Hydrologic models provide the ability to simulate hydrologic fluxes. However, these simulated fluxes may deviate from observed fluxes. Therefore, assessing the process realism of these hydrologic models require process-specific evaluation metrics. Hydrologic processes can be captured by hydrologic signatures. Including these hydrologic signatures into the evaluation process may improve the assessment of process realism in hydrologic models.

The aim of this MSc thesis is to define new process-specific evaluation metrics and test their applicability. Optionally, the model error quantified by the metric may be linked to model parameters or input data by statistical analysis (e.g. correlation, random forest).

McMillan (2020) provide an extensive summary on hydrologic signatures. Suitable hydrologic signatures have to be selected to define diagnostic efficiency metrics (Schwemmle et al., 2020) for specific processes (e.g. baseflow). In the following, these newly defined metrics can be calculated using the CAMELS US data set (Newman et al., 2015).

Robin Schwemmle, Markus Weiler

The CAMELS US data set (Addor et al., 2017; Newman et al., 2015) is publicly available and must be downloaded by the student. The data set contains observed streamflow time series and catchment attributes for each catchment. Moreover, simulated streamflow time series (coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model) and the accompanying parameters and input data are available for each catchment. The catchments span over a wide range of hydroclimatic conditions.

Robin Schwemmle robin.schwemmle@hydrology.uni-freiburg.de

Handling large data sets, advanced programming skills, knowledge in the programming language Python, (bivariate or multivariate statistics)

English (or German)

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293-5313, 10.5194/hess-21-5293-2017, 2017.

Beven, K.: Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process, Hydrol. Earth Syst. Sci., 11, 460-467, 10.5194/hess-11-460-2007, 2007.

Gnann, S. J., Coxon, G., Woods, R. A., Howden, N. J. K., and McMillan, H. K.: TOSSH: A Toolbox for Streamflow Signatures in Hydrology, Environmental Modelling & Software, 138, 104983, 10.1016/j.envsoft.2021.104983, 2021.

McMillan, H.: Linking hydrologic signatures to hydrologic processes: A review, Hydrological Processes, 34, 1393-1409, 10.1002/hyp.13632, 2020.

Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209-223, 10.5194/hess-19-209-2015, 2015.

Schwemmle, R., Demand, D., and Weiler, M.: Technical note: Diagnostic efficiency – specific evaluation of model performance, Hydrol. Earth Syst. Sci., 25, 2187-2198, 10.5194/hess-25-2187-2021, 2021.

Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resources Research, 44, 10.1029/2007wr006716, 2008.