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The degradation of water quality, pivotal for sustainable development, is exacerbated by the interconnected impacts of climate change (CC) and land-use/land-cover change (LULCC). Rapid global development has amplified these effects, disrupting key processes that regulate river flows and runoff. LULCC, particularly in agriculture, plays a central role in nutrient cycle and runoff to the water bodies, thus affecting the water quality and ecosystem resilience. Concurrently, climate change intensifies algal blooms, reduces dissolved oxygen, and alters aquatic ecosystems. Shifts in precipitation and rising temperatures amplify pollutant transport and compromise water quality. Moreover, the complex interactions among LULCC and CC have significant impacts on nutrients, pollution concentrations, and sedimentation rates in freshwater ecosystems. This research focuses on the analysis of the dynamics and impacts of LULCC and climate-induced changes on the state of water quality at the river basin scale in Italy, supporting the achievement of good chemical and ecological status of the Water Framework Directive. First, this study aims to analyse LULCC pattern changes across various spatial scales with the use of geoinformation system (GIS) techniques to identify sensitivity and changes at the pixel level within different land use types. This analysis allows the identification of linkages and dependencies between LULCC indicators and their relationships with water quality parameters in each river basin of Italy. Second, to address the compound effects of climate change, this study examines historical patterns of climate change indicators and their correlation with water quality over time. It also investigates the temporal and spatial occurrence of extreme events, which are linked to changes in nutrient and pollutant levels. The final phase of the methodology involves the development of a machine-learning model aiming at understanding and predicting the complex interplay of multiple risk factors such as the combined effects of land-use change, climate variability, and other anthropogenic influences on water quality. By using machine learning techniques, the study will be able to identify intricate patterns and non-linear relationships between multiple risk factors and their collective influence on water quality dynamics. The results will provide a comprehensive understanding required for adaptive measures and decision-making support in Italy.
Rufo et al. (Fri,) studied this question.