InVEST model-based estimation of water yield in the Upper Tuul river basin - Purevsuren, Munkhtur; Batnyam, Tseveengerel (Proceedings of the Mongolian Academy of Sciences, 2022)
The estimation and mapping of water yield are of significant importance to the effective planning and management of water resources in Mongolia. In this study, we quantified and assessed the water yield of the Upper Tuul River basin using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Water yield model. The study aimed to test whether it is possible to estimate the water yield of the selected research area using the model. The required input data included land use and land cover, mean annual precipitation and potential evapotranspiration, soil depth, and plant available water content. In addition, those data were obtained from Landsat 8, Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), MODIS Global Terrestrial Evapotranspiration Product (MOD16), and International Soil Reference and Information Centre (ISRIC) database. Finally, we generated spatial distribution maps, namely, mean actual evapotranspiration (mm), mean water yield (mm), and the volume of water yield (km3) by pixel within the research area. According to the modelling results, the estimated value for mean annual precipitation was 295.08 mm, and 827.09 mm respectively for potential evapotranspiration, 229.13 mm for average actual evapotranspiration, 55.89 mm and 0.43 km3 for water yield within the study area. The result was slightly higher (15.1 mm) in terms of mean actual evapotranspiration compared to the results of previous studies, conducted in the same research area, and it was found that the potential water yield in the study area has also been impacted. However, the InVEST (Water Yield) model can be used for future research studies concerning water yield and resource in river basins as it is possible to further improve the model results by using in-situ measurement data and satellite products with high spatial accuracy as input data.