Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016

Kalisa, Wilson; Zhang, Jiahua; Igbawua, Tertsea; Ujoh, Fanan; Ebohon, Obas John; Namugize, Jean Nepomuscene; Yao, Fengmei

doi:10.1016/j.agwat.2020.106195

Cited by 90 publications

(54 citation statements)

References 44 publications

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“…An overall analysis of the metrological event shows that a mixture of both dry and wet events is identified in the temporal study. Similar findings are found in the study of Mekonen et al [63], Kalisa et al [64]. In addition, the temporal study also shows that there are a lot of differences amongst these stations with regard to drought events.…”

Section: Temporal Variation Of Droughtsupporting

confidence: 89%

Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India

Das¹,

Gayen

Saha

et al. 2020

SN Appl. Sci.

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The study aimed to monitor the spatial extent and severity of drought events in the Luni River Basin using Standardized Precipitation Index (SPI), and hence, the long-term monthly precipitation records of 39 rain gauge stations (1973-2016) were used in the study. Both the long-term (24, 12, and 9 months) and short-term (6, 3, and 1 month) SPI were calculated to recognize the drought events and the percentage of the area covered by the severe drought conditions. The nonparametric Mann-Kendall test was performed for trend analysis in drought events to investigate the consistency of drought events. The frequency results of drought events revealed that Jalore station was the highest drought frequency station, while the lowest drought frequency was observed in Vijaynagar station. The annual SPI result showed that the following years witnessed major drought events:

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Section: Temporal Variation Of Droughtsupporting

confidence: 89%

Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India

Das¹,

Gayen

Saha

et al. 2020

SN Appl. Sci.

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show abstract

“…Kalisa et al [121] Calculated SPI over East Africa from 1920 to 2015. Adequately estimated dryness or wetness, and the study proved it can be used to assess drought intensity, especially in drought-prone regions.…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

et al. 2020

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Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.

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“…To perform this study, a wide range of physical and socio-economic parameters (twelve for each) have been taken so that all the probable avenues of drought can be covered. The consideration of SPI based drought is well practiced, and it has been used for both analysis and prediction of drought (Karimi et al 2019;Kalisa et al 2020). The parameters have been selected based on the previous literatures and its assessment has been done using well-appreciated MCDM techniques like fuzzy and AHP.…”

Section: Discussionmentioning

confidence: 99%

Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha

Saha

Kundu

Paul

et al. 2021

Geomatics, Natural Hazards and Risk

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Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016

Cited by 90 publications

References 44 publications

Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India

Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha

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