UC Davis

In many rural areas in arid and semi-arid regions, balancing agricultural and environmental water demands is a key challenge facing resource managers, which is complicated by the interconnection of groundwater and surface water resources. In California, water management decisions are increasingly supported by hydrologic models, but these can often provide confusing or overwhelming amounts of information. The Scott River watershed (HUC8 18010208), a 2,109 square km undammed rural basin in northern California, was used as a case study to develop a suite of tools, informed by a groundwater-surface water model, for water managers, including: 1) a hydrologic proxy for the ecological success of a key aquatic species; 2) applying a quantitative multi-benefit framework (i.e., incorporating ecological, agricultural, and cost objectives) to an existing groundwater planning process; and 3) two locally-tailored, seasonal, quantitative predictions of fall-season watershed behavior as a complement to historically-used water year type categories. Chapter 1 aims to quantify hydrologic conditions that support persistence of the Scott Valley coho salmon (Oncorhynchus kisutch) run. We applied the functional flows framework to characterize the hydrology of each water year measured at a key long-term stream gauge. Taking advantage of a nearly two-decade ecological monitoring dataset, we built linear models to predict coho salmon reproductive success using combinations of one and two hydrologic metric predictors. We used an ensemble of the three best linear models to formulate a Hydrologic Benefit function, summarizing the ecological services provided by the hydrology in different seasons into a single index value per water year.

In Chapter 2, we apply a multi-benefit framework to a portfolio of management actions, which were proposed for the Scott Valley jurisdiction during the recent development of a long-term Groundwater Sustainability Plan (GSP). We developed a summary statistic or proxy for each of the three primary policy objectives in the GSP (environmental, agricultural, and project cost). We then used them to summarize the results of 40 management scenarios developed for the Groundwater Sustainability Plan (GSP) in Scott Valley in Northern California, which were simulated using an existing integrated surface- and groundwater model. We found that a trade-off in benefits for fish and farms was evident in every category of infrastructure investment (a proxy for project cost), though greater infrastructure investment can achieve some reductions in this trade-off. Additionally, although the GSP management priorities emphasized infrastructure investments, both infrastructure-based and regulatory approaches fell within the Pareto-optimal set of management options under a strict application of these objective functions. Finally, regarding regulatory actions, management interventions targeted at low-flow periods produced a more efficient gain in environmental flow value per cost in agricultural productivity.

In Chapter 3, we propose methods to predict, approximately five months in advance, two key hydrologic metrics in the Scott River watershed. Both metrics are intended to quantify the transition from the dry to the wet season, to characterize the severity of a dry year and support seasonal adaptive management. The first metric is the minimum 30-day dry season baseflow volume, V_min, which occurs at the end of the dry season (September-October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting Sept. 1st, necessary to bring the watershed to a "full" or "spilling" condition (i.e. initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as P_spill. As potential predictors of these two values, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference ET, and a subset of these metrics from the previous water year. We find that, though many of these predictors are correlated with the two metrics of interest, of the predictors considered here, the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October-April precipitation. These two linear models could reproduce historic values of V_min and P_spill with an RMSE of 1.4 Mm3 / 30 days (19.4 cfs) and 20.7 mm (0.8 inches), respectively.

The tools developed for this case study could be of value for other local jurisdictions with similar features, including a Mediterranean and/or intermontane (snow-fed) climate, an undammed watershed, and challenges balancing agricultural and environmental water needs. The method for empirically deriving the highest-priority hydrologic functions for a threatened species could be used in other watersheds (if sufficient ecological data records are available) to evaluate trade-offs and support water management decisions in human-altered novel ecosystems. The development of basin-specific objective functions, especially ones using output from existing hydrologic models, could help quantify management decision trade-offs and improve stakeholder communication in ongoing water planning efforts throughout the region. And finally, although careful consideration of baseline conditions used as a basis for prediction is necessary, seasonal predictive indices could be used by governance entities to support adaptive management in an uncertain future climate.