Modelling runoff generation of a small catchment in the context of climate change by using an ensemble of different climate model outputs and bias correction methods

2020 ◽  
Author(s):  
Kai Sonntag ◽  
Matthias Gassmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Linear Scaling ◽  
Power Transformation ◽  
Quantile Mapping ◽  
The Future ◽  
Distribution Mapping

<p>Due to climate change, meteorological extremes affect the environment and our society in the past decades. But not only the extremes are piling up, the average temperatures and the precipitation regimes have changed in recent decades. The change in meteorological conditions also affects the water balance and thus also the generation processes of runoff. The aim of this work is to estimate this future change for a small low-mountain catchment in central Germany using climate projections and hydrological modelling.</p><p>As input to the hydrological model HBV Light, climate data from seven different combinations of global and regional climate models are used. However, due to their substantial bias it is necessary to apply bias correction. For each of the three climate input time series used by HBV Light, different bias correction methods are tested: Precipitation (Linear Scaling Multiplication, Quantile Mapping, Power Transformation, Distribution Mapping Gamma), Temperature (Linear Scaling Addition, Quantile Mapping, Variance Scaling, Distribution Mapping Normal) and Potential Evapotranspiration (Linear Scaling Multiplication, Linear Scaling Addition, Quantile Mapping). The corrected climate model outputs are compared to the observed timeseries and rated based on three different efficiency criteria. Overall, the combination of different climate models and bias correction methods generates 63 future hydrological projections. Based on this ensemble, the future water balance of the catchment is assessed. The results show that (1) the biggest uncertainties in the hydrological simulation were generated by uncorrected climate model outputs; (2) the uncertainties in hydrological simulations increase till the end of the century; (3) Power Transformation and Quantile Mapping perform best for precipitation, Linear Scaling Addition and Quantile Mapping for temperature, Linear Scaling Addition and Quantile Mapping for potential evapotranspiration; (4) the total annual outflow increases till 2070 because of an increase of the outflow in winter and spring; (5) in the future, interflow will increase in spring and winter and reduce in summer and autumn; (6) till the end of the century the baseflow will rise in spring and in the rest of year the baseflow will decrease. This study shows that even if changes in the annual total discharge for small catchments have no significant trend, the generation processes and the seasonal values may change in the future.</p>

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals Improving Hydro-Climatic Projections with Bias-Correction in Sahelian Niger Basin

2016 ◽  
Author(s):  
Ganiyu Titilope Oyerinde ◽  
Fabien C.C. Hountondji ◽  
Agnide E. Lawin ◽  
Ayo J. Odofin ◽  
Abel Afouda ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Local Level ◽  
Global Climate Models ◽  
Climate Projections ◽  
Quantile Mapping ◽  
Impacts Of Climate Change

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrology impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction significantly improved the accuracy of rainfall and temperature simulations compared to observations. Nash coefficient (NSE) for monthly rainfall comparisons of 8 GCMs to the observed was improved by bias correction from 0.69 to 0.84. The standard deviations among the 8 GCM rainfall data were significantly reduced from 0.13 to 0.03. Increasing rainfall, temperature, PET and river discharge were projected for all GCMs used in this study under the RCP8.5 scenario. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

2018 ◽  
Vol 31 (16) ◽  
pp. 6591-6610 ◽  
Author(s):  
Martin Aleksandrov Ivanov ◽  
Jürg Luterbacher ◽  
Sven Kotlarski
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Analytical Description ◽  
Analytical Theory ◽  
Climate Change Signal ◽  
Future Research ◽  
Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

scholarly journals Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

scholarly journals Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

2017 ◽  
Vol 21 (6) ◽  
pp. 2649-2666 ◽  
Author(s):  
Matthew B. Switanek ◽  
Peter A. Troch ◽  
Christopher L. Castro ◽  
Armin Leuprecht ◽  
Hsin-I Chang ◽  
...  
Keyword(s):  
Error Correction ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Climate Change Signal ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
Distribution Mapping ◽  
Projected Changes ◽  
Time Invariant

Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

Analysis of EURO-CORDEX sub-daily rainfall simulations and derived event characteristics

2021 ◽  
Author(s):  
Paola Nanni ◽  
David J. Peres ◽  
Rosaria E. Musumeci ◽  
Antonino Cancelliere
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Rainfall Event ◽  
Historical Period ◽  
High Temporal Resolution ◽  
Hydrological Hazards ◽  
The Future

<p>Climate change is a phenomenon that is claimed to be responsible for a significant alteration of the precipitation regime in different regions worldwide and for the induced potential changes on related hydrological hazards. In particular, some consensus has raised about the fact that climate changes can induce a shift to shorter but more intense rainfall events, causing an intensification of urban and flash flooding hazards.  Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted. For this reason, it is of key importance to ascertain the quality of regional climate models (RCMs), especially with reference to their ability to reproduce the main climatological regimes with respect to an historical period. To this end, several studies have focused on the analysis of annual or monthly data, while few studies do exist that analyze the sub-daily data that are made available by the regional climate projection initiatives. In this study, with reference to specific locations in eastern Sicily (Italy), we first evaluate historical simulations of precipitation data from selected RCMs belonging to the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) with high temporal resolution (three-hourly), in order to understand how they compare to fine-resolution observations. In particular, we investigate the ability to reproduce rainfall event characteristics, as well as annual maxima precipitation at different durations. With reference to rainfall event characteristics, we specifically focus on duration, intensity, and inter-arrival time between events. Annual maxima are analyzed at sub-daily durations. We then analyze the future simulations according to different Representative concentration scenarios. The proposed analysis highlights the differences between the different RCMs, supporting the selection of the most suitable climate model for assessing the impacts in the considered locations, and to understand what trends for intense precipitation are to be expected in the future.</p>

scholarly journals Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

2015 ◽  
Vol 28 (17) ◽  
pp. 6938-6959 ◽  
Author(s):  
Alex J. Cannon ◽  
Stephen R. Sobie ◽  
Trevor Q. Murdock
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Coupled Model ◽  
Precipitation Extremes ◽  
Extreme Value Analysis ◽  
Quantile Mapping ◽  
Value Analysis

Abstract Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

The implications of soil erosion model conceptualization, bias-correction methods and climate model ensembles on soil erosion projections under climate change

2020 ◽  
Author(s):  
Joris de Vente ◽  
Joris Eekhout
Keyword(s):  
Climate Change ◽  
Soil Erosion ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Erosion Model ◽  
Distribution Mapping ◽  
Climate Model Ensembles ◽  
Model Ensembles ◽  
The Impact

<p>Climate models project increased extreme precipitation for the coming decades, which may lead to higher soil erosion in many locations worldwide. The impact of climate change on soil erosion is most often assessed by applying a soil erosion model forced by bias-corrected climate model output. A literature review among more than 100 papers showed that many studies use different soil erosion models, bias-correction methods and climate model ensembles. In this study, we assessed how these differences affect the outcome of climate change impact assessments on soil erosion. The study was performed in two contrasting Mediterranean catchments (SE Spain), where climate change is projected to lead to a decrease in annual precipitation sum and an increase in extreme precipitation, based on the RCP8.5 emission scenario. First, we assessed the impact of soil erosion model selection using the three most widely used model concepts, i.e. a model forced by precipitation (RUSLE), a model forced by runoff (MUSLE), and a model forced by precipitation and runoff (MMF). Depending on the model, soil erosion in the study area is projected to decrease (RUSLE) or increase (MUSLE and MMF). The differences between the model projections are inherently a result of their model conceptualization, such as a decrease of soil loss due to decreased annual precipitation sum (RUSLE) and an increase of soil loss due to increased extreme precipitation and, consequently, increased runoff (MUSLE). An intermediate result is obtained with MMF, where a projected decrease in detachment by raindrop impact is counteracted by a projected increase in detachment by runoff. Second, we evaluated the implications of three bias‐correction methods, i.e. delta change, quantile mapping and scaled distribution mapping. Scaled distribution mapping best reproduces the raw climate change signal, in particular for extreme precipitation. Depending on the bias‐correction method, soil erosion is projected to decrease (delta change) or increase (quantile mapping and scaled distribution mapping). Finally, we assessed the effect of climate model ensembles on soil erosion projections. We showed that individual climate models may project opposite changes with respect to the ensemble average, hence, climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that in climate change impact assessments it is important to select a soil erosion model that is forced by both precipitation and runoff, which under climate change may have a contrasting effect on soil erosion. Furthermore, the impact of climate change on soil erosion can only accurately be assessed with a bias‐correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models.</p>

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