Basin-scale Evaluation of the Noah-MP Land Surface Model for Runoff and Snow Generation in the Missouri River Basin: Insights and Recommendations for Parameterization Scheme Selection

Abstract. Process-based land surface models, such as the Noah-Multiparameterization (Noah-MP) model, are widelyused for large-scale hydrologic simulations because of their flexibility in selecting multiple parameterization schemes. However, limited guidance on choosing appropriate configurations constrains their reliability in representing runoff and snowmelt dynamics across diverse land-cover and snow conditions. This study evaluates the defaultparameterization scheme and four alternative parameterization schemes in the Noah-MP land surface model, includingRunoff and Groundwater (RUN), Surface Exchange Coefficient for Heat (SFC), Frozen Soil Permeability (INF), and Snow/Soil Temperature Time Scheme (STC), across 50 Hydro-Climate Data Networks (HCDNs) in the MissouriRiver Basin. Model performance was evaluated using USGS streamflow observations and snow water equivalent (SWE) estimates from the University of Arizona dataset for 2014 to 2023. Results showed that the alternative schemes generally improved runoff simulation compared to the default scheme through better representing key hydrologicaland thermodynamic processes. Specifically, the RUN, SFC, INF, and STC experiments improved the Kling–GuptaEfficiency (KGE) by 0.19, 0.37, 0.48, and 0.14, respectively in representative subbasins, through enhancedgroundwater dynamics, reduced evapotranspiration bias, improved rapid runoff response, and more accurate SWE evolution. SWE evaluation further indicates that the STC experiment reduced the mean bias of the April–July runoff- to-maximum SWE ratio by 12–32 % in high-elevation subbasins, reflecting improved representation of snowmeltdriven runoff. These results highlight the importance of basin-specific parameterization schemes within Noah-MP toimprove hydrological prediction and water management across diverse hydroclimatic regions. The findings further indicate optimal parameterization schemes for different climates, land cover, and snow regimes.