Artificial Intelligence and Data-Driven Insights into Hydrochemical Dynamics under Human and Climate Pressures

Watersheds play a crucial role in the hydrological cycle, encompassing interactions between surface water and groundwater and acting as reservoirs of both natural variability and human activity. These areas significantly capture hydrochemical signatures influenced by geological settings, land use, and climate variability, serving as sensitive indicators of environmental change. In many regions, particularly Mediterranean and semi-arid zones, water resources face increasing pressure from agricultural practices, urban expansion, wastewater discharge, and industrial pollution. Added to this is the impact of climate change, which alters precipitation patterns, exacerbates droughts, and accelerates seawater intrusion into coastal aquifers. Together, these forces threaten water quality and availability, posing significant challenges to ecosystems, agricultural productivity, and human livelihoods. Despite advancements in monitoring and process-based modeling, significant gaps exist in linking hydrochemical dynamics with effective water management strategies. Addressing these gaps requires innovative data-driven approaches, the use of artificial intelligence, and inter-disciplinary collaboration to support sustainable watershed management amidst accelerating global changes.This Research Topic aims to address the urgent challenges facing global water resources by advancing the understanding of hydrochemical dynamics under the stresses of human and climate pressures. We are focused on generating new insights into the mechanisms that control water quality and availability across various watershed settings. Special attention is given to the interaction between natural variability and human activities in shaping hydrological processes. Our objective is to integrate field observations, tracer and isotope applications, and innovative monitoring with advanced modeling techniques. Emphasis is placed on data assimilation, machine learning, and artificial intelligence as tools to connect process understanding with predictive capacity.To gather further insights into this dynamic domain, we welcome articles that address, but are not limited to, the following themes:• Hydrochemical characterization and temporal variability of water resourcesSeawater intrusion, salinization, and drought-driven impacts on aquifers• Anthropogenic contamination from agriculture, urbanization, and industry• Innovative monitoring, tracer, and isotope techniques• Hydrological and hydrochemical modelingWe invite original research articles, reviews, methods papers, and case studies that enhance the understanding of hydrochemical processes and human impacts on surface and groundwater systems. Contributions can focus on individual watersheds or provide broader regional and global perspectives, directly contributing to sustainability frameworks like the UN Sustainable Development Goals. Topic editors: Salah Elsayed Mohamed Elsayed, Saâdia BENMAMAR, Salah Eddine Tachi, Aidi Huo Source: https://www.frontiersin.org/research-topics/75207/artificial-intelligence-and-data-driven-insights-into-hydrochemical-dynamics-under-human-and-climate-pressures