Quantitative Assessment of Water Resource Sustainability Under Compound Climate Change and Anthropogenic Disturbances

This Research Topic focuses on the quantitative assessment of water resource sustainability within the context of changing environments. As climate change intensifies, the increasing frequency of extreme hydrological events (e.g., droughts and floods), coupled with frequent human interventions (e.g., dam construction, intensive agriculture, rapid urbanization, inter-basin water transfer), renders the assessment of water sustainability highly complex and non-linear. Traditional static evaluation frameworks are inadequate for capturing these dynamic compound risks. The objective of this Research Topic is to gather cutting-edge quantitative methodologies—ranging from process-based hydrological models (e.g., distributed and semi-distributed watershed models, water footprint accounting) and statistical approaches (e.g., multivariate trend analysis, Bayesian inference, extreme value theory) to data-driven and AI-augmented techniques (e.g., deep learning for spatiotemporal forecasting, physics-informed machine learning, causal representation learning, and large language model-driven decision support)—that address the spatial and temporal heterogeneity of water systems. Special attention is given to approaches that integrate multi-source big data—satellite remote sensing, in-situ IoT sensor networks, geospatial human activity data, and reanalysis products—with process-based understanding. By synthesizing novel data-driven, AI-augmented, and hybrid physics-ML approaches, this collection aims to provide robust, evidence-based scientific support for regional water management, climate adaptation policies, and the realization of SDG 6.The problem we aim to address is the inadequacy of traditional static evaluation frameworks in capturing the highly complex, non-linear, and compound risks to water resource sustainability posed by concurrent climate change and anthropogenic disturbances. Current studies often evaluate these drivers in isolation, fail to reflect the acute shocks of sub-seasonal hydrological extremes, and lack actionable downscaling coupled with socio-economic feedback loops.To achieve a robust quantitative assessment, this Research Topic will gather cutting-edge methodologies that integrate multi-source big data (e.g., remote sensing, IoT sensor networks) with advanced techniques, including process-based hydrological models, AI-augmented algorithms, and hybrid physics-ML approaches. By bridging the gaps in uncoupled drivers analysis, scale mismatches, and socio-economic integration, our ultimate goal is to provide robust, evidence-based scientific support for regional water management, climate adaptation policies, and the realization of SDG 6.This Research Topic focuses on the quantitative assessment of water resource sustainability under compound climate change and anthropogenic disturbances. Contributors are encouraged to address specific themes including:1. Disentangling the compound effects of climate and human interventions on water budgets;2. Developing AI-augmented and hybrid physics-ML models for forecasting;3. Fusing multi-source big data for dynamic water resource assessment;4. Downscaling global assessments (e.g., water footprints, planetary boundaries) to actionable local levels;5. Dynamically coupling hydro-ecological processes with socio-economic feedback loops;6. Water resource management under climate change.We welcome original research, methodological advances, and systematic reviews that provide robust, evidence-based scientific support for regional water management, climate adaptation policies, and the realization of SDG 6. Topic editors: Wanshu Nie, Kangle Mo, Hu Qing fang Source: https://www.frontiersin.org/research-topics/85240/quantitative-assessment-of-water-resource-sustainability-under-compound-climate-change-and-anthropogenic-disturbances