Resumen de Advanced techniques for solving groundwater and surface water problems in the context of inverse methods and climate change
This work focuses on the investigation of advanced techniques to handle groundwater and surface water problems in the framework of inverse methods and climate change. The Ensemble Kalman filter methods, with particular attention to the Ensemble Smoother with Multiple Data Assimilation (ES-MDA), are extensively analyzed and improved for the solution of different types of inverse problems. In particular, the main novelty is the application of these methods for the identification of time series function.
In the first part of the thesis, after the description of the ES-MDA method, the development of a Python software package for the application of the proposed methodology is presented. It is designed with a flexible workflow that can be easily adapted to implement different variants of the Ensemble Kalman filter and to be applied for the solution of various types of inverse problems. A complemented tool package provides several functionalities that allow to setup the algorithm configuration suiting the specific analyzed problem.
The first novelty application of the ES-MDA method aimed at solving the reverse flow routing problem. The objective of the inverse procedure is the estimation of an unknown inflow hydrograph to a hydraulic system on the basis of information collected downstream and a given forward routing model that relates inflow hydrograph and downstream observations. The procedure is tested by means of two synthetic examples and a real case study; the impact of ensemble sizes and the application of covariance localization and inflation techniques are also investigated. The tests show the capability of the proposed method to solve this type of problem; the performance of ES-MDA improves, especially for small ensemble sizes, when covariance localization and inflation techniques are applied.
The second application, in the context of surface water, concerns the calibration of a hydrological-hydraulic model that simulates rainfall-runoff processes. The ES-MDA is coupled with the numerical model by parallel way for the estimation of roughness and infiltration coefficients based on the knowledge of a discharge hydrograph at the basin outlet. The results of two synthetic tests and a real case study demonstrate the capability of the proposed method to calibrate the hydrological-hydraulic model with a reasonable computational time.
In the groundwater field, ES-MDA is applied for the first time to simultaneously identify the source location and the release history of a contaminant spill in an aquifer from a sparse set of concentration data collected in few points of the aquifer. The impacts of the concentration sampling scheme, the ensemble size and the use of covariance localization and covariance inflation techniques are tested; furthermore, a new procedure to perform a spatiotemporal iterative localization is presented. The methodology is tested by means of an analytical example and a study case that uses real data collected in a laboratory sandbox. ES-MDA leads to a good estimation of the investigated parameters; a well-designed monitoring network and the use of covariance corrections improve the performance of the method and help to minimize ill-posedness and equifinality.
A part of the thesis investigates the impact of climate change on the groundwater availability. A surrogate model that describes the response of groundwater levels to meteorological variables up to 2100 is presented. It is a simple statistical approach based on the correlations between groundwater levels and two drought indices that depend on precipitation and temperature data. The presented method is used to evaluate the impact of climate change on groundwater resources in a study area located in Northern Italy using historical and regional climate model data. The results denote a progressive increase of groundwater droughts in the investigated area.
