Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments Article Swipe
Daniel Bannister
,
Andrew Orr
,
Sanjay K. Jain
,
Ian Holman
,
Andrea Momblanch
,
Tony Phillips
,
Adebayo J. Adeloye
,
Boris Snapir
,
Toby Waine
,
J. Scott Hosking
,
Clare Allen‐Sader
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1029/2019jd030804
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1029/2019jd030804
The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5‐km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2019jd030804
- https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2019JD030804
- OA Status
- hybrid
- Cited By
- 74
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2996413371
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- OpenAlex ID
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https://openalex.org/W2996413371Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2019jd030804Digital Object Identifier
- Title
-
Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan CatchmentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-14Full publication date if available
- Authors
-
Daniel Bannister, Andrew Orr, Sanjay K. Jain, Ian Holman, Andrea Momblanch, Tony Phillips, Adebayo J. Adeloye, Boris Snapir, Toby Waine, J. Scott Hosking, Clare Allen‐SaderList of authors in order
- Landing page
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https://doi.org/10.1029/2019jd030804Publisher landing page
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https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2019JD030804Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2019JD030804Direct OA link when available
- Concepts
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Precipitation, Environmental science, Climate model, Climatology, High resolution, Climate change, Meteorology, Geology, Geography, Remote sensing, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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74Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 12, 2023: 8, 2022: 13, 2021: 30Per-year citation counts (last 5 years)
- References (count)
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72Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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