Regional cooperation among water utilities can improve the robustness of urban water supply systems to challenging and deeply uncertain futures. Through coordination mechanisms such as water transfers and regional demand management, water utilities can improve the efficiency of resource allocation and delay the need for new infrastructure investments. Though cooperation provides utilities with the potential for reduced cost strategies for improving the reliability of their services, two important challenges are worthy of careful consideration. First, regional utilities often have to navigate robustness conflicts stemming from potential asymmetries in their risk exposure, demand dynamics, and the availability of supply resources. Second, successful implementation of candidate compromise water portfolios requires that cooperating utilities understand their operational tolerances to deviations from the recommended regional actions (“imperfect implementation”). This study contributes a framework for identifying compromises across regional robustness conflicts and quantifying tolerances to implementation uncertainties. The framework is demonstrated on a system of four interdependent but institutionally independent water utilities in the Research Triangle region of North Carolina that are confronting robustness conflicts given asymmetries in their vulnerabilities to growing demands and hydroclimatic uncertainties. Our findings highlight that seemingly balanced compromise management strategies can yield significant and potentially surprising unintended consequences that could degrade regional cooperation. Moreover, asymmetries in regional robustness can be amplified with modest deviations from their agreed upon actions. Results of this analysis are broadly applicable to water supply regionalization as a global challenge and provide insights for discovering robust compromises and safe operating spaces for multi‐actor water supply systems.
Water management usually considers economic and ecological objectives, and involves tradeoffs, conflicts, compromise, and cooperation among objectives. Pareto optimality often is championed in water management, but its relationships with the mathematical representation of objectives, and implications of tradeoffs for Pareto optimal decisions, are rarely examined. We evaluate the mathematical properties of optimized tradeoffs to identify promising regions for compromise, suggest strategies for reducing conflicts, and better understand whether decision‐makers are more or less likely to cooperate on environmental water allocations. Cooperation and compromise among objectives can be easier when tradeoff curves are concave and more adversarial when tradeoff curves are convex. “Knees,” or areas with maximum curvature, bulges, or breakpoints in concave Pareto frontiers, suggest more promising areas for compromise. Evaluating the shape of Pareto curves based on each objective's performance function can screen for the existence of knees amenable to compromise. We explore water management and restorations actions that improve and shift the location and prominence of knees in concave Pareto frontiers. Connecting river habitats and other non‐flow management actions may add knees on locally concave regions of Pareto frontiers. Managing multiple streams regionally, rather than individually, can sometimes turn convex local tradeoffs into concave regional tradeoffs more amenable to compromise. Overall, this analysis provides a deep investigation of how the shape of tradeoffs influences the range and promise of decisions to improve performance, and illustrates that management actions may encourage cooperation and reduce conflict.
more » « less- Award ID(s):
- 1653452
- NSF-PAR ID:
- 10371447
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 10
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
We consider a collection of distributed sensor nodes periodically exchanging information to achieve real- time situational awareness in a communication constrained setting, e.g., collaborative sensing amongst vehicles to improve safety-critical decisions. Nodes may be both con- sumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, whilst producers broadcast updates on what they are currently able to see. Accordingly, we introduce and explore optimiz- ing trade-offs between the coverage and the space-time in- terest weighted average “age” of the information available to consumers. We consider two settings that capture the fundamental character of the problem. The first addresses selecting a subset of producers that maximizes the cover- age of the consumers preferred regions and minimizes the average age of these regions given that producers provide updates at a fixed rate. The second addresses the mini- mization of the interest weighted average age achieved by a fixed subset of producers with possibly overlapping cov- erage by optimizing their update rates. The first problem is shown to be submodular and thus amenable to greedy op- timization while the second has a non-convex/non-concave cost function which is amenable to effective optimization using the Frank-Wolfe algorithm. Numerical results exhibit the benefits of context dependent optimization information sharing among obstructed sensing nodes.more » « less
-
more » « less
Abstract Conservation translocation projects must carefully balance multiple, potentially competing objectives (e.g. population viability, retention of genetic diversity, delivery of key ecological services) against conflicting stakeholder values and severe time and cost constraints. Advanced decision support tools would facilitate identifying practical solutions.
We examined how to achieve compromise across competing objectives in conservation translocations via an examination of giant tortoises in the Galapagos Islands with ancestry from the extinct Floreana Island species (
Chelonoidis niger ). Efforts have begun to populate Floreana Island with tortoises genetically similar to its historical inhabitants while balancing three potentially competing objectives – restoring ecosystem services (sustaining a high tortoise population size), maximizing genome representation of the extinctC. niger species and maintaining a genetically diverse population – under realistic cost constraints.We developed a novel approach to this conservation decision problem by coupling an individual‐based simulation model with generalized additive models and global optimization. We identified several incompatibilities among programme objectives, with quasi‐optimal single‐objective solutions (sets of management actions) differing substantially in programme duration, translocation age, incubation temperature (determinant of sex ratio) and the number of individuals directly translocated from the source population.
Quasi‐optimal single‐objective solutions were able to produce outcomes (i.e. population size and measures of genetic diversity and
C. niger genome representation) to within 75% of their highest simulated outcomes (e.g. highest population size achieved across all simulations) within a cost constraint ofc . $2m USD, but these solutions resulted in severe declines (up to 74% reduction) in outcomes for non‐focal objectives. However, when all programme objectives were equally weighted to produce a multi‐objective solution, all objectives were met to within 90% of the highest achievable mean values across all cost constraints.Synthesis and applications . Multi‐objective conservation translocations are likely to encounter complex trade‐offs and conflicts among programme objectives. Here, we developed a novel combination of modelling approaches to identify optimal management strategies. We found that solutions that simultaneously addressed multiple, competing objectives performed better than single‐objective solutions. Our model‐based decision support tool demonstrates that timely, cost‐effective solutions can be identified in cases where management objectives appear to be incompatible. -
UAVs (unmanned aerial vehicles) or drones are promising instruments for video-based surveillance. Various applications of aerial surveillance use object detection programs to detect target objects. In such applications, three parameters influence a drone deployment strategy: the area covered by the drone, the latency of target (object) detection, and the quality of the detection output by the object detector. Previous works have focused on improving Pareto optimality along the area-latency frontier or the area-quality frontier, but not on the combined area-latency-quality frontier, because of which these solutions are sub-optimal for drone-based surveillance. We explore a three way tradeoff between area, latency, and quality in the context of autonomous aerial surveillance of targets in an area using drones with cameras and an object detection program. We propose Vega, a drone deployment framework that captures these tradeoffs to deploy drones efficiently. We make three contributions with Vega. First, we characterize the ability of the state-of-the-art mobile object detector, EfficientDet [CPVR '20], to detect objects from varying drone altitudes using confidence and IoU curves vs. drone altitude. Second, based on these characteristics of the detector, we propose a set of two algorithmic primitives for drone-based maneuvers, namely DroneZoom and DroneCycle. Using these two primitives, we obtain a more optimal Pareto frontier between our three target parameters - coverage area, detection latency, and detection quality for a single drone system. Third, we scale out our findings to a swarm deployment using higher-order Voronoi tessellations, where we control the swarm's spatial density using the Voronoi order to further lower the detection latency while maintaining detection quality.more » « less
-
We consider a collection of distributed sensor nodes periodically exchanging information to achieve real-time situa- tional awareness in a communication constrained setting, e.g., collaborative sensing amongst vehicles to enable safety-critical decisions. Nodes may be both consumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, whilst producers provide updates on what they are currently able to see. Accordingly, we introduce and explore optimizing trade-offs between the coverage and the space-time average of the “age” of the information available to consumers. We consider two settings that capture the fundamental character of the problem. The first addresses selecting a subset of producers which optimizes a weighted sum of the coverage and the average age given that producers provide updates at a fixed rate. The second addresses the minimization of the weighted average age achieved by a fixed subset of producers with possibly overlapping coverage by optimizing their update rates. The former is shown to be submodular and thus amenable to greedy optimization while the latter has a non-convex/non-concave cost function which is amenable to effective optimization using tools such as the Frank- Wolfe algorithm. Numerical results exhibit the benefits of context dependent optimization information exchanges among obstructed sensing nodes in a communication constrained environment.more » « less
