Toward reliable water resource assessment: Correcting bias in satellite-based rainfall and model-derived discharge in Kenya’s Central Highlands
5 affiliations
- 1Department of Earth Science, University of Ghana, Legon, Accra, Ghana
- 2International Water Management Institute (IWMI), Accra, Ghana
- 3International Water Management Institute (IWMI), Colombo, Sri Lanka
- 4International Water Management Institute (IWMI), Addis Ababa, Ethiopia
- 5State Department for Irrigation, Nairobi, Kenya
Abstract
Reliable precipitation and streamflow data are essential for hydrological modelling and water resources management, yet observational networks in many African regions remain sparse. This study evaluated five satellite precipitation products (SPPs) CHIRPS, PERSIANN-CCS, PERSIANN-PDIR, TAMSAT, and MSWEP and modelled stream-discharge data from VegET–mizuRoute model against in situ observations across the Central Highlands of Kenya for 2012–2021. Performance was assessed at eight precipitation and six discharge stations using pairwise statistics (RMSE, NSE, CC, RMB) and categorical metrics (POD, FAR) on a monthly scale. Four bias correction techniques (Least Squares, Quantile Mapping, Delta Method, Random Forest) were assessed to enhance the utility of the best-performing SPP and the modelled discharge data. CHIRPS and MSWEP consistently outperformed the other products: CHIRPS achieved RMSE of 34.85–134.26 mm, NSE of −0.94 to 0.44, and CC of 0.52–0.87, while MSWEP recorded RMSE of 24.17–125.66 mm, NSE of −0.91 to 0.65, and CC of 0.45–0.84. The VegET–mizuRoute model reproduced seasonal flow dynamics but showed station-dependent biases, with NSE from −2.28 to 0.23, RMB from −0.59 to 0.77, and CC from 0.46 to 0.71. Following bias correction, Random Forest yielded substantial improvements for both precipitation and discharge, underscoring the capacity of machine learning to address complex, nonlinear biases. It raised precipitation NSE to a consistently positive 0.85–0.95 across all eight stations while halving RMSE at most sites (9.05–46.23 mm). For discharge, Random Forest improved NSE at most stations to 0.80–0.88, with CC rising to 0.92–0.95, except at one station. Our findings confirm that systematic bias remains a dominant error source in global hydrological products in this region, and that machine learning-based bias correction is essential for the operational reliability and accuracy of these datasets for water management and flood applications.
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BibTeX
@article{yeboah2026toward,
title = {{Toward reliable water resource assessment: Correcting bias in satellite-based rainfall and model-derived discharge in Kenya’s Central Highlands}},
author = {Felicia Yeboah and Afua Owusu and Mansoor Leh and Komlavi Akpoti and Kirubel Mekonnen and Elias Muluken Adamseged and Emma Odera and Naga Velpuri},
journal = {PLOS Water},
year = {2026},
month = sep,
doi = {10.1371/journal.pwat.0000623},
url = {https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000623},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Toward reliable water resource assessment: Correcting bias in satellite-based rainfall and model-derived discharge in Kenya’s Central Highlands
AU - Felicia Yeboah
AU - Afua Owusu
AU - Mansoor Leh
AU - Komlavi Akpoti
AU - Kirubel Mekonnen
AU - Elias Muluken Adamseged
AU - Emma Odera
AU - Naga Velpuri
PY - 2026
DA - 2026/09/25
JO - PLOS Water
DO - 10.1371/journal.pwat.0000623
UR - https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000623
AB - Reliable precipitation and streamflow data are essential for hydrological modelling and water resources management, yet observational networks in many African regions remain sparse. This study evaluated five satellite precipitation products (SPPs) CHIRPS, PERSIANN-CCS, PERSIANN-PDIR, TAMSAT, and MSWEP and modelled stream-discharge data from VegET–mizuRoute model against in situ observations across the Central Highlands of Kenya for 2012–2021. Performance was assessed at eight precipitation and six discharge stations using pairwise statistics (RMSE, NSE, CC, RMB) and categorical metrics (POD, FAR) on a monthly scale. Four bias correction techniques (Least Squares, Quantile Mapping, Delta Method, Random Forest) were assessed to enhance the utility of the best-performing SPP and the modelled discharge data. CHIRPS and MSWEP consistently outperformed the other products: CHIRPS achieved RMSE of 34.85–134.26 mm, NSE of −0.94 to 0.44, and CC of 0.52–0.87, while MSWEP recorded RMSE of 24.17–125.66 mm, NSE of −0.91 to 0.65, and CC of 0.45–0.84. The VegET–mizuRoute model reproduced seasonal flow dynamics but showed station-dependent biases, with NSE from −2.28 to 0.23, RMB from −0.59 to 0.77, and CC from 0.46 to 0.71. Following bias correction, Random Forest yielded substantial improvements for both precipitation and discharge, underscoring the capacity of machine learning to address complex, nonlinear biases. It raised precipitation NSE to a consistently positive 0.85–0.95 across all eight stations while halving RMSE at most sites (9.05–46.23 mm). For discharge, Random Forest improved NSE at most stations to 0.80–0.88, with CC rising to 0.92–0.95, except at one station. Our findings confirm that systematic bias remains a dominant error source in global hydrological products in this region, and that machine learning-based bias correction is essential for the operational reliability and accuracy of these datasets for water management and flood applications.
SN - 2767-3219
ER -