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ZEDD lab research is dedicated to advancing our understanding of water sustainability and developing innovative solutions to complex water resource challenges. Through interdisciplinary collaborations and the integration of cutting-edge data mining and control techniques, the lab aims to address pressing issues of water scarcity, equitable access of resources, and climate change impacts. |
1. Multi-Objective Tradeoffs in Water Pricing Structures.
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Rapid urbanization and increasing hydrologic variability are threatening the water security of urban water utilities. In response to these concerns, utility managers must set water pricing structures to incentivize water conservation and recover the costs of water treatment and infrastructure investments, while providing affordable prices to their service population. Despite their importance in achieving these objectives, the comparative performance of alternative pricing strategies on utility objectives remains poorly defined.
The impact of a utility’s water rate structure depends on the distribution of household water demand within its service population. We develop a Bayesian hierarchical model to characterize the household water demand distributions for hundreds of water utilities, leveraging reported demand, revenue, and rate structure data. Using these demand distributions, we quantify utility performance across affordability, conservation, and cost-recovery objectives, and identify how political, environmental, and socioeconomic contexts shape these outcomes. By quantifying the link between alternative pricing strategies and utility performance outcomes, this research informs rate-setting decisions tailored to the diverse operating environments of individual utilities. |
2. Optimizing hydrological monitoring for Adaptive Water Management.
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Designing operating rules for water systems is a challenging control problem that typically features a complex decision space and multiple conflicting operating objectives.
While robust, adaptive decision rules can leverage a wide range of observable hydrologic information to improve water release outcomes, monitoring and modeling all possible hydrologic features across a basin is expensive and infeasible in reality. This motivates the need to integrate hydrological monitoring, modeling, and management to identify key hydrological variables to monitor (when, what, & where to monitor) that improve decision-making at the lowest cost. We hypothesize that collecting better data at key locations throughout a basin will improve water management, especially for areas or objectives that lacked proper monitoring in the past. This project will quantify the trade-offs between monitoring cost, model performance, and management outcomes in a case study basin. |
3. Mountain Ecosystem-Aware Reservoir Optimization.
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Mountain regions are of fundamental importance for freshwater provision, biodiversity conservation, and downstream socio-economic activities. At the same time, mountain river ecosystems are inherently fragile, characterized by narrow ecological tolerances and a strong dependence on natural flow and water quality regimes.
In many anthropized alpine basins, reservoir operational rules governing storage and release decisions exert first-order control on downstream hydrological regimes and water quality across sub-daily to seasonal timescales. In particular, hydropeaking and operation-induced alterations of water quality, including downstream temperature regimes, represent the dominant anthropogenic pressures on regulated mountain rivers. Although the ecological relevance of these operation-driven impacts is well established, they are still represented in most reservoir planning and optimization frameworks in a highly simplified or aggregated manner, limiting their ability to inform ecologically consistent operational strategies. In our project, we address this limitation by developing a sub-daily simulation and multi-objective optimization framework specifically designed for reservoir systems in mountain environments. The framework explicitly represents two key operation-driven stressors: (i) hydropeaking and (ii) downstream water temperature alteration, optimized jointly with traditional operations objectives of hydropower revenue and irrigation reliability, allowing trade-offs to be explored within a unified decision-support framework. By embedding ecosystem-relevant processes into operational planning, the project advances state-of-the-art reservoir optimization and provides a practical pathway toward more balanced and ecologically aware management of regulated mountain river systems. |
4. Quantifying Agricultural losses to Heat Stress Using Insurance Data.
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The Federal Crop Insurance Program (FCIP) is the largest government-supported risk management program in U.S. agriculture, insuring nearly all planted acreage by 2024. Historical cause-of-loss records consistently identify drought as the leading source of crop damage. However, losses attributed to precipitation deficits may be overstated, as some impacts classified as drought-related are in fact driven by heat stress during the growing season.
In this study, we integrate drought indices, soil characteristics, and county-by-year crop insurance observations into a hierarchical Bayesian model. This framework enables us to quantify the fraction of indemnities attributable specifically to heat stress across different counties. By combining these estimates with future climate projections, we assess how the relative contribution of heat stress to crop losses may evolve over time. Our findings provide new empirical evidence on the distinct financial impacts of heat stress within the broader category of drought-related losses. By disentangling the effects of precipitation deficits and heat stress, this project contributes to more accurate crop-loss predictions, improved actuarial pricing, and stronger agricultural risk assessment under a warming climate. |
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[1] Zaniolo, M., Fletcher, S., & Mauter, M. S. (2023). Multi-scale planning model for robust urban drought response. Environmental Research Letters, 18(5), 054014. [2] Zaniolo, M., Giuliani, M., Sinclair, S., Burlando, P., & Castelletti, A. (2021). When timing matters—misdesigned dam filling impacts hydropower sustainability. Nature Communications, 12(1), 3056. [3] Giuliani, M., Zaniolo, M., Castelletti, A., Davoli, G., & Block, P. (2019). Detecting the state of the climate system via artificial intelligence to improve seasonal forecasts and inform reservoir operations. Water Resources Research, 55(11), 9133-9147. [4] Zaniolo, M., Giuliani, M., Bantider, A., & Castelletti, A. (2021). Hydropower development: Economic and environmental benefits and risks. In The Omo-Turkana Basin (pp. 37-57). Routledge. [5] Zaniolo, M., Giuliani, M., & Castelletti, A. (2021). Neuro-evolutionary direct policy search for multiobjective optimal control. IEEE Transactions on Neural Networks and Learning Systems, 33(10), 5926-5938. [6] Zaniolo, M., Giuliani, M., & Castelletti, A. (2021). Policy Representation Learning for multiobjective reservoir policy design with different objective dynamics. Water Resources Research, 57(12), e2020WR029329. |
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All are welcome.
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