Stochastic Watershed Models (SWMs) are an important innovation in hydrologic modeling that propagate uncertainty into model predictions by adding samples of model error to deterministic simulations. A growing body of work shows that univariate SWMs effectively reduce bias in hydrologic simulations, especially at the upper and lower flow quantiles. This has important implications for short term forecasting and the estimation of design events for long term planning. However, the application of SWMs in a regional context across many sites is underexplored. Streamflow across nearby sites is highly correlated, and so too are hydrologic model errors. Further, in arid and semi-arid regions streamflow can be intermittent, but SWMs rarely model zero flows at one site, let alone correlated intermittency across sites. In this technical note, we contribute a multisite SWM that captures univariate attributes of model error (heteroscedasticity, autocorrelation, non-normality, conditional bias), as well as multisite attributes of model error (cross-correlated error magnitude and persistence). The SWM also incorporates a multisite, auto-logistic regression model to account for multisite persistence in streamflow intermittency. The model is applied and tested in a case study that spans 14 watersheds in the Sacramento, San Joaquin, and Tulare basins in California. We find that the multisite SWM is able to better reproduce regional low and high flow events and design statistics as compared to a single-site SWM applied independently to all locations.

Many water markets in the Western United States (U.S.) have the ability to reallocate water temporarily during drought, often as short-term water rights leases from lower value irrigated activities to higher value urban uses. Regulatory approval of water transfers, however, typically takes time and involves high transaction costs that arise from technical and legal analyses, discouraging short-term leasing. This leads municipalities to protect against drought-related shortfalls by purchasing large volumes of infrequently used permanent water rights. High transaction costs also result in municipal water rights rarely being leased back to irrigators in wet or normal years, reducing agricultural productivity. This research explores the development of a multi-year two-way option (TWO) contract that facilitates leasing from agricultural-to-urban users during drought and leasing from urban-to agricultural users during wet periods. The modeling framework developed to assess performance of the TWO contracts includes consideration of the hydrologic, engineered, and institutional systems governing the South Platte River Basin in Colorado where there is growing competition for water between municipalities (e.g., the city of Boulder) and irrigators. The modeling framework is built around StateMod, a network-based water allocation model used by state regulators to evaluate water rights allocations and potential rights transfers. Results suggest that the TWO contracts could allow municipalities to maintain supply reliability with significantly reduced rights holdings at lower cost, while increasing agricultural productivity in wet and normal years. Additionally, the TWO contracts provide irrigators with additional revenues via net payments of option fees from municipalities.

To aid California's water sector to better manage future climate extremes, we present a method for creating a regional ensemble of plausible daily future climate and streamflow scenarios that represent natural climate variability captured in a network of tree-ring chronologies, and then embed anthropogenic climate change trends within those scenarios. We use 600 years of paleo-reconstructed weather regimes to force a stochastic weather generator, which we develop for five subbasins in the San Joaquin River in the Central Valley region of California. To assess the compound effects of climate change, we create temperature series that reflect scenarios of warming and precipitation series that are scaled to reflect thermodynamically driven shifts in the daily precipitation distribution. We then use these weather scenarios to force hydrologic models for each of the San Joaquin subbasins. The paleo-forced streamflow scenarios highlight periods in the region's past that produce flood and drought extremes that surpass those in the modern record and exhibit large non-stationarity through the reconstruction. Variance decomposition is employed to characterize the contribution of natural variability and climate change to variability in decision-relevant metrics related to floods and drought. Our results show that a large portion of variability in individual subbasin and spatially compounding extreme events can be attributed to natural variability, but that anthropogenic climate changes become more influential at longer planning horizons. The joint importance of climate change and natural variability in shaping extreme floods and droughts is critical to resilient water systems planning and management in the Central Valley region.

The field of MultiSector Dynamics (MSD) explores the dynamics and co-evolutionary pathways of human and Earth systems with a focus on critical goods, services, and amenities delivered to people through interdependent sectors. This commentary lays out core definitions and concepts, identifies MSD science questions in the context of the current state of knowledge, and describes ongoing activities to expand capacities for open science, leverage revolutions in data and computing, and grow and diversify the MSD workforce. Central to our vision is the ambition of advancing the next generation of complex adaptive human-Earth systems science to better address interconnected risks, increase resilience, and improve sustainability. This will require convergent research and the integration of ideas and methods from multiple disciplines. Understanding the tradeoffs, synergies, and complexities that exist in coupled human-Earth systems is particularly important in the context of energy transitions and increased future shocks.