Global hydrological models have been applied for large-scale water resources estimation, as well as flood and drought hazard assessment. However, despite the knowledge that global models may have strongly limited prediction performance, strict methods benchmark those models have rarely been applied mostly due to the lack of large data set and the difficulty to compare gridded large-scale simulations with discharge observations that are available for certain catchments.
The aim of this MSc thesis is systematic comparison of a recently developed global set of hydrological simulations with a large set of observed discharge time series (~7000) around the globe. In a second step the model error will be compared to climatic, topographic and geologic descriptors to identify those catchment settings, at which large scale models have to be improved.
The simulations are derived from the Earth2Observe model ensemble (Schellekens et al., 2016), while the catchment observations are provided by Dr Hylke Beck, Princeton University (Beck et al., 2015). Similar to previous studies (Beck et al., 2017; Gudmundsson et al., 2012; Tallaksen and Stahl, 2014) a strategy to compare the gridded data with the catchment-based observations has to be developed. Deviations between observations and simulations will be derived for different ranges of flow percentiles. Multi-variate statistics (multivariate regression, regression trees, random forests, principal component analysis, mixed linear models, etc) will be used how the simulation error correlates with the three dimensions climate, topography and geology that are considered to describe a hydrological system (Winter, 2001).
The primary challenge of this MSc thesis is the handling of very large data sets that exceed 100s of gigabytes. To succeed, advanced programming and parallel computing skills are required. Further assets are interest in multivariate statistic and the knowledge to apply some of the abovementioned methods with a big data set.
Andreas Hartmann, Inge de Graaf, Hylke Beck, Paolo Perona
Andreas Hartmann email@example.com Tel. +49 (0)761 / 203-3520
The data of the Earth2Observe model ensemble is openly available and must be downloaded by the student. The catchment observations, as well as a range of climatic, topographic and geological descriptors are provided by Dr Hylke Beck, Princeton University.
Beck, H. E., de Roo, A. and van Dijk, A. I. J. M.: Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments*, J. Hydrometeorol., 16(4), 1478–1501, doi:10.1175/JHM-D-14-0155.1, 2015.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R. and Schellekens, J.: Global evaluation of runoff from ten state-of-the-art hydrological models, Hydrol. Earth Syst. Sci. Discuss., 21, 2881–2903, doi:10.5194/hess-2016-124, 2017.
Gudmundsson, L., Tallaksen, L. M., Stahl, K., Clark, D. B., Dumont, E., Hagemann, S., Bertrand, N., Gerten, D., Heinke, J., Hanasaki, N., Voss, F. and Koirala, S.: Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe, J. Hydrometeorol., 13(2), 604–620, doi:10.1175/JHM-D-11-083.1, 2012.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W. and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., 1, 1–35, doi:10.5194/essd-2016-55, 2016.
Tallaksen, L. M. and Stahl, K.: Spatial and temporal patterns of large-scale droughts in Europe: Model dispersion and performance, Geophys. Res. Lett., 41(2), 2013GL058573, doi:10.1002/2013gl058573, 2014.
Winter, T. C.: The Concept of Hydrologic Landscapes, JAWRA J. Am. Water Resour. Assoc., 37(2), 335–349, doi:10.1111/j.1752-1688.2001.tb00973.x, 2001.