The impact of bias correcting regional climate model results on hydrological indicators for Bavarian catchments
| Autor: | F. J. Schmid, Jane Korck, Marco Braun, Ralf Ludwig, Holger Komischke, Florian Willkofer |
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| Rok vydání: | 2018 |
| Předmět: |
geography
geography.geographical_feature_category 010504 meteorology & atmospheric sciences lcsh:QE1-996.5 0208 environmental biotechnology Drainage basin Climate change 02 engineering and technology 01 natural sciences 020801 environmental engineering lcsh:Geology Catchment hydrology Climatology Earth and Planetary Sciences (miscellaneous) Environmental science Climate model Indicator value lcsh:GB3-5030 Extreme value theory Surface runoff lcsh:Physical geography 0105 earth and related environmental sciences Water Science and Technology Quantile |
| Zdroj: | Journal of Hydrology: Regional Studies, Vol 19, Iss, Pp 25-41 (2018) |
| ISSN: | 2214-5818 |
| Popis: | Study region: The Mindel river catchment, gauge Offingen, Bavaria, Germany. Study focus: The study investigates the potential interference of climate change signals (CCS) in hydrological indicators due to the application of bias correction (BC) of regional climate models (RCM). A validated setup of the hydrological model WaSiM was used for runoff modeling. The CCS, gained by the application of three RCMs (CCLM, REMO-UBA, RACMO2) for a reference period (1971–2000) and a scenario period (2021–2050), are evaluated according to eight hydrological indicators derived from modeled runoff. Three different BC techniques (linear scaling, quantile mapping, local intensity scaling) are applied.New hydrological insights for the region: Runoff indicators are calculated for the investigated catchment using bias corrected RCM data. The quantile mapping approach proves superior to linear scaling and local intensity scaling and is recommended as the bias correction method of choice when assessing climate change impacts on catchment hydrology. Extreme flow indicators (high flows), however, are poorly represented by any bias corrected model results, as current approaches fail to properly capture extreme value statistics. The CCS of mean hydrological indicator values (e.g. mean flow) is well preserved by almost every BC technique. For extreme indicator values (e.g. high flows), the CCS shows distinct differences between the original RCM and BC data. Keywords: Bias correction, Regional climate model, Climate change signal, Hydrological modeling, Runoff indicators, Bavaria |
| Databáze: | OpenAIRE |
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