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  • 1
    Publication Date: 2013-10-10
    Print ISSN: 1466-4879
    Electronic ISSN: 1476-5470
    Topics: Biology , Medicine
    Published by Springer Nature
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  • 2
    Publication Date: 2015-04-21
    Description: As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2014-10-30
    Description: As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA) method for snow cover fraction input data (SCF) for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.
    Print ISSN: 1812-2108
    Electronic ISSN: 1812-2116
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
    Publication Date: 2015-10-01
    Print ISSN: 0022-1694
    Electronic ISSN: 1879-2707
    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
    Published by Elsevier
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  • 5
    Publication Date: 2023-04-20
    Description: Drought is a complex phenomenon due to its spatial and temporal variability, hence, the uncertainty related to its estimation is large. It is customary to use standardized drought indices to assess drought, and mostly fitting a probability distribution function is the primary step. Most studies focus on probability distribution function selection based on historical observation data, and climate projection studies use these studies as a benchmark. However, this stationary distribution assumption in drought index estimation methodologies (for future climate scenarios) might not be valid. Therefore, different distributions are tested under changing climate for estimating SPI and SPEI drought indices. Additionally, we assessed the impact of climate change on streamflow drought at Grote Nete catchment, Belgium, by forcing climate projection data on a new geohydrological model called SWAT+gwflow.The Weibull distribution is appropriate for SPI for both future scenarios (rcp 2.6 and rcp 8.5) for a 1-month accumulation period better than the gamma distribution, which is mostly preferred to fit precipitation. However, the gamma distribution remains valid for the other accumulation periods. As for SPEI, Pearson type 3 (PE3) is appropriate for fitting the water balance (difference between precipitation and potential evapotranspiration) for both shorter and longer accumulation periods. This is contrary to a previous study made using observations, which suggested Generalized extreme value distribution (GEV) for Belgium. Finally, the hydrological drought assessment on the Grote Nete watershed indicated a sharp increase in drought frequency for a one-month accumulation period, where the amount of drought events increased by a factor of 5.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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