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Climate change-induced shifts in snow storage and snowmelt patterns pose risks for adverse impacts to people, the environment, and irrigated agriculture. Existing research primarily focuses on evaluating these risks to irrigated agriculture at large scales, overlooking the role of local context in shaping risk dynamics. Consequently, many “at-risk” areas lack insight into how adaptation strategies for managing risk through water supply augmentation or water conservation vary across contexts and over time. To address this gap, we develop a comprehensive index for evaluating irrigated agriculture's risk and adaptation potential to changes in snow at local scales and apply it throughout the western US. Results confirm trends toward escalating risk for changes in snow storage and snowmelt patterns over the century. However, substantial heterogeneity in the extent and drivers of risk exists due to variability in localized interactions between declines in water supply (approximately −9% ± 13% by 2100) and increased agricultural demand (approximately 7% ± 5% by 2100). Despite an existing focus on supply augmentation as a critical adaptation strategy to reduce risk, we show its effectiveness diminishes for many areas over time, declining to an average of −54% of historical augmentation potential by 2100. Conserving water through historical changes in crop acreage and type emerges as a more stable adaptation measure, reducing demand by 7%–8% regardless of time. While particularly relevant for higher elevation, less intensive agricultural settings in snowmelt-dependent regions, findings underscore the need for strategies that support local-scale, context-appropriate adaptation to effectively manage escalating risk as snow changes.

Hydrological drought is one of the main hydroclimatic hazards worldwide, affecting water availability, ecosystems and socioeconomic activities. This phenomenon is commonly characterized by the Standardized Streamflow Index (SSI), which is widely used because of its straightforward formulation and calculation. Nevertheless, there is limited understanding of what the SSI actually reveals about how climate anomalies propagate through the terrestrial water cycle. To find possible explanations, we implemented the SUMMA hydrological model coupled with the mizuRoute routing model in six hydroclimatically different case study basins located on the western slopes of the extratropical Andes, and examined correlations between the SSI (computed from the models for 1, 3 and 6-month time scales) and potential explanatory variables – including precipitation and simulated catchment-scale storages – aggregated at different time scales. Additionally, we analyzed the impacts of adopting commonly used time scales on propagation analyses of specific drought events – from meteorological to soil moisture and hydrological drought – with focus on their duration and intensity. The results reveal that the choice of time scale for the SSI has larger effects on correlations with explanatory variables in rainfall-dominated regimes compared to snowmelt-driven basins, especially when simulated fluxes and storages are aggregated to time scales longer than 9 months. In all the basins analyzed, the strongest relationships (Spearman rank correlation values over 0.7) were obtained when using 6-month aggregations to compute the SSI and 9–12 months to compute the explanatory variables, excepting aquifer storage in snowmelt-driven basins. Finally, the results show that the trajectories of drought propagation obtained with the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the SSI may change drastically with the selection of time scale. Overall, this study highlights the need for caution when selecting standardized drought indices and associated time scales, since their choice impacts event characterizations, monitoring and propagation analyses. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

The Office of Hydrologic Development (OHD) of the U.S. National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) conducted the two phases of the Distributed Model Intercomparison Project (DMIP) as cost-effective studies to guide the transition to spatially distributed hydrologic modeling for operational forecasting at NWS River Forecast Centers (RFCs). Phase 2 of the Distributed Model Intercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the U.S. NWS as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models forced by high quality operational data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for the testing, evaluation, and improvement of distributed models.DMIP 2 contained experiments in two regions: in the DMIP 1 Oklahoma basins, and second, in two basins in the Sierra Nevada Mountains in the western USA. This paper presents the overview and results of the DMIP 2 experiments conducted for the two Sierra Nevada basins. Simulations from five independent groups from France, Italy, Spain and the USA were analyzed. Experiments included comparison of lumped and distributed model streamflow simulations generated with uncalibrated and calibrated parameters, and simulations of snow water equivalent (SWE) at interior locations. As in other phases of DMIP, the participant simulations were evaluated against observed hourly streamflow and SWE data and compared with simulations provided by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on a run-period and event basis. Differences between uncalibrated and calibrated model simulations are assessed.Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites.Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographically-enhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations. © 2013.

Spatially-distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model calibration results in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model, coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin Hypercube Sampling (LHS) method to generate 3500 different VIC model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing case), and subsequently compared against outputs from four routing schemes (Unit Hydrograph, Lagrangian Kinematic Wave, Muskingum-Cunge and Diffusive Wave) applied with five different routing time steps (1, 3, 6, 12 and 24 hours). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily and even monthly time scales. The maximum Kling-Gupta Efficiency (KGE) obtained for daily streamflow increases from 0.73 (no routing) to 0.82 (for the best scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in frequency curves and lowers the segment with high discharge values in the flow duration curve (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration. [ABSTRACT FROM AUTHOR]