Flow analysis with WaSiM-ETH – model parameter sensitivity at different scales

Cullmann, Johannes; Mishra, Vijay Kumar; Peters, Richard L.

doi:10.5194/adgeo-9-73-2006

Cited by 26 publications

(11 citation statements)

References 3 publications

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“…Those SA methods have been widely used for land surface and hydrologic models (e.g., Salehi et al 2000;Cullmann et al 2006;Demaria et al 2007;Tang et al 2007;Pappenberger et al 2008;Hall et al 2009;Reusser et al 2011;Massmann and Holzmann 2012). The comparison of SA methods has been the interest of many studies (e.g., Saltelli et al 2005;Frey and Patil 2002;Hall et al 2009;Confalonieri et al 2010;Reusser et al 2011;Massmann and Holzmann 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Shi

Davis

Zhang

et al. 2014

Journal of Hydrometeorology

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Land surface models (LSMs) and hydrologic models are parameterized models. The number of involved parameters is often large. Sensitivity analysis (SA) is a key step to understand the complex relationships between parameters and between state variables and parameters. SA is also critical to understand system dynamics and to examine the parameter identifiability. In this paper, parameter SA for a fully coupled, physically based, distributed land surface hydrologic model, namely, the Flux-Penn State Integrated Hydrologic Model (Flux-PIHM), is performed. Multiparameter and single-parameter tests are performed to examine the three dimensions of identifiability: distinguishability, observability, and simplicity. Results show that Flux-PIHM model predictions of discharge, water table depth, soil moisture, land surface temperature, and surface heat fluxes are very sensitive to the selection of parameter values. Parameter uncertainties produce large uncertainties in hydrologic and land surface variable predictions. The van Genuchten parameters a and b and the Zilitinkevich parameter C zil are the most identifiable among the 20 tested parameters. Results indicate that the land surface and the subsurface are closely coupled. Hydrologic parameters have significant influence on land surface simulations. At the same time, land surface parameters have considerable impacts on hydrologic simulations; the evapotranspiration prediction prior to a strong precipitation event is critical for initializing accurate prediction of discharge peaks. Results also show that parameter identifiability depends on seasons and canopy wetness. Parameter identifiability at high and low flow conditions can be extremely different. Complex system dynamics have been revealed during the SA.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Shi

Davis

Zhang

et al. 2014

Journal of Hydrometeorology

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“…The remaining parameters per soil mapping unit, such as the van Genuchten values, were calculated from available soil data. The model outputs were found to be very sensitive to the parameters K rec , dr, K D and K H (Cullmann et al 2006, Schulla and Jasper 2007, Kasei 2010. As shown by Elfert and Bormann (2010), the conceptual parameters of WaSiM-ETH are more sensitive than parameters with a physical meaning.…”

Section: Model Calibration and Validationmentioning

confidence: 97%

Comparative study of a physically based distributed hydrological model versus a conceptual hydrological model for assessment of climate change response in the Upper Nile, Baro-Akobo basin: a case study of the Sore watershed, Ethiopia

Kebede

Diekkrüger

Moges

2014

International Journal of River Basin Management

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This paper presents a comparative study of two distinctively different hydrological models for simulating future discharge response in climate change scenarios. The mostly agricultural Sore watershed (1117 km 2 ), Ethiopia, was used as a case study. Outputs from the climate models REMO (Regional Model) and CGCM3.1 (Canadian Global Climate Model) were used as inputs for hydrological models after statistical downscaling. Data from the REMO A1B and B1 and CGCM3.1 A1B scenarios were selected to represent future conditions. The models used in this study, the physically based distributed hydrological model WaSiM (Water Flow and Balance Simulation Model)-ETH and the conceptual model HBV (Hydrologiska Byråns Vattenbalansavdelning)-Light, were applied to simulate the flow conditions for a reference period (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997) and a future period . The results confirm that the uncertainty caused by using different climate model inputs is larger than the uncertainty caused by using different hydrological models. In both hydrological models, the future peak discharge decreases in the future climate change scenarios regardless of the climate model and emission scenario considered. Whereas peak discharge shifted from August/September for the reference period to June in the future with CGCM3.1, discharge generally shifted to month earlier for both climate models. For low-flow conditions, the HBV-Light model always computed slightly higher values than the WaSiM-ETH model. This study demonstrates that both the hydrological and climate models were consistent concerning the overall direction of change, regardless of magnitude.

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“…The scaling dilemma exists when approaching better model performance with higher resolution data: the limited effect to bring more accurate results after a certain resolution threshold and the massive demand of storage capacity and computer time required by high resolved data (Vazquez et al 2002;Omer et al 2003;Cullmann et al 2006). Even when a detailed database is preferred, it is not always available for every catchment of interest.…”

Section: Introductionmentioning

confidence: 96%

Estimating the impacts and uncertainty of changing spatial input data resolutions on streamflow simulations in two basins

Zhang

Hörmann

Fohrer

et al. 2012

Journal of Hydroinformatics

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The impact of different grid resolutions of spatial input data on modelled river runoff are investigated using the simple rainfall-runoff model KIDS (Kielstau Discharge Simulations) in PCRaster modelling language for two watersheds -Kielstau and XitaoXi. In this study, the grid-based spatial data are aggregated to coarser resolutions to support the multi-resolution, multi-calibration and multi-site analysis for grid-scale investigations. Daily streamflow is simulated and model parameters are calibrated at each spatial resolution. The study suggests that re-calibration is critically needed when the grid resolution is changed. Altering grid sizes has an apparent impact on the parameter distribution patterns. Resolution uncertainty bands obtained by the overlapping hydrographs generated with different resolutions of input data are reported with a sufficient coverage of the observations for both basins. The analysis of model efficiency in terms of IC-ratio (a ratio between the input grid area and the catchment area) indicates that coarser resolutions with an IC-ratio of <0.001 may be used as an effective alternative for conducting preliminary analyses in streamflow simulation for the Kielstau basin. The modelling outputs are more sensitive to the spatial distribution of input data at the XitaoXi watershed, showing that accurate input data are required to achieve optimum modelling performance.

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Flow analysis with WaSiM-ETH – model parameter sensitivity at different scales

Cited by 26 publications

References 3 publications

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Comparative study of a physically based distributed hydrological model versus a conceptual hydrological model for assessment of climate change response in the Upper Nile, Baro-Akobo basin: a case study of the Sore watershed, Ethiopia

Estimating the impacts and uncertainty of changing spatial input data resolutions on streamflow simulations in two basins

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