WATRES

A Data-Driven approach to estimate WATershed RESponses

Started
October 24, 2022
Status
In Progress
Share this project

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

SDSC Team:
Quentin Duchemin
Guillaume Obozinski

PI | Partners:

UNIL, Hydrology group:

  • Dr. Paolo Benettin

More info

ETH Zurich, Department of Environmental Systems Science:

  • Prof. James Kirchner
  • Dr. Maria Grazia Zanoni

More info

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?

Figure 1. Example of a watershed and the two related problems: the hydrologic response, which involves the propagation of a pressure wave (celerity) through the subsurface and is responsible for the quick streamflow generation; the hydrologic transport, which involves the actual residence time of water within the system (velocity) and can be revealed through the use of tracers. Individual rainfall events result in large, but short-lived, changes in streamflow (a) and smaller, but much more persistent, changes in streamwater composition (b).

Gallery

Annexe

Additional resources

Bibliography

  1. 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
  2. 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
  3. 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
  4. 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

Publications

Related Pages

More projects

IRMA

In Progress
Interpretable and Robust Machine Learning for Mobility Analysis
No items found.

Feature Learning for Bayesian Inference

In Progress
No items found.

STIMO

In Progress
Personalized epidural electrical stimulation of the lumbar spinal cord for clinically applicable therapy to restore mobility after paralyzing spinal cord injury
No items found.

LAMP

In Progress
Lensless Actinic Metrology for EUV Photomasks
No items found.

News

Latest news

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data
May 1, 2024

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.
The Promise of AI in Pharmaceutical Manufacturing
April 22, 2024

The Promise of AI in Pharmaceutical Manufacturing

The Promise of AI in Pharmaceutical Manufacturing

Innovation in pharmaceutical manufacturing raises key questions: How will AI change our operations? What does this mean for the skills of our workforce? How will it reshape our collaborative efforts? And crucially, how can we fully leverage these changes?
Efficient and scalable graph generation through iterative local expansion
March 20, 2024

Efficient and scalable graph generation through iterative local expansion

Efficient and scalable graph generation through iterative local expansion

Have you ever considered the complexity of generating large-scale, intricate graphs akin to those that represent the vast relational structures of our world? Our research introduces a pioneering approach to graph generation that tackles the scalability and complexity of creating such expansive, real-world graphs.

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!