Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F25%3A105968" target="_blank" >RIV/60460709:41320/25:105968 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41330/25:105968 RIV/00216208:11310/25:10500603

Výsledek na webu

<a href="https://doi.org/10.1186/s40537-025-01208-4" target="_blank" >https://doi.org/10.1186/s40537-025-01208-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40537-025-01208-4" target="_blank" >10.1186/s40537-025-01208-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Clustering

Popis výsledku v původním jazyce

Accurate precipitation estimation at high spatial and temporal resolutions is essential for hydrological and meteorological applications, especially in regions experiencing water resource degradation. This study presents a robust non-parametric framework for disaggregating coarse-resolution satellite precipitation data to finer scales, using a hybrid model that integrates Extreme Gradient Boosting (XGBoost) with multivariate spatio-temporal fuzzy clustering. Eight clusters were delineated based on Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation and Shuttle Radar Topography Mission (SRTM) elevation data, with one representative station per cluster used for training and validation, and an additional 19 stations employed solely for independent validation. We downscaled 255 months (June 2000-September 2021) of IMERG precipitation data from 11 to 1 km spatial resolution across the Czech Republic. The disaggregated precipitation demonstrated marked accuracy improvements when evaluated against observed station data, with R2documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$R<^>2$$end{document} values ranging from 0.63 to 0.85, RMSE between 17.43 mm and 32.41 mm, NSE from 0.39 to 0.82, and KGE spanning 0.67 to 0.86-indicating a significant reduction in the bias inherent in the original IMERG data. The proposed methodology achieved (1) enhanced agreement between disaggregated and observed monthly precipitation, (2) significant improvement in IMERG data accuracy at finer scales, and (3) demonstrated operational potential in regions with sparse ground-based observations. This approach offers a promising solution for generating reliable, high-resolution precipitation datasets in data-scarce environments, with broad applicability in global hydrological and meteorological modelling.

Název v anglickém jazyce

Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Clustering

Popis výsledku anglicky

Accurate precipitation estimation at high spatial and temporal resolutions is essential for hydrological and meteorological applications, especially in regions experiencing water resource degradation. This study presents a robust non-parametric framework for disaggregating coarse-resolution satellite precipitation data to finer scales, using a hybrid model that integrates Extreme Gradient Boosting (XGBoost) with multivariate spatio-temporal fuzzy clustering. Eight clusters were delineated based on Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation and Shuttle Radar Topography Mission (SRTM) elevation data, with one representative station per cluster used for training and validation, and an additional 19 stations employed solely for independent validation. We downscaled 255 months (June 2000-September 2021) of IMERG precipitation data from 11 to 1 km spatial resolution across the Czech Republic. The disaggregated precipitation demonstrated marked accuracy improvements when evaluated against observed station data, with R2documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$R<^>2$$end{document} values ranging from 0.63 to 0.85, RMSE between 17.43 mm and 32.41 mm, NSE from 0.39 to 0.82, and KGE spanning 0.67 to 0.86-indicating a significant reduction in the bias inherent in the original IMERG data. The proposed methodology achieved (1) enhanced agreement between disaggregated and observed monthly precipitation, (2) significant improvement in IMERG data accuracy at finer scales, and (3) demonstrated operational potential in regions with sparse ground-based observations. This approach offers a promising solution for generating reliable, high-resolution precipitation datasets in data-scarce environments, with broad applicability in global hydrological and meteorological modelling.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Big Data

ISSN

2196-1115

e-ISSN

2196-1115

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

1-24

Kód UT WoS článku

001512305700001

EID výsledku v databázi Scopus

2-s2.0-105008494662