Performance Evaluation of Bias Correction Methods and Projection of Future Precipitation Changes Using Regional Climate Model over Thanjavur, Tamil Nadu, India

Sundaram, Gunavathi; Radhakrishnan, Selvakumar

doi:10.1007/s12205-022-0151-0

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…However, selecting the best method for bias correction is challenging. But, mostly it is evaluated by two ways of evaluation indices such as frequency-based indices (mean, median, standard deviation, and 10th and 90th percentiles) and time series-based indices using different performance evaluation criteria, including correlation coefficient (R), percent of bias (P Bias ), mean absolute error (MAE), and root-mean-square error (RMSE) (Li et al 2019;Mendez et al 2020;Sundaram & Radhakrishnan 2022). This evaluation is used to obtain the best bias correction method to obtain the future climate change scenario.…”

Section: Performance Evaluation Of Bias Correction Methodsmentioning

confidence: 99%

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Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

Dinku,

Gibre

2024

Journal of Water and Climate Change

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The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very good with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, delta change method seems to be with no biases for maximum temperature, but it increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C amount from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

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Section: Performance Evaluation Of Bias Correction Methodsmentioning

confidence: 99%

“… Mendez et al 2020;Enayati et al 2021; Derdour et al 2022;Gado et al 2022;Sundaram & Radhakrishnan 2022), this method is evaluated using the following equations.…”

mentioning

confidence: 99%

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

Dinku,

Gibre

2024

Journal of Water and Climate Change

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“…QMMs include statistical transformations for the post-processing of climate modeling outputs based on different methods such as the parametric transformation function (PTF), distribution derived transformation (DIST), empirical quantiles (QUANT), robust empirical quantiles (RQUANT), and smoothing spline (SSPLIN) [15]. Because of the reliability of QMMs, these methods have been successfully applied to precipitation and temperature datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in different catchments worldwide [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Impact of Future Climate Scenarios and Bias Correction Methods on the Achibueno River Basin

Moya,

Althoff,

Celis-Diez

et al. 2024

Water

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Future climate scenarios based on regional climate models (RCMs) have been evaluated widely. However, the use of RCMs without bias correction may increase the uncertainty in the assessment of climate change impacts, especially in mountain areas. Five quantile mapping methods (QMMs) were evaluated as bias correction methods for precipitation and temperature in the historical period (1979–2005) of one local climate model and three RCMs at the Achibueno River Basin, southcentral Chile. Additionally, bias-corrected climate scenarios from 2025 to 2050 under two Representative Concentration Pathways (RCPs) were evaluated on the hydrological response of the catchment with the Soil and Water Assessment Tool (SWAT+). The parametric transformation function and robust empirical quantile were the most promising bias correction methods for precipitation and temperature, respectively. Climate scenarios suggest changes in the frequency and amount of precipitation with fluctuations in temperatures. Under RCP 2.6, partial increases in precipitation, water yield, and evapotranspiration are projected, while for RCP 8.5, strong peaks of precipitation and water yield in short periods of time, together with increases in evapotranspiration, are expected. Consequently, flooding events and increasing irrigation demand are changes likely to take place. Therefore, considering adaptation of current and future management practices for the protection of water resources in southcentral Chile is mandatory.

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Selection of representative near-future climate simulations by minimizing bias in average monthly temperature and precipitation

Khokhlov,

Tuchkovenko,

Loboda

2023

Theor Appl Climatol

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The bias in the global and regional climate models signi cantly complicates their use in impact studies. A signi cant difference between the observed and model precipitation in the warm months is registered in Odesa for 1970-2005. This difference is probably determined by complex orography and inappropriate parameterization methods for convective processes climate models. In the last fteen years, the average temperature has increased by about 1°C in winter and by 2°C in summer compared with 1970-2005.Considering decreasing precipitation during summer months, it seems that the climate of Odesa is moving towards the Mediterranean climate -warm to hot, dry summers and mild, moderately wet winters. The approach based on selecting representative simulations with minimum average bias and adjusting the choice to the present-day climate is described and applied for Odesa using data from the RCP8.5 scenario simulations of the EURO-CORDEX project and ERA5-Land reanalysis. The approach can be applied separately for monthly near-surface temperature and total precipitation, as well as jointly for these variables, and provides the satisfactory ability to select models for use then in impact studies. The output variables of simulations selected are close to observed ones in recent years and are well to coincide with the ensemble-mean values in the near future, 2021-2050. On the other hand, the scatter of output variables in the selected simulations adequately describes the uncertainty of the future climate.

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Performance Evaluation of Bias Correction Methods and Projection of Future Precipitation Changes Using Regional Climate Model over Thanjavur, Tamil Nadu, India

Cited by 7 publications

References 40 publications

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

Impact of Future Climate Scenarios and Bias Correction Methods on the Achibueno River Basin

Selection of representative near-future climate simulations by minimizing bias in average monthly temperature and precipitation

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