Change in Hydrological Regimes and Extremes from the Impact of Climate Change in the Largest Tributary of the Tonle Sap Lake Basin

Abstract

The Tonle Sap Lake (TSL) Basins of the Lower Mekong are one of the world’s most productive ecosystems and have recently been disturbed by climate change. The SWAT (Soil & Water Assessment Tool) hydrological model is utilized to investigate the effect of future climate scenarios. This study focused on two climate scenarios (RCP2.6 and RCP8.5) with three GCMs (GFDL-CM3, GISS-E2-R-CC, and IPSL-CM5A-MR) and their impact on the hydrological process and extremes in the Sen River Basin, the largest tributary of the TSL basin. The annual precipitation, surface runoff, lateral flow, groundwater flow, and total water yield are projected to decrease in both the near-future (2020–2040) and mid-future period (2050–2070), while actual evapotranspiration is projected to increase by 3.3% and 5.3%. Monthly precipitation is projected to increase by 11.2% during the rainy season and decrease by 7.5% during the dry season. Two climate models (GISS and IPSL model) lead to decreases in 1-day, 3-day, 7-day, 30-day, and 90-day maximum flows and minimum flows flow. Thus, the prediction results depend on the climate model used.

Metrics Assessment and Streamflow Modeling under Changing Climate in a Data-Scarce Heterogeneous Region: A Case Study of the Kabul River Basin

“…This model has been used globally in small and medium watersheds [40] to large mountainous watersheds [24]. SWAT has also proven to be very effective in simulating the eventual effects of climate change (CC) scenarios on streamflow [41], erosion [42], and nutrient transport [43], etc. The mathematical expression of the water balance equation, which is the core driving engine of the SWAT model, is given below in Equation (1) [44]:…”

Prolonged and Severe Drought in the Most Dammed Tributaries of the Lower Mekong Basin

“…We calibrated and validated SWAT using observed streamflow data from 2001 to 2010, as well as observed streamflow data from 2011 to 2019, respectively. We manually calibrated the model by altering sensitive model parameters, a process that is commonly used (see Arnold et al [32], Ich et al [38], Sok et al [39], and Nair et al [40]). For the Sesan River, we calibrated one parameter, "effective hydraulic conductivity in main channel alluvium," while we adjusted eight sensitive parameters for the Srepok River model (see Appendix A, Table A1).…”