Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas

Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981-2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed.

Comparison of Two Hydrological Models, HEC-HMS and SWAT in Runoff Estimation: Application to Huai Bang Sai Tropical Watershed, Thailand

In the present study, the streamflow simulation capacities between the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) were compared for the Huai Bang Sai (HBS) watershed in northeastern Thailand. During calibration (2007–2010) and validation (2011–2014), the SWAT model demonstrated a Coefficient of Determination (R2) and a Nash Sutcliffe Efficiency (NSE) of 0.83 and 0.82, and 0.78 and 0.77, respectively. During the same periods, the HEC-HMS model demonstrated values of 0.80 and 0.79, and 0.84 and 0.82. The exceedance probabilities at 10%, 40%, and 90% were 144.5, 14.5, and 0.9 mm in the flow duration curves (FDCs) obtained for observed flow. From the HEC-HMS and SWAT models, these indices yielded 109.0, 15.0, and 0.02 mm, and 123.5, 16.95, and 0.02 mm. These results inferred those high flows were captured well by the SWAT model, while medium flows were captured well by the HEC-HMS model. It is noteworthy that the low flows were accurately simulated by both models. Furthermore, dry and wet seasonal flows were simulated reasonably well by the SWAT model with slight under-predictions of 2.12% and 13.52% compared to the observed values. The HEC-HMS model under-predicted the dry and wet seasonal flows by 10.76% and 18.54% compared to observed flows. The results of the present study will provide valuable recommendations for the stakeholders of the HBS watershed to improve water usage policies. In addition, the present study will be helpful to select the most appropriate hydrologic model for humid tropical watersheds in Thailand and elsewhere in the world.

Hydrological Modeling of Karst Watershed Containing Subterranean River Using a Modified SWAT Model: A Case Study of the Daotian River Basin, Southwest China

Karst watershed refers to the total range of surface and underground recharge areas of rivers (including subterranean rivers and surface rivers) in karst areas. Karst water resources, as the primary source of domestic water supply in southwest China, are vital for the social and economic development of these regions. It is greatly significant to establish a high-precision hydrological model of karst watershed for guiding water resources management in karst areas. Choosing the Daotian river basin in the Wumeng Mountains of Southwest China as the study area, this paper proposed a method for simplifying karst subterranean rivers into surface rivers by modifying the digital elevation model (DEM) based on a field survey and tracer test. This method aims to solve the inconsistency between the topographical drainage divides and actual catchment boundaries in karst areas. The Soil and Water Assessment Tool (SWAT) model was modified by replacing the single-reservoir model in the groundwater module with a three-reservoir model to depict the constraints of multiple media on groundwater discharge in the karst system. The results show that the catchment areas beyond topographic watershed were effectively identified after simplifying subterranean rivers to surface rivers based on the modified DEM data, which ensured the accuracy of the basic model. For the calibration and two validation periods, the Nash–Sutcliffe efficiencies (NSE) of the modified SWAT model were 0.87, 0.83, and 0.85, respectively, and R2 were 0.88, 0.84, and 0.86, respectively. The NSE of the modified SWAT model was 0.09 higher than that of the original SWAT model in simulating baseflow, which effectively improved the simulation accuracy of daily runoff. In addition, the modified SWAT model had a lower uncertainty within the same parameter ranges than the original one. Therefore, the modified SWAT model is more applicable to karst watersheds.