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This paper describes a public domain semiautomated data screening and quality assurance processing tool useful for both discrete and continuous data. This application serves both a field research project assessing salinity and methylmercury drainage load control options and the requirements of a public domain watershed-based water quality forecasting model used to provide decision support for short-term salinity management in the San Joaquin River Basin of California. The US Army Corps of Engineers HEC-DSSVue data management and visualization platform was enhanced in the former application with Python scripts that performed the basic data quality assurance functions. Analogous tools were developed for use on the HOSTGATOR cloud server using the same Python scripts and algorithms to migrate quality-controlled data directly to a server for use by the Watershed Analysis Risk Management Framework (WARMF) water quality forecasting model. The Python scripts for both applications are easily adaptable by other potential users who do not currently have the resources to implement an enterprise-level hydrological data management and real-time quality assurance software system. [ABSTRACT FROM AUTHOR]

In California, after three decades of water market development, volume of water traded in the market remains lower than desired. Total volume of water traded annually accounts for just around 2%–5% of total water used. Vast difference in the marginal values of water in agriculture and urban or environmental uses suggest that significant economic gains can be achieved by easing the barriers to trading water across sectors. This paper draws attention to the role of state and local policies in restricting water transfers. Using novel, purpose-built, satellite-mapped data of a sample of water-trading irrigation districts in California, this paper shows how districts' water transfer decisions are made in response to conflicting pressures of drought-induced high-water demand against the need to support the agricultural economy and the environment in water-exporting areas. The time period under study is 1991–2019, which spans the entire history of water market activity in California from inception to maturity, and helps illustrate the role of changing local and state policies on irrigation districts' water market participation. Despite policy-induced restrictions, this paper shows that sample irrigation districts are highly responsive to demand in water-buying areas, have increased the volume and frequency of water transfers, particularly in the last decade. This paper highlights important research and policy questions about the distribution of economic and environmental effects of water transfers in source area communities that could affect the future potential of water markets in easing structural and drought-induced water scarcity in California. [ABSTRACT FROM AUTHOR]

Changes in demand, various hydrological inputs, and environmental stressors are among the issues that reservoir managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increases, it becomes more difficult to effectively represent a real-world system using traditional methods such as dynamic programming and stochastic dynamic programming for determining the best reservoir operation policy. One of the challenges is the "curse of dimensionality," which means the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with respect to the number of input variables (i.e., dimensionality) of the function. Deep reinforcement learning (DRL) is an intelligent approach to overcome the curses of stochastic optimization problems for reservoir operation policy decisions. To our knowledge, this study is the first attempt that examines various novel DRL continuous-action policy gradient methods, including deep deterministic policy gradients, twin delayed DDPG (TD3), and two different versions of Soft Actor-Critic (SAC18 and SAC19) for optimizing reservoir operation policy. In this study, multiple DRL techniques were implemented to find an optimal operation policy for Folsom Reservoir in California. The reservoir system supplies agricultural, municipal, hydropower, and environmental flow demands and flood control operations to the City of Sacramento. Analysis suggests that the TD3 and SAC are robust to meet the Folsom Reservoir's demands and optimize reservoir operation policies. Experiments on continuous-action spaces of reservoir policy decisions demonstrated that the DRL techniques can efficiently learn strategic policies in spaces and can overcome the curse of dimensionality and modeling. [ABSTRACT FROM AUTHOR]

Conservation in working landscapes is an important aspect of stream restoration. Landowners with agricultural water rights are increasingly adding instream flow as a beneficial use of their allocated water. However, stream restoration projects commonly focus on the quantity of water dedicated to the environment and overlook water quality. For cold-water ecosystems, the preservation of natural thermal regimes is a key to conservation efforts in the face of a changing climate. This study explored the effects of increasing stream flow using alternative water sources (e.g., instream runoff versus offchannel springs) to enhance cold-water stream habitat on a working cattle ranch. Using empirical data to create a steady-state stream flow and water temperature model, we evaluated temperature benefits and management trade-offs for ecological and agricultural water use. The modeled results showed that the source of water and the method used to deliver water to the channel affected the quality and extent of cold-water habitat. Offchannel spring water cooled 7-day average maximum temperatures and 7-day average mean temperatures by as much as 3.7°C and 2.9°C, respectively, but only when potential offchannel aquatic habitat was sacrificed for mainstem habitat. When offchannel, spring-fed habitat was provided, the value of spring water to the main channel decreased. Our findings suggest that source water thermal quality is of paramount importance for enhancing ecosystem function and improving cold-water habitat in working landscapes. In addition, the restoration of a historic spring channel may provide added ecosystem benefits by enhancing aquatic habitat for cold-water species. In the western United States, understanding the best possible uses for high-quality cold-water sources may provide the greatest benefits to the environment and adjacent working landscapes. Whereas water purchases and leases tend to focus on water quantity, this study could direct such investments toward higher value sources for the environment. In places like California, where drought has resulted in water right curtailments for agricultural diverters, understanding the best uses for high quality cold water compared with other sources can provide the greatest benefits to both agriculture and the environment. [ABSTRACT FROM AUTHOR]

The evaluation of water systems based on historical statistics is problematic when shifts in the hydrologic system occur due to a changing climate. An explicit link to thermodynamic and dynamic pathways in the climate system that may control water system performance is missing from current operational policies prescribed by regulation manuals within the US. In response, this study contributes an extended version of an existing weather regime (WR)–based stochastic weather generator (SWG) that allows (1) hourly simulation, (2) over the entire year, and (3) with a corrected representation of extremes. A range of climate scenarios is developed to demonstrate the insights that can be gained from linking the impacts of climate change to their thermodynamic and dynamic causal mechanisms, in this case for inflows to the Don Pedro Reservoir within the Tuolumne River Watershed of California. Application of the WR-SWG and water system modeling chain shows that the magnitude of flood events can be heavily influenced by antecedent hydrologic factors such as snow water equivalent (SWE) and soil moisture. Our results suggest that, under all climate change scenarios, SWE decreases as temperature increases and contributes more (sometimes up to 2.5 times more than the baseline) inflow as part of rain-on-snow events. The monthly reservoir inflows show the potential to cause extreme floods as the average rate of inflow increases by up to 80% with temperature increases, whereas SWE tends to increase by 50%, adding water to the stream during the high flow season. In addition to the temperature increase, if the water-holding capacity of the atmosphere increases with Clausius-Clapeyron scaling, reservoir inflows are projected to increase. This provides insight for risk-hedging policies: winter storm and spring snowmelt release and storage decisions that drive flood and drought risk, respectively. [ABSTRACT FROM AUTHOR]

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes. [ABSTRACT FROM AUTHOR]

A climatically driven, water system modeling framework that captures a full range of interacting components at necessary spatiotemporal scales was developed for the Southwestern United States, including a detailed representation of California's water supply and demand network, within the Water Evaluation and Planning (WEAP) system. Credibility of the model as a tool for water-energy analysis was garnered through evaluation of the historic regional water balances, including metrics of water supply, water use, groundwater storage, environmental requirements, and the energy use associated with water. The ability of the model to address policy-relevant questions was demonstrated by evaluating the impacts of a future 30-year drought scenario that included a realistic 2°C warming trend through 2050. With drought, the total energy used to supply water decreases by about 3% compared to the historic use, and warming alone reduces overall energy use by a modest 1% due to overall reductions in water delivery. The strongest signal of persistent warming expresses itself in groundwater storage, which decreases by an additional 9% beyond the impact of drought alone. A water conservation scenario was used to demonstrate the response of the water sector to the types of conservation strategies being pursued at local and state levels, with conservation reducing water use by about 10% and energy use by 3% relative to historic levels. Such measures overcome the warming trend, but it is demonstrated that persistent drought challenges groundwater conservation strategies and reductions in energy use by the water sector, leading to further overdraft of aquifers and increased energy use. [ABSTRACT FROM AUTHOR]

Domain-specific architectures of artificial neural networks (ANNs) have been developed to estimate salinity levels for planning at key monitoring stations in the Sacramento-San Joaquin Delta, California. In this work, we propose three major enhancements to existing ANN architectures for purposes of training time reduction, estimation error reduction, and better feature extraction. Specifically, we design a novel multitask ANN architecture with shared hidden layers for joint salinity estimation at multiple stations, achieving a reduction of 90% training and inference time. As another major structural redesign, we replace predetermined preprocessing on input data by a trainable convolution layer. We further enhance the multitask ANN design and training for salinity forecasting. Test results indicate that these enhancements substantially improve the efficiency and expand the capacity of the current salinity modeling ANNs in the Delta. Our enhanced ANN design methodologies have the potential for incorporation into the current modeling practice and provide more robust and timely information to guide water resource planning and management in the Delta. [ABSTRACT FROM AUTHOR]

For California water resource planning in the face of climate change, hydrological and water distribution models require inputs of high spatial– and temporal–resolution temperature and precipitation projections. We used a quantile delta mapping (QDM) procedure along with bias correction and localized constructed analogs (LOCA) downscaling to produce 6-km temperature and precipitation fields that preserve the relative changes in these quantities from climate model projections. We developed a wetter moderate warming (WMW) case from the Representative Concentration Pathway (RCP) 4.5 emissions scenario and a dry extreme warming (DEW) case from the RCP8.5 scenario to establish a range of projected hydroclimatological conditions. In both cases, we found that extreme precipitation becomes more extreme, but the sign of changes in moderate precipitation events differs between the two cases. The precipitation estimate range is most broad in southern California, where it varies by a factor of 2 and is 50% across the Sierra Nevada. This approach, adopted by the California Department of Water Resources, balances a host of practical water resource planning considerations with the evolving state of the science for future hydroclimatological projections. [ABSTRACT FROM AUTHOR]