skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Dynamic Adaptation of Water Resources Systems Under Uncertainty by Learning Policy Structure and Indicators
Abstract The challenge of adapting water resources systems to uncertain hydroclimatic and socioeconomic conditions warrants a dynamic planning approach. Recent studies have designed policies with structures linking infrastructure and management actions to threshold values of indicator variables observed over time. Typically, one or more of these components are held fixed while the others are optimized, constraining the flexibility of policy generation. Here we develop a framework to address this challenge by designing and testing dynamic adaptation policies that combine indicators, actions, and thresholds in a flexible structure. The approach is demonstrated for a case study of northern California, where a mix of infrastructure, management, and operational adaptations are considered over time in response to an ensemble of nonstationary hydrology and water demands. We first identify a subset of non‐dominated policies that are robust to held‐out scenarios, and then analyze their most common actions and indicators compared to non‐robust policies. Results show that the robust policies are not differentiated by the actions they select, but show substantial differences in their indicator variables, which can be interpreted in the context of physical hydrologic trends. In particular, the most frequent statistical transformations of indicator variables highlight the balance between adapting quickly versus correctly. Additionally, we determine the indicators most frequently associated with each action, as well as the distribution of action timing across scenarios. This study presents a new and transferable problem framing for adaptation under uncertainty in which indicator variables, actions, and policy structure are identified simultaneously during the optimization.  more » « less
Award ID(s):
1639268 2041826
PAR ID:
10446461
Author(s) / Creator(s):
 ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Water Resources Research
Volume:
57
Issue:
11
ISSN:
0043-1397
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Long-term snowpack decline is among the best-understood impacts of climate change on water resources systems. This trend has been observed for decades and is projected to continue even in climate projections in which total runoff volumes do not change significantly. For basins in which snowpack has historically provided intra-annual water storage, snowpack decline creates several issues that may require adaptation to infrastructure, operations, or both. This study develops an approach to analyze vulnerabilities and adaptations specifically focused on the challenge of snowpack decline, using the northern California reservoir system as a case study. We first introduce an open-source daily time-step simulation model of this system, which is validated against historical observations of operations. Multiobjective vulnerabilities to snowpack decline are then examined using a set of downscaled climate scenarios to capture the physically based effects of rising temperatures. A statistical analysis shows that the primary impacts include water supply shortage and lower reservoir storage resulting from the seasonal shift in runoff timing. These challenges identified from the vulnerability assessment inform proposed adaptations to operations to maintain multiobjective performance across the ensemble of plausible future scenarios, which include other uncertain hydrologic changes. To adapt seasonal reservoir management without the cost of additional infrastructure, we specifically propose and test adaptations that parameterize the structure of existing operating policies: a dynamic flood control rule curve and revised snowpack-to-streamflow forecasting methods to improve seasonal runoff predictability given declining snowpack. These adaptations are shown to mitigate the majority of vulnerabilities caused by snowpack decline across the scenario ensemble, with remaining opportunities for improvement using formal policy search and dynamic adaptation techniques. The coupled approach to vulnerability assessment and adaptation is generalizable to other snowmelt-dominated water resources systems facing the loss of seasonal storage due to rising temperatures. 
    more » « less
  2. The sensitivity of forecast-informed reservoir operating policies to forecast attributes (lead-time and skill) in many-objective water systems has been well-established. However, the viability of forecast-informed operations as a climate change adaptation strategy remains underexplored, especially in many-objective systems with complex trade-offs across interests. Little is known about the relationships between forecast attribute and policy robustness under deep uncertainty in future conditions and the relationships between forecast-informed performance and future hydrologic state. This study explores the sensitivity of forecast-informed policy robustness to forecast lead-time and skill in the outflow management plan of the Lake Ontario basin. We create water supply forecasts at four different subseasonal-to-annual lead-times and two levels of skill and further employ a many-objective evolutionary algorithm to discover policies tailored for each forecast case, historical supply conditions, and six objectives. We also leverage a partnership with decision-makers to identify a subset of candidate policies, which are reevaluated under a large set of plausible hydrologic conditions that reflect stationary and nonstationary climates. Scenario discovery techniques are used to map attributes of future hydrology to forecast-informed policy performance. Results show policy robustness is directly related to forecast lead-time, where policies conditioned on 12-month forecasts were more robust under future hydrology. Policies tailored for noisier long-lead forecasts were more robust under a wide range of plausible futures compared with policies trained to perfect forecasts, which highlights the potential to overfit control policies to historical information, even for a forecast-informed policy with perfect foresight. The relationship between performance and the hydrologic regime is dependent on the complexity of the interactions between control decisions and objectives. A threshold of objective performance as a function of supply conditions can support adaptive management of the system. However, more complex interactions make it difficult to identify simple hydrologic indicators that can serve as triggers for dynamic management. 
    more » « less
  3. Abstract As climate change impacts increase in frequency and magnitude, policies, and actions to promote climate change adaptation are critical to reduce negative consequences to infrastructure and society. Despite the urgency of adaptation, there have been few systematic efforts to understand the dynamics of public support for adaptation efforts at the local level in the U.S., partly because of the context- and location-specific nature of many adaptation actions. In this paper we use novel survey data to identify the role of demographics, extreme weather experience, awareness of climate change adaptation, risk perceptions, and perceived efficacy in predicting general support for local climate adaptation policy. We utilize a large national sample of U.S. adults (N = 37,088) collected over 12 waves between 2019 and 2022. We find that risk perceptions, beliefs about global warming, awareness of climate change adaptation, and perceived efficacy of local governments are key drivers of support for local adaptation policy. We provide policymakers, educators, and communicators with key guidelines for enhancing public support for adaptation policies. These insights are critical to expanding climate adaptation efforts and policy implementation at the local and national levels in the U.S. 
    more » « less
  4. Abstract Urban water management is increasingly challenged by the need to balance cost-effectiveness with equity considerations. This study presents a multi-objective approach to water conservation within the Las Vegas valley water district, analyzing a comprehensive dataset of water consumption and socioeconomic indicators across all single-family residences. We assess policy scenarios under two primary objectives: maximizing water savings to enhance economic efficiency and improving water affordability to promote equity. Our analysis reveals that while strategies focused on water savings reduce water use more efficiently, they tend to favor higher-income, predominantly white neighborhoods whereas prioritizing water affordability shifts resources towards lower-income, communities of color. The analysis of intermediate policy scenarios reveals the trade-offs and potential synergies between water savings and affordability. Our findings suggest that local water sustainability can be achieved by allocating resources to both high-demand and socioeconomically disadvantaged households. Highlighting the importance of integrating equity considerations into water management policies, this study provides insights for policymakers in crafting more inclusive and sustainable urban water management practices. 
    more » « less
  5. We study self-supervised adaptation of a robot's policy for social interaction, i.e., a policy for active communication with surrounding pedestrians through audio or visual signals. Inspired by the observation that humans continually adapt their behavior when interacting under varying social context, we propose Adaptive EXP4 (A-EXP4), a novel online learning algorithm for adapting the robot-pedestrian interaction policy. To address limitations of bandit algorithms in adaptation to unseen and highly dynamic scenarios, we employ a mixture model over the policy parameter space. Specifically, a Dirichlet Process Gaussian Mixture Model (DPMM) is used to cluster the parameters of sampled policies and maintain a mixture model over the clusters, hence effectively discovering policies that are suitable to the current environmental context in an unsupervised manner. Our simulated and real-world experiments demonstrate the feasibility of A-EXP4 in accommodating interaction with different types of pedestrians while jointly minimizing social disruption through the adaptation process. While the A-EXP4 formulation is kept general for application in a variety of domains requiring continual adaptation of a robot's policy, we specifically evaluate the performance of our algorithm using a suitcase-inspired assistive robotic platform. In this concrete assistive scenario, the algorithm observes how audio signals produced by the navigational system affect the behavior of pedestrians and adapts accordingly. Consequently, we find A-EXP4 to effectively adapt the interaction policy for gently clearing a navigation path in crowded settings, resulting in significant reduction in empirical regret compared to the EXP4 baseline. 
    more » « less