Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia

Bayissa, Yared A.; Maskey, Shreedhar; Tadesse, Tsegaye; Andel, Schalk Jan van; Moges, Semu Ayalew; Griensven, Ann van; Solomatine, Dimitri

doi:10.3390/geosciences8030081

Cited by 134 publications

(88 citation statements)

References 41 publications

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“…Additionally, among the rainfall-based drought indices, the SPI9 had the lowest percentage of months with drought events compared with the SPI3 and SPI6. This is in line with other studies [46], where the author found that agricultural-based drought indicators depict relatively larger values of drought months compared with the meteorological drought indices. The maximum drought duration varied among the different drought indicators.…”

Section: Comparison Of Drought Characteristicssupporting

confidence: 92%

“…Based on our analysis, the maximum drought duration appeared to be higher in the meteorological-based indices than the agricultural-based indices. This is primarily because the meteorological-based drought indices integrate the drought condition over a longer period of time than do the agricultural-based drought indices [46]. The drought intensity was found to be higher in the agricultural-based drought variables and lower in the metrological-based drought indices.…”

Section: Comparison Of Drought Characteristicsmentioning

confidence: 95%

“…Positive values of RZSM anomalies, SPI3, SPI6, and SPI9 are masked out to identify only months with drought conditions, the corresponding number is divided by the total number of months to calculate the percentage of months with drought conditions. Each product has been examined in terms of the following drought characteristics: drought duration (defined as the period during which the drought indices are continuously negative); drought severity (computed as the absolute value of the sum of all drought indices during a drought event); and drought intensity (calculated by dividing the severity by the drought duration) [46,47]. This routine also computes cross-correlations between agricultural-and meteorological-based drought indices and allows us to estimate the Pearson, Spearman, and lag correlation coefficients using the GEE 'reducerpearsonscorrelation' function between the paired monthly time series of soil moisture anomalies and SPI, as well soil moisture and NDVI (Figure 4).…”

Section: Drought Assessment Routinementioning

confidence: 99%

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Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

2018

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Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions' satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation-and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them. soil moisture monitoring offers, such as global coverage, high accuracy, and high temporal frequency essential for operational applications, the shallow penetration depth of the microwave frequencies used for soil moisture monitoring remains a limiting factor [3]. In fact, many hydrological processes and forecasting and decision-making activities linked to soil moisture require knowledge of the root zone soil moisture (RZSM) information. The latter is typically estimated using hydrologic land surface models, which are traditionally driven by some weather-related observations such as precipitation, temperature, and so forth. The credibility of the modeled RZSM estimates is strongly dependent on the quality of the for...

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Section: Comparison Of Drought Characteristicssupporting

confidence: 92%

Section: Comparison Of Drought Characteristicsmentioning

confidence: 95%

Section: Drought Assessment Routinementioning

confidence: 99%

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Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

2018

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“…In 2015-2016, another drought event associated with El Niño affected EA, with 24 million people facing critical and emergency food insecurity by August 2016. Drought indices have recently been used to characterize historical episodes in the Upper Blue Nile Basin in Ethiopia [8] and intraseasonal descriptors of wet and dry spells were applied to equatorial EA using rain gauge data [9]. Excessive rainfall was recorded during late 2015 and the March-May 2016 rainy season, leading to flooding in parts of Somalia, Kenya, Ethiopia, and Uganda (source: United Nation Office for the Coordination of Humanitarian Affairs, UN OCHA, http://www.unocha.org, last accessed 13 March 2018).…”

Section: Introductionmentioning

confidence: 99%

East Africa Rainfall Trends and Variability 1983–2015 Using Three Long-Term Satellite Products

et al. 2018

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Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S-20N, 28-52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October-November-December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March-April-May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved.

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“…In many parts of the world, the coverage of this information is not known or properly documentend because of data scarcity. As a result, many drought analysis studies, especially in sub-Saharan Africa, have been using point data, particularly observed rainfall datasets [4,5]. This has been causing inadequate information about drought coverage, thus affecting the livelihood of many people in this region.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

Wambura

Dietrich

2020

Water

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Understanding spatial drought characteristics is vital for planning adaptation and mitigation measures in river catchments. In many parts of the world, spatial drought information is not available due to lack of adequate evenly distributed data for spatial drought analyses. This study elucidates a spatial drought analysis in a data-scarce tropical catchment using remote sensing actual evapotranspiration (ET) and potential evapotranspiration (PET) data. Firstly, the time series of 690 images of remotely sensed ET and PET between the years 2000 and 2014 were spatially analyzed using the evapotranspiration deficit index (ETDI) approach to obtain ETDIs in the Kilombero River catchment (Tanzania). Then, spatio-temporal patterns of ETDIs were used to characterize drought frequency, total drought durations, total drought severity, and drought intensity. The frequency, durations, severity, and intensity of drought increased from the year 2000 towards 2014, causing substantial drought changes in the catchment. However, drought intensity revealed that those changes were mainly from no drought and mild drought to moderate drought. Between the years 2000 and 2014, no-drought areas and mild drought areas declined from 10% to 0% and from 42% to 19%, respectively, whereas moderate drought areas increased from 47% to 81% of the catchment size. Those changes of drought conditions were partly attributed to anthropogenic land cover change, especially in the southwest grasslands, and were partly attributed to meteorological factors in other parts of the catchment. This information is crucial for further land cover change and climate change investigations, as well as planning water and land resources in the Kilombero River catchment. Moreover, the study also demonstrates the potential of using publicly available remote sensing ET products and the ETDI approach for spatially characterizing drought in ungauged regions.

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Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia

Abstract: The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system.

Cited by 134 publications

References 41 publications

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

East Africa Rainfall Trends and Variability 1983–2015 Using Three Long-Term Satellite Products

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

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