Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward

Hinge, Gilbert; Mohamed, Mohamed M.; Long, Di; Hamouda, Mohamed A.

doi:10.3390/rs13214353

Cited by 21 publications

(6 citation statements)

References 80 publications

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“…These technological advancements significantly enhance the reliability of precipitation data, which is crucial for accurate hydrological modeling and climate research. There are a number of SPPs available to the public that have been extensively used in many studies including water resource management [13,14], hydrological modeling [15][16][17][18][19], and extreme events analysis [16,[20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Ejaz,

Khan,

Shahid

et al. 2024

Water

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Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.

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Section: Introductionmentioning

confidence: 99%

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Ejaz,

Khan,

Shahid

et al. 2024

Water

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“…Nonetheless, there are many challenges associated with the lack of consistency between multi-sensor or even single-sensor data products (e.g., [9]). To address this issue, a number of studies have been performed to evaluate and/or compare different precipitation datasets at regional to global scales (e.g., [10]), with most results indicating that multi-sensor approaches are advantageous for drought monitoring [11]. Soil moisture is another important variable that defines agricultural drought, and therefore its observations have been widely used in agricultural and water resources applications (e.g., [12]).…”

Section: Introductionmentioning

confidence: 99%

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Andreadis,

Meason,

Corbett-Lad

et al. 2024

Remote Sensing

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Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions.

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“…Satellite precipitation products (SPPs) are the most important sources of data for analyzing the spatial distribution of precipitation and seasonal drought monitoring. There are various SPPs available, such as the Climate Prediction Center morphing method, Tropical Rainfall Measuring Mission (TRMM), and Global Satellite Mapping of Precipitation to produce drought indices [23], [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Jalayer

Sharifi

Abbasi‐Moghadam

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The in situ drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the in situ drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and

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Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward

Cited by 21 publications

References 80 publications

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Hydrologic Consistency of Multi-Sensor Drought Observations in Forested Environments

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

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