We estimate net groundwater storage change in the Central Valley from April 2002 to September 2016 as the difference between inflows and outflows, precipitation, evapotranspiration, and changes in soil moisture and surface water storage. We also estimate total water storage change attributable to groundwater change using Gravity Recovery and Climate Experiment (GRACE) satellite data, which should be equivalent to our water balance estimates. Over two drought periods within our 14‐1/2 years study period (January 2007 to December 2009 and October 2012 to September 2016), we estimate from our water balance that a total of 16.5 km3 and 40.0 km3 of groundwater was lost, respectively. Our water balance‐based estimate of the overall groundwater loss over the 14‐1/2 years is −20.7 km3, which includes substantial recovery during nondrought periods The estimated rate of groundwater loss is greater during the recent drought (10.0 ± 0.2 versus 5.5 ± 0.3 km3/yr) than in the 2007–2009 drought, due to lower net inflows, a transition from row crops to trees, and higher crop water use, notwithstanding a reduction in irrigated area. The GRACE estimates of groundwater loss (−5.0 km3/yr for both water balance and GRACE during 2007–2009, and −11.2 km3/yr for GRACE versus −10 km3/yr for water balance during 2012–2016) are quite consistent for the two methods. However, over the entire study period, the GRACE‐based groundwater loss estimate is almost triple that from the water balance, mostly because GRACE does not indicate the between‐drought groundwater recovery that is inferred from our water balance.

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Despite offering spatially and temporally continuous measurements, the use of remotely sensed P and E in data‐scarce catchments is hindered by the lack of ground‐based measurements that enable comprehensive validation. This study proposes a novel validation framework that characterizes the combined error in the long‐term average estimates of remotely sensed P and E by making use of the Budyko hypothesis, specifically Fu's equation. A Root Mean Square Error (RMSE)‐based error metric that is capable of translating individual biases in P and E estimates onto the Budyko space is developed. A controlled sensitivity experiment using data from Model Parameter Estimation Experiment (MOPEX) catchments in the United States showed that the developed error metric is more sensitive to biases in P compared to biases in estimates of E. Validating the framework using combinations of different satellite‐based estimates of P and E revealed that the framework succeeds in arriving at the same conclusions as a traditional validation method with regards to the quality of P and E data sets. The framework offers a physically consistent, parametrically efficient basis for the selection of remotely sensed P and E data sets for hydrologic studies. Due to lack of consideration for catchment storage in the formulation of Fu's equation, the developed error metric is limited to long temporal time scales. As a result, the error metric is capable of characterizing the bias in P and E data sets and not the variance.

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Dry conditions in 2013-16 in much of the western United States were responsible for severe drought and led to an exceptional fire season in the Pacific Northwest in 2015. Winter 2015/16 was forecasted to relieve drought in the southern portion of the region as a result of increased precipitation due to a very strong El Niño signal. A student forecasting challenge is summarized in which forecasts of winter hydroclimate across the western United States were made on 1 January 2016 for the winter hydroclimate using several dynamical and statistical forecast methods. They show that the precipitation forecasts had a large spread and none were skillful, while anomalously high observed temperatures were forecasted with a higher skill and precision. The poor forecast performance, particularly for precipitation, is traceable to high uncertainty in the North American Multi-Model Ensemble (NMME) forecast, which appears to be related to the inability of the models to predict an atmospheric blocking pattern over the region. It is found that strong El Niño sensitivities in dynamical models resulted in an overprediction of precipitation in the southern part of the domain. The results suggest the need for a more detailed attribution study of the anomalous meteorological patterns of the 2015/16 El Niño event compared to previous major events.

In data‐scarce regions, seasonal hydropower planning is hindered by the unavailability of reliable long‐term streamflow observations, which are required for the construction of inflow scenario trees. In this study, we develop a methodological framework to overcome the problem of streamflow data scarcity by combining precipitation forecasts from ensemble numerical weather prediction models, spatially distributed hydrologic models, and stochastic programming. We use evapotranspiration as a proxy for streamflow in generating reliable reservoir inflow forecasts. Using the framework, we compare three different formulations of inflow scenario structures and their applicability to data‐scarce regions: (1) a single deterministic forecast, (2) a scenario fan with the first stage deterministic, and (3) a scenario fan with all stages stochastic. We apply the framework to a cascade of two reservoirs in the Omo‐Gibe River basin in Ethiopia. Future reservoir inflows are generated using a 3‐model 30‐member ensemble seasonal precipitation forecast from the North American Multimodel Ensemble and the Noah‐MP hydrologic model. We then perform deterministic and stochastic optimization for hydropower operation and planning. Comparing the results from the three different inflow scenario structures, we observe that the uncertainty in reservoir inflows is significant only for the dry stages of the planning horizon. In addition, we find that the impact of model parameter uncertainty on hydropower production is significant (0.14–0.18×106 MWh).

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Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, Et) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (St), i.e., the reduction of Et from its theoretical maximum. Then, we embed the new St formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the St formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate St and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.