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<p>Water allocation in arid and semiarid regions remains a critical challenge due to high climatic variability, structural scarcity, and persistent evaporative losses. This study proposes a transferable decision-support framework that integrates hydrological data, reservoir morphometry, and hierarchical clustering to support allocation under uncertainty. The approach combines elevation–area–volume (EAV) relationships, precipitation and evaporation data, and multi-scenario simulations to generate graphical allocation abaci that relate initial storage, demand, and planning horizon. The framework is demonstrated using a dataset of 130 reservoirs, enabling the identification of five morphometric classes with distinct sensitivities to evaporation and storage depletion. Results show that reservoir geometry strongly controls both the magnitude and rate of evaporative losses, particularly in systems with large surface-area expansion near maximum storage levels. The resulting abaci provide an intuitive and technically robust representation of allocation feasibility, enabling rapid assessment of trade-offs among storage, demand, and time horizon. By moving beyond fixed operating rules and single-horizon assumptions, the framework offers a flexible and scalable alternative for reservoir allocation analysis. Although demonstrated for a semiarid system, the methodology is generic and applicable to reservoir systems facing water scarcity worldwide.</p> Graphical Abstract <p></p>

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From Situation to Decision: A Transferable Graphical Decision-Support Framework for Water Allocation in Arid and Semiarid Reservoir Systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Reginaldo Moura Brasil Neto,
  • Antonio Rondinelly da Silva Pinheiro,
  • Richarde Marques da Silva,
  • Leandeson Pinheiro Santos de Araújo,
  • Celso Augusto Guimarães Santos

摘要

Water allocation in arid and semiarid regions remains a critical challenge due to high climatic variability, structural scarcity, and persistent evaporative losses. This study proposes a transferable decision-support framework that integrates hydrological data, reservoir morphometry, and hierarchical clustering to support allocation under uncertainty. The approach combines elevation–area–volume (EAV) relationships, precipitation and evaporation data, and multi-scenario simulations to generate graphical allocation abaci that relate initial storage, demand, and planning horizon. The framework is demonstrated using a dataset of 130 reservoirs, enabling the identification of five morphometric classes with distinct sensitivities to evaporation and storage depletion. Results show that reservoir geometry strongly controls both the magnitude and rate of evaporative losses, particularly in systems with large surface-area expansion near maximum storage levels. The resulting abaci provide an intuitive and technically robust representation of allocation feasibility, enabling rapid assessment of trade-offs among storage, demand, and time horizon. By moving beyond fixed operating rules and single-horizon assumptions, the framework offers a flexible and scalable alternative for reservoir allocation analysis. Although demonstrated for a semiarid system, the methodology is generic and applicable to reservoir systems facing water scarcity worldwide.

Graphical Abstract