scholarly journals Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

2021 ◽  
Vol 25 (6) ◽  
pp. 3493-3517
Author(s):  
Hossein Tabari ◽  
Santiago Mendoza Paz ◽  
Daan Buekenhout ◽  
Patrick Willems
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Change Impact ◽  
General Circulation ◽  
Frequency Change ◽  
Total Precipitation ◽  
Dry Spells ◽  
Dry Spell ◽  
Change Impact

Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

scholarly journals Comparison of statistical downscaling methods for climate change impact analysis on drought

2020 ◽  
Author(s):  
Hossein Tabari ◽  
Daan Buekenhout ◽  
Patrick Willems
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Change Impact ◽  
General Circulation ◽  
Total Precipitation ◽  
Worst Case ◽  
Dry Spells ◽  
Dry Spell ◽  
Change Impact

Abstract. General circulation models (GCMs) are the primary tools to evaluate the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Besides the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small and large scale variables. This study compares four statistical downscaling methods (SDMs) of bias correction (BC), change factor of mean (CFM), quantile perturbation (QP) and event based weather generator (EBWG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000. A set of drought related aspects is analysed: dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 14-member ensemble of CMIP6 GCMs, each powered by four future scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability of the climate. While all methods show a good agreement on downscaling total precipitation, the CFM method fails to downscale dry day frequency well. The QP method outperforms the others in downscaling dry spells. Using this method, dry day frequency is projected to increase significantly in the summer months, with relative changes of up to 20.4 % in the worst-case climate change scenario. At the same time, total precipitation is projected to decrease significantly by up to 41.9 % in these months. Lastly, extreme dry spells are projected to increase in length by up to 7.4 %.

scholarly journals Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

2014 ◽  
Vol 11 (6) ◽  
pp. 6167-6214 ◽  
Author(s):  
M. A. Sunyer ◽  
Y. Hundecha ◽  
D. Lawrence ◽  
H. Madsen ◽  
P. Willems ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Climate Change Impact ◽  
Climate Models ◽  
Control Period ◽  
Large Variability ◽  
Impact Studies ◽  
Change Impact

Abstract. Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods often used in climate change impact studies. Four methods are based on change factors, three are bias correction methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from fifteen regional climate models (RCMs) from the ENSEMBLES project for eleven catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the statistical downscaling methods vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between change factor and bias correction methods. The performance of the bias correction methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and statistical downscaling methods indicates that up to half of the total variance is derived from the statistical downscaling methods. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need of considering an ensemble of both statistical downscaling methods and climate models.

scholarly journals Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2377 ◽  
Author(s):  
Georgy Ayzel ◽  
Alexander Izhitskiy
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Hybrid Model ◽  
Climate Change Impact ◽  
General Circulation ◽  
Freshwater Inflow ◽  
Aral Sea ◽  
Climate Change Impact Assessment ◽  
Ecological Conditions ◽  
Change Impact

During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007–2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash–Sutcliffe efficiency of 0.72 and a Kling–Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.

scholarly journals Climate change impact on river flow extremes in the Upper Blue Nile River basin

2018 ◽  
Vol 10 (4) ◽  
pp. 759-781 ◽  
Author(s):  
Hadush K. Meresa ◽  
Mulusew T. Gatachew
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impact ◽  
River Flow ◽  
Nile River ◽  
Hydrological Models ◽  
Hydrological Extremes ◽  
Dry Spell ◽  
Change Impact ◽  
Blue Nile River Basin

Abstract This paper aims to study climate change impact on the hydrological extremes and projected precipitation extremes in far future (2071–2100) period in the Upper Blue Nile River basin (UBNRB). The changes in precipitation extremes were derived from the most recent AFROCORDEX climate data base projection scenarios compared to the reference period (1971–2000). The climate change impacts on the hydrological extremes were evaluated using three conceptual hydrological models: GR4 J, HBV, and HMETS; and two objective functions: NSE and LogNSE. These hydrological models are calibrated and validated in the periods 1971–2000 and 2001–2010, respectively. The results indicate that the wet/dry spell will significantly decrease/increase due to climate change in some sites of the region, while in others, there is increase/decrease in wet/dry spell but not significantly, respectively. The extreme river flow will be less attenuated and more variable in terms of magnitude, and more irregular in terms of seasonal occurrence than at present. Low flows are projected to increase most prominently for lowland sites, due to the combined effects of projected decreases in Belg and Bega precipitation, and projected increases in evapotranspiration that will reduce residual soil moisture in Bega and Belg seasons.

scholarly journals Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

2012 ◽  
Vol 9 (11) ◽  
pp. 12765-12795 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Impact Studies ◽  
Split Sample ◽  
Sample Test ◽  
Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

scholarly journals Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1516
Author(s):  
Tse-Yu Teng ◽  
Tzu-Ming Liu ◽  
Yu-Shiang Tung ◽  
Ke-Sheng Cheng
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Daily Rainfall ◽  
Correction Method ◽  
Two Stage ◽  
Climate Change Impact Assessment ◽  
Daily Data ◽  
Change Impact

With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan.

scholarly journals HESS Opinions "Should we apply bias correction to global and regional climate model data?"

2012 ◽  
Vol 16 (9) ◽  
pp. 3391-3404 ◽  
Author(s):  
U. Ehret ◽  
E. Zehe ◽  
V. Wulfmeyer ◽  
K. Warrach-Sagi ◽  
J. Liebert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
End Users ◽  
Model Output ◽  
Uncertainty Range ◽  
Impact Studies ◽  
Regional Circulation ◽  
Change Impact

Abstract. Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step) has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement) as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation. With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies.

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