Microseismic Estimates of Stimulated Rock Volume Using a Detection-Range Bias Correction: Theory and Case Study

2014 ◽  
Author(s):  
X. Zhang ◽  
M. Holland ◽  
W. Van Der Zee ◽  
D. Moos
Keyword(s):  
Bias Correction ◽  
Detection Range ◽  
Range Bias

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 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.

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

Bias-Correction of Regression Models: A Case Study on hERG Inhibition

2009 ◽  
Vol 49 (6) ◽  
pp. 1486-1496 ◽  
Author(s):  
Katja Hansen ◽  
Fabian Rathke ◽  
Timon Schroeter ◽  
Georg Rast ◽  
Thomas Fox ◽  
...  
Keyword(s):  
Bias Correction ◽  
Regression Models

scholarly journals Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe

2013 ◽  
Vol 170 ◽  
pp. 19-31 ◽  
Author(s):  
Ed Hawkins ◽  
Thomas M. Osborne ◽  
Chun Kit Ho ◽  
Andrew J. Challinor
Keyword(s):  
Bias Correction ◽  
Climate Projections ◽  
Crop Modelling
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