Evaluation of Bias Correction Methods for Regional Climate Models: Downscaled Rainfall Analysis Over Diverse Agroclimatic Zones of India

Jaiswal, Rohit Kumar; Mall, R. K.; Singh, Nidhi; Kumar, T. V. Lakshmi; Niyogi, Dev

doi:10.1029/2021ea001981

Cited by 22 publications

(7 citation statements)

References 88 publications

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“…By significantly aligning the monthly mean values, this process effectively reduces bias in the mean values [36]. It should be noted that, however to a little lesser degree, the other two methods also significantly lessen the difference in mean values [37].…”

Section: Bias Correctionmentioning

confidence: 99%

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Evaluation of bias correction methods for multi-satellite rainfall estimation products

Andari,

Nurhamidah,

Daoed

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Rainfall data from satellites provides hydrological studies with special temporal and spatial advantages. However, recognising the biases in satellite data is critical, so robust validation and correction methods using ground-based observational data are necessary. This research seeks to correct and validate multi-satellite rainfall data (TRMM, GPM-IMERG, and GSMaP) in order to enable hydrological applications. The corrective methods include linear scaling (LS), empirical quantile mapping (EQM), and local intensity scaling (LOCI). In validation, three statistical metrics are employed: Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Assessing ten years of monthly data from the Kuranji watershed, LS and EQM emerged as optimal bias correction algorithms for all satellites, with LOCI outperforming TRMM and GSMaP. Corrected monthly rainfall patterns using LS and EQM closely correlate with observed data, substantially reducing discrepancies between field records and satellite-derived rainfall data. This enhances the usability of satellite data for in-depth hydrological studies.

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Section: Bias Correctionmentioning

confidence: 99%

“…The goal of this two-step procedure is to reduce model biases by aligning the monthly mean rainfall of the model with observed data. This approach is represented mathematically as follows [37]:…”

Section: Linear Scaling (Ls)mentioning

confidence: 99%

Evaluation of bias correction methods for multi-satellite rainfall estimation products

Andari,

Nurhamidah,

Daoed

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…The projected extremes of GCMs data sets show high uncertainty in extreme precipitation and are associated with projected emission scenarios, regional climate variability, model parametrization schemes, internal models physics, etc (John et al., 2022; Latif, 2011). To reduce the above uncertainties, a different bias correction method is applied by researchers, in which mean‐based bias correction is found to be more suitable for climate scenarios (Jaiswal et al., 2022; Saha & Sateesh, 2022; Shrestha et al., 2020; Xu et al., 2021). To illustrate the hydroclimate extremes during near, mid, and far future, the study used the indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) used by the previous researcher (Chaubey et al., 2022; Sarkar & Maity, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

Chaubey,

Mall

2023

Earth's Future

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The changing frequency of extreme rain events in the past few decades over the Indian river basins (IRBs) contributed to floods and drought and resulted in economic losses and gross domestic product. In this study, we evaluated the performance of 12 Global Circulation Models from the Coupled Model Intercomparison Project Phase 6‐ experiment with India Meteorological Department observed data sets to reproduce the extreme rainfall events as well as project the changes in frequency and intensity of the hydroclimate extremes in future. We found that under low emission scenarios (SSP1‐2.6), the frequency of extreme rainfall is going to increase over the western ghat and northeast IRBs, while an increase in heavy rainfall intensity (14.3%) noticed under SSP2‐4.5 in the upper Ganga and Indus basin. Also, approximately 4%–10% of the heavy rainfall is projected to increase over the western part of IRBs during the Near (2021–2040) and Mid (2041–2060) future. The study explored the new hotspot regions for future urban flooding due to increasing pattern of heavy rainfall in future. Moreover, the lower Ganga basin will experience agricultural drought in near future due to decreasing areal mean rainfall, which needs to be seen by policymakers for managing the excess (less) water. Also, India's northern, central, and western river basins may experience more extremes under high‐emission (SSP5‐8.5) scenarios that indicate challenges to mitigation. The findings of this study highlight the importance of developing long‐term adaptation and mitigation strategies aimed at reducing hydroclimate vulnerability. It emphasizes the need to implement measures that enhance resilience and minimize risks associated with hydroclimate extremes at the basin level.

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“…The statistical methods for reducing the bias (Acharya et al, 2013) are based on the application of transformation functions. Various statistical approaches such as the Regression technique, Quantile mapping method, Principal Component Analysis, and methods that do not involve the usage of transformation function like mean bias removal technique, multiplicative shift technique, and standardization reconstruction techniques are used to correct the model bias (Jaiswal et al, 2022). One of the efficient statistical methods of bias correction is Quantile Mapping.…”

Section: Introductionmentioning

confidence: 99%

Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms

Kesavavarthini

Anandan

Srinivas

et al. 2023

Intl Journal of Climatology

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General Circulation Models or Global Climate Models (GCMs) output consists of inevitable bias due to insufficient knowledge about parameterization schemes and other mathematical computations that involve thermodynamical and physical laws while designing climate models. Indian summer monsoon (southwest monsoon) accounts for 75%-90% of the annual rainfall over most climatic zones of India during the months, June, July, August, and September, which has a direct impact on the agricultural economy of India. The aim of this study is to bias correct the Coupled Model Intercomparison Project Phase -6 (CMIP6) GCMs' precipitation data for the historical period from 1985 to 2014 and two Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, from the period 2015 to 2100, with reference to the India Meteorological Department (IMD) observed rainfall gridded dataset. The datasets used are for the rain-bearing Indian southwest monsoon season from the months, June to September. Monsoon Core Region is selected to carry out the bias correction using a couple of deep learning algorithms, namely one-dimensional Convolutional Neural Network (CNN1D) and Long Short-Term Memory Encoder-Decoder (LSTM-ED) Neural Network. The performance of both algorithms is evaluated with metrics. The LSTM-ED algorithm yielded better results with least error output. The bias-corrected data obtained using the LSTM-ED algorithm is then compared with IMD observed rainfall data for the climatic events such as ENSO (El Niño and La Niña) and Positive and Negative IOD (Indian Ocean Dipole).

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Evaluation of Bias Correction Methods for Regional Climate Models: Downscaled Rainfall Analysis Over Diverse Agroclimatic Zones of India

Abstract: The increase in extreme events, particularly heavy rainfall events, is a major threat to sustained crop yields across

Cited by 22 publications

References 88 publications

Evaluation of bias correction methods for multi-satellite rainfall estimation products

Evaluation of bias correction methods for multi-satellite rainfall estimation products

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms

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