Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya
<p>This study introduces a novel approach to post-processing (i.e. downscaling and bias-correcting) reanalysis-driven regional climate model daily precipitation outputs that can be generalised to ungauged mountain locations by leveraging sparse in situ observations and a probabilistic regres...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/3073/2025/hess-29-3073-2025.pdf |
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| Summary: | <p>This study introduces a novel approach to post-processing (i.e. downscaling and bias-correcting) reanalysis-driven regional climate model daily precipitation outputs that can be generalised to ungauged mountain locations by leveraging sparse in situ observations and a probabilistic regression framework. We call this post-processing approach generalised probabilistic regression (GPR) and implement it using both generalised linear models and artificial neural networks (i.e. multi-layer perceptrons). By testing the GPR post-processing approach across three Hindu Kush Himalaya (HKH) basins with varying hydro-meteorological characteristics and four experiments, which are representative of real-world scenarios, we find it performs consistently much better than both raw regional climate model output and deterministic bias correction methods for generalising daily precipitation post-processing to ungauged locations. We also find that GPR models are flexible and can be trained using data from a single region or multiple regions combined together, without major impacts on model performance. Additionally, we show that the GPR approach results in superior skill for post-processing entirely ungauged regions, by leveraging data from other regions as well as ungauged high-elevation ranges. This suggests that GPR models have potential for extending post-processing of daily precipitation to ungauged areas of HKH. Whilst multi-layer perceptrons yield marginally improved results overall, generalised linear models are a robust choice, particularly for data-scarce scenarios, i.e. post-processing extreme precipitation events and generalising to completely ungauged regions.</p> |
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| ISSN: | 1027-5606 1607-7938 |