The state of Colorado’s West Slope Basins are critical headwaters of the Colorado River and play a vital role in supporting Colorado’s local economy and natural environment. However, balancing the multi-sectoral water demands in the West Slope Basins is an increasing challenge for water managers. Internal variability - irreducible uncertainty stemming from interactions across non-linear processes within the hydroclimate system - complicates future vulnerability assessments. Climate change may exacerbate drought vulnerability in the West Slope Basins, with significant streamflow declines possible by mid-century. In this work, we introduce a novel multi-site Hidden Markov Model (HMM)-based synthetic streamflow generator to create an ensemble of streamflows for all six West Slope Basins that better characterizes the region’s hydroclimate and drought extremes. We capture the effects of climate change by perturbing the HMM to generate a climate-adjusted ensemble of streamflows that reflects plausible changes in climate. We then route both ensembles of streamflows through StateMod, the state of Colorado’s water allocation model, to evaluate spatially compounding drought impacts across the West Slope basins. Our results illustrate how drought events emerging from the system’s stationary internal variability in the absence of climate change can have significant impacts that exceed extreme conditions in the historical record. Further, we find that even relatively modest levels of plausible climate changes can cause a regime shift where extreme drought impacts become routine. These results can inform future Colorado River planning efforts, and our methodology can be expanded to other snow-dominated regions that face persistent droughts.

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.

California faces cycles of drought and flooding that are projected to intensify, but these extremes may impact water users across the state differently due to the region’s natural hydroclimate variability and complex institutional framework governing water deliveries. To assess these risks, this study introduces a novel exploratory modeling framework informed by paleo and climate-change based scenarios to better understand how impacts propagate through California’s complex water system. A stochastic weather generator, conditioned on tree-ring data, produces a large ensemble of daily weather sequences conditioned on drought and flood conditions under the Late Renaissance Megadrought period (1550-1580 CE). Regional climate changes are applied to this weather data and drive hydrologic projections for the Sacramento, San Joaquin, and Tulare Basins. The resulting streamflow ensembles are used in an exploratory stress test using the California Food-Energy-Water System model (CALFEWS), a highly resolved, daily model of water storage and conveyance throughout California. Results show that megadrought conditions lead to unprecedented reductions in inflows and storage at major California reservoirs. Both junior and senior water rights holders experience multi-year periods of curtailed water deliveries and complete drawdowns of groundwater assets. When megadrought dynamics are combined with climate change, risks for unprecedented depletion of reservoir storage and sustained curtailment of water deliveries across multiple years emerge. Asymmetries in risk emerge depending on water source, rights, and access to groundwater banks.

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.

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.