Standardized Precipitation Index Values Research Articles

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator

Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modeling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the “Kaplan–Meier estimator”, a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to apply this methodology to assess drought risk by treating past droughts as “events” and using drought indicators such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Mapping these results for a better understanding of the drought risks on larger spatial scales such as a river basin is also within the expected outcomes. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope gets steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps, which expose more vulnerable areas within the river basin.

Evaluation of Drought in the Ken River Basin in Bundelkhand Region, India

The present study exhibits the analyses of the meteorological drought events in the Ken Basin using various indices viz., Standardized Precipitation Index (SPI) for different time-scale and Rainfall Departure (RD) method. Precipitation data from 1990 to 2020 for six districts falling in the basin have been used to compute SPI and RD values. The computation of SPI series has been carried out for 1-month, 3-month, 6-month, 9-month and 12-month time-scales. The analysis indicated that the most parts of the basin experienced drought events in the years 2007, 2015 and 2017 region during the years. However, the basin was affected partially in the other drought years. The maximum severity of drought has been recorded as -65% and -52% in the year 1997 in Mahoba and Banda districts respectively. However, the up-reach parts of the basin were not affected in the year 1997.  The comparative analysis of RD and SPI exhibited a good correlation with 3-month and 6-month SPI time steps. It is found that the SPI-3 identifies the meteorological drought event appropriately followed by SPI-6.

Variation of standardized precipitation index (SPI) over southern West Bengal and its effect on jute yield

West Bengal is a key producer of raw jute fiber in the country. Identifying and managing dry spells during the jute growing period is crucial, necessitating contingency crop planning for enhanced productivity. Keeping this view in mind, standardized precipitation index (SPI) was calculated over five locations, representing five different districts of southern West Bengal. These locations are Barrackpore (North 24 Parganas District), Panagarh (Burdwan District), Bagati (Hooghly District), Krishnanagar (Nadia District) and Uluberia (Howrah District). This rainfall dependent dryness index (SPI) was calculated in 1 month and 3 months interval to identify short term dryness as well as mid-term dryness, applicable for seasonal crops. The trend analysis of the SPI values indicated that North 24 Parganas and Nadia experienced increased dryness during vegetative phase of Jute. Nadia district showed a significant increase in both short term and long-term dryness. The yield reduction index is well correlated with SPI values in all the study locations except Howrah. Arrangement of irrigation during the early stages of Jute can help the crop to cope up with the break of monsoon in this region

Relationship of ENSO and Standardized Precipitation Index (SPI) to Characterize Drought at Different Locations of Punjab, India

Drought is generally considered as a deficiency in precipitation over an extended period usually a season or more. It is a natural hazard that differs from other hazards since it has a slow onset, evolves over months or even years and its severity is often difficult to determine. Standardized Precipitation Index (SPI) is a widely used index to characterize drought on a range of timescales. In this study SPI was calculated annually and season (Kharif and Rabi) wise to correlate drought with ENSO events at different locations of Punjab viz. Amritsar, Ballowal Saunkhri, Bathinda, Ludhiana and Patiala. The historical data of rainfall for different locations of Punjab viz. Amritsar (1971-2017), Ballowal Saunkhri (1984-2018), Bathinda (1971-2018), Ludhiana (1971-2018) and Patiala (1971-2018) were collected from the Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana and Met Centre, India Meteorological Department, Chandigarh. It was found that at all the locations very strong El Nino of 1997-98 resulted in above normal rainfall. Drought like conditions was observed during El Nino years as value of SPI was negative but with few exceptions. The SPI values were positive during La Nina and neutral years. Droughts during kharif season were found to be more related to El Nino years compared to La Nina years.

Rainfall trends and spatiotemporal patterns of meteorological drought in Menna watershed, northwestern Ethiopia

Understanding the spatiotemporal patterns of drought is crucial for planning, disaster preparedness, vulnerability assessment, impact evaluation, and policy formulation to mitigate drought-induced effects. The purpose of this study was to assess rainfall trends and spatiotemporal patterns of meteorological drought using geospatial techniques in Menna watershed. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall, and station-based observed rainfall were the datasets used. The station-based rainfall was used to confirm the accuracy of CHIRPS rainfall data. The Mann-Kendall (MK) test and Sen's slope estimator were utilized to assess trends and ascertain the extent of change. To characterize meteorological droughts, percent of normal (PN), standardized anomaly index (SAI), and standardized precipitation index (SPI) were computed during the crop growing seasons (2000–2022). The validation result confirmed a strong agreement between the observed and CHIRPS rainfall data (R2 = 0.88). Based on the MK test, an increasing trend has been observed in annual (3.7 mm/year) and belg (3.4 mm/year) rainfall, which was significant at p < 0.05. But the kiremt season was slightly decreasing (−0.7 mm/year). The PN, SAI, and SPI values detected that 2002, 2004, 2009, 2011, 2014, 2015, and 2019 were drought years in the area. Even only 1.4, 0.2, and 0.5% of the watershed were free from drought in 2009, 2014, and 2015, respectively, due to extremely high rainfall deficiency. Conversely, 2001, 2010, and 2016 were notable for having the highest amounts of rainfall compared to the other years. Generally, the region could be classified as an area highly susceptible to meteorological drought in northwestern Ethiopia. There was no even a single year free from drought in the entire study period. To that extent, about 86% of it had repeatedly encountered extreme rainfall deficit (7–23 times) during the study period. Thus, the population has always been repeatedly smashed down by the frequent droughts. To tackle existing challenges and mitigate upcoming risks, continual droughts monitoring and implementation of efficient early warning systems are vital for the region.

Drought analysis based on SPI and RDI drought indices in the Burdur Basin

Drought is the most complex of the recurrent extreme weather events and is defined as a natural disaster with severe environmental, economic, and agricultural impacts resulting from a significant decrease in the average rainfall recorded in an area and the average rainfall recorded in the same place. Droughts have become more frequent and severe in many parts of the world, including Türkiye, due to global warming and climate change (increasing temperatures and changing precipitation patterns). Water resources and the agricultural sector are most severely affected by droughts. In this study, drought analyses of the Burdur Basin, located in the Aegean region, one of Türkiye's seven geographical regions, were carried out. For drought analysis, annual average total precipitation, annual maximum temperature, annual minimum temperature, and annual average temperature data of 17238 Burdur and 17892 Tefenni meteorological observation stations were used. Both meteorological and agricultural drought analyzes are included in the analysis of droughts. Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods were used to determine meteorological and agricultural drought, respectively. SPI and RDI values were obtained for 1-, 3-, 6- and 12-month time periods, and the severity, size, and distribution of dry and humid periods were determined for both stations separately. When the results of both methods were examined, severe droughts were observed in the study area in 1973, 1978, 1981, and 2017.

Agroecological-based modeling of meteorological drought at 12-month time scale in the Western Cape Province of South Africa

AbstractTo mitigate the effects of drought in water-stressed agro-ecological zones (AEZs), it is crucial to implement efficient monitoring of drought characteristics and establish proactive water-use strategies. The Western Cape (WC) province is being confronted with an escalating threat of diminished water availability due to unsustainable human activities. The objective of this study was to assess the spatio-temporal characteristics of meteorological drought within a 12-month timescale in the WC province of South Africa spanning from 1980 to 2020. The University of East Anglia's Climate Research Unit provided precipitation datasets from fifteen stations across the six AEZs of the WC province. These datasets were processed using the Standardized Precipitation Index (SPI). The Mann–Kendall (M–K) test was used to analyze the precipitation trend and SPI values at a significance level of p &lt; 0.05. The results indicated substantial negative trends in annual precipitation across all AEZs. An examination of seasonal precipitation patterns revealed a rise in summer precipitation in subtropical cool-arid regions and subtropical warm semi-arid regions, with increases of 0.4 and 0.2 mm/year, respectively. Conversely, the overall annual precipitation trend fell by -1.0 mm/year in both AEZs. The SPI values consistently fell within the range of -2 to 0 in the subtropical warm-arid, subtropical cool-arid, and subtropical cool-arid zones. Conversely, the SPI values were consistently positive in the subtropical warm-arid, subtropical cool-humid, and subtropical warm-humid zones. This finding suggests that there will be persistent drought conditions that will affect agricultural production, surface flows in rivers, and groundwater levels across the WC province.

Standardized Precipitation Index for Metrological Drought Analysis In Kashmir Valley Of Jammu And Kashmir

Abstract: The term "drought" is multifaceted, with varying interpretations based on personal experiences. It is a natural disaster that is distinguished by a severe and ongoing precipitation deficit. The planning and management of freshwater resources as well as the forecasting of the arrival and intensity of droughts depend heavily on drought monitoring. A number of indices have been put forth over time to track and measure data related to droughts. The Standardized Precipitation index (SPI), which is based on the idea of probability, is the most commonly used index. The current study examines the possibility of analysing the temporal pattern and severity of drought in the Kashmir Valley (Jammu and Kashmir) using precipitation-based SPI. Utilizing the "DrinC" software application, the Indices were calculated. SPI values were calculated using monthly precipitation data for the IMD Srinagar 2003 to 2022. SPI series computations were carried out for short, moderate, and long time scales. Based on the data, the Kashmir region experienced four periods of moderate drought in the years 2007, 2009, 2012, and 2016. Additionally, there was no significant drought from 2003 to 2022. 2007 had the maximum intensity with an SPI value of -1.37 for a moderate drought.

Analyzing Temporal Patterns of Temperature, Precipitation, and Drought Incidents: A Comprehensive Study of Environmental Trends in the Upper Draa Basin, Morocco

Quantifying variation in precipitation and drought in the context of a changing climate is important to assess climate-induced changes and propose feasible mitigation strategies, particularly in agrarian economies. This study investigates the main characteristics and historical drought trend for the period 1980–2016 using the Standard Precipitation Index (SPI), Standard Precipitation Evaporation Index (SPEI), Run Theory and Mann–Kendall Trend Test at seven stations across the Upper Draa Basin. The results indicate that rainfall has the largest magnitude over the M’semrir and Agouim (&gt;218 mm/pa) and the lowest in the Agouilal, Mansour Eddahbi Dam, and Assaka subregions (104 mm–134 mm/pa). The annual rainfall exhibited high variability with a coefficient of variation between 35−57% and was positively related to altitude with a correlation coefficient of 0.86. However, no significant annual rainfall trend was detected for all stations. The drought analysis results showed severe drought in 1981–1984, 2000–2001, and 2013–2014, with 2001 being the driest year during the study period and over 75% of both SPEI and SPI values returned drought. Conversely, wet years were experienced in 1988–1990 and 2007–2010, with 1989 being the wettest year. The drought frequency was low (&lt;19%) across all the timescales considered for both SPI and SPEI, with Mansour Eddahbi Dam and Assaka recording the highest frequencies for SPI-3 and SPEI-3, respectively.

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

A long-term drought assessment over India using CMIP6 framework : present and future perspectives

Research on the characteristics and spread of droughts has progressed significantly for future climate scenarios. However, studies on drought mitigation in relation to climate change have been largely inadequate. This study focuses on the severity and frequency of drought events based on meteorological properties of drought under two climate change scenarios: Shared Socioeconomic Pathway (SSP2 4.5 and SSP5 8.5). We utilized the Sixth International Coupled Model Inter-comparison Project sixth phase (CMIP6) ensemble General Circulation Models (GCMs) to collect historical (1901-2014) and future (2025-2100) precipitation data. IMD gridded precipitation was used as a reference data for comparative studies. We constructed the Standardized Precipitation Index (SPI) under two different Socioeconomic Shared Pathways (SSPs) to analyze future drought scenarios in the Indian region. Our results show a gradual increase in SPI values for future years, indicating an increase in the severity of drought events in the Indian region. The increase is more pronounced under the SSP5 8.5 scenario, which assumes high greenhouse gas emissions and limited climate change mitigation efforts. Furthermore, our results suggest that major dry spells are likely to occur in the first half of the future period, particularly in the case of ACCESS-ESM, one of the GCMs used in our analysis. In contrast, the NOR-ESM-MM model indicates that dry spells are anticipated throughout the entire future period. Overall, our study provides valuable insights into the potential impacts of climate change on drought events in the Indian region.

Spatially Consistent Drought Hazard Modeling Approach Applied to West Africa

A critical stage in drought risk assessment is the measurement of drought hazard, the probability of occurrence of a potentially damaging event. The standard approach to assess drought hazard is based on the standardized precipitation index (SPI) and a drought intensity classification established according to a fixed set of SPI values. We show that this method does not allow for the assessment of region-specific hazards, and we propose an alternative method based on the extreme value theory. We model precipitation using an extreme value mixture model, with a normal distribution for the bulk, and a generalized Pareto distribution for the upper and lower tails. The model estimation allows us to identify the threshold value below which precipitation can be qualified as extreme. The quantile function is used to measure the intensity of each category of droughts and calculate the drought hazard index (DHI). By construction, the DHI value varies according to the specific characteristics of the left tail of the precipitation distribution. To test the relevance of our approach, we estimate the DHI over a gridded set of rainfall data covering West Africa, a large and climatically heterogeneous region. The results show that our mixture model fits the data better than the model used for SPI calculation. In particular, our model performs better to identify extreme precipitation in the left tail of the distribution. The DHI map highlights clusters of high drought hazard located in the central part of the region under study.

Assessment of Meteorological Drought Trends in a Selected Coastal Basin Area in Poland—A Case Study

The aim of this study is to investigate the patterns and trends of drought occurrence in the northern part of Poland on the example of the Łeba river basin in the years 1956–2015. The study of meteorological drought was conducted on the basis of the Standardized Precipitation Index (SPI) on the scale of 1, 3, 6, 9, and 12 months. Annual precipitation totals did not show significant changes in the analyzed period, except for the station in Wejherowo, which is characterized by a significant increasing trend. The analysis of the long term of the variability average annual air temperature showed its statistically significant increase in the analyzed area at the rate of about 0.2 °C per decade. During the analyzed period, 14 to 84 meteorological droughts were identified, with durations ranging from 200 to 300 months. As the period of accumulating values of SPI, the number of droughts decreased, while their total duration increased. Most droughts were mild in nature, while extreme droughts accounted for between 5.2% and 10.7% of the duration. Drought intensification was shown only for SPI-1 in February and March in Wierzchucino station. On the other hand, a decreasing trend in SPI values was noted during longer periods of accumulation (SPI-6, 9, and 12).

Assessment of drought trends in the Upper Ewaso Ng'iro River Basin using the SPI and SPEI

Abstract Drought assessment is necessary for creating adaptation and resilience measures for the livelihoods of the affected communities. This study assessed drought trends in Kenya's Upper Ewaso Ng'iro River Basin (UENB) from 1981 to 2020. A Standardized Precipitation Index (SPI), a precipitation-based index, and a Standardized Precipitation Evapotranspiration Index (SPEI), a multivariate index that considers the difference between precipitation and potential evapotranspiration (PET), were used to evaluate drought severity and frequency over varying timescales. Monthly rainfall and temperature data for 10 stations within the basin were analyzed to calculate the SPI and SPEI time series values for 3, 6, and 12 months. The results demonstrate an alarming increase in the severity and frequency of drought events in the UENB since 1999. Additionally, the study reveals that the SPI and SPEI indices differ in identifying temporal and spatial drought characteristics, with longer timescales showing improved accuracy. Notably, the SPEI identifies more extensive and severe drought periods in the region compared to the SPI. The research findings are crucial in guiding policy decisions related to SDGs as they provide valuable information on drought trends necessary for implementing effective drought adaptation and resilience measures and promoting sustainable development in the UENB.

Quantitative analysis of input data uncertainty for SPI and SPEI in Peninsular Malaysia based on the bootstrap method

ABSTRACT Drought assessment has attracted attention in the research community, especially regarding the accuracy of drought indices due to input data uncertainty. This study addressed the impacts of input data uncertainty on the estimation of the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for drought assessment in Peninsular Malaysia. The Kolmogorov-Smirnov test recommended the gamma distribution function for the SPI and the log-logistic distribution function for the SPEI. The bootstrap method was used to estimate SPIU and SPEIU, which account for input data uncertainty, and provided estimates for SPI and SPEI values. However, the standard deviation indicated significant input data uncertainty, with values ranging from 0.1038 to 0.1378. The two drought indices exhibited similar classifications of drought categories, but SPIU showed greater uncertainty for very dry and extremely dry events. The findings emphasize the importance of input data uncertainty, especially when dealing with extreme drought events.

Meteorological Drought Characterization in the Calabria Region (Southern Italy)

Due to the important role of water resources in the growth of the world’s economy, drought causes global concern for its severe worldwide implications on different sectors, such as biodiversity, farming, public water supply, energy, tourism, human health, and ecosystem services. In particular, drought events can have strong environmental and socioeconomic impacts in countries depending on rain-fed agriculture such as the ones in the Mediterranean region, which, due to a detected increase in warming and precipitation decrease, is considered a climate change hotspot. In this context, in this paper, meteorological drought in the Calabria region (southern Italy) has been characterized considering the Standardized Precipitation Index (SPI) evaluated at different timescales. First, the temporal distribution of the most severe dry episodes has been evaluated. Then, a trend analysis has been conducted considering the different seasons, the wet (autumn and winter) and dry (spring and summer) periods, and the annual scale. Finally, the relationship between drought and some teleconnection patterns (the North Atlantic Oscillation—NAO, the El Niño–Southern Oscillation—ENSO, and the Mediterranean Oscillation—MO) has been investigated. Results show that the majority of the severe/extreme drought events have been observed between 1985 and 2008. Moreover, a decrease in SPI values has been observed in winter and spring, in both the wet and dry periods, and upon the annual scale considering the 12-month SPI and the 24-month SPI. Finally, a link between the drought episodes in the Calabria region and the NAO phases and the MO has been identified. Since drought episodes can severely impact water resources and their uses, the findings presented in this work can be useful to plan and manage the water supply for household, farming, and industrial uses.

Explainable machine learning for the prediction and assessment of complex drought impacts

Drought is a common and costly natural disaster with broad social, economic, and environmental impacts. Machine learning (ML) has been widely applied in scientific research because of its outstanding performance on predictive tasks. However, for practical applications like disaster monitoring and assessment, the cost of the models failure, especially false negative predictions, might significantly affect society. Stakeholders are not satisfied with or do not “trust” the predictions from a so-called black box. The explainability of ML models becomes progressively crucial in studying drought and its impacts. In this work, we propose an explainable ML pipeline using the XGBoost model and SHAP model based on a comprehensive database of drought impacts in the U.S. The XGBoost models significantly outperformed the baseline models in predicting the occurrence of multi-dimensional drought impacts derived from the text-based Drought Impact Reporter, attaining an average F2 score of 0.883 at the national level and 0.942 at the state level. The interpretation of the models at the state scale indicates that the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) contribute significantly to predicting multi-dimensional drought impacts. The time scalar, importance, and relationships of the SPI and STI vary depending on the types of drought impacts and locations. The patterns between the SPI variables and drought impacts indicated by the SHAP values reveal an expected relationship in which negative SPI values positively contribute to complex drought impacts. The explainability based on the SPI variables improves the trustworthiness of the XGBoost models. Overall, this study reveals promising results in accurately predicting complex drought impacts and rendering the relationships between the impacts and indicators more interpretable. This study also reveals the potential of utilizing explainable ML for the general social good to help stakeholders better understand the multi-dimensional drought impacts at the regional level and motivate appropriate responses.

Drought monitoring performance of global precipitation products in three wet seasons in Ethiopia: Part I—Quasi‐objective examination

AbstractThe need for representative and accurate climate data such as precipitation useful for drought monitoring has been rapidly increasing among policymakers and practitioners to tackle climate‐change‐induced drought events. Hence, the objective of this article is to evaluate the drought monitoring performance of global precipitation products for the three wet seasons and rainfall regions in Ethiopia. Drought indices were calculated using the Standardized Precipitation Index (SPI) at 3‐month timescale for Belg (March–May) and Autumn (September–November) seasons and at 4‐month timescale for the Kiremt (June–September) seasons. Data products were evaluated for their accuracy in representing drought magnitude, geographical coverage and frequency using quasi‐objective (visual inspection), and frequency and correlation analysis methods. The performance of gridded precipitation products was compared against the SPI value computed for 126 reference stations and the Ethiopian satellite‐gauge merged precipitation data. Precipitation products showed different levels of performance in representing the magnitude, frequency and geographical coverage of drought events for the three wet seasons and rainfall regions. None of the data products outperformed in representing the occurrence of drought for all three wet seasons and the corresponding three rainfall regions. However, the Ethiopian merged precipitation, Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) precipitation products are relatively better than others. The study results generally indicate that no single data outperform the other precipitation products in representing the complex spatiotemporal characteristics of drought events in a mountainous region like Ethiopia.

Meteorological Drought Variability over Africa from Multisource Datasets

This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The standardized precipitation index (SPI) is used to represent drought on a 3-month scale. We analyse various drought characteristics (duration, events, frequency, intensity, and severity) for all drought months, and moderate, severe, and extreme drought conditions. The results show that drought occurs across the continent, with the equatorial regions displaying more negative SPI values, especially for moderate and severe droughts. On the other hand, Eastern Sahara and Western Southern Africa portray less negative SPI values. The study also reveals that extreme drought months have the largest interannual variability, followed by all drought months and severe drought months. The trend analysis of SPI shows a significantly increasing trend in drought episodes over most regions of Africa, especially tropical areas. Drought characteristics vary greatly across different regions of Africa, with some areas experiencing longer and more severe droughts than others. The equatorial region has the highest number of drought events, with longer durations for severe and extreme drought months. The Eastern Sahara region has a low number of drought events but with longer durations for moderate, severe, and extreme drought months, leading to an overall higher drought severity over the area. In contrast, Western Southern Africa and Madagascar display a consistently low drought severity for all categories. The study demonstrates the importance of conducting drought analysis for different drought levels instead of using all drought months. Drought management and adaptation strategies need to enhance community resilience to changing drought situations and consider drought variability in order to mitigate different impacts of drought across the continent.