Unterschiede
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- | ===== Linking specific evaluation metrics to hydrologic processes | + | ===== Vorlage |
== Motivation == | == Motivation == | ||
- | 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 metrics. | ||
- | Hydrologic processes can be captured by hydrologic signatures. Including these | ||
- | hydrologic signatures into the evaluation process may improve the assessment of | ||
- | process realism of hydrologic models. | ||
- | == Research objectives == | ||
- | The aim of this MSc thesis is to define new process-specific 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, | ||
- | == Methodology | + | == Ziele der Arbeit |
- | McMillan (2020) provide an extensive summary on hydrologic signatures. Suitable | + | |
- | hydrologic signatures have to be selected to define process-specific evaluation | + | |
- | 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). | + | |
- | == Supervision == | ||
- | Robin Schwemmle, Markus Weiler | ||
- | == Further notes == | + | == Methode |
- | 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. | + | |
- | == Contact == | ||
- | Robin Schwemmle | ||
- | [[robin.schwemmle@hydrology.uni-freiburg.de]] | ||
- | == Skills and challenges | + | == Betreuung |
- | Handling large data sets, advanced programming skills, knowledge in the | + | |
- | programming language Python, (bivariate or multivariate statistics) | + | |
- | == Sprache == | ||
- | English (or German) | ||
- | == Literatur | + | == Zusätzliche Hinweise |
- | 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/ | + | |
- | 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/ | ||
- | McMillan, H.: Linking hydrologic signatures to hydrologic processes: A review, Hydrological Processes, 34, 1393-1409, 10.1002/ | + | == Kontakt == |
+ | Name | ||
+ | [[name@hydrology.uni-freiburg.de]] | ||
- | 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, | + | == Herausforderungen == |
+ | |||
+ | |||
+ | == Sprache == | ||
+ | |||
+ | |||
+ | == Literatur == | ||
- | Schwemmle, R., Demand, D., and Weiler, M.: Technical note: Diagnostic efficiency – specific evaluation of model performance, | ||
- | 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/ | + | {{tag>}} |
- | {{tag>master daten offen}} | + | |