CaRDS - the statewide California Residential water Demand and Supply open dataset.

As water scarcity becomes the new norm in the Western United States, states such as California have increased their efforts to improve water resilience. Achieving water security under climate change, population growth, and urbanization requires an integrated multi-sectoral approach, where adaptation strategies combine supply and demand management interventions. Yet, most studies consider supply-side and demand-side management strategies separately. Water conservation efforts are mainly driven by policy requirements and publicly available data to assess the effectiveness of demand- and supply-side management policies is often hard to find and unstructured. Here we present CaRDS - the statewide California Residential water Demand and Supply open dataset. CaRDS encompasses nine years (2013-2021) of monthly water supply and demand time series for 404 water suppliers in California, USA, compiled from different open-access data sources. Access to detailed temporal and spatial water supply operations and demands at the state-level can be useful to researchers and practitioners to realize applications such as evaluating the effectiveness of water conservation policies and discovering regional differences in water resilience measures.

Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change.

Agricultural water demand, groundwater extraction, surface water delivery and climate have complex nonlinear relationships with groundwater storage in agricultural regions. As an alternative to elaborate computationally intensive physical models, machine learning methods are often adopted as surrogate to capture such complex relationships due to their high computational efficiency. Inevitably, using only one machine learning model is prone to underestimate prediction uncertainty and subjected to poor accuracy. This study presents a novel machine learning-based groundwater ensemble modeling framework in conjunction with a Bayesian model averaging approach to predict groundwater storage change and improve overall model predicting reliability. Three different machine learning models have been developed namely artificial neural network, support vector machine and response surface regression. To explicitly quantify uncertainty from machine learning model parameter and structure, Bayesian model averaging is employed to produce a forecast distribution associated with each machine learning prediction. Model weights and variances are obtained based on model performance to construct ensemble models. Then, the developed individual and Bayesian model averaging machine learning ensemble models are applied, evaluated and validated at different spatial scales including subregional and regional scales in an overdrafted agricultural region-the San Joaquin River Basin, through independent training and testing dataset. Results shows the machine learning models have remarkable predicting capability without sacrificing accuracy but with higher computational efficiency. Compared to a single-model approach, the ensemble model is able to produce consistently reliable predictions across the basin, yet it does not always outperform the best model in the ensemble. Additionally, model results suggest that groundwater pumping for agricultural irrigation is the primary driving force of groundwater storage change across the region. The modeling framework can serve as an alternative approach to simulating groundwater response, especially in those agricultural regions where lack of subsurface data hinders physically-based modeling.

Optimizing the dammed: water supply losses and fish habitat gains from dam removal in California.

Dams provide water supply, flood protection, and hydropower generation benefits, but also harm native species by altering the natural flow regime and degrading aquatic and riparian habitat. Restoring some rivers reaches to free-flowing conditions may restore substantial environmental benefits, but at some economic cost. This study uses a systems analysis approach to preliminarily evaluate removing rim dams in California's Central Valley to highlight promising habitat and unpromising economic use tradeoffs for water supply and hydropower. CALVIN, an economic-engineering optimization model, is used to evaluate water storage and scarcity from removing dams. A warm and dry climate model for a 30-year period centered at 2085, and a population growth scenario for year 2050 water demands represent future conditions. Tradeoffs between hydropower generation and water scarcity to urban, agricultural, and instream flow requirements were compared with additional river kilometers of habitat accessible to anadromous fish species following dam removal. Results show that existing infrastructure is most beneficial if operated as a system (ignoring many current institutional constraints). Removing all rim dams is not beneficial for California, but a subset of existing dams are potentially promising candidates for removal from an optimized water supply and free-flowing river perspective. Removing individual dams decreases statewide delivered water by 0-2282 million cubic meters and provides access to 0 to 3200 km of salmonid habitat upstream of dams. The method described here can help prioritize dam removal, although more detailed, project-specific studies also are needed. Similarly, improving environmental protection can come at substantially lower economic cost, when evaluated and operated as a system.