Observing a climate change adaptation solution’s linkages with groundwater dependent ecosystems: a modeling and field based approach
Groundwater plays a crucial role in the water cycle and in sustaining life and human activities, and can help alleviate hydrological drought periods serving as a buffer reservoir. Groundwater sustainability, however, relies on a delicate balance between recharge and discharge, in which humans play an important role, with influences on groundwater dependent ecosystems. Agricultural Managed Aquifer Recharge (Ag-MAR) can be an adaptation measure to drought periods and expected climate change effects on water resources. As part of the Interreg CE project MAURICE, an off-season irrigation practice is proposed in Lombardy as a climate change adaptation strategy, storing surface water into aquifers in periods of exceedance (autumn/winter) using the existing irrigation network’s canals as a "natural" infiltration system.
In the case study area, a great number of typical Northern Italy lowland springs, called “fontanili”, is present, used since the XIV century to irrigate fields while generating biodiversity hotspots right in a urbanized area. In the past decades, they have been largely abandoned and endangered by infrastructures and decreasing groundwater levels. Their relationship with groundwater still holds some uncertainties related to their interaction with the aquifer along their course and their influence on the surrounding groundwater system, which could hinder the performance of measures like the proposed Ag-MAR.
This work presents numerical models in MODFLOW that, with increasing complexity, reproduce a single lowland spring’s behavior based on in-situ observations and literature, considering pros and cons of the different methods. Then, the lowland springs have been implemented in a larger scale model, to simulate scenarios assessing their response to the implementation of off-season irrigation. Results bring more light to these unique systems’ behavior and show concrete and successful possibilities of modeling them and applying Ag-MAR also in their presence.