High-Resolution Flow and Nutrient Modeling Under Climate Change in the Flat, Urbanized and Intensively Cultivated Adige River Lowland Basin (Italy) Using SWAT

This study describes the challenges and solutions encountered when developing a high-resolution, process-based hydrological model of the Adige River Lowland Basin (ARLB), a flat, intensively managed agricultural region in northeastern Italy. The model was based on the Soil and Water Assessment Tool (SWAT) and simulates streamflow and nutrient dynamics. Using detailed local hydrological, agricultural, and point-source data, the model robustly reproduces current conditions and projects future scenarios under climate change. Streamflow calibration demonstrated strong performance (NSE up to 0.76), with simulated monthly average discharge (192 m3/s) closely matching observed values (218 m3/s) and capturing intra- and inter-annual variability. Nutrient simulations also aligned well with observations. Total nitrogen (TN) concentrations averaged 1.08 mg/L versus 1.09 mg/L observed. Spatial TN loads were satisfactorily predicted across the subbasins, without additional nutrient calibration to prevent overfitting. Spatial analysis revealed that point sources, notably wastewater treatment plants (WWTPs) along the main river, contribute approximately 65% of the total nitrogen loads, while diffuse agricultural runoff (though secondary in load magnitude) is concentrated in the northern subbasins and is sensitive to climate variability. Climate change projections under 2 °C and 3 °C warming scenarios indicate increases in TN loadings by about 150 and 300 t/y, respectively. Phosphorus loadings exhibited weaker and more variable responses to warming than TN, reflecting model and scenario uncertainties. Overall, this work demonstrates the capability of the proposed modeling approach, based on high-resolution spatio-temporal variables, to model complex lowland hydrology and nutrient fluxes. The model can be used as a decision-support tool for regional nutrient management and climate adaptation strategies.