10.5880/GFZ.4.4.2019.004
eng
dataset
GFZ German Research Centre for Geosciences
datapub@gfz-potsdam.de
http://www.gfz-potsdam.de
Helmholtz-Centre Potsdam - GFZ German Research Centre for Geosciences
Helmholtz-Centre Potsdam - GFZ German Research Centre for Geosciences
pointOfContact
2021-04-26
urn:ogc:def:crs:EPSG:4326
Simulated sensitivity time series and model performance in three German catchments
2021-04-26
revision
doi:10.5880/GFZ.4.4.2019.004
Guse, Björn
GFZ German Research Centre for Geosciences, Potsdam, Germany
author
Pfannerstill, Matthias
Christian-Albrechts University of Kiel, Department of Hydrology and Water Management, Kiel, Germany
author
Kiesel, Jens
Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
author
Strauch, Michael
UFZ-Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Leipzig, Germany
author
Volk, Martin
UFZ-Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Leipzig, Germany
author
Gupta, Hoshin
The University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, Arizona, USA.
author
Fohrer, Nicola
Christian-Albrechts University of Kiel, Department of Hydrology and Water Management, Kiel, Germany
author
GFZ Data Services
publisher
The data sets contains the major results of the article “Improving information extraction from model data using sensitivity-weighted performance criteria“ written by Guse et al. (2020). In this article, it is analysed how a sensitivity-weighted performance criterion improves parameter identifiability and model performance. More details are given the in article.
The files of this dataset are described as follows.
Parameter sampling: FAST parameter sampling.xlsx:
To estimate the sensitivity, the Fourier Amplitude Sensitivity Test (FAST) was used (R-routine FAST, Reusser, 2013). Each column shows the values of the model parameter of the SWAT model (Arnold et al., 1998). All parameters are explained in detail in Neitsch et al. (2011). The FAST parameter sampling defines the number of model runs. For twelve model parameters as in this case, 579 model runs are required. The same parameter sets were used for all catchments.
Daily sensitivity time series: Sensitivity_2000_2005.xlsx:
Daily time series of parameter sensitivity for the period 2000-2005 for three catchments in Germany (Treene, Saale, Kinzig). Each column shows the sensitivity of one parameter of the SWAT model. The methodological approach of the temporal dynamics of parameter sensitivity (TEDPAS) was developed by Reusser et al. (2011) and firstly applied to the SWAT model in Guse et al. (2014). As sensitivity index, the first-order partial variance is used that is the ratio of the partial variance of one parameter divided by the total variance. The sensitivity is thus always between 0 and 1. The sum in one row, i.e. the sensitivity of all model parameters on one day, could not be higher than 1.
Parameter sampling: LH parameter sampling.xlsx:
To calculate parameter identifiability, Latin Hypercube sampling was used to generate 2000 parameter sets (R-package FME, Soetaert and Petzoldt, 2010). Each column shows the values of the model parameter of the SWAT model (Arnold et al., 1998). All parameters are explained in detail in Neitsch et al. (2011). The same parameter sets were used for all catchments.
Performance criteria with and without sensitivity weights: RSR_RSRw_cal.xlsx:
• Calculation of the RSR once and RSRw separately for each model parameter.
• RSR: Typical RSR (RMSE divided by standard deviation)
• RSR_w: RSR with weights according to daily sensitivity time series.
The calculation was carried out in all three catchments.
• The column RSR shows the results of the RSR (RMSE divided by standard deviation) for the different model runs.
• The column RSR[_parameter name] shows the calculation of the RSR_w for the specific model parameter.
• RSR_w give weights on each day based on the daily parameter sensitivity (as shown in sensitivity_2000_2005.xlsx). This means that days with a higher parameter sensitivity are higher weighted.
In the methodological approach the best 25% of the model runs were calculated (best 500 model runs) and the model parameters were constrained to the most appropriate parameter values (see methodological description in the article).
Performance criteria for the three catchments: GOFrun_[catchment name]_RSR.xlsx:
These three tables are organised identical and are available for the three catchments in Germany (Treene, Saale, Kinzig). In using the different parameter ranges for the catchments as defined in the previous steps, 2000 model simulation were carried out. Therefore, a Latin-Hypercube sampling was used (R-package FME, Soetaert and Petzoldt, 2010). The three tables show the results of 2000 model simulations for ten different performance criteria for the two different methodological approaches (RSR and swRSR) and two periods (calibration: 2000-2005 and validation: 2006-2010).
Performance criteria for the three catchments: GOFrun_[catchment name]_MAE.xlsx:
The three tables show the results of 2000 model simulations for ten different performance criteria for the two different methodological approaches (MAE and swMAE) and two periods (calibration: 2000-2005 and validation: 2006-2010).
Complete
Guse, Björn
bfguse@gfz-potsdam.de, bguse@hydrology.uni-kiel.de
pointOfContact
Hydrological modeling
Sensitivity analysis
Temporal dynamics of parameter sensitivity
Parameter identifiability
Performance criteria
Catchment hydrology
EARTH SCIENCE > TERRESTRIAL HYDROSPHERE
EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > WATERSHED CHARACTERISTICS
EARTH SCIENCE SERVICES > MODELS > HYDROLOGIC AND TERRESTRIAL WATER CYCLE MODELS
NASA/GCMD Earth Science Keywords
publication
CC BY 4.0
CC BY 4.0
10.1111/j.1752-1688.1998.tb05961.x
DOI
References
10.1002/hyp.9777
DOI
References
https://swat.tamu.edu/media/99192/swat2009-theory.pdf
URL
References
10.1029/2010WR009947
DOI
References
http://CRAN.R-project.org/package=fast
URL
References
10.18637/jss.v033.i03
DOI
References
10.1029/2019WR025605
DOI
IsSupplementTo
eng
geoscientificInformation
Treene catchment
9.24092
9.68106
54.503
54.749
Saale catchment
11.5736
11.9983
50.1709
50.4141
Kinzig catchment
8.91014
9.6682
50.0955
50.3251
http://doi.org/10.5880/GFZ.4.4.2019.004
WWW:LINK-1.0-http--link
Data Access - DOI
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