Evaluating the Influence of Precipitation Forcing and Vegetation Dynamics on Hydrologic Simulations in Semi-Arid Snow-Dominated Catchments Using Noah-MP and RAPID
Sadaf Moghisi1, Ali Behrangi1, Aniket Gupta1, Mohammad Farmani1, Bo Svoma2, Guo-Yue Niu1
1Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
2Salt River Project (SRP), Phoenix, AZ, USA
Abstract:
The Salt and Verde River basins in central Arizona provide water to more than two million people, yet predicting streamflow in these snow-influenced, semi-arid watersheds remains challenging due to uncertainties in precipitation inputs and land surface processes. In this study, we use the Noah-MP land surface model coupled with the RAPID routing model to simulate hydrologic processes at 1 km spatial and hourly temporal resolution over the period 1981–2020.
We evaluate four precipitation datasets (AORC, NLDAS2, CONUS404, and IMERG) and assess the role of both static and dynamic vegetation representations. Model performance is evaluated against multiple observational datasets, including streamflow records, snow water equivalent (SNOTEL, SNODAS, and UA SWE), and leaf area index (LAI). We also examine precipitation biases and their impacts on hydrologic simulations.
Our results show that precipitation forcing strongly controls the magnitude and variability of both streamflow and snow water equivalent, while vegetation dynamics primarily influence snow processes through canopy interception. Dynamic vegetation improves the representation of seasonal vegetation changes and affects evapotranspiration and runoff timing.
These findings highlight the importance of accurate precipitation inputs and realistic vegetation representation—particularly canopy–snow interactions—for improving hydrologic predictions in snow-affected, semi-arid basins.