The impact of bias correcting regional climate model results on hydrological indicators for Bavarian catchments

Willkofer, Florian; Schmid, F. J.; Komischke, Holger; Korck, Jane; Braun, Marco; Ludwig, Ralf

doi:10.1016/j.ejrh.2018.06.010

Cited by 36 publications

(27 citation statements)

References 29 publications

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“…These grids consist of station measurements interpolated at a desired resolution using one of the various interpolation methods available. They are used as inputs for various models (Heininger and Cullmann (2015), Gusyev et al (2016), Stahl et al (2017), Höllering et al (2018)), and they can also be used for the bias correction of climate models (Imbery et al (2013), Willkofer et al (2018), Meyer et al (2019), Krähenmann et al (2020)). High-resolution observation grids furthermore allow for spatial analyses based on continuous surfaces rather than on irregularly spaced point measurements, and long-term daily grids allow for analyses on different spatial scales and for the analysis of trends.…”

Section: Introductionmentioning

confidence: 99%

New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

Razafimaharo

Krähenmann

Höpp

et al. 2020

Theor Appl Climatol

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This study presents daily high-resolution (5 km × 5 km) grids of mean, minimum, and maximum temperature and relative humidity for Germany and its catchment areas, from 1951 to 2015. These observational datasets (HYRAS) are based upon measurements gathered for Germany and its neighbouring countries, in total more than 1300 stations, gridded in two steps: first, the generation of a background field, using non-linear vertical temperature profiles, and then an inverse distance weighting scheme to interpolate the residuals, subsequently added onto the background field. The modified Euclidian distances used integrate elevation, distance to the coast, and urban heat island (UHI) effect. A direct station-grid comparison and cross-validation yield low errors for the temperature grids over most of the domain and greater deviations in more complex terrain. The interpolation of relative humidity is more uncertain due to its inherent spatial inhomogeneity and indirect derivation using dew point temperature. Compared with other gridded observational datasets, HYRAS benefits from its high resolution and captures complex topographic effects. HYRAS improves upon its predecessor by providing datasets for additional variables (minimum and maximum temperature), integrating temperature inversions, maritime influence and UHI effect, and representing a larger area. With a long-term observational dataset of multiple meteorological variables also including precipitation, various climatological analyses are possible. We present long-term historical climate trends and relevant indices of climate extremes, pointing towards a significantly warming climate over Germany, with no significant change in total precipitation. We also evaluate extreme events, specifically the summer heat waves of 2003 and 2015.

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Section: Introductionmentioning

confidence: 99%

New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

Razafimaharo

Krähenmann

Höpp

et al. 2020

Theor Appl Climatol

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“…Hydrologic processes are simulated at a daily time step using WaSiM-ETH (Schulla, 2019), a distributed, physically based hydrologic model mostly operated for local and regional water resources assessments in Europe (Willkofer et al, 2018;Rössler et al, 2019) and more marginally in North America (Velázquez et al, 2013). An exhaustive description of the modeling setup is provided by .…”

Section: Hydrologic Modelingmentioning

confidence: 99%

“…(B) The suggested asynchronous configuration circumventing the requirement for meteorological observations. (Willkofer et al, 2018) or by using variables issued from reanalyses (Roy et al, 2018;Didovets et al, 2019;.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Hydroclimatic Modeling for the Construction of Physically Based Streamflow Projections in a Context of Observation Scarcity

2020

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Asynchronous hydroclimatic modeling is proposed for the construction of physically based streamflow projections over regions characterized by meteorological observation scarcity. The novel approach circumvents the requirement for meteorological observations by 1) calibrating quantile mapping transfer functions simultaneously to the parameters of the hydrologic model, 2) forcing the hydrologic model with post-processed climate simulations, and 3) intentionally ignoring the correlation between simulated streamflow values and observations. As a result, relative humidity, solar radiation and wind speed are integrated to a full hydroclimatic modeling chain, allowing the construction of streamflow projections forcing the Penman-Montheith reference evapotranspiration formulation over a forested catchment that flows into the St-Lawrence River, Canada. Results confirm a more accurate simulated hydrological response relative to a conventional hydroclimatic modeling chain employing reanalyses as description of the climate system. They also highlight the contribution to uncertainty in streamflow projections from biased climate variables issued by the reanalyses. The suggested framework assumes the hydrologic regime as a functional proxy to corresponding climate drivers. We believe the latter opens promising perspectives in the scope of producing more reliable estimations of waterrelated and energy-driven processes such as streamflow generation, snow accumulation and melt, river ice jams, water temperature, or vegetation growth under evolving climate conditions.

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“…Maraun [41] also discussed the possibility of alteration of climate change trends and nonstationarity signals after bias correction. Moreover Willkofer [42] stated the poor performance of bias correction approaches with extreme values. Thus, in this study bias correction did not applied to the results of models in order to preserve nonstationary and trend directions.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

2022

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This study examines the potential impacts of climate change on extreme precipitation. Rainfall analysis with stationary and nonstationary approach for observed and future conditions is performed for (1950-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 6 hour and projections (2015-2098 period) of 10, 15 minutes and 1, 6 hour for Ankara province, Turkey. Daily projections are disaggregated to finer scales, 5 minutes storm durations, then five minutes time series aggregated to the storm durations that are subject of interest. Nonstationary Generalized Extreme Value (GEV) models and stationary GEV models for observed and future data are obtained. Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of each storm duration for observed data driven model results. Considering the projected data driven model results; on average nonstationary models produce mostly lower return levels for mid and longer return periods for all storm durations and return periods except one hour storm duration. Depending on the models and Representative Concentration Pathways (RCP), there are different results for the future extreme rainfall input; yet all results indicate a decreasing extreme trend.

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The impact of bias correcting regional climate model results on hydrological indicators for Bavarian catchments

Cited by 36 publications

References 29 publications

New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

Asynchronous Hydroclimatic Modeling for the Construction of Physically Based Streamflow Projections in a Context of Observation Scarcity

Investigation of the Effect of Climate Change on Extreme Precipitation: Capital Ankara Case

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