Assessment of climate change impacts on the hydrology of the Lech Valley in northern Alps

2010 ◽  
Vol 1 (3) ◽  
pp. 207-218 ◽  
Author(s):  
C. Dobler ◽  
J. Stötter ◽  
F. Schöberl
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Danube River ◽  
Distributed Model ◽  
Climate Projections ◽  
Scenario Period ◽  
Northern Alps

The objective of this investigation is to assess the impacts of climate change on the hydrology of the Lech Valley (1,000 km2), a sub-catchment of the Danube River basin located in the northern Alps. An ensemble of nine climate projections is used to simulate the climate of a mid-21st-century scenario period (2040–2069) and an end-21st-century scenario period (2070–2099). The delta change approach overcame the gap between regional climate models (RCMs) and the hydrological model. An observed 30-year time series (1971–2000) of precipitation and temperature was perturbed according to mean monthly changes between the RCM runs. The hydrological simulations have been employed with the semi-distributed model HQsim in an off-line mode. The climate scenarios show an increase in monthly temperatures and accompanying significant changes in the seasonal precipitation patterns, including an increase in the precipitation during winter and spring and a considerable decrease in the precipitation during summer. The resulting effects on the runoff indicate large, seasonal varying changes. A decrease in monthly runoff during summer and increases in winter minimize the inter-annual disparities between low runoff in winter and high runoff in spring and summer. The overall agreement of RCM runs suggests confidence in the projections.

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Urban-rural contrasts and extreme weather conditions in regional climate change projections across spatial and temporal scales

2020 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Climate Projections ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales. </p>

scholarly journals ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan

2012 ◽  
Vol 370 (1962) ◽  
pp. 1121-1139 ◽  
Author(s):  
Toshichika Iizumi ◽  
Mikhail A. Semenov ◽  
Motoki Nishimori ◽  
Yasushi Ishigooka ◽  
Tsuneo Kuwagata
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Local Scale ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Emission Scenarios ◽  
Model Ensemble ◽  
Daily Weather Data

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.

scholarly journals Assessment of potential hydrological climate change impacts in Kastoria basin (Western Macedonia, Greece) using EUROCORDEX regional climate models

2021 ◽  
Keyword(s):  
Climate Change ◽  
Groundwater Level ◽  
Climate Models ◽  
Regional Climate ◽  
Substantial Reduction ◽  
Regional Climate Models ◽  
Water Yield ◽  
Climate Projections ◽  
Climate Data ◽  
Northern Greece

<p>The Mediterranean region is expected to present reduced availability of water resources due to climate change. This study aims to assess the potential hydrological responses to climate change in the Kastoria basin (Western Macedonia, Northern Greece) for the period 2019-2078. Climate projections from eight regional climate models from EURO-CORDEX were bias-adjusted using the linear scaling method. The bias-adjusted climate data were used to force the FeFLOW hydro-logical model to predict the discharge of the Kastoria aquifer towards lake Orestiada along with the projected groundwater level distribution. Precipitation (temperature) shows a tendency to decrease (increase) mainly in late spring to early autumn while increase (decrease) in the other sea-sons. Moreover, results indicate a significant increase in temperature and a slight decrease in precipitation towards 2078, while the predicted groundwater level of Kastoria aquifer will reduce slightly. However, the future hydrological behavior of the basin indicates a substantial reduction by approximately 15% of total water yield towards the end of the century.</p>

Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

2010 ◽  
Vol 41 (3-4) ◽  
pp. 211-229 ◽  
Author(s):  
Wei Yang ◽  
Johan Andréasson ◽  
L. Phil Graham ◽  
Jonas Olsson ◽  
Jörgen Rosberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Impact Studies ◽  
Emissions Scenarios ◽  
Precipitation And Temperature

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

scholarly journals On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

2013 ◽  
Vol 17 (3) ◽  
pp. 1189-1204 ◽  
Author(s):  
M. J. Muerth ◽  
B. Gauvin St-Denis ◽  
S. Ricard ◽  
J. A. Velázquez ◽  
J. Schmid ◽  
...  
Keyword(s):  
Climate Change ◽  
River Runoff ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Hydrological Models

Abstract. In climate change impact research, the assessment of future river runoff as well as the catchment-scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate projections originating from the climate models and the downscaling techniques, as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of uncertainty. Within the QBic3 project (Québec–Bavarian International Collaboration on Climate Change), the relative contributions to the overall uncertainty from the whole model chain (from global climate models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use regional climate models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to facilitate the reproduction of historic runoff conditions when used in hydrological models, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For these reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary to obtain the change signal in hydro-climatic projections, or safe to use for the production of present and future river runoff scenarios as it does not alter the change signal. The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the past, regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future periods is weak for most indicators, with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations.

scholarly journals Estimation of Climate-Change Impacts on the Urban Heat Load Using an Urban Climate Model and Regional Climate Projections

2011 ◽  
Vol 50 (1) ◽  
pp. 167-184 ◽  
Author(s):  
Barbara Früh ◽  
Paul Becker ◽  
Thomas Deutschländer ◽  
Johann-Dirk Hessel ◽  
Meinolf Kossmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Load ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Urban Climate ◽  
Regional Climate Models ◽  
Annual Number ◽  
Climate Projections ◽  
Urban Heat

Abstract A pragmatic approach to estimate the impact of climate change on the urban environment, here called the cuboid method, is presented. This method allows one to simulate the urban heat load and the frequency of air temperature threshold exceedances using only eight microscale urban climate simulations for each relevant wind direction and time series of daily meteorological parameters either from observations or regional climate projections. Eight representative simulations are designed to encompass all major potential urban heat-stress conditions. From these representative simulations, the urban-heat-load conditions in any weather situation are derived by interpolation. The presented approach is applied to study possible future heat load in Frankfurt, Germany, using the high-resolution Microscale Urban Climate Model in three dimensions (MUKLIMO_3). To estimate future changes in heat-load-related climate indices in Frankfurt, climate projections from the regional climate models Max Planck Institute Regional Model (REMO), Climate Limited-Area Model (CLM), Wetterlagen-basierte Regionalisierungsmethode (WETTREG), and Statistical Regional Model (STAR) are used. These regional climate models are driven by the “ECHAM5” general circulation model and Intergovernmental Panel on Climate Change emission scenario A1B. For the mean annual number of days with a maximum daily temperature exceeding 25°C, a comparison between the cuboid method results from observed and projected regional climate time series of the period 1971–2000 shows good agreement, except for CLM for which a clear underestimation is found. On the basis of the 90% significance level of all four regional climate models, the mean annual number of days with a maximum daily temperature exceeding 25°C in Frankfurt is expected to increase by 5–32 days for 2021–50 as compared with 1971–2000.

scholarly journals On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

2012 ◽  
Vol 9 (9) ◽  
pp. 10205-10243 ◽  
Author(s):  
M. J. Muerth ◽  
B. Gauvin St-Denis ◽  
S. Ricard ◽  
J. A. Velázquez ◽  
J. Schmid ◽  
...  
Keyword(s):  
Climate Change ◽  
River Runoff ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Hydrological Models

Abstract. In climate change impact research, the assessment of future river runoff as well as the catchment scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate projections originating from the climate models and the downscaling techniques as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of incertitude. Within the QBic3 project (Québec-Bavaria International Collaboration on Climate Change) the relative contributions to the overall uncertainty from the whole model chain (from global climate models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use Regional Climate Models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to reproduce historic runoff conditions from hydrological models using them, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For those reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary in hydro-climatic projections, or safe to use as it does not alter the change signal of river runoff. The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the past regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future period is weak for most indicators with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations.

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