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Lakes and reservoirs are an integral part of the terrestrial water cycle. In this work, we present the implementation of different water balance models of lakes and reservoirs into mizuRoute, a vector‐based routing model, termed mizuRoute‐Lake. As the main advantage of mizuRoute‐Lake, users can choose between various parametric models implemented in mizuRoute‐Lake. So far, three parametric models of lake and reservoir water balance, namely Hanasaki, HYPE, and Döll are implemented in mizuRoute‐Lake. In general, the parametric models relate the outflow from lakes or reservoirs to the storage and various parameters including inflow, demand, volume of storage, etc. Additionally, this flexibility allows users to easily evaluate and compare the effect of various water balance models for a lake without needing to reconfigure the routing model or change the parameters of other lakes or reservoirs in the modeling domain. Users can also use existing data such as historical observations or water management models to specify the behavior of a selected number of lakes and reservoirs within the modeling domain using the data‐driven capability of mizuRoute‐Lake. We demonstrate the flexibility of mizuRoute‐Lake by presenting global, regional, and local scale applications. The development of mizuRoute‐Lake paves the way for better integration of water management models, locally measured, and remotely sensed data sets in the context of Earth system modeling. Lakes and reservoirs impact the hydrological cycle. While there are many lake and reservoir models, a modeling framework typically encompasses a single selected lake and/or reservoir model representation. Including various lake discharge models in one model framework is useful for many applications of water resources. In this study, we present the implementation of various lake and reservoir water balance models in the mizuRoute‐Lake. mizuRoute‐Lake provides the flexibility to use and evaluate different lake and reservoir water balance models in river basins. Such flexibility can facilitate the integration with Earth system models, land surface models, water management models, and in situ or remote sensing observations. Additionally, it can help shed light on often neglected interactions and uncertainties in lake and reservoir models and their parameterizations within the hydrological cycle. mizuRoute‐Lake offers the flexibility to select from multiple parametric models for lakes and reservoirs' water balanceUsers can leverage existing data, historical observations, water management models, and remotely sensed data, to specify lake behaviorThe versatility of mizuRoute‐Lake is demonstrated through its application at different scales, global to local mizuRoute‐Lake offers the flexibility to select from multiple parametric models for lakes and reservoirs' water balance Users can leverage existing data, historical observations, water management models, and remotely sensed data, to specify lake behavior The versatility of mizuRoute‐Lake is demonstrated through its application at different scales, global to local

This study presents diagnostic evaluation of two large‐domain hydrologic models: the mesoscale Hydrologic Model (mHM) and the Variable Infiltration Capacity (VIC) over the contiguous United States (CONUS). These models have been calibrated using the Multiscale Parameter Regionalization scheme in a joint, multibasin approach using 492 medium‐sized basins across the CONUS yielding spatially distributed model parameter sets. The mHM simulations are used as a performance benchmark to examine performance deficiencies in the VIC model. We find that after calibration to streamflow, VIC generally overestimates the magnitude and temporal variability of evapotranspiration (ET) as compared to mHM as well as the FLUXNET observation‐based ET product, resulting in underestimation of the mean and variability of runoff. We perform a controlled calibration experiment to investigate the effect of varying number of transfer function parameters in mHM and to enable a fair comparison between both models (14 and 48 for mHM vs. 14 for VIC). Results of this experiment show similar behavior of mHM with 14 and 48 parameters. Furthermore, we diagnose the internal functioning of the VIC model by looking at the relationship of the evaporative fraction versus the degree of soil saturation and compare it with that of the mHM model, which has a different model structure, a prescribed nonlinear relationship between these variables and exhibits better model skill than VIC. Despite these limitations, the VIC‐based CONUS‐wide calibration constrained against streamflow exhibits better ET skill as compared to two preexisting independent VIC studies. Continental domain fluxes and states derived by two calibrated hydrologic models (mHM and VIC) are evaluated to diagnose model functional deficienciesThe more skillful (benchmark) model can help to identify the other model's functional deficienciesThe new VIC parameters based on streamflow calibration exhibit better ET skill compared to two preexisting VIC configurations

Estimating spatially distributed parameters remains one of the biggest challenges for large‐domain hydrologic modeling. Many large‐domain modeling efforts rely on spatially inconsistent parameter fields, e.g., patchwork patterns resulting from individual basin calibrations, parameter fields generated through default transfer functions that relate geophysical attributes to model parameters, or spatially constant, default parameter values. This paper provides an initial assessment of a multiscale parameter regionalization (MPR) method over large geographical domains to derive seamless parameters in a spatially consistent manner. MPR applies transfer functions at the native scale of the geophysical data, and then scales these model parameters to the desired model resolution. We developed a stand‐alone framework called MPR‐flex for multimodel use and applied MPR‐flex to the variable infiltration capacity model to produce hydrologic simulations over the contiguous United States (CONUS). We first independently calibrate 531 basins across CONUS to obtain a performance benchmark for each basin. To derive the CONUS parameter fields, we perform a joint MPR calibration using all but the poorest behaved basins to obtain a single set of transfer function parameters that are applied to the entire CONUS. Results show that CONUS‐wide calibration has similar performance compared to previous simulations using a patchwork quilt of partially calibrated parameter sets, but without the spatial discontinuities in parameters that characterize some previous CONUS‐domain model simulations. Several avenues to improve CONUS‐wide calibration remain, including selection of calibration basins, objective function formulation, as well as MPR‐flex improvements including transfer function formulations and scaling operator optimization. Hydrologic models compute surface and subsurface water transmission and storage using many equations with parameters that vary spatially. Many parameter values are commonly estimated through systematic adjustment of parameter values to match historical streamflow over a watershed where a stream gauge is available. This method is difficult to apply to large domains contiguously. Consequently, many current large scale hydrologic assessments rely on spatially inconsistent parameter fields such as a patchwork quilt resulting from individual basin calibration or spatially constant parameters resulting from the adoption of default estimates. Facing this challenge, we developed a parameter estimation methodology that incorporates effects of landscape properties on parameter values to enable continental‐domain applications of hydrologic models in a spatially consistent way. We applied this methodology to a model that has been used for many hydrologic studies such as climate impact studies to generate a spatially smooth parameter set over the contiguous United States. Simulations using this parameter set produce similar accuracy to the previously developed parameter set. The Multiscale Parameter Regionalization (MPR) framework facilitates estimates of consistent hydrologic model parameters over large domainsA model‐independent MPR tool was developed for use with multiple modelsImprovements of model simulation skill require enhancement of MPR and calibration strategies

The implementation of elevation bands is a popular strategy to account for topographic heterogeneities in snowpack modeling. Here, we characterize the implications of subgrid temperature distribution along elevation bands through numerical experiments in nine mountainous basins of the Andes Cordillera, central Chile. Specifically, we analyze outputs from the Variable Infiltration Capacity model with six different setups: no elevation bands (i.e., flat grid cells; benchmark model) and elevation bands with vertical discretizations of 1,000, 750, 500, 200, and 100 m. The analyses are conducted in a wet period (April/1982–March/1987), dry period (April/2010–March/2015) and a climatological period (April/1982–March/2015). The results show that adding elevation bands yields little variations in simulated monthly or daily streamflow; however, there are important effects on the partitioning of precipitation between snowfall and rainfall, snowmelt, sublimation, and the spatial variability in 1 September snow water equivalent (SWE), suggesting a form of model‐structure equifinality. Vertical temperature distribution generally yields less basin‐averaged snowmelt and more (less) catchment‐scale sublimation across water‐limited (energy‐limited) basins. Further, the implications of subgrid temperature distribution vary with the analysis period: fluxes are more affected during the wet period, while variations in 1 September SWE are more noticeable during the dry period. In general, the effects of topographic temperature distribution are reduced with increasing vertical discretization and can differ among catchments. Finally, the grid cells that yield the largest sensitivities to vertical discretization have relatively more humid conditions, large intra‐annual variations in the water/energy budget, lower mean altitude, elevation ranges >1,000 m, and steep slopes (>15°). Spatially distributed computer‐based models are widely used to make predictions on water availability. In mountainous areas, it is common to distribute air temperature using elevation bands in modeling units with complex topography; however, the effects of the selected number of bands and/or elevation range on model results have not been assessed in detail. We use a suite of diverse Andean basins to document how the vertical distribution of air temperature along elevation bands affects the simulation of the water cycle at different spatial scales. Our results show that, although the incorporation of air temperature variability has little effects on the simulation of discharge at the basin outlets, similar results can arise from different spatial distributions of rainfall, snowfall, snowmelt, sublimation, and maximum annual accumulation. The implications of adding elevation bands may vary with the climate conditions (i.e., wet/dry) of the analysis period. Finally, we identify climate seasonality, mean altitude, elevation range, and slope as the key variables that should be examined carefully to decide where (i.e., which grid cells) the choice of elevation band configuration should be made with more caution. Subgrid temperature distribution does not greatly affect basin‐scale runoff but perturbs other water fluxes and their spatial variabilitySimulated 1 September snow water equivalent is more affected by subgrid temperature distribution in dry periods, without clear connections with elevation band widthTemperature distribution is more relevant for grid cells with pronounced seasonality, low altitude, high‐elevation ranges, and steep slopes Subgrid temperature distribution does not greatly affect basin‐scale runoff but perturbs other water fluxes and their spatial variability Simulated 1 September snow water equivalent is more affected by subgrid temperature distribution in dry periods, without clear connections with elevation band width Temperature distribution is more relevant for grid cells with pronounced seasonality, low altitude, high‐elevation ranges, and steep slopes

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash‐Sutcliffe Efficiency (NSE) and the Kling‐Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large‐sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models. We provide tools to quantify the sampling uncertainty in the Nash‐Sutcliffe Efficiency (NSE) and Kling Gupta Efficiency (KGE) metricsOur large‐sample analysis demonstrates that there is substantial sampling uncertainty in the estimates of NSE and KGEWe prescribe further research to improve the estimation interpretation, and use of system‐scale performance metrics in hydrologic modeling We provide tools to quantify the sampling uncertainty in the Nash‐Sutcliffe Efficiency (NSE) and Kling Gupta Efficiency (KGE) metrics Our large‐sample analysis demonstrates that there is substantial sampling uncertainty in the estimates of NSE and KGE We prescribe further research to improve the estimation interpretation, and use of system‐scale performance metrics in hydrologic modeling

Snow cover area affects snowmelt, soil moisture, evapotranspiration, and ultimately streamflow. For the Distributed Model Intercomparison Project – Phase 2 Western basins, we assimilate satellite‐based fractional snow cover area (fSCA) from the Moderate Resolution Imaging Spectroradiometer, or MODIS, into the National Weather Service (NWS) SNOW‐17 model. This model is coupled with the NWS Sacramento Heat Transfer (SAC‐HT) model inside the National Aeronautics and Space Administration's (NASA) Land Information System. SNOW‐17 computes fSCA from snow water equivalent (SWE) values using an areal depletion curve. Using a direct insertion, we assimilate fSCAs in two fully distributed ways: (1) we update the curve by attempting SWE preservation, and (2) we reconstruct SWEs using the curve. The preceding are refinements of an existing simple, conceptually guided NWS algorithm. Satellite fSCA over dense forests inadequately accounts for below‐canopy snow, degrading simulated streamflow upon assimilation during snowmelt. Accordingly, we implement a below‐canopy allowance during assimilation. This simplistic allowance and direct insertion are found to be inadequate for improving calibrated results, still degrading them as mentioned above. However, for streamflow volume for the uncalibrated runs, we obtain: (1) substantial to major improvements (64–81%) as a percentage of the control run residuals (or distance from observations), and (2) minor improvements (16–22%) as a percentage of observed values. We highlight the need for detailed representations of canopy‐snow optical radiative transfer processes in mountainous, dense forest regions if assimilation‐based improvements are to be seen in calibrated runs over these areas. Unaccounted below‐canopy snow extent importantBelow‐canopy snow accounting improves streamflow simulationsModels should incorporate detailed snow and vegetation representations