Satellite rainfall performance evaluation and application to monitor meteorological drought: a case of Omo-Gibe basin, Ethiopia

Dejene, Indale Niguse; Wedajo, Gizachew Kabite; Bayissa, Yared A.; Abraham, Ashenif Melese; Cherinet, Kefalegn Getahun

doi:10.1007/s11069-023-06127-2

Cited by 4 publications

(2 citation statements)

References 67 publications

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“…Drought, a significant natural disaster driven by climate variability, impacts economic, social, and environmental aspects (Feng et al, 2020). Understanding its multifaceted nature is crucial for effective water resource management (Dejene et al, 2023). While more frequent in dry regions and often linked to insufficient precipitation due to global warming, droughts can happen in any climate (Zhao et al, 2018;Nawaz et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based precipitation products for meteorological drought

Suliman,

Rajab,

Shahid

2024

Preprint

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Drought, a serious natural hazard, significantly impacts agriculture, especially in dry regions. Iraq faces a high drought risk yet suffers from a scarcity of rainfall gauges for effective monitoring. Therefore, adequate and accurate datasets are crucial for the country's spatiotemporal classification and monitoring of droughts. This study investigates the feasibility of calculating the Standardized Precipitation Index (SPI) at various meteorological ground stations (GS) using monthly precipitation data from satellite-based precipitation products (SbPP). For spatial comparisons, Iraq was divided into three Köppen climate zones. Monthly time series from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) (1983–2007) and Climate Hazards Group of Infra-Red Precipitation with Stations (CHIRPS) (1983–2017) were statistically compared against GS data in these zones for estimating droughts at 3-, 6-, and 12-month timescales. The SbPPs exhibited reasonable correlations with GS data and effectively represented the spatial distribution of rainfall. A good level of consistency was observed between APHRODITE and GS values in estimating SPI in high-rainfall zones. The study concludes that APHRODITE outperforms CHIRPS in determining the spatial and temporal distribution of droughts in Iraq.

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Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based precipitation products for meteorological drought

Suliman,

Rajab,

Shahid

2024

Preprint

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“…However, drought has been categorized into four types: agriculture, hydrological, meteorological, and socio-economic 14 . The consensus in the scientific community is that all categorised droughts originated from meteorological droughts 15 , 16 . Over the past few decades, numerous meteorological drought indices have been introduced, such as the Palmer Drought Severity Index (PDSI) 17 Standardized Precipitation Index (SPI) 18 , and Standardized Precipitation Evapotranspiration Index (SPEI) 19 .…”

Section: Introductionmentioning

confidence: 99%

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

Das,

Zhang,

Ghosh

et al. 2024

Sci Rep

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This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

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Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Suliman,

Rajab,

Shahid

2024

Theor Appl Climatol

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Satellite rainfall performance evaluation and application to monitor meteorological drought: a case of Omo-Gibe basin, Ethiopia

Cited by 4 publications

References 67 publications

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based precipitation products for meteorological drought

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based precipitation products for meteorological drought

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

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