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WATRES
A Data-Driven approach to estimate WATershed RESponses
![](https://cdn.prod.website-files.com/63f1f58039379743bd96333e/666028cb0d5ad61045ec7b8d_academic-project_thumbnail_watres.webp)
Abstract
Water flowing through the landscape as groundwater or streamflow is made of innumerable water parcels of different ages (or residence times), which enter through precipitation and mix along their journey. Rivers often react quickly to rainfall events and can cause water quantity problems like
floods. But rivers are also known to transport significant amounts of “old” water, which is stored for years before being discharged and can have a large influence on water quality. While the timing of watershed responses is key to our understanding of flood generation and water quality processes, quantifying these responses is complex because they can be nonlinear, time-variable and may take irregular shapes that are difficult to predict a priori. The sensor revolution now provides both flow and tracer measurements at high resolution, but these technical advances have not been matched by data analysis techniques that can unleash the full power of the new data. Current methodologies typically rely on strong assumptions (e.g., stationarity) and on models that are calibrated against data but not yet data-driven. The goal of this project is to develop a new knowledge-guided but data-driven methodology to estimate the timing of watershed responses. This methodology will leverage streamflow data from over 150 sites across Switzerland and streamflow concentration data
from the highest-resolution Swiss water quality dataset.
This project aims to develop a statistical learning algorithm to quantify water residence time distributions (and the associated uncertainty) from water quantity and water quality data. The algorithms will be applied to real-world watersheds to quantify the characteristic landscape responses in terms of water flow and water age. This project will allow us to advance a scientific problem that is also highly relevant for society, as water security from floods and droughts, and a fair distribution of water are among our most fundamental needs.
People
Collaborators
![Quentin Duchemin](https://cdn.prod.website-files.com/63f1f58039379743bd96333e/6413245b5da3725fdb0c419d_collaborator_quentin-duchemin_picture1.jpeg)
![](https://cdn.prod.website-files.com/63f1f58039379743bd96333e/6413245b5da3725fdb0c419d_collaborator_quentin-duchemin_picture1.jpeg)
Quentin graduated with an engineering degree in mathematics and computer science from École des Ponts ParisTech in 2019. After a 6-month experience at the Center for Data Science of the New York University working on applied Machine Learning for medical imaging, he did a PhD in Statistics at Gustave Eiffel University (Paris). During his PhD, Quentin worked on random graphs and selective inference. His recent cross-disciplinary collaborations involve applications in biology and hydrology.
![Guillaume Obozinski](https://cdn.prod.website-files.com/63f1f58039379743bd96333e/6601363e5d95ac39e0b92a4b_collaborator_guillaume-obozinski_picture2.jpg)
![](https://cdn.prod.website-files.com/63f1f58039379743bd96333e/6601363e5d95ac39e0b92a4b_collaborator_guillaume-obozinski_picture2.jpg)
Guillaume Obozinski graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.
description
Motivation
Watershed responses are key to our understanding of flood generation and water quality processes, but quantifying these responses is complex because they can be nonlinear, time-variable and may take irregular shapes that are difficult to predict a priori. The sensor revolution now provides both flow and tracer measurements at high resolution, but these technical advances have not been matched by data analysis techniques that can unleash the full power of the new data.
Proposed Approach / Solution
The goal of this project is to develop a new knowledge-guided but data-driven methodology to estimate the response of watersheds in terms of water quantity and water quality (cf. Figure 1). The fundamental milestones that will mark progress of the project are i) the release of a statistical learning algorithm to quantify watershed response times (and the associated uncertainty) from water quantity and water quality data and ii) the successful application of the algorithm to real-world sites.
Impact
By exploiting advanced data science techniques, this projects aims to achieve a systematic assessment of water flow and transport characteristic timescales. We will investigate the following questions: what are the timescales of flow and transport response in real-world watersheds? How are they related to each other? How do they vary over time? Which factors (climate, topography, watershed size) control these timescales?
![](https://cdn.prod.website-files.com/63f1f58039379743bd96333e/66602b3fbf2f1976e25e31fd_academic-project_watres_figure1.webp)
Presentation
Gallery
Annexe
Additional resources
Bibliography
- Kirchner, J. W. (2019). Quantifying new water fractions and transit time distributions using ensemble hydrograph separation: theory and benchmark tests. Hydrology and Earth System Sciences, 23(1), 303–349. doi:10.5194/hess-23-303-2019
- Benettin, P., & Bertuzzo, E. (2018). tran-SAS v1.0: a numerical model to compute catchment-scale hydrologic transport using StorAge Selection functions. Geoscientific Model Development, 11(4), 1627–1639. doi:10.5194/gmd-11-1627-2018
- Harman, C. J. (2015). Time-variable transit time distributions and transport: Theory and application to storage-dependent transport of chloride in a watershed. Water Resources Research, 51(1), 1–30. doi:10.1002/2014wr015707
- Kirchner, J. W. (2022). Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and demixing of noisy time series. Sensors (Basel, Switzerland), 22(9), 3291. doi:10.3390/s22093291
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