Analysis of Groundwater Recharge in Mongolian Drylands Using Composite Vadose Zone Modeling

Knowledge of groundwater recharge (GR) is important for the effective management of water resources under semi-arid continental climates. Unfortunately, studies and data in Mongolia are limited due to the constraints in funding and lack of research infrastructures. Currently, the wide accessibility of freely available global-scale digital datasets of physical and chemical soil properties, weather data, vegetation characteristics, and depths to the water table offers new tools and basic information that can support low-cost physically based and process-oriented models. Estimates of GR over 41 study sites in Mongolia were obtained using HYDRUS-1D in a 2-m-thick soil profile with root depths of either 0.30 or 0.97 m by exploiting the daily precipitation and biome-specific potential evapotranspiration values. The GR simulated by HYDRUS-1D arrives at the water table and becomes the actual GR with a lag time that has been calculated using a simplified form of the Richards equation and a traveling wave model. The mean annual precipitation ranges from 57 to 316 mm year(-1), and on average about 95% of it is lost by mean annual actual evapotranspiration. In the steppe region, the vegetation cover induces higher-than-normal actual transpiration losses and consequently lower GR. The mean annual GR rates span between 0.3 and 12.0 mm year(-1), while travel times range between 4 and 558 years. Model prediction uncertainty was quantified by comparing actual evapotranspiration and GR with available maps and by a sensitivity assessment of lag time to the soil moisture in the deep vadose zone. The partial least squares regression (PLSR) was used to evaluate the impact of available environmental properties in explaining the 47.1 and 59.1% variability of the spatially averaged mean annual GR and travel time, respectively. The most relevant contributors are clay content, aridity index, and leaf area index for GR, and depth to the water table and silt content for the lag time. In data-poor, arid, and semi-arid regions such as Mongolia, where the mean annual GR rates are low and poorly correlated to precipitation, the ever-increasing availability of world databases and remote sensing products offers promise in estimating GR.