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

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1475 ◽  
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.

scholarly journals Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

2020 ◽  
Author(s):  
Maedeh Enayati ◽  
Omid Bozorg-Haddad ◽  
Javad Bazrafshan ◽  
Somayeh Hejabi ◽  
Xuefeng Chu
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Correction Method ◽  
Quantile Mapping ◽  
Topographic Features ◽  
Temperature Variable ◽  
Transformation Functions ◽  
Karkheh River Basin ◽  
The Karkheh River

Abstract This study aims to conduct a thorough investigation to compare the abilities of QM techniques as a bias correction method for the raw outputs from GCM/RCM combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the CORDEX dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

scholarly journals Levee Overtopping Risk Assessment under Climate Change Scenario in Kao-Ping River, Taiwan

2020 ◽  
Vol 12 (11) ◽  
pp. 4511
Author(s):  
Hsiao-Ping Wei ◽  
Yuan-Fong Su ◽  
Chao-Tzuen Cheng ◽  
Keh-Chia Yeh
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Climate Change Scenario ◽  
Mapping Method ◽  
Change Scenario ◽  
Quantile Mapping ◽  
Meteorological Research Institute ◽  
The Future ◽  
Overtopping Probability ◽  
Southern Taiwan

With the growing concern about the failure risk of river embankments in a rapidly changing climate, this study aims to quantify the overtopping probability of river embankment in Kao-Ping River basin in southern Taiwan. A water level simulation model is calibrated and validated with historical typhoon events and the calibrated model is further used to assess overtopping risk in the future under a climate change scenario. A dynamic downscaled projection dataset, provided by Meteorological Research Institute (MRI) has been further downscaled to 5-km grids and bias-corrected with a quantile mapping method, is used to simulate the water level of Kao-Ping River in the future. Our results highlighted that the overtopping risk of Kao-Ping River increased by a factor of 5.7~8.0 by the end of the 21st century.

Does bias correction increase reliability of flood projections under climate change? A case study of large rivers in Germany

2014 ◽  
Vol 34 (14) ◽  
pp. 3780-3800 ◽  
Author(s):  
Shaochun Huang ◽  
Valentina Krysanova ◽  
Fred F. Hattermann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Large Rivers

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>

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