Sai Veena Sunkara

Colorado's West Slope Basins are a critical source of water for the Colorado River, contributing approximately 70% of the inflows to Lake Powell in a typical year. Whether these basins will face intensifying water shortages by mid‐century remains highly debated due to deep uncertainties in future climate conditions, including the possibility of wetter or drier futures, persistence of severe drought, population growth, and evolving multisectoral water demands. Identifying the primary drivers of plausible mid‐century water shortages is particularly challenging given the region's high internal climate variability, changing hydrology, and complex institutional framework governing water rights for thousands of users. This study integrates large‐scale exploratory modeling with diagnostic sensitivity analysis to clarify the relative influence of uncertain natural and human drivers of water shortages in the West Slope basins. A multi‐site Hidden Markov Model (HMM) is used to generate a wide range of synthetic streamflow scenarios representing plausible mid‐century changes relative to the historical baseline. These stochastic hydrologic scenarios span both wetter and drier futures and are combined with projected demand changes across sectors. The resulting scenarios are simulated within Colorado's StateMod water allocation model to estimate water shortages at basin, district, and sectoral scales. Diagnostic sensitivity analysis reveals that the dominant drivers of shortages vary markedly by basin, district, major reservoir and sector. These findings provide actionable, scale‐specific insights into the most influential factors shaping future water stress in Colorado's West Slope basins.

Hydropower is a key energy source in the western interconnection of the United States, comprising about 25% of the annual installed nameplate capacity in 2020. However, its generation is increasingly impacted by changing hydrological conditions, operational constraints, environmental factors, water availability, and aging dam infrastructure. Assessing hydropower availability and resource adequacy is valuable for shaping energy policies and infrastructure requirements. In this study we evaluate the sensitivity of resource adequacy in the Western Interconnection to the unavailability of hydropower plants, under a wide-ranging set of 16,384 hydropower loss scenarios derived from a combination of 14 major hydrologic regions. Although a complete loss of hydropower capacity in any region is unlikely, studying such scenarios helps in identifying hydrologic regions most critical for maintaining resource adequacy. So, we identify key hydrologic regions with disproportionately large adequacy impacts relative to their installed hydropower capacity and study compounding effects between regions using classification and regression trees. Even after controlling for total installed capacity, we find that hydropower resources in the Pacific Northwest contribute the most to interconnection-wide adequacy outcomes, with resources in Northern California and the Desert Southwest providing more moderate incremental contributions.

Baseflow plays a crucial role in sustaining river aquatic ecosystems, reduced drought impacts necessitating the need to understand how human activities might influence it. Here, we implement a comparative socio-hydrology based approach to identify dominant controls on baseflow reductions across three regions in India irrigated by large reservoirs. We evaluate the effect of conjunctive use of surface water and groundwater on downstream baseflow in areas irrigated by the Nagarjuna Sagar (NSR) in Krishna basin, Hirakud (HRD) in Mahanadi basin and Indira Sagar (ISR) in Narmada basin, three large reservoirs in India. We apply a socio-hydrologic model to simulate surface water and groundwater withdrawals as a function of reservoir inflows, reservoir characteristics, water demands, and aquifer characteristics of the regions. The model constitutes a reservoir module that simulates water releases from the reservoir, a water use module that simulates how farmers use surface water and groundwater to meet irrigation demands, and a conceptual groundwater module to simulate groundwater levels. Farmers extract groundwater when water supplied from reservoirs does not meet irrigation demands. A classification and regression tree (CART) based algorithm was used to quantify the relative influence of different socio-hydrological factors on baseflow reductions due to upstream irrigation. We found an average annual reduction of 323 MCM (1968-2022), 24 MCM (1958-2021) and 13.72 MCM (2005-2022) in baseflow due to the groundwater pumping for NSR, HRD, and ISR, respectively. These translate to 11 %, 5%, and 3% reduction in baseflow compared to a baseline no pumping scenario. Though the relative reduction in baseflow was primarily governed by the volume of groundwater pumped in all cases, accurate characterization of the reduction required information on climate and reservoir characteristics at annual time scales.

Colorado’s West Slope Basins are a critical source of water for the Colorado River, contributing approximately 70% of the inflows to Lake Powell in a typical year. Whether these basins will face intensifying water shortages by mid-century remains highly debated due to deep uncertainties in future climate conditions, including the possibility of wetter or drier futures, persistence of severe drought, population growth, and evolving multisectoral water demands. Identifying the primary drivers of plausible mid-century water shortages is particularly challenging given the region’s high internal climate variability, changing hydrology, and complex institutional framework governing water rights for thousands of users. This study integrates large-scale exploratory modeling with diagnostic sensitivity analysis to clarify the relative influence of uncertain natural and human drivers of water shortages in the West Slope basins. A multi-site Hidden Markov Model (HMM) is used to generate a wide range of synthetic streamflow scenarios representing plausible mid-century changes relative to the historical baseline. These stochastic hydrologic scenarios span both wetter and drier futures and are combined with projected demand changes across sectors. The resulting scenarios are simulated within Colorado’s StateMod water allocation model to estimate water shortages at basin, district, and sectoral scales. Diagnostic sensitivity analysis reveals that the dominant drivers of shortages vary markedly by basin, district, major reservoir and sector. These findings provide actionable, scale-specific insights into the most influential factors shaping future water stress in Colorado’s West Slope basins.

Recent studies have shown the potential for reservoir control policies to adapt to uncertain future climate and demand by reoptimizing on a fixed time interval. However, this strategy is independent of the system evolution and might implement late or unnecessary adaptation. This study develops a framework to identify dynamic decisions on two levels: an “outer loop” adaptation policy that establishes indicator thresholds for reoptimization based on recently observed data, and an “inner loop” control policy that undergoes reoptimization according to these thresholds. We demonstrate this method for a case study of Oroville Reservoir, California, using an ensemble of climate model projections split into training and testing sets. The control policy uses inputs of storage, day of year, and a 5‐day inflow forecast, while the adaptation policy indicators include long‐term statistics of climate and demand as well as the recent system performance. Both policy levels are optimized simultaneously using heuristic policy search and analyzed with policy interpretation methods, including Shapley Additive Explanations (SHAP) and global sensitivity analysis. Results show that the adaptation solutions provide equal or better performance compared to the historical benchmark and are robust to out‐of‐sample scenarios. Additionally, the decision to reoptimize is primarily driven by demand, flood cost and mean annual flow indicators on different timescales. The proposed methodology identifies how control policy reoptimization can be initiated using observed thresholds of climate, demand, and system performance to improve adaptation under future uncertainty.

Compound coastal flooding due to astronomic, atmospheric, oceanographic, and hydrologic drivers poses severe threats to coastal communities. While physics-driven approaches are able to dynamically simulate temporally and spatially varying compound flooding generated by multiple drivers with correlations between some of them, computational burdens limit their capability to explore the full range of conditions that contribute to compound coastal hazards. Data-driven statistical approaches address some of these computational challenges; however, they are also unable to explore all possible forcing combinations due to short observational records, and projections are typically limited to a few locations. This study proposes a hybrid statistical–dynamical framework for compound coastal flooding analysis that integrates a stochastic generator of compound flooding drivers, a hydrodynamic model, and machine learning-based surrogate models. The framework was demonstrated in San Francisco Bay (SF) over the past 500 years with accuracy similar to the physics-driven approach but with much higher computational efficiency. The stochastic generator of compound flooding drivers is developed by coupling a sea surface temperature (SST) reconstruction model with a climate emulator, weather generator, and model of the hydrological and reservoir system. Using reconstructed SSTs as input, the generator of compound flooding drivers is employed to simulate time series of the forcing factors contributing to compound flooding (e.g. surge, waves, river discharge, etc) in SF Bay. A process-based hydrodynamic model is built to predict total water levels varying in time and space throughout SF Bay based on stochastically generated drivers. The machine learning-based surrogate models are then developed from a relatively small library (several hundred) of hydrodynamic model simulations to efficiently predict water levels for compound flooding analysis under the full range of stochastic drivers. This study contributes a hybrid statistical–dynamical framework to better understand the spatial distribution and temporal evolution of compound coastal-fluvial flooding, along with the relative contributions of drivers in complex nearshore, estuarine, and river environments for centennial timescales under past, present, and future climates.

Causal loop diagrams (CLDs) based on expert and/or stakeholder inputs inform the quantitative structure of socio-hydrological models (SHMs). However, a systematic exploration of the sensitivity of CLDs and SHMs to different levels of stakeholder inputs is lacking. For a large multi-purpose reservoir in southern India, we explore this sensitivity by developing three CLDs that integrate reservoir water balance, groundwater pumping, and consumer water use patterns. CLD1 is a conventional water balance-based reservoir model, while CLD2 additionally incorporates the reservoir operatorâ\euro™s judgment and groundwater pumping. CLD3 further incorporates the adaptive behavior of water users by adjusting demands in response to long-term (5-year) droughts. The correlation between observed and simulated monthly reservoir storage (2000-2013) for SHM1, SHM2, and SHM3 is 0.57, 0.85, and 0.87, respectively. SHM3 also outperforms SHM1 and SHM2 in simulating the relative use of surface and groundwater for irrigation purposes in the command area of the reservoir. Simulated demand deficits, command area groundwater levels, and minimum environmental flow satisfaction downstream of the reservoir for 1968-2013 using the three models exhibit substantial differences. SHM1 and SHM2 simulate deteriorating groundwater levels under the multi-year drought of 2001-2003 while SHM3 does not due to the consideration of adaptive farmer behavior. Thus, our understanding of water and food security during a multi-year drought can be significantly affected by the level of stakeholder inputs incorporated in the models. We highlight the importance of testing different SHMs structures to better understand human-water interactions under extreme conditions.