Forecasting the groundwater monitoring network using hybrid time series models(Case study:Nalochay)

Document Type : Complete scientific research article

Authors

1 Associate Professor of water engineering Deot.

2 Assistant Professor of Water engineering Dept.

3 Ph.D. student. water engineering Dept. University of Birjand

Abstract

Background and objectives: Designing of water quantity and quality monitoring system has been raised as one of the most complex issues in the field of water resources and the environment. The siutable quality of groundwatertable information recorded in the groundwater networks plays an important role in the sustainable design of water projects. In order to create an efficient and efficient network, groundwater networks should be periodically evaluated according to the needs and plans of the forward water resources The study area is the Nazlouchai catchment area located in west of Urmia Lake.
Materials and Methods: In this research, entropy theory was used to monitoring the quantity water level in the two historical statistical periods (2001-2016) and updated (2016-2021). The updated statistical period was developed using hybrid time series models (CARMA-ARCH). After initial data analysis and changes in the time series, the data were simulated to create the interaction of piezometers with multivariate regression. After confirming the accuracy of the multivariate regression model, entropy indicators were calculated and zoned on the Nazlouchai plain. After evaluating the groundwater network monitoring during the statistical period of 2001-2016, the Nazlochai plain groundwater network monitoring was updated for the statistical period of 2016-2021.
Results: The results of evaluation of the CARMA-ARCH hybrid model accuracy indicate the ability of the hybrid model to simulate and predict the annual values of groundwater level in the study area. The performance factor of the model also confirmed this. The results of the evaluation of the groundwater network monitoring in Nazlouchai plain showed that more than 99% of the studied area is located in the surplus and relatively surplus situation in terms of the number of piezometers. The status of the plain in the statistical period of 2011-2016 is good and the transmission of information between the piezometers is complete. During the statistical period of 2016-2021, groundwater level changes in the study area have been reduced, which has affected the network's groundwater monitoring. So that the areas with excess wells has been reduced to moderate monitoring areas. In general, the results of the research indicate the necessity of using the groundwater monitoring network and it is recommended that this monitoring be carried out annually for different plains of Iran. Also, the results showed that with decreasing groundwater level in the studied area, information transfer between wells is also reduced.
Conclusion: The results show that there is no complete transfer of information between the piezometers in the study area during the statistical period of 2016-2021.

Keywords


1.Allbed, A., Kumar, L., and Aldakheel, Y. Y. 2014. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 1: 230. 1-8.
2.Baatz, M., Benz, U., Dehghani, S., Heynen, M., Höltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M., and Willhauck, G. 2004. eCognition Professional User Guide 4. Published by:Trimble Germany GmbH, Arnulfstrasse 126, D-80636 Munich, Germany. 270p.
3.Babaei, R. 2017. Evaluation of land use change using satellite images processing (Case Study: Moghan Plain). Master's thesis. Remote sensing and GIS Field in soil and water studies. Tabriz University. 130p. (In Persian)
4.Bertani, T.C., Novack, T., Hayakawa, E.H., and Zani, H. 2010. Detection of Saline and Non-Saline Lakes on the Pantanal of Nhecolândia (Brazil) Using Object-Based Image Analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-4/C7.32-38.
5.Blaschke, T., and Strobl, J. 2001.What’s wrong with pixels? Somerecent developments interfacing remotesensing and GIS. GIS-Zeitschrift für Geoinformations system. 14: 6. 12-17.
6.Campbell, J.B., and Wynne, R.H. 2011. Introduction to remote sensing. Fifth edition, Guilford Press. 667p. 7.
7.Dashtakian, K., Pakparvar, M., and Abdollahi, J. 2008. Study of Soil Salinity Mapping Methods Using Landsat Satellite Data in Marvast Region. Res.J. Iran Grass. Des. 15: 2. 139-157.(In Persian)   
8.ECognition. 2012. Ecognition User Guide and Reference book. http://www.Definiens-imaging.com (Munich, Germany: Definiens Imaging) Published by: Trimble Germany GmbH, Arnulfstrasse 126, D-80636 Munich, Germany. 441p.
9.Farifteh, J., Van der Meer, F.,Atzberger, C., and Carranza, E.J.M. 2007. Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN). J. Rem. Sens. Environ. 110: 1. 59-78.
10.Farifteh, J., Farshad, A., and George, R.J. 2006. Assessing salt-affectedsoil using remote sensing, solute modelling, and geophysics. Geoderma 130: 4. 191-206.
11.Feizizadeh, B., and Hossein, H. 2008. Comparison of object based and pixel based methods and effective parameters in coverage / Land Use Classification in West Azarbaijan Province. Natural Geography Research, Spring number, 71: 42. 73-84. (In Persian)    
12.Hall, O., Hay, G.J., Bouchard, A., and Marceau, D.J. 2004. Detecting dominant landscape objects through multiple scales: an integration of object-specific methods and watershed segmentation. Landscape Ecology, 19: 1. 59-76. 
13.Hatafi, A.A., Karimi Ahmadabad, M., Ekhtesasi, M.R., and Payedar Ardakani, A. 2017. Evaluation of modeling methods and supervised classification for mapping soil salinity using ASTER and ETM images. J. Water Soil Cons. 23: 5. 123-140. (In Persian)
14.Hoffmann, A., and Van der Vegt, J.W. 2001. New Sensor systems and new Classification .Methods: Laser- and Digital Camera-data meet object-oriented strategies. GIS – Zeitschrift für Geoinformationssysteme 6: 01. 18-23.
15.James, D., Hurad Daniel, L., Civco Martha, S., Gilmore Emily, H., and Wilson. 2006. Tidal Wetland Classification From Landsat Imagery Using An Integrated Pixel-based and Object-based Classification Approach. ASPRS 2006 Annual Conference Reno, Nevada. May 1-5. 11p.
16.Karam, A., Kiyani, T., Dadrasi Sbzvari, A., and Davarzani, Z. 2018. Estimation of Soil Salinity Using Remote Sensing and Spatial Statistics in Sabzevar. Quantitative Geomorphology Research, Seventh Year, No. 4: 31-53. (In Persian)
17.Khademi, F., Pirokharati, H., and Sajjad, Sh. 2014. Study of the trend of increasing saline soils around Urmia lake using GIS and RS. Earth Sciences, 24: 94. 93-98. (In Persian)
18.Lees, B. 2006. The spatial analysis of spectral data: Extracting the neglected data. Applied GIS, 2: 2. 14-1.   
19.Lemma, H., Frankl, A., Poesen,J., Adgo, E., and Nyssen, J.2017. Classifying land cover from an object-oriented approach-applied to LANDSAT 8 at the regional scale of the Lake Tana Basin (Ethiopia). 19th
EGU General Assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, Austria.p. 3526.
20.Matinfar, H.R., Sarmadian, F., and Kazem, A. 2007. Identification of saline soils in dry area (Kashan) based on digital processing of IRS satellitedata and field studies J. Water Water.
2: 3. 99-111. (In Persian)
21.Metternicht, G.I. 2001. Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological Modelling, 144: 3. 163-179.
22.Moharami, M. 2017. Modeling the effects of the Urmia Lake on the eastern coastal villages by object-oriented satellite imagery, Master's thesis, Remote Sensing and GIS, University of Tabriz. 145p. (In Persian)
23.Nguyen, K.A., Liou, Y.A., Tran, H.P., Hoang, P.P., and Nguyen, T.H. 2020. Soil salinity assessment by usingnear-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science, 7: 1. 1-16.   
24.Schiewe, J., Tufte, L., and Ehlers,M. 2001. Potential and problems of multi-scale segmentation methods in remote sensing. GIS - Zeitschrift für Geoinformationssysteme 6: 01. 34-39.
25.Schiewe, J. 2002. Segmentation of high-resolution remotely sensed data-concepts, applications and problems. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34: 4. 380-385.             
26.Shrivastava, P., and Kumar, R. 2015. Soil salinity: a serious environmental issue and plant growth promoting bacteria as one of the tools forits alleviation. Saudi J. Biol. Sci.22: 2. 123-131.  
27.Stals, J.P. 2007. Mapping potential soil salinization using rule based object-oriented image analysis PHD Thesis (Geography and Environmental Studies). University of Stellenbosch. 96p. 
28.Stocking, M. 1995. Soil erosion andland degradation. Environmentalscience for environmental management, Pp: 223-242.     
29.Tajgardan, T., Ayoubi, Sh., Shataii, Sh., and Khormali, F. 2009. Mapping soil surface salinity using remote sensing data of ETM+ (Case study: North of Agh Ghala, Golestan Province). J. Water Soil Cons. 16: 2. 1-18. (In Persian)
30.Volschenk, T., Fey, M.V., and Zietsman, H.L. 2005. Situation Analysis of Problems for Water Quality Management in the Lower Orange River Region with Special Reference to the Contribution of the Foothills to Salinization. Final report to the Water Research Commission and Northern Cape Department of Agriculture and Land Reform. 170p.      
31.Yan, G. 2003. Pixel based and object oriented image for coal fire research (Doctoral dissertation, Thesis (MSc) International institute for geo -information science and earth and observation Enschede. ITC, Netherlands). 93p.
32.Zhang, Y., and Maxwell, T. 2006.A fuzzy logic approach to supervised segmentation for object-oriented classification. In ASPRS 2006 Annual Conference Reno, Nevada May 1-5. 11p.