Accurate precipitation estimation is essential for hydrological research and applications. This study assesses the performance of IMERG V07, IMERG V06, and ERA5 over snow-ice-covered and snow-ice-free surfaces, using three years (2018-2020) of MRMS gauge-adjusted data as a reference. Besides the IMERG final products, the study compares precipitation estimates from the IR and PMW components of IMERG V07 under different surface and environmental conditions. This is particularly relevant as both IR and PMW products face significant limitations over cold regions, especially over snow-ice-covered surfaces, complicating the choice of products for integration in the merged products like IMERG. We found that IMERG V07 offers notable improvements over IMERG V06 and generally outperforms ERA5 over snow-ice-free regions, demonstrating enhanced accuracy in precipitation intensity and spatial coverage. Conversely, ERA5 outperforms IMERG V07 over snow-ice surfaces, highlighting remaining challenges in satellite-based precipitation products over cold regions. An evaluation of PMW precipitation products indicates that while they generally perform better than IR precipitation product in warmer conditions, IR precipitation is still invaluable in cold regions with snow-ice cover. Among the PMW products and over snow-ice surfaces, AMSR 2 underperforms other PMW precipitation products for most statistical metrics, while GMI, SSMIS, and MHS products perform relatively better than others. The results emphasize the need for improving spaceborne sensors and algorithms to improve their accuracy across diverse environmental conditions, especially over cold regions in the presence of snow or ice on the surface.

Most land surface models (LSMs) do not explicitly represent surface ponding, infiltration of ponded water, or the soil macropore effects on infiltration, percolation, and groundwater recharge. In this study, we implement a dual-permeability model (DPM) based on the mixed-form Richards’ equation, which solves pressure head continuously across unsaturated and saturated zones while conserves mass, into the Noah-MP LSM to represent slow flow through soil matrix and rapid flow through macropore networks. The model explicitly computes surface ponding depth, infiltration of ponded water, and runoff beyond a ponding threshold (infiltration-excess runoff) by switching the atmospheric boundary condition between head and flux boundary conditions. The new model also provides two optional soil water retention models of Van Genuchten (VG) and Brooks-Corey (BC). Model experiments over the conterminous US indicate that 1) surface ponded water and its runoff contribute substantially to seasonal variations in total water storage and peak flows in wet regions with low soil permeability (e.g., the Lower Mississippi River and surrounding regions), 2) the VG model produces drier topsoil with less soil surface evaporation than does the BC model with the Clapp-Hornberger parameters, especially during droughts and in dry regions, better matching remote sensing soil moisture, and 3) DMP produces more runoff with increased subsurface runoff, thereby improving the modeling skill at monthly scale over all subbasins of the Mississippi River, especially for low flow events. This study also highlights the importance of consistent representations of soil and plant hydraulics in Earth System Models to modeling ecosystem drought resilience.

Understanding factors controlling baseflow (or groundwater discharge) is critical for improving streamflow prediction skills in the arid southwest US. We used a version of Noah-MP with newly-advanced hydrology features and the Routing Application for Parallel computation of Discharge (RAPID) to investigate the impacts of uncertainties in representations of hydrological processes, soil hydraulic parameters, and precipitation data on baseflow production and streamflow prediction skill. We conducted model experiments by combining different options of hydrological processes, hydraulic parameters, and precipitation datasets in the southwest US. These experiments were driven by three gridded precipitation products: the NLDAS-2, the IMERG Final, and AORC. RAPID was then used to route Noah-MP modeled surface and subsurface runoff to predict daily streamflow at 390 USGS gauges. We evaluated the modeled ratio of baseflow to total streamflow (or baseflow index, BFI) against those derived from the USGS streamflow. Our results suggest that 1) soil water retention curve model plays a dominant role, with the Van-Genuchten hydraulic scheme reducing the overestimated BFI produced by the Brooks-Corey (also used by the National Water Model, NWM), 2) hydraulic parameters strongly affect streamflow prediction, a machine learning-based dataset captures the USGS BFI, showing a better performance than the optimized NWM by a median KGE of 21%, and 3) the ponding depth threshold that increases infiltration is preferred. Overall, most of our models with the advanced hydrology show a better performance in modeling BFI and thus a better skill in streamflow predictions than the optimized NWM in the dry southwestern river basins.