SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Water quality sustainability evaluation under uncertainty: A multi-scenario analysis based on Bayesian networks 
Authors:Sperotto, A., J.L. Molina, S. Torresan, A. Critto, M. Pulido-Velazquez and A. Marcomini 
Volume (Issue):11(17) 
Article ID:4764 
URL (non-DOI journals): 
Broad Application Category:hydrologic & pollutant 
Primary Application Category:climate change 
Secondary Application Category:nutrient cycling/loss and transport 
Watershed Description:140 km^2 Zero River, which is located within the Venetian floodplain and drains to the Venice Logoon in northeast Italy. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 different combinations of a global climate model (GCM)–regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041–2070) and long-term (2071–2100) periods with respect to the baseline (1983–2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between different GCM–RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources. 
Keywords:water quality; climate change; Bayesian networks; uncertainty; multi-models