Review of Applications of Geographic Information Systems (GIS) in Water Resource Engineering

Geographic Information Systems (GIS) have evolved from primarily cartographic and spatial-data management environments into core analytical infrastructure for water-resource engineering. Their application now spans watershed delineation, rainfall–runoff modelling, groundwater assessment, flood and drought analysis, irrigation planning, water-quality assessment, river management and water-distribution infrastructure. However, the literature is fragmented across application domains and frequently attributes the predictive capability of remote sensing, hydrological models or machine-learning algorithms to GIS itself. This review critically synthesizes the evidence contained in a 20-study working corpus and supplements it with recent peer-reviewed literature to identify how GIS contributes to water-resource engineering, where its contribution is strongest, and which methodological weaknesses continue to limit transferability and decision reliability. The review is organized around six application domains and evaluates the GIS role across data integration, terrain analysis, spatial modelling, decision support, visualization and model coupling. The synthesis shows a transition from conventional GIS-based watershed and groundwater mapping toward integrated workflows combining digital elevation models, satellite observations, hydrological/hydraulic models, geostatistics, artificial intelligence and cloud computing. Nevertheless, recurring weaknesses are evident: non-reproducible literature selection, inconsistent validation, inadequate uncertainty quantification, scale mismatch, dependence on subjective weighting in multi-criteria methods, insufficient benchmarking between GIS-enabled and non-GIS workflows, and geographical concentration of evidence.Recent systematic reviews reinforce these concerns, particularly the need for standardized validation and uncertainty protocols and for multi-source, physically informed modelling. The review therefore proposes a GIS–water-resource engineering framework in which GIS is treated not as the predictive model itself but as the spatial integration and decision-support layer connecting observations, process models, data-driven models and management actions. A research agenda is proposed around uncertainty-aware GIS, hybrid physics–AI modelling, cloud-native geospatial workflows, real-time sensor integration, digital twins, explainable AI and reproducible open science. The review provides a conceptual basis for researchers and practitioners to design more transparent, validated and decision-oriented GIS applications in water-resource engineering.