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March 14, 2026Sustainable Water Resources Management3 citationsOpen Access

Review of multi-sectoral water management simulation–optimization approaches

RMRafael MagnaboscoVGVanessa A. GodoyJGJ. Jaime Gómez-Hernández

Key Points

  • The review aims to analyze diverse simulation-optimization approaches in multi-sectoral water management to support sustainability.
  • Reviewed 74 case studies from 2014 to 2024
  • Examined modeling strategies and tool sets in simulation-optimization
  • Identified trends in environmental sustainability and cross-sectoral synergy
  • Highlighted methodologies including metaheuristic methods, machine learning, and conflict resolution methods
  • Increased focus on environmental sustainability and ecological restoration
  • Shift towards advanced optimization methods and machine learning in hydrological models
  • Integration of climate scenarios into water management frameworks
  • Enhanced stakeholder engagement recommended through multi-criteria analysis and visual tools

Abstract

In response to growing concerns over climate change, water scarcity, and conflicting sectoral objectives, the simulation–optimization (S–O) modeling approach remains one of the most effective tools to support informed, adaptive, and sustainable decision-making. This is particularly relevant for integrated water resources management (IWRM) and to achieve Sustainable Development Goal 6.5.1. Consequently, this review provides a comprehensive and quantitative analysis of diverse S–O frameworks, specifically examining trends, modeling strategies, and tool sets adopted in the context of multi-sectoral water management (MSWM), based on 74 case studies published between 2014 and 2024. Recent literature reveals an increasing emphasis on environmental sustainability, including water quality and ecological restoration, as well as the adoption of water–food–energy–ecosystem nexus approaches to foster cross-sectoral synergy. Notable trends include the shift toward advanced metaheuristic and hybrid optimization methods, a growing use of machine learning and surrogate models for real-time applications, and the integration of climate and land use change scenarios into hydrological models. Additionally, the use of conflict resolution methods, multi-criteria analysis, and interactive visual tools are recommended to enhance stakeholder engagement and support robust decision making. This article contributes by identifying key patterns, tools, and methodological choices in recent S–O frameworks, offering valuable insights to guide future MSWM studies and applications.

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Cite This Study

Magnabosco et al. (2026) studied this question.

synapsesocial.com/papers/69b4fac6b39f7826a300b799https://doi.org/10.1007/s40899-026-01334-8
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