WATRES

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.