Monitoring and predicting the groundwater-level fluctuations for better management of groundwater resource in Lowlands Using Geographic Information System (GIS)

Subject Areas : Journal of Radar and Optical Remote Sensing and GIS

Abolfazl  Bameri ¹ (Faculty Member of Soil Engineering Department, Faculty of Water and Soil, University of Zabol, Iran)
Moazam  Khaleghi ² (Ph.D. Graduated, Water Engineering Department, Faculty of Water and Soil, University of Zabol, Iran)

Received: 2021-03-25 Accepted : 2021-06-18 Published : 2021-06-22

Keywords: Geostatistics, Sistan Plain, Groundwater Depth, Hamoon Lake,

Abstract :

 In order to be aware of groundwater-level fluctuations in arid and semi-arid regions, it is necessary to make an accurate forecast of the groundwater depth situation. The drying of Hamoon lake, severe water shortages and significant reduction in groundwater levels have led to critical environmental conditions in the Sistan plain. Spatial understanding of groundwater depth changes in the region and awareness of the severity of groundwater depletion are important for the development of water resources management strategies. Therefore, this study was conducted with the aim of zoning groundwater depth using geostatistics and GIS techniques in the agricultural lands of Sistan plain located in the east of Hamoon Lake, with an area of about 201000 ha. For this purpose, groundwater depth data were collected from 846 wells by field survey using piezometric wells in the study area.  In this research, various geostatistical methods including deterministic interpolation method and geostatistical methods were evaluated to compare the prediction ability of groundwater depth spatial variations. The results showed that the intensity of groundwater depth changes in the study area with a coefficient of variation of 19.87% is moderate. The spherical model could better explain the spatial variation of the experimental variogram of the studied parameter in the region. Finally, the results related to the deterministic method of inverse distance weighted with power 2 estimates a better prediction for groundwater depth zoning than kriging and cokriging geostatistical methods.

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