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The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub‐grid or sub‐watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation‐runoff modelling system (NHM‐PRMS) represents the sub‐grid variability of SWE with snow depletion curves (SDCs), which relate snow‐covered area to watershed‐mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM‐PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM‐PRMS was simulated at a daily time step for each SDC over a 14‐year period. Results highlight that increasing the sub‐grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day−1 resulted in decreases in runoff ratio by 1.8% on average in snow‐dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water‐limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications. [ABSTRACT FROM AUTHOR]

This study explores the viability of using simulated monthly runoff as a proxy for landscape‐scale surface‐depression storage processes simulated by the United States Geological Survey's National Hydrologic Model (NHM) infrastructure across the conterminous United States (CONUS). Two different temporal resolution model codes (daily and monthly) were run in the NHM with the same spatial discretization. Simulated values of daily surface‐depression storage (treated as a decimal fraction of maximum volume) as computed by the daily Precipitation‐Runoff Modeling System (NHM‐PRMS) and normalized runoff (0 to 1) as computed by the Monthly Water Balance Model (NHM‐MWBM) were aggregated to monthly and annual values for each hydrologic response unit (HRU) in the CONUS geospatial fabric (HRU; n = 109,951) and analyzed using Spearman's rank correlation test. Correlations between simulated runoff and surface‐depression storage aggregated to monthly and annual values were compared to identify where which time scale had relatively higher correlation values across the CONUS. Results show Spearman's rank values >0.75 (highly correlated) for the monthly time scale in 28,279 HRUs (53.35%) compared to the annual time scale in 41,655 HRUs (78.58%). The geographic distribution of HRUs with highly correlated monthly values show areas where surface‐depression storage features are known to be common (e.g., Prairie Pothole Region, Florida). Research Impact Statement: Correlation between simulated runoff and surface depression storage using continental‐extent models to improve understanding and representation of surface‐depression storage processes. [ABSTRACT FROM AUTHOR]

In this research, we characterized the changes in the Gravity Recovery and Climate Experiment (GRACE) monthly total water storage anomaly (TWSA) in 18 surface basins and 12 principal aquifers in the conterminous United States during 2003–2016. Regions with high variability in storage were identified. Ten basins and four aquifers showed significant changes in storage. Eight surface basins and eight aquifers were found to show decadal stability in storage. A pixel-based analysis of storage showed that the New England basin and North Atlantic Coastal Plain aquifer showed the largest area under positive storage change. By contrast, the Lower Colorado and California basins showed the largest area under negative change. This study found that historically wetter regions (with more storage) are becoming wetter, and drier regions (with less storage) are becoming drier. Fourier analysis of the GRACE data showed that while all basins exhibited prominent annual periodicities, significant sub-annual and multi-annual cycles also exist in some basins. The storage turnover period was estimated to range between 6 and 12 months. The primary explanatory variable (PEV) of TWSA was identified for each region. This study provides new insights on several aspects of basin or aquifer storage that are important for understanding basin and aquifer hydrology. [ABSTRACT FROM AUTHOR]