ORIGINAL_ARTICLE
Assessment of basin hydrological components by modified conceptual continuous rainfall-runoff SCS-CN
Background and objectives: Since the problem of predicting and runoff estimating play a key role in integrated water resources management, therefore hydrological modeling especially continuous rainfall-runoff modeling may be most important part of water resource planning which is released from reservoir dams. Thus continuous daily hydrological models are useful tools for estimating runoff from rainfall. These models are able to estimate the runoff in ungagged basin. The purpose of this paper is to provide a continuous simulation model for Hydrologic forecasting so that investigate dominancy or dormancy of the processes.Materials and methods: In this study rainfall-runoff processes involved in modified SCS-CN model calibrated in Leaf River Watershed located in US and Qarasou subbasin located in west of Iran through PSO optimization algorithm developed in MATLAB programming language with 9000 simulation numbers. Nash-Sutcliffe Efficiency (NSE) is used as objective function and the decision variables (14 parameters) within the specified range are randomly initialized. Optimum parameters were extracted through PSO. This model is calibrated and validated with two periods 1957-1961 and 1953 for Leaf River Watershed and two periods 1381-1384 and 1387 for Qarasou subbasin respectively.Results: Model parameters were calibrated and Validation for two case studies. Comparison of the observed and simulated runoff carried out based on three performance criteria: Nash-Sutcliffe (NSE) and Kling-Gupta Efficiency (KGE) and Root Mean Square Error (RMSE). Proposed model performed these three statistics respectively for leaf River Watershed 0.81,0.87,1.40 as calibration period and 0.83, 0.86, 2.53 as validation period. Reasonable values for these criteria is also attained in Qarasou subbasin but due to more reliable data, better results is expected in Leaf River watershed. A result comparison of the SCS-CN model with Hymod as a simple conceptual model, both with the same inputs revealed latter model can simulate hydrology behavior of Leaf River Watershed and Qareso River Watershed slightly better. This may be originated due to fewer model complexities and thus less parameter uncertainty of Hydmod. In spite of this superior skill in runoff simulation of Hymod, special capabilities of modified SCS-CN model which calculate hydrological components (baseflow, percolation, throughflow, surface runoff and initial abstraction) may prove usefulness and efficiency of this new model easily. Conclusion: modified SCS-CN model as a conceptual model calculates daily runoff using rainfall and potential evapotranspiration dataset. This model may be used to assess annual hydrologic components as well as total runoff values. Based on the results, the dominancy of the infiltration, evaporation and surface runoff processes were approved in Leaf River Watershed. These three processes but in reverse order is ranked in Qarasou subbasin as main hydrological components.
https://jwsc.gau.ac.ir/article_3574_a6c3124ad680b0fc59cc56f1657ac899.pdf
2017-03-21
1
23
10.22069/jwfst.2017.12085.2660
long-term hydrologic simulation
Hydrological components
curve number method
Optimization algorithm PSO
Conceptual Model Hymod
Soraya
Golnarkar
sgolnarkar@gmail.com
1
دانشجو
LEAD_AUTHOR
Mohsen
Pourreza
mohsen.pourreza@birjand.ac.ir
2
عض هیئت علمی
AUTHOR
Abbas
Khashei
abbaskhashei@birjand.ac.ir
3
عضو هیئت علمی
AUTHOR
Mahdi
Amirabadizadeh
mamirabadizadeh@birjand.ac.ir
4
مدیر گروه
AUTHOR
.Arnold, J.G., Engel, B.A., and Srinivasan, R. 1993. Continuous time, grid cell watershed model,
1
application of advanced information technologies. Effective management of natural resources.
2
ASAE Publication, 04–93. American Society of Agricultural Engineers, Pp: 267-278.
3
2.Boughton, W.C. 1966. A mathematical model for relating runoff to rainfall with daily data.
4
Civil Engineering Trans I.E Australia, 38: 2. 779-787.
5
3.Boughton, W.C. 1968. A mathematical catchment model for estimating runoff. J. Hydrol.
6
(New Zealand), Pp: 75-100.
7
4.Boyle, D.P., Gupta, H.V., and Sorooshian, S. 2000. Toward improved calibration of
8
hydrologic models: Combining the strengths of manual and automatic methods. Water
9
Resources Research, 36: 12. 3663-3674.
10
5.Choi, J.Y., Engel, B.A., and Chung, H.W. 2002. Daily streamflow modelling and assessment
11
based on the curve-number technique. Hydrological Processes, 16: 16. 3131-3150.
12
6.Cooper, V.A., Nguyen, V.T.V., and Nicell, J.A. 2007. Calibration of conceptual rainfall–
13
runoff models using global optimisation methods with hydrologic process-based parameter
14
constraints. J. Hydrol. 334: 3. 455-466.
15
7.Crawford, N.H., and Linsley, R.K. 1966. Digital Simulation in Hydrology'Stanford Watershed
16
8.Douglas, E.M., Jacobs, J.M., Sumner, D.M., and Ray, R.L. 2009. A comparison of models
17
for estimating potential evapotranspiration for Florida land cover types. J. Hydrol.
18
373: 3. 366-376.
19
9.Geetha, K., Mishra, S.K., Eldho, T.I., Rastogi, A.K., and Pandey, R.P. 2008. SCS-CN-based
20
continuous simulation model for hydrologic forecasting. Water Resources Management,
21
22: 2. 165-190.
22
10.Gupta, H.V., Kling, H., Yilmaz, K.K., and Martinez, G.F. 2009. Decomposition of the mean
23
squared error and NSE performance criteria: Implications for improving hydrological
24
modelling. J. Hydrol. 377: 1. 80-91.
25
11.Heaney, J.P., Sample, D., Wright, L., and Koustas, R. 1999. Geographical information
26
systems, decision support systems and urban stormwater management. US Environmental
27
Protection Agency, National Risk Management Research Laboratory, Office of Research and
28
Development.
29
12.James, D. 1970. An Evaluation of Relationships Between Streamflow Patterns and
30
Watershed Characteristics Through the Use of OPSET: A Self Calibrating Version of the
31
Stanford Watershed Model.
32
13.James, L.D. 1972. Hydrologic modeling, parameter estimation and watershed characteristics.
33
J. Hydrol. 17: 4. 283-307.
34
14.Liou, E.Y. 1970. Opset: program for computerized selection of watershed parameter values
35
for the Stanford watershed model.
36
15.Mandeville, A.N., O'connell, P.E., Sutcliffe, J.V., and Nash, J.E. 1970. River flow
37
forecasting through conceptual models part III-The Ray catchment at Grendon Underwood.
38
J. Hydrol. 11: 2. 109-128.
39
16.Michel, C., Andréassian, V., and Perrin, C. 2005. Soil conservation service curve number
40
method: how to mend a wrong soil moisture accounting procedure?. Water Resources
41
Research, 41: 2.
42
17.Mishra, S.K. 1998. Long-term hydrologic simulation using SCS-CN method. Tech report.
43
National Institute of Hydrology, Roorkee-247 667. UP, India.
44
18.Mishra, S.K., and Singh, V. 2013. Soil conservation service curve number (SCS-CN)
45
methodology (Vol. 42). Springer Science and Business Media.
46
19.Mishra, S.K., and Singh, V.P. 2002. SCS-CN-based hydrologic simulation package.
47
Mathematical Models of Small Watershed Hydrology and Applications, Water Resources
48
Publs., LLC, Highlands Ranch, Pp: 391-464.
49
20.Mishra, S.K., and Singh, V.P. 2004a. Long-term hydrological simulation based on the Soil
50
Conservation Service curve number. Hydrological Processes, 18: 7. 1291-1313.
51
21.Mishra, S.K., and Singh, V.P. 2004b. Validity and extension of the SCS-CN method for
52
computing infiltration and rainfall-excess rates. Hydrological Processes, 18: 17. 3323-3345.
53
22.Nash, J.E., and Sutcliffe, J.V. 1970. River flow forecasting through conceptual models part IA discussion of principles. J. Hydrol. 10: 3. 282-290.
54
23.Ponce, V.M., and Hawkins, R.H. 1996. Runoff curve number: Has it reached maturity?. J.
55
Hydrol. Engin. 1: 1. 11-19.
56
24.Saghafian, B., Noroozpour, S., Kiani, M., and Nasab, A.R. 2016. A coupled ModClark-curve
57
number rainfall-runon-runoff model. Arab. J. Geosci. 9: 4. 1-13.
58
25.Singh, V.P. 1989. Hydrologic systems: watershed modeling (Vol. 2). Prentice Hall.
59
26.Singh, V.P. 1995. Computer Models of Watershed Hydrology1 Water Resources
60
Publications. Littleton, Colorado.
61
27.Singh, V.P., Frevert, D.K., Rieker, J.D., Leverson, V., Meyer, S., and Meyer, S. 2006.
62
Hydrologic modeling inventory: cooperative research effort. J. Irrig. Drain. Engin.
63
132: 2. 98-103.
64
28.Vrugt, J.A., Gupta, H.V., Bouten, W., and Sorooshian, S. 2003. A Shuffled Complex
65
Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic
66
model parameters. Water Resources Research, 39: 8.
67
29.Vrugt, J.A., Ter Braak, C.J., Gupta, H.V., and Robinson, B.A. 2009. Equifinality of formal
68
(DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?. Stochastic
69
environmental research and risk assessment, 23: 7. 1011-1026.
70
30.Wagener, T., Boyle, D.P., Lees, M.J., Wheater, H.S., Gupta, H.V., and Sorooshian, S. 2001.
71
A framework for development and application of hydrological models. Hydrology and Earth
72
System Sciences, 5: 1. 13-26.
73
ORIGINAL_ARTICLE
Quantiles Trend Estimation of Variables of Annual Maximum Floods
Background and objectives: Investigation of the basin floods in most cases is only based on flood peak trend analysis using conventional parametric or non-parametric (ordinary linear regression (OLR), Mann-Kendall, Sen) tests. In addition to the primary restrictions, these methods usually are provided to estimate the conditional mean or median and do not consider different quantiles while assessing the appropriate domain of conditional quantiles leads to a very good understanding of trend pattern. The objective of this study is using quantile regression (QR) to estimate the time trend (conditional quantiles) of flood variables including peak, volume and duration that result in better understanding of variables of annual maximum floods (AMF). Materials and methods: In the first step, AMF time series of Taleh-Zang hydrometry station located in southwestern Iran was considered and the time series of AMF peak flow, volume and duration were extracted.In the next step, trend analysis of AMF variables time series performed using OLR and their efficiency were investigated using fitting precision criteria, statistical significant test and residuals analysis. Then, QR lines were estimated for AMF variables trend analysis considering (0.05-0.95 with 0.01 steps) and their fitting precision criteria and statistical significant test were determined. Considering selected quantiles0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85 and 0.95 QR lines were plotted for AMF variables. Results: The OLR results indicated positive trends for AMF variables but complementary analysis showed that this method cannot be a suitable analysis for AMF variables trend analysis in this research. The QR application resulted in wide range of line slopes in comparison with OLR method. For all three variables 15% of estimated line slopes using QR were more than their estimation by OLR. Investigation of QR lines indicated statistical significant regression lines of AMF volume were related to upper bound quantiles while for AMF peak and duration were related to quantiles mid bound plus upper bound and there were a few acceptable QR lines for lower bound for all three variables so that for AMF peak, volume and duration 59%, 31% and 73% of QR lines were statistical significant considering 0.05 significance level. The fitting precisions of QR lines of upper and mid bounds were more than lower bound. Conclusion: The quantile regression can be used without affecting the limitations of conventional methods for AMF variables trend analysis to access a wider range of applied trend analysis. Also there are certain differences between AMF variables trend slopes (especially for upper bound quantiles) in comparison with those estimated with OLR therefore the OLR method could not be a useful tool for trend assessment of extreme events. The results show trend of extreme flood variables are significantly more than those estimated by OLR and in other words the OLR led to underestimation of AMF variables increasing trend slope. Moreover, multiple variables flood trend analysis using QR revealed that considering significant trends for three flood variables, the flood potential risk are significantly more than those estimated using single variable analysis.
https://jwsc.gau.ac.ir/article_3575_da2cb3640609ab8436fcd96ca96cae29.pdf
2017-03-21
25
46
10.22069/jwfst.2017.11738.2623
Quantile Regression
Ordinary Linear Regression
Trend
Flood Variables
Meysam
Salarijazi
meysam.salarijazi@gmail.com
1
Gorgan University of Agricultural Sciences and Natural Resources
LEAD_AUTHOR
1.Adib, A., Ahmadeanfar, I., Salarijazi, M., Labibzadeh, M., and Vaghefi, M. 2012.
1
Optimization of released water from the Dez dam for supply of water demands in the
2
downstream of dam. Applied Mechanics and Materials (147: 187-190). Trans. Tech.
3
Publications.
4
2.Adib, A., Salarijazi, M., and Najafpour, K. 2010. Evaluation of synthetic outlet runoff
5
assessment models. J. Appl. Sci. Environ. Manage. 14: 3. 13-18.
6
3.Adib, A., Salarijazi, M., Shooshtari, M. M., and Akhondali, A.M. 2011. Comparison between
7
characteristics of geomorphoclimatic instantaneous unit hydrograph be produced by GcIUH
8
based Clark Model and Clark IUH model. J. Mar. Sci. Technol. 19: 2. 201-209.
9
4.Adib, A., Salarijazi, M., Vaghefi, M., Shooshtari, M.M., and Akhondali, A.M. 2010.
10
Comparison between GcIUH-Clark, GIUH-Nash, Clark-IUH and Nash-IUH models. Turk. J.
11
Engin. Environ. Sci. 34: 2. 91-104.
12
5.Ahmadianfar, I., Adib, A., and Salarijazi, M. 2015. Optimizing multireservoir operation:
13
Hybrid of bat algorithm and differential evolution. J. Water Resour. Plan. Manage.
14
142: 2. 05015010.
15
6.Anghileri, D., Pianosi, F., and Soncini-Sessa, R. 2014. Trend detection in seasonal data: from
16
hydrology to water resources. J. Hydrol. 511: 171-179.
17
7.Arnell, N.W., and Gosling, S.N. 2016. The impacts of climate change on river flood risk at the
18
global scale. Climatic Change, 134: 3. 387-401.
19
8.Bačová Mitková, V., and Halmová, D. 2014. Joint modeling of flood peak discharges,
20
volume and duration: a case study of the Danube River in Bratislava. J. Hydrol. Hydromech.
21
62: 3. 186-196.
22
9.Barbosa, S.M. 2008. Quantile trends in Baltic sea level. Geophysical Research Letters, 35: 22.
23
10.Barbosa, S.M., Scotto, M.G., and Alonso, A.M. 2011. Summarizing changes in air
24
temperature over Central Europe by quantile regression and clustering. Natural Hazards and
25
Earth System Sciences, 11: 12. 3227-3233.
26
11.Bouza-Deaño, R., Ternero-Rodriguez, M., and Fernández-Espinosa, A.J. 2008. Trend study
27
and assessment of surface water quality in the Ebro River (Spain). J. Hydrol. 361: 3. 227-239.
28
12.Bremnes, J.B. 2006. A comparison of a few statistical models for making quantile wind
29
power forecasts. Wind Energy, 9: 1‐2. 3-11.
30
13.Brody, S.D., Highfield, W.E., and Blessing, R. 2015. An analysis of the effects of land use
31
and land cover on flood losses along the gulf of Mexico coast from 1999 to 2009. JAWRA
32
J. Amer. Water Resour. Assoc. 51: 6. 1556-1567.
33
14.Brunetti, M., Buffoni, L., Mangianti, F., Maugeri, M., and Nanni, T. 2004. Temperature,
34
precipitation and extreme events during the last century in Italy. Global and planetary
35
change, 40: 1. 141-149.
36
15.Buchinsky, M. 1998. Recent advances in quantile regression models: a practical guideline for
37
empirical research. J. Human Resour. Pp: 88-126.
38
16.Burn, D.H., and Elnur, M.A.H. 2002. Detection of hydrologic trends and variability.
39
J. Hydrol. 255: 1. 107-122.
40
17.Chamaillé-Jammes, S., Fritz, H., and Murindagomo, F. 2007. Detecting climate changes of
41
concern in highly variable environments: Quantile regressions reveal that droughts worsen in
42
Hwange National Park, Zimbabwe. J. Arid Environ. 71: 3. 321-326.
43
18.Changnon, S.A., and Demissie, M. 1996. Detection of changes in streamflow and floods
44
resulting from climate fluctuations and land use-drainage changes. Climatic change,
45
32: 4. 411-421.
46
19.Chen, H., Guo, S., Xu, C.Y., and Singh, V.P. 2007. Historical temporal trends of hydroclimatic variables and runoff response to climate variability and their relevance in water
47
resource management in the Hanjiang basin. J. Hydrol. 344: 3. 171-184.
48
20.Cunderlik, J.M., and Ouarda, T.B. 2006. Regional flood-duration–frequency modeling in the
49
changing environment. J. Hydrol. 318: 1. 276-291.
50
21.Delgado, J.M., Apel, H., and Merz, B. 2010. Flood trends and variability in the Mekong
51
river. Hydrology and Earth System Sciences, 14: 3. 407-418.
52
22.Eidipour, A., Akhondali, A.M., Zarei, H., and Salarijazi, M. 2016. Flood hydrograph
53
estimation using GIUH model in ungauged karst basins (Case study: Abolabbas Basin).
54
TUEXENIA, 36: 36. 26-33.
55
23.Eilers, P.H., and De Menezes, R.X. 2005. Quantile smoothing of array CGH data.
56
Bioinformatics, 21: 7. 1146-1153.
57
24.Elsner, J.B., Kossin, J.P., and Jagger, T.H. 2008. The increasing intensity of the strongest
58
tropical cyclones. Nature, 455: 7209. 92-95.
59
25.Francke, T., López‐Tarazón, J.A., and Schröder, B. 2008. Estimation of suspended sediment
60
concentration and yield using linear models, random forests and quantile regression forests.
61
Hydrological Processes, 22: 25. 4892-4904.
62
26.Friederichs, P., and Hense, A. 2007. Statistical downscaling of extreme precipitation events
63
using censored quantile regression. Monthly weather review, 135: 6. 2365-2378.
64
27.Ganguli, P., and Reddy, M.J. 2013. Probabilistic assessment of flood risks using trivariate
65
copulas. Theoretical and applied climatology, 111: 1-2. 341-360.
66
28.Gao, G., Chen, D., Xu, C.Y., and Simelton, E. 2007. Trend of estimated actual
67
evapotranspiration over China during 1960-2002. J. Geophysic. Res. Atm. 112 (D11).
68
29.Ghorbani, Kh., Sohrabian, E., and Salarijazi, M. 2016. Evaluation of hydrological and
69
data mining models in monthly river discharge simulation and prediction (Case study:
70
Araz-Kouseh watershed). J. Water Soil Cons. 23: 1. 203-217.
71
30.Ghorbani, Kh., Sohrabian, E., Salarijazi, M., and Abdolhoseini, M. 2016. Prediction of
72
climate change impact on monthly river discharge trend using IHACRES hydrological model
73
(case study: Galikesh watershed). J. Water Soil Resour. Cons. 5: 4. 18-34.
74
31.Gocic, M., and Trajkovic, S. 2013. Analysis of changes in meteorological variables using
75
Mann-Kendall and Sen's slope estimator statistical tests in Serbia. Global and Planetary
76
Change, 100: 172-182.
77
32.Greenville, A.C., Wardle, G.M., and Dickman, C.R. 2012. Extreme climatic events drive
78
mammal irruptions: regression analysis of 100‐year trends in desert rainfall and temperature.
79
Ecology and evolution, 2: 11. 2645-2658.
80
33.Guo, Y., and Shen, Y. 2015. Quantifying water and energy budgets and the impacts of
81
climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to
82
water resources. J. Hydrol. 527: 251-261.
83
34.Gustavsen, G.W., and Rickertsen, K. 2006. A censored quantile regression analysis of
84
vegetable demand: the effects of changes in prices and total expenditure. Can. J. Agric.
85
Econ. /Rev. Canadienne d'agroeconomie, 54: 4. 631-645.
86
35.Haddad, K., and Rahman, A. 2012. Regional flood frequency analysis in eastern Australia:
87
Bayesian GLS regression-based methods within fixed region and ROI framework–Quantile
88
Regression vs. Parameter Regression Technique. J. Hydrol. 430: 142-161.
89
36.Hardwick Jones, R., Westra, S., and Sharma, A. 2010. Observed relationships between
90
extreme sub‐daily precipitation, surface temperature and relative humidity. Geophysical
91
Research Letters, 37: 22.
92
37.Hooshmand, A., Salarijazi, M., Bahrami, M., Zahiri, J., and Soleimani, S. 2013. Assessment
93
of pan evaporation changes in South Western Iran. Afric. J. Agric. Res. 8: 16. 1449-1456.
94
38.Jiang, Y., Luo, Y., Zhao, Z., and Tao, S. 2010. Changes in wind speed over China during
95
1956–2004. Theoretical and Applied Climatology, 99: 3-4. 421-430.
96
39.Karmakar, S., and Simonovic, S.P. 2008. Bivariate flood frequency analysis: Part 1.
97
Determination of marginals by parametric and nonparametric techniques. J. Flood Risk
98
Manage. 1: 4. 190-200.
99
40.Karpouzos, D.K., Kavalieratou, S., and Babajimopoulos, C. 2010. Trend analysis of
100
precipitation data in Pieria Region (Greece). European Water, 30: 31-40.
101
41.Khang, Y.H., and Yun, S.C. 2010. Trends in general and abdominal obesity among Korean
102
adults: findings from 1998, 2001, 2005 and 2007 Korea National Health and Nutrition
103
Examination Surveys. J. Korean Med. Sci. 25: 11. 1582-1588.
104
42.Kisi, O., and Ay, M. 2014. Comparison of Mann–Kendall and innovative trend method for
105
water quality parameters of the Kizilirmak River, Turk. J. Hydrol. 513: 362-375.
106
43.Koenker, R. 2005. Quantile regression (No. 38). Cambridge university press.
107
44.Kumar, K.R., Kumar, K.K., and Pant, G.B. 1994. Diurnal asymmetry of surface temperature
108
trends over India. Geophysical Research Letters, 21: 8. 677-680.
109
45.Lee, K., Baek, H.J., and Cho, C. 2013. Analysis of changes in extreme temperatures using
110
quantile regression. Asia-Pacific J. Atm. Sci. 49: 3. 313-323.
111
46.Linares, J.C., Delgado-Huertas, A., and Carreira, J.A. 2011. Climatic trends and different
112
drought adaptive capacity and vulnerability in a mixed Abies pinsapo–Pinus halepensis
113
forest. Climatic change, 105: 1-2. 67-90.
114
47.López López, P., Verkade, J.S., Weerts, A.H., and Solomatine, D.P. 2014. Alternative
115
configurations of quantile regression for estimating predictive uncertainty in water level
116
forecasts for the upper Severn River: a comparison. Hydrology and Earth System Sciences
117
Discussions, 11: 2014.
118
48.Luce, C.H., and Holden, ZA. 2009. Declining annual streamflow distributions in the Pacific
119
Northwest United States, 1948-2006. Geophysical Research Letters, 36: 16.
120
49.Luo, P., He, B., Takara, K., Razafindrabe, B.H., Nover, D., and Yamashiki, Y. 2011.
121
Spatiotemporal trend analysis of recent river water quality conditions in Japan. J. Environ.
122
Monitor. 13: 10. 2819-2829.
123
50.Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M., and Wanner, H. 2004. European
124
seasonal and annual temperature variability, trends and extremes since 1500. Science,
125
303: 5663. 1499-1503.
126
51.Machado, J.A., and Mata, J. 2005. Counterfactual decomposition of changes in wage
127
distributions using quantile regression. J. Appl. Econom. 20: 4. 445-465.
128
52.Mallakpour, I., and Villarini, G. 2015. The changing nature of flooding across the central
129
United States. Nature Climate Change, 5: 3. 250-254.
130
53.Marofi, S., Soleymani, S., Salarijazi, M., and Marofi, H. 2012. Watershed-wide trend
131
analysis of temperature characteristics in Karun-Dez watershed, southwestern Iran.
132
Theoretical and Applied Climatology, 110: 1-2. 311-320.
133
54.Mazvimavi, D. 2010. Investigating changes over time of annual rainfall in Zimbabwe.
134
Hydrology and Earth System Sciences, 14: 12. 2671-2679.
135
55.Melly, B. 2005. Public-private sector wage differentials in Germany: Evidence from quantile
136
regression. Empirical Economics, 30: 2. 505-520.
137
56.Moazed, H., Salarijazi, M., Moradzadeh, M., and Soleymani, S. 2012. Changes in rainfall
138
characteristics in Southwestern Iran. Afric. J. Agric. Res. 7: 18. 2835-2843.
139
57.Mondal, A., Kundu, S., and Mukhopadhyay, A. 2012. Rainfall trend analysis by MannKendall test: A case study of north-eastern part of Cuttack district, Orissa. Int. J. Geol. Earth
140
Environ. Sci. 2: 1. 70-78.
141
58.Monteiro, A., Carvalho, A., Ribeiro, I., Scotto, M., Barbosa, S., Alonso, A., and Borrego, C.
142
2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and
143
clustering. Atmospheric environment, 56: 184-193.
144
59.Moslemzadeh, M., Salarizazi, M., and Soleymani, S. 2011. Application and assessment of
145
kriging and cokriging methods on groundwater level estimation. J. Amer. Sci. 7: 7. 34-39.
146
60.Muzik, I. 2002. A first-order analysis of the climate change effect on flood frequencies
147
in a subalpine watershed by means of a hydrological rainfall–runoff model. J. Hydrol.
148
267: 1. 65-73.
149
61.Nielsen, H.A., Madsen, H., and Nielsen, T.S. 2006. Using quantile regression to extend
150
an existing wind power forecasting system with probabilistic forecasts. Wind Energy,
151
9: 1‐2. 95-108.
152
62.Ohana-Levi, N., Karnieli, A., Egozi, R., Givati, A., and Peeters, A. 2015. Modeling the
153
Effects of Land-Cover Change on Rainfall-Runoff Relationships in a Semiarid, Eastern
154
Mediterranean Watershed. Advances in Meteorology, 2015.
155
63.Partal, T., and Kahya, E. 2006. Trend analysis in Turkish precipitation data. Hydrological
156
processes, 20: 9. 2011-2026.
157
64.Petrow, T., and Merz, B. 2009. Trends in flood magnitude, frequency and seasonality in
158
Germany in the period 1951–2002. J. Hydrol. 371: 1. 129-141.
159
65.Piticar, A., Mihăilă, D., Lazurca, L.G., Bistricean, P.I., Puţuntică, A., and Briciu, A.E. 2016.
160
Spatiotemporal distribution of reference evapotranspiration in the Republic of Moldova.
161
Theoretical and Applied Climatology, 124: 3-4. 1133-1144.
162
66.Poff, N.L., and Zimmerman, J.K. 2010. Ecological responses to altered flow regimes: a
163
literature review to inform the science and management of environmental flows. Freshwater
164
Biology, 55: 1. 194-205.
165
67.Quesada, B., Vautard, R., Yiou, P., Hirschi, M., and Seneviratne, S.I. 2012. Asymmetric
166
European summer heat predictability from wet and dry southern winters and springs. Nature
167
Climate Change, 2: 10. 736-741.
168
68.Reich, B.J. 2012. Spatiotemporal quantile regression for detecting distributional changes in
169
environmental processes. J. Royal Stat. Soc. Series C (Applied Statistics), 61: 4. 535-553.
170
69.Rodrigo, F.S., and Trigo, R.M. 2007. Trends in daily rainfall in the Iberian Peninsula from
171
1951 to 2002. Inter. J. Climatol. 27: 4. 513-529.
172
70.Roscoe, K.L., Weerts, A.H., and Schroevers, M. 2012. Estimation of the uncertainty in water
173
level forecasts at ungauged river locations using quantile regression. Inter. J. River Basin
174
Manage. 10: 4. 383-394.
175
71.Sadeghian, M.S., Salarijazi, M., Ahmadianfar, I., and Heydari, M. 2016. Stage-Discharge
176
relationship in tidal rivers for tidal flood condition. Fresenius Environmental Bulletin,
177
25: 10. 4111-4117.
178
72.Salarijazi, M., Abdolhosseini, M., Ghorbani, K., and Eslamian, S. 2016. Evaluation of quasimaximum likelihood and smearing estimator to improve sediment rating curve estimation.
179
Inter. J. Hydrol. Sci. Technol. 6: 4. 359-370.
180
73.Salarijazi, M., Akhond-Ali, A.M., Adib, A., and Daneshkhah, A. 2012. Trend and
181
change-point detection for the annual stream-flow series of the Karun River at the Ahvaz
182
hydrometric station. Afric. J. Agric. Res. 7: 32. 4540-4552.
183
4574.Salarijazi, M., Akhond-Ali, A.M., Adib, A., and Dneshkhah, A.R. 2015. Bivariate FloodFrequency Analysis Using the Copula Functions. J. Irrig. Sci. Engin. 37: 4. 29-38.75.Sankarasubramanian, A., and Lall, U. 2003. Flood quantiles in a changing climate: Seasonalforecasts and causal relations. Water Resources Research, 39: 5.76.Schmocker-Fackel, P., and Naef, F. 2010. More frequent flooding? Changes in floodfrequency in Switzerland since 1850. J. Hydrol. 381: 1. 1-8.77.Shamsudduha, M., Chandler, R.E., Taylor, R.G., and Ahmed, K.M. 2009. Recent trends ingroundwater levels in a highly seasonal hydrological system: the Ganges-BrahmaputraMeghna Delta. Hydrology and Earth System Sciences, 13: 12. 2373-2385.78.Shiau, J.T., and Chen, T.J. 2015. Quantile regression-based probabilistic estimationscheme for daily and annual suspended sediment loads. Water Resources Management,29: 8. 2805-2818.79.Shiau, J.T., and Huang, W.H. 2015. Detecting distributional changes of annual rainfallindices in Taiwan using quantile regression. J. Hydro-Environ. Res. 9: 3. 368-380.80.Shiau, J.T., and Lin, J.W. 2016. Clustering quantile regression-based drought trends inTaiwan. Water Resources Management, 30: 3. 1053-1069.81.Stojković, M., Ilić, A., Prohaska, S., and Plavšić, J. 2014. Multi-temporal analysis of meanannual and seasonal stream flow trends, including periodicity and multiple non-linearregression. Water Resources Management, 28: 12. 4319-4335.82.Tareghian, R., and Rasmussen, P.F. 2013. Statistical downscaling of precipitation usingquantile regression. J. Hydrol. 487: 122-135.83.Tharme, R.E. 2003. A global perspective on environmental flow assessment: emergingtrends in the development and application of environmental flow methodologies for rivers.River research and applications, 19: 5‐6. 397-441.84.Timofeev, A.A., and Sterin, A.M. 2010.Using the quantile regression method to analyzechanges in climate characteristics. Russian Meteorology and Hydrology, 35: 5. 310-319.85.Tøttrup, A.P., Thorup, K., and Rahbek, C. 2006. Patterns of change in timing of springmigration in North European songbird populations. J. Avian Biol. 37: 1. 84-92.86.Villarini, G., Smith, J.A., Serinaldi, F., and Ntelekos, A.A. 2011. Analyses of seasonal andannual maximum daily discharge records for central Europe. J. Hydrol. 399: 3. 299-312.87.Wang, Y., Jiang, T., Bothe, O., and Fraedrich, K. 2007. Changes of pan evaporationand reference evapotranspiration in the Yangtze River basin. Theoretical and AppliedClimatology, 90: 1-2. 13-23.88.Wasko, C., and Sharma, A. 2014. Quantile regression for investigating scaling of extremeprecipitation with temperature. Water Resources Research, 50: 4. 3608-3614.89.Weerts, A.H., Winsemius, H.C., and Verkade, J.S. 2011. Estimation of predictivehydrological uncertainty using quantile regression: examples from the National FloodForecasting System (England and Wales). Hydrology and Earth System Sciences, 15: 1.90.Xiao, Z. 2009. Quantile cointegrating regression. J. Econom. 150: 2. 248-260.91.Yenilmez, F., Keskin, F., and Aksoy, A. 2011.Water quality trend analysis in Eymir Lake,Ankara. Physics and Chemistry of the Earth, Parts A/B/C, 36: 5. 135-140.92.Yue, S., and Wang, C. 2004. The Mann-Kendall test modified by effective sample sizeto detect trend in serially correlated hydrological series. Water Resources Management,18: 3. 201-218.93.Yue, S., Ouarda, T.B., Bobée, B., Legendre, P., and Bruneau, P. 2002. Approach fordescribing statistical properties of flood hydrograph. J. Hydrol. Engin. 7: 2. 147-153.94.Yue, S., Pilon, P., and Cavadias, G. 2002. Power of the Mann–Kendall and Spearman's rhotests for detecting monotonic trends in hydrological series. J. Hydrol. 259: 1. 254-271.95.Zhang, L., and Singh, V.P. 2006. Bivariate flood frequency analysis using the copulamethod. J. Hydrol. Engin. 11: 2. 150-164.
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ORIGINAL_ARTICLE
Effect of EDTA and Citric acid on soil enzyme activities and phytoextraction of lead by sun flower and Indian mustard from a contaminated soil
Background and Objectives: Chelate induced-phytoextraction is one of the methods for remediation of heavy metal contaminated soils that have been attracted a lot of attention in the past decade. So far more attentions have been placed to effects of chelating agents on heavy metal solubility in soil and their uptake by plants, while there are less information about their side effects on soil environment and organisms. Soil enzyme activities can be suitable indicators to assess soil recovery after different remediation processes. The aim of this study was to evaluate the effects of EDTA and Citric acid (CA) on soil enzyme activities as well as lead (Pb) uptake by Indian mustard and sun flower.Material and Methods: This study was conducted in a completely randomized design with factorial arrangement and three replications in greenhouse condition. The experimental factors were chelating agent treatments and plant types. The chelating agent treatments were including Control (without chelating agent), EDTA3 and EDTA5 (3 and 5 mmol EDTA per kg dry soil), CA3 and CA5 (3 and 5 mmol CA per kg dry soil). The plant species were Indian mustard (Brassica juncea) and sun flower (Helianthus annus). Also additional treatment (without Pb and without chelating agent) was considered to evaluate the effect of Pb on plant dry weight and soil enzymes activities (NP treatment).Results: The results showed that EDTA was more effective than CA for increasing available Pb concentration. Unexpectedly, the addition of CA into soil significantly decreased available Pb concentration compared with the control treatment. The results showed that between two studied chelating agents, the EDTA was appropriate for increasing Pb uptake by shoots and CA was appropriate for increasing Pb uptake by roots. The highest Pb uptake by root (2.99 mg Pb per pot) was observed in Indian mustard using 5 mmol CA per kg dry soil. Also the highest Pb uptake by shoot (1.74 mg Pb per pot) was obtained by Indian mustard with EDTA3 treatment. The results showed that soil treated with EDTA led to hormesis effect on dehydrogenase and phosphomonoestrase activity, GMea and TEA indices. The EDTA5 treatment decreased GMea and TEA indices while, the EDTA3 treatment increased these indices compared with the control treatment. The addition of both concentration of CA into soil significantly and considerably increased the studied soil enzyme activities as well as GMea and TEA indices compared with the control treatment. Conclusion: In EDTA3 treatment the shoot Pb uptake amount was higher than control treatment and, furthermore, it improved GMea and TEA indices. The EDTA5 treatment had lower efficiency than EDTA3 in increasing of shoot Pb uptake and also it decreased GMea and TEA indices compared with the control treatment. The addition of CA into soil was probably more suitable option for Pb phytostabilization in the studied soil and also considerably increased TEA and GMea indices compared with the control and NP treatments.
https://jwsc.gau.ac.ir/article_3576_65bb6f31342a4ea39c1db2713f49ee07.pdf
2017-03-21
47
65
10.22069/jwfst.2017.12039.2655
chelating agents
TEA and GMea indices
available Pb concentration
phytoextraction
Seyed Sajad
Hoseini
sajjadhosseini1369@gmail.com
1
دانشگاه فردوسی مشهد
AUTHOR
Amir
Lakzian
lakzian@ferdowsi.um.ac.ir
2
دانشگاه فردوسی مشهد
LEAD_AUTHOR
Akram
Halajnia
halajnia@yahoo.com
3
دانشگاه فردوسی مشهد
AUTHOR
1.Babaeian, E., Homaee, M., and Rahnemaie, R. 2016. Chelate-enhanced phytoextraction and
1
phytostabilization of lead-contaminated soils by carrot (Daucus carota). Arch. Agron. Soil
2
Sci. 62: 3. 339-358.
3
2.Bremner, J.M., and Mulvaney, C.S. 1982. Nitrogen-total. P 595-624, In: A.L. Page (Ed.),
4
Methods of Soil Analysis, American Society of Agronomy, Madison, WI.
5
3.Calabrese, E.J. 2008. Hormesis: why it is important to toxicology and toxicologists. Environ.
6
Toxicol. Chem. 27: 7. 1451-1474.
7
4.Cay, S., Uyanik, A., Engin, M.S., and Kutbay, H.G. 2015. Effect of EDTA and tannic acid on
8
the removal of Cd, Ni, Pb and Cu from artificially contaminated soil by Althaearosea Cavan.
9
Int. J. Phytoremediation. 17: 6. 568-574.
10
5.Chander, K., and Joergensen, R.G. 2008. Decomposition of Zn-rich Arabidopsis halleri
11
litter in low and high metal soil in the presence and absence of EDTA. Water Air Soil Pollut.
12
188: 1-4. 195-204.
13
6.Chander, K., and Joergensen, R.G. 2011. Soil microorganisms and the growth of Lupinus
14
albus on a high metal soil in the presence of EDTA. Arch. Agron. Soil Sci. 57: 2. 115-126.
15
7.Chapman, H.D. 1965. Cation exchange capacity. P 891-901, In: C.A. Black (Ed.), Methods of
16
Soil Analysis, American Society of Agronomy, Madison, WI.
17
8.Ciarkowska, K., Sołek-Podwika, K., and Wieczorek, J. 2014. Enzyme activity as an indicator
18
of soil-rehabilitation processes at a zinc and lead ore mining and processing area. J. Environ.
19
Manage. 132: 250-256.
20
9.Dick, R.P., Breakwell, D.P., Turco, R.F., Doran, J.W., and Jones, A.J. 1996. Soil
21
enzyme activities and biodiversity measurements as integrative microbiological indicators.
22
P 247-271, In: J.W. Doran and A.J. Jones (Ed.), Methods for assessing soil quality, Soil
23
Science Society of America, Madison, Wisconsin.
24
10.DoNascimento, C.W.A., Amarasiriwardena, D., and Xing, B. 2006. Comparison of natural
25
organic acids and synthetic chelates at enhancing phytoextraction of metals from a
26
multi-metal contaminated soil. Environ. Pollut. 140: 1. 114-123.
27
11.Evangelou, M.W., Ebel, M., Hommes, G., and Schaeffer, A. 2008. Biodegradation: the
28
reason for the inefficiency of small organic acids in chelant-assisted phytoextraction. Water
29
Air soil Pollut. 195: 1-4. 177-188.
30
12.Evangelou, M.W., Kutschinski-Klöss, S., Ebel, M., and Schaeffer, A. 2007. Potential of
31
Borago officinalis, Sinapis alba L. and Phacelia boratus for phytoextraction of Cd and Pb
32
from soil. Water Air Soil Pollut. 182: 1-4. 407-416.
33
13.Fatahi, E., Fotovat, A., Astaraei, A.R., and Haghnia, G.H. 2010. The effects of H2SO4 and
34
EDTA on phytoremediation of Pb in soil with three plant Sun flower, Zea mays and Cotton.
35
Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil
36
Science. 51: 57-68. (In Persian)
37
14.Feng, D., Teng, Y., Wang, J., and Wu, J. 2016. The Combined Effect of Cu, Zn and Pb on
38
Enzyme Activities in Soil from the Vicinity of a Wellhead Protection Area. Soil Sediment
39
Contam. 25: 3. 279-295.
40
15.Fine, P., Paresh, R., Beriozkin, A., and Hass, A. 2014. Chelant-enhanced heavy metal uptake
41
by eucalyptus trees under controlled deficit irrigation. Sci. Total Environ. 493: 995-1005.
42
16.García-Ruiz, R., Ochoa, V., Hinojosa, M.B., and Carreira, J.A. 2008. Suitability of enzyme
43
activities for the monitoring of soil quality improvement in organic agricultural systems. Soil
44
Biol. Biochem. 40: 9. 2137-2145.
45
17.Gee, G.H., and Bauder, J.W. 1986. Particle size analysis. P 383-409, In: A. Klute (Ed.),
46
Methods of Soil Analysis, Part 2 physical properties, Soil Science Society of America,
47
Madison, Wisconsin.
48
18.Gupta, D.K., Huang, H.G., and Corpas, F.J. 2013. Lead tolerance in plants: strategies for
49
phytoremediation. Environ. Sci. Pollut. Res. 20: 4. 2150-2161.
50
19.Han, Y., Zhang, L., Gu, J., Zhao, J., and Fu, J. 2016. Citric acid and EDTA on the growth,
51
photosynthetic properties and heavy metal accumulation of Iris halophila Pall. cultivated in
52
Pb mine tailings. Int. Biodeterior. Biodegrad. Pp: 1-7.
53
20.He, Z.L., Yang, X.E., Baligar, V.C., and Calvert, D.V. 2003. Microbiological and
54
biochemical indexing systems for assessing quality of acid soils. Adv. Agron. 78: 89-138.
55
21.Hernández-allica, J., Garbisu, C., Becerril, J.M., Barrutia, O., García-plazaola, J.I., Zhao,
56
F.J., and McGrath, S.P. 2006. Synthesis of low molecular weight thiols in response to Cd
57
exposure in Thlaspi caerulescens. Plant Cell Environ. 29: 7. 1422-1429.
58
22.Hinojosa, M.B., Carreira, J.A., Rodríguez-Maroto, J.M., and García-Ruíz, R. 2008. Effects
59
of pyrite sludge pollution on soil enzyme activities: ecological dose–response model. Sci.
60
Total Environ. 396: 2. 89-99.
61
23.Huang, H., Li, T., Tian, S., Gupta, D.K., Zhang, X., and Yang, X.E. 2008. Role of EDTA in
62
alleviating lead toxicity in accumulator species of Sedum alfredii H. Bioresource Technol.
63
99: 14. 6088-6096.
64
24.Jones Jr, J.B. 2001. Laboratory guide for conducting soil tests and plant analysis. CRC press.
65
LLC, New York, 365p.
66
25.Kos, B., and Lestan, D. 2003. Influence of a biodegradable ([S, S]-EDDS) and
67
nondegradable (EDTA) chelate and hydrogel modified soil water sorption capacity on Pb
68
phytoextraction and leaching. Plant Soil. 253: 2. 403-411.
69
26.Lambrechts, T., Gustot, Q., Couder, E., Houben, D., Iserentant, A., and Lutts, S. 2011.
70
Comparison of EDTA-enhanced phytoextraction and phytostabilisation strategies with
71
Lolium perenne on a heavy metal contaminated soil. Chemosphere. 85: 8. 1290-1298.
72
27.Lee, J., and Sung, K. 2014. Effects of chelates on soil microbial properties, plant growth and
73
heavy metal accumulation in plants. Ecol. Eng. 73: 386-394.
74
28.Lindsay, W.L., and Norvell, W.A. 1978. Development of a DTPA soil test for zinc, iron,
75
manganese and copper. Soil Sci. Soc. Am. J. 42: 3. 421-428.
76
29.Loeppert, R.H., and Sparks, D.L. 1996. Carbonate and gypsum. P 437-474, In: D.L. Sparks
77
(Ed.), Methods of Soil Analysis, Part 3 chemical methods, Soil Science Society of America,
78
Madison, Wisconsin.
79
30.Luo, C., Shen, Z., and Li, X. 2005. Enhanced phytoextraction of Cu, Pb, Zn and Cd with
80
EDTA and EDDS. Chemosphere. 59: 1. 1-11.
81
31.Mao, L., Tang, D., Feng, H., Gao, Y., Zhou, P., Xu, L., and Wang, L. 2015. Determining soil
82
enzyme activities for the assessment of fungi and citric acid-assisted phytoextraction under
83
cadmium and lead contamination. Environ. Sci. Pollut. Res. 22: 24. 19860-19869.
84
32.Martens, R. 1992. A comparison of soil adenine nucleotide measurements by HPLC and
85
enzymatic analysis. Soil Bio. Biochem. 24: 7. 639-645.
86
33.McGrath, S.P., and Cunliffe, C.H. 1985. A simplified method for the extraction of the metals
87
Fe, Zn, Cu, Ni, Cd, Pb, Cr, Co and Mn from soils and sewage sludges. J. Sci. Food Agric.
88
36: 9. 794-798.
89
34.Muhammad, D., Chen, F., Zhao, J., Zhang, G., and Wu, F. 2009. Comparison of EDTA-and
90
citric acid-enhanced phytoextraction of heavy metals in artificially metal contaminated soil
91
by Typha angustifolia. Int. J. Phytoremediation. 11: 6. 558-574.
92
35.Mühlbachová, G. 2011. Soil microbial activities and heavy metal mobility in long-term
93
contaminated soils after addition of EDTA and EDDS. Ecol. Eng. 37: 7. 1064-1071.
94
36.Nannipieri, P., Grego, S., Ceccanti, B., Bollag, J.M., and Stotzky, G. 1990. Ecological
95
significance of the biological activity in soil. P 293-355, In: J.M. Bollag and G. Stotozky
96
(Eds.), Soil biochemistry, Volume 6, Marcel Dekker, New York.
97
37.Nannipieri, P., Kandeler, E., and Ruggiero, P. 2002. Enzyme Activities and Microbiological
98
and Biochemical Processes In Soil. P 1-33, In: R.G. Burn and R. Dick (Eds.), Enzymes in the
99
Environment, Marcel Dekker, New York.
100
38.Nannipieri, P., Pankhurst, C.E., Doube, B.M., Gupta, V.V.S.R., and Grace, P.R. 1994.
101
The potential use of soil enzymes as indicators of productivity, sustainability and pollution.
102
P 238-244, In: C.E. Pankhurst, B.M. Doube, V.V.S.R. Gupta and P.R. Grace (Eds.), Soil
103
biota: management in sustainable farming systems, CSIRO Publications, Madison.
104
39.Nowack, B., Schulin, R., and Robinson, B.H. 2006. Critical assessment of chelant-enhanced
105
metal phytoextraction. Environ. Sci. Technol. 40: 17. 5225-5232.
106
40.Olsen, S.R., and Sommers, L.E. 1982. Phosphorus. P 4013-430, In: A. Klute (Ed.), Methods
107
of Soil Analysis, Part1 chemical and biological properties, Soil Science Society of America,
108
Madison, Wisconsin.
109
41.Pan, J., and Yu, L. 2011. Effects of Cd or/and Pb on soil enzyme activities and microbial
110
community structure. Ecol. Eng. 37: 11. 1889-1894.
111
42.Renella, G., Egamberdiyeva, D., Landi, L., Mench, M., and Nannipieri, P. 2006. Microbial
112
activity and hydrolase activities during decomposition of root exudates released by an
113
artificial root surface in Cd-contaminated soils. Soil Biol. Biochem. 38: 4. 702-708.
114
43.Sabir, M., Hanafi, M.M., Zia-Ur-Rehman, M., Saifullah, M.H., Ahmad, H.R., Hakeem,
115
K.R., and Aziz, T. 2014. Comparison of low-molecular-weight organic acids and
116
ethylenediaminetetraacetic acid to enhance phytoextraction of heavy metals by maize.
117
Commun. Soil Sci. Plant Anal. 45: 1. 42-52.
118
44.Saifullah, M.H., Ghafoor, A., Zia, M.H., Murtaza, G., Waraich, E.A., Bibi, S., and Srivastava,
119
P. 2010. Comparison of organic and inorganic amendments for enhancing soil lead
120
phytoextraction by wheat (Triticum aestivum L.). Int. J. Phytoremediation. 12: 7. 633-649.
121
45.Saifullah, M.H., Shahid, M., Zia-Ur-Rehman, M., Sabir, M., and Ahmad, H.R.
122
2014. Phytoremediation of Pb-Contaminated Soils Using Synthetic Chelates. P 397-414,
123
In: M. Sabir and H.R. Ahmad (Eds.), Soil Remediation and Plants: Prospects and
124
Challenges, Elsevier Inc.
125
46.Sapoundjieva, K., Kartalska, Y., Vassilev, A., Naidenov, M., Kuzmanova, I., and Krastev, S.
126
2003. Effects of the chelating agent EDTA on metal solubility in the soil, metal uptake and
127
performance of maize plants and soil microorganisms. Bulg. J. Agric. Sci. 9: 659-663
128
(Bulgaria).
129
47.Shakoor, M.B., Ali, S., Hameed, A., Farid, M., Hussain, S., Yasmeen, T., Najeeb, U.,
130
Bharwana, S.A., and Abbasi, G.H. 2014. Citric acid improves lead (Pb) phytoextraction in
131
Brassica napus L. by mitigating Pb-induced morphological and biochemical damages.
132
Ecotoxicol. Environ. Saf. 109: 38-47.
133
48.Sun, Y.B., Zhou, Q.X., An, J., Liu, W.T., and Liu, R. 2009. Chelator-enhanced
134
phytoextraction of heavy metals from contaminated soil irrigated by industrial wastewater
135
with the hyperaccumulator plant (Sedum alfredii Hance). Geoderma. 150: 1. 106-112.
136
49.Tabatabai, M.A., and Bremner, J.M. 1969. Use of p-nitrophenyl phosphate for assay of soil
137
phosphatase activity. Soil Biol. Biochem. 1: 4. 301-307.
138
50.Tabatabai, M.A., and Bremner, J.M. 1972. Assay of urease activity in soils. Soil Biol.
139
Biochem. 4: 4. 479-487.
140
51.Thalmann, A. 1966. The determination of the dehydrogenase activity in soil by means of
141
TTC (triphenyltetrazolium). Soil Biol. 6: 46-49.
142
52.Tian, S.K., Lu, L.L., Yang, X.E., Huang, H.G., Brown, P., Labavitch, J., Liao, H.B., and He,
143
Z.L. 2011. The impact of EDTA on lead distribution and speciation in the accumulator
144
Sedum alfredii by synchrotron X-ray investigation. Environ. Pollut. 159: 3. 782-788.
145
53.Usman, A.R., Almaroai, Y.A., Ahmad, M., Vithanage, M., and Ok, Y.S. 2013. Toxicity of
146
synthetic chelators and metal availability in poultry manure amended Cd, Pb and As
147
contaminated agricultural soil. J. Hazard. Mater. 262: 1022-1030.
148
54.Vassil, A.D., Kapulnik, Y., Raskin, I., and Salt, D.E. 1998. The role of EDTA in lead
149
transport and accumulation by Indian mustard. Plant Physiol. 117: 2. 447-453.
150
55.Vigliotta, G., Matrella, S., Cicatelli, A., Guarino, F., and Castiglione, S. 2016. Effects of
151
heavy metals and chelants on phytoremediation capacity and on rhizobacterial communities
152
of maize. J. Environ. Manage. 179: 93-102.
153
56.Walkley, A., and Black, I.A. 1934. An examination of the Degtjareff method for determining
154
soil organic matter and a proposed modification of the chromic acid titration method. Soil
155
Sci. 37: 1. 29-38.
156
57.Wenzel, W.W., Unterbrunner, R., Sommer, P., and Sacco, P. 2003. Chelate-assisted
157
phytoextraction using canola (Brassica napus L.) in outdoors pot and lysimeter experiments.Plant Soil. 249: 1. 83-96.58.Wu, L.H., Luo, Y.M., Xing, X.R., and Christie, P. 2004. EDTA-enhanced phytoremediationof heavy metal contaminated soil with Indian mustard and associated potential leaching risk.Agric. Ecosyst. Environ. 102: 3. 307-318.59.Yang, L., Wang, G., Cheng, Z., Liu, Y., Shen, Z., and Luo, C. 2013. Influence of theapplication of chelant EDDS on soil enzymatic activity and microbial community structure.J. Hazard. Mater. 262: 561-570.60.Yang, Z.X., Liu, S.Q., Zheng, D.W., and Feng, S.D. 2006. Effects of cadmium, zinc and leadon soil enzyme activities. J. Environ. Sci. 18: 6. 1135-1141.61.Zhang, H., Chen, X., He, C., Liang, X., Oh, K., Liu, X., and Lei, Y. 2015. Use of energycrop (Ricinus communis L.) for phytoextraction of heavy metals assisted with citric acid.Int. J. Phytoremediation. 17: 7. 632-639.
158
ORIGINAL_ARTICLE
Application of Random Forest method for predicting soil classes in low relief lands (case study: Hirmand county)
AbstractBackground and Objectives: Base of soil information for environmental modeling is soil survey and mapping as a way to determine soil distribution patterns, describe and display it to understood and interpreted for different users. Digital soil mapping creates link between classes or soil characteristics and environmental factors affected soil formation and development by using mathematical models which can provide more precise and accurate soil maps and reducing cost of soil survey and mapping projects. This study was done to mapping soil great groups and subgroups by using Random Forest technique in the Hirmand county lands in Sistan plain.Materials and Methods: In this study 108 soil profiles were dug on about 60.000 hectares of Hirmand county lands. Sixteen environmental variables were used as estimator for soil mapping including land properties, salinity and vegetation index. After classification of soil profiles to great groups and subgroups, soil classes map provided by using random forest (RF) method. It should be mentioned 80 percent of data was used for model training and 20 percent for independent validation. Results: Pedological studies showed soils that formed in Sistan plain haven’t high development and most of them are Entisol and Aridisol. Most soil profiles classified as Torrifluvents on great groups and Typic Torrifluvents as subgroups. Also the result of RF showed the lowest estimation error of out of bag (OOB) samples in soil great groups and subgroups were 43.53 and 50.59 respectively. Independent validation results showed the best accuracy obtained for soil great groups and subgroups were 48 and 53 percent respectively. Grooves depth, convergence index, channel network between and salinity in soil great groups and grooves depth, elevation and catchment area in soil subgroups were the most important environmental variables to estimate soil classes.Conclusion: The results showed most soils are young in the low relief lands in arid regions and these regions have also low soil diversity. Soil digital mapping and random forest technique could be useful for soil classes prediction and soil mapping in this kind of regions. Results: Pedological studies showed soils that formed in Sistan plain haven’t high development and most of them are Entisol and Aridisol. Most soil profiles classified as Torrifluvents on great groups and Typic Torrifluvents as subgroups. Also the result of RF showed the lowest estimation error of out of bag (OOB) samples in soil great groups and subgroups were 43.53 and 50.59 respectively. Independent validation results showed the best accuracy obtained for soil great groups and subgroups were 48 and 53 percent respectively. Grooves depth, convergence index, channel network between and salinity in soil great groups and grooves depth, elevation and catchment area in soil subgroups were the most important environmental variables to estimate soil classes.Conclusion: The results showed most soils are young in the low relief lands in arid regions and these regions have also low soil diversity. Soil digital mapping and random forest technique could be useful for soil classes prediction and soil mapping in this kind of regions. Keywords: Soil digital mapping, Random forest technique, Map accuracy, Arid regions, Sistan plain
https://jwsc.gau.ac.ir/article_3577_c7851b1a49d89163d15a9c96321ac348.pdf
2017-03-21
67
84
10.22069/jwfst.2017.12396.2700
Soil digital mapping
Random forest technique
Map accuracy
Arid regions
Sistan plain
Khalililah
Mirak Zehi
mirakzahi93@gmail.com
1
گروه علوم و مهندسی خاک، دانشگاه زابل، زابل، ایران
AUTHOR
Ali
Shahriari
shahriariali.uoz@gmail.com
2
گروه علوم خاک، دانشگاه زابل
LEAD_AUTHOR
Mohammd Reza
Pahlevanrad
pahlevan354@yahoo.com
3
بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی سیستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، زابل، ایران
AUTHOR
Abolfazl
Bameri
abolfazl_bameri@yahoo.com
4
گروه علوم و مهندسی خاک، دانشگاه زابل، زابل، ایران
AUTHOR
ebkit-text-size-adjust: auto; -webkit-1.Al-Masrahy, M.A., and Mountney, N.P. 2015. A classification scheme for fluvial–aeolian
1
system interaction in desert-margin settings. Aeolian Research. 17: 67-88.
2
2.Barthold, F.K., Wiesmeier, M., Breuer, L., Frede, H.G., Wu, J., and Blank, F.B. 2013. Land
3
use and climate control the spatial distribution of soil types in the grasslands of Inner
4
Mongolia. J. Arid Environ. 88: 194-205.
5
3.Behrens, T., Förster, H., Scholten, T., Steinrücken, U., Spies, E.D., and Goldschmitt, M. 2005.
6
Digital soil mapping using artificial neural networks. J. Plant Nutr. Soil Sci. 168: 21-33.
7
4.Behrens, T., Schmidt, K., Zhu, A.X., and Scholten, T. 2010. The ConMap approach for
8
terrainbased digital soil mapping. Eur. J. Soil Sci. 61: 133-143.
9
5.Boer, M., DelBarrio, G., and Puigdefabregas, J. 1996. Mapping soil depth classes in dry
10
Mediterranean areas using terrain attributes derived from a digital elevationmodel.
11
Geoderma. 72: 99-118.
12
6.Breiman, L., and Cutler, A. 2004. Random Forests. Department of Statistics, University of
13
Berkeley. http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm.
14
7.Brungard, C.B., and Boettinger, J.L. 2012. Spatial prediction of biological soil crust classes;
15
value added DSM from soil survey. P 57-60, In: B. Minasny, B.P. Malone and A.
16
McBratney (Eds.), Digital Soil Assessments and Beyond Proceedings of the 5th
17
GlobalWorkshop on Digital Soil Mapping. CRC Press, Sydney.
18
8.Brungard, C.W. 2009. Alternative Sampling and Analysis Methods for Digital Soil Mapping
19
in Southwestern Utah. Thesis for Master of Science, Utah State University. USA.
20
9.Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A., and Edwards Jr., T.C. 2015.
21
Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma.
22
239-240: 68-83.
23
10.Buol, S.W., Southard, R.J., Graham, R.C., and McDaniel, P.A. 2011. Soil genesis and
24
classification. 6th edition. Iowa State Univ. Press. Ames. Iowa, 556p.
25
11.Campling, P., Gobin, A., and Feyen, J. 2002. Logisticmodeling to spatially predict the
26
probability of soil drainage classes. Soil Sci. Soc. Am. J. 66: 1390-1401.
27
12.Cook, S.E., Jarvis, A., and Gonzalez, J.P. 2008. A New Global Demand for Digital Soil
28
Information. P 31-43, In: A.E. Hartemink, A. McBratney and M.L. Mendonca-Santos (Eds.),
29
Digital Soil Mapping with Limited Data. Springer, Dordrecht Heidelberg London New York.
30
13.Grunwald, S. 2010. Current State of Digital Soil Mapping and What Is Next. P 3-12, In: J.L.
31
Boettinger, D.W. Howel, A.C. Moore, A.E. Hartemink and S. Kienast-Brown (Eds.), Digital
32
Soil Mapping: Bridging Research, Environmental Application and Operation. Springer.
33
Dordrecht Heidelberg London New York.
34
14.Hastie, T., Tibshirani, R., and Friedman, J.H. 2001. The Elements of Statistical Learning:
35
Data Mining, Inference and Prediction. Springer, New York.
36
15.Hengl, T., and Reuter, H.I. 2008. Geomorphometry. Concepts, Software, Applications.
37
Developments in Soil Science. Elsevier, Amsterdam.
38
16.Hengl, T., Toomanian, N., Reuter, H.I., and Malakouti, M.J. 2007. Methods to interpolate soil
39
categorical variables from profile observations: lessons from Iran. Geoderma. 140: 417-427.
40
17.Heung, B., Bulmer, C.E., and Schmidt, M.G. 2014. Predictive soil parent material mapping
41
at a regional–Scale: A random forest approach. Geoderma. 214-215: 141-154.
42
18.Jafari, A., Ayoubi, S., Khademi, H., Finke, P.A., and Toomanian, N. 2013. Selection of a
43
taxonomic level for soil mapping using diversity and map purity indices: a case study from
44
an Iranian arid region. Geomorphology. 201: 86-97.
45
19.Jafari, A., Finke, P.A., Van deWauw, J., Ayoubi, S., and Khademi, H. 2012. Spatial prediction
46
of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression
47
approaches to predict diagnostic horizons and soil types. Eur. J. Soil Sci. 63: 284-298.
48
20.Jenny, H. 1941. Factors of Soil Formation: A System of Quantitative Pedology. McGrawHill, New York.
49
21.Lieb, M., Glaser, B., and Huwe, B. 2012. Uncertainty in the spatial prediction of soil texture
50
comparison of regression tree and random forest models. Geoderma. 170: 70-79.
51
22.Liu, J., Pattey, E., Nolin, M.C., Miller, J.R., and Ka, O. 2008. Mapping within-field soil
52
drainage using remote sensing, DEM and apparent soil electrical conductivity. Geoderma.
53
143: 261-272.
54
23.McBratney, A.B., Mendonça Santos, M.L., and Minasny, B. 2003. On digital soil mapping.
55
Geoderma. 117: 1-2. 3-52.
56
24.Minasny, B., McBratney, A.B., and Hartemink, A.E. 2010. Global pedodiversity, taxonomic
57
distance and the World Reference Base. Geoderma. 155: 132-139.
58
25.Moonjun, R., Farshad, A., Shrestha, D.P., and Vaiphasa, C. 2010. Artificial neural network
59
and decision tree in predictive soil mapping of Hoi Num Rin sub-watershed, Thailand.
60
P 151-164, In: J.L. Boettinger, D.W. Howell, A.C. Moore, A.E. Hartemink and S.
61
Kienast-Brown (Eds.), Digital Soil Mapping: Bridging Research, Environmental Application
62
and Operation. Springer, Dordrecht.
63
26.National soil survey center. 2012. Field book for describing and sampling soils, Ver. 3. U.S.
64
department of agriculture, Natural resources conservation service.
65
27.Pahlavan Rad, M.R., Toomanian, N., Khormali, F., Brungard, C.W., Komaki, C.B., and
66
Bogaert, P. 2014. Updating soil survey maps using random forest and conditional latin
67
hypercube sampling in the loess soil of northern Iran. Geoderma. 232-234: 97-106.
68
28.Pahlavan Rad, M.R., Toomanian, N., Khormali, F., Brungard, C.W., Komaki, C.B., and
69
Bogaert, P. 2016. Legacy soil maps as a covariate in digital soil mapping: A case study from
70
northern Iran. Geoderma. 279: 141-148.
71
29.Pahlavan Rad, M.R. 2014. Mapping and Updating Soil Map Using Random Forest and
72
Multinomial Logistic Regression in Golestan Province. Phd Thesis, Gorgan University of
73
Agricultural Sciences and Natural Resources, 114p.
74
30.Pahlavan Rad, M.R., Toomanian, N., Khormali, F., Brungard, C.W., Komaki, C.B., and
75
Bogaert, P. 2014. Digital soil mapping using random decision tree models in Golestan
76
province. J. Water Soil Cons. 21: 6. 73-93. (In Persian)
77
31.Poggio, L., Gimona, A., and Brewer, M.J. 2013. Regional scale mapping of soil properties
78
and their uncertainty with a large number of satellite-derived covariates. Geoderma.
79
209-210: 1–14.
80
32.Roecker, S.M., Howell, D.W., Haydu-Houdeshell, C.A., and Blinn, C. 2010. A Qualitative
81
Comparison of Conventional SoilSurvey and Digital Soil Mapping Approaches. P 369-384,
82
In: J.L. Boettinger, D.W. Howell, A.C. Moore, E.A. Hartemink and S. Kienast-Brown
83
(Eds.), Digital Soil Mapping: Bridging Research, Environmental Application and Operation.
84
Progress in Soil Science. Springer, New York.
85
33.Schaetzl, R.J., and Anderson, S. 2005. Soils: Genesis and Geomorphology. Cambridge
86
University Press, 833p.
87
34.Soil Survey Staff. 2014. Keys to soil Taxonomy, 12th ed. U.S. department of agriculture,
88
Natural resources conservation service.
89
35.Stum, A.K., Boettinger, J.L., White, M.A., and Ramsey, R.D. 2010. Random Forests applied
90
as a soil spatialpredictive model in arid Utah. P 179-189, In: J.L. Boettinger, D. Howell,
91
A.C. Moore, A. Hartemink and E.S. Kienast-Brown (Eds.), Digital SoilMapping:Bridging
92
Research, Environmental Application and Operation. Progress in Soil Science. Springer,
93
Logan, USA.
94
36.Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., and Malone, B.P. 2014. Digital
95
mapping of soil salinity in Ardakan region, central Iran. Geoderma. 213: 15-28.
96
37.Were, K., Bui, D.T., Disk, B., and Singl, B.R. 2015. A comparative assessment of support
97
vector regression, artificial neural networks and random forest for predicting soil organic
98
carbon stocks across an afromonkane land scape. Ecological indicator. Pp: 394-403.
99
38.Wilson, J.P., and Gallant, J.C. 2000. Terrain Analysis: Principles and Applications. In: G.J.
100
Wilson JP (Ed.), Digital terrain analysis. John Wiley, New York, 478p.
101
39.Xiong, X., Grunwald, S., Myers, D.B., Kim, J., Harris, W.G., and Comerford, N.B. 2012.
102
Which soil, environmental and anthropogenic covariates for soil carbon models in Florida
103
are needed? P 335-339, In: B. Minasny, B.P. Malone and A. McBratney (Eds.), Digital Soil
104
Assessments and Beyond: Proceedings of the 5th Global Workshop on Digital SoilMapping.
105
CRC Press, Sydney.
106
40.Yokoyama, R., Shirasawa, M., and Pike, R.J. 2002. Visualizing topography by openness: a
107
new application of image processing to digital elevation models. Photogramm. Eng. Remote
108
Sens. 68: 257-266.
109
ORIGINAL_ARTICLE
The Elilination of Nitrate from Urban Storm Runoffs by Multi-Filters Process
Background and Objectives: Over the last two centuries, a substantial increase has been observed in the rate of production and consumption of nitrate, particularly in agricultural sector. Currently, in many parts of the world and even in Iran, the high concentration of nitrate in drinking water has shown to be a serious problem, mainly caused by the introduction of agricultural wastewater and home and industrial sewage runoffs in the water resources, and especially in the groundwater. Use of the contaminated water with nitrate along with some food products containing high levels of nitrate can result in the entrance of excessive amounts of nitrate into the body. The ultimate goal of the present study is to design and investigate the bio-geo-filters in order for the elimination of nitrate from the runoffs.Materials and Methods: In this research, alternate layers of non-woven geotextile filters and granular soil have been used for reduction and removal of pollution. These layers are of paramount importance in terms of their permissibility and absorption capability. For selection of materials some points have been considered, which include the material capability for pollution elimination, their accessibility, and maximal cost-effectiveness. Results: After conduction of permissibility tests, the ratio of the weight mixture of the applied materials in PRB has been considered as 25% sand, 20% zeolite, 20% iron borings, and 10% poplar wood sawdust. It has been observed that for pH=7, the maximal nitrate absorption efficiency by zeolite is about 69%, sawdust 29%, and iron borings 12%. As indicated by the results of nitrate absorption through the final mixture of PRB in different concentrations of nitrate under optimal pH conditions while other parameters being constant, maximal absorption is due to the concentration of 150 mg L-1 and occurs in about 83%. The more the original nitrate concentration increases, the more the absorption amount goes up. Moreover, nitrate elimination with equal amounts of absorbent and optimal pH has been performed in different times for determination of equilibrium time, and the maximal elimination of 100% has been obtained in equilibrium time of 96 hours. in the administered test for removal of the pollution, after the growth of biologic mass in its environment, the filter was able to decrease the amount of nitrate up to 99% after the elapse of 9 days, and consequently its final amount was decreased from 100 mg L-1 to 1 mg L-1.Conclusion: The designed permeable reactive barriers with the percentage of weight mix has the capability of adsorbing a quite large amount of nitrate in a short time. Washing of adsorbent materials and removal of the pollutants result in the increase in the especial surface of the adsorbent, and thus the adsorption power increases.
https://jwsc.gau.ac.ir/article_3578_f11b09cee65d056b4fb12d642488592b.pdf
2017-03-21
85
101
10.22069/jwfst.2017.12111.2665
bio-geo-filter
Nitrate removal
Treating urban runoff
absorption
low-priced absorbents
Somayeh
Sirouspour
s.sirospor@gmail.com
1
مربی دانشگاه آزاد اسلامی واحد رامهرمز
AUTHOR
Mansour
Parvizi
parvizi@yu.ac.ir
2
استادیار دانشگاه دولتی یاسوج، گروه مهندسی عمران
LEAD_AUTHOR
Mohammad
Parvin Nia
mparvinnia@yu.ac.ir
3
استادیار گروه مهندسی عمران و محیط زیست، دانشگاه یاسوج
AUTHOR
Ardeshir
Shokrollahi
ashokrollahi@yu.ac.ir
4
دانشیار گروه شیمی، دانشگاه علوم پایه یاسوج
AUTHOR
auto; -webkit-text-stroke-width: 0px; 1.World Health Organization. 1971. International standards for drinking-water, 3rd ed. Geneva:
1
World Health Organiza-tion.
2
2.Gilchrist, M., Winyard, P.G., and Benjamin, N. 2010. Review; Dietary nitrate – Good or bad?,
3
Nitric Oxide. 22: 104-109.
4
3.Camargo, J.A., Alonso, A., and Salamanca, A. 2005. Nitrate Toxicityto Aquatic Animals: a
5
Review with New Data for Freshwater Invertebrates, Chemosphere. 58: 1255-1267.
6
4.EVS, Nitrate and Nitrite. 2005. Human Health Fact Sheet. Argonne National Laboratory,
7
5.DES. Nitrate and Nitrite: Health Information Summary. 2006. Environmental Fact Sheet. New
8
Hampshire Department of Environmental Services. ARD-EHP-16.
9
6.Soejima, T. 2002. In Situ Remediation of Nitrate-Contaminated Grounwater Using a
10
Permeable Reactive Barrier, Environmental Geotechnics (4th ICEG). de Mello and Almeida.
11
2: 811-816.
12
7.Parvinnia, M. 2007. Filterability and Recovery of Civic Floods Using Active Penetrable
13
Layers, PhD Thesis. University of Shiraz. Department of civil and road engineering.
14
8.Yaman, C. 2003. Geotextiles as Biofilm Filters in Wastewater Treatment, PhD Thesis.
15
Department of Environmental Engineering. Drexel University. Philadelphia.
16
9.Mohammed, T., Vigneswaran, S., and Kandasamy, J. 2010. Biofiltration as Pre Treatment to
17
Water Harvesting and Recycling, Water Sci. Technol.
18
10.Amini, S. 1997. Filtration of color sewage in sewing factories using surface adsorbents with
19
the aim of recovery, the 4th international conference of civil engineering.
20
11.Patil, S.B., and Chore, H.S. 2015. Experimental and Numerical Modeling of Solute
21
Transport Through Porous Media, Inter. J. Engin. Res. Pp: 244-249.
22
12.Harris, B. 2004. PRB’s and their role in thesustainable remediation of groundwater, Belfast
23
Northern Irland.
24
13.Delbazi, N., Ahmadi Moghadam, M., Takdastan, A., and Jafar Zade Haghighi Fard, N. 2011.
25
A Comparison of Filter Performance Layer of Sand-Floor and Bilayer Filter with Lika and
26
Anthracite Floors in the Removal of Organic Matter and Turbidity, J. Health Environ. J. Sci.
27
Res. 3: 301-312.
28
14.Afandi Zade, SH. 1988. Geotextiles (Textiles species), J. Road. 17: 13-21.
29
15.Naddafi, K., and Gholami, M. 2014. Removal of Reactive Red 120 from aqueous solutions
30
using surface modified natural zeolite, J. Health Environ. 3: 7. 276-288.
31
16.Kamali, M., and Haji, S. 2011. Application of zeolite in water and wastewater treatment,
32
First Conference on Biology Environmental Refining Technologies.
33
17.Hosseini, M., Kholghi, M., Ataee Ashtiani, B., and Bagheri Mohagheghi, M.M. 2011.
34
Laboratorial Study of Reduction of Nitrate from Drinkable Water Using Bimetal
35
Nanoparticles Of Iron/ Copper, J. Water Soil. 1: 94-103.
36
18.Ghasemian, M.K. 2010. Modeling of Biologic Soil Filters for Removal of Organic Materials
37
Solved in Civil Floods, M.Sc. Thesis. Department of Civil Engineering. Yasouj University.
38
19.Öztürk, N., and Bektaş, T.E. 2004. Nitrate Removal from Aqueous Solution by Adsorption
39
Onto Various Materials, J. Hazard. Mater. 112: 1. 62-155.
40
20.Fouladshekan, F., and Rahnemaie, R. 2015. Using Quartz-Supported Zero-Valent Iron
41
Nanoparticles for Removing Nitrate in Equilibrium and Fluid Systems, J. Water Soil Cons.
42
22: 2. 219-227.
43
21.Huang, C.P. et al. 1998. Nitrate Reduction by Metallic Iron, Wat. Res. 32: 8. 2257-2264.
44
22.Islam, M., and Patel, R. 2010. Synthesis and Physicochemical Characterization of Zn/Al
45
Chloride Layered Double Hydroxide and Evaluation of Its Nitrate Removal Efficiency,
46
Desalination. 256: 1-3. 8-120.
47
ORIGINAL_ARTICLE
Measurement of changes in labile pools of soil organic carbon and some soil properties under forest tree species in Northern Iran
(Case study: Shalman Seed and Seedling of Forest Tree Species Research Station, Guilan Province)
Background and Objectives: Deep insight about the different effect of forest tree species on soil quality properties have made soil health monitoring perspective clear concerning sustainable management; however, restoration and reclamation of deteriorates inflicted on natural ecosystems may be managed through proper selection of tree species. In this study, in order to select suitable tree species in afforestation projects, Shalman Seed and Seedling of Forest Tree Species Research Station (Guilan province) was chosen as a study area to investigate carbon storage rates and effect of conifers and broadleaves on soil properties, Consequently, soil carbon labile pools were evaluated to present unique sensitive indicator of health and soil quality.Materials and Methods: Sampling of 10 layers with thickness of 20 cm were taken from 0-200 cm depth under selected tree species plots, including Populus caspica, Oak (Quercus castaneifolia), Alder (Alnus glutinosa), Bald cypress (Taxodium distichum), Loblolly pine (Pinus taeda) and Juniper ( Juniperus polycarpos). Cation exchang capacity, mean weight diameter, EC, pH, bulk density, total nitrogen, soil organic carbon and its labile pools were analyzed in soil samples of 0-20, 20-40, 40-60 and 60-80 cm and just for carbon storage measurements all of 10 layers (0-200 cm) were considered. The experiments were of randomized complete block (RCB) designs. Data for the same soil interval were subjected to two-way analysis of variance (ANOVA). Person linear Correlation method was used to determine sensitive indicators of soil quality. Results: Preliminary results indicated the significant effects of tree species on soil properties during soil depth. Despite insignificant differences in EC, pH and BD, our results showed that significant alterations by tree species types were found in the 0-20 cm soil layer. However, the greatest difference on CEC and MWD values were also observed in the 0-20 cm soil thickness between Alder vs. Juniper; and Alder vs. Bald cypress, respectively. All species had also higher total nitrogen (TN) and soil organic carbon (SOC) in the top soil layer (0-20 cm) followed the order: A. glutinosa > Q. castaneifolia > P. caspica > J. polycarpos > T. distichum > P. taeda. The measured amount of total soil organic carbon as a carbon storage was the highest and the lowest under Alder (A. glutinosa) and Loblolly pine (Pinus taeda) with 206.24 and 136.94 (ton OC ha-1), respectively.Conclusion: Broadleaves, especially N fixer species such as Alder had the greatest effect on soil quality properties. Broadleaves had also great potential for carbon storage with more uniform distribution during soil depth. However, among broadleaves, Alder had great effect on soil properties and soil organic matter. Finally, according to correlation values, no single and more sensitive organic carbon pool as a soil quality indicator of forest tree species changes was selected, but the complex of soil organic carbon pools could be used as sensitive indicators of soil quality and health.
https://jwsc.gau.ac.ir/article_3579_aade2d612ebafaeada1474116295c2f5.pdf
2017-03-21
103
119
10.22069/jwfst.2017.11171.2552
"Labile soil organic carbon"
"Water soluble organic carbon"
"Carbon storage (stocks)"
Karim
Atashnama
k_atashnama@znu.ac.ir
1
جهاد کشاورزی استان قم
LEAD_AUTHOR
Ahmad
Golchin
agolchin2011@yahoo.com
2
استاد گروه خاکشناسی دانشگاه زنجان
AUTHOR
Abdollah
Mosavi Kopar
abdy_mo@yahoo.com
3
مرکز تحقیقات کشاورزی و منابع طبیعی گیلان مسئول ایستگاه تحقیقات صنوبر صفرابسته
AUTHOR
2; word-spacing: 0px; -webkit-text-si1.Alvarez, E., Fernandez Marcos, M.L., Torrado, V., and Fernandez Sanjurjo, M.J. 2008.
1
Dynamics of macronutrients during first stage of litter decomposition from forest species in a
2
temperate area (Galicia, NW Spain). Nutrient Cycling in Agroecosystems. 80: 3. 243-256.
3
2.An, S., Mentler, A., Mayer, H., and Blum, W.E.H. 2010. Soil aggregation, aggregate stability,
4
organic carbon and nitrogen in different soil aggregate fractions under forest and shrub
5
vegetation on the Loess Plateau, China. Catena. 81: 226-233.
6
3.Barbier, S., Gosseline, F., and Balandier, P. 2008. Influence of tree species on understory
7
vegetation diversity and mechanisms involved-A critical review for temperate and boreal
8
forests. Forest Ecology and Management. 254: 1-15.
9
4.Beheshti, A., Raiesia, F., and Golchin, A. 2012. Soil properties, C fractions and their
10
dynamics in land use conversion from native forests to croplands in northern Iran.
11
Agriculture, Ecosystems & Environment. 148: 121-133.
12
5.Binkley, D., and Giardina, C. 1998. Why do tree species affect soils? The warp and woof of
13
tree-soil interactions. Biogeochemistry. 42: 89-106.
14
6.Bremner, J.M., and Mulvaney, C.S. 1982. Nitrogen-total. P 595-624, In: A.L. Page, R.H.
15
Miller and D.R. Keeney (Eds.), Methods of Soil Analyses. Part 2: Chemical and
16
Microbiological Properties. 2nd ed. American Society of Agronomy, Madison, WI.
17
7.Celik, I. 2005. Land use effects on organic matter and physical properties of soil in a southern
18
Mediterranean high land of Turkey. Soil and Tillage Research. 83: 270-277.
19
8.Chen, C.R., Xu, Z.H., and Mathers, N.J. 2004. Soil carbon pools in adjacent natural and
20
plantation forests of subtropical Australia. Soil Sci. Soc. Am. J. 68: 282-291.
21
9.Chiti, T., Cerini, A., Puglisi, A., Sanesi, A., and Capperucci, C. 2006. Effects of associating an
22
N-fixer species to monotypic oak plantations on the quantity and quality of organic matter in
23
mine soils. Geoderma. 61: 35-43.
24
10.Cools, N., Vesterdal, L., De Vos, B., Vanguelova, E., and Hansen, K. 2014. Tree species is
25
the major factor explaining C: N ratios in European forest soils. Forest Ecology and
26
Management. 311: 3-16.
27
11.Ghani, A., Dexter, M., and Perrott, K. 2003. Hot-water extractable carbon in soils: a sensitive
28
measurement for determining impacts of fertilisation, grazing and cultivation. Soil Biology
29
and Biochemistry. 35: 9. 1231-1243.
30
12.Golchin, A., and Asgari, H. 2008. Land use effects on soil quality indicators in north-eastern
31
Iran. Soil Research. 46: 1. 27-36.
32
13.Golchin, A., Clarke, R., Oades, J.M., and Skjemstad, J.O. 1995. The effects of cultivation on
33
the composition of organic matter and structural stability of soils. Soil Research. 33: 975-993.
34
14.Gregorich, E.G., Beare, M.H., Stoklas, U., and St-Georges, P. 2003. Biodegradability of
35
soluble organic matter in maize-cropped soils. Geoderma. 113: 237-252.
36
15.Hagen-Thorn, A., Callesen, I., Armolaitis, K., and Nihlgard, B. 2004a. The impact of six
37
European tree species on the chemistry of mineral topsoil in forest plantations on former
38
agricultural land. Forest Ecology and Management. 195: 373-384.
39
16.Hamkalo, Z., and Bedernichek, T. 2014. Total, cold and hot water extractable organic carbon
40
in soil profile: impact of land-use change. Zemdirbyste –Agriculture. 101: 2. 125-132.
41
17.Hao, X., Ball, B.C., Culley, J.L.B., Carter, M.R., and Parkin, G.W. 2008. Soil density and
42
porosity. P 743-759, In: M.R. Carter and E.G. Gregorich (Eds.), Soil Sampling and Methods
43
of Analysis. Canadian Society of Soil Science, CRC Press, Taylor & Francis Group, Boca
44
Raton, FL.
45
18.Harison, K.G., Broecker, W.S., and Bonani, G. 1993b. The effect of changing land use on
46
soil radiocarbon. Science. 262: 725-726.
47
19.Haynes, R.J., and Francis, G.S. 1993. Changes in microbial biomass C, soil carbohydrate
48
composition and aggregate stability induced by growth of selected crop and forage species
49
under field conditions. Europ. J. Soil Sci. 44: 665-675.
50
20.Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D.W.,
51
Minkkinen, K., and Byrne, K.A. 2007. How strongly can forest management influence soil
52
carbon sequestration. Geoaderma. 137: 253-268.
53
21.Jiang, P.K., and Xu, Q.F. 2006. Abundance and dynamics of soil labile carbon pools under
54
different types of forest vegetation. Pedosphere. 16: 4. 505-511.
55
22.Jinenez, M.P., Horra, A.M., Pruzzo, L., and Palma, R.M. 2002. Soil quality: a new index based
56
on microbiological and biochemical parameter. Biology and Fertility of Soils. 35: 302-306.
57
23.Jobbágy, E.G., and Jackson, R.B. 2000. The vertical distribution of soil organic carbon and
58
its relation to climate and vegetation. Ecological Applications. 10: 423-436.
59
24.Johnson, D.W., and Curtis, P.S. 2001. Effects of forest management on soil C and N storage:
60
Meta analysis. Forest Ecology and Management. 140: 227-238.
61
25.Kara, O., and Baykara, M. 2014. Changes in soil microbial biomass and aggregate stability
62
under different land use in the northern Turkey. Environmental Monitoring and Assessment.
63
186: 3801-3808.
64
26.Kavvadias, V.A., Alifragis, A., Tsiontsis, G., Brofas, G., and Stamatelos, G. 2001. Litterfall,
65
litter accumulation and litter decomposition rates in four forest ecosystem in northern
66
Greece. Forest Ecology and Management. 144: 113-127.
67
27.Kolar, L., Kuzel, S., Horacek, J., Cechova, V., Borova-Batt, J., and Peterka, J. 2009. Labile
68
fraction of soil organic matter, their quantity and quality. Plant, Soil and Environment.
69
55: 245-251.
70
28.Kroetsch, D., and Wang, C. 2008. Particle size distribution. P 713-725, In: M.R. Carter and
71
E.G. Gregorich (Eds.), Soil Sampling and Methods of Analysis. Canadian Society of Soil
72
Science, CRC Press, Taylor and Francis Group, Boca Raton, FL.
73
29.Lal, R., Negassa, W., and Lorenz, K. 2015. Carbon sequestration in soil. Current Opinion in
74
Environmental Sustainability. 15: 79-86.
75
30.Liu, C.H., and Luo, R.Y. 1990. Chemical characteristics of humus in forest soils of NanjingZhenjiang Hills. J. Nanjing Forest. Univ. In: Jiang, P.K., and Xu, Q.F. 2006. Abundance and
76
dynamics of soil labile carbon pools under different types of forest vegetation. Pedosphere,
77
16: 4. 505-511.
78
31.McLean, E.O. 1982. Soil pH and lime requirement. P 199-224, In: A.L. Page, R.H. Miller
79
and D.R. Keeney (Eds.), Methods of Soil Analyses. Part 2: Chemical and Microbiological
80
Properties. 2nd ed. American Society of Agronomy, Madison, WI.
81
32.Miller, J.J., and Curtin, D. 2008. Electrical Conductivity and Soluble Ions. P 161-171,
82
In: M.R. Carter and E.G. Gregorich (Eds.), Soil Sampling and Methods of Analysis.
83
Canadian Society of Soil Science, CRC Press, Taylor and Francis Group, Boca Raton, FL.
84
33.Nelson, D.W., and Sommers, L.E. 1982. Total carbon, organic carbon, and organic matter.
85
P 539-579, In: A.L. Page, R.H. Miller and D.R. Keeney (Eds.), Methods of Soil Analysis.
86
Part 2: Chemical and Microbiological Properties. 2nd ed. American Society of Agronomy,
87
Soil Science Society of America, Madison, WI.
88
34.Oostra, S., Majdi, H., and Olsson, M. 2006. Impact of tree species on soil carbon stocks and
89
soil acidity in southern Sweden. Scandinav. J. For. Res. 21: 364-371.
90
35.Parsakhoo, A., Lotfalian, M., Kavian, A., and Hosseini, S.A. 2014. Assessment of
91
soil erodibility and aggregate stability for different parts of a forest road. J. For. Res.
92
25: 1. 193-200.
93
36.Pérez-Cruzado, C., mansilla-salinero, P., Rodríguez-Soalleiro, R., and Merino, A. 2012.
94
Influence of tree species on carbon sequestration in afforested pastures in a humid temperate
95
region. Plant and Soil. 353: 333-353.
96
37.Piccolo, A. 1996. Humic substances in terrestrial ecosystems. Elsevier. Netherlands, 675p.
97
38.Powlson, D.S., Whitmore, A.P., and Goulding, K.W.T. 2011. Soil carbon sequestration to
98
mitigate climate change: a critical re-examination to identify the true and the false. Europ. J.
99
Soil Sci. 62: 42-55.
100
39.Ramesh, T., Manjaiah, K., Mohopatra, K., Rajasekar, K., and Ngachan, S. 2015. Assessment
101
of soil organic carbon stocks and fractions under different agroforestry systems in
102
subtropical hill agroecosystems of north-east India. Agroforestry Systems. 89: 677-690.
103
40.Rasse, D.P., Li, J.H., and Drake, B.G. 2005. Seventeen years of elevated CO2 exposure in a
104
Chesapeake Bay wetland: Sustained but contrasting responses of plant growth and CO2
105
uptake. Global Change Biology. 11: 369-377.
106
41.Razavi, S.A. 2010. Comparison of Soil Characteristics and Biodiversity in Plantations of
107
Bald Cypress and Caucasian Alder (Case Study: Kludeh-Mazandaran Province). J. Wood
108
For. Sci. Technol. 17: 2. 41-56. (In Persian)
109
42.Reich, P.B., Oleksyn, J., Modrzynski, J., Mrozinski, P., Hobbie, S.E., Eissenstat, D.M.,
110
Chorover, J., Chadwick, O.A., Hale, C.M., and Tjoelker, M.G. 2005. Linking litter calcium,
111
earthworms and soil properties: a common garden test with 14 tree species. Ecology Letters.
112
8: 811-818.
113
43.Resh, S.C., Binkley, D., and Parrotta, J.A. 2002. Greater soil carbon sequestration under
114
Nitrogen-fixing trees compared with Eucalyptus species. Ecosystems. 5: 217-231.
115
44.Rilling, M.C., and Mummey, D.L. 2006. Mycorrhizas and soil structure. New Phytolgist.
116
171: 41-53.
117
45.Rhoades, J.D. 1982a. Cation exchange capacity. P 149-157, In: A.L. Page, R.H. Miller and
118
R. Keeney (Eds.), Methods of Soil Analysis., Part 2: Chemical and Microbiological
119
Properties., 2nd ed. American Society of Agronomy, Madison WI.
120
46.Sagheb-Talebi, K., Sajedi, T., and Pourhashemi, M. 2014. Forests of Iran: A treasure from
121
the past, a hope for future. Plant and Vegetation, Vol. 10, Springer Verlag, Dordrecht, 152p.
122
47.SAS Institute, Inc. 2002. Statistical Analysis Software Version 8.2 for Microsoft Windows.
123
SAS Institute Inc. SAS Institute, Cary, NC.
124
48.Six, J., Bossuyt, H., Degryze, S., and Denef, K. 2004. A history of research on the link
125
between (micro) aggregates, soil biota, and soil organic matter dynamics. Soil and Tillage
126
Research. 79: 7-31.
127
49.Six, J., Callewaert, P., Lenders, S., Gryze, S.D., Morris, S.J., Gregorich, E.G., Paul, E.A.,
128
and Paustian, K. 2002a. Measuring and understanding carbon storage in afforested soils by
129
physical fractionation. Soil Sci. Soc. Amer. J. 66: 1981-1987.
130
50.Smolander, A., and Kitunen, V. 2002. Soil microbial activities and characteristics of
131
dissolved organic C and N in relation to tree species. Soil Biology and Biochemistry.
132
34: 651-660.
133
51.Soil Survey Staff. 2010. Keys to soil taxonomy. USDA Natural Resources Conservation
134
Service, Washington, DC.
135
52.Theng, B.K.G., Ristori, G.G., Santi, C.A., and Percival, H.J. 1999. An improved method for
136
determining the specific surface areas of top soils with varied organic matter content, texture
137
and clay mineral composition. Europ. J. Soil Sci. 50: 309-316.
138
53.Vance, E.D., Brookes, P.C., and Jenkinson, D.S. 1987. Microbial biomass measurements in
139
forest soils: the use of the chloroform fumigation incubation method for strongly acid soils.
140
Soil Biology and Biochemistry. 19: 697-702.
141
54.Varamesh, S., Hosseini, S.M., Abdi, N., and Akbarinia, M. 2010. Increment of soil carbon
142
sequestration due to forestation and its relation with some physical and chemical factors of
143
soil. Iran. J. For. 2: 1. 25-35. (In Persian)
144
55.Wang, D., Wang, B., and Niu, X. 2014. Effects of natural forest types on soil carbon fraction
145
in North-East China. J. Trop. For. Sci. 26: 3. 362-370.
146
56.Wang, Q., and Wang, S. 2007. Soil organic matter under different forest types in Southern
147
China. Geoderma. 142: 3. 349-356.
148
57.Wang, Q., and Wang, S. 2011. Response of labile soil organic matter to changes in forest
149
vegetation in subtropical regions. Applied soil ecology. 47: 3. 210-216.
150
58.Withington, J.M., Reich, P.B., Oleksyn, J., and Eissenstat, D.M. 2006. Comparisons of
151
structure and life span in roots and leaves among temperate trees. Ecological Monographs.
152
76: 3. 81-397.
153
59.Yousefi, M., Hajabbasi, M., and Shariatmadari, H. 2008. Cropping system effects on
154
carbohydrate content and water-stable aggregates in a calcareous soil of Central Iran. Soil
155
and Tillage Research. 101: 57-61
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ORIGINAL_ARTICLE
Experimental Study of Shock Waves in Open-Channels Transition with Trapezoidal and Rectangular Sections
Background and objectives: Contractions have many uses in supercritical flows, such as flow conveyance from intake channels of dams to tunnel spillways, reduction of chutes width and reduction of flow conveyance time in the flood conduits. In supercritical flows studies, the formation of the shock waves has an important role. Technically, production and development of the mentioned waves are undesirable due to water depth increase because of several times increasing of inflow water depth, its spread at a wide range in downstream of channel and water surface roughness. Any weak design of channels under supercritical condition can cause to scour channel’s bed and walls, damage to equipment in the flow direction, raising maintenance costs and reduce water conveyance efficiency. In the present research, the formation of shock waves in converged transitions of open channel with rectangular and trapezoidal sections was investigated using laboratory and physical models.Materials and methods: In order to investigate hydraulic parameters of shock waves in the converged transitions, twelve models with different geometries were used. In the present research, the studied geometric variables were the diagonal length of transition walls (0.5, 0.75 and 1m) and side wall angle (33.69º, 45º, 60º and 90º). In all used models, the convergence ratio was 0.5. The height and instantaneous velocity were measured in different points of formed shock waves in the mentioned models for four different Froude number in the range of 3.25 to 9.23. Results: The measured values in the converged transitions showed that the velocity distribution was not uniform in the vertical direction of shock waves. Also, the results showed that by traveling wave front toward downstream cause to reduce wave velocity and increase wave height so that for various geometries, the changes trend was different. The results showed that on average, and for side slopes angels of 33.69º, 45º and 60º, the maximum height of shock waves was reduced 64.8%, 54.3% and 39.6% respectively in the comparison of trapezoidal and rectangular sections. Also, in the converged transitions and for the mentioned side slope angles, maximum shock wave velocity was reduced 39.1%, 31.6% and 16.5% respectively in the comparison of trapezoidal and rectangular sections. Increasing of side slope angle was accompanied with energy dissipation increment of shock waves for a constant Froude number and transition wall length. Also, maximum value of energy dissipation was seen for 0.5m of wall length. The values of energy dissipation for the mentioned length, Fr1=7.26 and side slopes angels of 33.69º, 45º, 60º and 90º were achieved 14.69%, 15.43%, 16.34% and 18.72%, respectively. Conclusion: The analysis of the velocity profiles and free surface of shock waves showed that in general the reduction of side slope angle (increasing side slopes) of the transition wall, increase of diagonal wall length of the transition and reduction of Froude number have a direct relationship with the reduction of waves velocity and height. Since channels are constructed in the form of trapezoidal, the obtained results of the present research can be very useful for designer engineers.
https://jwsc.gau.ac.ir/article_3580_b73c7c343df216464028544ab333717f.pdf
2017-03-21
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138
10.22069/jwfst.2017.11113.2548
Contraction
Diagonal length
Slide slope angle
Shock waves
Supercritical flow
Javad
Behmanesh
j.behmanesh@urmia.ac.ir
1
دانشگاه ارومیه- گروه مهندسی آب
LEAD_AUTHOR
Soheila
Alipour
soheila70a@gmail.com
2
دانشگاه ارومیه
AUTHOR
Mohamd Reza
Nikpour
rezanikpoor@yahoo.com
3
دانشگاه محقق اردبیلی
AUTHOR
1.Bhallamudi, S.M., and Chaudhry, M.H. 1992. Computation of flows in open-channel
1
transitions. J. Hydr. Res. 30: 1. 77-93.
2
2.Chow, V.T. 1959. Open channel hydraulics. Mc Graw-Hill Press, Michigan, 680p.
3
3.Ghazanfari hashemi, R., and Montazeri Namin, M. 2012. Investigation of turbulence effects of
4
supercritical flow in contractions using 3D numerical modeling. 11th Iranian Conference on
5
Hydraulic, Pp: 171-179. (In Persian)
6
4.Gonzalo, R., Nanía, L.S., and Gómez, M. 2014. Influence of Channel Width on Flow
7
Distribution in Four-Branch Junctions with Supercritical Flow: Exp. App. J. Hydr. Eng.
8
140: 1. 77-88.
9
5.Hager, W.H. 1989. Supercritical flow in channel junction. J. Hydr. Eng. 115: 5. 595-616.
10
6.Hager, W.H., Schwalt, M., Jimenez, O.F., and Chaudhry, M.H. 1994. Supercritical flow near
11
an abrupt wall deflection. J. Hydr. Res. 32: 1. 103-118.
12
7.Jafarzadeh, M.R., Shamkhalchian, A., and Jomehzadeh, M. 2012. Supercritical flow profile
13
improvement by means of a convex corner at a bend inlet. J. Hydr. Res. 50: 6. 623-630.
14
8.Jimenez, O.F., and Chaudhry, M.H. 1988. Computation of Supercritical Free-Surface Flows.
15
J. Hydr. Eng. 114: 4. 377-395.
16
9.Kolarević, M., Savić, L., Kapor, R., and Mladenović, N. 2013. Supercritical flow in circular
17
pipe bends. J. Scineks. Ceon. 42: 128-133.
18
10.Krüger, S., and Rutschmann, P. 2006. 3D Modeling supercritical flow with extended
19
shallow-water approach. J. Hydr. Eng. 132: 9. 916-926.
20
11.Mignot, E., Rivière, N., Perkins, R., and Paquier, A. 2008. Flow patterns in a four-branch
21
junction with supercritical flow. J. Hydr. Eng. 134: 6. 701-713.
22
12.Nikpour, M.R. 2013. Investigation of Supercritical flow in open-channels transition using
23
experimental and numerical models. In: A thesis submitted to the Faculty of Agriculture,
24
University of Tabriz for the Ph.D. Degree, 200p. (In Persian)
25
13.Reinauer, R., and Hager, W.H. 1997. Supercritical bend flow. J. Hydr. Eng. 123: 3. 208-218.
26
14.Reinauer, R., and Hager, W. 1998. Supercritical flow in chute contraction. J. Hydra. Eng.
27
124: 1. 55-64.
28
15.Saldarriaga, J., Bermudez, N., and Rubio, D.P. 2012. Hydraulic behavior of junction
29
manholes under supercritical flow conditions. J. Hydr. Res. 50: 6. 631-636.
30
16.Ya Kun, L., and Han Gen, N. 2008. Abrupt deflected supercritical water flow in slopped
31
channels. J. Hydrodyn. 20: 3. 293-298.
32
ORIGINAL_ARTICLE
Climate change impacts on the maximum daily discharge under conditions of uncertainty (Dinavar basin in Kermanshah)
Background and Objectives: The phenomenon of climate change and its impact on water resources is of utmost importance that has been less investigated in our country.In this study, The meteorological variables in terms of predicted climate change and were compared with the present situation. The effect of this phenomenon on Dinavar Kermanshah discharge basin taking into account the uncertainty was evaluated.Materials and Methods: To this end, results of 6 model coupled atmosphere - ocean general circulation of the atmosphere contains MPEH5, IPCM4, INCM3, HADCM3, GFCM21 and NCCCSM under scenarios of greenhouse gas emissions SRESS includes A1B, A2 and B1 were Downscaling using the LARS-WG software. To determine the accuracy of the models and scenarios, temperature and precipitation observational data were compared with temperature and precipitation available data on Canada base models and scenarios and weighted method was used to evaluate uncertainty models and scenarios. Then, base of scenario and models uncertainty, was predicted variables in coming period (2011-2034) and (2046-2069) compared with the base period (1987-2010). After the downscaling of climate variables, IHACRES rainfall-runoff models used to simulate runoff in future periods.Results: Based on the results, it's expected that temperature will be increased respectively 1.72 ,1.55 and 1.39 ° C in 2011-2034 and 3.27, 2.88 and 2.26 ° C in 2046-2069, for A1B, A2 and B1 scenarios compared to the baseline in Dinavar basin. As well as precipitation changes respectively has been 15.22, 17.94 and 23.27 mm for A1B, A2 and B1 scenarios in 2011-2034, and -35.4, 7.97 and 2.58 mm for A1B, A2 and B1 scenarios in 2046-2069 compared to the baseline in this basin. The results showed that the amount of average flow and runoff volume has been increased in future periods except A1B scenario (2046-2069). But, flow regime of maximum daily discharges showed that it is adjust in future period. Flow - Frequency curve analysis with different probability showed that it is required to build large reservoirs to water supply in low flow seasons in future periods.Conclusion: The results showed that the amount of average temperature and precipitation will be increased in future periods. So that the increase of temperature in the second period is more than the first period and increase of precipitation in the first period will be more than the second period. Also the amount of discharges in future period will be increased so that the increase in the first period will be more than the second period, and the volume of runoff in the first period will be more than the second period and in both periods were higher than the base period. But flow regime of maximum daily discharges showed the decreasing in future period, So that the maximum discharge rate decrease in the second period is more than the first period. Flow - Frequency curve analysis also showed that in the absence ofwater storage, agriculture andindustry and drinkingin the area faced withsupply problems.
https://jwsc.gau.ac.ir/article_3581_620ad58345144ec5ea4e553bdfbfabe4.pdf
2017-03-21
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156
10.22069/jwfst.2017.10715.2513
Climate Change
AOGCM Models
Emission Scenarios
IHACRES
LARS-WG
Sahar
Najafian
s.najafian65@yahoo.com
1
دانشجوی کارشناسی ارشد هواشناسی کشاورزی دانشگاه سمنان
AUTHOR
Mohamd Reza
Yazdani
m_yazdani@semnan.ac.ir
2
استادیار گروه بیابانزدایی دانشکده کویرشناسی دانشگاه سمنان
LEAD_AUTHOR
Arash
Azari
arashazari.ir@gmail.com
3
استادیار گروه مهندسی آب دانشگاه رازی کرمانشاه
AUTHOR
Mohamad
Rahimi
mrahimi@sun.semnan.ac.ir
4
استادیار گروه بیابانزدایی دانشکده کویرشناسی دانشگاه سمنان
AUTHOR
1.Ashofteh, P. 2012. Climate change Impact on the crop water requirement using HadCM3
1
model in Aidoghmoush irrigation network. Iran. J. Irrig. Drain. 6: 3. 142-151. (In Persian)
2
2.Ashraf, B., Mousavi-Baygi, M., Kamali, G.A., and Davari, K. 2012. Evaluation of wheat and
3
Sugar beet water use Variation due to climate change effects in two Coming Decades in the
4
selected plains of Khorasan Razavi Province. Iran. J. Irrig. Drain. 6: 2. 105-117. (In Persian)
5
3.Booij, M.J., Tollenaar, D., van Beek, E., and Kwadijk, J.C. 2011. Simulating impacts of
6
climate change on river discharges in the Nile basin. Physics and Chemistry of the Earth,
7
Parts A/B/C. 36: 13. 696-709.
8
4.Carcano, E.C., Bartolini, P., Muselli, M., and Piroddi, L. 2008. Jordan recurrent neural
9
network versus IHACRES in modelling daily streamflows. J. Hydrol. 362: 3. 291-307.
10
5.COP21. 2015. UN climate change conference | Paris, http://www.cop21paris.org/about/cop21.
11
6.Dobler, C., Hagemann, S., Wilby, R.L., and Stötter, J. 2012. Quantifying different sources of
12
uncertainty in hydrological projections in an Alpine watershed. Hydrology and Earth System
13
Sciences. 16: 11. 4343-4360.
14
7.Ehteramian, K., Shahabfar, A., and Alizadeh, A. 2004. Evaluation ofthe ENSO phenomenonon
15
the precipitation regime in Khorasan province. J. Geograph. Reg. Dev. 3: 29-42.
16
8.Eslamian, S., Nosrati, K., and Shahbazi, A. 2004. Climate change impacts on the hydrological
17
drought. J. Agric. Tehran Univ. 6: 1. 49-56. (In Persian)
18
9.IPCC. 2014. Climate Change 2014: Impacts, Adaptation and Vulnerability. Contribution of
19
Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate
20
Change. Yokohama, Japan.
21
10.IPCC. 2007. Synthesis Report of the Forth Assessment Report. Cambridge University Press,
22
Cambridge.
23
11.Kabiri, R., Kanani, V., and Andrew, C. 2012. Climate Change Impacts on River Runoff in
24
Klang Watershed in West malasia. J. Clim. Res. 48: 57-71.
25
12.Kunreuther, H., Heal, G., Allen, M., Edenhofer, O., Field, C., and Yohe, G. 2013. Risk
26
management and climate change. Nature Climate Change. 3: 447-450.
27
13.Lee, H. 2015. The Climate System and Climate Change; Climate Change Biology. Chapter
28
2. (Second Edition), Pp: 13-53.
29
14.Nash, J.E., and Sutcliffe, J.V. 1970. River flow forecasting through conceptual models part IA discussion of principles. J. Hydrol. 10: 3. 282-290.
30
15.Phillips, J. 2010. Evaluating the level and nature of sustainable development for a
31
geothermal power plant. Renewable and sustainable energy reviews. 14: 8. 2414-2425.
32
16.Souvignet, M., Gaese, H., Ribbe, L., Kretschmer, N., and Oyarzun, R. 2008. Climate change
33
impacts on water availability in the Arid Elqui Valley, North Central Chile: a preliminary
34
assessment. In IWRA World Water Congress, Montpellier, France.
35
17.Teng, J., Vaze, J., Chiew, F.H., Wang, B., and Perraud, J.M. 2012. Estimating the relative
36
uncertainties sourced from GCMs and hydrological models in modeling climate change
37
impact on runoff. J. Hydrometeorol. 13: 1. 122-139.
38
18.Vaseghi, R., Massah, A.R., Meshkati, A.H., and Rahimzadeh, F. 2011. Investigation of
39
runoff impact of Ensembles scenarios AOGCM models, 4th Conference of Water Resources
40
Management of Iran, Tehran, Iran, Pp: 23-35. (In Persian)
41
19.Vaze, J., Post, D.A., Chiew, F.H.S., Perraud, J.M., Viney, N.R., and Teng, J. 2010. Climate
42
non-stationarity-validity of calibrated rainfall–runoff models for use in climate change
43
studies. J. Hydrol. 394: 3. 447-457.
44
20.Velazquez, D., Garrote, L., Andreu, J., Martin-Carrasco, F.J., and Iglesias, A. 2011. A
45
methodology to diagnose the effect of climate change and to identify adaptive strategies to
46
reduce its impacts in conjunctive-use systems at basin scale. J. Hydrol. 405: 1. 110-122.
47
21.Zhu, Q., Jiang, H., Peng, C., Liu, J., Fang, X., Wei, X., Liu, S., and Zhou, G. 2012. Effects of
48
future climate change, CO2 enrichment and vegetation structure variation on hydrological
49
processes in China. Global and Planetary Change. 80: 123-135.
50
ORIGINAL_ARTICLE
Effect of particle size and surfactant concentration on nitrate absorption efficiency and release by modified zeolite with HDTMA in aqueous solution
Background and Objectives: Nitrate anion can be repelled by the negative charges on clay minerals' surface and leached from soil profile to surface and groundwater. Natural clays are not effective adsorbents and entrapment media for anions, low water soluble, non polar and non ionic organic molecules. However, the natural clays may be modified using organic cations (surfactant) to adsorb and trap varieties of non ionic, anionic compounds and enhanced anions retention capacity that are detrimental to our aqueous environments. The objective was to study the adsorption efficiency and desorption of nitrate in aqueous solutions by modified Iranian zeolite clinoptilolite (Semnan) with hexa decyl tri methyl ammonium bromide (HDTMA-Br), a cationic surfactant. Materials and Methods: The micro and nano zeolite was separated by centrifuge method. The micro and nano-zeolites were first modified by hexa decyl tri methyl ammonium bromide (HDTMA-Br). In this study, adsorption efficiency in initial concentrations of nitrate by modified zeolite with surfactant loading of 100 and 200% external cation exchange capacity)(ECEC) was investigated in a completely randomized factorial design. The nitrate release as affected by time at 4 and 14 mM of nitrate in surfactant loading 200% ECEC were also evaluated. The external cation exchange capacity (ECEC) of zeolite was determined by replacing the Na in non zeolitic exchange sites with tert butyl ammonium ions. Structure and morphology of zeolite was determined using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and atomic force microscope (AFM).Results: The results showed that adsorption efficiency of nitrate by nano organo zeolite with surfactant loading of 200% ECEC in 3, 6, 14, 20 and 30 mM nitrate were 92, 88, 77, 67 and 56 %, whereas in micro-zeolite were 75, 67, 50, 41 and 33 % respectively. Adsorption efficiency of nitrate by micro organo zeolite with surfactant loading of 100 % ECEC were 53, 46, 35, 28 and 20 % respectively. In nano-organo zeolite, nitrate desorption were 2.6 to 5.7 % and 8.9 to 12.2 % in 3 and 14 mM, respectively, whereas for micro organo zeolite were 21% and 33 % in 3 and 14 mM of initial nitrate concentration, respectively. Conclusion: Results of this research showed that the particular separation of zeolite, initial nitrate concentration and level of surfactant loading had a highly effect on adsorption efficiency and cleaning of nitrate in aqueous solutions. Moreover, nano-organozelite showed high adsorption efficiency of nitrate and good quality to trap and retain of nitrate.
https://jwsc.gau.ac.ir/article_3583_da6e5301dae563bba2c7120d6e259d1f.pdf
2017-03-21
157
172
10.22069/jwfst.2017.10885.2527
Nano and micro zeolite
organoclay
hexadecyltrimethylammonium
ECEC
clinoptilolite
Fariba
Nemati
nemati.fariba@gmail.com
1
1. M.Sc. Student, Department of Soil Sciences, Faculty of Agricultural Sciences, Shahed University
AUTHOR
Hossein
Torabi Golsefidi
htorabi@shahed.ac.ir
2
Assist. prof. of Shahed University
LEAD_AUTHOR
Amir Mohammad
Naji
amnaji1976@yahoo.com
3
Assist. Prof., Department of Plant Breeding and Biothecnology, Faculty of Agricultural Sciences, Shahed University
AUTHOR
1.Armstrong, G.A. 1963. Determination of nitrate in water by ultraviolet Spectrophotometry.
1
Analytical chemistry. 35: 1292-1294.
2
2.Aroke, U.O., El-Nafaty, U.A., and Osha, O.A. 2014. Removal of oxyanion contaminant from
3
waste water by sorption onto HDTMA-Br modified organo-kaolinite clay, North-Eastern,
4
Nigeria. Inter. J. Emer. Technol. Adv. Engin. 4: 1. 475-484.
5
3.Azam, N., Eslamian, S., Gheisari, M., and Abedi-Koupani, J. 2013. Reduce Nitrate from
6
Aqueous Solution Using Surfactant-Modified Bentonite. 1st national conference planning,
7
conservation, environmental protection and sustainable development, 3 December, Shahid
8
Mofateh University of Hamadan. (In Persian)
9
4.Bhattacharya, S., and Aadhar, M. 2014. Studies on preparation and analysis of organoclay
10
nano Particles. Res. J. Engin. Sci. 3: 3. 10-16.
11
5.Bhardwaja, D., Sharmab, M., Sharmac, P., and Tomar, R. 2012. Synthesis and surfactant
12
modification of clinoptilolite and montmorillonite for the removal of nitrate and preparation
13
of slow release nitrogen fertilizer. J. Hazard. Mater. 227-228: 292-300.
14
6.Cho, H.H., Lee, T., Hwang, S.J., and Park, J.W. 2005. Iron and organo-bentonite for the
15
reduction and sorption. Chemosphere. 58: 1. 103-108.
16
7.Dezfoli, A., and Abdolahi, H. 2010. Nitrate monitoring design, Agricultural Jihad
17
Organization of Fars province, Deputy of improve the production of plant, Crop
18
management of Shiraz, No: 89/280.
19
8.El-Nahhal, Y. 2003. Adsorptive behavior of acetochlor on organoclay complexes.
20
Environmental Contamination and Toxicology (1104-1111). Michigan State University:
21
Department of Crops and Soil Sciences.
22
9.Gitipour, S., Heidarzadeh, N., Hosseinpour, M.A., and Abolfazlzadeh, M. 2010. Adsorption of
23
crude oil and PAHs by ordinary and modified bentonites. Res. J. Chem. Environ. 14: 1. 46-51.
24
10.Gunay, A., Arslankaya, E., and Tosun, I. 2007. Lead removal from aqueous solution by
25
natural and pretreated clinoptilolite: Adsorption equilibrium and kinetics. J. Hazard. Mater.
26
146: 1-2. 362-371.
27
11.Hoidy, H.W., Ahmad, M., Mulla, E., and Bt Ibrahim, N. 2009. Synthesis and
28
characterization of organoclay from sodium-montmorillonite and fatty hydroxamic acids.
29
Amer. J. Appl. Sci. 6: 8. 1567-1572.
30
12.Jaynes, W.F., and Boyd, S.A. 1990. Trimethylammonium-smectite as an effective adsorbent of
31
water soluble aromatic hydrocarbons. Air and waste Management Association. 40: 1649-1653.
32
13.Kittrick, J.A., and Hope, E.W. 1963. A procedure for particle size separations of soils for
33
x-ray diffraction analysis. Soil Science. 96: 5. 319-325.
34
14.Lee, J., Choi, J., and Park, J.W. 2002. Simultaneous sorption of lead and chlorobenzene by
35
organobentonite. Chemosphere. 49: 1309-1315.
36
15.Li, Z. 2003. Use of surfactant-modified zeolite as fertilizer carrier sto control nitrate release.
37
Micropor. Mesopor. Mat. 61: 1-3. 181-188.
38
16.Li, Z., and Bowman, R.S. 2001. Regeneration of surfactant-modified zeolite after saturation
39
with choromate and percholoroethylene. Pergamon. 35: 1. 322-326.
40
17.Mahdavi Mazde, A., Liaghat, A., and Sheikh mohamadi, Y. 2011. Nitrate Removal from
41
agricultural wastes using modified zeolite. Iran Water Res. J. 5: 8. 117-124. (In Persian)
42
18.Malekian, R., Abedi-Koupai, J., and Eslamian, S.S. 2013. Ion-Exchange Process for nitrate
43
removal and release using surfactant modified zeolite. Sci. Technol. Agric. Natur. Resour.
44
Water and Soil Sience. 17: 63. 190-202. (In Persian)
45
19.Malla, P.B. 2002. Vermiculite. Pp 501-530, In: J.B. Dixon and D.G. Schulze (Eds.), Soil
46
mineralogy with environmental application. Soil Science Society of America, Inc. Madison,
47
Wisconsin, USA.
48
20.Ming, D., and Dixon, J.B. 1987. Quantitative determination of Clinoptilolite. clay and clay
49
mineralogy. 35: 6. 463-468.
50
21.Nabizadeh, R., Mahdavi, A.H., Ghadiri, S., Nasseri, S., Mesdaghinia, A., and Abouee, A.
51
2012. MTBE adsorption on Surfactant-Modified Zeolites from aqueous solutions. J. North
52
Khorasan Univ. Med. Sci. 4: 3. 483-492. (In Persian)
53
22.Pernyeszi, T., Kasteel, R., Witthuhn, B., Klahre, P., Vereecken, H., and Klumpp, E. 2006.
54
Organoclays for soil remediation: Adsorption of 2,4-dichlorophenol on organoclay/aquifer
55
material mixtures studiedunder static and flow conditions. Applied Clay Science. 32: 179-189.
56
23.Rafiei, H., Shirvani, M., and Behzad, T. 2014. Performance of cationic surfactant modified
57
sepiolite and bentonite in lead sorption from aqueous solutions. J. Water Soil. 28: 4. 818-835.
58
(In Persian)
59
24.Rhoades, J.D. 1982. Cation-exchange capacity. P 149-157, In: A.L. Page, R.H., Miller and
60
D.R. Keeny (Eds.), Methods of soil analysis. Part 2. 2nd ed. Agron. Monogr. No. 9. ASA
61
and SSSA, Madison, WI.
62
25.Sharafi, M., Bazigar, S., Tamizifar, M., Nemati, A., and Validi, M. 2009. The use of
63
nanoclay as an absorbent mineral materials. 5th Student Conference on Nanotechnology,
64
29-31 May, Tehran University of Medicinal Science. Retrieved March 30, 2016, from
65
http://www.civilica.com/Paper-NANOSC05-NANOSC05_171.html.
66
26.Schick, J., Caullet, P., Paillaud, J.L., Patarin, J., and Callarec, C. 2011. Nitrate sorption from
67
water on a surfactant-modified zeolite. Microporous and Mesoporous Materials. 142: 2. 549-556.
68
27.Schon, F., Gronski, W., and Freiburg. 2003. Filler networking of silica and organoclay in
69
rubber composites: reinforcement and dynamic-mechanical properties. Kautsch. Gummi
70
Kunstst. 54: 166-171.
71
28.Tillman Jr, F.D., Bartelt-Hunt, S.L., Smith, J.A., and Alther, G.R. 2004. Evaluation of an
72
organoclay, an organoclay-anthracite blend, clinoptilolite and hydroxyl-apatite as sorbents
73
for heavy metal removal from water. Bull. Environ. Contam. Toxicol. 72: 1134-1141.
74
29.Trigo, C., Celis, R., Hermosín, M., and Cornejo, J. 2009. Organoclay-based formulations to
75
reduce the environmental impact of the herbicide Diuron in olive groves. Soil Sci. Soc. Am.
76
J. 73: 5. 1652-1657.
77
30.Xi, Y., Mallavarapu, M., and Naidu, R. 2010. Preparation, characterization of surfactants
78
modified clay minerals and nitrate adsorption. Applied Clay Science. 48: 92-96.
79
ORIGINAL_ARTICLE
Mapping and Assessment of Land Degradation Risk using MEDALUS Model in Siyahpoush Watershed, Ardabil Province
Background and objectives: Nowadays, land degradation is a serious problem in many parts of the world. Land degradation occurs as a result of various factors including climatic change, improper land use and management in arid, semi-arid and dry sub-humid areas. It has been recognized as a major socioeconomic, social and environmental problem in many countries of the world. Various models are provided in order to assess desertification in the world. It seems that the MEDALUS model has apparent advantages compared to the other ones, such as easy style, data accessibility and taking geometric mean. The objectives of this study were to mapping and quantitative evaluation of land degradation in Siyahpoush catchment using MEDALUS and adjusted MEDALUS model.Materials and methods: In this study, MEDALUS and adjusted MEDALUS models were applied to desertification assessment and mapping in Siyahpoush catchment. For this purpose, four important criteria (soil quality, climate, vegetation cover, management and policy) which were effective on desertification have been selected. Indices for each criterion are defined in the MEDALUS model. Index layers for each criterion were prepared using GIS. These indices were ranked in accordance with MEDALUS model. The geometric mean was then calculated and map was produced for each criterion. Land degradation map of the study area was finally prepared using the geometric mean criteria. Results: The result showed that management quality and climate quality criteria with a geometric average of 1.91 and 1.62 have played the most important role in sensitivity of the area to desertification. Soil quality criterion with a geometric average of 1.39 and vegetation quality criterion with a geometric average of 1.41 were classified in moderate and high quality, respectively. Therefore, vegetation quality was determined as the most appropriate criterion. The ESAI index for MEDALUS and adjusted MEDALUS model ranged 1.38 to 1.79 and 1.37 to 1.93, respectively. This means that all area is located in critical class of desertification.Conclusion: The management and climate quality were identified as the most inappropriate criteria and vegetation quality was found as the most appropriate criterion. According to the obtained results, the study area is classified as critical class by ESAs model, so that 90.1% and 99.2% of the study area is located in the severe critical sub-class (C3) whit MEDALUS and adjusted MEDALUS model, respectively. However, implementing management policies would help to restrain this phenomenon at field or regional level. In addition, monitoring of land degradation needs to be considered that have involved more effective indices in this region.
https://jwsc.gau.ac.ir/article_3584_2484856710c24acd709f7e653ec9a389.pdf
2017-03-21
173
187
10.22069/jwfst.2017.11351.2576
Desertification
MEDALUS
geographic information systems
Soil quality
Nafiseh
Yaghmaieyan
yaghmaeian_na@yahoo.com
1
دانشگاه گیلان
AUTHOR
Hosein
Asadi
ho.asadi@ut.ac.ir
2
گروه خاکشناسی، دانشکده علوم کشاورزی، دانشگاه گیلان
LEAD_AUTHOR
Sedigheh
Rezaie
rezaei.s2012@yahoo.com
3
دانش آموخته دانشگاه گیلان
AUTHOR
1.Abbasi, A.P., Amani, H., and Zareian, M. 2014. Quantitative assessment of desertification
1
status using MEDALUS model and GIS (Case study: Shamil Plain–Hormozgan province).
2
RS & GIS for Natural Resources.5: 1. 87-97. (In Persian)
3
2.Bakhshandemehr, L., Soltani, S., and Sepehr, A. 2013. Assessment of present status of
4
desertification and modifying the MEDALUS model in Segzi plain of Isfahan, J. Range
5
Water. Manage. 66: 1. 27-41. (In Persian)
6
3.Bakr, N. 2013. Sustainable natural resource management in regional ecosystems: case study in
7
semi-arid and humid regions. Ph.D. Thesis, School of Plant, Environmental and Soil
8
Sciences, Louisiana State University.
9
4.El Baroudy, A.A. 2011. Monitoring land degradation using remote sensing and GIS
10
techniques in an area of the middle Nile Delta, Egypt. Catena. 87: 2. 201-208.
11
5.Elena Topa, M., Iavazzo, P., Terracciano, S., Adamo, P., Coly, A., De Paola, F., Giardano, S.,
12
Giugni, M., and Eric Traore, S. 2013. Evaluation of sensitivity to desertification by a
13
modified ESAs method in two sub-Saharan peri-urban areas: Ouagadougou (Burkina Faso)
14
and Saint Louis (Senegal). Geophysical Research Abstracts. 15: 2013-2229.
15
6.Farajzadeh, M., and Egbal, M.N. 2007. Evaluation of MEDALUS model for desertification
16
hazard zonation using GIS; study area: Iyzad Khast plain, Iran. Pak. J. Biol. Sci.
17
16: 2622-2630.
18
7.Fozooni, L., Fakhrieh, A., and Ekhtesasi, M.R. 2012. Assessment of desertification using of
19
modify MEDALUS model in Sistan plain (the east of IRAN). J. Elixir Geosci. 47: 8950-8955.
20
8.Gao, J., and Liu, Y. 2010. Determination of land degradation causes in Tongyu County,
21
northeast China via land cover change detection. J. Appl. Earth Obs. Geoinf. 12: 9-16.
22
9.Goudie, A.S. 2011. Desertification. P 30-35, In: J.O. Nriagu (Ed.), Encyclopedia of
23
Environmental Health, Burlington. Elsevier.
24
10.Hadeel, A.S., Mushtak, T., Jabbar, M.T., and Chen, X. 2010. Application of remote sensing
25
and GIS in the study of environmental sensitivity to desertification: a case study in Basrah
26
Province, southern part of Iraq. J. Appl. Geomat. 2: 101-112.
27
11.Honardoost, F., Nikouie, A., and Ghezelesflou, A. 2012. Mapping of present Status
28
of desertification using Medalus (Case study: Tarvati- Gonbade Kavous watershed).
29
First National Congress on Desert. International Desert Research Center, Tehran. Pp: 52-59.
30
(In Persian)
31
12.Khanamani, A., Karim Zadeh, H.R., Jafari, R., and Golshahi, A. 2013. Quantitative
32
assessment of current desertification using MEDALUS model (Case study: Segzi plain). RS
33
& GIS for Natural Resources. 4: 1. 13-25. (In Persian)
34
13.Kosmas, C., Kirkby, M., and Geeson, N. 1999. The Medalus project: Mediterranean
35
desertification and land use, Manual on key indicators of desertification and mapping
36
environmentally sensitive areas to desertification. European Commission, Project ENV4 CT
37
95 0119 (EUR 18882).
38
14.Lavado Conntador, J.F., Schanabel, S., Mezo Gutierrez, A.G., and Pulido, F.M. 2009.
39
Mapping sensitivity to land degradation Extremadura. SW Spain. 1: 1. 25-41.
40
15.Motandon, L.M., and Small, E.E. 2008. The impact of soil reflectance on the quantification
41
of the green vegetation fraction from NDVI. Rem. Sens. Environ. 112: 1835-1845.
42
16.Netpa Consulting Engineering. 2007. Integrated multipurpose project of Siyahposh
43
watershed. Guilan Office of Natural Resources, Ministry of Jihad-e-Agriculture. (In Persian)
44
17.Parvari, S.H., Pahlavanravi, A., Moghaddam Nia, A.R., Dehvari, A., and Parvari, D. 2011.
45
Application of methodology for mapping environmentally sensitive areas (ESAs) to
46
desertification in dry bed of Hamoun wetland (Iran). Inter. J. Natur. Resour. Mar. Sci. 1: 1. 65-8.
47
18.Rangzan, K., Sulaimani, B., Sarsangi, A., and Abshirini, A. 2008. Change detection
48
mineralogy, desertification mapping in East and Northeast of Ahvaz city, SW Iran using
49
combination of Remote sensing methods, GIS and ESA model. Global J. Environ. Res.
50
2: 1. 42-52.
51
19.Sepehr, A., Hassanli, A.M., Ekhtesasi, M., and Jamali, J. 2007. Quantitative assessment of
52
desertification in south of Iran using MEDALUS method. Environmental Monitoring and
53
Assessment. 134: 1-3. 243-254.
54
20.Shoshanya, M., Goldshleger, N., and Chudnovsky, A. 2013. Monitoring of agricultural soil
55
degradation by remote-sensing methods: a review. Inter. J. Rem. Sens. 34: 6152-6181.
56
21.Silakhori, E. 2014. Mapping of desertification hazard intensity based on soil index
57
using ESAs methodology in Mazinan of Sabzevar. Emergency Management. 3: 2.63-57.
58
(In Persian)
59
22.Soil Survey Staff. 2014. Keys to Soil Taxonomy, 12th ed., NRCS, USDA. 358p.
60
23.Yang, X., Zhang, K., Jia, B., and Ci, L. 2005. Desertification assessment in China: An
61
overview. J. Arid Environ. 63: 2. 517-531.
62
ORIGINAL_ARTICLE
Uncertainty analysis of rainfall projections (case study: Bojnourd and Mashhad synoptic gauge station)
Despite recent progress in developing reliable climate models, the different uncertainties inherent in climate change projections. Climate can change due to a number of anthropogenic and natural factors in spatial and temporal large scales. Therefore, a successful application of a climate parameters simulation in applied water research strongly depends on uncertainty analysis of model output. Here we present a detailed and quantitative uncertainty assessment of rainfall for first future epoch (2011-2040) and second future epoch (2040-2070), based on the projections of wide range of rainfall projections resulting from the factorial combination of four emission scenarios, five GCMs and two downscaling methods (LARS-WG و SDSM) in Bojnourd and Mashhad synoptic stations. This enabled us to decompose the uncertainty in the ensemble of projections using Box-whisker plot and Bootstrapping method. The uncertainty in precipitation change in response to the general circulation model (GCM) from HadCM3, NCPCM, CNCM3, GFCM2, CGCM3, SRES emission scenarios (A1B, A2, B1, and B2) and two downscaling method (SDSM and LARS-WG) was investigated in two future epochs. In this study, we evaluate the impact of uncertainty in climate change projections on the future precipitation by Box-whisker plots and Bootstrap technique. In the first step, the outliers were excluded by box-and-whisker plots. In the next step the precipitation projected which is reported by ten different scenarios, is then a vector of about 6000 bootstrap replications (500 per model), from which we take the 2.5th and 97.5th percentiles to calculate the range containing 95% of projected estimates. The GCM models show wide variation in their results, particularly for Bojnourd precipitation forecasting. According to Box-whisker graph in Bojnourd synoptic station (BSS), the projected precipitations by CGCM3 and HadCM3 in first and second epoch fall under the 2.5th and 97.5th percentiles. In Mashhad synoptic station (MSS) some scenarios projected precipitation significantly different from other scenarios which were belonging to CGCM3 in January and March and GFCM3 in summer months. On the basis of these results, it is clear that both stations will experience an increase in precipitation for epoch1 and epoch2, with the largest increase found for epoch2. In the next step confidence interval estimation by the bootstrap method is investigated for the uncertainty quantification of precipitation projections using the random sampling method. In BSS the confidence interval band is large in all month except in August and October. It is interesting that for MSS, the range in GCM predictions is relatively small for all seasons except in spring. This means that the uncertainty in climate predictions is considerably smaller for these months. All GCM and downscaling outputs are inherently uncertain because no model can ever fully describe physical systems. Most studies in the literature on the climate change projection do not capture the full range of plausible future climate variation, making their findings seem more precise than they actually are, and as a result making them less credible among climate scientists and potentially misleading for policymakers. We feel that the methodological approach presented here addresses a fundamental shortcoming in the past research. We show that failing to account for climate uncertainty lead to a false sense of confidence about the likely future impacts of climate change, when in fact impacts are actually far less certain
https://jwsc.gau.ac.ir/article_3585_347b04474386d995a89c1a953110bf15.pdf
2017-03-21
189
204
10.22069/jwfst.2017.10433.2486
Box-Whisker
Bootstrap
Climate Change
Rainfall
Uncertainty
Hamed
Rohani
rouhani.hamed@yahoo.com
1
دانشگاه گنبد
LEAD_AUTHOR
Azam
Ghandi
ghandi.azam@yahoo.com
2
Former M.Sc. Student of Watershed Management Dept. Gonbad Kavuse University
AUTHOR
Seyed Morteza
Seyedian
s.m.seyedian@gmail.com
3
Assistance Professor ofGonbad Kavuse University
AUTHOR
Mojtaba
Kashani
kashani.mojtaba@yahoo.com
4
Lecturer in Gonbad University
AUTHOR
2; Abasi, F., Babaeyan, A., Malbosi, Sh., Asmari, M., and Goli Mokhtari, L. 2012. Assessment
1
of climate change in the coming decades (2025 to 2100) using General Circulation Model’s
2
downscaling climate data. J. Geograph. Res. 1: 27. 205-230. (In Persian)
3
2.Abasnia, M., Tavosi, T., Khosravi, M., and Torous, H. 2016. Uncertainty analysis of future
4
changes in daily maximum temperatures over Iran by GIS. Geographical Data. 25: 97. 29-43.
5
(In Persian)
6
3.Alexander, L., Zhang, X., Peterson, T., Caesar, J., Gleason, B., Klein Tank, A., Haylock, M.,
7
Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J.,
8
Griffiths, G., Vincent, L., Stephenson, D., Burn, J., Aguilar, E., Brunet, M., Taylor, M.,
9
New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. 2006. Global observed
10
changes in daily climate extremes of temperature and precipitation. J. Geophysic. Res. Atm.
11
111, D05. 1-22.
12
1- With of margin
13
4.Alexandru, A., and Sushama, L. 2015. Current climate and climate change over India as
14
simulated by the Canadian Regional Climate Model. Climate Dynamics. 45: 1059-1084.
15
5.Ansari, H., Khadivi, M., Saleh Niya, N., and Babaiyan, A. 2014. Evaluation of uncertainty of
16
LARS-WG under scenario A1B, A2 and B1 in predicting precipitation and temperature
17
(Case Study: Mashhad synoptic station). J. Irrig. Drain. 4: 8. 664-672. (In Persian)
18
6.Arnell, N. 2004. Climate change and global water resources: SRES emissions and
19
socio-economic scenarios. Global Environmental Change. 14: 131-52.
20
7.Ashofte, P., and Massah, A.R. 2009. Uncertainty of climate change impact on the
21
flood regime. Case study: Aidoghmoush basin, East Azarbaijan. Water Resources Research.
22
5: 2. 27-39.
23
8.Ashraf, B., Alizadeh, A., Mousavi Baygi, M., and Bannayan Aval, M. 2013. Verification of
24
temperature and precipitation data simulated by implementing individual and group five
25
AOGCM models for North East Iran. J. Soil Water (Agricultural Science and Technology).
26
2: 28. 253-266. (In Persian)
27
9.Babaiyan, A., and Najafi Nik, Z. 2006. Introduction and evaluation of LARS-WG to simulate
28
meteorological parameters Khorasan period (2003-1961). Quarterly maker. 62: 49-65.
29
(In Persian)
30
10.Chen, J., Brissettea, F.P., Chaumontb, D., and Braunb, M. 2013. Performance and
31
uncertainty evaluation of empirical downscaling methods in quantifying the climate change
32
impacts on hydrology over two North American river basins. J. Hydrol. 479: 4. 200-214.
33
11.Christensen, J., and Christensen, O. 2007. A summary of the PRUDENCE model projections
34
of changes in European climate by the end of this century. Climatic Change. 81: 7. 7-30.
35
12.Ebrahim, G.Y., Jonoski, A., Griensven, A., and Baldassarre, G.D. 2013. Downscaling
36
technique uncertainty in assessing hydrological impact of climate change in the Upper Beles
37
River Basin, Ethiopia. J. Hydrol. Res. 44: 2. 37-44.
38
13.Efron, B., and Tibshirani, V. 1993. An introduction to the bootstrap. Chapman and Hall,
39
14.Etemadi, E., Samadi, Z., and Sharifikia, M. 2014. Uncertainty analysis of statistical
40
downscaling models using general circulation model over an international wetland. Climate
41
Dynamics. 42: 2899-2920.
42
15.Fowler, H.J., Blenkinsop, S., and Tebaldi, C. 2007. Linking climate change modeling to
43
impacts studies: Recent advances in downscaling techniques for hydrologic modeling. Inter.
44
J. Climatol. 27: 1547-1578.
45
16.Gao, Y., Lu, J., and Leung, L.R. 2016. Uncertainties in projection future changes
46
in atmospheric rivers and their impacts on heavy precipitation over Europe. J. Clim.
47
29: 18. 6711-6726.
48
17.Ghandi, A. 2015. Evaluation of uncertainty in estimates of climate parameters by different
49
statistical downscaling methods. Master thesis, University of Gonbad.
50
18.Ghermez Cheshmeh, B., Rasoli, A., Rezayi Banafsheh, M., Mesah Bavani, A., and Khorshid
51
Dost, A. 2015. Evaluation of uncertainty in the simulated neural network handling
52
HADCM3 using bootstrap confidence intervals. J. Engin. Water. Manage. 3: 7. 306-316.
53
(In Persian)
54
19.Graham, P., Hagemann, S., Juan, S., and Beniston, M. 2007. On interpreting hydrological
55
change from regional climate models. J. Clim. Change. 81: 97-122.
56
20.Hoshmand, D., and Khordadi, M.J. 2014. Uncertainty Assessment of AOGCMs and
57
Emission Scenarios in Climatic Parameters Estimation (Case Study in Mashhad Synoptic
58
Station). Geography and Environmental Hazards. 3: 11. 77-92. (In Persian)
59
21.Hughes, D.A., Mantel, S., and Mohobane, T. 2014. An assessment of the skill of downscaled
60
GCM outputs in simulating historical patterns of rainfall variability in South Africa.
61
Hydrology Research. 45: 1. 134-147.
62
22.Huth, R. 2004. Sensitivity of local daily temperature change estimates to the selection of
63
downscaling models and predictors. J. Clim. 17: 640-652.
64
23.Kent, C., Chadwick, R., and Rowell, P.D. 2015. Understanding Uncertainties in Future
65
Projections of Seasonal Tropical Precipitation. J. Clim. 28: 4390-4413.
66
24.Knutti, R. 2008. Should we believe model predictions of future climate change,
67
Philosophical transactions Series A. Mathematical, Physical and Engineering Sciences.
68
366: 1885. 4647-4664.
69
25.Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. 2010. Challenges in combining
70
projections from multiple climate models. J. Clim. 23: 10. 2739-2758.
71
26.Kohi, M., and Sanayi Nejad, H. 2013. Climate change scenarios based on the results of the
72
two methods of handling statistical downscaled variable reference evapotranspiration in
73
Orumiyeh. J. Irrig. Drain. 4: 7. 559-574. (In Persian)
74
27.Kripalanai, R.H., and Kulkarni, A. 2007. South Asian summer monsoon precipitation
75
variability, 2007: coupled climate model simulations and projections under IPCC AR4.
76
Theor. Appl. Climatol. 90: 133-159.
77
28.Kumar, P., Wiltshire, A., Mathison, C., Asharaf, Sh., Ahrens, B., Lucas-Picher, P.,
78
Christensen, H.J., Gobiet, A., Saeed, F., Hagemann, S., and Jacob, D. 2013. Downscaled
79
climate change projections with uncertainty assessment over India using a high resolution
80
multi-model approach. Science of the Total Environment. 468: 18-30.
81
29.Lavaysse, C., Vrac, M., Drobinski, P., Lengaigne, M., and Vischel, T. 2012. Present
82
and projection in an anthropogenic scenario. Natural Hazards and Earth System Science.
83
12: 3. 651-670.
84
30.Meinshausen, M., Raper, S., and Wigley, T. 2008. Emulating IPCC AR4 atmosphere ocean
85
and carbon cycle models for projecting global-mean, hemispheric and land/ocean temperatures:
86
MAGICC 6.0. Atmospheric Chemistry and Physics Discussions. 8: 2. 6153-6272.
87
31.Mojtahedi, S.M.H., and Oo, B.L. 2014. Coastal buildings and infrastructure flood risk
88
analysis using multi-attribute decision-making. J. Flood Risk Manage. 9: 1. 87-96.
89
32.Pir moradian, N., Hadinia, H., and Ashrafzadeh, A. 2016. Prediction of Minimum and
90
Maximum Temperature, Radiation and Precipitation in Rasht Synoptic Station under
91
Different Climate Change Scenarios. J. Geograph. Plan. 20: 55. 29-44. (In Persian)
92
33.Rowell, D.P., Senior, C.A., Vellinga, M., and Graham, R.J. 2016. Can climate projection
93
uncertainty be constrained over Africa using metrics of contemporary performance? Climate
94
Change. 134: 621-633.
95
34.Samadi, S., Wilson, A.M.E., and Moradkhani, H. 2013. Uncertainty analysis of statistical
96
downscaling models using Hadly Center Coupled Model. Theoretical and Applied
97
Climatology. 113: 3-4. 673-690.
98
35.Semenov, M., and Stratonovitch, P. 2010. Use of multi-model ensembles from global climate
99
models for assessment of climate change impacts. Climate Research. 41: 1-14.
100
36.Sheffield, J., and Wood, E. 2008a. Global Trends and Variability in Soil Moisture and
101
Drought Characteristics, 1950-2000, from Observation-Driven Simulations of the Terrestrial
102
Hydrologic Cycle. J. Clim. 21: 3. 432-458.
103
37.Sheffield, J., and Wood, E. 2008b. Projected changes in drought occurrence under future
104
global warming from multi-model, multi-scenario. IPCC AR4 simulations, Climate
105
Dynamics. 31: 1. 79-105.
106
38.Stainforth, D., Allen, M., Tredger, E., and Smith, L. 2007. Confidence, uncertainty and
107
decision-support relevance in climate predictions. Philosophical Transactions of the Royal
108
Society A – Mathematical. Physical and Engineering Sciences. 365: 2145-2161.
109
39.Sunyer, M.A., Hundecha, Y., Lawence, D., Willems, P., Martinkova, M., Vormoor, K.,
110
Burger, G., Hanel, M., Kriauciuniene, J., Loukas Osuch, M., and Yucel, I. 2014.
111
Inter-comparison of projection of extreme precipitation in Europe. Hydrology and Earth
112
System Sciences Discussions. 11: 6167-6214.
113
40.Tao, H., Gemmer, M., Jiang, J., Lai, X., and Zhang, Z. 2012. Assessment of CMIP3 climate
114
models and projected changes of precipitation and temperature in the Yangtze River Basin,
115
China. Climate Change. 111: 737-751.
116
41.Tebaldi, C., and Knutt, R. 2007. The use of the multi-model ensemble in probabilistic
117
climate projections, Philosophical Transactions of the Royal Society. Series A.
118
Mathematical. Physical and Engineering Sciences. 365: 1857. 2053-2075.
119
42.Turley, M.C., and Ford, E.D. 2009. Definition and calculation of uncertainty in ecological
120
process models. Ecological Modelling. 220: 1968-1983.
121
43.van Asselt, M., and Rotmans, J. 2002. Uncertainty in Integrated Assessment Modelling.
122
Climatic Change. 54: 1-2. 75-105.
123
44.Vasiliades, L., Loukas, A., and Patsonas, G. 2009. Evaluation of a statistical downscaling
124
procedure for the estimation of climate change impacts on droughts. Natural Hazards and
125
Earth System Science. 9: 3. 879-894.
126
45.Yu, W., Nakakita, E., Kim, S., and Yamaguchi, K. 2016. Impact assessment of uncertainty
127
propagation of ensemble NWP rainfall to flood forecasting with catchment scale. Advances
128
in Meteorology. 2016: 1-17.
129
46.Zhang, H., Huang, G., Wang, D., and Zhang, X. 2011. Uncertainty assessment of climate
130
change impacts on the hydrology of small prairie wetlands. J. Hydrol. 396:1-2. 94-103.
131
47.Zhang, X., Zwiers, F.W., Hegerl, G.C., Lambert, F.H, Gillett, N.P, Solomon, S., Stott, P.,
132
and Nozawa, T. 2007. Detection of human influence on twentieth century precipitation
133
trends. Nature. 448: 461-465
134
ORIGINAL_ARTICLE
Effect of enriched cow manure with converter sludge on Fe bio-availability in a lead polluted soil
Background and objectives: Nowadays, different materials such as applying Fe chelates, soil acidifying materials and industrial wastes are used to correct soil Fe deficiency. Slag and convertor sludge of steel factories are useful as a reclamation material for Fe nutrition among the industrials wastes for this purpose. These materials contain considerable amount of Fe produced in large quantities every year. Application of slag and convertor sludge to soil may affect bioavailability and chemical forms of Fe in soil. On the other hand, environmental pollution caused by heavy metals such as lead (Pb) is a serious and growing problem and can affect nutrient management such as Fe. Considering interaction of Fe and Pb, this research was performed to investigate the effect of converter sludge enriched cow manure on the changes in Fe bio-availability in a Pb polluted soil. Materials and Methods: A factorial experiment with a randomized complete block design with 3 factors in three replications was conducted in greenhouse conditions. Treatments were consisting of applying enriched cow manure (0, 15 and 30 t ha-1) with 0 and 5% pure Fe from converter sludge. In addition, the soil was polluted with Pb from Pb(NO3)2 source at the rates of 0, 200, 300 and 400 mg Pb kg-1 soil and incubated for one month. Then, the enriched cow manure was added to the Pb polluted soil and corn (Zea mays L. single grass 704) seeds were sown. After 60 days from the experiment, soil physio-chemical properties and soil and plant Fe concentration were measured. Results: Increasing the loading rate of cow manure from 0 to 15 and 30 t ha-1 in a Pb polluted soil (300 mg Pb soil-1) caused an increasing in DTPA extractable-Fe by 21 and 35 times, respectively. Similar to this result, root and shoot Fe concentration was also increased, as, applying 30 t ha-1 cow manure in a polluted soil (200 mg Pb soil-1) caused an increasing in root and shoot Fe concentration by 7 and 12.3 times, respectively. Enriched cow manure with converter sludge had also a positive effect on root and shoot Fe concentration, as, applying 30 t ha-1 enriched cow manure in a Pb polluted soil (200 mg Pb soil-1) caused an increasing in root and shoot Fe concentration by 2 and 7.7 times, respectively. Conclusion: The greatest DTPA extractable Fe and root and shoot Fe concentration was belong to the non- polluted soil treated with 30 t ha-1 cow manure enriched with 5% Fe pure from converter sludge. Considering the interaction effect of Fe and Pb, increasing the soil Pb pollution caused the significant decreasing in soil Fe availability and root and shoot Fe concentration. The result of this study showed that applying cow manure enriched with 5% Fe pure from converter sludge can probably increase soil and plant Fe bio-availability. However, the role of applying cow manure on decreasing Pb bio-availability and thereby, increasing soil Fe bio-availability (iron and lead competitive effect) cannot be ignored. Keywords: Iron, Converter sludge, Enriched cow manure, Lead
https://jwsc.gau.ac.ir/article_3586_7b3d3f88463186377ccd3349b877525f.pdf
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10.22069/jwfst.2017.11657.2612
Iron
Converter sludge
Enriched cow manure
Lead
Narges
Tabrateh Farahani
nargesban13@gmail.com
1
دانش اموخته گروه خاکشناسی دانشگاه آزاد اسلامی واحد اراک
AUTHOR
Amir Hoseini
Baghaie
a-baghaie@iau-arak.ac.ir
2
عضو هیات علمی گروه خاکشناسی دانشگاه آزاد اسلامی واحد اراک
LEAD_AUTHOR
Anahita
Polous
anaita.plous@gmail.com
3
عضو هیات علمی گروه خاکشناسی دانشگاه آزاد اسلامی واحد اراک
AUTHOR
1.Abbaspour, A., Kalbasi, M., and Shariatmadari, H. 2004. Effect of steel converter sludge as
1
iron fertilizer and soil amendment in some calcareous soils. J. Plant Nutr. 27: 2. 377-394.
2
2.Agegnehu, G., Nelson, P.N., and Bird, M.I. 2016. Crop yield ,plant nutrient uptake and soil
3
physicochemical properties under organic soil amendments and nitrogen fertilization on
4
Nitisols. Soil Till. Res. 160: 1-13.
5
3.Alidadi Khaliliha, M., Dordipour, E., and Barani Motlagh, M. 2016. Interactive effect of iron
6
and lead on growth and their uptake in Cress (Lepidium sativum L.). J. Soil Manage. Sust.
7
Prod. 5: 4. 41-59. (In Persian)
8
4.Allen, S.E., Grimshaw, H.M., and Rowland, A.P. 1986. Chemical analysis. P 285-344,
9
In: P.D. Moore and S.B. Chapman (Eds.), Methods in Plant Ecology, Blackwell Scientific
10
Publication, Oxford, London.
11
5.Azizi, P., and Glaser, B. 2006. Organic Iron-fertilizers from Hornbeam-leaves, Outer
12
Rice-husks and Charcoal. J. Appl. Sci. 6: 673-677.
13
6.Baghaie, A., Khoshgoftarmanesh, A.H., Afyuni, M., and Schulin, R. 2011. The role of organic
14
and inorganic fractions of cow manure and biosolids on lead sorption. Soil Sci. Plant Nutr.
15
57: 1. 11-18.
16
7.Bremner, J.M. 1996. Nitrogen-total. P 1-89, In: D.L. Sparks, Methods of Soil Analysis. Part 3,
17
3rd Ed. Am. Soc. Agron. Madison. WI.
18
8.Cohen, C.K., Fox, T.C., Garvin, D.F., and Kochian, L.V. 1998. The role of iron-deficiency stress
19
responses in stimulating heavy-metal transport in plants. Plant Physiol. 116: 3. 1063-1072.
20
9.Das, A., Patel, D.P., Lal, R., Kumar, M., Ramkrushna, G.I., Layek, J., Buragohain, J.,
21
Ngachan, S.V., Ghosh, P.K., Choudhury, B.U., Mohapatra, K.P., and Shivakumar, B.G.
22
2016. Impact of fodder grasses and organic amendments on productivity and soil and crop
23
quality in a subtropical region of eastern Himalayas, India. Agric. Ecosyst. Environ.
24
216: 274-282.
25
10.Fodor, F. 2006. Heavy metals competing with iron under conditions involving
26
phytoremediation, P 129-151, In: L.L. Barton and J. Abadía (Eds.), Iron Nutrition in Plants
27
and Rhizospheric Microorganisms, Springer, Dordrecht, The Netherlands.
28
11.Gee, G.W., and Bauder, J.W., 1986. Particle-size analysis. P 383-411, In: A. Klute (Eds.),
29
Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods, American society of
30
agronomy, Madison, WI.
31
12.Gopal, R., and Rizvi, A.H. 2008. Excess lead alters growth, metabolism and translocation of
32
certain nutrients in radish. Chemosphere. 70: 9. 1539-1544.
33
13.Hasegawa, H., Rahman, M.A., Saitou, K., Kobayashi, M., and Okumura, C. 2011. Influence
34
of chelating ligands on bioavailability and mobility of iron in plant growth media and their
35
effect on radish growth. Environ. Exp. Bot. 71: 3. 345-351.
36
14.He, W., Shohag, M.J.I., Wei, Y., Feng, Y., and Yang, X. 2013. Iron concentration,
37
bioavailability and nutritional quality of polished rice affected by different forms of foliar
38
iron fertilizer. Food Chem. 141: 4. 4122-4126.
39
15.Heidari Kohal, H., Samar, S.M., and Moez Ardalan, M. 2014. Soil injection of Iron Sulfate,
40
an Inexpensive Method for Controlling Iron Deficiency of Fruit Trees. Land Manage. J.
41
2: 2. 151-160. (In Persian)
42
16.Heidari, M., Galavi, M., and Hassani, M. 2011. Effect of sulfur and iron fertilizers on yield,
43
yield components and nutrient uptake in sesame (Sesamum indicum L.) under water stress.
44
Afr. J. Biotechnol. 10: 44. 8816-8822.
45
17.Jokar, L., and Ronaghi, A. 2015. Effect of foliar application of different Fe levels and
46
sources on growth and concentration of some nutrients in sorghum. J. Sci. Technol.
47
Greenhouse Cul. 6: 22. 163-174. (In Persian)
48
18.Lee, P.K., Choi, B.Y., and Kang, M.J. 2015. Assessment of mobility and bio-availability of
49
heavy metals in dry depositions of Asian dust and implications for environmental risk.
50
Chemosphere. 119: 1411-1421.
51
19.Li, J., Gan, J., and Hu, Y. 2016. Characteristics of Heavy Metal Species Transformation of
52
Pb, Cu, Zn from Municipal Sewage Sludge by Thermal Drying. Procedia Environ. Sci.
53
31: 961-969.
54
20.Mansouri, T., Golchin, A., and Fereidooni, J. 2016. The Effects of EDTA and H2SO4
55
on Phyto-extraction of Pb from contaminated Soils by Radish. J. Water Soil. 30: 1. 194-209.
56
(In Persian)
57
21.Martínez-Cuenca, M.R., Forner-Giner, M.Á., Iglesias, D.J., Primo-Millo, E., and Legaz, F.
58
2013. Strategy I responses to Fe-deficiency of two Citrus rootstocks differing in their
59
tolerance to iron chlorosis. Sci. Hort. 153: 56-63.
60
22.Melali, A.R., and Shariatmadari, H. 2008. Application of Steel Making Slag and Converter
61
Sludge in Farm Manure Enrichment for Corn Nutrition in Greenhouse Conditions. J. Water
62
Soil Sci. 11: 42. 505-513. (In Persian)
63
23.Mohammadi Torkashvand, A. 2011. Effect of steel converter slag as iron fertilizer in some
64
calcareous soils. Acta Agric. Scand. Sect. B Soil Plant Sci. 61: 1. 14-22.
65
24.Motesharezadeh, B., and SavaghebI, G.R. 2011. Study of sunflower plant response to cadmium
66
and lead toxicity by usage of PGPR in a calcareous soil. J. Water Soil. 25: 1069-1079.
67
(In Persian)
68
25.Nelson, D.W., and Sommers, L.E. 1996. Total carbon, organic carbon and organic matter.
69
Methods of soil analysis, 3: 961-1010.
70
26.Nelson, R.E. 1982. Carbonate and gypsum. P 81-197, In: A.L. Page, R.H. Miller and D.R.
71
Keeney (Eds.), Methods of Soil Analysis ,Part 2. Chemical and Microbiological Properties,
72
American Society of Agronomy, Madison, Wisconsin, USA.
73
27.Olsen, S.R., and Sommers, L.E. 1982. Phosphorus. P 403-430, In: A.L. Page, R.H. Miller
74
and D.R. Keeney (Eds.), Methods of Soil Analysis, Part 2. Chemical and Microbiological
75
Properties, American Society of Agronomy, Madison, Wisconsin, USA.
76
28.Rezvani, M., Zaefarian, F., and Gholizadeh, A. 2012. Lead and nutrients uptake by aeluropus
77
littoralis under different levels of lead in soil. Water Soil Sci. 22: 3. 73-86.
78
29.Rhoades, J.D. 1982. Cation exchange capacity. P 49-157, In: A.L. Page, R.H. Miller and
79
D.R. Keeney (Eds.), Methods of Soil Analysis, Part 2. Chemical and Microbiological
80
Properties, American Society of Agronomy, Madison, Wisconsin ,USA.
81
30.Saadat, K., and Barani Motlagh, M. 2013. Influence of Iranian natural zeolites, clinoptilolite
82
on uptake of lead and cadmium in applied sewage sludge by Maize (Zea mays L.). J. Water
83
Soil Cons. 20: 123-143. (In Persian)
84
31.Sharifi, M., Afyuni, M., and Khoshgoftarmanesh, A.H. 2010. Effects of sewage sludge,
85
animal manure, compost and cadmium chloride on cadmium accumulation in corn and
86
alfalfa. J. Residuals Sci. Tech. 7: 4. 219-225.
87
32.Sharma, A., Johri, B., Sharma, A., and Glick, B. 2003. Plant growth-promoting bacterium
88
Pseudomonas sp. strain GRP 3 influences iron acquisition in mung bean (Vigna radiata L.
89
Wilzeck). Soil Biol. Biochem. 35: 7. 887-894.
90
33.Shirani, H., Hajabbasi, M.A., Afyuni, M., and Hemmat, A. 2010. Impact of Tillage Systems
91
and Farmyard Manure on Soil Penetration Resistance under Corn Cropping. J. Water Soil
92
Sci. 14: 51. 141-155. (In Persian)
93
34.Siedlecka, A. 1995. Some aspects of interactions between heavy metals and plant mineral
94
nutrients. Acta Soc. Bot. Pol. 64: 3. 265-272.
95
35.Sinha, P., Dube, B., Srivastava, P., and Chatterjee, C. 2006. Alteration in uptake and
96
translocation of essential nutrients in cabbage by excess lead. Chemosphere. 65: 4. 651-656.
97
36.Solgi, E., Esmaili-Sari, A., Riyahi-Bakhtiari, A., and Hadipour, M. 2012. Soil contamination
98
of metals in the three industrial estates, Arak, Iran. Bull. Environ. Contam. Toxicol.
99
88: 4. 634-638.
100
37.Tafvizi, M., and Motesharezadeh, B. 2014. Effects of Lead on Iron, Manganese and Zinc
101
Concentrations in Different Varieties of Maize (Zea mays). Commun. Soil Sci. Plant Anal.
102
45: 14. 1853-1865.
103
38.Wang, X., and Cai, Q.S. 2006. Steel Slag as an Iron Fertilizer for Corn Growth and Soil
104
Improvement in a Pot Experiment1. Pedosphere. 16: 4. 519-524.
105
39.Westerman, R.L. 1990. Soil testing and plant analysis. SSSA, No. 3, Madison,Wisconsin,
106
40.Zhong, S., Shi, J., and Xu, J. 2010. Influence of iron plaque on accumulation of lead
107
by yellow flag (Iris pseudacorus L.) grown in artificial Pb-contaminated soil. J. Soils Sed.
108
10: 5. 964-970.
109
ORIGINAL_ARTICLE
The effect of submergence depth on evaporation losses in paddy fields
Background and objective: Evaporation is one of the main components of water losses in submerged irrigation method in paddy fields. The amount of evaporation is a function of temperature, relative humidity, wind speed, vegetated surface, submerges depth, water table level and other elements. In different intermittent irrigation managements, paddy fields frequently are under submerged and non-submerged situation. In each irrigation practice, the water level changes from submerged to capillary crack. This research aims measuring of evaporation rate during rice growth in different submerged depths in Guilan Province paddy fields in Rice Research Institute near meteorological research station in 2013. Material and methods: Five different water level treatments (5, 2.5, 0, -5, -10 cm) where applied to the farm in three repetition and using mini Lysimeters the evaporation is measured in daily scale in the middle of pig plots. Results: The results show that evaporation in different submerged levels is significantly different in 5%.The most and least evaporation amounts are consequently seen in 0 cm and 10 cm treatments respectively 120.8 millimeter and 94 millimeter. In all treatments the evaporation reduces during the time to the half. Precipitation minimize also evaporation rate till 75 %. Neglecting precipitation dates also does not change the difference between treatments. The comparisons show that higher levels of water on the soil surface cause higher evaporation losses. By reducing water level and narrowing water depth on soil surface, especially in vegetation period evaporation reduces. If the thickness of this layer reduces and reaches to zero or soil became semi saturated, evaporation increases again. If the thickness of this layer reduces and reaches to zero or soil became semi saturated evaporation increases again. Then when the soil became dryer and the water level stays at -10 cm below soil level, the evaporation decreases significantly.Conclusion: The results of evaporation measurements and its fluctuations are highly strongly to fluctuations of soil temperature in every treatment (in depth of 5 and 10 cm under the top soil) and water temperature and treatments which have higher records of temperature in the soil and water environment, have severer evaporation rates. In case of enough available water, presence a thin layer of water on top soil surface can reduce effectively evaporation. But in the absence of water necessary to maintain submergence, to reduce evaporation losses it is recommended to keep water level table in lower than 5 cm from top soil surface.
https://jwsc.gau.ac.ir/article_3587_6e4e522ad309bb7b6dc56c486f565bcb.pdf
2017-03-21
221
235
10.22069/jwfst.2017.12237.2674
depth of submerged
Evaporation
Mini lysimeter
Paddy field
Vegetation period
Mohammad
Mosavi Baigi
mousavib@um.ac.ir
1
دانشکده کشاورزی-دانشگاه فردوسی مشهد
LEAD_AUTHOR
Ebrahim
AsadiOskouei
e.asadi.o@gmail.com
2
دانشجوی رشته هواشناسی کشاورزی دانشگاه فردوسی مشهد
AUTHOR
Mohammad Reza
Yazdani
smryazdani@yahoo.com
3
استادیار پژوهشی - موسسه تحقیقات برنج کشور
AUTHOR
Amin
Alizadeh
alizadeh@gmail.com
4
دانشگاه فردوسی مشهد
AUTHOR
-1.Agam, N., Evett, S.R., Tolk, J.A., Kustas, W.P., Colaizzi, P.D., Alfieri, J.G., Mckee, L.G.,
1
Copeland, K.S., Howell, T.A., and Chavez, J.L. 2012. Evaporative loss from irrigatedinter
2
rows in a highly advective semi-arid agricultural area. Adv. Water Res. 50: 20-30.
3
2.Alizadeh, A. 2004. Soil, Water, Plant relationship. Astan Quds Razavi, press, 470p.
4
3.Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. 1998. Crop Evapotranspiration: Guidelines
5
for Computing Crop Requirements. FAO irrigation and drainage paper no. 56. Food and
6
Agricultural Organisation of the United Nations, Rome, Italy.
7
4.Ashktorab, H., Pruitt, W., and Paw, U.K. 1994. Partitioning of Evapotranspiration Using
8
Lysimeter and Micro-Bowen-Ratio System. J. Irrig. Drain. Eng. 120: 450-464.
9
5.Balwinder, S., Eberbach, P.L., Humphreys, E., and Kukal, S.S. 2011. The effect of ricestraw
10
mulch on evapotranspiration, transpiration and soil evaporation of irrigated wheat in Punjab.
11
India. Agric. Water Manage. 98: 1847-1855.
12
6.Borhan, A. 1990. Plant water requirement and irrigation planning. Interior ministry.
13
(In Persian)
14
7.Ding, R., Kang, S., Zhang, Y., Xinmei, H., Tong, L., and Du, T. 2013. Partitioning
15
evapotranspiration into soil evaporation and transpiration using a modified dual crop
16
coefficient model in irrigated maize field with ground-mulching. Elsevier. 127: 85-96.
17
8.Ehleringer, J.R., Roden, J.R., and Dawson, T.E. 2000. Assessing ecosystem-level water
18
relations through stable isotoperation analyses. P 181-198, In: O.E. Sala, R. Jackson, H.A.
19
Mooney and R. Howarth (Eds.), Methods in Ecosystem Science. SpringerVerlag, New York,
20
9.FAO. Crop Evapotranspiration (Guidelines for Computing Crop Water Requirements), FAO
21
Irrigation and Drainage Paper No.56.
22
10.Ferretti, D.F., Pendall, E., Morgan, J.A., Nelson, J.A., LeCain, D., and Mosier, A.R. 2003.
23
Partitioning evapotranspiration fluxes from a Colorado grassland using stable isotopes:
24
seasonal variations and ecosystem implications of elevated atmospheric CO2. Plant and Soil
25
J. 254: 291-303.
26
11.Ham, J.M., Heilman, J.L., and Lascano, R.J. 1990. Determination of soil water evaporation
27
and transpiration from energy balance and stem flow measurement. Agricultural and Forest
28
Meteorology. 52: 287-301.
29
12.Harrold, L.L., Peters, D.B., Driebelbis, F.R., and Mc-Guiness, J.L. 1959. Transpiration
30
evaluation of corn grown on a plastic-cove red lysimeter. Soil Sci. Soc. Of Am. Proc.
31
23: 174-178.
32
13.Jara, J., Stockle, C.O., and Kjelgard, J. 1998. Measurement of evapotranspiration and its
33
components in a corn (Zea Mays L.) field. Agric. For. Meteorol. 92: 131-145.
34
14.Kool, D., Agam, N., Lazarovitch, N., Heitman, J.L., Sauer, T.J., and Ben-gal, A. 2014. A
35
review of approaches for evapotranspiration partioning. Agric. For. Meteorol. 184: 56-70.
36
15.Kustas, W.P., and Agam, N. 2014. Soil Evaporation. Encyclopedia of Natural Resources.
37
DOI: 10.1081/E-ENRL-120049129.
38
16.Kustas, W.P., and Norman, J.M. 1999a. Evaluation of soil and vegetation heat flux
39
predictions using a simple two-source model with radiometric temperatures for partial
40
canopy cover. Agric. For. Meteorol. 94: 13-29.
41
17.Kustas, W.P., and Norman, J.M. 1999b. Reply to comments about the basic equations of
42
dual-source vegetation-atmosphere transfer models. Agric. For. Meteorol. 94: 275-278.
43
18.Lauenroth, W.K., and Bradford, J.B. 2006. Ecohydrology and the Partitioning AET between
44
Transpiration and Evaporation in a Semiarid Steppe. Ecosystems. 9: 756-767.
45
19.Lawrence, D.M., Thornton, P.E., Oleson, K.W., and Bonan, G.B. 2007. The Partitioning of
46
Evapotranspiration into Transpiration, Soil Evaporation and Canopy Evaporation in a GCM:
47
Impacts on Land–Atmosphere Interaction. J. Hydrometeor. 8: 862-880.
48
20.Modabberi, H. 2010. Determining evapotranspiration and crop coefficient of rice varieties in
49
swamp plain (Guilan). Irrigation and Drainage Master's Thesis. Agricultural Faculty, Tarbiat
50
Modarres University. (In Persian)
51
21.Peters, D.B., and Russell, M.B. 1959. Relative water losses by evaporation andtranspiration
52
in field corn. Soil Sci. Soc. Am. J. 23: 170-173.
53
22.Sakuratani, T. 1987. Studies on evapotranspiration from crops. (2) Separate estimation of
54
transpiration and evaporation from a soybean field without water shortage. J. Agric.
55
Meteorol. 42: 309-317.
56
23.Shaw, R.H. 1959. Water use from plastic covered and uncovers corn plots. Agron. J.
57
51: 172-173.
58
24.Shawcroft, R.W., and Gardner, H.R. 1983. Direct evaporation from soil under a row crop
59
canopy. Agric. Meteorol. 28: 229-238.
60
25.Sutanto, S.J., Wenninger, J., Coenders-Gerrits, A.M.J., and Uhlenbrook, S. 2012.
61
Partitioning of evaporation into transpiration, soil evaporation and interception: a
62
comparison between isotope measurements and a HYDRUS-1D model, Hydrol. Earth Syst.
63
Sci. 16: 2605-2616.
64
26.Tolk, J.A., Howell, T.A., Steiner, J.L., Krieg, D.R., and Schneider, A.D. 1995. Role
65
of transpi-ration suppression by evaporation of intercepted water in improving irrigation
66
efficiency. Irrig. Sci. 16: 89-95.
67
27.Yazdani, M.R., Sharifi, M.M., Razavi poor, T., and Sharafi, N. 2002. Comparison of several
68
water management methods in rice fields Of Guilan province. 11th National Committee
69
Conference on Irrigation and Drainage. (In Persian)
70
ORIGINAL_ARTICLE
Investigation of canola yield as a second crop in paddy fields under subsurface drainage
Background and objectives: In order to provide feasibility of winter cropping in paddy fields, subsurface drainage systems should be installed to overcome waterlogging problems and to remove excess rainfall. In different countries, installation of subsurface drainage in paddy fields caused increases in yield and facilitated working conditions on the land. In Pakistan (Azhar et al., 2005) and India (Ritzema et al., 2008), the installation of subsurface drainage system resulted in increases in cotton, sugarcane, rice, and wheat yields. In a research (Carter and Camp, 1994), it was shown that subsurface drainage systems increased sugarcane yield. Totally, evaluation of influences of subsurface drainage systems showed positive effects on rice yields (Darzi et al., 2012; Mathew et al., 2001; Satyarayana and Bonestra, 2007), also it can provide possibility of second crop in paddy fields. Because of new installation of subsurface drainage systems in Northern Iran paddy fields, investigation of canola yield as a second crop has a great importance. By determining amount of yield improvement and harvested yield, farmers and government will have good point of view in future work. Materials and methods: In this study, the effect of three conventional subsurface drainage systems and a bi-level drainage system along with a control treatment on canola yield was investigated in paddy fields of Sari Agricultural Sciences and Natural Resources University. Experiments were done in randomized complete block design with 5 treatments in 2014-15. Water table depth were measured daily and in harvest time some of crop index like plant number in one square meter, pod number in plant, grain number in pod, 1000 grain weight and yield of canola were determined. Data were compared statistically by Combined ANOVA with least significant difference (LSD) test at the 0.05 probability level in SAS statistical software. Results: The results of statistical analysis showed that plant number, pod number, 1000 grain weight in subsurface drainage treatments were significantly more than control treatment. Also, the canola yield in subsurface drainage treatments were significantly 425 to 1025 kg ha-1 more than that in control treatment. However the rainfall during germination time was much, the drains worked well and water table was lower than 30 cm. Conclusion: Improvement of aeration and quicker discharge of excess water in subsurface drainage treatments during canola growing season caused more canola yield. Generally, grain yield in drainage treatment with 0.9 m depth and 30 m spacing, Bi-level drainage treatment, drainage treatment with 0.65 m depth and 30 m spacing and drainage treatment with 0.65 m depth and 15 m spacing were 55, 35, 29 and 22 % more than that in control treatment. Due to these results and large areas of paddy fields in North of Iran, use of these areas during wet seasons for canola cultivation can be a helpful solution for producing oil grains and achieving to self-sufficiency.
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249
10.22069/jwfst.2017.11160.2549
Bi-level drainage
conventional drainage
Hyola 401
water table
Samaneh
Dousti Pashakolaee
dostipasha@yahoo.com
1
M.Sc. student of irrigation and drainage engineering, Sari Agricultural Sciences and Natural Resources University
AUTHOR
Ali
Shahnazari
aliponh@yahoo.com
2
علوم کشاورزی و منابع طبیعی ساری
LEAD_AUTHOR
Mehdi
Jafari Talukolaee
mehdijafari_89@yahoo.com
3
Ph.D. student of irrigation and drainage engineering, Sari Agricultural Sciences and Natural Resources University
AUTHOR
1.Agricultural Statistics. 2015. Ministy of Jihad-e-Agriculture, economical planning section.
1
Farmig year of 2013-14. First volume, 169p. (In Persian)
2
2.Azhar, A.H., Alam, M.M., and Rafiq, M. 2005. Agricultural impact assessment of subsurface
3
drainage projects in Pakistan– crop yield analysis. Pak. J. Water Resour. 9: 1. 1-7.
4
3.Bange, M.P., Milroy, S.P., and Thongbai, P. 2004. Growth and yield of cotton in response to
5
waterlogging. Field Crops Res. 88: 129-142.
6
4.Bils Borrow, P.E., Evans, E.J., and Zhoa, F.D. 1993. The influence of spring nitrogen on yield
7
components and glucosinolat content of autumn sown oilseed rape (B. napus). J. Agric. Sci
8
(Camb.) 120: 219-224.
9
5.Carter, C.E., and Camp, C.R. 1994. Drain spacing effects on water table control and sugarcane
10
yields. Transactions of the ASAE. 37: 5. 1509-1513.
11
6.Charlief, R., Warmann, G., and Heerm, W. 2000. Great Plains canola research. K-State
12
research and extension are available on the http://www.oznet.ksu.edu.
13
7.Darzi-Naftchali, A., Mirlatifi, S.M., Shahnazari, A., Ejlali, F., and Mahdian, M.H. 2013.
14
Effect of subsurface drainage on water balance and water table in poorly drained paddy
15
fields. Agric. Water Manage. 130: 61-68.
16
8.Darzi, A., Mirlatifi, M., Shahnazari, A., Ejlali, F., and Mahdian, M.H. 2012. Influence of
17
surface and subsurface drainage on rice yield and its component in paddy fields. J. Water
18
Res. Agric. 26: 1. 61-71. (In Persian)
19
9.Evans, J. 1982. Symbiosis, nitrogen and dry matter distribution in chickpea (Cicer arietinum
20
L.). Exp Agric. 18: 339-351.
21
10.Gardner, W.K., Drendel, M.F., and Mc Donald, G.K. 1994. Growth and yield response of
22
grain legumes to different soil management practices after rained lowland rice. Aust. J. Exp.
23
Agri. 34: 3. 41-48.
24
11.Ismail, A.M., Ella, E.S., Vergara, G.V., and Mackill, D.J. 2009. Mechanisms associated with
25
tolerance to flooding during germination and early seedling growth in rice. Annual. Botany.
26
103: 197-209.
27
12.Johnston, T.H., and Scott, G.C. 1998. Gravel and conventional mole drainage for dryland
28
cropping in SE Australia. The Australian Society of Agronomy. Available on the Url:
29
http://www.regional.org.au/au/asa/1998/7/179johnston.htm.
30
13.Khajapoor, M.A. 1996. Production of Industrial Crops. Isfahan University Press. 182p.
31
(In Persian)
32
14.Khajehpur, M.R. 2006. Industrial plants, academic jihad publications. Esfahan University
33
Jahad. (In Persian)
34
15.Malik, A., Colmer, T.D., Lambers, H., and Schortemyer, M. 2001. Changes inphysiological
35
and morphological traits of roots and shoots of wheat in response to different depths of
36
waterlogging. Australian J. Plant. Physiol. 28: 1121-1131.
37
16.Mandham, N.J., Shipway, P.A., and Scott, R.K. 1981. The effect of delayed sowing and
38
weather on growth, development and yield of winter oilseed rape (Brassica napus L.).
39
J. Agric. Sci. 96: 389-416.
40
17.Mathew, E.K., Panda, R.K., and Nair, M. 2001. Influence of subsurface drainage on
41
crop production and soil quality in a low-lying acid sulphate soil. Agric. Water Manage.
42
47: 191-209.
43
18.Mohajer, A.R. 2004. Iran will be selfficient in edible oil production in next 12 years.
44
J. Livestock, Cul. Ind. 54: 120. (In Persian)
45
19.Musgrave, M.E., and Ding, N. 1998. Evaluation wheat cultivars for waterlogging tolerance.
46
Crop Sci. 38: 90-97.
47
20.Palta, J.A., Ganjeali, A., Turner, N.C., and Siddique, KH.M. 2010. Effects of transient
48
subsurface waterlogging on root growth, biomass and yield of chickpea. Agric. Water
49
Manage. 97: 1469-1476.
50
21.Peries, R., Johnson, T., Bluett, C., and Wightman, B. 2001. Raised-bed cropping leading the
51
way in high rainfall southern Australia. Proc. 10th Australian Agronomy Conference,
52
Australian Society of Agronomy, Hobart, Jan 2001.
53
22.Rabiee, M. 2012. Effect of row spacing and nitrogen fertilizer rates on grain yield and
54
agronomic characteristics of rapeseed cv. Hayola 308 as second crop in paddy fields of
55
Guilan in Iran. Seed Plant Prod. J. 27: 4. 399-415. (In Persian)
56
23.Rabiee, M., Karimi, M.M., and Safa, F. 2004. Effect of planting dates on grain yield and
57
agronomic characteristics of rapeseed cultivars as a second crop after rice at Kouchesfahan.
58
Iran. J. Agric. Sci. 35: 1. 177-187. (In Persian)
59
24.Rabiei, M., and Rahimi, M. 2013. Selection of the best rapeseed genotypes as second crop in
60
paddy fields of Guilan. Elec. J. Crop Prod. 7: 1. 201-213. (In Persian)
61
25.Rasouli, S.F., Galeshi, S., Pirdashti, H., and Zeinali, E. 2014. Evaluation of waterlogging
62
stress effect on yield and yield components of rapeseed. Elec. J. Crop Prod. 7: 2. 23-41.
63
(In Persian)
64
26.Ritzema, H.P., Satyanarayana, T.V., Raman, S., and Boonstra, J. 2008. Subsurface drainage
65
to combat waterlogging and salinity in irrigated lands in India: Lessons learned in farmers’
66
fields. Agric. Water Manage. 95: 3. 179-189.
67
27.Satyanarayana, T.V., and Boonstra, J. 2007. Subsurface drainage pilot area experiences in
68
three irrigated project commands of Andhra Pradesh in India. Irrig and Drain. 56: 245-252.
69
28.Seefeldt, S.S., Kidwell, K.K., and Waller, J.E. 2002. Base growth temperatures, germination
70
rates and growth response of contemporary spring wheet (Triticum aestivum L.) cultivars
71
from the US Pacific Northwest. Field Crop Res. 75: 47-52.
72
29.Seyed Ahmadi, A.R., Gharineh, M.H., Bakhshandeh, A.M., Fathi, G., and Naderi, A. 2012.
73
Study of phenological and growth of canola cultivars to thermal unit accumulation in three
74
planting dates Ahvaz climate. J. Plant Prod. 19: 4. 97-116. (In Persian)
75
30.Soltani, A., Kamkar, B., Galeshi, S., and Akramghaderi, F. 2008. Effects of seed
76
deterioration on the depletion of seed and seedling growth of wheat Hetrotrophic. J. Gorgan
77
Univ. Agric. Sci. Natur. Res. 15p. (In Persian)
78
31.Tahmasbi, M., Galeshi, S., and Sadeghipoor, H. 2011. Morphological and physiological
79
characteristics of wheat in response to the effects of flooding and temperature. Abstracts of
80
articles 1st Conference of strategies to achieve sustainable agriculture. Ahvaz. (In Persian)
81
32.Visser, E.J.W., and Voesenek, L.A.C.J. 2004. Acclimation to soil flooding sensing and
82
signaltransduction. Plant Soil. 244: 197-214.
83
33.Wiskow, E., and van der Ploeg, R. 2003. Calculation of drain spacings for optimal rainstorm
84
flood control. J. Hydrol. 272: 163-174.
85
34.Zhang, H., Turner, N.C., and Poole, M.L. 2004. Yield of wheat and canola in the high
86
rainfall zone of south-western Australia in years with and without a transient perched water
87
table. Austr. J. Agric. Res. 55: 4. 461-470.
88
ORIGINAL_ARTICLE
Bayesian analysis and particle filter application in rainfall-runoff models and quantification of uncertainty
Background and objectives: Applying hydrologic models and forecast is a necessity in different studies in water resources. There should be multiple assumptions in forecasting the outflow of watersheds due to different complex relations in hydrologic cycle. Because of assumptions and simplifications those applied in the structure of models and developed relations, forecasts made by rainfall runoff models are always subject to uncertainties. Different sources of uncertainty are categorized into three parts: first, the uncertainty attributed to the applied data, second, the structure of model and third, and the parameters. It is also necessary to address uncertainties and improve the precision of the forecasts. Therefore, there are multiple methods developed to analyze uncertainties. For this aim, data assimilation is a recommended approach and particle filter method is one of the developed models in this regard. The main goal of this research is to apply particle filter to update and improve the HYMOD rainfall runoff model forecasts based on observed stream flow. In addition, by the use of this approach, quantification and decreasing the uncertainty is evaluated based on different sources of error. Materials and methods: In this study, improving the forecasts is implemented by data assimilation approach. To this aim, particle filter method, successive Bayesian estimation and posterior probability density function are applied for obtaining the soil moisture and Hymod parameters in daily scale in Kassilian river basin with approximately 67 square kilometers area. Particle filter is based on Bayes equation and maximum likelihood function of errors for the given time period. Moreover, this method should be combined with statistical resampling that prevents divergence of the analysis, and corrects degeneracy, sample impoverishment of particles and tendency of the state variables particle weights to unit value (1).Results: Applying particle filter method makes it possible to use the intended model parameters for simulating and forecasting by random ensemble parameters generation and calculating prior probability density function. This method is also effective for precising forecasts and simultaneous application of parameters and soil moisture variable in analysis. Also this method helps to modify the forecasts using Baysian theory and definition of primary errors maximum likelihood function. In addition, this method also represents the posterior probability density function and corrects the prior density function.Conclusion: The results show applicability of particle filter method in combination with statistical resampling for hydrological data assimilation and improvement of the precision of forecasts of outflow from Kassilian river basin. It is shown that, the applied method improved the Nash-Sutcliffe statistic in comparison with open loop procedure. As the Nash-Sutcliffe statistic improved by 22%, rising from 0.55 to 0.67.
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10.22069/jwfst.2017.12108.2663
HyMod model
particle filter
Data assimilation
Resampling
Degeneracy
Mojtaba
Ahmadizade
mojtaba_ahmadizade@yahoo.com
1
Bualisinauniversity
AUTHOR
Safar
Maroufi
smarofi@yahoo.com
2
گروه آب دانشگاه بو علی سینا همدان
LEAD_AUTHOR
1.Arulampalam, S., Maskell, S., Gordon, N., and Clapp, T. 2002. A tutorial on particle
1
filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processess.
2
50: 2. 174-189.
3
2.Beven, K.J., and Freer, J. 2001. Equifinality, data assimilation and uncertainty estimation in
4
mechanistic modelling of complex environmental systems. J. Hydrol. 249: 11-29.
5
3.Boyle, D.P. 2000. Multicriteria calibration of hydrological models. PhD Dissertation,
6
Department of Hydrology and Water Resources. University of Arizona, 145p.
7
4.Bulygina, N., and Gupta, H. 2009. Estimating the uncertain mathematical structure of a water
8
balance model via Bayesian data assimilation. Water Resour. Res. 45: W00B13.
9
5.Clark, M.P., and Vrugt, J.A. 2006. Unraveling uncertainties in hydrologic model calibration:
10
Addressing the problem of compensatory parameters. Geophys. Res. Lett. 33 (L06406): 1-5.
11
6.DeChant, C., and Moradkhani, H. 2012. Examining the effectiveness and robustness of
12
sequential data assimilation methods for quantification of uncertainty in hydrologic
13
forecasting. Water Resour. Res. 48: W04518.
14
7.Duan, Q., Sorooshian, S., and Gupta, V.K. 1992. Effective and efficient global optimization
15
for conceptual rainfall-runoff models. Water Resour. Res. 28: 4. 1015-1031.
16
8.Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi geostrophic model
17
using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99: 10143-10162.
18
9.Gordon, N., Salmond, D., and Smith, A.F.M. 1993. Novel approach to nonlinear and
19
non-Gaussian Bayesian state estimation, Proc. Inst. Electr. Eng. 140: 107-113.
20
10.Leisenring, M., and Moradkhani, H. 2011. Snow water equivalent prediction using Bayesian
21
data assimilation methods. Stoch. Environ. Res. Risk Assess. 25: 2. 253-270.
22
11.Li, T., Gannan, Y., and Wang, L. 2016. Particle Filter with Novel Nonlinear Error
23
Model for Miniature Gyroscope-Based Measurement While Drilling Navigation. Sensors.
24
16: 3. 371-394.
25
12.Liu, J.S., Chen, R., and Logvinenko, T. 2001. A theoretical framework for sequential
26
importance sampling and resampling, in Sequential Monte Carlo Methods in Practice.
27
Springer, New York, Pp: 225-246.
28
13.Miller, R.N., Ghil, M., and Guathiez, F. 1994. Advanced data assimilation in strongly
29
nonlinear dynamical systems. J. Atmos. Sci. 51: 8. 1037-1056.
30
14.Moore, R.J. 1985. The probability-distributed principle and runoff production at point and
31
basin scales. Hydrol. Sci. J. 30: 2. 273-297.
32
15.Moradkhani, H., Hsu, K.L., Gupta, H., and Sorooshian, S. 2005. Uncertainty assessment of
33
hydrologic model states and parameters: Sequential data assimilation using the particle filter.
34
Water Resour. Res. 41: 5. 1001-1017.
35
16.Pourreza Bilondi, M., Akhoond Ali, A.M., Gharaman, B., and Telvari, A.R. 2015.
36
Uncertainty analysis of a single event distributed rainfall-runoff model by using two
37
different Markov Chain Monte Carlo methods. J. Water Soil Conservation. 21: 5. 1-26.
38
(In Persian)
39
17.Salamon, P., and Feyen, L. 2009. Assessing Parameter, Precipitation and Predictive
40
Uncertainty in a Distributed Hydrological Model Using Sequential Data Assimilation with
41
the Particle Filter. J. Hydrol. 376: 428-442.
42
18.Sorooshian, S., Duan, Q., and Gupta, V.K. 1993. Calibration of rainfall-runoff models:
43
application of global optimization to the soil moisture accounting model. Water Resour. Res.
44
29: 4. 1185-1194.
45
19.Vrugt, J.A.C., Diks, G.H., Gupta, H.V., Bouten, W., and Verstraten, J.M. 2005. Improved
46
treatment of uncertainty in hydrologic modeling: Combining the strengths of global
47
optimization and data assimilation. Water Resour. Res. 41: 1-17.
48
20.Weerts, A.H., and El Serafy, G.Y.H. 2006. Particle filtering and ensemble Kalman filtering
49
for state updating with hydrological conceptual rainfall-runoff models. Water Resour. Res.
50
42: W09403.
51
ORIGINAL_ARTICLE
Experimental and numerical Study of Hydraulic characteristics of flow over the sharp-crested weirs in the effect of increasing upstream bed level
AbstractBackground and objectives: Sharp crested weirs are used for the purpose of flow measurement, flow diversion and water level control in hydraulics, irrigation, and environmental projects. So exploring the features and characteristics of the hydraulic properties are an important issue in the design of these structures. Various studies have been done about sharp-crested weir. Few studies have been done about the impact of inequalityin the upstream and downstream bed level on hydraulic properties. The sharp-crested weirs like other weirs, unequal in the upstream and downstream bed level (such as the Check drop) cause changes on the hydraulic characteristics that must be studied.Materials and Methods: Research conducted on Hydraulic laboratory which Situated in Research Institute of Soil Conservation and Watershed Management. The experiments were performed in the flume with 14 meters length, width of 60 cm and a height of 50 cm. Sharp crested weirs was built of Plexiglas with a thickness of 6 mm, edge thickness of 2 mm, a height of 20 cm and a length of 60 cm in the workshop and was placed within the flume. Upstream bed level increased with proper materials in three level 5, 10 and 15 cm from floor. At any stage, values of the weir crest level and upstream and downstream water level were recorded for different discharges. Computational Fluid Dynamics (CFD) was used to generalize the results. For this purpose, FLOW 3D software was used for modeling of Free-surface flow over weir. In This software, weir and it’s free surface are considered by using Fractional Area Volume Obstacle Representation and Volume Of Fluid methods respectively. The governing equations were Navier-Stokes and continuity equations for incompressible flows. For modeling turbulence, was used Re-Normalization Group (RNG) model.Results: The results showed a good agreement Between experimental data and numerical simulation. changing procedure of discharge coefficient was the same in both methods. Maximum deference in the H, extracted from two methods, is 5% that is acceptable. The results showed that by increasing the upstream bed level, the upstream flow depth decreases, velocity and Froude number increase. But rising the upstream bed level to 0.75 (Z/P=0.75) does not affect on the discharge coefficient. In numerical method, Discharge coefficient values for H/P≥0.5 can be considered the average value of 0.73 for all cases. With increasing discharge Froude numbers are converging in different ratios of upstream bed level. In the special case where the upstream bed level is rised to Crest (vertical drop or Z/P=1), the discharge coefficient value will be 0.6. This value is the lowest between all cases and its magnitude is equal to discharge coefficient of the broad-crested weir. So in this case, the level of water is higher than the same rate of discharge in the other cases and this difference goes up by increasing discharge. In Z/P=1 he Froude number will be equal to a fixed value Fr = 0.94Cd.Conclusion: In summary it can be concluded that by increasing the upstream bed level, the Froude number will increase and thus the nape becomes more horizontal. In the range of H / P≥0.5, except when the Z/P tend towards one, in other cases, the rising bed level and the increasing H/P have no significant impact on discharge coefficient. In the Z/P Keywords: vertical drop, sharp-crested weirs, discharge coefficient, Froude number, Check Drop
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10.22069/jwfst.2017.11245.2561
vertical drop
sharp-crested weirs
Discharge coefficient
Froude Number
Check Drop
Davoud
Davoud Maghami
ddm_maghami@yahoo.com
1
دانشجوی دکترا سازه های آبی دانشگاه همدان
LEAD_AUTHOR
Hossein
Banejad
hossein_banejad@yahoo.com
2
دانشیار گروه مهندسی آب دانشگاه بو علی سینا همدان
AUTHOR
Mojtaba
Saneie
drsaneie@gmail.com
3
دانشیار پژوهشکده حفاظت خاک و آبخیزداری
AUTHOR
Seyed Asadolah
Mohseni Movahed
movahed244@yahoo.com
4
استادیار دانشگاه اراک
AUTHOR
1.Arvanaghi, H., and Nasehi Oskuei, N. 2013. Sharp-Crested Weir Discharge Coefficient. J.
1
Civil Engin. Urban. 3: 3. 87-91.
2
2.Azimian, A. 2006. Computational Fluid Dynamics. Isfahan University Publication Center.
3
604p. (In Persian)
4
3.Bagheri, S., and Heidarpour, M. 2010. Flow over rectangular sharp crested weirs. J. Irrig. Sci.
5
28: 2. 173-179.
6
4.Bos, M.G. 1989. Discharge measurement structures. 3rd edn. Publisher: International institute
7
for land reclamation and improvement. 401p.
8
5.Dastorani, M., and Nasrabadi, M. 2012. The effect of sedimentation in the ogee spillway on
9
flow conditions. Iran. J. Water Res. 10: 47-56. (In Persian)
10
6.Flow Science Incorporated. 2015. Flow-3D user's manuals, version 11.1, Santa Fe, NM.
11
7.Ghasemzadeh, F. 2013. Simulation hydraulic issues in Flow-3D. Noavar Publications. 144p.
12
(In Persian)
13
8.Henderson, F.M. 1964. Open-channel flow. New York: Macmillan. 522p.
14
9.Hirt, C.W., and Nichols, B.D. 1981. Volume of Fluid (VOF) method for the dynamics of free
15
boundaries. J. Comput. Physic. 39: 201-225.
16
10.Khosrojerdi, A., and Kavianpour, M.R. 2002. Hydraulic Behavior of Straight and Curved
17
Broad Crested Weirs. 5th International Conference on Hydroscience Engineering, Poland.
18
11.Kumar, S., Ahmad, Z., and Mansoor, T. 2011. A new approach to improve the discharging
19
capacity of sharp-crested triangular plan form weirs. Flow Measurement and
20
Instrumentation. 22: 175-180.
21
12.Kumar, S., Ahmad, Z., Mansoor, T., and Himanshu, S.K. 2012. Discharge Characteristics of
22
Sharp Crested Weir of Curved Plan-form. Res. J. Engin. Sci. 1: 4. 16-20.
23
13.Ramamurthy, A.S., Qu, J., and Zhai, C. 2007. Multisite weir characteristics. J. Irrig. Drain.
24
Engin. 133: 2. 198-200.
25
14.Reda, M.A. 2011. 2D-3D Modeling of Flow Over Sharp-Crested Weirs. J. Appl. Sci. Res.
26
7: 12. 2495-2505.
27
15.Naderi, V., Sadeghi Nasrabadi, M., and Arvanaghi, H. 2014. Effect of Height of SharpCrested Weir on Discharge Coefficient. Inter. J. Basic Sci. Appl. Res. 3: 6. 325-330.
28
16.Swamee, P.K. 1988. Generalized rectangular weir equations. J. Hydr. Engin. 114: 8. 945-949.
29
ORIGINAL_ARTICLE
Application of Gene Expression Programming Approach to Estimate the Aeration Coefficient of Bottom Outlet Gates of Dams
AbstractBackground and objectives: The use of storage dams plays a key role in the development of industry, agriculture and employment communities Bottom outlet tunnels are one of the most significant components of the reservoir dams which are used in flood evacuation and control. They consist of inlet duct, main conveyance tunnel and flow regulator structures including gates and valves. A major problem with bottom outlet gate of dams is cavitation which happens in the high flow discharge. This phenomenon would destroy the surface of structure. It has been demonstrated that flow aeration is an effective way to reduce the cavitation damages. In this regard, the flow aeration rate is an important discussion that must be noted. Since, in this paper aeration coefficient evaluation is assessed.Materials and methods: This study, is to estimate the aeration coefficient of bottom outlet gate of four dams (Alborz, Zhaveh, Gotvand Olia, Jareh) using Gene Expression Programming (GEP) approach. To achieve this aim, experimental data were used collecting from hydraulic structures laboratory of Tehran Water Research Institute to train and test the model. The aeration coefficient was influenced by compressed Froude number (Frc) and aerator area to gate area ratio (Aa/Ag). 30 chromosomes and 3 genes were chosen to GEP performance. The model ability was assessed by two statistical parameters of correlation coefficient (R2) and root of mean square error (RMSE).Results: The results show that GEP predicted the aeration coefficient of bottom outlet gates of dams with R2 of 0.803 and 0.639 and RMSE of 0.096 and 0.125 for training and testing stages, respectively. This model gave better results compared by regression equation with R2 of 0.718 and 0.402 and RMSE of 0.114 and 0.171 for training and testing parts, respectively. In the other words, the error of aeration coefficient prediction was decreased about 28% using GEP approach.Conclusion: The results show that GEP intelligence approach is an adequate model to predict aeration coefficient of bottom outlet gates of dams. Also, the results of traditional regression equations were improved using this method. In the other words, these results indicated that GEP is reliable to evaluate the aeration coefficient of bottom outlet gates of dams by more accurate estimation to prevent cavitation phenomenon. So, use of this way is suggested in future studies related to this topic. Conclusion: The results show that GEP intelligence approach is an adequate model to predict aeration coefficient of bottom outlet gates of dams. Also, the results of traditional regression equations were improved using this method. In the other words, these results indicated that GEP is reliable to evaluate the aeration coefficient of bottom outlet gates of dams by more accurate estimation to prevent cavitation phenomenon. So, use of this way is suggested in future studies related to this topic.
https://jwsc.gau.ac.ir/article_3591_37ce8e5356731d50ea51be13b18b228a.pdf
2017-03-21
279
286
10.22069/jwfst.2017.10446.2487
aeration coefficient
Gene expression programming
Cavitation
bottom outlet gate
Samad
Emamgholizadeh
s_gholizadeh517@yahoo.com
1
Shahrood uni
LEAD_AUTHOR
Razieh
Karimi Demneh
r.karimi1017@yahoo.com
2
دانشگاه صنعتی شاهرود
AUTHOR
1.Chanson, H. 1995. Predicting oxygen content downstream of weirs, spillways and waterways.
1
Proc. Inst. Civil Eng-Water Maritime Energy. 112: 1. 20-30.
2
2.Emamgholizadeh, S., Bateni, S.M., Shahsavani, D., Ashrafi, T., and Ghorbani, H. 2015.
3
Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP)
4
and Multivariate Adaptive Regression Splines (MARS). J. Hydrol. 529: 1590-1600.
5
3.Ferreira, C. 2001. Gene expression programming: a new adaptive algorithm for solving
6
problems. J. Complex Syst. 13: 2. 87-129.
7
4.Jian-hua, W., and Chao, L. 2011. Effects of entrained air manner on cavitation damage. J.
8
Hydrodyn. 23: 3. 333-338.
9
5.Kavianpour, M.R. 1997. The Reattaching Flow Downstream of Deflectors Including the
10
Effect of Air Injection. A thesis submitted to the University of Manchester Institute of
11
Science and Technology for the degree of PHD. Manchester, UK.
12
6.Kisi, O., Hosseinzadeh Dalir, A., Cimen, M., and Shiri, J. 2012. Suspended sediment modeling
13
using genetic programing and soft computing techniques. J. Hydrol. 450-451: 48-58.
14
7.Ozkan F., and Kaya T. 2010. Using intelligence methods to predict air-demand ratio in venturi
15
weirs. Advances in Engineering Software. 41: 1073-1079.
16
8.Peterka, A.J. 1953. The effect of entrained air on cavitation pitting. In: Proc. IAHR Minnesota
17
conference, Minnesota, USA, Pp: 507-518.
18
9.Sutopo, Y., Wignyosukarto, B.S., Yulistyanto, B., and Istiarto. 2015. Self and artificial air
19
entrainment in steep channel. Procedia Engineering. 125: 158-165.
20
10.Zahiri, A., Dehghani, A.A., and Azamathulla, H.Md. 2015. Application of Gene-Expression
21
Programming in Hydraulic Engineering. Chapter Handbook of Genetic Programming
22
Applications. Pp: 71-97.
23
11.Zhi-yong, D., and Pei-lan, S. 2006. Cavitation control by aeration and its compressible
24
characteristics. J. Hydrodyn. 18: 4. 499-504.
25
ORIGINAL_ARTICLE
Investigation of the Effects of Land use Change on Low flow Indices
(Case study: Taleghan catchment)
Background and Objectives: Low flows are the most important parameters for the qualitative and quantitative hydrological analysis of the catchments and have a significant role in the planning and water resource management. Several factors are involved in low flow trend, including, land use and vegetation that directly and indirectly affected by the interference of humans. Low flow data, within a watershed is used for a wide range of activities including: drought planning, investigation of ecosystem status, planning water demand, water pollution issues, development projects in the field of power generation and environmental studies. Low flows from various aspects have been investigated. Some of these cases can be pointed to research Riggs (1990), Warner (2003) and McMahon and Nathan (1991). They were used a linear correlation and multivariate regression methods to estimate low flows. For prediction and investigation of the effect of land cover variation on flow parameters, several studies have been done, including research of Zhao, (2010) and Wei and Zhang (2010). The impact of land use and climate change on the hydrology of Alabama coastal basins by Ruoyu, et al was evaluated by hydrological modeling. Direct and indirect effects of human in land use changing and its role on water resources have studied by some researcher. Including: Kashaigili, (2008) and Delgado et al (2010) the aim of this study was to investigate the role of land use change on a number of low flow indices in Taleghan catchment.Materials and methods: In this research, by using topographical maps with the scale of 1:250000 and 1:50000 and Positioning the Galinak gauging stations in Taleghan river, the study area was Determined. Then using aerial photographs with the scale of1:20000 and TM and ETM satellite images of 1366 and 1381, land use map in the four-level of rangeland, dry land farming, irrigated and rock outcrop were prepared. Then land use change was calculated. Base flow index using daily data and based on, one parameter recursive digital filter algorithm were extracted by HydroOffice, 2012. Low flow indices with 3, 7, 15, 30 and 60 days duration using daily data were extracted. Then relationship between low flow indices and land use in the period of study were investigated. In this research, land use changes in the basin using the interpretation of aerial photographs and satellite imagery in three intervals of the years 1349, 1366 and 1381 were investigated. Base flow index using daily data and one parameter recursive digital filter algorithm were extracted. Low flow indices with 3, 7, 15, 30 and 60 days duration using daily data were extracted. Then relationship between low flow indices and land use in the period of study were investigated.Results: The results showed that all of the low flow indices in the first period of study have experienced a steep upward trend. This trend in the second period during the years 1349 to 1366 also shows an increasing trend but with little slope. In the period 1366- 1381 all indices, including base flow index and other low flow indices a minimal decline have experienced. Increasing rangeland coverage of 81 with respect to 49 was in accordance with the increasing of indices in the period studied.Conclusion: Land use Changes due to direct and indirect human intervention has a direct impact on the trend of low flow indices.Conformity of vegetation cover trend in 49 to 81 years with trend of low flow indices, indicating a positive role of rangeland on increasing the low flow indices. So rangeland protection to ensure, base flow continuity in the research area, is essential.
https://jwsc.gau.ac.ir/article_3592_6e67e95232ef1c937934a32e6bc524c1.pdf
2017-03-21
287
294
10.22069/jwfst.2017.11473.2590
Agriculture land use
Digital filter
Low flow indices
Rangeland land use
satellite images
Rahim
Kazemi
ra_hkazemi@yahoo.com
1
عضو هیئت علمی پژوهشکده حفاظت خاک و آبخیز داری
LEAD_AUTHOR
Reza
Bayat
iran1400@yahoo.com
2
عضو هیات علمی
AUTHOR
1.Brown, A.E., Zhang, L., McMahon, T.A., Western, A.W., and Vertessy, R.A. 2005. A review
1
of paired catchment studies for determining changes in water yield resulting from alterations
2
in vegetation. J. Hydrol. 310: 1-4. 28-61.
3
2.Bouzari, S. 1992. Taleghan from Seismotectonic point of view, J. Geol. Growth Educ.
4
8: 31. 44-51. (In Persian)
5
3.Delgado, J., Llorens, P., Nord, G., Calder, I.R., and Gallart, F. 2010. Modelling the
6
hydrological response of a Mediterranean medium-sized headwater basin subject to land
7
cover change: the Cardener River basin (NE Spain), J. Hydrol. 383: 1-2. 125-134.
8
4.Dibyajyoti, T. 2007. Changing climate and land-use impacts on Indiana's stream base flow.
9
Geological Society of America Abstracts, 39: 6. 432-441.
10
5.Ghorbani Dashtaki, Sh., Homaee, M., and Mahdian, M.H. 2010. Effect of Land Use Change
11
on Spatial Variability of Infiltration Parameters. Iran. J. Irrig. Drain. 2: 4. 206-221.
12
(In Persian)
13
6.Gustard, A., Young, A.R., Rees, G., and Holmes, M.G.R. 2004.Operational hydrology.
14
In: Hydrological drought: Processes and Estimation Methods for Stream flow and
15
Groundwater (ed. by L.M. Tallaksen & H.A.J. van Lanen), P 455-484. Developments in
16
Water Science 48, Elsevier, Netherlands.
17
7.Hosseini, M., Ghafouri, A.M., Amin, M.S.M., Tabatabaei, M.R., Goodarzi, M., and Abde
18
Kolahchi, A. 2012. Effects of Land Use Changes on Water Balance in Taleghan Catchment,
19
Iran, J. Agric. Sci. Technol. 14: 1159-1172.
20
8.Kashaigili, J.J. 2008. Impacts of land-use and land-cover changes on flow regimes of the
21
Usangu wetland and the Great Ruaha River, Tanzania, Physics and Chemistry of the Earth,
22
Parts A/B/C, 33: 8-13. 640-647.
23
9.Line, D.E., and White, N.M. 2007. Effects of development on runoff and pollutant export.
24
J. Water Environ. Res. 79: 2. 185-190.
25
10.Longobardi, A., and Villani, P. 2008. Baseflow index regionalization analysis in a
26
Mediterranean area and data scarcity context: Role of the catchment permeability index.
27
J. Hydrol. 355: 1-4. 63-75.
28
11.Mahe, G., Paturel, J.E., Servat, E., Conway, D., and Dezetter, A. 2005.The impact of land
29
use change on soil water holding capacity and river flow modeling in the Nakambe River,
30
Burkina-Faso, J. Hydrol. 300: 33-43.
31
12.Mehdi, B., Lehner, B., Gombault, C., Michaud, A., Beaudin, I., Sottile, M.F., and Blondlot,
32
A. 2015. Simulated impacts of climate change and agricultural land use change on surface
33
water quality with and without adaptation management strategies, Agriculture, Ecosystems
34
& Environment, Pp: 213-47.
35
13.Rahman, K., Gago da Silva, A., Tejeda, E.M., Gobiet, A., Beniston, M., and Lehmann, A.
36
2015. An independent and combined effect analysis of land use and climate change in the
37
upper Rhone River watershed, Switzerland, J. Appl. Geograph. 63: 264-272.
38
14.Ruoyu, W., Kalin, L., Kuang, W., and Tian, H. 2015. Individual and combined effects of
39
land use/cover and climate change on Wolf Bay watershed stream flow in southern Alabama.
40
Hydrological Processes, 28: 22. 5530-5546.
41
15.Tallaksen, L.M., and Lanen, H.A.J. van. 2004. Introduction. In: Hydrological Drought –
42
Processes and Estimation Methods for Streamflow and Groundwater (ed. by L.M. Tallaksen
43
& H.A.J. van Lanen), Developments in Water Sciences 48, Elsevier Science B.V.,
44
Amsterdam, the Netherlands, Pp: 3-17.
45
16.Tan, M., Ibrahim, Ab, L., Yusop, Z., Duan, Z., and Ling, L. 2015. Impacts of land-use and
46
climate variability on hydrological components in the Johor River basin, Malaysia, Hydrol.
47
Sci. J. 60: 5. 873-889.
48
17.Wei, X.H., and Zhang, M.F. 2010. Quantifying stream flow change caused by forest
49
disturbance at a large spatial scale: a single watershed study. Water Resour. Res. 46p.
50
18.Zhao, F., Zhang, L., Xu, Z., and Scott, D.F. 2010. Evaluation of methods for estimating the
51
effects of vegetation change and climate variability on stream flow. Water Resource. Res. 46p.
52
ORIGINAL_ARTICLE
Experimental study of the effect of asymmetric T- shaped spur dike on the scour reduction in bridge abutment
Abstract Background and objectives: Bridges are the most important river structures and lots of bridges are damaging every years by river floods. One of the reason in bridge damage is local scour in around of bridge abutment. Investigations are reveal that controlling of scour methods are based on mechanism of scour. If the design criteria perfectly do not use, extinctive damages will be occurs in bridges foundation. Proper design and management of spur dike structure can control scour and crate stability for bridge .Effect f scours on flow properties and sediment transport are different and depend on scour design parameters, flow hydrology and amount of sediment. Different factors have effected scour phenomena. For study of these factors different researches are necessary. The aim of this research are study of effective parameters on maximum scour depth in bridges abutment in asymmetric composite sections. Material and methods: This research has contacted on a rectangular shape flume with dimension of 1 m width,12 m length and 60 cm depth in hydraulic laboratory of Shahrood University. In this research the amount of scour in cape of abutment have been studied with asymmetric T- Shaped spur dike by 4 relative conjunction of 0.2, 0.5, 2 and 5 for length of wing by the 9, 18, 27, 36 and 45 cm distance from abutment. The amount of discharges used in study were 18,20, 22, 24 and 26 litter per second. Results: Result of study reveal that maximum depth of scour is decreasing with increasing of the upstream length of spur dike to downstream, also it is showing that depth of scour is decreasing with increasing the distance of spur dike from abutment. Decreasing of the scour depth was 100 percent with 18 lit/s discharge and 70 percent with 26 lit/s. Results also showed that dimensions of scour hole is increasing with increasing discharge. In this research assessment of hydraulic and geometric parameters like discharge, average flow velocity, depth of water, length of spur dike, length of spur wing and distance of abutment was done. Analysis of these factors led to obtain a dimensionless equation. Final results of study was a new equation to estimate maximum depth of scour around the abutment.Conclusion: Main results of this research revealed that with increasing of the distance of spore dike from abutment amount of dike effect to abutment erosion is increasing so that depth of scour in cap of abutment is decreasing.
https://jwsc.gau.ac.ir/article_3593_6f0cc366d1d08fac789a72b5c12c78bc.pdf
2017-03-21
295
301
10.22069/jwfst.2017.9516.2371
Keywords:spur dike
scour
abutment
bridge
asymmetric
Khalil
Azhdary
azhdary2005@yahoo.co.in
1
دانشگاه صنعتی شاهرود
LEAD_AUTHOR
Samad
Emamgholizadeh
s_gholizadeh517@yahoo.com
2
دانشگاه صنعتی شاهرود
AUTHOR
Hourieh
Rezaie
rezaeihoorie@yahoo.com
3
دانش آموخته کارشناسی ارشد
AUTHOR
1.Abbasi, A.A., and Malek Nejad, Y.M. 2012. Experimental investigation the effect of
1
geometry parameters of straight and T shape gabions on local scouring. J. Irrig. Water Engin.
2
8: 95-107.
3
2.Douglas, M.T. 2006. The role of vortex shedding in the scour of pools. Advances in Water
4
Resources. 29: 121-129.
5
3.Hashemi Najafi, F., Ayoub Zade, A., and Dehghani, A.A. 2009. Laboratory Study scour depth
6
around the L-shaped breakwater in clear water. J. Agric. Sci. Natur. Resour. 15: 1. 57-70.
7
4.Hakimzadeh, H., Azari, N., and Mehrzad, R. 2012. Experimental study of lateral structural
8
slopes of groynes on scour reduction. 6th International Conference on Scour and Erosion
9
Paris-August 27-31. 273: 1035-1040.
10
5.Saneie, M., and Mosavi, B. 2011. Experimental investigation of groin placement on
11
minimizing river bank erosion. J. Water Sci. Res. 2: 2. 59-68.
12
6.Sturm, T.W. 2006. Scour around bankline and setback abutments in compound channels.
13
J. Hydr. Engin. 132: 21-32.
14
7.Vaghefi, M., Ghodsian, M., and Adib, A. 2011. Experimental study on Froude number on
15
temporal variation of scour around a T shaped spur dike in a 90 degree bend. Applied
16
Mechanics and Materials. 147: 5. 75-79.
17
8.Zarrati, A.R., Gholami, H., and Mashahir, M.B. 2004. Application of collar to control scour
18
around rectangular bridge pier. J. Hydr. Res. 42: 1. 97-103.
19
ORIGINAL_ARTICLE
Temporal variation of runoff production and rill erosion in a marl soil under different rainfall intensities
Background and Objectives: Rill erosion is a major factor of soil loss in the marl formations. The marl formations are very susceptible to water erosion processes and cover a wide area in some watersheds in arid and semi-arid regions. Rill erosion is active water erosion in these areas which temporally varies during year, from each event to other or during each rainfall event. Temporal variation of rill erosion during a given rainfall event can occur due to the change of soil properties and its effect on the characteristics of concentrated flow. Knowledge of temporal variation of rill erosion and effect of during a rainfall event can provide information on the mechanism of rill erosion in the hillslopes. The rate of temporal variation of flow characteristics and rill erosion can be affected by the rainfall intensity. Therefore, this study was conducted to the study of temporal variation of flow characteristics and rill erosion in a marl soil under different simulated rainfalls. Materials and Methods: A laboratory experiment was carried out using six simulated rainfall intensities ranging from 10 mm h-1 to 60 mm h-1 with three replications. Soil samples were collected from the marl formations in west of Zanjan and separately purred to a flume with 4m in length and 0.94 m in width putted on 10% slope. Rill erosion and flow characteristics (discharge and concentration) were measured at 5-min from starting flow/ runoff in each rainfall intensity. Rate of rill erosion and flow characteristics versus rainfall duration was obtained and differences among the different rainfall intensities were computed using the variance analysis method. The dependency of rill erosion on the flow characteristics (discharge and concentration) was determined for all rainfall intensities. All data analysis was performed using SPSS version 21. Results: Results indicated that there are substantial differences in the flow starting time, flow concentration and rill erosion among the rainfall intensities (P< 0.001). Rill flow and erosion rapidly occurred with increasing rainfall intensity. Rill erosion increased speedily during rainfall and reached to approximately constant value in the last times (about 45 min). The flow concentration appeared also a similar trend with the rill erosion, while flow discharge showed an increasing trend in the last times. Most of erodible particles were eroded during 45 min from rainfall and after this time, large flows containing lower concentration/sediment were observed in the rills. A strong relationship was found between rill erosion and flow discharge in different rainfall intensities. Conclusion: The study revealed that the threshold and pick time of rill erosion were strongly varied during rainfall. The variation trend of rill erosion during rainfall increases with increasing the rainfall intensity. Rill erosion temporally varies during rainfall. Rill erosion increases during rainfall due to increases in flow discharge as well as flow concentration. Rill erosion is strongly dependent on the flow discharge in initial times of rainfall, while in the last times its trend is very different from flow discharge
https://jwsc.gau.ac.ir/article_3594_2cd882afa67728f768e8a5d9f7d6f232.pdf
2017-03-21
303
309
10.22069/jwfst.2017.12311.2687
Flow discharge
Erodible particles
Marl formation
Rill
Flow concentration
Ali Reza
Vaezi
vaezi.alireza@gmail.com
1
Associate Prof. of Soil Sci., Univ. of Zanjan, Iran
LEAD_AUTHOR
Majid
Foroumadi
majid.foroumadi@znu.ac.ir
2
Ph D. Student of Soil Sci., Univ. of Zanjan, Iran
AUTHOR
1.Asadi, H., Aligoli, M., and Gorji, M. 2017. Dynamic changes of sediment concentration in rill
1
erosion at field experiments. J. Water Soil Sci. 20: 78. 125-139. (In Persian)
2
2.Bagnold, R.A. 1966. An approach to the sediment transport problem from general physics. US
3
Geological Survey Paper. 422-1, Washington.
4
3.Chen, X.Y., Zhao, Y., Mo, B., and Mi, H.X. 2014. An improved experimental method for
5
simulating erosion processes by concentrated channel flow. Plos one.9: 6. p.e99660.
6
4.Dunjo, G., Pardini, G., and Gispert, M. 2004. The role of land use-land cover on runoff
7
generation and sediment yield at a microplot scale, in a small Mediterranean catchment.
8
J. Arid Environ. 57: 99-116.
9
5.Franti, T.G., Laflen, J.M., and Watson, D.A. 1985. Soil erodibility and critical shear under
10
concentrated flow. American Society of Agricultural Engineers. 42: 329-335.
11
6.Jain, M.K., Kothyari, U.C., and RangaRaju, K.G. 2004. A GIS based distributed rainfall–
12
runoff model. J. Hydrol. 299: 107-135.
13
7.Lili, M., Bralts, V.F., Yinghua, P., Han, L., and Tingwu, L. 2008. Methods for measuring soil
14
infiltration: State of the art. Inter. J. Agric. Biol. Engin. 1: 1. 22-30.
15
8.Liu, H., Lei, T.W., Zhao, J., Yuan, C.P., Fan, Y.T., and Qu, L.Q. 2011. Effects of rainfall
16
intensity and antecedent soil water content on soil infiltrability under rainfall conditions
17
using the run off-on-out method. J. Hydrol. 396: 1. 24-32.
18
9.Merz, R., Bloschl, G., and Parajka, J. 2006. Spatiotemporal variability of event runoff
19
coefficients. J. Hydrol. 331: 591-604.
20
10.Sadeghi, S.H.R., Mohammadpour, K., and Dianatytilaki, G.E. 2010. Temporal variability of
21
runoff coefficient in the summer pastures of Kadir, Proceedings of the 6th National
22
Conference of Science and Watershed Engineering and 4th National Conference of Erosion
23
and Sediment, 28-29 April, Tehran, Iran. 52-60. (In Persian)
24
11.Shen, H., Zheng, F., Wen, L., Han, Y., and Hu, W. 2016. Impacts of rainfall intensity
25
and slope gradient on rill erosion processes at loessial hillslope. Soil and Tillage Research.
26
155: 429-436.
27
12.Vaezi, A.R., and Gharehdaghlii, H. 2013. Quantification of rill erosion development in Marl
28
soils of Zanjanroud watershed in North West of Zanjan, Iran. J. Water Soil. 27: 5. 872-881.
29
(In Persian)
30
13.Vaezi, A.R., Ahmadi, M., and Cerda, A. 2017. Contribution of raindrop impact to the change
31
of soil physical properties and water erosion under semi-arid rainfalls. Science of the Total
32
Enviroment. Pp: 1-11.
33
14.Vaezi, A.R., Bahrami, H.A., Sadeghi, S.H.R., and Mahdian, M.H. 2008. Spatial variations of
34
runoff in a port of calcareous soils of semi-arid region in northwest of Iran. J. Agric. Sci.
35
Natur. Resour. 15: 5. 56-65. (In Persian)
36
15.Williams, B.M., Martinez-Menaa, S., and Deeksb, L. 2004. Exponential distribution theory
37
and aggregate erosion. Soil Sci. Soc. Amer. J. 6: 382-391.
38
ORIGINAL_ARTICLE
Technical evaluation sprinkler irrigation system implemented in some of the fields in Fars province
Introduction and objectives: Precision in designing and correct managements on sprinkler irrigation systems can help to improve and develop of these systems and cause efficiency raising in agriculture. Materials and methods: In this research, constant classic sprinkle irrigation systems (four systems) and one wheel move sprinkle system were evaluated and compared (in 2015). To evaluate of irrigation systems, Christiansen’s uniformity coefficient (CU), distribution uniformity (DU), application efficiency of low-quarter (AELQ), and potential application efficiency of low quarter (PELQ) were calculated in the experimental plots and adjusted with pressure changes for the whole system. Results: The maximum an uniformity coefficient and distribution uniformity in all the systems were in Darab, Arsenjan and Sarvestan counties and they were 80.78, 69.56 and 68.21 percent for uniformity coefficient, respectively. These values showed normal distribution of data and symmetry measurements than the average and the distribution uniformities were 66.12, 55.4 and 53 percent, respectively.Conclusion: The reason of low distribution uniformity in systems was water losses for deep percolation, outdated system, pressure loss, and pressure and discharge variations of sprinklers. Homogenization of application efficiency potential of low quarter and the actual application efficiency in all evaluated irrigation systems were showed supply water less than plants water requirement. These values were low that showed water losses because of deep percolation and outdated systemsIntroduction and objectives: Precision in designing and correct managements on sprinkler irrigation systems can help to improve and develop of these systems and cause efficiency raising in agriculture. Materials and methods: In this research, constant classic sprinkle irrigation systems (four systems) and one wheel move sprinkle system were evaluated and compared (in 2015). To evaluate of irrigation systems, Christiansen’s uniformity coefficient (CU), distribution uniformity (DU), application efficiency of low-quarter (AELQ), and potential application efficiency of low quarter (PELQ) were calculated in the experimental plots and adjusted with pressure changes for the whole system. Results: The maximum an uniformity coefficient and distribution uniformity in all the systems were in Darab, Arsenjan and Sarvestan counties and they were 80.78, 69.56 and 68.21 percent for uniformity coefficient, respectively. These values showed normal distribution of data and symmetry measurements than the average and the distribution uniformities were 66.12, 55.4 and 53 percent, respectively.Conclusion: The reason of low distribution uniformity in systems was water losses for deep percolation, outdated system, pressure loss, and pressure and discharge variations of sprinklers. Homogenization of application efficiency potential of low quarter and the actual application efficiency in all evaluated irrigation systems were showed supply water less than plants water requirement. These values were low that showed water losses because of deep percolation and outdated systems
https://jwsc.gau.ac.ir/article_3595_4d5253c653bd8b67f62330a0661e7d66.pdf
2017-03-21
311
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10.22069/jwfst.2017.12139.2668
Evaluation of sprinkler irrigation
Irrigation adequacy
wheel move system
uniformity coefficient
Mehdi
Bahrami
bahrami@fasau.ac.ir
1
استادیار دانشگاه فسا
AUTHOR
Farzaneh
Khajeie
far48.khajehi@yahoo.com
2
دانشجوی کارشناسی ارشد مهندسی آب دانشگاه فسا
AUTHOR
Ali
Dindarlou
adindarlou@gmail.com
3
instructor of Persian gulf university
LEAD_AUTHOR
Mehdi
Dastourani
mdastourani@birjand.ac.ir
4
استادیار گروه مهندسی آب دانشگاه بیرجند
AUTHOR
1.Alizadeh, A. 2005. Irrigation system design. Ferdowsi University Press, 367p. (In Persian)
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2.Christiansen, J. 1942. The Uniformity of Application of Water by Sprinkler Systems. Agri.
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Eng. 22: 89-92.
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3.Majd-Salimi, K. 2015. Technical evaluation of performed classcal sperinkler system on tea
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farms in Gilan province. J. Water Soil. 29: 2. 336-349. (In Persian)
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4.Merriam, J., Shearer, M., and Bort, C. 1983. Evaluation irrigation systems and practices.
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In: M.E. Jensen (ED), Design and operation of farm irrigation system. ASAE. Monograph.
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Pp: 719-760.
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5.Sanaei, A., Eizad-Panah, Z., and Boroumand-Nasab, S. 2015. Technical evaluation center
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pivot systems performed in Bardsir and Rain citys in Kerman province. Irrigation Sciences
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and Engineering. 38: 2. 171-180. (In Persian)
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