The EU Nitrate Directive has been ruling for almost 30 years, nevertheless nitrate concentration in the Lombardy Plain did not decrease. Together with failures of management implementation, a possible cause for such field observations is that management actions were taken without adequately considering the actual hydrogeological dynamics. To consider this aspect, the paper presents a groundwater flow and transport numerical model of a specific area of the Lombardy Plain. The aim of this model is to demonstrate how modelling, as a management tool, can be useful in the governance process. The groundwater model, using well-known MODFLOW-MT3D codes, is based on existing hydrogeological information, while a nitrogen mass balance has been performed at municipal scale to determine the agricultural N surplus to the subsurface. The model adequately reproduces head levels and nitrate concentrations in observation wells for a 10-year simulation period, showing that 4.5% of the N annual input remains stored in the system. The model indicates the efficiency of rivers and springs to export N out from the system at an estimated rate of 77.5% of the annual N inputs. Back to governance, the model shows that management data at municipal level (e.g. irrigation rates, groundwater withdrawal, N net recharge) provide a satisfactory scale for successfully reproducing nitrate evolution. Hence those variables that can be object of debate during a governance process can be treated as input data to the numerical model. Therefore, backcasting exercises can be conducted to check whether the model outcome fits with the expected results of specific management actions. The model highlights how the N mass balance evolves, providing clues on which factors can be managed to reduce nitrate concentrations and meet the Directive's requirements. Numerical groundwater models, as an option to address water management issues, ultimately contribute to solve the information and capacity governance gaps.

Governance and groundwater modelling: Hints to boost the implementation of the EU Nitrate Directive. The Lombardy Plain case, N Italy

Re V.;
2021-01-01

Abstract

The EU Nitrate Directive has been ruling for almost 30 years, nevertheless nitrate concentration in the Lombardy Plain did not decrease. Together with failures of management implementation, a possible cause for such field observations is that management actions were taken without adequately considering the actual hydrogeological dynamics. To consider this aspect, the paper presents a groundwater flow and transport numerical model of a specific area of the Lombardy Plain. The aim of this model is to demonstrate how modelling, as a management tool, can be useful in the governance process. The groundwater model, using well-known MODFLOW-MT3D codes, is based on existing hydrogeological information, while a nitrogen mass balance has been performed at municipal scale to determine the agricultural N surplus to the subsurface. The model adequately reproduces head levels and nitrate concentrations in observation wells for a 10-year simulation period, showing that 4.5% of the N annual input remains stored in the system. The model indicates the efficiency of rivers and springs to export N out from the system at an estimated rate of 77.5% of the annual N inputs. Back to governance, the model shows that management data at municipal level (e.g. irrigation rates, groundwater withdrawal, N net recharge) provide a satisfactory scale for successfully reproducing nitrate evolution. Hence those variables that can be object of debate during a governance process can be treated as input data to the numerical model. Therefore, backcasting exercises can be conducted to check whether the model outcome fits with the expected results of specific management actions. The model highlights how the N mass balance evolves, providing clues on which factors can be managed to reduce nitrate concentrations and meet the Directive's requirements. Numerical groundwater models, as an option to address water management issues, ultimately contribute to solve the information and capacity governance gaps.
2021
Musacchio, A.; Mas-Pla, J.; Soana, E.; Re, V.; Sacchi, E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1106466
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