Author(s): Matteo Masi; Fabio Castelli; Maryam Barati Moghaddam; Chiara Arrighi

Linked Author(s): Fabio Castelli, Chiara Arrighi

Keywords: No Keywords

Abstract: Nutrient pollution in rivers remains a major environmental challenge, demanding modelling tools that can guide effective basin-scale water management. Water quality prediction at large scales is often affected by limited monitoring and high uncertainty in complex biogeochemical processes. This work introduces an integrated framework that couples a hydrological model with a reactive–transport module to simulate nutrient dynamics in large catchments. A spatially regularized calibration using the PEST++ iterative ensemble smoother estimates distributed nutrient loads while estimating predictive uncertainty. The method was applied to the Arno River basin in Italy, simulating eight water quality variables including biological oxygen demand, dissolved oxygen, nitrogen, phosphorus, and algal biomass over 2011–2020. Calibration used 8151 spot observations from 70 sampling points, showing good predictability of constituents. The model identified pollution hotspots, particularly urban-linked ammonium and organic loads, and provided spatially explicit nutrient estimates with uncertainty, offering a practical tool for decision-making in data-scarce catchments.

DOI:

Year: 2026