Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya
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| Publicado en: | Hydrology and Earth System Sciences vol. 29, no. 14 (2025), p. 3073 |
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| Autor principal: | |
| Otros Autores: | , , , , , , , , , |
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Copernicus GmbH
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.5194/hess-29-3073-2025 |2 doi | |
| 035 | |a 3231831022 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Girona-Mata, Marc |u British Antarctic Survey, UK Research and Innovation, Cambridge, UK; Department of Engineering, University of Cambridge, Cambridge, UK | |
| 245 | 1 | |a Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Asia | |
| 651 | 4 | |a Hindu Kush | |
| 653 | |a Extreme weather | ||
| 653 | |a Bias | ||
| 653 | |a Datasets | ||
| 653 | |a Models | ||
| 653 | |a Pilot projects | ||
| 653 | |a Multilayers | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Neural networks | ||
| 653 | |a Regional climate models | ||
| 653 | |a Hydrology | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Precipitation | ||
| 653 | |a Climate change | ||
| 653 | |a Mountains | ||
| 653 | |a Simulation | ||
| 653 | |a Hydrometeorology | ||
| 653 | |a Climate models | ||
| 653 | |a Water resources | ||
| 653 | |a Regional climates | ||
| 653 | |a Regions | ||
| 653 | |a Monsoons | ||
| 653 | |a Climate | ||
| 653 | |a Climate science | ||
| 653 | |a Daily precipitation | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Orr, Andrew |u British Antarctic Survey, UK Research and Innovation, Cambridge, UK | |
| 700 | 1 | |a Widmann, Martin |u School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK | |
| 700 | 1 | |a Bannister, Daniel |u WTW Research Network, WTW, London, UK | |
| 700 | 1 | |a Ghulam Hussain Dars |u U.S.-Pakistan Center for Advanced Studies in Water, Mehran University of Engineering and Technology, Jamshoro, Pakistan | |
| 700 | 1 | |a Hosking, Scott |u British Antarctic Survey, UK Research and Innovation, Cambridge, UK; The Alan Turing Institute, London, UK | |
| 700 | 1 | |a Norris, Jesse |u Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, CA, USA | |
| 700 | 1 | |a Ocio, David |u Mott MacDonald, Cambridge, UK | |
| 700 | 1 | |a Phillips, Tony |u British Antarctic Survey, UK Research and Innovation, Cambridge, UK | |
| 700 | 1 | |a Steiner, Jakob |u Institute of Geography and Regional Science, University of Graz, Graz, Austria; Himalayan University Consortium, Lalitpur, Nepal | |
| 700 | 1 | |a Turner, Richard E |u Department of Engineering, University of Cambridge, Cambridge, UK | |
| 773 | 0 | |t Hydrology and Earth System Sciences |g vol. 29, no. 14 (2025), p. 3073 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231831022/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3231831022/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231831022/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |