Value of data in hydrological modelling
Sustainable management of water resources and mitigation of natural hazards often rely on accurate and reliable discharge estimates that are predicted by runoff models. Most runoff models need to be calibrated, which is an essential step in their application. In my PhD project I look at the value of data for model calibration from two different perspectives:
1) Gauging the ungauged catchment:
In some situations it might be feasible to make a few discharge measurements in an ungauged catchment that can be used for parameter estimation. In particular we address the following questions: “Which discharge measurements are most informative?”, and “Do discharge measurements improve parameter regionalization?”
2) Effect of the objective function:
The selection of the objective function used to optimize model parameters has a crucial effect on the simulated hydrographs. Given that multiple years of discharge data are available, we test the effect of commonly used statistical metrics or hydrological signatures on the models ability to reproduce a range of hydrograph aspects.
Brunner, Manuela; Pool, Sandra; Kiewiet, Leonie; Acheson, Elise (2018). The other’s perception of a streamflow sample: from a bottle of water to a data point. Hydrological Processes: 1-6.
Pool, Sandra; Vis, Marc J P; Knight, Rodney R; Seibert, Jan (2017). Streamflow characteristics from modeled runoff time series – importance of calibration criteria selection. Hydrology and Earth System Sciences, 21(11):5443-5457.
Pool, Sandra; Viviroli, Daniel; Seibert, Jan (2017). Prediction of hydrographs and flow-duration curves in almost ungauged catchments: Which runoff measurements are most informative for model calibration? Journal of Hydrology, 554:613-622.
Vis, Marc; Knight, Rodney; Pool, Sandra; Wolfe, William; Seibert, Jan (2015). Model calibration criteria for estimating ecological flow characteristics. Water, 7(5):2358-2381.
Schneider, Philipp; Pool, Sandra; Strouhal, Ludek; Seibert, Jan (2014). True colors - experimental identification of hydrological processes at a hillslope prone to slide. Hydrology and Earth System Sciences, 18(2):875-892.
Outstanding Student Paper Award, AGU 2017 Fall Meeting, New Orleans, LA, USA