CONSIDERATIONS ON THE USE OF QUANTILE MAPPING BIAS CORRECTION FOR THE IMPACT ASSESSMENT OF CLIMATE CHANGE

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

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Relative impact of irrigation techniques and climate change on hydroclimatic regimes in the Mediterranean region

10.5194/egusphere-egu21-2242 ◽

2021 ◽

Author(s):

Sandra Pool ◽

Félix Francés ◽

Alberto Garcia-Prats ◽

Manuel Pulido-Velazquez ◽

Carles Sanichs-Ibor ◽

...

Keyword(s):

Climate Change ◽

Impact Assessment ◽

Drip Irrigation ◽

Mediterranean Region ◽

Control Period ◽

Irrigated Agriculture ◽

Flood Irrigation ◽

The Mediterranean ◽

Projected Changes ◽

The Impact

Irrigated agriculture is the major water consumer in the Mediterranean region. Improved irrigation techniques have been widely promoted to reduce water withdrawals and increase resilience to climate change impacts. In this study, we assess the impact of the ongoing transition from flood to drip irrigation on future hydroclimatic regimes in the agricultural areas of Valencia (Spain). The impact assessment is conducted for a control period (1971-2000), a near-term future (2020-2049) and a mid-term future (2045-2074) using a chain of models that includes five GCM-RCM combinations, two emission scenarios (RCP 4.5 and RCP 8.5), two irrigation scenarios (flood and drip irrigation), and twelve parameterizations of the hydrological model Tetis. Results of this modelling chain suggest considerable uncertainties regarding the magnitude and sign of future hydroclimatic changes. Yet, climate change could lead to a statistically significant decrease in future groundwater recharge of up -6.6% in flood irrigation and -9.3% in drip irrigation. Projected changes in actual evapotranspiration are as well statistically significant, but in the order of +1% in flood irrigation and -2.1% in drip irrigation under the assumption of business as usual irrigation schedules. The projected changes and the related uncertainties will pose a challenging context for future water management. However, our findings further indicate that the effect of the choice of irrigation technique may have a greater impact on hydroclimate than climate change alone. Explicitly considering irrigation techniques in climate change impact assessment might therefore be a way towards better informed decision-making.This study has been supported by the IRRIWAM research project funded by the Coop Research Program of the ETH Zurich World Food System Center and the ETH Zurich Foundation, and by the ADAPTAMED (RTI2018-101483-B-I00) and TETISCHANGE (RTI2018-093717-B-I00) research projects funded by the Ministerio de Economia y Competitividad (MINECO) of Spain including EU FEDER funds.

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An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment

Journal of Hydrology ◽

10.1016/j.jhydrol.2017.11.010 ◽

2018 ◽

Vol 556 ◽

pp. 100-118 ◽

Cited By ~ 26

Author(s):

P.S. Smitha ◽

B. Narasimhan ◽

K.P. Sudheer ◽

H. Annamalai

Keyword(s):

Climate Change ◽

Impact Assessment ◽

Bias Correction ◽

Climate Change Impact ◽

Daily Rainfall ◽

Sliding Window ◽

Correction Method ◽

Climate Change Impact Assessment ◽

Window Technique ◽

Change Impact

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A method to preserve trends in quantile mapping bias correction of climate modeled temperature

Earth System Dynamics ◽

10.5194/esd-8-889-2017 ◽

2017 ◽

Vol 8 (3) ◽

pp. 889-900 ◽

Cited By ~ 19

Author(s):

Manolis G. Grillakis ◽

Aristeidis G. Koutroulis ◽

Ioannis N. Daliakopoulos ◽

Ioannis K. Tsanis

Keyword(s):

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Quantile Mapping ◽

Temperature Bias ◽

Mapping Bias ◽

Temperature Signal ◽

Change Impact

Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

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

Water ◽

10.3390/w11112266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2266 ◽

Cited By ~ 4

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.

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Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

Water ◽

10.3390/w11071475 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1475 ◽

Cited By ~ 7

Author(s):

Jun-Haeng Heo ◽

Hyunjun Ahn ◽

Ju-Young Shin ◽

Thomas Rodding Kjeldsen ◽

Changsam Jeong

Keyword(s):

Climate Change ◽

Bias Correction ◽

Shape Parameter ◽

Mapping Method ◽

Distribution Model ◽

Parameter Distribution ◽

Precipitation Data ◽

Distribution Models ◽

Quantile Mapping

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.

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An application of super ensemble simulation with appropriate bias correction for river planning in Japan

10.5194/egusphere-egu2020-12875 ◽

2020 ◽

Author(s):

Satoshi Watanabe

Keyword(s):

Climate Change ◽

Decision Making ◽

Bias Correction ◽

River Basins ◽

Conventional Approach ◽

Ensemble Simulation ◽

Class A ◽

Design Rainfall ◽

The One ◽

The Impact

In this study, a methodology that uses super ensemble simulation with appropriate bias correction for river planning was proposed. The Database for Policy Decision-Making for Future Climate Change (d4PDF) is a super ensemble experiments that comprise over 1000-year output have been conducted. The d4PDF provides regional downscaling simulation that focuses around Japan. It is expected that the impact assessments of climate changes on various fields considering uncertainly are conducted.The impact of climate change on floods is a serious issue. In Japan, all class A river has design rainfall for the river planning that is defined considering historical observations of precipitation that happens once in several hundred years, which the planning year is different depending on the situation of a river. The design rainfall provides the fundamental information for planning river management.&#160; The Ministry of Land, Infrastructure, Transportation and Tourism defines the value of the rainfall in the planning year in each class A river basin by considering the hydro-meteorological and social characteristics of each basin. As the design rainfall was defined in the mid-1900s for most of the rivers, the method to estimate precipitation in the planning year was conducted with limited observation data using extreme statistical value. The super ensemble simulation data is expected to contribute for the decision making with appropriate setting of design rainfall.We proposed a method to correct the bias of super ensemble simulation and estimated the design rainfall in 47 river basins selected from class A river basins. The estimated design rainfall was compared between the one estimated with super ensemble simulation and the one estimated with conventional approach. The spread of results oriented from super ensemble simulation indicated that uncertainly of design rainfall estimated with conventional approach was so high that the consideration of uncertainty is necessary for river planning. The experiments indicated that the use of super ensemble simulation with appropriate bias correction could provide knowledge that aids us in understanding the hydrological extremes.

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Climate change impact assessment as function of model inaccuracy

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-2875-2007 ◽

2007 ◽

Vol 4 (5) ◽

pp. 2875-2899

Author(s):

P. Droogers ◽

A. van Loon ◽

W. Immerzeel

Keyword(s):

Climate Change ◽

Impact Assessment ◽

Climate Change Impact ◽

Simulation Models ◽

Climate Change Scenarios ◽

Model Errors ◽

Climate Change Impact Assessment ◽

Scenario Model ◽

Change Impact ◽

The Impact

Abstract. Numerical simulation models are frequently applied to assess the impact of climate change on hydrology and agriculture. A common hypothesis is that unavoidable model errors are reflected in the reference situation as well as in the climate change situation so that by comparing reference to scenario model errors will level out. For a polder in The Netherlands an innovative procedure has been introduced, referred to as the Model-Scenario-Ratio (MSR), to express model inaccuracy on climate change impact assessment. MSR values close to 1, indicating that impact assessment is mainly a function of the scenario itself rather than of the quality of the model, were found for most indicators evaluated. More extreme climate change scenarios and indicators based on threshold values showed lower MSR values, indicating that model accuracy is an important component of the climate change impact assessment. It was concluded that the MSR approach can be applied easily and will lead to more robust impact assessment analyses.

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