scholarly journals Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand

2020 ◽  
Author(s):  
Srisunee Wuthiwongtyohtin
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Rainfall ◽  
Bias Error ◽  
Statistical Bias ◽  
Study Region ◽  
Study Results ◽  
Nonparametric Transformation ◽  
Statistical Bias Correction

Abstract This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrated 30-year-period data, (2) seasonal subsampling, and (3) monthly subsampling. The bias correction techniques are classified into three groups, which are (1) distribution-derived transformation, (2) parametric transformation, and (3) nonparametric transformation. Eleven bias correction techniques with three different subsamples are used to derive transfer function parameters to adjust model bias error. Generally, appropriate bias correction methods with optimal subsampling are locally dependent and need to be defined specifically for a study area. The study results show that monthly subsampling would be well established by capturing the monthly mean variation after correcting the model's daily rainfall. The results also give the best-fitted parameter set of the different subsamples. However, applying the full calibrated data and the seasonal subsamples cannot substantially improve internal variability. Thus, the effect of internal climate variability of the study region is greater than the choice of bias correction methods. Of the bias correction approaches, nonparametric transformation performed best in correcting daily rainfall bias error in this study area as evaluated by statistics and frequency distributions. Therefore, using a combination of methods between the nonparametric transformation and monthly subsampling offered the best accuracy and robustness. However, the nonparametric transformation was quite sensitive to the calibration time period.

scholarly journals Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models

2011 ◽  
Vol 12 (4) ◽  
pp. 556-578 ◽  
Author(s):  
Stefan Hagemann ◽  
Cui Chen ◽  
Jan O. Haerter ◽  
Jens Heinke ◽  
Dieter Gerten ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Coupled Model ◽  
Model Output ◽  
Hydrological Models ◽  
Statistical Bias ◽  
Max Planck ◽  
Climate Model Output ◽  
Statistical Bias Correction

Abstract Future climate model scenarios depend crucially on the models’ adequate representation of the hydrological cycle. Within the EU integrated project Water and Global Change (WATCH), special care is taken to use state-of-the-art climate model output for impacts assessments with a suite of hydrological models. This coupling is expected to lead to a better assessment of changes in the hydrological cycle. However, given the systematic errors of climate models, their output is often not directly applicable as input for hydrological models. Thus, the methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations. As observations, global reanalyzed daily data of precipitation and temperature were used that were obtained in the WATCH project. Daily time series from three GCMs (GCMs) ECHAM5/Max Planck Institute Ocean Model (MPI-OM), Centre National de Recherches Météorologiques Coupled GCM, version 3 (CNRM-CM3), and the atmospheric component of the L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) coupled model (called LMDZ-4)—were bias corrected. After the validation of the bias-corrected data, the original and the bias-corrected GCM data were used to force two global hydrology models (GHMs): 1) the hydrological model of the Max Planck Institute for Meteorology (MPI-HM) consisting of the simplified land surface (SL) scheme and the hydrological discharge (HD) model, and 2) the dynamic global vegetation model called LPJmL. The impact of the bias correction on the projected simulated hydrological changes is analyzed, and the simulation results of the two GHMs are compared. Here, the projected changes in 2071–2100 are considered relative to 1961–90. It is shown for both GHMs that the usage of bias-corrected GCM data leads to an improved simulation of river runoff for most catchments. But it is also found that the bias correction has an impact on the climate change signal for specific locations and months, thereby identifying another level of uncertainty in the modeling chain from the GCM to the simulated changes calculated by the GHMs. This uncertainty may be of the same order of magnitude as uncertainty related to the choice of the GCM or GHM. Note that this uncertainty is primarily attached to the GCM and only becomes obvious by applying the statistical bias correction methodology.

scholarly journals Climate model bias correction and the role of timescales

2010 ◽  
Vol 7 (5) ◽  
pp. 7863-7898 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

scholarly journals Application of Statistical Bias correction method to the Yoshino River Basin

2014 ◽  
Vol 70 (4) ◽  
pp. I_193-I_198 ◽  
Author(s):  
CHOTHANDA NYUNT ◽  
TOSHIO KOIKE ◽  
AKIO YAMAMOTO ◽  
TOSHIHORO NEMOTO ◽  
MASARU KITSUREGAWA
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Correction Method ◽  
Statistical Bias ◽  
Bias Correction Method ◽  
Statistical Bias Correction

scholarly journals Statistical bias correction for climate change impact on the basin scale precipitation in Sri Lanka, Philippines, Japan and Tunisia

2016 ◽  
Author(s):  
Cho Thanda Nyunt ◽  
Toshio Koike ◽  
Akio Yamamoto
Keyword(s):  
Sri Lanka ◽  
Bias Correction ◽  
Water Resource Management ◽  
Climate Model ◽  
Global Climate Model ◽  
Rain Gauge ◽  
Statistical Bias ◽  
Regional Variability ◽  
Basin Scale ◽  
Statistical Bias Correction

Abstract. We introduce a 3-step statistical bias correction method to solve global climate model (GCM) bias by determining regional variability through multi-model selection. This is a generalized method to assess imperfect GCMs that are unable to simulate distinct regional climate characteristics, either spatially or temporally. More remaining local bias is eliminated using an all-inclusive statistical bias correction addressing the major shortcomings of GCM precipitation. First, the multi-GCM choice is determined according to spatial correlation (Scorr) and root mean square error (RMSE) of regional and mesoscale climate variation in a comparison with global references. After multi-GCM selection, there are three major steps in the proposed bias correction, i.e., the generalized Pareto distribution (GPD) for extreme rainfall bias correction, ranking order statistics for wet and dry day frequency errors, and a two-parameter gamma distribution for monthly normal rainfall bias correction. Best-fit GPD parameters are resolved by the RMSE, a Hill plot, and mean excess function. The capability of the method is examined by application to four catchments in diverse climate regions. These are the Kalu Ganga (Sri Lanka), Pampanga, Angat and Kaliwa (Philippines), Yoshino (Japan), and Medjerda (Tunisia). The assumption of GCM stationary error in the future is verified by calibration in 1981–2000 and validation over 1961–1980 at two observation sites. The results show a favorable outcome. Overall performance of catchments was good for bias-corrected extreme events and inter-seasonal climatology, compared with observations. However, the suggested method has no intermediate grid scale between the GCM grid and observation points, and requires a well-distributed observed rain gauge network to reproduce a reasonable rainfall distribution over a target basin. The results of holistic bias correction provide reliable, quantitative and qualitative information for basin or national scale integrated water resource management.

scholarly journals Climate model bias correction and the role of timescales

2011 ◽  
Vol 15 (3) ◽  
pp. 1065-1079 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a statistical bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

scholarly journals Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

2020 ◽  
Vol 12 (6) ◽  
pp. 1042 ◽  
Author(s):  
Xiaoying Yang ◽  
Yang Lu ◽  
Mou Leong Tan ◽  
Xiaogang Li ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Daily Rainfall ◽  
Central China ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
East Central ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Different Time Scales

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

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