The impact of rainfall distribution methods on streamflow throughout multiple elevations in the Rocky Mountains using the APEX model—Price River watershed, Utah

The hydrology of mountainous watersheds in the western United States is significantly influenced by snow year-round. It is widely known that topography affects precipitation; however, the knowledge of how watershed rainfall designation methods affect streamflow is not well understood for high-relief areas. The objectives of this study were to assess the predictive capability of the Agricultural Policy/Environmental eXtender (APEX) model to simulate streamflow in a snowmelt-dominated watershed with high spatial rainfall variability through (a) allocating weather stations to sub-basins based on a conventional Thiessen polygon method (CM) or a rainfall-elevation–based input (RE) and using an areal average Parameter-Elevation Regression on Independent Slopes Model (PRISM) rainfall designation and (b) improving the snowmelt processes in the Price River watershed, Utah. The updated APEX model with snowmelt parameters significantly improved spring flood simulation. The RE was the most robust method in snowmelt and seasonal streamflow simulations compared with the CM and PRISM rainfall designations. Adapting the APEX model to simulate snow-dominant complex terrains will provide crucial water quantity and quality predictions for reliable environmental and watershed management assessment

APEX-MODFLOW: A New integrated model to simulate hydrological processes in watershed systems

APEX (Agricultural Policy/Environmental eXtender) is an oft-used agroecosystem model but has limited use in groundwater-driven watersheds due to a simplistic representation of groundwater processes. This paper presents the linkage of APEX and the groundwater flow model MODFLOW into a single modeling code. The mapping of recharge, groundwater head, and groundwater-surface water interactions are handled internally via subroutines. The APEX-MODFLOW model is applied to three watersheds in the United States for testing code accuracy and hydrologic state variables and fluxes: the Animas River Watershed, Colorado and New Mexico (3543 km2); the Price River Watershed, Utah (4886 km2); and the Middle Bosque River Watershed, Texas (470 km2). Whereas the hydrology of the Animas River and Price River watersheds is driven by snowmelt and spring runoff, the hydrology of the Middle Bosque River Watershed is driven by summer thunderstorms. The model can be used for scenario analysis in groundwater-driven watersheds

Innovative approach to prognostic plant growth modeling in SWAT+ for forest and perennial vegetation in tropical and Sub-Tropical climates

The growth of vegetation in ecosystems is influenced by hydro-climatic factors and biogeochemical cycles. Accurately modeling annual vegetation growth dynamics is essential for eco-hydrological modeling to estimate watershed hydrologic balance and nutrient cycling under changing environmental conditions. The Soil and Water Assessment Tool (SWAT) and its upgraded version SWAT+ are process-oriented river basin models widely used. However, the temperature-based approach to plant growth simulation in tropical regions has limitations due to the importance of soil moisture availability as a key driver of plant growth. This study proposes an innovative approach that incorporates a proxy soil moisture availability index based on monthly rainfall and potential evapotranspiration ratio. This approach identifies the start of the growing season within prescribed transition months and controls leaf drop rate throughout the year, a crucial process during leaf senescence. We evaluated the reliability of this approach by comparing SWAT+ simulated Leaf Area Index (LAI), evapotranspiration (ET), and net primary productivity (NPP) with benchmark remote sensing-based datasets for three landcover classes in the Mara River Basin (Kenya/Tanzania). Our results demonstrate that the improved plant growth module in SWAT+ developed in this study can simulate temporal vegetation growth dynamics of evergreen forest, savanna grassland, and shrubland land cover types consistently with good correlations (r > 0.5) and low average bias (<10%). Thus, the SWAT+ model with the enhanced plant growth module can be a robust tool for investigating the coupled carbon, nutrient, and water cycling in tropical and sub-tropical climates