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<p>Potential evapotranspiration plays a crucial role in hydrological analysis. Therefore, reliable projection of potential evapotranspiration and/or reference evapotranspiration (ETo) at the local scale under various climate change scenario is essential. To achieve this, climate datasets from various General Circulation Models must be downscaled and bias corrected to enhance their accuracy. In this study, we developed daily bias-corrected datasets of ETo at 0.25° spatial resolution. We used outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the historical and four Shared Socioeconomic Pathways. The datasets include temperature, solar radiation, wind speed, and relative humidity from 12 different models at various spatial scales for South Asia. The bias correction was performed using the Quantile-Mapping approach. The resulting bias-corrected climate variables&#xa0;(dataset available in repository) were subsequently used to estimate historical and future ETo over South Asia. A significant difference was observed between the original and bias-corrected datasets when evaluated against the ERA-5 reanalysis observation data. Penman-Monteith method was utilized for ETo estimation to reduce the uncertainties of temperature-based methods.</p>

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Development of daily downscaled, bias-corrected CMIP6 climate datasets for estimating reference evapotranspiration (ETo) in South Asia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Aniruddha Saha,
  • Manoj Kumar Jain,
  • Pranita Joshi,
  • Subhankar Das,
  • Naimesh Singh Rawat

摘要

Potential evapotranspiration plays a crucial role in hydrological analysis. Therefore, reliable projection of potential evapotranspiration and/or reference evapotranspiration (ETo) at the local scale under various climate change scenario is essential. To achieve this, climate datasets from various General Circulation Models must be downscaled and bias corrected to enhance their accuracy. In this study, we developed daily bias-corrected datasets of ETo at 0.25° spatial resolution. We used outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the historical and four Shared Socioeconomic Pathways. The datasets include temperature, solar radiation, wind speed, and relative humidity from 12 different models at various spatial scales for South Asia. The bias correction was performed using the Quantile-Mapping approach. The resulting bias-corrected climate variables (dataset available in repository) were subsequently used to estimate historical and future ETo over South Asia. A significant difference was observed between the original and bias-corrected datasets when evaluated against the ERA-5 reanalysis observation data. Penman-Monteith method was utilized for ETo estimation to reduce the uncertainties of temperature-based methods.