Annual Water Yield Estimation for Different Land Uses by GIS-Based InVEST Model (Case Study: Mish-khas Catchment, Ilam Province, Iran)

Document Type : Research and Full Length Article


1 Faculty of natural resources, Ilam University, IRAN

2 Dept. Forest Sciences, University of Ilam

3 Department of Natural Resources and Environmental Engineering, Shiraz University, Shiraz, Iran

4 Department of Forest Economics and Forest Management Planning, ALU Freiburg, Germany


Fresh water supply and its security encounter a high level of fluctuating variability under global climate changes. To address these concerns in catchment water management, a good understanding of land use/cover impacts on the hydrological cycle affecting water supply is crucial. The objective of this study is to define a model to investigate the impact of existing land use/cover on water yielding in Mish-khas catchment of Zagros region, Ilam province, Iran. In this research, a water yield model of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) was employed to estimate annual water yield in the catchments as a basic foundation for policy and decision making. The input data set included land use/cover layers of the region produced in 2016, average annual precipitation and potential evapotranspiration from 1986-2016, soil depth, plant available water content and land use/cover bio-physical database. Based on the results, total annual water yield was estimated 30.2 million m3 for the whole Mish-khas catchment. The annual water yield percent for rangeland, forest, farmland and orchards land uses was 57%, 31%, 8.6% and 3.4% of the total water yield of the catchment, respectively. In addition, the results showed that the farmland had the highest water yield (2449 m3/ha) followed by forests (2269 m3/ha), orchards (2254 m3/ha) and rangeland (2196 m3/ha) land uses. In terms of water distribution, the northern regions with a volume of 2315 m3/ha had higher water yield than the southern regions (2210 m3/ha). The results also indicate that a GIS-based InVEST model is a useful instrument to identify more suitable areas for water-table recharge.


Main Subjects

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  • Receive Date: 24 December 2017
  • Revise Date: 16 April 2018
  • Accept Date: 12 May 2018
  • First Publish Date: 01 January 2019