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Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya

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Pubblicato in:Hydrology and Earth System Sciences vol. 29, no. 14 (2025), p. 3073
Autore principale: Girona-Mata, Marc
Altri autori: Orr, Andrew, Widmann, Martin, Bannister, Daniel, Ghulam Hussain Dars, Hosking, Scott, Norris, Jesse, Ocio, David, Phillips, Tony, Steiner, Jakob, Turner, Richard E
Pubblicazione:
Copernicus GmbH
Soggetti:
Asia
Hindu Kush
Extreme weather
Bias
Datasets
Models
Pilot projects
Multilayers
Artificial neural networks
Multilayer perceptrons
Neural networks
Regional climate models
Hydrology
Statistical analysis
Precipitation
Climate change
Mountains
Simulation
Hydrometeorology
Climate models
Water resources
Regional climates
Regions
Monsoons
Climate
Climate science
Daily precipitation
Environmental
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Abstract: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.
ISSN:1027-5606
1607-7938
DOI:10.5194/hess-29-3073-2025
Fonte:Engineering Database