A meta-modeling approach for hydrological forecasting under uncertainty: Application to groundwater nitrate response to climate change

We suggest a quantitative predictive approach based on meta-modeling techniques for long-term forecasting in hydrology. We apply our scalable methodological framework to a representative groundwater body in EmiliaRomagna region of Italy to evaluate the potential impact of climate change on groundwater nitrate concentration. The approach allows to (i) handle the uncertainty associated with the projection of climate variables, (ii) preserve an accurate description of flow and transport processes, and (iii) drastically accelerate computationally intensive simulations, at a time. A long-term climate scenario (i.e. referred to the 30-years 2061-2090) is developed for the study area by considering changes in the natural recharge to groundwater, the baseflow of the main rivers, and the rate of nitrate leaching. Global sensitivity analysis is performed to assess the relative influence of these factors on the quantity of interest (QoI), i.e. the long-term prediction of groundwater nitrate concentration. Our analysis enables also us to compute the spatial statistics and variability of the moments of the QoI over the study area and to perform Monte Carlo simulations to fully characterize the probabilistic behavior of the QoI at selected sensitive locations.

Automated summary

This study proposes a quantitative predictive approach in hydrology using meta-modeling techniques to assess the potential impact of climate change on groundwater nitrate concentration. Through global sensitivity analysis and Monte Carlo simulations, the relative influence of factors on long-term prediction of groundwater nitrate concentration can be evaluated, providing a comprehensive understanding of the probabilistic behavior of QoI in the study area.