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Mountain regions in arid and semi-arid climates, such as California, are considered particularly sensitive to climate change because global warming is expected to alter snowpack storage and related surface water supply. It is therefore important to accurately capture snowmelt processes in watershed models for climate change impact assessment. In this study we use the Soil and Water Assessment Tool (SWAT) to estimate projected changes in snowpack and streamflow in four alpine tributaries to the agriculturally important but less studied southern Central Valley, California. Watershed responses are evaluated for four CMIP5 climate models (HadGEM_ES, CNRM-CM5, CanESM2 and MIROC5) and two emission scenarios (RCP 4.5 and RCP 8.5) for 2020-2099. SWAT models are calibrated following a dual-objective, lumped calibration approach with an automatic calibration against observed streamflow (stage 1) and a manual calibration against reconstructed Parallel Energy Balance (ParBal) snow water equivalent (SWE) data (stage 2). Results indicate that under a warming climate, peak streamflow is expected to increase 0.5-4 times in magnitude in the coming decades and to arrive 2-4 months earlier in the year because of earlier snowmelt. In the foreseeable future, snow cover will reduce gradually in the lower elevations and diminish at higher rates at higher elevation towards the end of the 21st century. Surface water supply is predicted to increase in the southern Central Valley under the evaluated scenarios but increased temporal variability (wetter wet seasons and drier dry seasons) will create new challenges for managing supply. The study further highlights that the use of remote sensing based, reconstructed SWE data could fill the current gap of limited in-situ SWE observations to improve the snow calibration of SWAT to better predict climate change impacts in semi-arid, snow-dominated watersheds.

Satellite services are fundamental to the global economy, and their design reflects a tradeoff between coverage and cost. Here, we report the discovery of two alternative 4-satellite constellations with 24- and 48-hour periods, both of which attain nearly continuous global coverage. The 4-satellite constellations harness energy from nonlinear orbital perturbation forces (e.g., Earth's geopotential, gravitational effects of the sun and moon, and solar radiation pressure) to reduce their propellant and maintenance costs. Our findings demonstrate that small sacrifices in global coverage at user-specified longitudes allow operationally viable constellations with significantly reduced mass-to-orbit costs and increased design life. The 24-hour period constellation reduces the overall required vehicle mass budget for propellant by approximately 60% compared to a geostationary Earth orbit constellation with similar coverage over typical satellite lifetimes. Mass savings of this magnitude permit the use of less expensive launch vehicles, installation of additional instruments, and substantially improved mission life.

This short communication describes a new open-source implementation of the CALVIN model (CALifornia Value Integrated Network), a large-scale network flow optimization model of California's water supply system. The model is cross-platform, uses common data formats, and connects to several freely available linear programming solvers. Given inputs including hydrology, urban/agricultural demand curves, and variable operating costs, the model minimizes the systemwide cost of water scarcity and operations including surface and groundwater reservoirs, wastewater reuse, desalination, environmental flow requirements, and hydropower. Key outputs include water shortage costs and marginal economic values of water and infrastructure capacity. We benchmark the scalability of different solvers up to roughly 5 million decision variables, using shared-memory parallelization on a high performance computing cluster. Runtimes are reduced by two orders of magnitude relative to the original model when no initial solution is provided, in addition to the benefits such as accessibility and transparency that come with an open-source platform. While this model is specific to California, the data and model structure are separated, so a similar framework could be used in any system where water allocation has been formulated as a network flow problem.

Urban groundwater supply planning is often achieved at the regional scale while the effects of aquifer exploitation are experienced on a local scale. In the Mexico City metropolitan area, land and water use changes have caused massive overdraft and subsidence, threatening water supply in marginalized communities. While spatially distributed groundwater models have been developed to test model uncertainty and policy interventions, there is little understanding of how regional aggregation of planning objectives can exacerbate existing social inequities. This study develops socially informed spatial analysis to evaluate differential impacts of groundwater pumping policies at multiple scales. We compare four planning objectives at three spatial resolutions to determine if policy preference shows a relationship with socioeconomic indicators. We find that the multiobjective performance of pumping policies can neglect marginalized communities according to multiple indicators. Further, clustering model grid cells according to socioeconomic indicators allows for more accurate analysis of this effect. Finally, multiscale analysis of policy preference considering subregional socioeconomic characteristics can aid in equitable selection of aquifer management alternatives. [ABSTRACT FROM AUTHOR]

Reservoirs balance multiple conflicting objectives, including flood control and water supply. In California, shifts in seasonal hydrologic patterns under climate change will amplify the difficulties in balancing flood control with water supply. Current flood control policies are based on fixed seasonal rule curves determined by the observed timing and magnitude of floods in the record. These rule curves generally require the release of wet season inflows, reducing the available stored water for use during the dry season. Here we investigate the potential for forecast-informed reservoir operations (FIRO) to increase water supply availability while minimizing additional flood risk at 14 reservoirs in the Sacramento, San Joaquin, and Tulare river basins. We use a differential evolution algorithm to train risk-based reservoir operation policies with an ensemble of historical forecasts over the period 2013–2023. Results show an average 8.1% increase in storage normalized by capacity, though this varies across reservoirs. The forecast-informed policies also reduce the occurrence of high-magnitude releases throughout the system. The accumulation of benefits is sensitive to the timing and magnitude of flood events, and most of the cumulative benefit is obtained during a few years. Under cross-validation, we find that large floods are needed in the training data to avoid overfitting the policy. We further examine the relationship between reservoir properties and FIRO benefits, finding that the ratio of peak inflow magnitude to maximum safe release correlates with increased storage under the FIRO policy, while the ratio of mean inflow to capacity correlates to the reduction of high-magnitude releases. This study highlights how adaptive reservoir management policies can yield water supply benefits without an increase in flood risk, given adequate historical data for policy training. These policies may be a valuable adaptation to climate change but require careful validation and out-of-sample testing. [ABSTRACT FROM AUTHOR]

Dynamic planning of water infrastructure requires identifying signals for adaptation, including measures of system performance linked to vulnerabilities. However, it remains a challenge to detect projected changes in performance outside the envelope of natural variability, and to identify whether such detections can be attributed to one or more uncertain drivers. This study investigated these questions using a combination of ensemble simulation, nonparametric tests, and variance decomposition, which were demonstrated for a case study of the Sacramento–San Joaquin River Basin, California. We trained a logistic regression classifier to predict future detections given observed trends in performance over time. The scenario ensemble includes coupled climate and land-use change through the end of the century, evaluated using a multireservoir simulation model to determine changes in water supply reliability and flooding metrics relative to the historical period (1951–2000). The results show that the reliability metric is far more likely to exhibit a significant change within the century, with the most severe scenarios tending to be detected earlier, reflecting long-term trends. Changes in flooding often are not detected due to natural variability despite severe events in some scenarios. We found that the variance in detection times is attributable largely to the choice of climate model, and also to the emissions scenario and its interaction with the choice of climate model. Finally, in the prediction model for both cases, reliability and flooding, the model learns to associate more-recent observations of system performance with nonstationarity detection. These findings underscore the importance of differentiating between long-term change and natural variability in identifying signals for adaptation. [ABSTRACT FROM AUTHOR]