Water Residence Time Estimation by 1D Deconvolution in the Form of a l 2 -Regularized Inverse Problem With Smoothness, Positivity and Causality Constraints

Abstract : The Water Residence Time distribution is the equivalent of the impulse response of a linear system allowing the propagation of water through a medium, e.g. the propagation of rain water from the top of the mountain towards the aquifers. We consider the output aquifer levels as the convolution between the input rain levels and the Water Residence Time, starting with an initial aquifer base level. The estimation of Water Residence Time is important for a better understanding of hydro-bio-geochemical processes and mixing properties of wetlands used as filters in ecological applications, as well as protecting fresh water sources for wells from pollutants. Common methods of estimating the Water Residence Time focus on cross-correlation, parameter fitting and non-parametric deconvolution methods. Here we propose a 1D full-deconvolution, regularized, non-parametric inverse problem algorithm that enforces smoothness and uses constraints of causality and positivity to estimate the Water Residence Time curve. Compared to Bayesian non-parametric deconvolution approaches, it has a fast runtime per test case; compared to the popular and fast cross-correlation method, it produces a more precise Water Residence Time curve even in the case of noisy measurements. The algorithm needs only one regularization parameter to balance between smoothness of the Water Residence Time and accuracy of the reconstruction. We propose an approach on how to automatically find a suitable value of the regularization parameter from the input data only. Tests on real data illustrate the potential of this method to analyze hydrological datasets.
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Alina G. Meresescu, Matthieu Kowalski, Frédéric Schmidt, François Landais. Water Residence Time Estimation by 1D Deconvolution in the Form of a l 2 -Regularized Inverse Problem With Smoothness, Positivity and Causality Constraints. Computers and Geosciences, Elsevier, 2018, 115, pp.105-121. ⟨10.1016/j.cageo.2018.03.009⟩. ⟨hal-01735678⟩



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