Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

Tazi, Kenza; Orr, Andrew; Hernandez-González, Javier; Hosking, Scott; Turner, Richard E.

doi:https://doi.org/10.5194/hess-28-4903-2024

Articles | Volume 28, issue 22

https://doi.org/10.5194/hess-28-4903-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Hydrological response to climatic and cryospheric changes...

https://doi.org/10.5194/hess-28-4903-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 22

Research article

|

18 Nov 2024

Research article |

| 18 Nov 2024

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner

Publisher's note: the paper was adjusted on 23 April 2025. The adjustments included the typo "chloropeth", the incorrect formatting of the matrix K, the text of the caption of table C2, as well as incorrect years given in the captions of tables 4, C1, and C2.

Data sets

Downscaled ERA5 monthly precipitation data using Multi-Fidelity Gaussian Processes between 1980 and 2012 for the Upper Beas and Sutlej Basins, Himalayas Kenza Tazi https://doi.org/10.5285/b2099787-b57c-44ae-bf42-0d46d9ec87cc

ERA5 monthly averaged data on single levels from 1940 to present Copernicus Climate Change Service https://doi.org/10.24381/cds.f17050d7

VALUE ECA&D local observations VALUE http://www.value-cost.eu/data

GMTED2010 elevation data at different resolutions, Tropospheric Emission Monitoring Internet Service TROPOMI https://www.temis.nl/data/gmted2010/index.php

Global multi-resolution terrain elevation data 2010 (GMTED2010) Jeffrey J. Danielson and Dean B. Gesch https://doi.org/10.3133/ofr20111073

Model code and software

Code for 'Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5' Kenza Tazi https://doi.org/10.5281/zenodo.14106487

Short summary

This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.