ORIGINAL_ARTICLE
Investigation of the Terraces of the Zayandeh‒rud River’s Current Pathway Using the Harden’s Profile Development Index (PDI)
AbstractBackground and objectives: One of the most important goals of the soil science is to investigate the changes in the earth surface systems in the past and useing their patterns to predict environmental changes in the future in order to improve land management. Therefore, this study was conducted on the current Zayandeh‒rud River’s pathway in a semi-detailed scale in order to understand the development of alluvial plain of the river.Materials and methods: Geomorphic surfaces were determined using a stereoscopic interpretation of aerial photos with a scale of 1:20000 and based on the Zinck’s hierarchical classification system. Fourty eight profiles were drilled at 1 km/km network, according to the common method of semi-detailed soil studies in a grid survey pattern. Soil classification was finalized in accordance with the Soil Survey Staff. The degree of soil evolution was studied according to Harden's soil development index (PDI) for eight control pedons.Results: According to previous studies, the Zayandeh-e-Rud River during its flow time lave had a single pathway three terraces in the Zayandeh-rud plain. The last research showed that the Zayandeh-roud River runs through three separate way over time. In this study, the interpretation of aerial photos and field study cleared that the current pathway of the Zayandeh‒rud River includes a series of three terraces, which each terrace consists of sub-terraces. Also it was found that Fourty eight profiles in this research were classified in four suborder (including argids, calcids, cambids and orthents) with eight soil families. On the other hands, the calculated PDI values for the control pedons of these eight families also were different. This indicated difference of degree of soil evolution in the current river’s paethway.Conclusion: Therefore, it can be concluded that the soil of three terraces of the current the Zayandeh‒rud River’s way is more diverse than previously reported, and this variation indicates the difference in the age of these terraces. Pedologic study also revealed that the soils of the first terrace had the highest PDI and evolution. Presence of argillic and calcic horizons in these profiles confirms this conclusion. On the other hand, the soils in the second terrace also had a lower PDI index and less degree of development than the first terrace, and were more developed than third terrace’s soils. Therefore, it can be stated the curent pathway has three independent age terraces with multi-interior terraces. From the results of this study, it can be anticipated that also there are terraces on previous river pathway, which require more research to prove them.
https://jwsc.gau.ac.ir/article_4149_de40a7e069be2bf9a3eb1634de307c47.pdf
2018-05-22
1
23
10.22069/jwsc.2018.14152.2888
Geomorphic Surfaces
Zayandeh‒rud River’s Pathways
Sub Terraces
PDI index
Shaghayegh
Havaee
sh.havayi@yahoo.com
1
Department of Soil Science, College of Agriculture, Vali-e-Asr University of Rafasnjan, Iran.
LEAD_AUTHOR
Ardavan
Kamali
a.kamali@vru.ac.ir
2
Department of Soil Science, College of Agriculture, Vali-e-Asr University of Rafasnjan, Iran.
AUTHOR
Norair
Toomanian
norairtoomanian@gmail.com
3
Associate professor of soil science, Agriculture and natural resource research center of Esfahan
AUTHOR
Mohammad Reza
Mosaddeghi
mosaddeghi@cc.iut.ac.ir
4
Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran.
AUTHOR
1.Alonso, P.C., Sierra, E., Ortega, C., and Dorronsoro. 1994. Soil development indices of
1
soils developed on fluvial terraces (Peòaranda de Bracamonte, Sala manca, Spain). Catena.
2
23: 295-308.
3
2.Ayoubi, S. 2002. Pedogenic evidence of climate change in the quaternary period in the paleosols of Isfahan and Imam Khayes. Doctoral dissertation, Department of Soil Science, Faculty of Agriculture, Isfahan University of Technology.
4
3.Badía, D., Martí, C., Casanova, J., Gillot, T., Cuchí, J.A., Palacio, J., and Andrés, R., 2015.
5
A Quaternary soil chronosequence study on the terraces of the AlcanadreRiver (semiarid Ebro Basin, NESpain). Geoderma. 241-242: 158-167.
6
4.Barshad, I. 1959. Factors affecting clay formation. Clays Clay Miner. 6: 110-132.
7
5.Bilzi, A.F., and Ciolkosz, E.J. 1977. A field morphology rating scale for evaluating pedological development. Soil Sci. 124: 45-48.
8
6.Birkeland, P.W. 1984. Holocene soil chronofunctions, Southern Alps, New Zealand. Geoderma. 34: 115-134.
9
7.Bockheim, J.G., Kelsey, H.M., and Marshall III, J.G. 1992. Soil development, relative dating and correlation of late Quaternary marine terraces in southwestern Oregan. Quat. Res.
10
37: 60-74.
11
8.Bull, W.B. 1990. Stream-terrace genesis: implications for soil development. Geomorphology 3: 351-367.
12
9.Bull, W.B. 1991. Geomorphic responses to climatic change. OxfordUniversity Press,
13
New York, 336p.
14
10.Buol, S.W., Hole, F.D., and McCracken, R.J. 1973. Soil Genesis and Classification.
15
Iowa State Univ. Press, Ames, IO, 2nd ed., 404p.
16
11.Cohen, S., Willgoose, G., Svoray, T., Hancock, G., and Sela, S. 2015. The effects of sediment transport, weathering and aeolian mechanisms on soil evolution. J. Geophys. Res. F: EarthSurf. 120: 2. 260-274.
17
12.Dolatshahi, A.R., Esfandiari, K., Momeni, A., and Hajmolana, N. 2000. Instructions for laboratory analysis of soil and water samples. No. 467, Soil and Water Research Institute, Ministry of Agriculture and Natural Resources, Tehran, Iran.
18
13.Harden, J.W. 1982. A quantitative index of soil development from field descriptions, examples from a chronosequence in Central California. Geoderma. 28: 1-28.
19
14.Harden, J.W., and Taylor, E.M. 1983. A quantitative comparison of soil development in four climatic regimes. Quat. Res. 20: 342-359.
20
15.Ibáñez, J.J., Vargas, R.J., and Vázquez-Hoehne, A. 2013. PedodiversityState of the Art and Future Challenges. In: J.J. Ibáñez, and J. Bockheim (Ed.), Pedodiversity. Taylor & Francis Group, Boca Raton, FL, USA, Pp: 133-152.
21
16.Isadpanah, B., Farmanara, M., and Eskandarzadeh, I. 1974. Final Report on Semi-Sedimentary Soil Science in Vardoush Region, IsfahanProvince. No. 391, Soil Science and Fertility Institute, Ministry of Agriculture and Natural Resources, Tehran, Iran.
22
17.Jafarian, M.A. 1986. Geography of the past and the developmental stages of the Zayandehrud valley. Res.J. Isf. Univ. 1: 31-15.
23
18.Jenny, H. 1941. Factors in Soil Formation. McGraw-Hill, New York. Kao, H., Chen, W.P., 2000. The Chi-chi earthquake sequence: active out-of-sequence thrust faulting in Taiwan. Science. 288: 2346-2349.
24
19.Khademi, H., Mermut, A.R., and Krouse, H.R. 1997. Sulfur isotope geochemistry of gypsiferous Aridisoils from central Iran. Geoderma. 80: 195-209.
25
20.Khanaamani, A., Jafari, R., Sangouni, H., and Shahbazi, A. 2011. Evaluation of Soil Status Using Remote Sensing Technology and Geographic Information System (Case study: Segzi Plain of Isfahan). J. Rem. Sens. App. GIS Natur. Resour. Sci. 3: 37-25.
26
21.Leamy, M.L., Milne, J.D.G., Pullar, W.A., and Bruce, J.G. 1973. Paleopedology and stratigraphy in the New Zealand Quaternary succession. N. Z. J. Geol. Geophys. 16: 723-744.
27
22.Makarian, M., Pourkermani, M., Sherkati, S., and Motamedi, H. 2011. Structural analysis of Chinese carpets in part of central Iran basin. Monthly Exploration and Production, 78: 55-48.
28
23.Morrison, R.B. 1968. Means of time-stratigraphic division and longdistance correlation of Quaternary successions. In: Morrison, R.B., and WrightJr. Jr., H.E. (Eds.), Means of Correlation of Quaternary Successions. Int. Assoc. Quat. Res., VII Congress, Proc. 8: 1-113.
29
24.Mulcahy, M.J., and Churchward, H.M. 1973. Quaternary environments and soils in Australia. Soil Sci. 116: 156-169.
30
25.Nourbakhsh, F. 2002. A Study on the Soils of Zarrin Shahr, Talekhoncheh and Kharmhine. SoilWaterRes.Center J. No. 1143.
31
26.Phillips, J.D. 1999. Earth surface systems: complexity, order and scale. Oxford: Blackwell.
32
27.Ramesht, M.H. 1992. Zayandeh‒RudRiver and its Impact on Spatial Image of Isfahan. Thesis of Doctor, Department of Geography, TarbiatModaresUniversity, Tehran.
33
28.Saldana, A., and Ibanez, J.J., 2004. Pedodiversity analysis at large scales: an example of three fluvial terrain of the HenaresRiver (central Spain). Geoderma. 62: 123-138.
34
29.Salehi, M.H., and Khademi, H. 2007. Fundamentals of soil mapping. IsfahanUniversity of Technology Press. (In Persian)
35
30.Schaetzl, R.J., and Anderson, S. 2005. Soils: genesis and geomorphology. CambridgeUniversity Press.
36
31.Schoeneberger, P.J., Wysocki, D.A., Benham, E.C., and Staff, S.S. 2012. Field book for describing and sampling soils. Natural Resources Conservation Service. NationalSoilSurveyCenter, Lincoln, NE, USA.
37
32.Soil Taxonomy. 2014. Keys to Soil Taxonomy. 12th ed. USDA-Natural Resources Conservation Service, Washington, DC.
38
33.Toomanian, N. 2006. How to develop land, soil diversity and quantitative mapping of some pedogenic characteristics in some parts of Central Iran, Ph.D. Soil college, Faculty of Agriculture, Isfahan University of Technology.
39
34.Torrent, J., Schwertmann, U., and Schulze, D.J. 1980. Iron oxide mineralogy of some soils of two river terrace sequences in Spain. Geoderma. 23: 191-208.
40
35.Tsai, H., Huang, W.S., Hseu, Z.Y., and Chen, Z.S. 2006. A river terrace soil chronosequence of the Pakua tableland in Taiwan. Soil Sci. 171: 167-179.
41
36.Tsai, H., Huang, W.S., Hseu, Z.Y., and Chen, Z.S. 2007. Pedogenic approach to resolving the geomorphic evolution of the Pakua river terraces in central Taiwan. Geomorphology.
42
83: 14-28.
43
37.Zinck, J.A. 1988. Physiography and soils. Lecture Notes for Soil Students. Soil Science Division. Soil Survey Courses Subject Matter: K6 ITC, Enschede, Netherlands.
44
ORIGINAL_ARTICLE
Modeling and groundwater potential mapping using data driven ensemble model EBF-Index of entropy (case study: najaf abad aquifer)
Background and objectives: Groundwater considered as the main source of future water supply, irrigation, and food production under impacts of global climate changes phenomena. Main aquifers around the world are under pressure to meet the growing demands of water due to population growth. Management of groundwater reserves in a sustainable manner is a major challenge. A goal of groundwater resource assessment is to provide information on the current status of the resource and provide insights about the future availability of ground water. In recent years, several authors have attempted to assessment groundwater potential using different data-driven and knowledge-driven techniques combined with remote sensing (RS) and geographic information system (GIS). The main objective of this research is identification of effective parameters in groundwater recharge and assessment of groundwater potential using data-driven combined method in najaf-abad aquifer.Materials and methods: The study area lies between (32° 18′ 06″- 32° 50′ 12) latitude and (50° 52′ 46″- 51° 41′ 48″) longitude. It extends over an area of about 966.11 km2. In general, four steps must be implemented to groundwater potential mapping using combined approache. These steps are: (1) prepare groundwater well inventory map and divided into two sets: training and testing. The training data is used to investigate the statistical relationship between well locations and Geo-environmental factors influence on groundwater occurrences. The testing set is used to validate the results. (2) Build the database. In this step layers of groundwater occurrence factors are prepared using different resources such as field survey, and RS. All thematic layers must be converted to raster format to use in further analysis. (3) computation the relationship between training well locations and groundwater occurrence factors using shanoon model and their classes using EBF model are investigated. The groundwater potential map is then computed and classified into four classes using Natural Break scheme (4) the validation of the results and compare the effectiveness of model in prediction groundwater potential zones with indivdal models.Results: The results of the multicollinearity analysis among 20 Geo-environmental factors influence on groundwater occurrences used in this study showed that the Tolerance and VIF of 15 variables were ≥0.1 and ≤10, respectively. As a result, this parameters are selected for modeling. The computed weights for each factor using Index of entropy model, indicated that the most influencing factors on groundwater occurrence in the study area were distance from fault, LULC and geology. The results of validation of models indicate that The AUC for EBF, index of entropy and EBF-index of entropy models were 0.660, 0.431 and 0.899, respectively implying that the EBF-index of entropy was better than EBF and index of entropy.Conclution: The main conclusions of this study is that The ensembled approach of EBF-Index of entropy combining with RS and GIS technologies provide a powerful tool for groundwater potential mapping in the study area. The results of this study could be used for efficient managing groundwater resources in the study area. Based on results of ensemble model, The areas covered by very high groundwater potential zones occupy 45.26 % of the total area, indicating that the groundwater potential is high in study area.
https://jwsc.gau.ac.ir/article_4150_04fed6e975731fa0969b4f0c3316bc8e.pdf
2018-05-22
25
48
10.22069/jwsc.2018.14021.2879
Groundwater
Modeling
geo-environmental parameters
najaf abad
Ali Reza
Arab Amery
alireza.ameri91@yahoo.com
1
دانشگاه تربیت مدرس
LEAD_AUTHOR
Khalil
Rezaei
rezaei@yahoo.com
2
kharazmi university
AUTHOR
Mojtaba
Yamani
yamani@yahoo.com
3
tehran university
AUTHOR
Kourosh
Shirani
shirani@yahoo.com
4
esfahan university
AUTHOR
1.Al-Abad, A., Al-Temmeme, A., and Al-Ghanimy, A. 2016. A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq, Sustain. Water Resour. Manage. 2: 3. 265-283.
1
2.Ayazi, M.H., Pirasteh, S., Arvin, A.K.P., Pradhan, B., Nikouravan, B., and Mansor, S.
2
2010. Disasters and risk reduction in groundwater: Zagros mountain southwest Iran using geo-informatics techniques. Dis. Adv. 3: 1. 51-57.
3
3.Constantin, M., Bednarik, M., Jurchescu, M.C., and Vlaicu, M. 2011. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ. Earth Sci. 63: 2. 397-406.
4
4.Chenini, I., and Mammou, A.B. 2010. Groundwater recharge study in arid region: an approach using GIS techniques and numerical modelling. Comput. Geosci. 36: 6. 801-817.
5
5.Chen, W., Pourghasemi, H.R., and Naghibi, S.A. 2017. Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms, Bull. Eng. Geol. Environ. 23: 2. 1-19.
6
6.Dempster, A.P. 1968. Generalization of Bayesian inference. J. R. Stat. Soc. Series B.
7
30: 205-247.
8
7.Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., and Althuwaynee, F. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat. Hazards. 65: 1. 135-165.
9
8.Davoodi Moghaddam, D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., and Pradhan, B. 2015. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arab. J. Geosci. 8: 2. 913-929.
10
9.Ercanoglu, M., and Gokceoglu, C. 2002. Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol. 41: 6. 720-730.
11
10.Guo-Liang, D., Yong-Shuang, Z., Javed, I., and Xin, Y. 2017. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China, J. Mt. Sci. 14: 2. 249-268.
12
11.Glenn, C.R. 2012. Lahaina Groundwater Tracer Study-Lahaina, Maui, Hawaii. Final Interim Report prepared from the State of Hawaii DOH, the U.S. EPA and the U.S. Army Engineer Research and Development Center.
13
12.Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J., and Sattarian, A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int. J. Environ. Sci. Technol. 11: 4. 909-926.
14
13.Jothibasu, A., and Anbazhagan, S. 2016. Modeling groundwater probability index in Ponnaiyar River basin of South India using analytic hierarchy process, Model. Earth Syst. Environ. 2: 109.
15
14.Lee, S., Hwang, J., and Park, I. 2013. Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena. 100: 15-30.
16
15.Lee, S., Song, K.Y., Kim, Y., and Park, I. 2012. Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol. J. 20: 1511-1527.
17
16.Lee, S., and Pradhan, B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression model. Landslides. 4: 1. 33-41.
18
17.Molden, D. 2007. Water for food, water for life: a comprehensive assessment of water management in agriculture. Earthscan, London and International Water Management Institute, Colombo.
19
18.Magesh, N.S., Chandrasekar, N., and Soundranayagam, J.P. 2012. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci. Front. 3: 2. 189-196.
20
19.Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Soleiman, W.N.A., and Ramli, M.F. 2012. Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab. J. Geosci. 7: 2. 711-724.
21
20.Moore, I.D., Grayson, R.B., and Ladson, A.R. 1991. Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydro. Process. 5: 3-30.
22
21.Mirzapour, H., and Haghi Zadeh, A. 2017. Delineation of groundwater potential zones in Madian Roud watershed in Lorestan using Weighted Index Overlay Analysis (WIOA). Hydrogeology. 1: 83-98. (In Persian)
23
22.Mogaji, K.A., Lim, H.S., and Abdullah, K. 2014. Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model. Arab. J. Geosci. 8: 5. 3235-3258.
24
23.Naghibi, S.A., Pourghasemi, H.R., Pourtaghie, Z.S., and Rezaei, A. 2014. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. J. Earth Sci. 8: 1. 171-186.
25
24.Naghibi, S.A., Pourghasemi, H.R., and Dixon, B. 2016. Groundwater spring potential using boosted regression tree, classification and regression tree and random forest machine learning models in Iran. Environ. Monit. Assess. 188: 1. 44-64.
26
25.Nampak, H., Pradhan, B., and Manap, M.A. 2014. Application of GIS based data
27
driven evidential belief function model to predict groundwater potential zonation. J. Hydrol. 513: 283-300.
28
26.Ozdemir, A., and Altural, T. 2013. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J. Asia. Earth Sci. 64: 180-197.
29
27.Pourghasemi, H.R., and Beheshtirad, M. 2014. Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int. 30: 6. 662-685.
30
28.Pourghasemi, H.R., and Kerle, N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ. Earth Sci. 75:185.
31
29.Page, M.L., Berjamy, B., Fakir, Y., Bourgin, F., Jarlan, J., Abourida, A., Benrhanem, M., Jacob, G., Huber, M., Sghrer, F., Simonneaux, V., and Chehbouni, G. 2012. An integrated DSS for groundwater management based on remote sensing. The case of a semi-arid aquifer in Morocco. Water Resour. Manage. 26: 3209-3230.
32
30.Pourtaghi, Z.S., and Pourghasemi, H.R. 2014. GIS-based groundwater spring potential assessment and mapping in the Birj and Township, southern Khorasan Province, Iran. Hydrogeol. J. 22: 643-662.
33
31.Razandi, Y., Pourghasemi, H.R., Samani-Neisani, N., and Rahmati, O. 2015. Application of analytical hierarchy process, frequency ratio and certainty factor models for groundwater potential mapping using GIS. Earth Sci. Inf. 8: 4. 867-883.
34
32.Rahmati, O., Pourghasemi, H.R., and Melesse, A. 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. Catena. 137: 360-372.
35
33.Samy, I., Shattri, M., Bujang, B.K., and Ahmad, R.M. 2011. Structural geologic control with the limestone bedrock associated with piling problems using remote sensing and GIS: a modified geomorphological method. Environ. Earth Sci. 66: 8. 2185-2195.
36
34.Sharma, L.P., Patel, N., Ghose, M.K., and Debnath, P. 2010. Influence of Shannon’s entropy on lands lide -causing parameters for vulnerability study and zonation-a case study in Sikkim, India. Arab. J. Geosci. 5: 3. 421-431.
37
35.Shafer, G. 1976. A mathematical theory of evidence, vol. 1. Princeton University, Princeton.
38
36.Shekhar, S., and Pandey, A.C. 2014. Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques. Geocarto. Int. 30: 4. 402-421.
39
37.Singh, P., Gupta, A., and Singh, M. 2014. Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. Egypt J. Rem. Sens. Space Sci. 17: 111-121.
40
38.Tehrany, M.S., Pradhan, B., and Jebur, M.N. 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol. 504: 69-79.
41
39.Taheri, K., Gutie´rrez, F., Mohseni, H., Raeisi, E., and Taheri, M. 2015. Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude-frequency relationships: a case study in Hamadan province, Iran. Geomorphology. 234: 64-79.
42
40.Thapa, R., Gupta, S., Guin, S., and Kaur, H. 2017. Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West Bengal, Appl. Water Sci. 7: 7. 4117-4131.
43
41.Umar, Z., Pradhan, B., and Ahmad, A. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena. 118: 124-135.
44
42.Youssef, A.M., Pradhan, B., and Jebur, M.N. 2015. Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ. Earth Sci. 73: 7. 3745-3761.
45
43.Yesilnacar, E.K. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey, PhD Thesis. Department of Geomatics the University of Melbourne, 423p.
46
44.Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z.S., and Behzadfar, M. 2016. GIS based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ. Earth Sci. 75: 665.
47
45.Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z.S., and Behzadfar, M. 2015. Groundwater Potential Mapping using Shannon's Entropy and Random Forest Models in the Bojnourd Township. EcoHydrology. 2: 221-232. (In Persian)
48
ORIGINAL_ARTICLE
Spatial disaggregation semi-detailed soil map using DSMART approach
Background and objectives: Digital soil data with high spatial resolution and enough accuracy and precision are necessary for management of global challenges such as food security, environment problems. Generally, soil data are available in small scale. Nevertheless, in the last decades, with the advent of soil digital mapping and modeling approaches, it is possible to disaggregate soil map units. The spatial disaggregation of soil map units is a method for modeling the spatial distribution of individual soil classes. During this process, the soil map data from a small scale (coarse resolution) is converted to a large scale (fine resolution). The statistical and data mining methods are used to implement it. The purpose of this research was to predict the spatial distribution of soil classes by disaggregating the soil map units of a semi detailed soil map using disaggregating and harmonizing soil map units through resampled classification trees algorithm (DSMART method). Materials and methods: The study area is located in Kermanshah province. The total area of the study was approximately 14083.9 ha. Soil polygon map include 5 map units and 4 soil subgroups. In this study, elevation, slope, aspect, convexity, direct duration, sediment index, topographic wetness index, valley depth and Vertical distance to channel network as covariates produced using DEM 10 m. Grain size index, clay index and NDVI were also calculated using Landsat 7 ETM+ imagery. Geological map at scale of 1:100,000 were also used as a qualitative covariate. Then dsmart method is run as a novel approach for disaggregation soil maps. In this method, disaggregated soil classes are represented by raster probability surfaces. DSMART samples randomly within the soil map units and uses classification trees (C5.0 algorithm) to produce probability surface maps of soil class distribution. External validation was performed using 82 profiles. The validation dataset was intersected with the corresponding probability surface maps and validation quantified by overall accuracy, producer’s accuracy, user’s accuracy and kappa coefficients. Furthermore, confusion index calculated between the most probable and second-most-probable soil class. The CI expresses concisely degree of confusion about soil class given.Results: The most important predictive variables in the tree classification model were Vertical distance to channel network, Elevation, lithology, grain size index and MRVBF. The confusion index close to 1 has a large extent in the study area. It shows that occurrence probability of soil subgroups is near equal in each location in both the most probable soil class and second probable soil class maps. Validation of probability surfaces showed that overall accuracy the most probable soil class, second probable soil class and third probable soil class are 44%, 28% and 11%, respectively. These results indicated the relatively good performance of dsmart method for generating digital individual soil class map. However, kappa coefficients for first, second and third probable surfaces soil maps were obtained 0.04, 0.02, -0.08, respectively. Low kappa coefficients can be attributed to the true nature of the data, i.e the dominance of the Typic Calcixerepts subgroup as compared to other subgroups of the soil in the traditional soil map and the dsmart model prediction map and validation data. Conclusion: Dsmart method is able to predict the occurrence probability of all soil classes which its distribution is unclear in soil map unit. This provides the opportunity to produced digital soil class maps when legacy soil data and covariate information becomes available. Such outputs may help us to recognize better soil- landscape relationship.
https://jwsc.gau.ac.ir/article_4151_826badc6baa470a31fc33dfba0bae38a.pdf
2018-05-22
49
69
10.22069/jwsc.2018.13870.2860
spatial disaggregation
Digital soil mapping
soil class
map unit
Shahrokh
Fatehi
shahrokh.fatehi@gmail.com
1
Research Assistant Professor, Soil and Water Research Department, Kermanshah Agricultural and Natural Resources Research and Education Center, AREEO, Kermanshah, Iran.
LEAD_AUTHOR
Kamran
Eftekhari
kamran_eftekhari2001@yahoo.com
2
Research Assistant Professor, Soil and Water Research Institute, Agriculture Research, Education and Extension Organization (AREEO), karaj, Iran.
AUTHOR
Jalal
Ghaderi
ghaderij@yahoo.com
3
Staff Member, Soil and Water Research Department, Kermanshah Agricultural and Natural Resources Research and Education Center, AREEO, Kermanshah, Iran.
AUTHOR
1.Banaei, M.H., Momeni, A., Baybordi, M., and Malakouti, M.J. 2005. The soils of Iran (new achievements in perception, managements and use). Soil and Water Research Institute, AREO, Tehran, Iran, 482p. (In Persian)
1
2.Bui, E.N., and Moran, C.J. 2001. Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma. 103: 2. 79-94.
2
3.Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S.A., and Edwards, T.C. 2015. Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma.
3
240: 68-83.
4
4.Brus, D.J., Kempen, B., and Heuvelink, G.B.M. 2011. Sampling for validation of digital soil maps. Europ. J. Soil Sci. 62: 394-407.
5
5.Burrough, P.A., van Gaans, P.F.M., and Hootsmans, R. 1997. Continuous classification in soil survey: spatial correlation, confusion and boundaries. Geoderma. 77: 115-135.
6
6.Chaney, N., Hempel, J.W., Odgers, N.P., McBratney, A.B., and Wood, E.F. 2014. Spatial disaggregation and harmonization of gSSURGO. In: ASA, CSSA and SSSA international annual meeting, LongbBeach. ASA, CSSA and SSSA.
7
7.Collard, C., Kempen, B., Heuvelink, G.B.M., Saby, N.P.A., Richer de Forges, A.C., Lehmann, S., Nehlig, P., and Arrouays, D. 2014. Refining a reconnaissance soil map by calibrating regression models with data from the same map (Normandy, France). Geoderma Regional. 1: 21-30.
8
8.Dobos, E., Bialkó, T., Micheli, E., and Kobza, J. 2010. Legacy Soil Data Harmonization and Database Development. P 309-324, J.L. Boettinger et al. (eds.), Digital Soil Mapping, Progress in Soil Science 2, Springer.
9
9.Grinand, C., Arrouays, D., Laroche, B., and Martin, M.P. 2008. Extrapolating regional soil landscapes from an existing soil map: Sampling intensity validation procedures and integration of spatial context. Geoderma. 143: 180-190.
10
10.Fatehi, Sh. 2008. Semi-detailed soil survey of Merek plain in Karkheh river basin. Soil and Water Research Institute, 54p. (In Persian)
11
11.Fatehi, Sh., Mohammadi, J., Salehi, M.H., Toomanian, N., Momeni, A., and Jafari, A. 2015. Spatial disaggregating conventional soil map using multiple logistic regression and classification tree, (Case study: Merek sub catchment in Kermanshah province). 14th Iranian soil science congress, September 28-30, Rafsanjan, Iran, Pp: 208-213. (In Persian)
12
12.Häring, T., Dietz, E., Osenstetter, S., Koschitzki, T., and Schroder, B. 2012. Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils. Geoderma. 37: 185-186.
13
13.Holmes, K.W., Griffin, E.A., and Odgers, N.P. 2015. Large-area spatial disaggregation
14
of a mosaic of conventional soil maps: evaluation over Western Australia. Soil Research.
15
53: 865-880.
16
14.Lagacherie, P., Legros, J.P., and Burrough, P. 1995. A soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area. Geoderma.
17
65: 4. 283-301.
18
15.McBratney, A.B. 1998. Some considerations on methods for spatially aggregating and disaggregating soil information. Nutr. Cycl. Agroecosyst. 50: 3. 51-62.
19
16.McBratney, A.B., Field, D.J., and Koch, A. 2014. The dimensions of soil security. Geoderma. 13: 203-213.
20
17.Malone, B.P., Minasny, B., and McBratney, A.B. 2017. Using R for Digital Soil Mapping. Springer, the Netherlands, Pp: 221-230.
21
18.Nauman, T.W., and Thompson, J.A. 2014. Semi-automated disaggregation of conventional soil maps usingknowledge driven data mining and classification tree. Geoderma. 213: 385-399.
22
19.Odgers, N.P., Sun, W., McBratney, A.B., Minasny, B., and Clifford, D. 2014. Disaggregating and harmonizing soil map units through resampled classification trees. Geoderma. 215: 91-100.
23
20.Rouse, J.W., Hass, R.H.J., Schell, A., and Deering, D.W. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, Vol. 1, Washington, DC. Pp: 309-317.
24
21.Quinlan, J.R. 1994. C4.5: Programs for machine learning. Machine Learning. 16: 235-240.
25
22.Subburayalu, S., Jenhan, I., and Slater, B.K. 2014. Disaggregation of component soil series using possibilistic decision trees from an OhioCounty soil survey map. Geoderma. 213: 334-345.
26
23.Soil Survey Staff. 1993. Soil survey manual. U. S. Department of Agriculture Handbook. United States Department of Agriculture Soil Conservation Service.
27
24.Thompson, J.A., Prescott, T., Moore, A.C., Bell, J., Kautz, D.R., Hempel, J.W., Walt man, S.W., and Perry, C. 2010. Regional approach to soil property mapping using legacy data and spatial disaggregation. Techniques. In: 19th world congress of soil science. IUSS, Brisbane.
28
25.Van Deventer, A.P., Ward, A.D., Gowda, P.H., and Lyon, J.G. 1997. Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices. Photogrammetric Engineering & Remote Sensing. 63: 87-93.
29
26.Vincent, S., Lemercier, B., Berthier, L., and Walter, C. 2018. Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships. Geoderma. 311: 130-142.
30
27.Wei, S., McBratney, A., Hempel, J., Minasny, B., Malone, B., D’Avello, T., Burras, L., and Thompson, J. 2010. Digital harmonisation of adjacent analogue soil survey areas-4 Iowa counties. 19th World Congress of Soil Science: Soil solutions for a changing world 2010, Wien (Vienna), Austria: International Union of Soil Sciences (IUSS).
31
28.Xiao, J., Shen, Y., Tateishi, R., and Bayaer, W. 2006. Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. Inter. J. Rem. Sens. 27: 2411-2422.
32
ORIGINAL_ARTICLE
Water allocation decision making in the presence of uncertainty using robust counterpart programming and multiple objectives
Background and Objectives: Considering the existence of uncertainty in the data of water resource problems, it has become more essential to design a reliable water resource allocation model under uncertainty. Due to multi-dimensional nature of optimal water allocation problem, considering multiple conflicting objectives within the optimization models is inevitable. The aim of this study is to provide a quantity-quality optimization model in which not only balance the economic and environmental objectives, but also remain robust in the face of existing uncertainties. Materials and Methods: The nominal model of the study was constructed using the objectives of maximizing the income of the entire system and minimizing pollution load entered to the river. It was applied to the Dez-Karoon river system as a case study. By taking the uncertainties of river flow and water demands into account, the nominal model was promoted to a robust multi-objective optimization model using the Bertsimas and Sim's approach. The sensitivity of the robust model to changes in uncertainty levels and the probability of constraint violation was investigated. The ɛ-constraint method was used to solve the problem and the nominal model was applied to assess the results of the developed model. Among optimal solutions set, Knee point of the Pareto front of was selected as the solution of the problem. Results: Application of developed model into the case study demonstrates its ability in quickly finding the exact solution of the problem. Comparing the optimal solution of knee points showed that hedging the optimization model against uncertainties via considering the uncertainty level and violation probability of 0.1, requires the decrease in operating the river water from 8301.5 to 7368.9 MCM/year and adjustment of the economic income from 1,636,808 to 1,365,693 million Rial/year in comparison with the nominal model. Under such a condition in which prevents the failure of supplying water under a given level of risk, the pollution load discharged into the river will decrease from 53,949 to 48,505 ton/ year. The results illustrate that without adding extra complexity into the nominal model, it can be immunized against uncertainties via the robust approach. By determining the uncertainty level and the probability of constraint violation, the decision maker is able to select the robustness level of the water resource allocation model and therefore, explore tradeoff among the values of the objectives and reliability of the system. Conclution: The results demonstrate the satisfactory, high reliability and flexibility of the proposed robust model. Accordingly, the provided linear model of this study may be used as a user-friendly tool in the decision making process for optimal allocation of water resources.
https://jwsc.gau.ac.ir/article_4152_14cbd84bda05876e58d20e1dbda13818.pdf
2018-05-22
71
89
10.22069/jwsc.2018.13546.2821
ɛ-Constraint method
Knee point
Robust counterpart
Water allocation
Uncertainty
Omid
Nasiri Gheidari
omidnasiri13@gmail.com
1
Ph.D. candidate in water resource engineering, Bu-Ali Sina University
AUTHOR
Safar
Marofi
marofisafar59@gmail.com
2
Faculty member/ Bu-ali Sina University
LEAD_AUTHOR
1.Anghileri, D., Castelletti, A., Pianosi, F., Soncini-Sessa, R., and Weber, E. 2012. Optimizing watershed management by coordinated operation of storing facilities. J. Water Resour. Plan. Manage. 139: 5. 492-500.
1
2.Ardjmand, E., Weckman, G.R., Young, W.A., Sanei Bajgiran, O., and Aminipour, B. 2016. A robust optimisation model for production planning and pricing under demand uncertainty. Inter. J. Prod. Res. 54: 13. 1-21.
2
3.Babel, M., Gupta, A.D., and Nayak, D. 2005. A model for optimal allocation of water to competing demands. Water Resources Management. 19: 6. 693-712.
3
4.Ben-Tal, A., and Nemirovski, A. 1999. Robust solutions of uncertain linear programs. Operations Research Letters. 25: 1. 1-13.
4
5.Ben-Tal, A., and Nemirovski, A. 2000. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming. 88: 3. 411-424.
5
6.Bertsimas, D., and Sim, M. 2004. The price of robustness. Operations Research. 52: 1. 35-53.
6
7.Cai, X., Lasdon, L., and Michelsen, A.M. 2004. Group decision making in water resources planning using multiple objective analysis. J. Water Resour. Plan. Manage. 130: 1. 4-14.
7
8.Chung, G., Lansey, K., and Bayraksan, G. 2009. Reliable water supply system design under uncertainty. Environmental Modelling & Software. 24: 4. 449-462.
8
9.Das, I. 1999. On characterizing the “knee” of the Pareto curve based on normal-boundary intersection. Structural Optimization. 18: 2. 107-115.
9
10.Deb, K. 2003. Multi-objective evolutionary algorithms: Introducing bias among Pareto-optimal solutions. P 263-292, In: A. Ghosh and S. Tsutsu (eds), Advances in evolutionary computing, Springer, Berlin.
10
11.Deb, K., and Gupta, S. 2011. Understanding knee points in bicriteria problems and their implications as preferred solution principles. Engineering Optimization. 43: 11. 1175-1204.
11
12.El Ghaoui, L., and Lebret, H. 1997. Robust solutions to least-squares problems with uncertain data. SIAM Journal on Matrix Analysis and Applications. 18: 4. 1035-1064.
12
13.El Ghaoui, L., Oustry, F., and Lebret, H. 1998. Robust solutions to uncertain semidefinite programs. SIAM Journal on Optimization 9: 1. 33-52.
13
14.Haimes, Y.Y. 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man and Cybernetics. 1: 3. 296-297.
14
15.Homayounifar, M., and Rastegaripour, F. 2010. Water allocation of Latian dam between agricultural products under uncertainty. J. Agric. Econ. Dev. 24: 2. 259-267. (In Persian)
15
16.Housh, M., Ostfeld, A., and Shamir, U. 2011. Optimal multiyear management of a water supply system under uncertainty: Robust counterpart approach. Water Resources Research. 47: 10. 1-15.
16
17.Li, M., and Guo, P. 2014. A multi-objective optimal allocation model for irrigation water resources under multiple uncertainties. Applied Mathematical Modelling. 38: 19. 4897-4911.
17
18.Li, Y., Huang, G.H., Huang, Y., and Zhou, H. 2009. A multistage fuzzy-stochastic programming model for supporting sustainable water-resources allocation and management. Environmental Modelling & Software. 24: 7. 786-797.
18
19.Li, Z., and Ierapetritou, M.G. 2008. Robust optimization for process scheduling under uncertainty. Industrial & Engineering Chemistry Research. 47: 12. 4148-4157.
19
20.Maqsood, I., Huang, G.H., and Yeomans, J.S. 2005. An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty. Europ. J. Oper. Res. 167: 1. 208-225.
20
21.Miettinen, K. 1999. Nonlinear multiobjective optimization. volume 12, International Series in Operations Research and Management Science, Kluwer Academic Publishers, Dordrecht, Netherlands. 120p.
21
22.Mohaghar, A., Mehregan, M.R., and Naz-Abadi, M.R. 2009. Applying robust optimization to solve product mix problem in automotive industries. J. Ind. Manage. 1: 2. 139-152.
22
(In Persian)
23
23.Mulvey, J.M., Vanderbei, R.J., and Zenios, S.A. 1995. Robust optimization of large-scale systems. Operations Research 43: 2. 264-281.
24
24.Sabouhi, M., and Mardani, M. 2013. Optimal allocation strategies of irrigation water and coastal land of Nekooabad irrigation network under uncertainty. 7: 13. 109-119. (In Persian)
25
25.Sadeghi, H., and Khaksar Astaneh, S. 2014. Provide an optimum model for renewable energy development in Iran; robust optimization approach. Iranian Energy Economics.
26
3: 11. 159-195. (In Persian)
27
26.Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., and Vatani, B. 2016. A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Applied Mathematical Modelling, 40: 1. 169-191.
28
27.Soyster, A.L. 1973. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research. 21: 5. 1154-1157.
29
ORIGINAL_ARTICLE
Phytoremediation of Lead in the presence of individual and combined inoculation of earthworms, arbuscular mycorrhizal fungi and rhizobacteria by maize
AbstractIntroduction Soils may become polluted with high concentrations of heavy metals both naturally, as a result of proximity to mineral outcrops and ore bodies and anthropogenically, as a result of industrial activities. Lead (Pb), commonly caused soil pollution and considered to be responsible for significant decreases in biological activities in soils. Phytoremediation is an emerging and low cost technology that utilizes plants and associated organisms to remove, transform, or stabilize contaminants located in water, sediments, or soils. Phytostabilization focuses on the formation of a vegetation cover where sequestration (binding and sorption) processes immobilize metals within the plant rhizosphere reducing metal bioavailability. Therefore, the success of phytoremediation depends on the interactions between macro- and microorganisms and plant roots in the rhizosphere.Materials and Methods The contaminated soil was collected from Bama mining site located in the southwest of Isfahan. After surface-sterilization and germination, maize seeds were transplanted into each plastic pot containing 4 kg of contaminated soil that already autoclaved at 121 oC for 2 h. A completely randomized design with 2×2×2 factorial treatment combinations was used with the following factors: with or without earthworm treatments (Eisenia foetida), with or without arbuscular mycorrhizal (AM) fungal treatments (co-inoculated with Funneliformis mosseae and Septoglomus constrictum) and with or without rhizobacteria (co-inoculated with Bacillus sp. and Bacillus licheniformis). After three months of growth under greenhouse conditions, maize shoots were harvested. Shoot and root were oven dried, weighed and milled used to determine Pb concentration. Concentration of Pb in roots and shoots were measured by dry ash method and soil Pb concentration was determined with DTPA-TEA method. Bioaccumulation (BF) and translocation (TF) and remediation (RF) factors for each treatment were calculated.Results and Discussion In general, inoculation of these organisms increased plant growth, availability of Pb in soil, plant Pb concentration and bioaccumulation factor. The highest shoot dry weight was observed in earthworms-AM fungi (EM) and AM fungi-bacteria (MB) co-inoculations with 3.2 times increase compared to un-inoculated plants. Available Pb in soil in earthworm-AM fungi-bacteria co-inoculation (EMB) was about 3 times higher than un-inoculated treatments. The higher Pb uptake in maize shoot and root were recorded in EMB. Furthermore, the BF for root maize in all treatments was higher than 1, especially in AM fungal treatment, alone. Although the TF for maize was lower than 1, it was increased above 1 in polluted soil co-inoculated with earthworm and bacteria. However, AM fungi tended to decrease the TF compared to un-inoculated maize. The highest RF (0.14%) with 23 times increase compared to un-inoculated was showed in EMB treatment. Conclusions Despite the substantial enhancement of Pb concentration in the maize, Pb absorption was not high enough to achieve extraction rates which would be necessary for practical use. Furthermore, the amounts of BF, TF and RF in this study demonstrated that maize could useful for Pb phytostabilization. Hence, it appears that the presence of AM fungi (as factor improve phytostabilization) could result in a better plant growth and tolerance against Pb toxicity, when soil is co-inoculated with earthworm and/or bacteria, especially under natural conditions that the presence of these organisms' together, could reduce Pb toxicity and improve maize.
https://jwsc.gau.ac.ir/article_4153_c8df79121a93de6af3fbff7233dc3f54.pdf
2018-05-22
91
110
10.22069/jwsc.2018.14140.2887
Earthworms
AM fungi
rhizobacteria
Pb bioaccumulation
Pb translocation
Ali
Mahohi
alimahohi@yahoo.com
1
Soil Science and Engineering, Faculty of Agriculture, Shahrekord University
LEAD_AUTHOR
Fayez
Raiesi
f_raiesi@yahoo.com
2
Department of Soil Science, Faculty of Agriculture, Shahrekord University
AUTHOR
Alireza
Hosseinpur
hosseinpur-a@agr.sku.ac.ir
3
shahrekord univecsity
AUTHOR
1.Ali, H., Khan, E., and Sajad, M.A. 2013. Phytoremediation of heavy metals- Concepts and applications. Chemosphere, 91: 869-881.
1
2.Azcón, R., Perálvarez, M.D., Biró, B., Roldán, A., and Ruíz-Lozano, J.M. 2009. Antioxidant activities and metal acquisition in mycorrhizal plants growing in a heavy-metal multi contaminated soil amended with treated lignocellulosic agrowaste. Applied Soil Ecology,
2
41: 168-177.
3
3.Bafeel, S.O. 2008. Contribution of mycorrhizae in phytoremediation of lead contaminated soils by Eucalyptus ostrata plants. World Appl. Sci. J. 5: 490-498.
4
4.Braud, A., Jezequel, K., Bazot, S., and Lebeau, T. 2009. Enhanced phytoextraction of an agricultural Cr and Pb-contaminated soil by bioaugmentation with siderophore-producing bacteria. Chemosphere, 74: 280-286.
5
5.Campbell, C.R., and Plank, C.O. 1998. Preparation of plant tissue for laboratory analysis.
6
In: Kalra, Y.P. (ed.) Handbook of Reference Methods for Plant Analysis. CRC Press, Taylor & Francis Group, Pp: 37-50.
7
6.Chapman, H.D. 1995. Cation exchange capacity. In black C.A. (ed) Methods of soil analysis. Agronomy Monograph. G. ASA. Madison WI. Pp: 891-901.
8
7.Chen, B., Shen, H., Li, X., Feng, G., and Christie, P. 2004. Effects of EDTA application and arbuscular mycorrhizal colonization on growth and zinc uptake by maize (Zea mays L.) in soil experimentally contaminated with zinc. Plant and Soil, 261: 219-229.
9
8.Chen, X., Wu, C., Tang, J., and Hu, S. 2005. Arbuscular mycorrhizae enhance metal lead uptake and growth of host plants under sand culture experiment. Chemosphere, 60: 665-671.
10
9.Dary, M., Chamber-Pérez, M.A., Palomares, A.J., and Pajuelo, E. 2010. In situ phytostabilization of heavy metal polluted soils using Lupinus luteus inoculated with metal resistant plant-growth promoting rhizobacteria. J. Hazard. Mater. 177: 323-330.
11
10.Dell' Amico, E., Cavalca, L., and Andreoni, V. 2008. Improvment of Brasica napus growth under cadmium stress by cadmium-resistant rhizobacteria. Soil Biology and Biochemistry. 40: 74-84.
12
11.Del Val, C., Barea, J.M., and Azcon-Aguilar, C. 1999. Assessing the tolerance to heavy metals of arbuscular mycorrhizal fungi isolated from sewage sludge contaminated soils. Applied Soil Ecology, 11: 261-269.
13
12.Elmer, W.H. 2009. Influence of earthworm activity on soil microbes and soil borne diseases of vegetables. Plant Disease, 93: 175-179.
14
13.Gee, G.W., and Bauder, J.W. 1986. Particle size analysis. In: Klute A. (ed.) Methods of Soil Analysis. Part 1. 2nd ed., Agron. Monogr. 9. ASA and SSSA, Madison, WI. Pp: 404-407.
15
14.Glickman, E., and Dessaux, Y. 1995. A critical examination of the specificity of the Salkowski reagent for indolic componds produced by phytopathogenic bacteria. Applied and Environmental Microbilogy. 61: 793-796.
16
15.Gonzalez-Guerrero, M., Azcon-Aguilar, C., Mooney, M., Valderas, A., MacDiarmid, C.W., Eide, D.J., and Ferrol, N. 2005. Characterization of a Glomus intraradices gene encoding a putative Zn transporter of the cation diffusion facilitator family. Fungal Genetics and Biology, 42: 130-140.
17
16.Hu, J., Wu, S., Wu, F., Leung, H.M., Lin, X., and Wong, M.H. 2013. Arbuscular mycorrhizal fungi enhance both absorption and stabilization of Cd by Alfred stonecrop (Sedum alfredii Hance) and perennial ryegrass (Lolium perenne L.) in a Cd-contaminated acidic soil. Chemosphere, 93: 1359-1365.
18
17.Jusselme, M.D., Poly, F., Miambi, E., Mora, P.H., Blouin, M. Pando, A., and Corinne Rouland-Lefèvre, C. 2012. Effect of earthworms on plant Lantana camara Pb-uptake and on bacterial communities in root-adhering soil. Science of the Total Environment, 416: 200-207.
19
18.Kabata-Pendias, A., and Mukherjee, A.B. 2007. Trace elements from soil to human. Springer-Verlag, Berlin, New York, 512p.
20
19.Li, M.S., Luo, Y.P., and Su, Z.Y. 2007. Heavy metal concentrations in soils and plant accumulation in a restored manganese mine land in Guangxi, South China. Environmental Pollution, 147: 168-175.
21
20.Li, Y., Peng, J., Shi, P., and Zhao, B., 2009. The effect of Cd on mycorrhizal development and enzyme activity of Glomus mosseae and Glomus intraradices in Astragalus sinicus L. Chemosphere, 75: 894-899.
22
21.Li, W.C., and Wong, M.H. 2010. Effects of bacteria on metal bioavailability, speciation and mobility in different metal mine soils: a column study. J. Soil Sed. 10: 313-325.
23
22.Lindsay, W.L., and Norvell, W.A. 1978. Development of DTPA soil test for zinc, iron, manganese and copper. Soil Sci. Soc. Amer. J. 42: 421-428.
24
23.Loeppert, R.H., and Suarez, D.L. 1996. Carbonate and gypsum. In: Sparks D.L. (ed) Methods of Soil Analysis. SSSA, Madison. Pp: 437-474.
25
24.Ma, Y., Dickinson, N.M., and Wong, M.H. 2002. Toxicity of Pb/Zn mine tailings to the earthworm Pheretima and the effects of burrowing on metal availability. Biology and Fertility of Soils, 36: 79-86.
26
25.Ma, Y., Dickinson, N.M., and Wong, M.H. 2006. Beneficial effects of earthworms and arbuscular mycorrhizal fungi on establishment of leguminous trees on Pb/Zn mine tailings. Soil Biology and Biochemistry, 38: 1403-1412.
27
26.Ma, Y., Prasad, M.N.V., Rajkumar, M., and Freitas, H. 2011. Plant growth promoting rhizobacteria and endophytes accelerate phytoremediation of metalliferous soils. Biotechnology Advances, 29: 248-258.
28
27.Malik, R.N., Husein, S.Z., and Nazir, I. 2010. Heavy metal contamination and accumulation in soil and wild plants species from industrial area of IslamabadPakistan. Pak. J. Bot.
29
42: 291-301.
30
28.Mojmali Renani, A. 2013. Effects of plant-growth promoted rhizobacteria on lead and zinc phytoremediation by Indian mustard. Thesis of Master of agricultural science in Agroecology. ShahrekordUniversity, Shahrekord. Iran. (In Persian)
31
29.Mosse, B. 1962. The establishment of mycorrhizal infection under aseptic conditions. Rothamsted Erperimental Station Report for 1961,p. SO.
32
30.Nadeem, S.M., Ahmad, M., Zahir, Z.A., Javaid, A., and Ashraf, M. 2014. The role of mycorrhizae and plant growth promoting rhizobacteria (PGPR) in improving crop productivity under stressful environments. Biotechnology Advances, 32: 429-448.
33
31.Naderi, M.R. 2012. Effects of plant-growth promoted rhizobacteria on lead (Pb) phytoremediation in soil contained Pb with long history. Thesis of Master of agricultural science in Agroecology. ShahrekordUniversity, Shahrekord. Iran. (In Persian)
34
32.Nardi, S., Pizzeghello, D., Muscolo, A., and Vianello, A. 2002. Physiological effects of humic substances on higher plants. Soil Biology and Biochemistry, 34: 1527-1536.
35
33.Nelson, D.W., and Sommers, L.E. 1996. Total carbon, organic carbon and organic matter. In: Sparks, D.L. (ed.) Methods of Soil Analysis, part 3, Chemical Methods. Soil Science Society of America: Madison, WI, Soil Science Society of America Book Series, Pp: 153-188.
36
34.Olsen, S.R., Cole, C.V., Watanabe, F.S., and Dean, L.A. 1954. Estimation of available phosphorus in soil by extraction with sodium bicarbonate. USDA. Circ. 939. U.S. GOV. Print Office, Washington, DC.
37
35.Prsadoust, F., Bahreini, Nejad, B., Safari, Sinegani, A.A., and Kaboli, M.M. 2007. Phytoremediation of lead (Pb) by pasture and native plants in soil contaminated of Irankouh area. Research and Manufacture, 75: 54-63. (In Persian)
38
36.Rajkumar, M., Sandhya, S., Prasad, M.N.V., and Freitas, H. 2012. Perspectives of plant-associated microbes in heavy metal phytoremediation. Biotechnology Advances, 30: 1562-1573.
39
37.Rhoades, J. 1986. Salinity: electrical conductivity and total dissolved salids. In: Sparks, D.L. (ed.) Methods of soil Analysis. Part 3: Chemial Properties. Soil Science Society of America. Madison, Wisconsin. Pp: 417-435.
40
38.Ruiz, E., Alonso-Azcarate, J., and Rodríguez, L. 2011. Lumbricus terrestris L. activity increases the availability of metals and their accumulation in maize and barley. Environmental Pollution, 159: 722-728.
41
39.Ruiz, E., Rodríguez, L., and Alonso-Azcarate, J. 2009. Effects of earthworms on metal uptake of heavy metals from polluted mine soils by different crop plants. Chemosphere,
42
75: 1035-1041.
43
40.Scheu, S. 2003. Effects of earthworms on plant growth: patterns and perspectives. Pedobiologia, 47: 846-856.
44
41.Schwyn, B., and Neilands, J.B. 1987. Universal chemical assay for detection and determination of siderophores. Analytical Biochemistry. 160: 47-56.
45
42.Sękara, A., Poniedzialek, M., Ciura, J., and Jędrszczyk, E. 2005. Cadmium and lead accumulation and distribution in the organs of nine crops: Implications for phytoremediation. Polish J. Environ. Stud. 14: 506-516.
46
43.Shen, H., Christie, P., and Li, X. 2006. Uptake of zinc, cadmium and phosphorus by arbuscular mycorrhizal maize (Zea mays L.) from a low available phosphorus calcareous soil spiked with zinc and cadmium. Environmental Geochemistry and Health, 28: 111-119.
47
44.Sheng, X.F., He, L.Y., Wang, Q.Y., Ye, H.S., and Jiang, C. 2008. Effects of inoculation of biosurfactant producing Bacillus sp. J119 on plant growth and cadmium uptake in a cadmium amended soil. J. Hazard. Mater. 155: 17-22.
48
45.Sheng, X.F., and Xia, J.J. 2006. Improvement of rape (Brassica napus) plant growth and cadmium uptake by cadmium-resistant bacteria. Chemosphere. 64: 1036-1042.
49
46.Shi, Z.Y., Zhang, L.Y., Li, X.L., Feng, G., Tian, C.Y., and Christie, P. 2007. Diversity of arbuscular mycorrhizal fungi associated with desert ephemerals in plant communities of JunggarBasin, northwest China. Applied Soil Ecology, 35: 10-20.
50
47.Sizmur, T., and Hodson, M.E. 2009. Do earthworms impact metal mobility and availability in soil? A review. Environmental Pollution. 157: 1981-1989.
51
48.Sizmur, T., Palumbo-Roe, B., Watts, M.J., and Hodson, M.E. 2011. Impact of the earthworm Lumbricus terrestris L. on As, Cu, Pb and Zn mobility and speciation in contaminated soils. Environmental Pollution, 159: 742-748.
52
49.Sposito, G., Lund, L.J., and Chang, A. 1982. Trace metal chemistry in arid-zone field soils amended with sewage sludge. I. Fractionation of Ni, Cu, Zn, Cd and Pb in solid phases. Soil Sci. Soc. Amer. J. 46: 260-264.
53
50.Subler, S., Baranski, C.M., and Edwards, C.A. 1997. Earthworm additions increased short-term nitrogen availability and leaching in two grain crop agroecosystems. Soil Biology and Biochemistry, 29: 413-421.
54
51.Sun, Y.B., Zhou, Q.X., An, J., Liu, W.T., and Liu, R. 2009. Chelator-enhanced phytoextraction of heavy metals from contaminated soil irrigated by industrial wastewater with the hyperaccumulator plant (Sedum alfredii Hance). Geoderma, 150: 106-112.
55
52.Thomas, G.W. 1996. Soil pH and soil acidity. In: Sparks D.L. (ed.) Methods of Soil Analysis. SSSA, Madison. Pp: 475-490.
56
53.Vivas, A., Voros, I., Biro, B., Campos, E., Barea, J.M., and Azcon, R. 2003. Symbiotic efficiency of autochthonous arbuscular mycorrhizal fungus (G. mosseae) and Brevibacillus sp. isolated from cadmium polluted soil under increasing cadmium levels. Environmental Pollution, 126: 179-189.
57
54.Vivas, A., Biro, B., Ruiz-Lozano, J.M., Barea, J.M., and Azcon, R. 2006. Two bacterial strains isolated from a Zn-polluted soil enhance plant growth and mycorrhizal efficiency under Zn-toxicity. Chemosphere, 62: 1523-1533.
58
55.Wu, F.Y., Bi, Y.L., Leung, H.M., Ye, Z.H., Lin, X.G., and Wong, M.H. 2010. Accumulation of As, Pb, Zn, Cd and Cu and arbuscular mycorrhizal status in populations of Cynodon dactylon grown on metal contaminated soils. Applied Soil Ecology, 44: 213-218.
59
56.Yoon, J., Cao, X., Zhou, Q., and Ma, L.Q. 2006. Accumulation of Pb, Cu and Zn in native plants growing on a contaminated Florida site. Science of the Total Environment, 368: 456-464.
60
57.Zhang, X.C., Lin, L., Chen, M.Y., Zhu, Z.Q., Yang, W.D., Chen, B., Yang, X.E., and An, Q.L. 2012. A nonpathogenic Fusarium oxysporum strain enhances phytoextraction of heavy metals by the hyperaccumulator Sedum alfredii Hance. J. Hazard. Mater. 229: 361-370.
61
ORIGINAL_ARTICLE
Stakeholder analysis in cooperative management of water resources in Qazvin plain: Investigation of powerful stakeholder’s impact
Stakeholder analysis is one of the key factors in success of management policies in all fields. This tool represents suitable information about the individuals that are effectively participate in water management plans implementation. The obtained information from stakeholder analysis can be considered as model input in researches in order to schedule the plans and evaluate success of water resources policies. Recently, researchers have tried to use participatory management methods for application of water management plans to investigate the level of cooperation of stakeholders. Often, the results indicate a lack of confidence and poor participation in this field. It has been observed that stakeholder participation is in correlation with stakeholders’ interests and power (importance). However, no model has been developed to simulate stakeholder power and importance. The purpose of this study is to represent a framework to accurate and ease stakeholder analysis process in primary studies in water management policies or plans. In this research, a novel model is proposed for simulation of stakeholders’ importance to analyze management policies in the field of water resources management. Qazvin irrigation network is chosen as a case study for this research. Lack of trust between all the stakeholders for implementation of plans and policies and failures of first attempts of creating and organizing groups of stakeholders, resulted in ineffective operation and maintenance of the irrigation network. These failures are often because of planning without complete knowledge about stakeholders and neglecting their interests and importance. In order to gather primary information and analyze input data for the stakeholder characteristic model, questioners and interviews with key stakeholders were performed. Key stakeholder groups were chosen using expert choice and analytic hierarchy process. On the other hand, stakeholder’s power and importance were evaluated using stakeholder characteristic modeling and calibration to compare its result with other stakeholder analysis frameworks. Genetic algorithm was used to calibrate stakeholder characteristic model as well as to estimate the model parameters. It is observed that the model is able to simulate the importance of stakeholders in an acceptable level with mean absolute error of 0.487. The results showed that there is correlations between knowledge level, resources, leadership, power and importance of stakeholders. One of the results of this study is that the hidden interactions between the stakeholders were defined using stakeholder analysis framework. This research was done to help managers and decision makers of Qazvin irrigation network to observe consequences of powerful stakeholder’s effect on excessive water withdrawals. It is concluded that using stakeholder analysis can help to better define stakeholder relationships and to recognize potential opportunities in management strategies.
https://jwsc.gau.ac.ir/article_4154_14f78f105a0be8a11c4314b2e488f548.pdf
2018-05-22
111
130
10.22069/jwsc.2018.12351.2692
water resources
stakeholder analysis
power
simulation
Optimization
Abdolah
Taheri Tizro
ttizro@yahoo.com
1
Associate Prof, Water Resources Engineering, Water Engineering Department, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
Milad
Ghallehban Tekmedash
milghatek@gmail.com
2
Ph.D student, Water Resources Engineering, Water Engineering Department, Bu-Ali Sina University, Hamedan, Iran,
LEAD_AUTHOR
Hamid
Zare abyaneh
zareabyaneh@gmail.com
3
Associate Prof, Water Resources Engineering, Water Engineering Department, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
1.Akhbari, M., and Grigg, N.S. 2013. A framework for an agent-based model to manage water resources conflicts. Water resources management. 27: 11. 4039-4052.
1
2.Akhbari, M., and Grigg, N.S. 2015. Managing Water Resources Conflicts: Modelling Behavior in a Decision Tool. Water Resources Management. 29: 14. 5201-5216.
2
3.Alizadeh, M.R., Nikoo, M.R., and Rakhshandehrou, G. 2016. Developing an Optimal Groundwater Allocation Model Considering Stakeholder Interactions; Application of Fallback Bargaining Models. Iran - water resources research. 11: 3. 14.
3
4.Bagherzadeh Karimi, M., Mammedov, R., and Fathi Saghezchi, F. 2011. Stakeholder Role Analysis for Integrated Management in Protected Areas (Case study: Urmia Lake, Iran). ECOPERSIA. 2: 101-110.
4
5.Billgren, C., and Holmén, H. 2008. Approaching reality: Comparing stakeholder analysis and cultural theory in the context of natural resource management. Land Use Policy.
5
25: 4. 550-562.
6
6.Cronbach L.J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika.
7
16: 297-334.
8
7.Ebrahimi, F. 2015. Analysis of local stakeholders relationaships in water resources policy using network analysis. In 1st National Conference on Society, Natural Resources, Water and Environment: Challanges and Solutions.
9
8.Frenken, K. 2009. Irrigation in the Middle East region in figures AQUASTAT Survey-2008. Water Reports. 34p.
10
9.Ghafourifard, S., Bagheri, A., and Shajari, S. 2015. Stakeholders Assessment in Water Sector (Case study: Rafsanjan Area). IR-WRR. 11: 2. 1-18.
11
10.Goodpaster, K.E. 1991. Business ethics and stakeholder analysis. Business ethics quarterly. 1: 01. 53-73.
12
11.Grimble, R., and Chan, M.K. 1995. Stakeholder analysis for natural resource management in developing countries. In Natural resources forum, pp. 113-124, Wiley Online Library.
13
12.Heikkila, T., and Gerlak, A.K. 2005. The Formation of Large scale Collaborative Resource Management Institutions: Clarifying the Roles of Stakeholders, Science and Institutions. Policy Stud. J. 33: 4. 583-612.
14
13.Karamouz, M., Akhbari, M., Moridi, A., and Kerachian, R. 2006. A system dynamics-based conflict resolution model for river water quality management. Iran. J. Environ. Health Sci. Engin. 3: 3. 147-160.
15
14.Ramirez, R. 1999. Stakeholder analysis and conflict management. Cultivating peace: conflict and collaboration in natural resource management. Pp: 101-126.
16
15.Rastogi, A., Badola, R., Hussain, S.A., and Hickey, G.M. 2010. Assessing the utility of stakeholder analysis to Protected Areas management: The case of Corbett National Park, India. Biological Conservation. 143: 12. 2956-2964.
17
16.Rogers, P., and Hall, A. 2003. Effective water governance, Global Water Partnership, Stockholm, Sweden. 32p.
18
17.Rosegrant, M.W., Cai, X., and Cline, S.A. 2002. World water and food to 2025: dealing with scarcity. Intl Food Policy Res. Inst. 322p.
19
18.Salari, F., Ghorbani, M., and Malekian, A. 2015. Social Monitoring in Local Stakeholders Network to Water Resources Local Governance (Case study: Razin Watershed, KermanshahCity). Iran. J. Natur. Resour. 68: 2. 1-9.
20
19.Schmeer, K. 1999. Stakeholder analysis guidelines. Policy toolkit for strengthening health sector reform. pp. 1-33.
21
20.Stanghellini, P.S.L. 2010. Stakeholder involvement in water management: the role of the stakeholder analysis within participatory processes. Water Policy. 12: 5. 675-694.
22
21.UNDP-United Nations Development Programme. 2002. Handbook on monitoring and evaluating for results, Evaluation Office, NY. 232p.
23
22.UNDP-United Nations Development Programme. 2013. User’s guide on assessing water overnance. Denmark. 115p.
24
23.Varvasovszky, Z., and Brugha, R. 2000. A stakeholder analysis. Health policy and planning. 15: 3. 338-345.
25
ORIGINAL_ARTICLE
Assessing the effect of forest degradation in different slope positions on soil quality and evolution in west of Kurdistan Province
Background and Objectives: Soil quality is one of the most important factors to assess soil management. Therefore, recognition of all soil quality properties such as physical, chemical and biological is essential. Forest degradation and land use change effect on soil properties variability and led to decrease soil quality factors on soil quality. Moreover, soil characteristics also are related to slope position. The region of Marivan in Kurdistan province is one of the forested areas of Zagros which in recent decades, due to population growth and the increased need for food, has been threatened and some parts are now cultivated. The aim of this research is assessing the effect of forest degradation and slope position on soil quality and evolution in west of Kurdistan Province.Materials and Methods: Eight soil profiles in different slope position (shoulder, back slope, foot slope and toe slope) of two adjacent hill slope, under land uses of cropland and forest (uniform condition) were dug and described. Moreover, in each land use three soil samples were taken from depth 0-20 cm in each slope position. Properties of soil texture, bulk density, particle density, fine sand, organic carbon, cation exchange capacity, field capacity moisture, permanent wilting point moisture, electrical conductivity, pH, carbonate calcium equivalent, total nitrogen, available phosphorous, available potash infiltration rate, microbial respiration rate, porosity, available moisture sodium adsorption ratio (SAR) and erodibility were measured and computed. Results: The results showed low slope positions (toe slope and foot slope) had higher contents of clay, organic carbon, available moisture, fine sand, silt, total nitrogen, available phosphorous, available potassium, CEC and microbial respiration rate and lower contents of electrical conductivity, soil erodibility, pH and SAR compared to high slope positions. Soils formed in low slope positions had higher depth and evolution compared to high slope positions. The results also showed two land uses (cropland and forest) in relation to bulk density, porosity, silt, clay, carbonate calcium equivalent, fine sand, pH, organic carbon, total nitrogen, microbial respiration rate, infiltration, soil erodibility and available moisture had significant difference and land use change of forest land to cropland has been led to degradation of Mollisols. Therefore, soil properties are dependent to slope position and land use kind and these factors have affected soil properties and evolution.Conclusion: The results showed forest degradation has led to decrease of soil quality using significant decreasing of organic carbon, microbial respiration, total nitrogen, CEC, porosity, infiltration and available moisture and significant increasing of bulk density, pH, SAR, fine sand, soil erodibility, and silt. Forest degradation and land use change also due to cultivation led to decrease organic carbon content and soil structure degradation of Mollic horizon. Therefore, Mollic horizon has converted to Ochric horizon and Entisols and Inceptisols have formed in cropland land use. Moreover, the results showed different slope position effect on bulk density, sand, silt, clay, infiltration, erodibility, available water, pH, organic carbon, carbonate calcium equivalent, microbial respiration rate, nitrogen, phosphorous, CEC and potassium and have significant difference. These results show current management of studied land effect on soil quality and led to land degradation. Therefore, soil conservation of steep area using prevention of deforestation in Marivan forests and use of land based on their capability to conserve of soil and land quality is essential.
https://jwsc.gau.ac.ir/article_4155_5ad105e28c89d97d4a706f4d25875e0a.pdf
2018-05-22
131
149
10.22069/jwsc.2018.14263.2900
Forest soils
Marivan
Mollisols
Land use change
Serve
Moradi
serve.moradi34@gmail.com
1
Soil science and engineering, university of Kurdistan
AUTHOR
Kamal
Nabiollahi
nabiollahy_k@yahoo.com
2
ُSoil science and engineering, university of Kurdistan
LEAD_AUTHOR
Sayed Mohamad Taher
Hosseini
t.hossaini@uok.ac.ir
3
Soil science and Engineering, university of Kurdistan
AUTHOR
1.Abu-hashim, M., Elsayed, M., and Belal, A.E. 2016. Effect of land-use changes and
1
site variables on surface soil organic carbon pool at Mediterranean region. J. Afr. Earth Sci. 114: 78-84.
2
2.Ajami, M., Khormali, F., and Ayoubi, Sh. 2009. Role of deforestation and land use change on soil erodibility of loess in eastern Golestan province. Watershed Management Research (Pajouhesh and Sazandegi). 94: 36-44. (In Persian)
3
3.Allen, K., Corre, M.D., Kurniawan, S., Utami, S.R., and Veldkamp, E. 2016. Spatial variability surpasses land-use change effects on soil biochemical properties of converted lowland landscapes in Sumatra, Indonesia. Geoderma. 284: 42-50.
4
4.Anderson, E., and John, P. 1982. P 831-870. Soil respiration. Methods of Soil Analysis Part 2. Amer. Soc. of Agron, Madison USA.
5
5.Asghari, Sh., Hashemian Soofian, S., Goli Kalanpa, E., and Mohebodini, M. 2015. Impacts of land use change on soil quality indicators in eastern Ardabil province. J. Soil Water Cons. 22: 3. 1-19. (In Persian)
6
6.Assefa, D., Rewald, B., Sanden, H., Rosinger, Ch., Abiyu, A., Yitaferu, B.L., and Godbold, D. 2017. Deforestation and land use strongly effect soil organic carbon and nitrogen stock in Northwest Ethiopia. Catena. 153: 89-99.
7
7.Blake, G.R., and Hartage, K.H. 1986. Bulk density, P 363-382. In: Klute, A. (Ed.), Methods of Soil Analysis. Part1: physical and Mineralogical Methods, 2nd ed. Agronomy Monograph. 9: ASA, Madison, WI.
8
8.Bonifacio, E., Zanini, E., Boero, V., and Franchini Angela, M. 1997. Pedogenesis in soil catena on serpentinite in north western Italy. Geoderma. 75: 33-51.
9
9.Bower, C.A., Reitemeier, R.F., and Fireman, M. 1952. Exchangeable cation analysis of saline and alkali soils. Soil Sci. 73: 251-262.
10
10.Brejda, J.J., Moorman, T.B., Karlan, D.L., and Dao, T.H. 2000. Identification of regional siol quality factors and indicators: I. Central and Southern High Plains, Soil Sci. Soc. Am. J.
11
64: 2115-2124.
12
11.Danielson, R.E., and Sutherland, P.L. 1986. Porosity, P 443-461. In: Klute, A. (Ed.). Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods. Agronomy Monograph, 9. 2nd edition, ASA and SSSA, Madison, WI.
13
12.Dotterweich, M. 2013. The history of human-induced soil erosion: geomorphic legacies, early descriptions and research, and the development of soil conservation-a global synopsis. Geomorphology. 201: 1-34.
14
13.Fujisakia, K., Perrin, A.S., Garric, B., Balesdent, J., and Brossard, M. 2017. Soil organic carbon changes after deforestation and agrosystem establishment in Amazonia: An assessment by diachronic approach. Agric. Ecosyst. Environ. 245: 63-73.
15
14.Gee, G.W., and Bauder, J.W. 1986. Particle size analysis, P 383-411. In: A. Klute. (ed). Methods of Soil Analysis. Part 1: Physical and mineralogical methods, second edition. American Society of Agronomy, Inc., Soil Science Society of America, Inc., Madison, WI.
16
15.Hattar, B., Taimeh, A., and Ziadat, F. 2010. Variation in soil chemical properties along toposequences in an arid region of the Levant. Catena. 83: 34-45.
17
16.Henok, K., Dondeyne, S., Poesen, J., Frankl, A., and Nyssen, J. 2017. Transition from Forestbased to Cereal-based Agricultural Systems: A Review of the Drivers of Land use Change and Degradation in Southwest Ethiopia. Land Degrad. Dev. 28: 431-449.
18
17.Jayachandran, K., Gamare, J.S., Nair, P.R., Xavier, M., and Aggarwal, S.K. 2012. A novel biamperometric methodology for thorium determination by EDTA complexometric titration. Radiochim. Acta. 100: 311-314.
19
18.Jiang, P., and Thelen, K.D. 2004. Effect of soil and topographic properties on crop yield in a north- central corn-soybean cropping system. Agron J. 96: 252-258.
20
19.Jones, B.J. 2001. Laboratory guide for conducting soil tests and plant analysis. Boca Raton, London, New York & Washington, D.C. CRC Press.
21
20.Karlen, D.L., Gardner, J.C., and Rosek, M.J. 1998. A soil quality framework for evaluating the impact of CRP. J. Prod. Agric. 11: 56-60.
22
21.Kassa, H., Dondeyne, S., Poesen, J., Frankl, A., and Nyssen, J. 2017. Impact of deforestation on soil fertility, soil carbon and nitrogen stocks: the case of the Gacheb catchment in the White Nile Basin, Ethiopia. Agric. Ecosyst. Environ. 247: 273-282.
23
22.Khaledian, Y., Kiani, F., Ebrahimi, S., and Movahedi Naeini, A. 2011. Impact of forest degradation, changing land use and building villas on some indicators of soil quality in the watershed, Golestan province. J. Soil Water Cons. 18: 3. 167-184. (In Persian)
24
23.Khaledian, Y., Kiani, F., Ebrahimi, S., Brevik, B.C., and Aitkenhead-Peterson, J. 2016. Assessment and monitoring of soil degradation during land use change using multivariate analysis. Land Degrad. Dev. 28: 128-141.
25
24.Khormali, F., Ajami, M., Ayoubi, S., Srinivasarao, Ch., and Wani, S.P. 2009. Role of deforestation and hill slope position on soil quality attributes of loess-derived soils in Golestan province, Iran. J. Agri. Ecosys. Environ. 134: 178-189.
26
25.Khormali, F., and Nabiollahy, K. 2009. Degradation of Mollisols in western Iran as affected by land use change. Jest. 11: 363-374.
27
26.Khormali, F., and Shamsi, S. 2009. Study of soil quality and micromorphology at
28
different sloped loess land use in the eastern of Golestan province. J. Agric. Sci. Natur. Resour. 16: 3. 14-29. (In Persian)
29
27.Kiani, F., Jalalian, A., Pashayee, A., and Khademi, H. 2007. The role of forest utilization, conservation and degradation of rangelands on soil quality indicators in Loss lands of Golestan province. Iran. J. Agric. Nat. Resour. Sci. 41: 453-463. (In Persian)
30
28.King, G.J., Acton, D.F., and Arnaud, R.J. 1983. Soil-landscape analysis in relation to soil distribution and mapping at site witan the Weyburn association. Canadian J. Soil Sci.
31
63: 657-670.
32
29.Klute, A., and Dirksen, C. 1986. Hydraulic conductivity of saturated soils (constant head),
33
P 694-696. In: Klute, A. (ed). Methods of Soil Analysis. Part 1, 2nd ed. Agronomy. Monograph 9. ASA and SSSA, Madison, WI.
34
30.Li, Zh., Liu, Ch., Dong, Y., Chang, X., Nie, X., Liu, L., Xiao, H., Lu, Y., and Zenga, G. 2017. Response of soil organic carbon and nitrogen stocks to soil erosion and land use types in the Loess hilly–gully region of China. Soil Tillage Res. 166: 1-9.
35
31.Maleki, S., Khormali, F., Kiani, F., and Karimi, A.R. 2013. Effect of slope position and aspect on some physical and chemical soil characteristics in a loess hillslope of Toshan area, Golestan province, Iran. J. Soil Water Cons. 20: 3. 93-112. (In Persian)
36
32.McLean, E.O. 1982. Soil pH and lime requirement, P 199-224. In: Page, A.L., Miller, R.H., and Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2 Chemical and Microbiological Properties, 2nd ed. ASA-SSSA, Madison, WI.
37
33.Nabiollahy, K., Khormali, F., and Ayoubi, Sh. 2006. Formation of Mollisols as affected by landscape position and depth of groundwater in Kharkeh research station, Kordestan province. 2006. J. Agric. Sci. Natur. Resour. 13: 20-30. (In Persian)
38
34.Nelson, D.W., and Sommers, L.E. 1982. Total carbon, organic carbon, and organic matter.
39
P 539-594. In: Page, A.L., R.H., D.R., Keeney (Eds.), Methods of Soil Analysis, Part 2-Chemical and Microbiological Properties. ASA-SSSA, Madison, WI.
40
35.Oliveira, D.M.S., Paustian, K., Cotrufo, M.F., Fiallos, A.R., Cerqueira, A.G., and Cerri, C.E.P. 2017. Assessing labile organic carbon in soils undergoing land use change in Brazil: A comparison of approaches. Ecol.Ind. 72: 411-419.
41
36.Olsen, S.R., and Sommers, L. 1982. Phosohorus, P 403-430. In: AL. Page: Methods of soil analysis, Agron. No. 9, Part 2: Chemical and microbiological properties, (ed.), Am. Soc. Agron. Madison, WI, USA.
42
37.Pajand, M.J., Emami, H., and Astaraee, A. 2016. Relationship between Topography and Some Soil Properties. J. Water Soil. 29: 6. 1699-1710. (In Persian)
43
38.Rahimi Ashjerdi, M.R., and Ayoubi, Sh. 2013. Impacts of Land Use Change and Slope Positions on some Soil Properties and Magnetic Susceptibility in Ferydunshahr District, Isfahan province. J. Water Soil. 27: 5. 882-895. (In Persian)
44
39.Ramezani, F., Jafari, S., Salavati, A., and Khalilimoghaddam, B. 2016. Study the Soil Quality Changes Indicators Using Nemoro and Integrated Quality Index Models in Some Khuzestan’s Soils. J. Water Soil. 29: 6. 1629-1639. (In Persian)
45
40.Ramezanpour, H., and Kalbasizadeh, F. 2013. Study the effect of slope position on soil physicochemical characteristics in broad leafed forests of Lahijan area. J. Soil Res. (Soil and Water Science). 27: 3. 388-395. (In Persian)
46
41.Rasouli-Sadaghiani, M.H., Ghodrat, K., Ashrafi-Saeidlou, S., Jafari, M., and Khodaverdiloo, H. 2016. Evaluation of soil quality indicators in a deforested region of Northen Zagros.
47
J. Soil Manage. Sust. Prod. 6: 3. 83-99. (In Persian)
48
42.Refahi, H.Gh. 2000. Water erosion and conservation. Tehran Univerity Press, 551p.
49
(In Persian)
50
43.Richards, L.A. 1954. Diagnosis and improvement of saline and alkali soils. Washington: United States Salinity Laboratory, 160p.
51
44.Salehi, M.H., Jazini, F., and Mohammadkhani, A. 2008. The Effect of Topography on Soil Properties with a Focus on Yield and Quality of Almond in the Saman Area, Shahrekord.
52
J. Water Soil Plant Agric. 8: 2. 79-92. (In Persian)
53
45.Sewerniak, P., Jankowski, M., and Dąbrowski, M. 2017. Effect of topography and deforestation on regular variation of soils on inland dunes in the TorunBasin (N Poland). Catena. 149: 318-330.
54
46.Shirazi, M.A., and Boersma, L. 1984. A unifying quantitative analysis of soil texture.
55
Soil. Sci. Soc. Am. J. 48: 142-147. (In Persian)
56
47.Soil and water research institute. 1377. Soil moisture and temperature regimes. Agricultural research organization, Agricultural minister.
57
48.Soil Survey Staff. 2014. Keys to Soil Taxonomy, 12th edn. United States Department of Agriculture, Washington.
58
49.Sparks, D.L., Page, A.L., Helmke, P.A., Leoppert, R.H., Soltanpour, P.N., Tabatabai, M.A., Johnston, G.T., and Summer, M.E. 1996. Methods of Soil Analysis. Soil Science Society of American Journal. Book Series No. 5. ASA and SSSA, Madison, Wisconsin, WI, USA.
59
50.Tesfaye, M.A., Bravo, F., Ruiz-Peinado, R., Pando, V., and Bravo-Oviedo, A. 2016. Impact of changes in land use, species and elevation on soil organic carbon and total nitrogen in Ethiopian central highlands. Geoderma. 261: 70-79.
60
51.Tsui, C.C., Chen, Z.S., and Hsieh, C.F. 2004. Relationships between soil properties and slope position in a lowland rain forest of southern Taiwan. Geoderma. 123: 131-142.
61
52.Wang, Zh., Hu, Y., Wang, R., Guo, Sh., Du, L., Zhao, M., and Yao, Zh. 2017. Soil organic carbon on the fragmented Chinese Loess Plateau: Combining effects of vegetation types and topographic positions. Soil Tillage Res. 174: 1-5.
62
53.Wang, Zh., Wang, R., Sun, Q., Du, L., Zhao, M., and Hu, Y. 2017. Soil CO2 emissions from different slope gradients and positions in thesemiarid Loess Plateau of China. Ecolo. Eng. 105: 231-239.
63
54.Weeb, A.A., and Dowling, A.J. 2005. Characterization of basaltic clay soils (Vertisols) from the Oxford land system in central Queensland. Aust. J. Soil Res. 28: 841-856.
64
55.Wei, S., Zhang, X., Mclaughlin, N.B., Liang, A., Jia, S., and Chen, X. 2014. Effectof soil temperature and soil moisture on CO2 flux from eroded landscapepositions on black soil in Northeast China. Soil Tillage Res. 144: 119-125.
65
56.Wischmeier, W.H., and Smith, D.D. 1978. Predicting rainfall erosion losses: a guide to conservation planning. In Agriculture Handbook 537, USA. Department of Agriculture, Washington, DC. 58p.
66
57.Yaghmaeian Mahabadi, N., Khosroabadi, M., and Asadi, H. 2017. Effect of Forest Clearing and Topography on Some Soil Physicochemical Properties Effective on Soil Quality in Saravan Region, Guilan. J. Soil Res. (Soil and Water Science). 31: 2. 277-291. (In Persian)
67
58.Zareian, Gh. 2003. Soil genesis, classification and Land suitability evaluation in darnegon, Shiraz province. 8th soil science congress, Iran, Pp: 200-201.
68
59.Zhu, H., Wu, J., Guo, Sh., Huang, D., Zhu, Q., Ge, T., and Lei, T. 2014. Land use and topographic position control soil organic C and N accumulation in eroded hilly watershed of the Loess Plateau. Catena. 120: 64-72.
69
ORIGINAL_ARTICLE
The use of new techniques in turf grass irrigation for optimum water usage in urban green spaces
Background and objectives: Since Iran is located in the arid and semi-arid belt in the world, it always confronts low water periods and frequent droughts. In recent years, attention has been paid to the protection of urban water resources, especially green space irrigation. Due to the key role of grass in designing and constructing green space and its high importance in community psychological safety, Maintaining the optimal quality of turf grass throughout the year is essential.Materials and methods: The current experiment is conducted with the aim of optimizing soil moisture using subsurface porous clay capsules in irrigating turf grass. The research was conducted in 2013-2014 in Agriculture Faculty of TarbiatModares University, as split plots in the form of totally random blocks with three treatments and irritations. The main factors include sprinkler irrigation (control), subsurface irrigation using subsurface porous clay capsule, and consolidated Irrigation; secondary factors include the lack of using superabsorbent, using A200 superabsorbent and acrylamide superabsorbent. soil moisture. In this study, indicators of moisture, volume of water consumed, turf color and density and root length, were measured.Results: The results showed that moisture change diagram represents the same trend in all treatments. In addition, the moisture has been between the plantation capacity and the maximum allowable discharge, and the plant has been under no moisture stress. The results of this experiment showed that the volume of consumed water in the treatments of sprinkler, consolidated and subsurface irrigation has been 7158.4, 4972.6, and 4243.8m3, respectively. In the subsurface and consolidated irrigation treatment , the volume of consumed water increased for 41 and 31% respectively, compared to sprinkler irrigation. This reduction trend was observed in the use of superabsorbent, in such a way that the subsurface irrigation treatment along with superabsorbent has shown the lowest water consumption in irrigation with the reduction of 51%. Irrigation method treatments had a significant effect on the root length. The highest and lowest root length was related to subsurface and sprinkler irrigation treatment, respectively, with the average of 23.56 and 20.50cm, respectively; and the significance level was 5%. In addition, increasing superabsorbent increased the root depth which was significant in 1% level.Conclusion: Therefore, with regard to the water needs of lawns with subsurface irrigation system using porous clay capsules, this method could be employed as an irrigation method to optimize water consumption and reduce water loss. Yet, to make this system an applied method in turf grass irrigation, regardless of reducing water consumption, It needs to reduce its implementation cost compared with other existing methods.
https://jwsc.gau.ac.ir/article_4156_512299df89192d2114143933fdf8be59.pdf
2018-05-22
151
167
10.22069/jwsc.2018.13732.2844
Subsurface Irrigation
Turf Grass
Water Conservation
Superabsorbent
Marzieh
Rashidi Joshaghan
rashidimarziye@yahoo.com
1
مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان شمالی
LEAD_AUTHOR
Hossein Ali
Bahrami
bahramih@modares.ac.ir
2
دانشیار فیزیک و حفاظت خاک دانشگاه تربیت مدرس
AUTHOR
Ghorban
Vagheie
ghorbani169@yahoo.com
3
استادیار گروه منابع طبیعی؛ دانشگاه گنبد کاووس؛ گنبد کاووس؛ ایران
AUTHOR
1.Aalami, M., Theranifar, A., Davari Nejat, Gh., and Sallah Varzi, Y. 2012. Study of the effect of paclobutrazol superabsorbent and irrigation interval on quality characteristics and turf grass in Mashhad weather conditions. J. Hort. Sci. 25: 3. 288-295. (In Persian)
1
2.Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage paper. 56: 209p.
2
3.Aydinsakir, K., Buyuktas, D., and Serpil Yilmaz, R.B. 2016. Evapotranspiration and quality characteristics of some bermudagrass turf cultivars under deficit irrigation. Grassland Science. 62: 224-232.
3
4.Bahrami, H.A., Ghorbani Vaghei, H., Alizadeh, P., Nasiri, F., and Mahallati, Z. 2010. Fuzzy modeling of soil water distribution using buried porous clay capsule irrigation from asubsurface point source. J. Sensorletters. 8: 75-80.
4
5.Bastani, Sh. 2003. Groundwater irrigation scheme with clay pipes. Proceeding of the Seventh Seminar of Iranian National Committee on Irrigation and Drainage. 26: 1-22. (In Persian)
5
6.Behnia, A., and Arab Fard, M. 2005. Determination of Discharge-Pressure Relationi of Pitches using in pitcher Irrigation. Agriculture Sciences and Technology Mashhad. 19: 1. 1-12.
6
(In Persian)
7
7.Brennan, D., Tapsuwan, S., and Ingram, G. 2007. The welfare costs of urban outdoor water restrictions. Austr. J. Agric. Resour. Econ. 51: 243-261.
8
8.Camp, C.R., Lamm, F.R., Evans, R.G., and Phene, C.J. 2000. Subsurface drip irrigation - Past, Present and Future. Proceeding of the 4th Decennial National Irrigation Symposium. Phoenix. Arizona. Pp: 363-372.
9
9.Chabon, J., Bremer, D.J., Fry, J.D., and Lavis, C. 2017. Effects of Soil Moisture–Based Irrigation Controllers, Mowing Height and Trinexapac-Ethyl on Tall Fescue Irrigation Amounts and Mowing Requirements. Inter. Turfgrass Soc. Res. J. 13: 1. 755-760.
10
10.Cote, C.M., Bristow, K.L., Charlesworth, P.B., Cook, F.J., and. Thorburn, P.J. 2003. Analysis of soil wetting and solute transport in subsurface trickle irrigation. Irrigation Science. 22: 143-156.
11
11.Ghorbani Vaghei, H., Bahrami, H.A., and Rashidi, M. 2014. Porous clay capsules and their application in supplying the water requirement of arid and semi-arid regions. International Bulletin of Water Resources and Development. 14: 2. 20-26. (In Persian)
12
12.Goss, M. 2017. Quality-Based Field Research Indicates Fertilization Reduces Irrigation Requirements of Four Turfgrass Species. Inter. Turfgrass Soc. Res. J. 13: 1. 761-767.
13
13.Hedayatnejat, R.M., Kafi, M., Fatahi Moghadam, R., and Parcynejat, M. 2010. Effect of bed heat on some quantitative and qualitative characteristics of Sport turfgrass. Iran. J. Hort. Sci. Technol. 11: 4. 321-336. (In Persian)
14
14.Kafi, M., and Kaviani, Sh. 2002. Managing the construction and maintenance of turf grass. Cultural Institute of ShaghayeghVillage. Press, 230p. (In Persian)
15
15.Kheirabi, J. 1998. Principles and methods of irrigation and water measurement. AcademicPublishingCenter. Press, 112p. (In Persian)
16
16.Kjelgren, R., Rupp, L., and Kilgren, D. 2000. Water conservation inurban landscapes. HortScience. 35: 1037-1040.
17
17.Leinauer, B., Sevostianova, E., Serena, M., Schiavon, M., and Macolino, S. 2010. Conservation of irrigation water for urban lawn areas. Acta Hortic. 881: 487-492.
18
18.Licata, M., Tuttolomondo, T., Leto, C., La Bella, S., and Virga, G. 2017. The use of constructed wetlands for the treatment and reuse of urban wastewater for the irrigation of two warm-season turfgrass species under Mediterranean climatic conditions, water science and technology, 76: 2. 459-470.
19
19.Martinez, J., and Reca, J. 2014. Water Use Efficiency of Surface Drip Irrigation versus an Alternative Subsurface Drip Irrigation Method. J. Irrig. Drain. 04014030: 1-9.
20
20.Mayer, P.W., D’Oreo, W.B., Opitz, E.M., Kiefer, J.C., Davis, W.Y., Dziegielewski, B., and Nelson, J.O. 1999. Residential End Uses of Water. AWWA Research Foundation, Denver, CO.
21
21.Mojedzadeh, B., Necoamal, M., and Rahnama, M.B. 2008. Investigating the performance of permeable irrigation in greenhouse cucumber. Second National Conference on Irrigation and Drainage Networks Management. (In Persian)
22
22.Montazeri, A.A. 2008. Effect of Stracuzorb super absorbent polymer on progression time and soil penetration parameters soil in Furrow irrigation method. Water Soil J. 2: 2. 341-356. (In Persian)
23
23.Morris, K.N. 2002. National bentgrass (fairway/tee) tests 1999-2002 data. National Turfgrss Evaluation Program, Beltsvill, Maryland. Yield. Comm. Soil Plant Analysis. 38: 921-933.
24
24.Mousavinia, M., and Atarpour, A. 2005. Investigation of the effect of A-200 superabsorbent polymer on reducing the irrigation interval and irrigation rate in some characteristics of cold sport turfgrass. Third of Specialized training courses and seminars for agricultural use super absorbent hydrogels. (In Persian)
25
25.Panayiotis, A., Nektarios, K., Nikolopoulou, A.E., and Chronopulos, I. 2004. Sod establishment and turf grass growth as affected by urea-formaldehyde resin foam soil amendment. Scientia Hort. 100: 203-213.
26
26.Peng, C., and De-Rong, S. 2008. Effects of Subsurface Drip Irrigation on Soil Moisture and Underground Root Distribution of Turfgrass. Modern Agricultural Sciences.
27
27.Schiavon, M., Leinauer, B., Serena, M., Sallenave, R., and Maier, B. 2013. Establishing Tall Fescue and Kentucky Blue grass Using Subsurface Irrigation and Saline Water. Agron. J. 105: 183-190.
28
28.Serena, M., Leinauer, B., Schiavon, M., Maier, B., and Sallenave, R. 2014. Establishment and Rooting Responseof Bermudagrass Propagated with Saline Water and Subsurface Irrigation. Crop Science Society of America. 54: 827-836.
29
29.Zohurian-Mehr, M.J., and Kabiri, K. 2008. Superabsorbent polymer materials: A review. Iran. Polymer J. 17: 6. 451-477.
30
ORIGINAL_ARTICLE
Evaluating the susceptibility of aggregate sizes to interrill erosion using aggregate stability indices
Background and objectives: Soil aggregate stability is an important physical indicator of the soil’s susceptibility to water erosion. Aggregate stability can vary, depending on the aggregate size. Some methods including the dry-sieving, wet-sieving and water-drop test were currently used to evaluate the stability of aggregates in the worldwide. Mean weight diameter of stable aggregates was used for the dry-sieving and wet-siewing method. In the water-drop test, aggregate stability is evaluated using the number of water drops needed for disrupting the aggregates. These indices are used to evaluate the soil structural stability for given size of aggregates. However, there are different sizes of aggregates in the soil. So, application of these indices may cause some errors in evaluating the soil’s susceptibility to water erosion processes. Therefore, this study was conducted to develop a proper aggregate stability for different aggregate sizes in view point of interrill erosion in a semi-arid soil sample. Materials and Methods: Four aggregate size classes including < 2, 2-4, 4-8, 8-11 mm were collected from an agricultural soil with texture of clay loam in west of Zanjan, north west of Iran. A-600 kg aggregate sample was taken from 0-30 cm surface soil with about 10 m3 in volume for each aggregate sizes by sieving the aggregates in the field. The aggregate samples were packed to the erosion plots with 120 cm × 130 cm in dimensions installed in a 9% uniform slope. A total of twelve plots were investigated using the randomized complete block design for four aggregate size classes with three replications. The plots were exposed to seven simulated rainfalls with 70 mm h-1 in intensity for 30-min with 7-day interval. Soil loss resulted by interrill erosion from each aggregate size was determined during each rainfall simulation. The stability of each aggregate size against mechanical impact (MWDdry), wetting force (MWDwet) and rainderop impact (WDT) was determined using the dry-sieving, wet-sieving and water-drop test methods for each aggregate size class, respectively. Additionally, the aggregate stability per aggregate mass were computed and defined as MWDwet-m, MWDdry-m and WDTm, respectively. Beside this, other physicochemical properties including particle size distribution, gravel, bulk density, saturated hydraulic conductivity, organic carbon and calcium carbonate were determined using the conventional methods in the lab. Results: Based on the results, significant positive correlations were found between the aggregate size and the stability of aggregates determined using the methods of dry-sieving (r= 0.99), wet-sieving (r= 0.89) and water-drop-test (r= 0.93). The aggregate stability determined using all methods increased with an increase in the aggregate size. Newetheless, evaluating the aggregate stability per aggregate mass indicated that negative correlations existe between the aggregate size and MWDwet-m (r= -0.95), MWDdry-m, (r= -0.88) and WDTm (r= -0.88). Although the coarse aggregates rather than smaller aggregates are resitant against external stresses such as mechanical impacts, wetting force and rainderop impact but their stability per their mass is small. Contrary to our expectation, soil loss by interrill erosion of each aggregate size classes increased with increasing the aggregate stability determined using the dry-sieving, wet-sieving and water-drop-test methods whereas it decreased with increasing the aggregate stability determined using these methods on the basis of the aggregate mass.Conclusion: This study revealed that MWDwet, MWDdry and WDT are not the proper indices to evaluate the stability of aggregate size with the view point of its resistance to interrill erosion. The aggregate stability determined in these methods per aggregate mas is a new approch to evaluate the susceptibility of various aggregate sizes of a soil to interrill erosion. Among these indices, MWDwet-m is the best indicator in this field.
https://jwsc.gau.ac.ir/article_4157_c0fe8c3063d53effb99a6d6f6fbc8d74.pdf
2018-05-22
169
185
10.22069/jwsc.2018.12419.2705
Aggregate stability per mass
Water-drop test method
Wet sieving method
Dry-sieving method
Zanjan
Ali Reza
Vaezi
vaezi.alireza@gmail.com
1
گروه خاکشناسی دانشکده کشاورزی دانشگاه زنجان
LEAD_AUTHOR
Saeid
Rahmati
saeidrahmati564@yahoo.com
2
دانشجوی کارشناسی ارشد فیزیک و حفاظت خاک دانشگاه زنجان
AUTHOR
Hossein
Bayat
hosseinbayat220@yahoo.com
3
کارشناسی ارشد فیزیک-حفاظت دانشگاه زنجان
AUTHOR
1.Ahmadi, A., Neyshabouri, M.R., Rouhipour, H., and Asadi, H. 2011. Fractal dimension of soil aggregates as an index of soil erodibility. J. Hydrol. 400: 3. 305-311.
1
2.Akbari, S., and Vaezi, A.R. 2015. Investigating aggregates stability against raindrops impact in some soils of a semi-arid region, North west of Zanjan. Water and Soil Science. 25: 2. 65-77. (In Persian)
2
3.An, S., Mentler, A., Mayer, H., and Blumc, W.E.H. 2010. Soil aggragation, aggregate stability, organic carbon and nitrogen in different soil aggregate fractions under forest and shrub vegatiotion on the Loess Plateau, China. Catena. 81: 226-233.
3
4.Arjmand Sajjadi, S., and Mahmoodabadi, M. 2014. Aggregate breakdown and surface seal development influenced by rain intensity, slope gradient and soil particle size. Solid Earth Discussions. 6: 3303-3331.
4
5.Bare, A., Kainz, M., and Veihe, A. 2010. The spatial variability of erodibility and ites relation to soil type, a study from northern Ghana. Geoderma. 106: 101-120.
5
6.Barthes, B.G., Kouoa Kouoa, E., Larre-Larrouy, M.C., Razafimbelo, T.M., de Luca, E.F., Azontonde, A., Neves, C.S., de Freitas, P.L., and Feller, C.L. 2008. Texture and sesquioxide effects on water stable aggregates and organic matter in some tropical soils. Geoderma.
6
143: 14-25.
7
7.Belaid, H., and Habaieb, H. 2015. Soil aggregate stability in a Tunisian semi-arid environment with reference to fractal analysis. J. Soil Sci. Environ. Manage. 6: 2. 16-23.
8
8.Besharat, F., and Vaezi, A.R. 2015. Soil Loss under Simulated Rainfalls Rainfall During Events on Runoff and Soil Loss under Simulated Rainfalls. Iran. J. Water. Manage. Sci. Engin. 9: 29. 9-18. (In Persian)
9
9.Boix-Fayos, C., Calvo-Cases, A., Imeson, A.C., and Soriano-Soto, M.D. 2001. Influence of soil properties on the aggregation of some Mediterranean soils and the use of aggregate size and stability as land degradation indicators. Catena. 44: 47-67.
10
10.Bouwer, H., and Jackson, R.D. 1974. Determining soil properties, P 611-627, Drainage for Agriculture, ASA Monograph Noumber 17, Madison, WI.
11
11.Bryan, R.B. 1968. The development, use and efficiency of indices of soil erodibility. Geoderma. 2: 5-26.
12
12.Canasveras, J.C., Barron, V., Del Campillo, M.C., Torrent, J., and Gomez, J.A. 2010. Estimation of aggregate stability indices in Mediterranean soils by diffuse reflectance spectroscopy. Geoderma. 158: 78-84.
13
13.Canton, Y., Sole-Benet, A., Asensio, C., Chamizo, S., and Puigdefabregas, J. 2009. Aggregate stability in range sandy loam soils relationship with runoff and erosion. Catena. 77: 192-199.
14
14.Carter-Cade, E., Greer, D., Braud, J., and Floy, M. 1974. Raindrop characteristics in southcentral United States. Transactions of ASAE. 17: 6. 1033-1037.
15
15.Culley, J.L.B. 1993. Density and compressibility. Soil sampling and methods of analysis.
16
Pp: 529-539.
17
16.Dominguez, J., Negrin, M.A., and Rodriguez, C.M. 2001. Aggregate water stability, particle size and soil solution properties in conducive and suppressive soils to Fusarium wilt of banana from Canary island (Spain). Soil Biology and Biochemistry. 33: 449-455.
18
17.Egashira, K., Kaetsu, Y., and Takuma, K. 1983. Aggregate stability as an index of erodibility of Andosoils. Soil Science and Plant Nnutrition. 29: 473-481.
19
18.Eynard, A., Schumacher, T.E., Lindstrom, M.J., and Malo, D.D. 2004. Aggregate sizes and stability in cultivated South Dakota prairie Ustolls and Usterts. Soil Sci. Soc. Amer. J.
20
68: 1360-1365.
21
19.Fallahzade, J., and Hajabbasi, M.A. 2010. Evaluation of organic matter storage in aggregate of clayey soils under degraded pasture and cropland in central Zagros. J. Water Soil Cons. 17: 179-194. (In Persian)
22
20.Gee, G.W., Bauder, J.W., and Klute, A. 1986. Particle-size analysis. Methods of soil analysis. Part 1. Physical and mineralogical methods. Pp: 383-411.
23
21.Girmay, G., Sing, B.R., Nyssen, J., and Borrosen, T. 2009. Runoff and sediment associated nutrient losses under different land uses in Tigray, Northern Ethiopia. J. Hydrol. 376: 70-80.
24
22.Gupta, O.P. 2002. Water in relation to soils and plants. Agrobios, India. Pp: 31-34.
25
23.Hoyos, N. 2005. Spatial modeling of soil erosion potential in a tropical watershed of the Colombian Andes. Catena. 63: 85-108.
26
24.Hoyos, N., and Comerford, N.B. 2005. Land use and landscape effects on aggregate stability and total carbon of Andisols from the Colombian Andes. Geoderma. 129: 268-278.
27
25.Imeson, A., and Vis, M. 1984. Assessing soil aggregate stability by water-drop impact and ultrasonic dispersion. Geoderma. 34: 185-200.
28
26.Jackson, M.L. 1967. Soil chemical analysis, Prentice-Hall of India, Private Limited,
29
New Delhi. Kloke, A. 1-3.
30
27.Kemper, W.D., and Rosenau, R.C. 1986. Aggregate stability and size distribution, in: Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods, Klute, A., Ed.
31
Pp: 425-442.
32
28.Klute, A. 1986. Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods.
33
2nd edition. Agron. Monog. 9. ASA ana SSSA, Madison, WI.
34
29.Mahmoodabadi, M. 2011. Consecutive application of organic matter and sodicity on secondary particle size distribution. Environmental Erosion Researchs. Noumber 2.
35
(In Persian)
36
30.Mahmoodabadi, M., and Ahmadbeygi, B. 2013. Effect of primary particle size distribution on aggregate stability at different size classes. Water and Soil Science. 23: 3. 207-219.
37
(In Persian)
38
31.Mataix-Solera, J., Cerda, A., Arcenegui, V., Jordan, A., and Zavala, L.M. 2011. Fire effects on soil aggregation: a review. Earth-Science Reviews. 109: 44-60.
39
32.Meyer, L.D., and Harmon, W.C. 1984. Susceptibility of agricultural soils to interrill erosion. Soil Sci. Soc. Amer. J. 48: 1152-1157.
40
33.Nelson, D.W., and Sommers, L. 1982. Total carbon, organic carbon, and organic matter. Methods of soil analysis. Part 2. Chemical and microbiological properties methods of soil, Pp: 539-579.
41
34.Nzeyimana, I., Hartemink, A.E., Ritsema, C., Stroosnijder, L., Lwanga, E.H., and Geissen, V. 2017. Mulching as a strategy to improve soil properties and reduce soil erodibility in coffee farming systems of Rwanda. Catena. 149: 43-51.
42
35.Rouhipour, H., Farzanea, H., and Asadi, H. 2004. Relationship between some indicators of soil aggregate stability with soil erodibility factor using a rainfall simulator. Iran. J. Range. Des. Res. 11: 236-254. (In Persian)
43
36.Sadeghi, S.H.R., Hazbavi, Z., Younesi, H., and Bahramifar, N. 2016. Trade-off between runoff and sediments from treated erosion plots and polyacrylamide and acrylamide residues. Catena. 142: 213-220.
44
37.Unjer, P.W., Fulton, J.L., and Jones, O.R. 1990. Land-leveling effects on soil texture, organic matter content, and aggregate stability. J. Soil Water Cons. Pp: 412-415.
45
38.Vaezi, A.R. 2014. Modeling runoff from semi-arid agricultural lands in Northwest Iran. Pedosphere. 24: 595-604.
46
39.Vaezi, A.R., Sadeghi, S.H.R., Bahrami, H.A., and Mahdian, M.H. 2008. Modeling the USLE K-factor for calcareous soils in northwestern Iran. Geomorphology. 97: 414-423.
47
40.Veihe, A. 2002. The spatial variability of erodibility and its relation to soil types: a study from northern Ghana. Geoderma. 106: 101-120.
48
41.Wang, J.G., Yang, W., Yu, B., Li, Z.X., Cai, C.F., and Ma, R.M. 2016. Estimating the influence of related soil properties on macro-and micro-aggregate stability in ultisols of south-central China. Catena. 137: 545-553.
49
42.Williams, B.M., Martinez, M., and Deeksb, L. 2004. Exponential distribution theory and aggregate size. Soil Sci. Soc. Amer. J. 6: 382-391.
50
43.Wischmeier, W.H., and Smith, D.D. 1978. Predicting rainfall erosion losses: a guide to conservation Planning, Agriculture Handbook. U.S. Department of Agriculture, Washington, DC. 537: 13-27.
51
44.Yoder, R.E. 1936. A direct method of aggregate analysis and a study of a physical nature of erosion losses. J. Amer. Agron. 28: 337-351.
52
45.Zhi-Hua, Sh., Feng-Ling, Y., Lu, L., Zhao-Xia, L., and Chong-Fa, C. 2010. Interrill erosion from disturbed and undisturbed samples in relation to topsoil aggregate stability in red soils from subtropical China. Catena. 81: 240-248.
53
ORIGINAL_ARTICLE
Availability of zinc in the rhizosphere of corn in texturally different contaminated soils treated with chelators
AbstractBackground and objectives: Rhizosphere processes have a major impact on zinc (Zn) availability in soils. The chemical and biological characteristics of the rhizosphere soils can be very different from those of the bulk soils. In peresent study, the effects of EDTA, citric acid and poultry manure extract (PME) on availability of Zn in the rhizosphere of corn (hybrid (KSC.704)) investigated in two texturally different contaminated soils as factoriel in a completely randomized design with three replications in greenhouse condition. Materials and methods: Citric acid and EDTA were used at concentrations level 0, 0.5 and 1 mmol kg-1 soil and poultry manure extract at concentrations level 0, 0.5 and 1 g kg-1 soil. Three seeds of corn were plant in the rhizobox. After 10 weeks, plants were harvested and rhizosphere and bulk soils were separated. Dissolved organic carbon (DOC), microbial biomass carbon (MBC) and available Zn (by using 4 chemical procedures including DTPA-TEA, AB-DTPA, Mehlich3 and rhizosphere-based method) were determined in the rhizosphere and bulk soils.Results: Rhizosphere soils properties was different with bulk soils. In both soils, the results indicated that DOC and MBC in the rhizosphere were significantly (p < 0.05) increased, while, pH in the rhizosphere was significantly (p < 0.05) decreased comare to bulk soils. In both soils, Zn extracted by different methods in the rhizosphere were significantly (p < 0.05) lower than those in the bulk soils. Amount of exteracted Zn with extractants ranged from 9.00 to 75.00 mg kg-1 in sandy loam soil, and 0.78 to 75.00 mg kg-1 in sandy loam soil. The maximum amount of Zn by mehlich3 and the least amount of Zn by rhizosphere based method were exteracted. Available Zn increased as added chelators to soil.Conclusion: In both soils, Zn extracted by different methods in the rhizosphere were significantly (p < 0.05) lower than those in the bulk soils. In sandy loam soil, The maximum amount of Zn in the citric acid treatment (1 mmol kg-1) and the least amount of Zn in control condition, were exteracted, while, In clay loam soil, The maximum amount of Zn in the EDTA treatment (1 mmol kg-1) and the least amount of Zn in the PME treatment (1 g kg-1), were exteracted. Mean of Zn extracted by DTPA-TEA, AB-DTPA, Mehlich3, in clay loam soil was significantly (p < 0.05) higher than those in sandy loam soil. Mean of Zn extracted by rhizosphere-based method in sandy loam soil was significantly (p < 0.05) lower than those in clay loam soil.
https://jwsc.gau.ac.ir/article_4158_26e0acee61f6d238e9319c40af0c98ba.pdf
2018-05-22
187
202
10.22069/jwsc.2018.14502.2930
Keywords: chelator
Availability
rhizosphere
sandy loam soil
clay loam soil
Mohammad
Rahmanian
m.rahmanian10@yahoo.com
1
استادیار گروه علوم خاک دانشگاه یاسوج
LEAD_AUTHOR
Alireza
Hosseinpur
hosseinpur-a@agr.sku.ac.ir
2
shahrekord univecsity
AUTHOR
1.Campbell, C.R., and Plank, C.O. 1998. Preparation of plant tissue for laboratory analysis.
1
P 37-50, In: Y.P. Kalra (ed), Handbook of reference methods for plant analysis, CRC Press, Taylor and Francis Group.
2
2.Chen, Y., Li, X., and Shen, Z. 2004. Leaching and uptake of heavy metals by ten differentspecies of plants during an EDTA-assisted phytoextraction process. Chemosphere. 57: 187-196.
3
3.Corre, M.D., Schnabel, R.R., and Shaffer, J.A. 1999. Evaluation of soil organic carbon under forests, cool-season and warm-season grasses in the northeastern US. Soil Biology and Biochemistry. 31: 1531-1539.
4
4.Dessureault-Rompre, J., Nowack, B., Schulin, R., Tercier-Waeber, M.L., and Luster, J. 2008. Metal solubility and speciation in the rhizosphere of Lupinus albus cluster roots. Environmental Science and Technology. 42: 7146-7151.
5
5.Evangelou, M.W.H., Bauer, U., Ebel, M., and Schaeffer, A. 2007. The influence of EDDS and EDTA on the uptake of heavy metals of Cd and Cu from soil with tobacco nicotiana tabacum. Chemosphere. 68: 345-353.
6
6.Feng, M.H., Shan, X.Q., Zhang, S., and Wen, B. 2005. A comparison of the rhizosphere-based method with DTPA, EDTA, CaCl2 and NaNO3 extraction methods for prediction of bioavailability of metals in soil to barley. Environmental Pollution. 137: 231-240.
7
7.Gee, G.H., and Bauder, J.W. 1986. Partial size analysis. P 383-411, In: A. Klute (ed), Methods of soil analysis, Part 2: Physical properties. Soil Science Society of America, Madison, Wisconsin.
8
8.Hinsinger, P. 1998. How do plant roots acquire mineral nutrients? Chemical processes involved in the rhizosphere. Advances in Agronomy. 64: 225-265.
9
9.Jenkinson, D.S., and Powlson, D.S. 1976. The effects of biocidal treatments on metabolism in soil. I. Fumigation with chloroform. Soil Biology and Biochemistry. 8: 209-213.
10
10.Kabata-Pendias, A., and Pendias, H. 2001. Trace element in soils and plants. 3rd ed. CRC Press, Boca Raton, FL, 413p.
11
11.Karczewska, A., Orlow, K., Kabala, C., Szopka, K., and Galka, B. 2011. Effects of chelating compounds on mobilization and phytoextraction of copper and lead in contaminated soils. Communications in Soil Science and Plant Analysis. 42: 1379-1389.
12
12.Kim K.R., Owens G., and Kwon S.I. 2010. Influence of Indian mustard (Brassica juncea) on rhizosphere soil solution chemistry in long-term contaminated soils: A rhizobox study. J. Environ. Sci. 22: 1. 98-105.
13
13.Lai, H.Y., and Chen, Z.S. 2005. The EDTA effect on phytoextraction of single
14
and combined metals-contaminated soils using rainbow pink (Dianthus chinensis). Chemosphere. 60: 1062-1071.
15
14.Lesage, E., Meers, E., Vervaeke, P., Lamsal, S., Hopgood, M., Tack, F.M.G., and Verloo, M.G. 2005. Enhanced phytoextraction: II. Effect of EDTA and citric acid on heavy metal uptake by Helianthus annuus from a calcareous soil. Inter. J. Phytoremed. 7: 2. 143-152.
16
15.Li, H., Shen, J., Zhang, F.M., Clairotte, J.J., LeCadre, E., and Hinsinger, P. 2008. Dynamics of phosphorus fractions in the rhizosphere of common bean (Phaseolus vulgaris L.) and durum wheat (Triticum turgidum durum L.) grown in monocropping and intercropping systems. Plant and Soil. 312: 139-150.
17
16.Li, Z., and Shuman, L.M. 1997. Mobility of Zn, Cd and Pb in soils as affected by poultry litter extract- I. leaching in soil columns. Environmental Pollution. 95: 219-226.
18
17.Lindsay, W.L., and Norvell, W.A. 1978. Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Sci. Soc. Amer. J. 42: 421-428.
19
18.Loeppert, R.H., and Sparks, D.L. 1996. Carbonate and gypsum. P 437-474, In: D.L. Sparks (ed), Methods of soil analysis. Part 3: Chemical properties. Soil Science Society of America, Madison, Wisconsin.
20
19.Lombi, E., Wenzel, W.W., Gobran, G.R., and Adriano, D.C. 2001. Dependency of phytoavailability of metals on indigenous and induced rhizosphere processes: a review.
21
P 3-24, In: G.R. Gobran,W.W. Wenzel and E. Lombi (eds), Trace elements in the rhizosphere, CRC Press LLC.
22
20.Mehlich, A. 1984. Mehlich 3 soil test extractant: A modification of Mehlich 2 extractant. Communications in Soil Science and Plant Analysis. 15: 1409-1416.
23
21.Nelson, D.W., and Sommers, L.E. 1996. Total carbon, organic carbon and organic matter.
24
P 961-1010, In: D.L. Sparks (ed), Methods of soil analysis. Part 3: Chemical properties. Soil Science Society of America, Madison. Wisconsin.
25
22.Perez – esteban, J., Escolastico, C., Masaguerb, A., and Moliner, A. 2012. Effects of sheep and horse manure and pine bark amendments on metal distribution and chemical properties of contaminated mine soils. Europ. J. Soil Sci. 63: 733-742.
26
23.Petra, K., Juan, B., Pilar Bernal, M., Flavia, N., Charlotte, P., Stefan, S., Rafael, C., and Carmela, M. 2009. Trace element behaviour at the root–soil interface. Implications in phytoremediation. Environmental and Experimental Botany. 67: 243-259.
27
24.Safari Singani, A.A., and Ahmadi, P. 2012. Manure application and cannabis cultivation influence on speciation of lead and cadmium by selective sequential extraction. Soil Sedimentary Contamination. 21: 305-321.
28
25.Saifullah Zia, M.H., Meers, E., Ghafoor, A., Murtaza, G., Sabir, M., Zia-ur-Rehman, M., and Tack, F.M.G. 2010. Chemically enhanced phytoextraction of Pb by wheat in texturally different soils. Chemosphere. 79: 652-658.
29
26.Soltanpour, P.N., and Schwab, A.P. 1977. A new soil test for simultaneous extraction of macro- and micro-nutrients in alkaline soils. Communication in Soil Science and Plant Analysis. 8: 195-207.
30
27.Sposito, G., Lund, L.J., and Chang, A. 1982. Trace metal chemistry in arid-zone field soils amended with sewage sludge. I. Fractionation of Ni, Cu, Zn, Cd and Pb in solid phases.
31
Soil Sci. Soc. Amer. J. 46: 260-264.
32
28.Tembo, B.D., Sichilongo, K., and Cernak, J. 2006. Distribution of copper, lead, cadmium and zinc concentrations in soils around Kabwe town in Zambia. Chemosphere. 63: 497-501.
33
29.Udovic, M., and Lestan, D. 2009. Pb, Zn and Cd mobility, availability and fractionation in aged soil remediated by EDTA leaching. Chemosphere. 74: 1367-1373.
34
30.Wang, Z., Shan, X.Q., and Zhang, S. 2002. Comparison between fractionation and bioavailability of trace elements in rhizosphere and bulk soils. Chemosphere. 46: 1163-1171.
35
ORIGINAL_ARTICLE
Experimental investigation of the effect of different cluster shapes on resistance coefficient
AbstractBackground and objectives: It is important to study the coefficient of resistance in streams, especially in open channels, canals and rivers. One of the factors influencing the flow resistance is bed-forms. Cluster microforms are types of bed-forms in mountainous rivers, which are important both in biological and in hydraulic as well as in the secondary currents. Study on the recognition and influence of clusters on flow resistance is novel. The purpose of this study is to investigate the effect of cluster shape and particle size of clusters on the flow roughness coefficients.Materials and methods: In order to investigate the effect of the shape and size of the particles that forming clusters, experiments were carried out in a laboratory channel of 20 meters in length, 0.6 m in width and 0.6 m in height. Using gravel particles with three different sizes of 9.5, 12.5, 15.5 mm, cluster three cluster types (linear, heap and rings) were constructed in laboratory flume. Two roughness coefficients namely Darcy-Wiesbach and Manning were calculated using the water surface slope measurements.Results: The results of this study showed that the linear cluster has the least effect on the rate of flow resistance coefficient. The ring and heap clusters have a roughness coefficient greater than the linear cluster, but both have almost same impact on the flow resistance. The results of these two forms of the cluster are very close for particles of 9.5 and 12.5 mm, but for a particle of 15.5 mm, the heap cluster coefficient of roughness is higher than the roughness coefficient of another. The manning’s roughness coefficient is also increased by increasing the diameter of the gravel particles of the cluster builder. By performing experiments with gravel particles of different diameters, the percentage of change in roughness coefficient relative to the non-cluster state for cluster clumps in particles of 9.5, 12.5 and 15.5 mm was 47, 52 and 75, respectively. For rings and particles of 9.5, 12.5 and 15.5 mm, the percentage changes were 48, 49 and 75 percent, respectively, and the percentage for linear clusters for particles of 9.5 and 19 percent, and particles 5 / 12 and 15.5 mm were observed at 37 and 67% respectively, indicating an increase in the flow resistance with an increase in the particle diameter. Also, the results showed that increase in Froude number would decrease the roughness coefficient.Conclusion: The results of the experiments made it clear that the cluster affect the flow resistance by increasing it. The results of this study showed that the heap cluster has the most effect on flow resistance.
https://jwsc.gau.ac.ir/article_4159_7bfe20fb15938319c6ec01f95d623f54.pdf
2018-05-22
203
218
10.22069/jwsc.2018.13919.2867
roughness coefficient
microform cluster
linear cluster
cobble cluster
cluster ring
Masoud
Karbasi
m.karbasi@znu.ac.ir
1
هیات علمی دانشگاه زنجان
LEAD_AUTHOR
Mohammad
Ghasemian
mghch25@yahoo.com
2
Water engineering department, Agriculture faculty, University of Zanjan
AUTHOR
Mahdi
Asadi
mahdi.asadi.a@gmail.com
3
assistant water engimering univercity of shahrekord
AUTHOR
1.Bahrami Yarahmadi, M., and Shafai Bejestan, M. 2011. Experimental Study of the Effect of Sediment Particles Shape on Manning's Coefficient. J. Water Soil. 25: 1. 51-60. (In Persian)
1
2.Bathurst, J.C. 1985. Flow resistance estimation in Mountain Rivers. J. Hydr. Engin.
2
111: 4. 625-643.
3
3.Biggs, B.J., Duncan, M.J., Francoeur, S.N., and Meyer, W.D. 1997. Physical characterization of microform bed cluster refugia in 12 headwater streams, New Zealand. New Zealand
4
J. Mar. Freshwater Res. 31: 4. 413-422.
5
4.Brayshaw, A.C., Frostick, L.E., and Reid, I. 1983. Hydrodynamics of particle clusters and sediment entrainment in coarse alluvial channels. Sedimentology. 30: 1. 137-143.
6
5.Buffington, J.M. 1995. Effects of hydraulic roughness and sediment supply on surface textures of Gravel-bed Rivers (Master's thesis, University of Washington).
7
6.Dal Cin, R. 1968. “Pebble clusters”: Their origin and utilization in the study of paleo currents. Sedimentary Geology. 2: 4. 233-241.
8
7.Esmaili, K., Kashefipour, S.M., and Shafaie Bajestan, M. 2009. The Effect of Bed Form on Roughness Coefficient in Unsteady Flows Using a Combined Numerical and Laboratory Method. J. Water Soil. 23: 3. 136-144. (In Persian)
9
8.Heays, K.G., Friedrich, H., and Melville, B.W. 2014. Laboratory study of gravel-bed cluster formation and disintegration. Water Resources Research, 50: 2227-2241.
10
9.Hemmatti, M., and Vafa, M. 2016. Investigation on the effect of gravel particles shape on Manning s roughness coefficient in Mountain Rivers. Applied research in irrigation and drainage structures engineering. 17: 66. 15-30. (In Persian)
11
10.Karbasi, M., Omid, M.H., and Farhoudi, J. 2011. Experimental investigation of 3D flow over cluster microforms. Iran. J. Irrig. Water Engin. 2: 5. 75-85. (In Persian)
12
11.Karbasi, M., Omid, M.H., and Farhoudi, J. 2012. Prediction of cluster bed-forms formation over gavel-bed Rivers. Iran. Water Res. J. 6: 10. 1-9. (In Persian)
13
12.Laronne, J.B., and Carson, M.A. 1976. Interrelationships between bed morphology and
14
bed-material transport for a small, gravel-bed channel. Sedimentology. 23: 1. 67-85.
15
13.Mianaee, S.J., Keshavarzi, A., and Sistani, B. 2008. Modeling erosion and deposition of particles on ripples using image processing technic. 4th national conference of civil engineering (University of Tehran). (In Persian)
16
14.Millar, R.G. 1999. Grain and form resistance in gravel-bed Rivers. J. Hydr. Res.
17
37: 3. 303-312.
18
15.Papanicolaou, A.N., and Schuyler, A. 2003. Cluster evolution and flow-frictional characteristics under different sediment availabilities and specific gravity. J. Engin. Mechanic. 129: 10. 1206-1219.
19
16.Papanicolaou, A.N., Strom, K., Schuyler, A., and Talebbeydokhti, N. 2003. The role of sediment specific gravity and availability on cluster evolution. Earth Surface Processes and Landforms. 28: 1. 69-86.
20
17.Reid, I., and Hassan, M.A. 1992. The influence of microform bed roughness elements on flow and sediment transport in Gravel-Bed Rivers: a reply. Earth Surface Processes and Landforms. 17: 5. 535-538.
21
18.Strom, K.B., and Papanicolaou, A.N. 2008. Morphological characterization of cluster microforms. Sedimentology. 55: 1. 137-153.
22
19.Teisseyre, A.K. 2013. Pebble clusters as a directional structure in fluvial gravels: modern and ancient examples. Geologia Sudetes. 12: 2. 79-90.
23
20.Wittenberg, L., and Newson, M.D. 2005. Particle clusters in Gravel-bed Rivers: an experimental morphological approach to bed material transport and stability concepts. Earth Surface Processes and Landforms. 30: 11. 1351-1368.
24
ORIGINAL_ARTICLE
Effect of vegetation cover percentage on runoff and soil loss of interill erosion in forest road cutslope (Case study: Kohmiyan Forest, Azadshahr).
Background and Objectives: Roads provide access to forest resources for properly management of timber production, transportation, forest protection and ecotourism. Also, forest roads are known as one of the main sources of sediment production in forest watersheds. Globally, soil erosion is one of the most important environmental problems which is threaten soil and water resources. Therefore, it is necessary to investigate on soil erosion of forest roads and determination of its quantity rate. The measurement of soil erosion rates under natural rainfall conditions is costly and time consuming. Data provided by rainfall simulation and static site measurements can be used to predict erosion rates under natural conditions, especially for erosion rates from forest roads. Various studies were investigated the factors affecting water erosion in forest roads and their results had shown a significant correlation among slope percentage, vegetation cover percentage, climate, Physical and chemical soil characteristics, road traffic with the amount of soil erosion. This study, specifically, aimed to assess the effect of vegetation cover on soil erosion, runoff production and sedimentation.Materials and Methods: This study was done to investigate the effects of vegetation cover of cut-slope forest road on erosion variables such as runoff amount, sediment concentration, soil loss, time to runoff and runoff coefficient in Kohmiyan Forest of Azadshahr. It was conducted five treatments of vegetation cover including 0, <25%, 25-50%, 50-75% and 75-100%, each with four replicates on one square meter plot by rainfall simulation at an intensity of 80 mm/h for 15 min and distance 3 minutes. Results: The results indicated that the average amount of runoff in five levels of vegetation cover including bare ground, less than 25%, 25-50%, 50-75% and 75-100% were 24.7,17.82, 12.78, 5.23 and 2.64 l/m2, average amount of sediment concentration were 15.66, 9.41, 7.37, 5.07 and 2.39 g/l and average time of runoff were 12.00,75.00, 150.00, 238.75 and 365.75 and runoff coefficient were 47.8, 35.62, 25.71, 10.58 and 5.27 percent, respectively. The results statistically showed significant differences between amount of runoff and soil loss due to vegetation cover of road cut-slope. The results of Tukey test indicated significant differences among mean runoff and sediment of five level and decrease with increased road cut-slope of vegetation cover percentage.Conclusion: The effect of vegetation cover on the cut-slope road is completely significant on the amount of runoff, sediment concentration, soil loss, time to runoff and runoff coefficient. In other words, the amount of runoff and soil loss has a reverse and significant relationship with vegetation cover percentage. Vegetation cover percentage has had a positive impact and role in the reduction of the amount of runoff and sediment in the forest roads.Keywords: Soil erosion, Runoff coefficient, Sediment concentration, Rainfall simulator, Rainfall intensity.
https://jwsc.gau.ac.ir/article_4160_d478e0b0d2e5584c7aa861afeec25dc2.pdf
2018-05-22
219
233
10.22069/jwsc.2018.12464.2719
Runoff
soil loss
Rainfall simulator
Vegetation cover
Rainfall intensity
Mohammad Hadi
Moayeri
moayeri38@yahoo.com
1
عضو هیات علمی - دانشگاه علوم کشاورزی و منابع طبیعی گرگان
LEAD_AUTHOR
Mostafa
Moghadamirad
moghadami.mostafa@yahoo.com
2
دانشگاه علوم کشاورزی و منابع طبیعی گرگان
AUTHOR
Ehsan
Abdi
abdie@ut.ac.ir
3
عضو هیات علمی - دانشگاه تهران
AUTHOR
Hojjat
Ghorbani Vagheie
ghorbani169@yahoo.com
4
عضو هیات علمی دانشگاه گنبد
AUTHOR
1.Abdollahi, Z., Sadeghi, S.H., and Khaledi Darvishan, A. 2016. Variation of simulated rainfall characteristics by permuting intake discharge and water pressure. J. Water. Manage. Sci. Engin. 10: 34. 51-62. (In Persian)
1
2.Akay, A.E., Erdas, E.M., Reis, M., and Yuksel, A. 2008. Estimating sediment yield from forest road network by using a sediment predication model and GIS techniques. J. Build. Environ. 43: 678-695.
2
3.Akbarimehr, M., and Naghdi, R. 2012. Assessing the relationship of slope and runoff volume on skid trails (Case study: Nav 3 district). J. For. Sci. 58: 8. 357-362. (In Persian)
3
4.Arnaez, J., Larrea, V., and Ortigosa, L. 2004. Surface runoff and soil erosion on unpaved forest roads from rainfall simulation tests in northeastern Spain. Catena. 57: 1-14.
4
5.Bakr, N., Tamer, A., Elbana, A.E., Arceneaux, Y.Z., Weindorf, D.C.H., and Magdi, S. 2015. Runoff and water quality from highway hillsides: Influence compost/mulch. Soil Tillage. Res. 150: 158-170.
5
6.Battany, M.C., and Grismer, M.E. 2000. Rainfall runoff and erosion in NapaValley vineyards: effects of slope, cover and surface roughness. J. Hydrol. Proc. 14: 1289-1304.
6
7.Casermeiro, M.A., Molina, J.A., Delacruz Caravaca, M.T., Hernando Massanet, M.I., and Moreno, P.S. 2004. Influence of scrubs on runoff and sediment loss in soils of Mediterranean climate. Catena. 57: 97-107.
7
8.Cerda, A. 2007. Soil water erosion on road embankments in eastern Spain. J. Sci. Total Environ. 378: 151-155.
8
9.Chaplot, V.A.M., and Bissonnais, Y.L. 2003. Runoff features for interrill erosion at different rainfall intensities, slope length and gradient in an agricultural Loessial hillslope.J. Soil Sci. Soc. Am. 67: 844-851.
9
10.De Ona, J., Osorio, F., and Garcia, P.A. 2009. Assessing the effects of using compost- sludge mixtures to reduce erosion in road embankments. J. Hazard. Mat. 164: 1257-1265.
10
11.Diseker, E.G., and Sheridan, J.M. 1971. Predicting sediment yield from roadbanks. Trans. Am. Soc. Agric. Eng. 14: 1. 102-105.
11
12.Duiker, S.W., Flanagan, D.C., and Lal, R. 2001. Erodibility and infiltration characteristics of five major soils of southwest Spain. Catena 45: 103-121.
12
13.Elliot, W.J., Foltz, R.B., and Robichaud, P.R. 2009. Recent findings related to measuring and modeling forest road erosion. In Proc. 18th World IMAC/MODSIM Congress Cairns, Australia. Pp: 4078-4084.
13
14.FAO. 2006. Guidelines for soil description. Fourth 12- edition, Rome, 108p.
14
15.Foltz, R.B., Copeland, N.S., and Elliot, W.J. 2009. Reopening abandoned forest roads in Northern Idaho, USA: Quantification of runoff, sediment concentration, infiltration and interrill erosion parameters. J. Environ. Manage. 90: 2542-2550.
15
16.Ford, E.D., and Deans, D. 1978. The effect of canopy structure on the stemflow, throughfall and interception loss in a young Sitka spruce plantation. J. Appl. Eco. 15: 3. 905-917.
16
17.Fu, B., Newham, L.T., and Ramos-Scharron, C.E. 2010. A review of surface erosion and sediment delivery models for unsealed roads. Environ Model and Soft, 25: 1-14.
17
18.Geissen, V., Sánchez-Hernández, R., Kampichler, C., Ramos-Reyes, R., Sepulveda-Lozada, A., Ochoa-Goana , S., de Jong, B.H.J., Huerta-Lwanga, E., and Hernández-Daumas, S. 2009. Effects of land-use change on some properties of tropical soils-An example from Southeast Mexico. Geoderma. 151: 87-97.
18
19.Ghahraman, B., and Abkhezr, H.R. 2004. Correction of the rainfall intensity- durationfrequency equations in Iran. J. Sci. Technol. Agric. Natur. Resour. 8: 2. 1-13.
19
(In Persian)
20
20.Grace, J.M. 2002. Effectiveness of vegetation in erosion control from forest road sideslopes. Trans. Asae. 45: 3. 681-685.
21
21.Hadson, N. 1993. Soil Conservation (Translating by Hossein Ghadiri). ShahidChamranUniversity Press. 470p. (In Persian)
22
22.Hematzadeh, Y., Barani, H., and Kabir, A. 2009. The role of vegetation management on surface runoff. J. Soil Water. Cons. 162: 2. 19-33. (In Persian)
23
23.Jordan, A., and Martınez-Zavala, L. 2008. Soil loss and runoff rates on unpaved forest roads in southern Spain after simulated rainfall. J. For. Ecol. Manage. 255: 913-919.
24
24.Jordan-Lopez, A., Martinez-Zavala, L., and Bellinfante, N. 2009. Impact of different parts of unpaved forest roads on runoff and sediment yield in a Meditrranean area. J. Sci. Total Environ. 4: 7. 937-944.
25
25.Kato, H., Onda, Y., Tanaka, Y., and Asano, M. 2009. Field measurement of infiltration rate using an oscillating nozzle Rainfall Simulator in the cold- semi arid Grass land of Mongolia. Catena. 76: 173-181.
26
26.Kavianpoor, A.H., Jafarian, Z., Smahli, A., Kavian, A. 2015. The effect of vegetation cover on runoff and soil loss using rainfall simulation. J. Geograp. Environ. Plan. 58: 2. 179-190.
27
27.Khaledi Darvishan, A., Homayonfar, V., and Sadeghi, S.H. 2016. Designing, construction and calibration of a portable rainfall simulator for field runoff and soil erosion studies.
28
J. Water. Manage. Sci. Engin. 10: 34. 105-112. (In Persian)
29
28.Li, X.Y. 2003. Gravel-sand mulch for soil and water conservation in the semiarid loess region of northwest China. Catena. 52: 105-127.
30
29.Lopez-Bermudez, F., Romero-Diaz, A., Martinez-Fernandez, J., Martinez-Fernandez, J. 1998. Vegetation and soil erosion under a semi-arid Mediterranean climate: a case study from Murcia (Spain). Geomorphology. 24: 51-58.
31
30.Lotfalian, M., Shirvani, Z., and Naghavi, H. 2009. Investigation of effective factors on skid roads erosion. J. For. 1: 2. 115-124. (In Persian)
32
31.Mingguo, Z., Qiangguo, C., and Hao, C. 2007. Effect of vegetation on runoff sediment yield relationship at different spatial scales in hilly areas of the Loess Plateau, North China. ActaEcologica Sinica. 27: 9. 3572-3581.
33
32.Moghadamirad, M., Abdi, E., Mohseni Saravi, M., Rouhani, H., and Majnounian, B. 2013. The effect of traffic on forest road surface erosion. J. For. Pop. Res. 20: 4. 634-644.
34
(In Persian)
35
33.Moreno, D.H., Merino, M.L., and Nicolau, J.M. 2009. Effect of vegetation Cover on the Hydrology of Reclaimed mining soils under Mediterranean- Continental Climate. Catena. 77: 39-47.
36
34.Morgan, R.P.C. 2005. Soil erosion and conservation, Third Edition, Blackwell. 304p.
37
35.Morin, J., and Kosovsky, A. 1995. The surface infiltration model. J. Soil Water. Cons.
38
50: 470-476.
39
36.Moslehi, M., Habashi, H., and Khormali, F. 2011. Evaluation of through fall and rainfall interception of Beech in Hyrcanian forest. J. For. W. Pro. 64: 3. 319-330. (In Persian)
40
37.Nadal-Romero, E., Lasanta, T., Regues, D., Lana- Renaul, N., and Cerda, A. 2011. Hydrological response and sediment production under different land covers in abandoned farmland fields in a Mediterranean mountain environment. Boletín de la Asociacion de Geógrafos Españoles. 55: 303-323.
41
38.Najafian, L., Kavian, A., Ghorbani, J., and Tamartash, R. 2010. Effect of soil properties on runoff and soil erosion. J. Raeng. 4: 2. 334-347. (In Persian)
42
39.Nekooimehr, M., Rafatnia, N., Raisian, R., Jahanbazi, H., Talebi, M., and Abdolahi, Kh. 2006. Impact of road construction on forest destruction in Bazoft region. J. For. Pop. Res. 14: 3. 228-243. (In Persian)
43
40.Pan, Ch., and Shangguan, Zh. 2006. Runoff hydraulic characteris and sediment generation in sloped grassplots under simulated rainfall condition. J. Hydrol. 331: 178-185.
44
41.Parsakhoo, A., Lotfalian, M., and Jalilvand, H. 2014. The effects of soil properties and vegetation cover on the sedimentation of forest roads. J. Soil Sci. Environ. Manage.
45
Pp: 20-28. (In Persian)
46
42.Puya, K., Majnounian, B., Feghhi, J., Lotfalian, M., and Abdi, E. 2009. The efficiency of Backmund method for evaluation of forest road networks with regard to capabilities of wheeled skidders in ground skidding method. J. For. 1: 1. 35-42. (In Persian)
47
43.Rafahi, H.Gh. 2006. Water erosion and conservation. University of Tehran Press (5 Ed), 671p.
48
44.Ramos-Scharrón, C.E., and MacDonald, L.H. 2005. Measurement and prediction of sediment production from unpaved roads, St John, USVirgin Islands. J. Earth Surf. Process. Landf. 30: 1283-1304.
49
45.Rastgar, Sh. 2013. Estimating and comprising the economic value of forage production and soil conservation functions of range vegetation, Ph.D. Thesis. Gorgan Agricultural Sciences and NaturalResourcesUniversity, Faculty of Natural Resources, 158p. (In Persian)
50
46.Reidel, M.S., and Vose, J.M. 2002. Forest road erosion, sediment transport and model validation in the southern appalachians. In: Second Federal Interagency Hydrologic Modelling Conference, July 28–August 1, Las Vegas, NV, USA.
51
47.Romero-Diaz, A., Cammeraat, L.H., Vacca, A., and Kosmas, C. 1999. Soil erosion at three experimental sites in the Mediterranean. J. Eart Surf. Process. Landf. 24: 1243-1256.
52
48.Sadeghi, S.H. 2010. Study and measurement of water erosion. Tarbiat modares university press, 199p. (In Persian)
53
49.Sheridan, G., Noske, P., Lane, P., and Sherwin, C. 2008. Using rainfall simulation and site measurements to predict annual inter rill erodibility and phosphorus generation rates from unsealed forest roads: Validation against in-situ erosion measurements. Catena. 73: 49-62.
54
50.Solgi, A., Najafi, A., and Sadeghi, S.H.R. 2014. Effects of traffic frequency and skid trail slope on surface runoff and sediment yield. J. For. Eng. 25: 2. 171-178. (In Persian)
55
51.Stroosnijder, L. 2005. Measurement of erosion: is it possible?. Catena. 64: 2-3. 162-173.
56
52.The natural resources and watershed management general office of Golestan province. Kouhmian's Forest management Plan Booklet, 1995. 250p. (In Persian)
57
53.Vahabi, J., and Mahdian, M.H. 2010. Investigation effect of vegetation cover and soil moisture on runoff by rainfall simulation. P 25-26, In: 4st National Conference on erosion and sediment, Noor, Iran. (In Persian)
58
54.Wainwright, J., Parsons, A.J., and Abrahams, A.D. 2000. Plot-scale studies of vegetation, overland flow and erosion interactions: case studies from Arizona and New Mexico.
59
J. Hydrol. Proc. 14: 2921-2943.
60
55.Williams, J.D., Wilkins, D.E., McCool, D.K., Baarstad, L.L., Klepper, B.L., and Papendick, R.I. 1998. A new rainfall simulator for use in low-energy rainfall areas. Am. Soc. Agric. Eng. 14: 3. 243-247.
61
56.Yousefi Fard, M., Jalalian, A., and Khademi, H. 2007. Estimating nutrient and soil loss
62
from pasture land use change using rainfall simulator. J. Sci. Technol. Agric. Natur. Resour. 40: 93-57.
63
58.Zhou, Z.C., and Shangguan, Z.P. 2007. The effects of ryegrass roots and shoots on loess erosion under simulated rainfall. Catena. 70: 350-355.
64
ORIGINAL_ARTICLE
Forecasting the effect of climate change on soil erosion hazard in Navrood watershed
Background and Objectives: Climate Change and its consequence changes in precipitation patterns can affect soil erosion, as the most important global problem of land degradation. Therefore, it is essential to assess soil erosion risk under climate change condition. The aim of this study was to evaluate the impacts of future climate change on soil erosion risk in Navrood watershed, located in west of Guilan province, North of Iran.Materials and Methods: In this study, the trend of climate change was evaluated through effective climatic parameters by XLSTAT software based on the data obtained from Rasht and Bandar Anzali stations. Also the soil erosion risk was predicted using RUSLE in combination with geographic information system and remote sensing, in Navrood watershed. The data of previous research were used to calculate the K, LS, C and P factors for the RUSLE model. The atmospheric general circulation model NCCCSM and three scenarios A1B, A2 and B1 were used to study climate change. The daily rainfall pattern were simulated for two 20-year periods of 2046-2065 and 2080-2099 for Kharajgil, Khalian and NAV stations located inside the watershed, based on the outputs of NCCCSM, daily rainfall values of the base period 2002-2007, and the LARS-WG model. Results: The results showed that a decrease will occur in rainfall at the Nav and Khalian stations; while there will be an increase for Kharajgil station. In contrast, the rainfall erosivity will increased for all scenarios and stations in future in compare with the base period due to increase of rainfall intensity. Based on the obtained results, soil erosion risk changes from zero to more than 77 tons per hectare per year, between zero and over 115 tons per hectare per year, and from zero to more than 98 ton per hectare per year across the watershed at the base period (2002-2007), and 2046-2065 and 2080-2099 periods, respectively.Conclusion: The results showed that rainfall erosivity will increased due to increase of rainfall intensity. Most of the watershed area is faced with low erosion risk, but the south-western and middle north parts of the watershed are experiencing high erosion. Additionally, although rainfall erosivity is at its highest level at some stations, but the erosion rate is low because of the positive impact of plant coverage in reducing soil erosion. Higher the density of plant coverage, particularly forest type, reduces the negative impacts of rainfall erosivity, resulted in lower soil erosion risk.
https://jwsc.gau.ac.ir/article_4161_2d968af80336298925c5ceec36f7e309.pdf
2018-05-22
235
250
10.22069/jwsc.2018.12223.2679
Climate change scenario
LARS-WG Model
Rainfall erosivity
RUSLE
Hossein
Asadi
ho.asadi@ut.ac.ir
1
گروه علوم خاک، دانشگاه تهران
LEAD_AUTHOR
Mohammad
Jafari
mhmdjfri@yahoo.com
2
دانشگاه گیلان
AUTHOR
Afshin
Ashrafzadeh
a_ashrafz@yahoo.com
3
دانشگاه گیلان
AUTHOR
Arezoo
Sharifi
arezoo_sha62@yahoo.com
4
دانشگاه کرمان
AUTHOR
1.Arnell, N.W., and Reynard, N.S. 1996. The effects of climate change due to global warming on river flows in Great Britain. J. Hydrol. 183: 397-424.
1
2.Arnoldus, H.M.J. 1980. An approximation of the rainfall factor in the Universal Soil
2
Loss Equation. In: M. DeBoodt, D. Gabriels, (Eds.), Assessment of Erosion. Chichester, New York. Pp: 127-132.
3
3.Asadi, H., Honarmand, M., Vazifedoust, M., and Mousavi, A. 2017. Assessment of Changes in Soil Erosion Risk Using RUSLE in Navrood Watershed, Iran. J. Agric. Sci. Tech. 19: 231-244.
4
4.Babaeian, I., Najafi Nik, Z., Zabol Abasi, F., Habibi Nokhandan, M., Adab, H., and Malbusi, Sh. 2009. Iranian climatic changes between 2010 and 2039 using small scale measurements of the general circulation model data on atmosphere (ECHO-G). J. Geograph. Dev. 16: 135-152.
5
(In Persian)
6
5.Booij, M.J. 2005. Impact of climate change on river flooding assessed with different spatial model resolutions. J. Hydrol. 303: 176-198.
7
6.Chmura, D.J., Anderson, P.D., Howe, G.T., Harrington, C.A., Halofsky, J.E., Peterson, D.L., Shaw, D.C., and Clair, J.B. 2011. Forest responses to climate change in the northwestern United States: Ecophysiological foundations for adaptive management. Forest Ecology and Management. 261: 7. 1121-1142.
8
7.Church, J.A., Gregory, J.M., Huybrechts, P., Kuhn, M., Lambeck, K., Nhuan, M.T., Qin, D., and Woodworth, P.L. 2001. Changes in sea level. In: Houghton J.T., Ding Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Xiaosu, D. (Eds.), Climate Change 2001. The Scientific Basis. Cambridge University Press, Cambridge, Pp: 639-693.
9
8.Diodato, N. 2004. Local models for rainstorm induced hazard analysis on Mediterranean river torrential geomorphological systems. Nat. Hazards Earth Syst. Sci. 4: 389-397.
10
9.Fantappiè, M., Priori, S., and Costantini, E.A.C. 2015. Soil erosion risk, Sicilian region (1:250,000 scale). J. Maps. 11: 2. 323-341.
11
10.Fatolazadeh, T. 2015. Examine the types and severity of erosion in the sub-basins watershed Navrood. J. Physic. Geograph. 8: 27. 25-38. (In Persian)
12
11.Gaatib, R., and Larabi, A. 2014. Integrated evaluation of soil erosion hazard and risk management in the Oued Beht watershed using remote sensing and GIS techniques: Impacts on El Kansra Dam Siltation (Morocco). J. Geogr. Inf. Syst. 6: 677-689.
13
12.Gholami, A., Shahedi, K., Habib-Nejad-Roshan, M., Vafakhah, M., and Soleimani, K. 2017. Forecasting and comparison of future climate change by using of GCM models under different scenarios in Talar watershed of Mazandaran province. J. Range Water. Manage.
14
70: 1. 181-196. (In Persian)
15
13.Haas, L. 2002. Mediterranean water resource planning and climate change adaptation. Water, wetlands and climate change, Building linkages for their integrated management. Mediterranean Regional Roundtable. Athens, Greece, December 10-11 Draft for Discussion, 62p.
16
14.Hadinia, H. 2013. Impact of climatic change on rice water demand in Rasht. M.Sc. Thesis, the Faculty of Agricultural Sciences, University of Guilan. 95p. (In Persian)
17
15.Hasanpour Kashani, M., Ghorbani, M.A., Dinpazhouh, Y., and Shahmorad, S. 2015. Rainfall-runoff simulation in the Navrood river basin using Truncated Volterra model and artificial neural networks. J. Water. Manage. Res. 6: 12. 1-10. (In Persian)
18
16.Honarmand, M. 2010. Assessment and mapping of soil erosion hazard using revised universal soil loss equation (RUSLE), geographic information system (GIS) and remote sensing (RS) in Navrood watershed (Guilan province). M.Sc. Thesis, Faculty of Agricultural Sciences, University of Guilan. 105p. (In Persian)
19
17.Katirayee, P.S., Hejam, S., and Iran Nejad, P. 2006. The role of frequency variation and daily rainfall intensity in shaping rainfall patterns during 1960-2001 in Iran. J. Earth Space Physic. 33: 67-83. (In Persian)
20
18.Kebede, W., Habitamu, T., Efrem, G., and Fantaw, Y. 2015. Soil erosion risk assessment in the Chaleleka wetland watershed, Central Rift Valley of Ethiopia. Environmental Systems Research 4:5, DOI 10.1186/s40068-015-0030-5.
21
19.Lu, D., Li, G., Valladares, G.S., and Batistella, M. 2004. Mapping soil erosion risk in Rondonia, Barzilian Amazonia using RUSLE, remote sensing and GIS. Land Degradation and Development, 15: 499-512.
22
20.Masoom Pour, F. 2005. Examination of the efficiency of MPSIAC model for estimating erosion and sediment in Navrood watershed. M.Sc. Thesis, Faculty of Natural Resources. The University of Mazandaran, Iran. 78p. (In Persian)
23
21.Massah Bovani, A., and Morid, S. 2005. Effects of climatic change on Zayandeh Rood water flow in Isfahan. J. Natur. Resour. Agric. Sci. 9: 4. 12-27. (In Persian)
24
22.Mohammadi, B. 2011. Analysis of annual precipitation trends in Iran. J. Geograph. Environ. Program. 22/43: 3. 95-106. (In Persian)
25
23.Nasiri, B., and Yarmoradi, Z. 2017. Predict changes in climate parameters Lorestan province in 50 years by using HADCM3. Scientific Research Quarterly of Geographical Data.
26
26: 101. 143-154. (In Persian)
27
24.Nunes, J., and Nearing, M. 2011. Modelling impacts of climatic change: Case studies using the new generation of erosion models. Wiley- Blackwell, Oxford, Pp: 289-312.
28
25.O’Neal, M.R., Nearing, M.A., Vining, Z.C., Southworth, J., and Pfeifer, R.A. 2005. Climate change impacts on soil erosion in Midwest United States with changes in crop management. Catena. 61: 165-184.
29
26.Paroissien, J.B., Darboux, F., Couturier, A., Devillers, B., Mouillot, F., Raclot, D., and
30
Le Bissonnais, Y. 2015. A method for modeling the effects of climate and land use changes on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France).
31
J. Environ. Manage. 150: 57-68.
32
27.Prasannakumar, V., Shiny, R., Geetha, N., and Vijith, H. 2011. Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: A case study of Siruvani river watershed in Attapady valley, Kerala, India. Environ. Earth Sci. 64: 965-972.
33
28.Prasannakumar, V., Vijith, H., Abinod, S., and Geetha, N. 2012. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers.
34
3: 2. 209-215.
35
29.Prasuhn, V., Liniger, H.P., Herweg, K., Candinas, A., and Clement, J.P. 2013. A
36
high-resolution soil erosion risk map of Switzerland as strategic policy support system. Land Use Policy. 32: 281-291.
37
30.Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., and Yoder, D.C. 1997. Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agriculture Handbook No. 703, USDA, Washington, DC, USA, 404p.
38
31.Routschek, A., Schmidt, J., and Kreienkamp, F. 2014. Impact of climate change on soil erosion: A- high-resolution projection on catchment scale until 2100 in Saxony/Germany. Catena 121: 99-109.
39
32.Sabz Gostar Consultation Engineering Co. 2003. Multi-purpose Comprehensive Scheme at the Watershed 7 Nav, Asalem. Natural Resources Administration of Guilan Province, Iran. Ministry of Agriculture. (In Persian)
40
33.Salahie, B., Ali Jahan, M., Eini, S., and Derakhshi, J. 2017. Prediction of initiation and ends dates of moderat and severe frosts in Kermanshah province selected on the outputs of some climate models. J. Geograph. Plan. 21: 59. 175-195.
41
34.Sereda, J., Bogard, M., Hudson, J., Helps, D., and Dessouki, T. 2011. Climate warming and the onset of salinization: Rapid changes in the limnology of two Northern Plains lakes. Limnologica. 41: 1-9.
42
35.Serpa, D., Nunes, J.P., Santos, J., Sampaio, E., Jacinto, R., Veiga, S., Lima, J.C., Moreira, M., Keizer, J.J., Abrantes, N., and Corte, J. 2015. Impacts of climate and land use changes on the hydrological and erosion processes of two contrasting Mediterranean catchments. Science of the Total Environment. 538: 64-77. 36.Sobhani, B., Eslahi, M., and Babaeian, I. 2015. The functionality of fine patterns of statistical downscaling model (SDSM) and LARS-WG patterns in simulation of meteorological variables at Orumiyeh lake watershed. The Quarterly of Investigations on Natural Geography. 47: 4. 499-516. (In Persian) 37.Zhang, X.C., Nearing, M.A., Garbrecht, J.D., and Steiner, J.L. 2004. Downscaling monthly forecasts to simulate impacts of climate change on soil erosion and wheat production. Soil Sci. Soc. Amer. J. 68: 1376-1385.
43
ORIGINAL_ARTICLE
Investigation the effect of different Salinity levels on Yield and Yield components of Quinoa (Cv. Titicaca)
Background and Objectives: Since the agriculture field is the main water consumer, using the approaches to increase water use efficiency is necessary. Due to the limited freshwater, farmers have to use exotic water, such as seawater. One of the management methods is the mixture use of freshwater and seawater. The goal of this study was to investigate the effect of different salinity levels on yield and yield components of Quinoa (Cv. Titicaca) in greenhouse condition.Materials and Methods: In this study, the effect of five mixing use of seawater and freshwater evaluated on yield and yield components of Quinoa (CV. Titicaca). The research was done based on completely randomized design including 3 replications as pot planting in Gorgan University of Agricultural Sciences and Natural Resources during 2016. Research Station is located in north of Iran at 36° 51' N latitude and 54° 16' E longitude at the south-east corner of Caspian Sea and its height from sea level is 13.3 meters. Soil texture is silty clay. In this study, five irrigation regimes included (0, 15, 30, 45 and 60 percent mixture of sea and tap water). The seeds of Quinoa were planted at a depth of 2.5 centimeter in soil of each pot and were irrigated with tap water. Plants harvested after 6 months, shoot and root dry weight, plant height, yield and thousand kernel weights were measured. Physical and chemical properties of irrigation water and of soil were determined before experiment. The obtained data analyzed using statistical software of SAS (Ver. 9.0) and the means were compared using LSD test at 5 % percent levels.Results: The results showed that effect of different salinity levels on shoot dry weight, plant height, yield and thousand kernel weights was significant at 1 percent level (p < 0.01), but on root dry weight was significant at 5 percent level (p < 0.05). In this study, all of these parameters decreased significantly with increasing water salinity. The result showed that the irrigation regime of 15 percent mixture of seawater and tap water compared to other regimes had the highest root and shoot dry weights, yield, and thousand kernel weights after control treatment. While, the 15 percent mixture of seawater and tap water irrigation regime had the highest plant height.Conclusion: The results indicated that increasing of salinity levels from 0 to 15 percent mixture of sea and tap water resulted in redaction of shoot and root dry weight, yield and thousand kernel weights to 9.8, 9.9, 2.2 and 23.4 percent, respectively. The results showed that this kind of saline and fresh water mixture, in any way, has a high efficiency in reducing salt stress on plant.
https://jwsc.gau.ac.ir/article_4162_de310ef2fda334f6cd18f5f4853de1e7.pdf
2018-05-22
251
266
10.22069/jwsc.2018.13721.2841
Quinoa
Seawater
shoot and root dry weight
thousand kernel weights
Yield
Saber
Jamali
sa13e12@gmail.com
1
Water Engineering Department, ferdowsi university of mashhad, mashhad, Iran.
LEAD_AUTHOR
Hossein
Sharifan
h_sharifan47@yahoo.com
2
Associate Professor, Department of water engineering, Faculty of Agricultural, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
1.Abedi, M.J., Nairizi, S., Ebrahimi Birang, N., Maherani, M., Khaledi, H., Mehrdadi, N., and Cheraghi, A.M. 2002. Saline Water Utilization in Sustainable Agriculture. Iranian National Committee on Irrigation and Drainage. 224p. (In Persian)
1
2.Abid, M., Qayyum, A., Dastai, A.A., and Abdul Wajid, R. 2001. Effect of Salinity and SAR of Irrigation water on yield, Physiological growth parameters of Maiz (Zea mayes L.) and Preperties of the soil. J. Res. (Science), Bahaudin Zakariya University, Multan Pakistan.
2
12: 1. 26-330.
3
3.Adolf, V.I., Shabala, S., Andersen, M.N., Razzaghi, F., and Jacobsen, S.E. 2012. Varietal differences of quinoa’s tolerance to saline conditions. Plant and Soil, 357: 1-2. 117-129.
4
4.Algosaibi, A.M., El-Garawany, M.M., Badran, A.E., and Almadini, A.M. 2015. Effect of Irrigation Water Salinity on the Growth of Quinoa Plant Seedlings. J. Agric. Sci. 7: 8. 205.
5
5.Alizadeh, A. 2014. Soil, Water and Plant Relationship. Sajad university of technology. 876p. (In Persian)
6
6.Allen, L.H.Jr. 1991. Effect of increasing carbon dioxide levels and climate change on plant growth, evaportanspiration and water resources in the West Under Conditions of Climatic Uncertainty. 14-16 Nov. 1990., Scottsdale, AZ. National Research Council, National Academy Press, Washington DC. Pp: 101-147.
7
7.Ashraf, M. 2001. Relation between growth and gas exchange characteristics in some salttolerance amphidiploid Brassica species in relation to their diploid parents. Environmental and Experimental Botany. 45: 155-163.
8
8.Bilalis, D., Kakabouki, I., Karkanis, A., Travlos, I., Triantafyllidis, V., and Dimitra, H.E.L.A. 2012. Seed and saponin production of organic quinoa (Chenopodium quinoa Willd.)
9
for different tillage and fertilization. Notulae Botanicae Horti Agrobotanici Cluj-Napoca.
10
40: 1. 42.
11
9.Blokhina O., Virolainen E., and Fagestedt. K.V. 2003. antioxidants, oxidative damage and oxygen deprivation stress: A review. Annuals of Botany, 91: 179-194.
12
10.Blum, A. 1988. Plant breeding for stress environments. CRC Press Inc., Boca Raton, Florida, USA. 233p.
13
11.Daneshvar, H.A., and Kiani, B. 2005. Effect of Salinity on some local cultivars of Russian olive (Elaeagnus angustifolia) in Isfahan province. 65: 76-83. (In Persian)
14
12.Davazdahemami, S., Sefidkon, F., Jahansooz, M.R., and Mazaheri, D. 2010. Evaluation of water salinity effects on yield and essential oil content and composition of Carum copticum L. Iran. J. Med. Arom. Plant. 25: 4. 504-512. (In Persian)
15
13.Dixit, A.A., Azar, K.M., Gardner, C.D. et al. 2011. Incorporation of whole, ancient grains into a modern Asian Indian diet to reduce the burden of chronic disease. Nutr Rev. Aug.
16
69: 8. 479-88.
17
14.Francois, L.E., Grieve, E.V., Mass, E.V., and Leseh, S.M. 1994. Time of salt stress affects growth and yield components of irrigated wheat. Agron. J. 86: 100-107.
18
15.Guo, F., and Tang, Z.C. 1999. Reduced Na+ and K+ permeability of K+ channel in plasma membrane isolated from roots of salt tolerant mutant of wheat. Chinese Science Bulletin,
19
44: 9. 816-821.
20
16.Hirose, Y., Fujita, T., Ishii, T., et al. 2010. Antioxidative properties and flavonoid composition of Chenopodium quinoa seeds cultivated in Japan. Food Chemistry, Volume 119, Issue 4, 15 April 2010, Pp: 1300-1306.
21
17.Jacobsen, S.E., Monteros, C., Christiansen, J.L., Bravo, L.A., Corcuera, L.J., and Mujica, A. 2005. Plant responses of quinoa (Chenopodium quinoa Willd.) to frost at various phenological stages. Eur. J. Agron. 22: 131-139.
22
18.Jacobsen, S.E., Liu, F., and Jensen, C.R. 2009. Does root-sourced ABA play a role for regulation of stomata under drought in quinoa (Chenopodium quinoa Willd.). Scientia Horticulturae, 122: 2. 281-287.
23
19.Jacobsen, S.E., Christiansen, J.L., and Rasmussen, J. 2010. Weed harrowing and inter-row hoeing in organic grown quinoa (Chenopodium quinoa Willd.). Outlook on Agriculture,
24
39: 3. 223-227.
25
20.Kafi, M., Borzoee, A., Salehi, M., Kamandi, A., Masoumi, A., and Nabati, J. 2014. Physiology of Environmental stresses in plants. iranian academic center for education culture and research of mashhad. (In Persian)
26
21.Kafi, M., Salehi, M., and Eshghizadeh, H.R. 2011. Biosaline Agriculture- plant, water and soil management Approaches. iranian academic center for education culture and research of mashhad. (In Persian)
27
22.Kerepesi, H., and Galiba, G. 2000. Osmotic and salt stress induced alteration in soluble carbohydrate content in wheat seedling. Crop Science. 40: 482-487.
28
23.Koyro, H.W., and Eisa, S.S. 2008. Effect of salinity on composition, viability and germination of seeds of Chenopodium quinoa Willd. Plant and Soil. 302: 1-2. 79-90.
29
24.Koyro, H.W., Lieth, H., and Eisa, S.S. 2008. Salt tolerance of chenopodium quinoa willd., grains of the Andes: Influence of salinity on biomass production, yield, composition of reziaves in the seeds, water and solute relations. Tasks for Vegetation Sciences. 43: 133-145.
30
25.Khorsandi, O., Hassani, A., Sefidkon, F., Shirzad, H., and Khorsand, A. 2010. Effect of salinity (NaCl) on growth, yield, essential oil content and composition of Agastache foeniculum Kuntz. Iran. J. Med. Arom. Plant. 26: 3. 438-451. (In Persian)
31
26.Mass, E.V., and Griev, C.M. 1990. Spike and leaf development in salt stress of wheat.
32
Crop Sci. 30: 1309-1313.
33
27.Munns, R. 1993. Physiological processes limiting plant growth in saline soil: some dogmas and hypotheses. Plant Cell Environment, 16: 15-24.
34
28.Munns, R., and Tester, M. 2008. Mechanisms of salinity tolerance. Annual Review of Plant Biology. 59: 651-681.
35
29.Nabati, J., Kafi, M., Nezami, A., Rezvani Moghaddam, P., Masoumi, A., and Zare Mehrgerdi, M. 2014. Evaluation of Quantitative and Qualitative Characteristic of Forage Kochia (Kochia scoparia) in Different Salinity Levels and Time. Iran. J. Field Crop Res.
36
12: 4. 613-620.
37
30.Nabizadeh Marvdust, M.R., Kafi, M., Rashed, M.H., and Hasel, M. 2003. Effect of salinity on growth, yield, collection of minerals and percentage of green cumin essence. J. Iran Arable Stud. 1: 1. 53-59.
38
31.Naseer, Sh. 2001. Response of barley (Hordeum vulgare L.) at various growth stages to salt stress. J. Biol. Sci. 1: 5. 259-326.
39
32.Panuccio, M.R., Jacobsen, S.E., Akhtar, S.S., and Muscolo, A. 2014. Effect of saline water on seed germination and early seedling growth of the halophyte quinoa. AoB Plants, 6, p. plu047.
40
33.Poustini, K. 2002. An Evaluation of 30 Wheat Cultivars Regarding the response to salinity stress. Iran. Agric. Sci. 33: 1. 57-64. (In Persian)
41
34.Ruley, A.T., Sharma, N.C., and Sahi, S.V. 2004. Antioxidant defense in a lead accumulation plant, Sensbania drummondii. Plant Physiology and Biochemical. 42: 899-906.
42
35.Sabet Teimouri, M., Khazaie, H.R., Nassiri Mahallati, M., and Nezami, A. 2010. Effect of salinity on seed yield and yield components of individual plants, morphological characteristics and leaf chlorophyll content of sesame (Sesamum indicum L.). Environmental stresses in crop science. 2: 2. 119-130. (In Persian)
43
36.Salehi, M., Kafi, M., and Kiani, A. 2009. Growth analysis of kochia (Kochia scoparia (L.) schrad) irrigated with saline water in summer cropping. Pak. J. Bot. 41: 1861-1870.
44
37.Shabala, S., Hariadi, Y., and Jacobsen, S.E. 2013. Genotypic difference in salinity tolerance in quinoa is determined by differential control of xylem Na+ loading and stomatal density.
45
J. Plant Physiol. 170: 10. 906-914.
46
38.Shahidi, R., Kamkar, B., Latifi, N., and Galeshi, S. 2010. Effect of different salinity levels and exposure times on individual’s seed yield and yield components of hull-less barley (Hordeum vulgare L.). crop production. 3: 2. 49-63. (In Persian)
47
39.Tadayon, M.R., and Emam, Y. 2007. Physiological and Morphological Responses of
48
Two Barley Cultivars to Salinity Stress in Relation to Grain Yield. J. Water Soil Sci.
49
11: 1. 253-263. (In Persian)
50
40.Talebnejad, R., and Sepaskhah, A.R. 2015a. Effect of different saline groundwater depths and irrigation water salinities on yield and water use of quinoa in lysimeter. Agric. Water. Manage. 148: 177-188.
51
41.Talebnejad, R., and Sepaskhah, A.R. 2015b. Effect of deficit irrigation and different
52
saline groundwater depths on yield and water productivity of quinoa.Agricultural Water Management, 159: 225-238.
53
42.Weisani, W., Sohrabi, Y., Heidarit, G., Siosemardeh, A., and Ghassemi, K. 2011. Physiological responses of soybean (Glycine max L.) to zinc application under salinity stress. Austr. J. Crop Sci. 5: 1441.
54
43.Zamani, S., Nezami, M.T., Habibi, D., and Baybordi, A. 2010. Study of yield and
55
yield components of winter Rapeseed under salt stress conditions. Crop Production Research. 1: 2. 109-121. (In Persian)
56
44.Zhu, J.K. 2001. Plant salt tolerance. Trends in Plant Science. 6: 2. 66-71.
57
ORIGINAL_ARTICLE
Experimental investigation of the effect of debris accumulation on the local scour at bridge pier and abutment
Accumulation of floating debris around the bridge's piers and abutments causes reduction of river flow area, flow diversion, flow accelerating and altering of scour pattern. The investigation of potential impacts of debris on the local scour processes is one of the main factors in design of bridge structures across the rivers. These wooden floating debris may have different shapes in terms of accumulation and position, often have rectangular shape in the nature. Although, the effect of debris on piers scour has been studied by different researchers, to the author's knowledge, no investigation has been conducted to study the effect of debris on flow pattern and scour hole characteristics in the case of combinative presence of pier and abutment. Therefore, in this study, the effect of debris with different geometrical characteristics on the pier and abutment scour and flow behavior was investigated experimentally. in this study, the effect of distance between bridge pier and abutment, geometrical characteristics of debris (including thickness, diameter and shape) on the scour was investigated experimentally. The experiments were conducted at the hydraulic and water structures laboratory of department of water engineering of Shahid Bahonar university of Kerman. The experimental flume has a rectangular cross section with 8 m length, 80 cm width and 60 cm depth. Model of bridge pier (diameter 3cm) and bridge abutment (6cm*12cm) was selected by stainless steel. Sedimentary bed with thickness of 16 cm, was composed of sediments with d50=0.91 mm. To avoid undesirable erosion of sediment, false bottoms were installed at the upstream and downstream parts of the study reach. Prismatic objects with different shapes of rectangular, triangular and semi-circular were used as debris. (The range of relative thickness of debris (T_d/D) was from 1 to 3 and the relative length of debris (D_d/D) from 4 to 10). The sediment threshold velocity and the maximum velocity of experiments of this study are 0.4 and 0.2 m⁄s respectively which shows that, all experiments were carried out at the clear water condition. The results showed that by decreasing the relative distance between bridge pier and abutment (G/D) from 6.66 to 3.33, the maximum scour depth at pier and abutment increased 8.1 and 12.5%, respectively. Also, the rectangular debris caused the most scour depth in comparison with the other debris shapes. By increasing the relative thickness of the semi-cylindrical debris (T_d/D) from 1 to 3, the maximum scour depth around the pier and abutment was respectively increased 7.64 and 24.21. In addition, the experimental results showed that the effective length of debris has a significant influence on the dimensions of scour hole, so that, the maximum scour depth in the presence of semi-cylindrical debris with relative effective length (D_d/D) of 10, increased 50.8 and 58 percent compared with that of the reference test, for the bridge pier and abutment, respectively. According to the results of this study, There was a direct relation between the scour depth and the debris thickness, so that, by doubling the relative thickness of rectangular debris, the scour depth around bridge pier and abutment is became 1.2 and 1.05, respectively. With increasing the relative length, the scour depth increased at first, thereafter reached to a constant value. For example, for rectangular debris, by changing the relative length from 4 to 10, the scour depth around bridge pier and abutment was increased 22.4 and 10.2 %, respectively, but for larger relative lengths, no change was observed in the scour depth. In addition, by decreasing the distance between pier and abutment, the maximum depth of scour hole was significantly increased compared with the reference test.
https://jwsc.gau.ac.ir/article_4163_b9099c83570d12f1b64c8c298a1b594b.pdf
2018-05-22
267
282
10.22069/jwsc.2018.12472.2714
Maximum scour depth
Debris
Bridge abutment
Bridge pier
Zahra
Abousaeidi
zahraabousaeidi@gmail.com
1
Shahid Bahonar University Of Kerman
AUTHOR
Kourosh
Ghaderi
kouroshqaderi@uk.ac.ir
2
استادیار دانشگاه شهید باهنر کرمان
LEAD_AUTHOR
Majid
Rahimpour
rahimpour@uk.ac.ir
3
Shahid Bahonar University Of Kerman
AUTHOR
Mohammad Mehdi
Ahmadi
ahmadi_mm@uk.ac.ir
4
null
AUTHOR
1.Breusers, H., Nicollet, G., and Shen, H. 1997. Local scour around cylindrical piers. J. Hydr. Res. IAHR, 15: 3. 211-252.
1
2.Diehl, T. 1997. Potential drift accumulation at bridge. Report No. FHWARD -97-028, Hydraulic Engineering No. 9, Federal Highway Administration, Washington, D.C.
2
3.Hagerty, D., Parola, A., and Fenske, T. 1995. Impacts of. 1993. Upper Mississippi river basin floods on highway systems. Report No. 1483. Transportation research board, Washington, DC. 121: 12. 869-876.
3
4.Hong S. 2005. Interaction of bridge contracrion scour and pier scour in a laboratory river model. M.Sc. thesis. Civil and Environmental Deep. Georgia Inst. of Technology. Atland.
4
5.Kumar, V., Rang Raju, K., and Vittal, N. 1999. Reduction of local scour around bridge piers using slot and collars. J. Hydr. Engin. ASCE. 125: 12. 1302-1305.
5
6.Lagasse, P., Clopper, P., and Zevenbergen, L. 2010. Effects of Debris on Bridge Pier Scour, NCHRP Report 653, Transportation Research Board, National Academies of Science, Washington, D.C. 117p.
6
7.Lagasse, P., Zevenbergen, L., Schall, J., and Clopper, P.E. 2007. Countermeasures to protect Bridge piers from scour. NCHRP Report No. 593, Transportation Research Record, Transportation Research Board, Washington, D.C. 6p.
7
8.Melville, B.W. 1992. Local Scour at bridge abutment. J. Hydr. Engin. 118: 4. 615-631.
8
9.Melville, B. 1997. Pier and abutment scour–an integrated approach. J. Hydr. Engin.
9
123: 2. 125-136.
10
10.Moshashaie, M. 2014. Experimental investigation of the effect of rectangular woody debris on scour of a sharp nose square and a square piers, M.Sc. dissertation, Faculty of agriculture, Shahr-e-Kord University. (In Persian)
11
11.Oben-nyarko, K., and Ettema, R. 2011. Pier and abutment scour interaction. J. Hydr. Engin. ASCE. Pp: 1599-1605.
12
12.Parola, A., Apelt, C., and Jempson, M. 2000. Debris Force on Highway Bridge. NCHRP Report No. 445, Transportation Research Record, Transportation Research Board, Washington, D.C. 176p.
13
13.Pagliara, S., and Carnacina, L. 2010. Temporal scour evolution at bridge piers: effect of wood debris roughness and porosity, J. Hydr. Res. 48: 1. 3-13.
14
14.Pagliara, S., and Carnacina, L. 2011. Influence of Wood Debris Accumulation on Bridge Pier Scour. J. Hydr. Engin. ASCE. 137: 254-261.
15
15.Park, J., Chamroeun, S., Park, C., and Young, D. 2015. A Study on the Effects of
16
Debris Accumulation at Sacrificial Piles on Bridge Pier Scour. KSCE J. Civil Engin.
17
20: 4. 1546-1551.
18
16.Raudkivi, A., and Ettema, R. 1983. Clear water scour at cylindrical piers. J. Hydr. Engin. ASCE, 103: 10. 1209-1213.
19
17.Schmocker, L., and Hanger, W. 2010. Drift accumulation at River Bridge. Laboratory
20
of Hydraulic, Hydrology and Glaciology VAW, ETH-Zurich, Zurich, Switzerland Bundesanstalt fur Wasserbau ISBN 978-3-939230-00-7.
21
18.Walleerstein, N., and Thome, C. 1996. Impact of wood debris on fluvial processes and channel morphology in stable and unstable stream. US Army Research Development and standardization Group., UK, London. 162p.
22
19.Walleerstein, N., and Thome, C., and Doyle, M. 1997. Spatial distribution and impact of large woody debris in norther Mississippi. Proceedings of the conference and Management of Landscapes Disturbed by channel Incision, May 19-23. Pp: 145-150.
23
ORIGINAL_ARTICLE
Estimation Discharge Cifficient in New System of Bottom Intake with Porous Media
Background and Objectives: Bottom intake is one of the suitable methods for diverting water in mountainous rivers. Different forms of bottom intake, which have so far been less studied, are the use of bottom intake with porous media which can be considered as a suitable substitute for bottom rack intakes due to the reduction difficults of bottom rack and low cost of design and execution. Since the idea of using bottom intake with porous media is new and information is limited to design and construct this kind of intake, the present research tried to consider the condition of the hydraulic behavior of these intakes in accordance with the reality.Materials and Methods: In order to model a bottom intake with porous medium and conduct experiments, a main flume with the walls of the glass materials in the dimensions of the 10* 0.30* 0.50 cube meter and a diverted flum by the dimensions of the 1* 0.45* 0.50 cube meter was used. To prepar an intake in the distance of 5 m at the beginning of main flume, the space has been considered so that the possibility of conduction is with three length, height and slope (L1=15 cm, L2=30 cm, L3=45 cm) (H1=10 cm, H2=15 cm, H3=20 cm) (S1=0%, S2=10%, S3=20%). The inner surrounding of intake was filled with four different types of gravel with average diameter P1=9.72mm, P2=13.41mm, P3=15.30 mm, P4=17.75mm. In every experiment by passing different discharges over intake,was measured the rate of diverted discharge for different models of intake was drawn and the effect of different parameters on the rate diverted discharge was studied, by bottom porous intake. We used rectangular weir at the end and beginning of main flume to measure discharge.Results and Conclusions: The results showed that inflow discharge increases the rate of diverted discharge but the proportion of diverted discharge to inflow discharge is on the decrease. By increasing the grain size, the diverted discharge increase, so that grain size P4 has most rate of diverted discharge. It ,s the result of void space increasing in this kind of grain size. By increasing uniform coefficient grain of intake, the porosity and void space of granular material decreases, and consequently diverted discharge decreases between 4 and 6 percent. Researching on diverted discharge with different length and height showed increasing intake length from L1 to L3 for intake with height H1 for P2 (d50=13.41mm) gradation and Qt=12.25 lit/s, the Qd/Qt is increased up to 23 percent. Increasing intake height from 10 cm to 30 cm for intake with length 30 cm for P4 (d50=17.75mm) gradation and Qt=17 lit/s, the Qd/Qt is increased up to 10 percent. By incearsing the slope of intake surface from 0% to 20% , diverted discharge decrease, this kind of decrease is the result of unexpected change in th surface slope of intake and separating flow in the in enterance of porous media intake. The minimum and maximum the rate of diverted discharge in the experiments in this study was 13% and 90% respectively.Conclusion: The results showed that by increasing the inflow discharge, the diverted discharge increases too; however, for larger values of the discharge, the ratio of the diverted to the upstream flow approaches a final constant value. Grain size of the porous media has a great influence on the diverted flow. By increasing the grain size, the diverted flow increases. By increasing the surface slope of bottom intake with porous media, the diverted flow decreases. Maximum diverted flow occurs at zero surface slope of the intake. Increasing intake length and height, causes increasing in diverted discharge.
https://jwsc.gau.ac.ir/article_4164_d0fe582cb9b7849de43eee8ab841f873.pdf
2018-05-22
283
296
10.22069/jwsc.2018.12378.2695
Bottom intake
Discharge cifficient
Porous media
River intake
Hossein
Shariati
shariaty13@gmail.com
1
دانشجوی دکترا رشته علوم و مهندسی آب دانشگاه فردوسی مشهد
AUTHOR
Saeed Reza
Khodashenas
khodashenas@um.ac.ir
2
Professor., Dept. of Water Engineering, Ferdowsi University of Mashhad
LEAD_AUTHOR
Kazem
Esmaeili
esmail@um.ac.ir
3
دانشیار گروه علوم و مهندسی آب دانشکده کشاورزی دانشگاه فردوسی مشهد.
AUTHOR
1.Bina, K., Maghrebi, M.F., and Abrishami, J. 2012. Experimental investigation of discharge coefficient in mesh panel bottom. J. Water Wastewater. 1: 24-33. (In Persian)
1
2.Castillo, L.G., García, J.T., and Carrillo, J.M. 2016. Experimental and numerical study of bottom rack occlusion by flow with gravel-sized sediment. Application to Ephemeral Streams in Semi-Arid Regions. 8: 1-18.
2
3.Hosseyni, S.M., and Abrishami, J. 2007. Open channel hydraulics. Press, 17p. (In Persian)
3
4.Kooroshvahid, F., Esmaili, K., Maghrebi, M.F., Alizadeh, A., and Naghavi, B. 2010. Flow discharge in bottom intakes with porous media. J. Water Soil. 24: 2. 347-358. (In Persian)
4
5.Kooroshvahid, F., Esmaili, K., and Naghavi, B. 2011. Experimental study on hydraulic characteristics of bottom intake with granular porous media. J. Special Topics & Reviews in Porous Media. 2: 301-311.
5
6.Kumar, S., Ahmad, Z., Kothyari, U.C., and Mittal, M.K. 2010. Discharge characteristics of a trench weir. J. Flow Measure. Instrument. 21:80-87.
6
7.Lund, S. 2005. Inntak til Smakraftverk. M.Sc. Thesis, Department of Hydraulic and Environmental Engineering., N.T.N.U, Norway.
7
8.Masjedi, A., and Taeedi, A. 2014. Laboratory study of channel slope and rod diameter effect on intake discharge coefficient under rack floor condition. J. Sci. Technol. Agric. Natur. Resour. 18: 67. 301-308. (In Persian)
8
9.Pouresmaeil, S., and Maghrebi, M.F. 2014. Experimental study on hydraulic characteristics of porous bottom intake in clear water. J. Water Soil. 28: 1. 35-45. (In Persian)
9
10.Righetti, M., and Lanzoni, S. 2008. Experimental study of the flow field over bottom intake racks. J. Hydr. Engin. 134: 15-22.
10
ORIGINAL_ARTICLE
Extraction of water from air moisture using underground temperature (case study: Examine the performance of the system in Bandar Abbas)
Background and objectives: Maintaining water resources as well as economic and fair exploitation of it is a global issue. Today, Due to the lack of water resources in many parts of the world, Disputes over the access to water resources pass national borders and access to these resources has become a strategic objective in interacting between countries. Based on the statistics released by the World Resources Institute in 2015, 33 countries will face the water stress in 2040in which Iran ranked 13. Considering the rainfall average of Iran, the amount of water resources, and per capita consumption of the country, Iran can be considered as the countries facing the lack of physical water resources. The objective of this research is to provide the safe water for agricultural use as the most widely used water sector in the country as well as beverage consumption without the use of fresh water sources, and only by exploiting the humidity of air.Materials and methods: In this research, designing a new system to extracting the clean water through the air moisture is considered. The methods applied in this research are the library method, statistical method, and analytical method. In this system, a part of the air humidity is separated and appears in the form of droplets of water on the channel wall. Then. The obtained water stored in a tank and use for the considered purpose.Results: The total amount of water obtained at a depth of 3 meters is equivalent to 67.53 in 4 meters 73.16 and in 5 meters 62.05 liters in 12 hours (total hours at 15th of every month). In general, the amount of water obtained by this method is varied depending on the climate and other conditions. In this research, the system is studied in Bandar Abbas. So, the water obtained ranged between 5 and 20 liters per hour in warm months.Conclusion: The results show by applying the stated method, especially in warm and humid areas, the acceptable levels of clean water can be achieved. It is necessary to note that all calculations in this paper are based on theoretical relations. So, the clean water obtained through the designed system may not be equal to the numerical value obtained. However, due to the water crisis in the country, achieving half of this amount could also be an acceptable contribution to reducing the water crisis and solving the problem of water scarcity.
https://jwsc.gau.ac.ir/article_4165_ce1199ca498be20db04696e0a2657d80.pdf
2018-05-22
297
305
10.22069/jwsc.2018.13025.2769
water crisis
water resources
air moisture
underground temperature
Iran
Amir Hossein
Janzadeh
amirhosseinj1990@yahoo.com
1
MA student, School of Architecture and Urbaism, Imam khomeini international univesity (IKIU), Qazvin, Iran.
LEAD_AUTHOR
1.Ackerman, E.B. 1968. Production of water from the atmosphere, Patent Citations: US 3400515 A, Application number: US3400515 A, Publication date: Sep 10, 1968. From: https://www.google.com/patents/US3400515?dq=U.S.+Pat.+No.+2,138,689&hl=en&sa=X&ved=0ahUKEwii2_aDlM3PAhWEjywKHdCPDKQQ6AEIIzAB.
1
2.Asgari, M. 2002. The new ratio between water resources and national security. J. Strategic Stud. 5: 489-502. (In Persian)
2
3.ASHRAE Handbook. 2009. Fundamental, American society of heating, refrigerating and
3
air-conditioning engineers, Atlanta. Chapter: 1.
4
4.Dastani, Z. 2016. Agriculture is a nation’s water killer. Ebtekar newspaper, NO 3457,
5
20 June 2016. (In Persian)
6
5.Gharibreza, Z. 2006. Water harvesting device from air humidity. Declaration NO: 38703587, Patent NO: 49836, Patent Data: 8 June 1999. Retrieved from: http://ip.ssaa.ir/Patent/ SearchResult.aspx?DecNo=38703587&RN=49863.
7
6.Ghasemi, A. 2015. Why “water” selected as “special issue” in 6th development program. Ministry of Economic and Finance Affairs, Department of Economic Affairs, Office of Research and Policy in productive sectors: association of research and politics of agriculture affairs.
8
7.Groth, W., and Hussmann, P. 1979. Process and system for recovering water from the atmosphere, Patent Citations: US 4146372 A, Application number: US 05/781,890, Publication date: Mar 27, 1979. From: https://www.google.com/patents/US4146372? dq=US+4146372+A&hl=en&sa=X&ved=0ahUKEwjq3LbPv7_RAhVIDZoKHcPJAF8Q6AEIGzAA.
9
8.Hillel, D. 1982. Introduction to Soil Physics, CA, San Diego: Academic press. Pp: 5-19.
10
9.Jajromi, K., Nosrati, Sh., and Bazdar, Sh. 2004. Water crisis: Future conflicts lines in South West Asia. Firs national congress on south west Asia Geopolitics, Taleghan, Iran's geopolitical association, university of Payamenoor. (In Persian)
11
10.Karami, M., and Norozzadeh, H. 2014. Factors that cause water crisis in Iran and solution to meet them. Second National Conference on Water Crisis (climate, water and environment change). Shahre Kord, the university of Shahre Kord. (In Persian)
12
11.Lehky, P. 2013. Extraction of water from air, Extraction of water from air, Patent Citations: US 20130227879 A1, Application number: US 13/824,784, Publication date: Sep 5, 2013. From: https://www.google.com/ patents/US20130227879?dq=20130227879+A1&hl= en&sa=X&ved=0ahUKEwjQp-HUgcHRAhXElSwKHUA6BpUQ6AEIGzAA.
13
12.Max, D. Michael. 2005. Apparatus and method for harvesting atmospheric moisture, Patent Citations: US 6945063 B2, Application number: US 10/603,600, Publication date: Sep 20, 2005. From: https://www.google.com/patents/US6945063?dq=harvest+water+from+air&hl =en&sa=X&ved=0ahUKEwiI3r_bkc3PAhUFhiwKHYU3Cp0Q6AEISDAG.
14
13.Micheal, M. 2013. Combination dehydrator, dry return air and condensed water generator/dispenser, Patent Citations: US 8607583 B2, Application number: US 13/252,132, Publication date: Oct 3, 2011. From: https://www.google.com/patents/US8607583? dq=U.S.+Pat.+No.+4,185,969&hl=en&sa=X&ved=0ahUKEwihlcevlM3PAhWE2CwKHcwGCqwQ6AEIODAE.
15
14.Norozi, A. 2008. Device for extraction water from air by utilizing sun. NO: 387061435, Patent NO: 53048, Patent Data: 20 Sep 2008. Retrieved from: http://ip.ssaa.ir/Patent/ SearchResult.aspx?DecNo=387061435&RN=53048.
16
15.Rosegrant, M.W., Cai, X., and Cline, S.A. 2002. World Water and Food to 2025: Dealing with Scarcity, Washington D.C: International Food Policy Research Institute (IFPRI).
17
16.Rosta, F. 2012. Management of water resource is a solution to go out of water crisis. First congress on Development of water resources. Abarkoh, Islamic Azad university of Abarkoh. (In Persian)
18
17.Shokohi, H. 2010. New perspectives in urban geography. Samt Press, V.1. (In Persian)
19
18.Tongue, S. 2009. Water-from-air using liquid desiccant and vehicle exhaust, Patent Citations: US 7601208 B2, Application number: US 11/267,978, Publication date: Oct 13, 2009. From: https://www.google.com/patents/US7601208.
20
ORIGINAL_ARTICLE
Fractal classification of typical meteorological day based on solar behavior
(Case study: Karaj synoptic station)
Background and objectives: Today the main part of human`s needed energy is provided by fossil fuels. Due to the reduction of fossil fuel reserves and climate changes caused by increased emissions, the production and use of new sources of clean renewable energy with fewer emissions is a necessity. Due to the efficiency of energy production, solar energy is more pronounced; among other renewable energy sources.The utilization of the information of solar irradiance is in many industrial applications, photovoltaic systems, agriculture and solar collectors design. For this purpose, the fractal dimension is used as a classification criterion. In order to provide a model that allows classification of days to three types this study estimated the daily fractal index and the index of purity of sky. In this method, with the advantage of cumulative distribution function, the fractal dimension classifies the days of Karaj station in three classes: clear sky day, partially clouded sky day and clouded sky day.Materials and methods: The experimental database contains global irradiance data and sunshain measured at Karaj site during 2014-2016 year. In the first stage after data quality control daily fractal dimension of solar radiation time series and clearness index was calculated. For each year two fractal thresholds was obtained using the cumulative distribution function (CDF). The days of year was classified in three classes of clear sky, partially cloudy sky and cloudy sky based on obtained thresholds. In the next step monthly analyses was done.Conclusion: Results showed that high frequency of class 1 was occurred in August 2015, high frequency of class 2 was occurred in February 2016 and high frequency of class 3 was occurred in March 2015 respectively. Also these statistical properties show that our classification method leads to homogeneous groupings of the studied days since the standard deviations of D and KT are low in compared to their averages. The more important value of this standard deviation for class III (upper than 10%) is due to the fact that this class contains rainy days whose irradiance signals have a regular form thus a fractal dimension near to 1. However, the analysis of monthly values of D permits the detection of the months where the fluctuations of their radiances are most intense and those where these irradiances are very regular. This information is very beneficial to refine the assessing of photovoltaic systems and to reduce the initial costs by appropriate design and construction of solar energy systems suitable to the climate of the site of interest.Results: This paper offers a method for the classification of radiation per day according to weather different classes, in order to exploit photovoltaic systems in using solar energy in the study area. This method has been proposed to classify the daily global irradiances into typical days using the fractal dimension as a basic criterion since it allows quantifying the irradiance fluctuations. This method defines fractal dimension thresholds using the cumulative distribution function. Then shown that it is possible to realize daily solar irradiances classification using the D thresholds obtained from the CDF method.Classification of the daily solar irradiance is important in design and installation of solar energy systems, especially PV arrays. Trends in the patterns of daily solar irradiance became significant information due to the recent interests in renewable technologies. This interest is essentially due to global warming and other negative effects to our environment. Such analyses presented in this purpose are of great interest as they reduce the initial costs by appropriate design and construction of solar energy systems suitable to the climate of the site of interest.
https://jwsc.gau.ac.ir/article_4166_a2aa7f3031859919595cd067a903229d.pdf
2018-05-22
307
314
10.22069/jwsc.2018.12995.2764
solar Irradiance
Fractal dimension
Classification
typical day
cumulative distribution function
Zahra
Agha Shariatmadary
zagha@ut.ac.ir
1
عضو هیأت علمی گروه هواشناسی دانشکده مهندسی آب و خاک دانشگاه تهران
LEAD_AUTHOR
Fatemeh
Bikhabi arani
f.bikhabiarani@ut.ac.ir
2
دانشجو
AUTHOR
1.Badescu, V. 2014. Modeling solar radiation at the earth's surface. Springer, Germany, 537p.
1
2.Dubuc, B., Quiniou, J.F., Roques-Carmes, C., Tricot, C., and Zucker, S.W. 1989. Evaluating the fractal dimension of profiles. Physical Review A. 39: 3. 1500-1512.
2
3.Ghaffari, H. 2012. Fractal dimension and impact of some of its mathematical operators, Master's Thesis, Department of Mathematics and Computer Science at the University of Damghan, 72p. (In Persian)
3
4.Harrouni, S., and Maafi, A. 2002. Classification des éclairements solaires à l’aide de l’analyse fractale. Revue Internationale des énergies renouvelables (CDER), 5: 107-122.
4
5.Maafi, A., and Harrouni, S. 2003. Preliminary results of the fractal classification of daily solar irradiances. Solar Energy, 75: 1. 53-61.
5
6.Moradi, I. 2009. Quality control of global solar radiation using sunshine duration hours. Energy, 34: 1. 1-6.
6
ORIGINAL_ARTICLE
Assessing the Accuracy of Contemporaneous Time Series and Neural Network Models in Modeling Rainfall-Runoff (Case Study: Nazloochaei Catchment)
Background and Objectives: Rainfall-runoff modeling is an essential process and very complicated phenomena that is necessary for proper reservoir system operation and successful water resources planning and management. There are different methods like conceptual and numerical methods for modeling of this process. Theoretically, a system modeling required explicit mathematical relationships between inputs and outputs variables. Developing such explicit model is very difficult because of unknown relationship between variables and substantial uncertainty of variables. So far performance models such as neural networks, multivariate models with auto moving average is studied for modeling the rainfall-runoff. So, in this study CARMA and ANN models studied in rainfall-runoff modeling.Materials and Methods: In this research, the multivariate contemporaneous autoregressive moving average (CARMA) models and artificial neural networks (ANN) were evaluated to rainfall-runoff modeling. we define 3 scenario for ANN model. In order to use CARMA and ANN models total annual precipitation and runoff time series in the period of 1975-2015 as for Nazloochaei the catchment area, in 44° 49 ' in latitude and 37° 40 ' longitude in the province of West Azerbaijan was used. At first, we checked the data in terms of randomness, trend and Homogeneity by run test, Mann-Kendall test and Wilcoxon test. And then we separated data in two group. One group including 80 presents of data for training and 20 percent of data for validation was assigned. Performance criteria that used, was root mean square error, Nash-Sutcliffe and correlation coefficient. Performance criteria that used, was root mean square error, Nash-Sutcliffe and correlation coefficient.Results: The results of this research indicated that the CARMA model had the efficiency accuracy more than ANN model, because root mean square error in CARMA model was equal 7.7 and that was in ANN model 9.50 m3/s. Also, CARMA model according to the Nash-Sutcliffe criteria and R2 equal to 0.41 and 0.54 had better performance than the ANN model. However, the value of these performance criteria in ANN model was equal 0.45 and 0.80. So CARMA model has more Accuracy than ANN model in rainfall-runoff modeling.Conclusion: According to the obtained results, using multivariate ARMA models caused to decrease in model error up to 18 percentages. So CARMA model had suitable performance in compare with ANN model, and this subject show the importance of to consider Random component in modeling. So CARMA model had suitable performance in compare with ANN model, and this subject show the importance of to consider Random component in modeling.
https://jwsc.gau.ac.ir/article_4167_565cb1ce399893febbf9d4efc5120998.pdf
2018-05-22
315
321
10.22069/jwsc.2018.12584.2728
Artificial neural networks
Multivariate models
Rainfall-runoff modeling
Time series
Mohammad Javad
Zeinali
mj.zeynali@yahoo.com
1
دانشجوی دانشگاه بیرجند
LEAD_AUTHOR
Abbas
Khashei Siuki
abbaskhashei@yahoo.com
2
Associate Professor Department of Water Engineering College of Agriculture University of Birjand
AUTHOR
1.Firat, M. 2008. Comparison of artificial intelligence techniques for river flow forecasting. Hydrology and Earth System Sciences Discussions. 12: 1. 123-39.
1
2.Khalili, K., and Nazeri Tahroudi, M. 2016. Performance evaluation of ARMA and CARMA models in modeling annual precipitation of Urmia synoptic station. J. Water Soil Sci.
2
26: 2-1. 13-28. (In Persian)
3
3.Moeeni, H., Bonakdari, H., Fatemi, S.E., and Ebtehaj, E. 2016. Modeling the monthly inflow to Jamishan dam reservoir using autoregressive integrated moving average and adaptive neuro-fuzzy inference system models. J. Water Soil Sci. 26: 2-1. 273-285. (In Persian)
4
4.Mohammadrezapour, O., and Zeynali, M.J. 2014. Comparison of ant colony, elite ant system and maximum – minimum ant system algorithms for optimizing coefficients of sediment rating curve (Case study: Sistan river). J. Appl. Hydrol. 1: 2. 55-66.
5
5.Nawaz, N., Harun, S., and Talei, A. 2015. Application of adaptive network-based fuzzy inference system (ANFIS) for river stage prediction in a tropical catchment. Applied mechanics and materials. Trans Tech Publisher, Switzerland. 735: 195-199.
6
6.Nazeri Tahroudi, M., Ahmadi, F., and Nazeri Tahroudim, Z. 2013. SAMS2007 software application in modeling the future climate to predict, temperature and rainfall of Kurdistan province (Case study: synoptic station in Sanandaj). 1th Semi-Arid Hydrology National Conference in KurdistanProvince. August 25. Sanandaj. (In Persian)
7
7.Salas, J.D. 1980. Applied modeling of hydrologic time series. Water Resources Publication.
8
8.Zou, P., Jingsong, Y., Jianrong, F., Guangming, L., and Dongshun, L. 2010. Artificial neural network and time series models for predicting soil salt and water content. J. Agric. Water Manage. 97: 2009-2019.
9