Next Article in Journal
Feasibility Study on Geothermal Dolomite Reservoir Reinjection with Surface Water in Tianjin, China
Previous Article in Journal
Impact of the Draft Plate on the Wall Erosion and Flow Field Stability of a Cyclone Separator
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 21
10.3390/w16213143
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia

by
Dessalegn Obsi Gemeda
1,*,
Béchir Bejaoui
2,
Nasser Farhat
3,
Indale Niguse Dejene
4,
Soreti Fufa Eticha
4,
Tadelu Girma
4,
Tadesse Mosissa Ejeta
1,5,
Gamachu Biftu Jabana
1,
Gadise Edilu Tufa
6,
Marta Hailemariam Mamo
6,
Zera Kedir Alo
7,
Fedhasa Benti Chalchisa
8,
Jale Amanuel
8,
Getachew Abeshu Disassa
9,
Diribe Makonene Kumsa
9,
Lidiya Dereje Mekonen
9,
Elfenesh Muleta Beyene
10,
Gudetu Wakgari Bortola
11,
Meseret Wagari
12,
Ayantu Habtamu Nemera
13,
Habtamu Tamiru
14,15,
Dereje Hinew Dehu
16,
Hasen M. Yusuf
17,
Diriba Diba
18,
Solomon Tulu Tadesse
19 and
Mitiku Badasa Moisa
4add Show full author list remove Hide full author list
1
Department of Natural Resource Management, College of Agriculture and Veterinary Medicine, Jimma University, Jimma P.O. Box 307, Ethiopia
2
Marine Environment Laboratory (LMM), National Institute of Marine Sciences and Technologies (INSTM), Tunis 2025, Tunisia
3
The Lebanese Center for Water and Environment LCWE, Beirut 1710, Lebanon
4
Department of Earth Science, College of Natural and Computational Science, Wollega University, Nekemte P.O. Box 395, Ethiopia
5
Department of Water and Climate, Vrije University of Brussels, 1050 Brussels, Belgium
6
Department of Rural Development and Agricultural Extension, College of Agriculture and Veterinary Medicine, Jimma University, Jimma P.O. Box 307, Ethiopia
7
Department of Agricultural Economist and Agribusiness Management, College of Agriculture and Veterinary Medicine, Jimma University, Jimma P.O. Box 307, Ethiopia
8
Department of Environmental Science, College of Natural and Computational Sciences, Wollega University, Nekemte P.O. Box 395, Ethiopia
9
Department of Psychology, College of Education and Behavioral Sciences, Jimma University, Jimma P.O. Box 307, Ethiopia
10
Institute of Foreign Affairs, Addis Ababa, Ethiopia P.O. Box 18529, Ethiopia
11
Department of Cooperative Business Management, College of Business and Economics, Wollega University, Nekemte P.O. Box 395, Ethiopia
12
Department of Natural Resource Management, Faculty of Resource Management and Economics, Wollega University, Shambu P.O. Box 38, Ethiopia
13
Department of Anthropology, College of Social Sciences and Humanities, Wollega University, Gimbi P.O. Box 44, Ethiopia
14
Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204, USA
15
Département of Water Resource and Irrigation Engineering, Wollega University, Shambu P.O. Box 38, Ethiopia
16
Department of History, College of Social Sciences and Humanities, Wollega University, Gimbi P.O. Box 44, Ethiopia
17
Department of Plant Sciences, Faculty of Agriculture, Wollega University, Nekemte P.O. Box 395, Ethiopia
18
Department of Animal Sciences, Faculty of Agriculture, Wollega University, Nekemte P.O. Box 395, Ethiopia
19
Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, Jimma P.O. Box 307, Ethiopia
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
Water 2024, 16(21), 3143; https://doi.org/10.3390/w16213143
Submission received: 10 September 2024 / Revised: 9 October 2024 / Accepted: 31 October 2024 / Published: 3 November 2024
(This article belongs to the Section Hydrology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Analyzing agricultural and hydrological drought at different timescales is essential for designing adaptation strategies. This study aimed to assess agricultural and hydrological drought in the Abbay Basin of Ethiopia by using multiple indices, namely the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and drought severity index (DSI). Climate extremes were assessed over the Abbay Basin between 1981 and 2022. The results indicate that the years 1982 and 2014 were the most drought-prone, while the year 1988 was the wettest year in the Abbay Basin. The results revealed the presence of extremely dry and severely dry conditions, potentially impacting agricultural output in the region. Agricultural drought was identified during the main crop seasons (June to September). The VCI results indicated the presence of extremely wet and severely wet conditions. In 2012, 65% of the area was affected by extreme drought conditions, while nearly half of the Basin experienced extreme drought in 2013 and 2022. The DSI results indicated the occurrence of agricultural drought, although the spatial coverage of extreme dry conditions was lower than that of the other indices. In 2003, 78.49% of the Basin experienced moderate drought conditions, whereas severe drought affected 20% of the region. In 2010, about 90% of the Basin experienced moderate drought. This study provides valuable insights for agricultural communities, enabling them to mitigate the impact of drought on crop yields by utilizing different adaptation strategies. An adequate knowledge of agricultural and hydrological drought is essential for policymakers to assess the potential effects of drought on socioeconomic activities and to recognize the significance of implementing climate change adaptation measures.

1. Introduction

Agricultural and hydrological drought assessments are crucial for providing reliable information for farming communities and decision-makers to design appropriate adaptation and mitigation measures. Both agricultural and hydrological droughts are aggravated by climate change. Climate change remains a pressing issue in the 21st century. It is agreed that climate change is having a greater impact on humanity than ever before. Climate change, particularly weather extremes, has a greater impact on food supply and demand. Accordingly, it significantly affects agricultural outputs, incomes, food quality, and food safety [1,2]. Many studies have confirmed that temperatures are rising almost everywhere in the world [3,4,5,6]. On a global scale, between 2001 and 2020, the surface air temperature increased within the range of 0.84 to 1.10 °C, surpassing the increase observed from 1850 to 1900, which was approximately 0.61 °C [7]. The IPCC report highlights the significant impact of the projected increase in the global mean temperature, which ranges from 1.5 °C to 2 °C. While temperatures are increasing worldwide, there is variation in rainfall patterns, with some areas experiencing an increase while others experiencing a decreasing trend [8].
Droughts are categorized into four classes: meteorological, agricultural, hydrological, and socioeconomic droughts [9]. Meteorological drought can be detected by using the SPI at one month timescales [10,11], agricultural drought at thee–six month timescales, while hydrological drought at twelve month timescales [12] and socioeconomic drought is associated with water supply and demand against the minimum requirements [13]. All drought types are interconnected. Meteorological droughts can lead to hydrological and agricultural droughts [14,15]. Meteorological drought refers to the absence of precipitation or rainfall for an extended period, while agricultural drought refers to a water deficiency to sustain plant growth [16]. Hydrological drought occurs when there is a lack of surface and subsurface water, resulting in below-average water availability [17,18]. On the other hand, socioeconomic drought refers to a situation where a watershed fails to meet the demand of various water-use sectors and their socioeconomic benefits [19], leading to water scarcity [20].
Drought and extreme rainfall are common in many countries. They can occur over years or decades and can have a negative impact on sustainable development goals (SDGs), mainly SDG 2 (zero hunger), SDG 6 (clean water and sanitation), and SDG 7 (affordable and clean energy). While various factors contribute to the occurrence of extreme weather events, evidence strongly points to human activities as the major causes of floods and droughts [21,22,23]. The increasing trend of greenhouse gas emissions is the primary cause of global warming. Scientific evidence shows that the concentration of carbon dioxide has increased by more than 50% (from preindustrial levels, when it was about 280 parts per million (ppm) in the 1700s, reaching more than 420 ppm in 2023 [24]. It is universally agreed that the global community should minimize the increase in global temperature to less than 2 °C above pre-industrial levels [4,25]. As global warming continues to intensify, it becomes evident that climate extremes, such as drought and floods, pose significant challenges to humanity [26,27,28].
Like other countries, Ethiopia has been exposed to climate extremes for many years [29,30,31] and will continue to be one of the most vulnerable countries in the future under business as usual. The 1983–1985 Ethiopian famine, driven by prolonged droughts, threatened the lives and livelihoods of millions of people [32]. As climate extremes are expected to increase in the future, the probability of rural food security in Ethiopia is unreliable [33]. The agricultural sector faces significant risks due to the impacts of climate change, which have become more frequent and severe in recent decades. This research highlights the importance of employing multiple drought indices such as SPI, SPEI, DSI, and VCI to accurately assess drought conditions. Based on these findings, specific adaptation strategies are necessary to mitigate the adverse impacts of drought on crop yield and water availability. These strategies include the adoption of drought resistant crop varieties and improved livestock breeds, improved water resource management, changing planting and harvesting dates, crop diversification, crop rotation, and the implementation of more efficient irrigation systems, all of which are critical for ensuring food security and agricultural sustainability under changing climate conditions.
In 2015, Ethiopia also experienced severe droughts [30,34]. Previous studies confirmed that droughts significantly affect the Ethiopian economy, which affects the lives of civilians [8,32,35]. Rainfall shocks are another climate-related disaster that significantly affects agricultural production and can lead to food insecurity and poverty in different parts of Ethiopia. The Lake Tana Basin, a region within the Abbay Basin, has recently experienced extreme and severe meteorological and hydrological drought [36], highlighting the urgent need for research on agricultural drought assessments to support farming communities in designing various adaptation strategies. This problem is also common around the Awash Basin as well in the pastoral communities in the southeastern part of the country.
A decrease in rainfall contributes to a 5% loss of agricultural gross domestic product around the Awash Basin of Ethiopia [37]. A previous study in southwestern Ethiopia found that rainfall during the main crop growing season declined [38]. This decline in rainfall has also had a considerable impact on livestock, which is highly vulnerable to climate change, within pastoral communities [39]. Between 2001 and 2003, the Somalia Regional State of Ethiopia experienced severe drought, which resulted in the loss of approximately 80% of the entire cattle population [40]. This loss of livestock increased the stress levels within communities and further exacerbated internal migration from areas affected by severe drought. Ethiopia, a rain-dependent farming community, is facing a significant challenge in livestock loss. This problem poses a serious threat to most of the population who rely heavily on agriculture, an economic sector highly susceptible to weather conditions [40].
Significant studies have been conducted to indicate the occurrence of drought in Ethiopia [27,29,37,41,42,43,44,45,46,47,48]. Some of these studies have focused on sub-watershed and micro-watershed scales, while others have been conducted at the country level. There is still a lack of drought characterization at the Basin level, especially in developing nations like Ethiopia. In addition to spatial limitations, most previous studies have focused on rainfall and temperature data, neglecting evapotranspiration and other environmental factors such as the normalized difference vegetation index (NDVI) and vegetation condition index (VCI), which could provide valuable information on the severity of agricultural and hydrological drought.
The World Meteorological Organization (WMO) has recommended the use of standardized precipitation index (SPI) for drought characterization [49]. This index utilizes only rainfall or precipitation data to quantify drought, without considering temperature, evaporation, and other environmental factors. The SPI is capable of detecting meteorological drought, agricultural drought, and hydrological drought at one month, six month, and twelve month timescales, respectively [10]. Another index, known as the standardized precipitation evapotranspiration index (SPEI), was proposed by Vicente-Serrano et al. [50] to address the limitation of SPI. The SPEI considers temperature and potential evapotranspiration to quantify drought. It assesses meteorological drought on a monthly scale, agricultural drought over a period of three to six months, and hydrological drought on a twelve month timescale [49,51,52].
Both SPI and SPEI are capable of identifying meteorological drought on timescales of less than three months, agricultural drought at three to six months, and hydrological drought on a timescale of twelve months. In addition to these indices, the Drought Severity Index (DSI) is widely used to characterize drought in a specific region. Initially proposed by Mu et al. [53] for drought assessment, the DSI was compared to the Palmer Drought Severity Index (PDSI) developed by Palmer [54]. The DSI is better than PDSI for drought monitoring [53]. This is because the PDSI relies solely on precipitation data, while the DSI considers evapotranspiration and vegetation conditions [55]. Vegetation condition index (VCI), developed by Kogan [56], is normalized by NDVI and quantifies the vegetation moisture status, directly indicating drought conditions [57]. Various agricultural crops have different tolerance levels to drought and require a depth investigation by using multi-indices. In the agricultural sector, drought stress indicators are consolidated based on several factors that account for the varying tolerance levels of different crops. These factors include physiological traits such as root depth, stomatal regulation, and water use efficiency. Genetic diversity also plays a role, with certain crops like sorghum and millet demonstrating a higher drought resilience compared to more sensitive crops such as maize. Additionally, mechanisms like osmotic adjustment and early maturation contribute to drought tolerance. The FAO’s Crop Water Requirement Index provides a standardized framework for evaluating water-use efficiency across different crops, helping to classify their drought tolerance levels.
Due to a lack of complete rain gauge climate data and high-resolution satellite data, drought monitoring in the Abbay Basin remains poorly studied, hindering the management of its water resources and livelihoods. To overcome these limitations, combinations of various indices and geospatial technologies have been used to characterize agricultural and hydrological drought. There is an increasing need for information on farming and hydrological drought to support effective intervention plans that enhance public resilience to drought events. This study aims to provide a comprehensive analysis of drought conditions using multiple indices, including SPI, SPEI, DSI, and VCI, to capture different dimensions of drought (meteorological, hydrological, agricultural). By utilizing multiple indices, the study enhances the understanding of drought’s effects on water resources, agricultural productivity, and broader climatic trends. Furthermore, the application of geospatial techniques to map areas susceptible to drought offers critical insights into spatial distribution of drought occurrences.

2. Materials and Methods

The study was conducted in Ethiopia, situated in the horn of Africa, where the majority of the population heavily relies on rain-fed agriculture. Geographically, the country is located between 30 and 150 north latitude and 330 and 480 east longitude. Ethiopia’s topography exhibits a wide range of variations, with elevations ranging from −125 m below sea level at the Danakil Depression in the Afar region to 4620 m above sea level at Ras Dashen. The Abbay Basin, which spans an extensive area of 199,812 square kilometers, covers 20% of Ethiopia’s land area. It stretches across three regions: the Amhara, Oromia, and Benishangul-Gumuz regions. In terms of coverage within these regions, the Abbay Basin covers approximately 60% of the Amhara region, 40% of Oromia, and 95% of Benishangul-Gumuz [58]. This basin comprises different sub-basins, including Anger, Beles, Dabus, Dedesa, Fincha, Gilgel Abbay, Gumara, Upper Gudar, Koga, Rib, and Jemma. The topography of the Abbay Basin varies from 493 to 4173 m above sea level (Figure 1).
This difference in topography may be a significant reason for the variety of climates in the Basin. As a result, the Abbay Basin experiences a semi-desert and desert climate along the border of Sudan and a temperate climate around the high plateau in the northwestern-central part of Ethiopia. This Basin receives maximum rainfall during the primary crop-growing season (June to September).
The annual rainfall in the Basin varies from 400 mm near the Sudan border to 2200 mm in the Didessa sub-basin. The minimum and maximum ranges of air temperatures are between 15–20 °C and 28–38 °C, respectively [59]. A study on hydro-climatic trends in the Abbay Basin by Tekleab et al. [60] highlighted significant temperature increases, while precipitation did not exhibit significant trends. The study area is characterized by rainfed agriculture. Areas receiving less than 500 mm of annual precipitation are generally classified as dry land, whereas regions with more than 500 mm are often considered suitable for rainfed agriculture [61]. The most part of our study area receives high precipitation during the main crop growing season, contributing to diverse agricultural practices that include both rainfed and supplementary irrigation methods.

2.1. Data Sources and Descriptions

In the present study, various data sources, such as Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), ground observed rainfall (rain gauge), and vegetation cover (eMODIS), were used to characterize agricultural and hydrological drought in the Abbay Basin (Table 1). The CHIRPS with station data version 2 was used for the SPI. The Moderate Resolution Imaging Spectroradiometer-based Normalized Difference Vegetation Index (eMODIS-NDVI) was used to assess the VCI and DSI.

2.2. Method of Data Analysis

2.2.1. SPI

SPI is widely used to assess and monitor agricultural drought at various timescales [10,62]. For this study, the SPI was employed to calculate agricultural drought at 3 to 6 month timescales [12] using the ArcGIS version 10.8, as given in Equation (1). Positive SPI values indicate wetness, while negative values represent dryness. CHRIPS gridded datasets were used to analyze the SPI from 1981 to 2022. SPI values were categorized into five classes as suggested by McKee et al. [63] extremely wet (SPI > 2), very wet (1.5 to 1.99), moderately moist (1 to 1.49), mild drought (0.99 to −0.99), moderate drought (−1 to −1.49), severe drought (−1.5 to −1.99) and extremely dry (>−2). The graph output was drawn using OriginPro 2019 (Northampton, MA 01060, USA, https://www.originlab.com/2019, accessed on 15 March 2024).
S P I = X i − μ   S D  
where SPI is the standardized precipitation index; Xi is the annual rainfall amount and μ is long term average rainfall, and SD is the standard deviation.

2.2.2. eMODIS-NDVI Based Vegetation Indices

The NDVI values show changes in vegetation greenness and are used for analyzing vegetation responses to drought [27,43,64,65]. The NDVI value ranges from −1 to +1 [66,67]. This study used MODIS NDVI data for the VCI and DSI. The NDVI value was derived from eMODIS data using ArcGIS, as given in Equation (2).
e M O D I S   N D V I = F l o a t ( S m o o t h e d   e M O D I S   N D V I − 100 )   100

2.2.3. Vegetation Condition Index (VCI)

The VCI is derived from the NDVI and a robust index for drought monitoring. This index was calculated from the minimum and maximum NDVI, and the values ranged from 0 to 100% [27,43]. Equation (4) was used to calculate the VCI:
V C I = N D V I − N D V I m i n N D V I m a x − N D V i × 100
where NDVI, NDVImin, and NDVImax are the mean monthly/seasonal NDVI and its absolute long-term minimum and maximum NDVI values, respectively, for each pixel. Low percentages of VCI indicate poor conditions, while high percentages indicate optimal conditions [68]. According to Kogan et al. [69], a VCI ranging from 0 to <20% indicates extreme drought, while a VCI ranging between 20% and <35%, 35% and <50%, and >50% indicates severe drought, moderate drought, and no drought, respectively.

2.2.4. Drought Severity Index (DSI)

Using a surface energy balance, the DSI was developed to estimate the relative soil water deficit in the root zone [70]. The long-term DSI was computed from composite images of eMODIS NDVI during the rainy season by using a raster calculator in the ArcGIS environment using Equation (4). To calculate the DSI, the monthly NDVI value and long-term mean NDVI for the same month were employed. We classified the DSI into five levels of drought severity based on Kogan et al. [69]: extreme drought (<−0.25), severe drought (−0.25 to −0.1), moderate drought (−0.1 to 0.1), mild drought (0.1 to 0.25), and no drought (>0.25).
D S I = N D V I i − N D V I m e a n
where NDVIi is the monthly NDVI value, and NDVImean is the long-term mean NDVI for the same month.

2.2.5. Assessment of Hydrological Drought

The SPI at a 12 month timescale was used to detect the presence of hydrological drought in the Abbay Basin following Vicente-Serrano [12]. The SPEI at a 12 month timescale was also employed to characterize hydrological drought in the Basin, as Vicente-Serrano et al. [50]. The SPEI is calculated from precipitation and temperature as climatic water balance [71,72]. In this study, we categorized the SPEI values into different categories: extremely wet (>2), very wet (1.50 to 1.99), moderately wet (1 to 1.49), and near normal (0.99 to −0.99). Conversely, moderately dry (−1 to −1.49), severely dry (−1.50 to −1.99), and extremely dry (>−2) following [8,73]. The SPEI was computed from the potential evapotranspiration (PET) and observed precipitation from 1981 to 2022, as given in Equation (5).
S P E I = P − P E T σ
where P is the observed precipitation, PET is the potential evapotranspiration, and σ is the standard deviation of the distribution of P-PET. In this study, we employed the L-moments technique [74] to estimate the parameters of the chosen distribution. L-moments are more robust for describing skewed data and outliers compared to traditional moments. This method ensures a better fit for non-normal precipitation distributions, which was a critical factor given the highly variable climate conditions in the study region.

3. Results

3.1. Agricultural Drought Analysis

3.1.1. SPI at 3-Month Timescales

In the present study, we analyzed drought severity classes at three–month timescales from 1981 to 2022. The results showed that 1982 and 2014 were the most drought-prone years throughout the Abbay Basin. Despite variations in drought severity, all ten Basin stations experienced drought conditions (Figure 2). In particular, from 1983 to 1987, drought frequently occurred in the Basin. During the five years (1983–1987), Dibate experienced extremely wet conditions with an SPI value of 3.24 and moderately moist conditions (SPI = 1.23) in 1985. The Dibate station also recorded moderately wet conditions in 1986 (SPI = 1.42). The results of this study will not only support farmers but will also assist different sectors in improving actual climate conditions to use climate change adaptation strategies. The historical Standardized Precipitation Index (SPI) plays a key role in supporting the design of climate change adaptation strategies. By analyzing historical SPI data, we identify drought trends and patterns as SPI helps in assessing long-term precipitation deficits or surpluses, providing valuable insights into historical drought events, their duration, intensity, and frequency. Thus, historical SPI provides a foundation for evidence-based adaptation planning by offering insights into past climate variability and guiding proactive responses to future changes. For instance, in areas experiencing agricultural drought, farmers can implement drought resistant crop varieties, changing or adjusting planting and harvesting dates, and adopt improved livestock breeds. Additionally, all key stakeholders should advocate the importance of soil and water conservation to minimize the impacts of agricultural droughts. Furthermore, the Disaster Preparedness and Emergency Response should focus on boosting public resilience by providing up-to date information and engaging in drought related disaster and hazardous management strategies. The national meteorological agency is also encouraged to deliver reliable climate information in real or near real time at no cost.
Between 1981 and 2022, the Abbay Basin experienced varying levels of drought and wetness. After several drought years, the year 1988 was the wettest year across the Basin. Four years later, in 1992, with the exception of Bedele, drought was prevalent at all stations. The years 1992 and 1997 were other drought years in the Basin. In these two years, nine out of ten stations recorded mild to severe drought conditions. The year 2006 was the second wettest year, occurring after almost two decades (19 years). Between 2007 and 2013, a significant number of stations experienced no drought conditions. Following the wetter years, the years 2014 and 2015 were affected by a shortage of precipitation, including extremely dry conditions. Specifically, three stations, namely Alibo, Bedele, and Fitche, recorded extremely dry conditions. In 2015, four stations, namely, Alibo, Bahirdar, Fitche, and Keranio, experienced extremely dry, while the Chewahit and Kelem Meda stations experienced severe dry. In 1987, 1992, 1997, and 2015, nine of the ten stations experienced drought. Overall, 1982 and 2014 were the most drought prone years, while 1988 and 2006 were the wettest years in the Abbay Basin at three months timescales.
It is very important to investigate the conditions of rainfall, especially during the primary crop-growing season. Insufficient precipitation during this critical period can significantly impact the region’s economy and lead to famine, if not managed well in advance. In the present study, we analyzed the drought categories in the years 1982, 1992, 2009, and 2015, specifically focusing on the main rainy season in the Abbay Basin. Our results revealed that extremely dry and severely dry conditions were observed, highlighting the problem of agricultural drought in the Basin. These extremely dry and severely dry conditions can affect crops’ growth, leading to yield loss and low productivity. Except for July 2015, August 1992 and 2009, the remaining months experienced both extremely dry and severely dry conditions, as shown in Figure 3. In September 2009, nearly half of the Basin experienced severe dry conditions. Overall, July and August exhibited lower levels of drought than June and September throughout the study period. Most of the northern, central, southern, and southwestern parts of Ethiopia received maximum rainfall during June and July.

3.1.2. SPEI at Three-Month Timescales

Agricultural droughts at three month timescales were analyzed using the SPEI. In this study, a low SPEI indicates dry conditions, while a high SPEI represents wet conditions (Supplementary Material (SM), Figure S1). The results revealed that all the Abbay Basin stations experienced extremely dry and extremely wet conditions (Table 2). Extremely dry areas were recorded at Alibo in 2005 and 2015. Additionally, severe dry was frequently recorded at this station in the 1980s, 1990s, and 2020s. In Bahirdar, extremely dry weather was recorded in 2015, while severely dry weather was recorded in the 1980s, 1990s, and 2020s. Bedele was extremely dry in 2003, 2006, and 2014. Additionally, significant numbers of severely dry were recorded at the Bedele station. The Chewahit station faced extremely dry conditions in 1982, 1984, 1990, 1999, and 2005. The Dibate station experienced extreme dryness in 2002 and 2005, while severely dry occurred in 1986, 1990, 2001, 2006, 2009, 2012, and 2013.
In Fitche, extremely dry conditions were observed in 1984, 1999, 2005–2006, 2008, and 2015. Severely dry conditions also occurred in 1982, 1988, 1989, 1992, 1994, 1997, 2000–2003, and 2012–2014. Kelem Meda was extremely dry in 1984, 1999, 2008, and 2012. Keranio was extremely dry in 2002–2003, 2005–2006, and 2015. At Mankush, extremely dry conditions occurred in 1982, 1991, 1995, 2001, and 2010. Mendi was extremely dry in 1995 and 1996.
Similar to extremely dry and severely dry, extremely wet and severely wet have also been recorded in the Basin. Alibo experienced extremely wet conditions in 1990 and 1998. At Bahirdar extremely wet conditions were recorded in 1990, 1992, 1994–1995, 1999, and 2013. The Bedele station experienced extremely wet conditions in 1988, 1990, 2007, 2010, 2014, and 2019. At Chewahit extremely wet conditions occurred in 1987, 1990, 1996, 1997, 1998, and 2014. At the Dibate station, extremely wet conditions occurred in 1982, 1985, 1990, 1997, 2000, 2008, 2014, and 2020. Fitche station experienced extremely wet conditions in 1987, 1988, 1990, 2016, and 2019. At Kelem Meda, extremely wet conditions occurred in 1990, 1998, and 2019. At Keranio extremely wet occurred in 1982, 1990, 1996, 1998, and 2014. Mankush recorded extremely wet conditions in 1981, 1990–1992, and 1998; the Mendi station experienced extremely wet conditions in 1997, 2016, and 2017.

3.1.3. Vegetation Condition Index

We utilized the VCI, to indicate the presence of agricultural drought in the Abbay Basin. Unlike other remote sensing-based indices, the VCI assessed drought conditions, specifically during the main growing season. The results showed the occurrence of all drought severity classes within the Basin. Spatially, about 130,253.18 km2 (65%) of the study area was affected by extreme drought conditions in 2012. In 2013 and 2022, approximately half of the Abbay Basin (49%) suffered from extreme drought (Table 3).
A significant portion of the Basin experienced extreme and severe drought conditions in 2003, 2009, and 2018. These drought conditions significantly negatively impact crop growth and agricultural production within the Basin. Based on the spatial distribution map of the VCI, it is evident that 2012 was the year with the most droughts, while 2003 had the least extreme and severe drought occurrences in the Basin (Figure 4). In 2022, the majority of the western and southwestern regions of the Basin were not affected by extreme drought or severe drought, while a considerable portion of the eastern, southeastern, and northern parts of the Basin experienced both extreme drought and severe drought.

3.1.4. Drought Severity Index (DSI)

The drought severity index (DSI) is widely used to analyze meteorological and agricultural droughts. In 2003, moderate drought affected 78.49% of the Basin, while severe drought impacted 20% of the area. In 2005, the coverage of severe drought decreased to 5.84%, while the proportion of the Basin affected by moderate drought increased to 88.53%. In 2009, the area affected by severe drought increased compared to previous years. In 2010, more than 90% of the Basin experienced moderate drought conditions. In 2012, about 45.40% of the Basin experienced severe drought, while 51.50% faced moderate drought. The spatial distribution map of the DSI showed that the most extreme drought occurred in 2012, followed by 2022 (Figure 5). A significant portion of the Basin was affected by severe drought. For instance, 45% in 2012, 36% in 2022, and 31.15% in 2013 were affected by severe drought (Supplementary Materials Table S1).

3.2. Hydrological Drought Assessment

3.2.1. SPI at 12-Month Timescales

Analyzing hydrological drought is particularly crucial in regions experiencing water scarcity, such as Ethiopia, where groundwater depletion and limited surface water availability pose significant challenges. Like three-month timescales, drought occurrences across the Abbay Basin have been detected at 12 month timescales (Figure 6). At 12 month timescales, all stations in the Basin experienced drought in 1982–1984, ranging from mild to extremely dry conditions. In 1985, except Dibate and Fitche, other stations in the Basin experienced mild and moderate moisture. In 1986, Chewahit and Kelem Meda were the only ones that did not record drought, while others experienced drought, including extremely dry conditions at Bedele and Mankush.
In 1988, no drought was detected in the Basin, which supports the SPI results at three month timescales. In 1990, all stations, except Fitche, experienced drought with varying degrees. In 1994 and 1995, the majority of the stations in the Basin experienced mild to moderately dry conditions. In 2002, all the stations in the Basin experienced drought, with extremely dry conditions occurring at Dibate. Between 2003 and 2005, most stations recorded mild to severely dry conditions. Conversely, in 2006, no drought was recorded in the Basin. In 2007, all stations, except Bahirdar and Dibate, experienced wetter conditions. Similarly, in 2008, only Fitche and Kelem Meda experienced drought, while the other eight stations received sufficient amounts of precipitation. In 2009, except for Bedele, all the stations experienced drought, including extremely dry conditions at Bairdar, with an SPI value of 2.50 (Supplementary Materials Figure S2). In 2017, no drought was detected at the 12 month timescale. In 2020, only mild drought was recorded at Mankush. The results revealed that all the stations in the Basin experienced mild to extremely dry conditions in 1982, 1983, 1984, and 2002. In contrast, in 1988 and 2017, no drought was detected in the Basin at 12 month timescales.

3.2.2. SPEI at 12 Month Timescales

A long-term drought assessment was performed using the SPEI-12. The analysis of SPEI was carried out at ten stations within the Abbay Basin to understand the severity of the hydrological drought conditions. Over 12 month timescales, except Chewahit, Dibate, and Mendi, other stations experienced extremely dry conditions. However, all the Basin stations experienced severe dry conditions between the 1980s and 2020s (Table 4). In contrast, the Basin also experienced extremely wet and severely wet periods. Except for Mankush, all stations in the Basin recorded highly wet conditions, although the frequency of occurrence was lower that at three month timescale.

4. Discussion

4.1. Impacts of Agricultural and Hydrological Droughts

The impacts of drought are very complex and can significantly influence social, political, economic, and environmental outcomes. Drought is a multifaceted natural hazard with substantial impacts on agriculture, industry, ecological environment, and peoples’ livelihoods [19]. When water availability for crops falls below the optimal level, it can greatly hinder their growth and overall productivity. Human activities significantly contribute to environmental changes, resulting in extreme climate events like drought and floods. There is ample evidence indicating that drought has a significant impact on the socioeconomics and livelihoods of countries that are heavily dependent on agriculture [1,2,8,32,35].
Our study aimed to assess agricultural and hydrological drought occurrences in the Abbay Basin of Ethiopia by utilizing multiple indices, such as the SPI, SPEI, NDVI, DSI, and VCI. Based on our assessment, all drought categories, namely, extreme drought, severe drought, and moderate drought, were detected in the Basin, which can hurt agricultural crop growth, yield productivity and water resource management. The SPI results over three month timescales indicated that the Abbay Basin experienced agricultural drought, but the severity levels varied across different periods and locations. In the early 1980s, particularly between 1983 and 1987, agricultural drought frequently occurred throughout the country, with high severity in the northern part of Ethiopia. Previous studies documented extreme and severe drought conditions during the 1980s [31,32,75]. Another study also highlights that drought was severe and intense in 1984–1985 over the horn of Africa [45].
This prolonged agricultural drought in the Abbay Basin led to significant losses in crop production and widespread food insecurity. More specifically, drought-driven famine in the early 1980s led to the loss of approximately 400,000 to one million lives [75]. Farmers were forced to abandon their fields or rely on emergency aid to survive. The government implemented various measures to mitigate the effects of the drought, including providing subsidies for farmers and improving irrigation infrastructures [76,77]. Despite these efforts, the impact of the agricultural drought on the local economy and population was devastating, highlighting the urgent need for sustainable water resource management and climate change adaptation strategies in the region. Thus, it is crucial to advise the agricultural communities to utilize drought-resistant crop varieties in order to minimize the risk of agricultural yield loss during the prolonged drought.
The results of the SPI and SPEI at 12 month timescales revealed different levels of drought, ranging from extremely dry to moderately dry. This indicates that the presence of hydrological droughts in the Abbay Basin significantly affects surface and subsurface water quality and availability [18,20]. It is clear that changes in land use and climate change can lead to hydrological reactions that have the potential to harm the entire ecosystems and the environment. The dynamics of land use and land cover can lead to hydrological reactions [78]. It is clear that changes in land use change can significantly impact the hydrological processes. Alterations in land use have played a significant role in the rise in the maximum temperature in the southwestern region of Ethiopia [38].
In 1983, 1984, 2002–2004, and 2015, extreme drought events were detected in the Abbay Basin. These extreme drought events severely impacted agriculture, water resources, and overall ecosystem health in the region. The decrease in surface water availability and the depletion of groundwater reserves during these drought years led to water scarcity and competition among various stakeholders [79]. The impact of hydrological drought extends beyond surface and subsurface water scarcity, as it also exerts and influences the environment and the whole climate system. Hydrological drought refers to negative anomalies in surface or subsurface water resources [17]. Analyzing hydrological drought is essential for understanding drought propagation and retrieval over a given region [80].
Additionally, these extreme drought events highlight the vulnerability of the Abbay Basin to climate variability and the importance of implementing sustainable water management practices to mitigate the impacts of future drought events. The findings of our study align with previous research that emphasizes the devastating consequences of drought events in Ethiopia. The 1980s drought resulted in substantial loss of life and property [32,75]. Another study reported widespread drought frequencies between 2002 and 2004 [81]. Our study also detected extreme drought in 2015. The El Nino-induced drought in 2015 contributed to the loss of agricultural production [34]. In 2015, Ethiopia experienced its worst drought due to El Nino. This El Nino-induced drought affected more than ten million people in 2016 [30,82].
Similar to three month timescales, different severity levels of drought were detected at 12 month timescales in the Basin. With the exception of Chewahit, Dibate, and Mendi, all the stations in the Abbay Basin experienced extreme drought. Different studies have documented the occurrence of extreme drought in various parts of Ethiopia [8,29,37,43,47]. Rainfall shocks have been reported as one of the driving factors of food insecurity in rainfed dependent economies. About 5% of the agricultural gross domestic product in the Awash Basin of Ethiopia was lost due to rainfall shortages [37].
It is clear that there is a direct link between food security and climate change, as the availability of food depends on climate conditions. Thus, climate related disruptions such as agricultural drought can have a significant impact on the livelihoods of smallholder farmers. A previous study by Tofu et al. [83] concluded that climate change-driven problems have played a significant role in contributing to food insecurity and poverty in the northern Ethiopia. The impact of drought is not limited to the northern part of the country, but also severely affects the southeastern part of the country, where numerous livestock have been lost due to shortage of pasture as the result of prolonged drought. Recently, the Borana people in the southeastern part of Ethiopia lost several million cattle due to a prolonged drought. In addition, a study conducted in the Somalia region between 2001 and 2003 resulted in the loss of approximately 80% of cattle in the area [39]. Our results indicate a shortage of precipitation during the main crop growing season, specifically in June and September, while July and August receive sufficient amounts of rainfall. Consequently, rain-fed dependent farming communities are facing enough challenges regarding crop and livestock losses. This is mainly attributed to the sensitivity of the Ethiopian economy to climate variability and extremes [30,31,42].
Similar to extremely dry and severely dry, this study revealed that the Abbay Basin also experienced excess precipitation, which can have a significant impact on people’s livelihoods, infrastructure, and natural environment. A study conducted by Sinore and Wang [84] in Ethiopia through meta-analysis indicates that both drought and flood have adverse effects on agricultural production. Based on the SPI and SPEI results at 3 month and 12 month timescales, extremely wet and severely wet conditions were recorded at different times in the Basin. However, the frequency of extremely wet and severely wet conditions was less frequent at the 12 month timescale than at the 3 month timescale. This study also detected extremely dry and severely dry. Duration and frequency of extremely dry and severely dry conditions, as well as highly wet and severely wet conditions, are significantly lower at the 12 month timescale than at the 3 month timescale [8,85].
In addition to the SPI and SPEI values, the presence of extreme climates was calculated using the VCI and DSI. Both indices are widely used to analyze agricultural drought [86]. The VCI is crucial to identifying agricultural drought during the main crop-growing season. We investigated the area covered by extremely dry and severely dry conditions in the Basin using the VCI. Our results are promising for policymakers since we calculated the area affected by extremely dry and severely wet conditions over the past few decades, specifically in the eastern, central, and northeastern parts of the Basin. In 2012, more than 65% of the Basin was severely dry, while nearly half of the Basin was severely dry in 2013 and 2022. This extreme drought and severe drought can hurt agricultural yields, which can lead to food insecurity in the region, as the main economy of the area is heavily dependent on a weather-sensitive economy. Household well-being was challenged due to food insecurity, driven by drought in Ethiopia [35]. In the present study, we identified drought severity classes, namely extreme drought, severe drought, and moderate drought, by using the DSI, a remote sensing-based drought assessment technique. Unlike other indices, the results of the DSI indicate the occurrence of extreme drought in the Basin is insignificant. However, moderate drought and severe drought were identified by using the DSI. In 2003, 2005, 2009, 2010, 2012, and 2015 significant areas in the Abbay Basin were affected by moderate drought conditions. Significant regions were affected by severe drought in 2012, 2013, and 2022.
The results of the drought assessment indicate a growing risk to agricultural productivity, particularly during critical growing seasons, as reflected in the three month SPI and VCI data. These findings underscore the need for targeted adaptation strategies to enhance resilience. For instance, the implementation of drought-resistant crop varieties, optimized irrigation techniques (such as drip irrigation), and soil moisture conservation practices are essential for maintaining crop productivity. Additionally, improved water management strategies, including rainwater harvesting and better groundwater usage practices, can help mitigate the impact of prolonged dry periods identified through the 12 month SPEI analysis. The integration of these adaptation strategies into local agricultural practices will not only enhance the sector’s resilience but also ensure long-term sustainability in the face of increasing climate variability.

4.2. Practical Implications

Assessing agricultural and hydrological drought in rainfed dependent farming communities can support policymakers to design climate change adaptation strategies, including the use of drought resistant crop varieties and improved livestock breeds [40,84], indigenous knowledge application [87], changing planting and harvesting dates [88], use of small-scale irrigation and agroforestry systems [84], and livelihood diversifications. An increasing frequency of agricultural and hydrological droughts is expected due to land use land cover changes and accelerating global climate change. The existing literature documented that rainfed dependent agricultural practices are one of the most susceptible to the impacts of climate change by significantly affecting crop yield [89,90]. Maintaining agricultural productivity requires the implementation of specific agronomic strategies, such as modifying fertilizer use, enhancing irrigation practices, selecting diverse crop varieties, promoting soil conservation and crop diversification [88]. Our results clearly indicate the occurrences of both agricultural and hydrological drought in the Abbay Basin of Ethiopia with different severity classes.
Contentious climate change assessment and monitoring are helpful to minimize the vulnerabilities of the farming communities from the effects of climate change, particularly extreme climate events such as drought and flood. It is essential to investigate the possible alteration in future water resources and water-related risks at the regional level due to global warming and local socioeconomic advancements [19]. Thus, the concerned stakeholders should provide reliable information, including short-term weather forecasts and longer-term climate change forecasts. Climate information can be connected to agricultural outcomes and management strategies to guarantee its accessibility and practicality for smallholder farmers [90]. Effectively conveying future climate change predictions to farming communities is vital for reducing the risks associated with agricultural yield loss, which can lead to food insecurity and poverty. Additionally, it is important to share information regarding agricultural and hydrological droughts with agricultural communities to support the adoption of diverse adaptation strategies.

5. Conclusions

This study employed multiple indices to assess agricultural and hydrological drought in the Abbay Basin of Ethiopia. The SPI, SPEI, VCI, and DSI indicate the presence of extremely dry and severely dry conditions in the Abbay Basin. In this study, agricultural drought is observed during the main crop-growing season, which can affect crop growth, yield, and quality characteristics. Both extremely dry and extremely wet conditions can significantly and negatively impact agriculture and other socioeconomic activities in the Basin. Like other countries, the problem of drought and excessive rainfall has been observed in the Abbay Basin during the study period (1982–2022). Under s business-as-usual scenario, there is a probability of future food and water insecurity due to extreme climate events in the Abbay Basin. Hydrological drought has the potential to adversely affect water resources in the reservoirs, leading to a reduction in energy potential such as hydroelectric power. The hydrological drought could potentially lead to a lack of water for irrigation and power generation in the neighboring nations. Thus, it is essential that all stakeholders should collaborate to address the underlying causes of both agricultural and hydrological drought. The present study provides valuable information for farmers to implement effective climate change adaptation strategies, thereby reducing the potential risks associated with climate extremes and disasters. The results of this study will not only support farmers but also assist different sectors in enhancing actual climate conditions on the ground for designing scenarios of climate change adaptation strategies. Additionally, further studies are needed to be undertaken to examine the impact of agricultural and hydrological drought on people’s livelihoods and the natural environment. The results of this study can provide robust evidence for policymakers to design appropriate adaptation and mitigation strategies to minimize the negative impacts of agricultural and hydrological drought on the region’s economy and beyond.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16213143/s1, Figure S1: SPEI at three month timescales in the selected station in the Abbay basin between 1981 and 2022. Table S1: Spatial coverage of drought severity index in the Abbay basin. Figure S2: SPEI at 12-month timescales in the selected station in the Abbay basin between 1981 and 2022.

Author Contributions

Conceptualization, D.O.G., M.B.M., B.B. and N.F.; methodology, D.O.G., I.N.D., F.B.C. and M.B.M.; software, D.O.G., M.B.M. and I.N.D.; validation, D.O.G., M.B.M. and T.M.E.; formal analysis, D.O.G. and M.B.M.; investigation, S.F.E., T.G., T.M.E., G.B.J., G.E.T., M.H.M., Z.K.A., F.B.C., J.A., G.A.D., D.M.K., L.D.M., E.M.B., G.W.B., M.W., A.H.N., H.T., D.H.D., H.M.Y., D.D. and S.T.T.; resources, D.O.G. and M.B.M.; data curation, D.O.G., M.B.M. and I.N.D.; writing—original draft preparation, D.O.G.; writing—review and editing, M.B.M., N.F., B.B., F.B.C., T.M.E. and S.T.T.; visualization, D.O.G., M.B.M. and I.N.D.; supervision, D.O.G. and M.B.M.; project administration, D.O.G. and M.B.M.; funding acquisition, D.O.G. and M.B.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by IHE Delft (DUPC3 Programme), 2023/080/111457/SMK in the context of the ABCDryBASIN. Additionally, we appreciate the resources provided by Wollega University and Jimma University College of Agriculture and Veterinary Medicine for enabling us to carry out this study.

Data Availability Statement

Additional data used for this study is available in the form of Supplementary Materials.

Acknowledgments

We acknowledge Jimma University College of Agriculture and Veterinary Medicine and Wollega University College of Natural and Computational Science to conduct this study. We also acknowledge our partner institutions; the National Institute of Marine Sciences and Technologies (INSTM), Tunis, Tunisia and The Lebanese Center for Water and Environment LCWE, Beirut, Lebanon, and Department of Civil and Environmental Engineering, University of Houston, Houston, USA for the existing facilities, which significantly contribute to this article.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Tchonkouang, R.D.; Onyeaka, H.; Nkoutchou, H. Assessing the vulnerability of food supply chains to climate change-induced disruptions. Sci. Total Environ. 2024, 920, 171047. [Google Scholar] [CrossRef] [PubMed]
  2. Godde, C.M.; Mason-D’Croz, D.; Mayberry, D.E.; Thornton, P.K.; Herrero, M. Impacts of climate change on the livestock food supply chain; a review of the evidence. Glob. Food Secur. 2021, 28, 100488. [Google Scholar] [CrossRef] [PubMed]
  3. Scafetta, N. Impacts and risks of “realistic” global warming projections for the 21st century. Geosci. Front. 2024, 15, 101774. [Google Scholar] [CrossRef]
  4. Wang, L.; Wang, L.; Li, Y.; Wang, J. A century-long analysis of global warming and earth temperature using a random walk with drift approach. Decis. Anal. J. 2023, 7, 100237. [Google Scholar] [CrossRef]
  5. Klein, T.; Anderegg, W.R.L. A vast increase in heat exposure in the 21st century is driven by global warming and urban population growth. Sustain. Cities Soc. 2021, 73, 103098. [Google Scholar] [CrossRef]
  6. Varotsos, C.A.; Efstathiou, M.N. Has global warming already arrived? J. Atmos. Sol.-Terr. Phys. 2019, 182, 31–38. [Google Scholar] [CrossRef]
  7. IPCC. 2023: Climate Change 2023: Synthesis Report. In Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Lee, H., Romero, J., Eds.; IPCC: Geneva, Switzerland, 2023; p. 184. [Google Scholar] [CrossRef]
  8. Gemeda, D.O.; Korecha, D.; Garedew, W. Monitoring climate extremes using standardized evapotranspiration index and future projection of rainfall and temperature in the wettest parts of southwest Ethiopia. Environ. Chall. 2022, 7, 100517. [Google Scholar] [CrossRef]
  9. Meilutytė-Lukauskienė, D.; Nazarenko, S.; Kobets, Y.; Akstinas, V.; Sharifi, A.; Haghighi, A.T.; Hashemi, H.; Kokorīte, I.; Ozolina, B. Hydro-meteorological droughts across the Baltic Region: The role of the accumulation periods. Sci. Total Environ. 2024, 913, 169669. [Google Scholar] [CrossRef]
  10. Dai, M.; Huang, S.; Huang, Q.; Leng, G.; Guo, Y.; Wang, L.; Fang, W.; Li, P.; Zheng, X. Assessing agricultural drought risk and its dynamic evolution characteristics. Agric. Water Manag. 2020, 231, 106003. [Google Scholar] [CrossRef]
  11. Gu, L.; Chen, J.; Yin, J.; Xu, C.-Y.; Chen, H. Drought hazard transferability from meteorological to hydrological propagation. J. Hydrol. 2020, 585, 124761. [Google Scholar] [CrossRef]
  12. Vicente-Serrano, S.M. Differences in Spatial Patterns of Drought on Different Time Scales: An Analysis of the Iberian Peninsula. Water Resour. Manag. 2006, 20, 37–60. [Google Scholar] [CrossRef]
  13. Liu, S.; Shi, H.; Niu, J.; Chen, J.; Kuang, X. Assessing future socioeconomic drought events under a changing climate over the Pearl River basin in South China. J. Hydrol. Reg. Stud. 2020, 30, 100700. [Google Scholar] [CrossRef]
  14. Sun, H.; Sun, X.; Chen, J.; Deng, X.; Yang, Y.; Qin, H.; Chen, F.; Zhang, W. Different types of meteorological drought and their impact on agriculture in Central China. J. Hydrol. 2023, 627, 130423. [Google Scholar] [CrossRef]
  15. Samantaray, A.K.; Ramadas, M.; Panda, R.K. Changes in drought characteristics based on rainfall pattern drought index and the CMIP6 multi-model ensemble. Agric. Water Manag. 2022, 266, 107568. [Google Scholar] [CrossRef]
  16. Vorobevskii, I.; Kronenberg, R.; Bernhofer, C. Linking different drought types in a small catchment from a statistical perspective—Case study of the Wernersbach catchment, Germany. J. Hydrol. X 2022, 15, 100122. [Google Scholar] [CrossRef]
  17. Cheng, H.; Wang, W.; van Oel, P.R.; Lu, J.; Wang, G.; Wang, H. Impacts of different human activities on hydrological drought in the Huaihe River Basin based on scenario comparison. J. Hydrol. Reg. Stud. 2021, 37, 100909. [Google Scholar] [CrossRef]
  18. Van Loon, A.F.; Laaha, G. Hydrological drought severity explained by climate and catchment characteristics. J. Hydrol. 2015, 526, 3–14. [Google Scholar] [CrossRef]
  19. Guo, Y.; Huang, S.; Huang, Q.; Wang, H.; Fang, W.; Yang, Y.; Wang, L. Assessing socioeconomic drought based on an improved Multivariate Standardized Reliability and Resilience Index. J. Hydrol. 2019, 568, 904–918. [Google Scholar] [CrossRef]
  20. Wang, T.; Tu, X.; Singh, V.P.; Chen, X.; Lin, K.; Lai, R.; Zhou, Z. Socioeconomic drought analysis by standardized water supply and demand index under changing environment. J. Clean. Prod. 2022, 347, 131248. [Google Scholar] [CrossRef]
  21. Yang, S.; Zhao, B.; Yang, D.; Wang, T.; Yang, Y.; Ma, T.; Santisirisomboon, J. Future changes in water resources, floods and droughts under the joint impact of climate and land-use changes in the Chao Phraya basin, Thailand. J. Hydrol. 2023, 620, 129454. [Google Scholar] [CrossRef]
  22. Ge, W.; Deng, L.; Wang, F.; Han, J. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci. Total Environ. 2021, 773, 145648. [Google Scholar] [CrossRef] [PubMed]
  23. Howe, P.D. Extreme weather experience and climate change opinion. Curr. Opin. Behav. Sci. 2021, 42, 127–131. [Google Scholar] [CrossRef]
  24. Luo, J.; Xie, Y.; Hou, M.Z.; Xiong, Y.; Wu, X.; Lüddeke, C.T.; Huang, L. Advances in subsea carbon dioxide utilization and storage. Energy Rev. 2023, 2, 100016. [Google Scholar] [CrossRef]
  25. Gao, Y.; Gao, X.; Zhang, X. The 2 °C Global Temperature Target and the Evolution of the Long-Term Goal of Addressing Climate Change—From the United Nations Framework Convention on Climate Change to the Paris Agreement. Engineering 2017, 3, 272–278. [Google Scholar] [CrossRef]
  26. Chalchissa, F.B.; Diga, G.M.; Feyisa, G.L.; Tolossa, A.R. Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia. Heliyon 2022, 8, e10136. [Google Scholar] [CrossRef]
  27. Moisa, M.B.; Merga, B.B.; Gemeda, D.O. Multiple indices-based assessment of agricultural drought: A case study in Gilgel Gibe Sub-basin, Southern Ethiopia. Theor. Appl. Clim. 2022, 148, 455–468. [Google Scholar] [CrossRef]
  28. Shao, W.; Kam, J. Retrospective and prospective evaluations of drought and flood. Sci. Total Environ. 2020, 748, 141155. [Google Scholar] [CrossRef]
  29. Gebremichael, H.B.; Raba, G.A.; Beketie, K.T.; Feyisa, G.L.; Siyoum, T. Changes in daily rainfall and temperature extremes of upper Awash Basin, Ethiopia. Sci. Afr. 2022, 16, e01173. [Google Scholar] [CrossRef]
  30. Mera, G.A. Drought and its impacts in Ethiopia. Weather Clim. Extrem. 2018, 22, 24–35. [Google Scholar] [CrossRef]
  31. Conway, D.; Schipper, E.L.F. Adaptation to climate change in Africa: Challenges and opportunities identified from Ethiopia. Glob. Environ. Chang. 2011, 21, 227–237. [Google Scholar] [CrossRef]
  32. Holden, S.; Shiferaw, B. Land degradation, drought and food security in a less-favoured area in the Ethiopian highlands: A bio-economic model with market imperfections. Agric. Econ. 2004, 30, 31–49. [Google Scholar]
  33. Wouterse, F.; Andrijevic, M.; Schaeffer, M. The microeconomics of adaptation: Evidence from smallholders in Ethiopia and Niger. World Dev. 2022, 154, 105884. [Google Scholar] [CrossRef]
  34. Hirvonen, K.; Sohnesen, T.P.; Bundervoet, T. Impact of Ethiopia’s 2015 drought on child undernutrition. World Dev. 2020, 131, 104964. [Google Scholar] [CrossRef]
  35. Smith, L.C.; Frankenberger, T.R. Recovering from severe drought in the drylands of Ethiopia: Impact of Comprehensive Resilience Programming. World Dev. 2022, 156, 105829. [Google Scholar] [CrossRef]
  36. Wubneh, M.A.; Alemu, M.G.; Fekadie, F.T.; Worku, T.A.; Demamu, M.T.; Aman, T.F. Meteorological and hydrological drought monitoring and trend analysis for selected gauged watersheds in the Lake Tana basin, Ethiopia: Under future climate change impact scenario. Sci. Afr. 2023, 20, e01738. [Google Scholar] [CrossRef]
  37. Borgomeo, E.; Vadheim, B.; Woldeyes, F.B.; Alamirew, T.; Tamru, S.; Charles, K.J.; Kebede, S.; Walker, O. The Distributional and Multi-Sectoral Impacts of Rainfall Shocks: Evidence From Computable General Equilibrium Modelling for the Awash Basin, Ethiopia. Ecol. Econ. 2018, 146, 621–632. [Google Scholar] [CrossRef]
  38. Gemeda, D.O.; Korecha, D.; Garedew, W. Evidences of climate change presences in the wettest parts of southwest Ethiopia. Heliyon 2021, 7, e08009. [Google Scholar] [CrossRef]
  39. Bogale, G.A.; Erena, Z.B. Drought vulnerability and impacts of climate change on livestock production and productivity in different agro-Ecological zones of Ethiopia. J. Appl. Anim. Res. 2022, 50, 471–489. [Google Scholar] [CrossRef]
  40. Gemeda, D.O.; Korecha, D.; Garedew, W. Determinants of climate change adaptation strategies and existing barriers in Southwestern parts of Ethiopia. Clim. Serv. 2023, 30, 100376. [Google Scholar] [CrossRef]
  41. Shitu, K.; Hymiro, A.; Tesfaw, M.; Abebe, T. Temporal rainfall variability and drought characterization in Cheleka Watershed, Awash River Basin, Ethiopia. J. Hydrol. Reg. Stud. 2024, 51, 101663. [Google Scholar] [CrossRef]
  42. Bedane, H.R.; Beketie, K.T.; Fantahun, E.E.; Feyisa, G.L.; Anose, F.A. The impact of rainfall variability and crop production on vertisols in the central highlands of Ethiopia. Environ. Syst. Res. 2022, 11, 26. [Google Scholar] [CrossRef]
  43. Wolteji, B.N.; Bedhadha, S.T.; Gebre, S.L.; Alemayehu, E.; Gemeda, D.O. Multiple Indices Based Agricultural Drought Assessment in the Rift Valley Region of Ethiopia. Environ. Chall. 2022, 7, 100488. [Google Scholar] [CrossRef]
  44. Gemeda, D.O.; Feyssa, D.H.; Garedew, W. Meteorological data trend analysis and local community perception towards climate change: A case study of Jimma City, Southwestern Ethiopia. Environ. Dev. Sustain. 2020, 23, 5885–5903. [Google Scholar] [CrossRef]
  45. Haile, G.G.; Tang, Q.; Leng, G.; Jia, G.; Wang, J.; Cai, D.; Sun, S.; Baniya, B.; Zhang, Q. Long-term spatiotemporal variation of drought patterns over the Greater Horn of Africa. Sci. Total Environ. 2020, 704, 135299. [Google Scholar] [CrossRef] [PubMed]
  46. Biazin, B.; Sterk, G. Drought vulnerability drives land-use and land cover changes in the Rift Valley dry lands of Ethiopia. Agric. Ecosyst. Environ. 2013, 164, 100–113. [Google Scholar] [CrossRef]
  47. Gebrehiwot, T.; van der Veen, A.; Maathuis, B. Spatial and temporal assessment of drought in the Northern highlands of Ethiopia. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 309–321. [Google Scholar] [CrossRef]
  48. Dercon, S. Growth and shocks: Evidence from rural Ethiopia. J. Dev. Econ. 2004, 74, 309–329. [Google Scholar] [CrossRef]
  49. Svoboda, M.; Hayes, M.; Wood, D. Standardized Precipitation Index User Guide (WMO—No. 1090); World Meteorological Organization (WMO): Geneva, Switzerland, 2012. [Google Scholar]
  50. Vicente-Serrano, S.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitivity to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718. [Google Scholar] [CrossRef]
  51. Wang, Q.; Zeng, J.; Qi, J.; Zhang, X.; Shui, W.; Xu, Z.; Zhang, R.; Wu, X.; Cong, J. A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018. Earth Syst. Sci. Data 2021, 13, 331–341. [Google Scholar] [CrossRef]
  52. Potop, V.; Boroneant, C.; Monzny, M.; Stepanek, P.; Shalak, P. Observed spatiotemporal characteristics of drought on various time scales over the Czech Republic. Theor. Appl. Climatol. 2014, 115, 563–581. [Google Scholar] [CrossRef]
  53. Mu, Q.; Zhao, M.; Kimball, J.S.; McDowell, N.G.; Running, S.W. A remotely sensed global terrestrial drought severity indez. Bull. Am. Meteorol. Soc. 2013, 94, 83–98. [Google Scholar] [CrossRef]
  54. Palmer, W.C. Meteorological Drought. In Research Paper No. 45; US Department of Commerce Weather Bureau: Washington, DC, USA, 1965. [Google Scholar]
  55. Zhang, Q.; Yu, H.; Sun, P.; Singh, V.P.; Shi, P. Multisource data based agricultural drought monitoring and agricultural loss in China. Glob. Planet. Chang. 2019, 172, 298–306. [Google Scholar] [CrossRef]
  56. Kogan, F.N. Application of Vegetation index and brightness temperature for drought detection. Adv. Space Res. 1995, 15, 91–100. [Google Scholar] [CrossRef]
  57. Bokusheva, R.; Kogan, F.; Vitkovskaya, I.; Conradt, S.; Batyrbayeva, M. Satellite-based vegetation health indices as a criteria for insuring against drought-related yield losses. Agric. For. Meteorol. 2016, 220, 200–206. [Google Scholar] [CrossRef]
  58. Federal Democratic Republic of Ethiopia Abbay Basin Authority. 2016. Available online: https://aba.gov.et/ (accessed on 27 March 2024).
  59. Tibebe, D.; Teferi, E.; Bewket, W.; Zeleke, G. Climate induced water risks on agriculture in the Abbay river basin: A review. Front. Water 2022, 4, 961948. [Google Scholar] [CrossRef]
  60. Tekleab, S.; Mohamed, Y.; Uhlenbrook, S. Hydro-climatic trends in the Abay/Upper Blue Nile basin, Ethiopia. Phys. Chem. Earth Parts A/B/C 2013, 61, 32–42. [Google Scholar] [CrossRef]
  61. Climate-Smart Agriculture Sourcebook; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2013; Available online: https://openknowledge.fao.org/server/api/core/bitstreams/b21f2087-f398-4718-8461-b92afc82e617/content (accessed on 25 September 2024).
  62. Harishnaika, N.; Ahmed, S.A.; Kumar, S.; Arpitha, M. Computation of the spatio-temporal extent of rainfall and long-term meteorological drought assessment using standardized precipitation index over Kolar and Chikkaballapura districts, Karnataka during 1951–2019. Remote Sens. Appl. 2022, 27, 100768. [Google Scholar] [CrossRef]
  63. McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993; American Meteorological Society: Boston, MA, USA, 1993; pp. 179–184. [Google Scholar]
  64. Dejene, I.N.; Wedajo, G.K.; Bayissa, Y.A.; Abraham, A.M.; Cherinet, K.G. Satellite rainfall performance evaluation and application to monitor meteorological drought: A case of Omo-Gibe basin, Ethiopia. Nat. Hazards 2023, 119, 167–201. [Google Scholar] [CrossRef]
  65. Ding, Y.; He, X.; Zhou, Z.; Hu, J.; Cai, H.; Wang, X.; Li, L.; Xu, J.; Shi, H. Response of vegetation to drought and yield monitoring based on NDVI and SIF. Catena 2022, 219, 106328. [Google Scholar] [CrossRef]
  66. Merga, B.B.; Moisa, M.B.; Negash, D.A.; Ahmed, Z.; Gemeda, D.O. Land Surface Temperature Variation in Response to Land Use Land-Cover Dynamics: A Case of Didessa River Sub-basin in Western Ethiopia. Earth Syst. Environ. 2022, 6, 803–815. [Google Scholar] [CrossRef]
  67. Huang, S.; Tang, L.; Hupy, J.P.; Wang, Y.; Shao, G. A comparative review on the use of normalized difference vegetation index (NDVI) in the ear of popular remote sensing. J. For. Res. 2021, 32, 1–6. [Google Scholar] [CrossRef]
  68. Nabizada, A.F.; Rousta, I.; Mozaffari, G.; Dalvi, M.; Olafsson, H.; Siedliska, A.; Krzyszczak, J. A remotely sensed study of the impact of meteorological parameters on vegetation for the eastern basins of Afghanistan. Earth Sci. Inform. 2023, 16, 1293–1312. [Google Scholar] [CrossRef]
  69. Kogan, F.; Adamenko, T.; Guo, W. Global and regional drought dynamics in the climate warming era. Remote Sens. Lett. 2013, 4, 364–372. [Google Scholar] [CrossRef]
  70. Wu, R.; Liu, Y.; Xing, X. Evaluation of evapotranspiration deficit index for agricultural drought monitoring in North China. J. Hydrol. 2021, 596, 126057. [Google Scholar] [CrossRef]
  71. Tirivarombo, S.; Eliasson, D.O.P. Drought monitoring and analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI). Phys. Chem. Earth Parts A/B/C 2018, 106, 1–10. [Google Scholar] [CrossRef]
  72. Vicente-Serrano, S.M.; Van der Schrier, G.; Beguería, S.; Azorin-Molina, C.; Lopez-Moreno, J.I. Contribution of precipitation and reference evapotranspiration to drought indices under different climates. J. Hydrol. 2015, 526, 42–54. [Google Scholar] [CrossRef]
  73. Li, X.-X.; Ju, H.; Sarah, G.; Yan, C.-R.; Batchelor, W.D.; Liu, Q. Spatiotemporal variation of drought characteristics in the HuangHuai-Hai Plain, China under the climate change scenario. J. Integr. Agric. 2017, 16, 2308–2322. [Google Scholar] [CrossRef]
  74. Anghel, C.G.; Stanca, S.C.; Ilinca, C. Two-Parameter Probability Distributions: Methods, Techniques and Comparative Analysis. Water 2023, 15, 3435. [Google Scholar] [CrossRef]
  75. Richman, M.B.; Leslie, L.M.; Segele, Z.T. Classifying Drought in Ethiopia Using Machine Learning. Procedia Comput. Sci. 2016, 95, 229–236. [Google Scholar] [CrossRef]
  76. Cherinet, A.; Tadesse, C.; Abebe, T. Drought and Flood Extreme Events and Management Strategies in Ethiopia. J. Geogr. Nat. Disasters 2022, 12, 248. [Google Scholar] [CrossRef]
  77. Hermans, K.; Garbe, L. Droughts, livelihoods, and human migration in northern Ethiopia. Reg. Env. Chang. 2019, 19, 1101–1111. [Google Scholar] [CrossRef]
  78. Berihun, M.L.; Tsunekawa, A.; Haregeweyn, N.; Meshesha, D.T.; Adgo, E.; Tsubo, M.; Masunaga, T.; Fenta, A.A.; Sultan, D.; Yibeltal, M.; et al. Hydrological responses to land use/land cover change and climate variability in contrasting agro-ecological environments of the Upper Blue Nile basin, Ethiopia. Sci. Total Environ. 2019, 689, 347–365. [Google Scholar] [CrossRef] [PubMed]
  79. Sanginabadi, H.; Saghafian, B.; Delavar, M. Couples Groundwater Drought and Water Scarcity Index for Intensively Overdrafted Aquifers. J. Hydrol. Eng. 2019, 24, 04019003. [Google Scholar] [CrossRef]
  80. Seka, A.M.; Zhang, J.; Zhang, D.; Ayele, E.G.; Han, J.; Prodhan, F.A.; Zhang, G.; Liu, Q. Hydrological drought evaluation using GRACE satellite-based drought index over the lake basins, East Africa. Sci. Total Environ. 2022, 852, 158425. [Google Scholar] [CrossRef]
  81. Bisrat, E.; Berhanu, B. Chapter 21—Drought in Ethiopia: Temporal and spatial characteristics. In Extreme Hydrology and Climate Variability; Melesse, A.M., Abtew, W., Senay, G., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 263–274. [Google Scholar] [CrossRef]
  82. Haile, B.T.; Zeleke, T.T.; Beketie, K.T.; Ayal, D.Y.; Feyisa, G.L. Analysis of El Niño Southern Oscillation and its impact on rainfall distribution and productivity of selected cereal crops in Kembata Alaba Tembaro zone. Clim. Serv. 2021, 23, 100254. [Google Scholar] [CrossRef]
  83. Tofu, D.A.; Woldeamanuel, T.; Haile, F. Smallholder farmers’ vulnerability and adaptation to climate change induced shocks: The case of Northern Ethiopia highlands. J. Agric. Food Res. 2022, 8, 100312. [Google Scholar] [CrossRef]
  84. Sinore, T.; Wang, F. Impact of climate change on agriculture and adaptation strategies in Ethiopia: A meta-analysis. Heliyon 2024, 10, e26103. [Google Scholar] [CrossRef] [PubMed]
  85. Hänsel, S.; Ustrnul, Z.; Łupikasza, E.; Skalak, P. Assessing seasonal drought variations and trends over Central Europe. Adv. Water Resour. 2019, 127, 53–75. [Google Scholar] [CrossRef]
  86. Al Kafy, A.; Bakshi, A.; Saha, M.; Faisal, A.A.; Almulhim, A.I.; Rahaman, Z.A.; Mohammad, P. Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms. Sci. Total Environ. 2023, 867, 161394. [Google Scholar] [CrossRef]
  87. Yeleliere, E.; Antwi-Agyei, P.; Guodaar, L. Farmers response to climate variability and change in rainfed farming systems: Insight from lived experiences of farmers. Heliyon 2023, 9, e19656. [Google Scholar] [CrossRef]
  88. Wu, L.; Elshorbagy, A.; Helgason, W. Assessment of agricultural adaptations to climate change from a water-energy-food nexus perspective. Agric. Water Manag. 2023, 284, 108343. [Google Scholar] [CrossRef]
  89. Bouteska, A.; Sharif, T.; Bhuiyan, F.; Zoynul Abedin, M. Impacts of the changing climate on agricultural productivity and food security: Evidence from Ethiopia. J. Clean. Prod. 2024, 449, 141793. [Google Scholar] [CrossRef]
  90. Abafogi, M.A.; Gemeda, D.O. Assessment of extreme climate indices in the Somalia National Regional State, eastern Ethiopia. Sustain. Environ. 2024, 10, 2391130. [Google Scholar] [CrossRef]
Water 16 03143 g001
Figure 1. Map of the study area.
Figure 1. Map of the study area.
Water 16 03143 g001
Water 16 03143 g002aWater 16 03143 g002b
Figure 2. SPI values at three-month timescales at ten stations in the Abbay Basin.
Figure 2. SPI values at three-month timescales at ten stations in the Abbay Basin.
Water 16 03143 g002aWater 16 03143 g002b
Water 16 03143 g003
Figure 3. Spatial distribution of drought conditions (1982, 1992, 2009, and 2015) in the Abbay River Basin during the main crop-growing season (June to September).
Figure 3. Spatial distribution of drought conditions (1982, 1992, 2009, and 2015) in the Abbay River Basin during the main crop-growing season (June to September).
Water 16 03143 g003
Water 16 03143 g004
Figure 4. The spatial pattern of the VCI during the main crop growing season.
Figure 4. The spatial pattern of the VCI during the main crop growing season.
Water 16 03143 g004
Water 16 03143 g005
Figure 5. Spatial distribution map of the DSI in the Abbay Basin.
Figure 5. Spatial distribution map of the DSI in the Abbay Basin.
Water 16 03143 g005
Water 16 03143 g006
Figure 6. SPI values at 12 month timescales at ten stations in the Abbay Basin.
Figure 6. SPI values at 12 month timescales at ten stations in the Abbay Basin.
Water 16 03143 g006
Table 1. Data sources and descriptions.
Table 1. Data sources and descriptions.
Data TypesYearSpatial ResolutionTemporal ResolutionSources
CHIRPS data1981–20220.05° (5 km)MonthlyFEWS NET
eMODIS data2003–2022250 mDekadalFEWS NET
Temperature data1981–2022Point dataMonthlyEMI
Where FEWS NET is the Famine Early Warning Systems Network and EMI is the Ethiopian Metrological Institute.
Table 2. Drought conditions based on SPEI three-month-timescales.
Table 2. Drought conditions based on SPEI three-month-timescales.
StationExtremely DrySeverely DryExtremely WetSeverely Wet
Alibo2005, 20151982, 1984–1985, 1990, 1994, 2001–2004, 2006, 2009, 2012–2015, 2017, 20181990, 19981987, 1989, 1993, 1996–1999, 2006, 2010, 2014, 2016, 2019
Bahirdar20151982, 1984, 1987, 1992, 2002, 2005, 2006, 2009, 2012, 20171990, 1992, 1994–1995, 1999, 20131987, 1996, 1997, 1998, 2000, 2006
Bedele2003, 2006, 20141984, 1991, 1994, 2000, 2002–2005, 2012, 20131988, 1990, 2007, 2010, 2014, 20191987, 1992, 1996–1997, 2007–2008, 2017, 2020, 2021
Chewahit1982,1984,
1990, 1999, 2005		1986, 1992, 1995, 1997, 2002–2004, 2009, 2011, 2015, 20171987, 1990, 1996, 1997, 1998, 20141983, 1985, 1988, 1992, 1999, 2001, 2006, 2012, 2019, 2021
Dibate2002, 20051986, 1990, 2001, 2006, 2009, 2012, 20131982, 1985, 1990,
1997, 2000, 2008, 2014, 2020		1981, 1987, 1990, 1991, 1999, 2008, 2016, 2017
Fitche1984, 1999, 2005, 2006, 2008, 20151982, 1988–1989, 1992, 1994, 1997, 2000–2003, 2012–20141987, 1988, 1990, 1997, 2016, 20191981–1982, 1985, 1993,
1996, 1998, 2007, 2020
Kelem-Meda1984, 1999, 2008, 20121982, 1987, 1992, 2000–2002, 2005, 2009, 2011–2012, 2015, 20171990, 1998, 20191981–1983, 1987, 1992, 1993, 1997, 2016, 2019, 2020, 2022
Keranio2002, 2003, 2005, 2006, 20151984, 1987, 1988, 1992, 1995, 1999, 2000, 2009, 2012–2013, 20171982, 1990, 1996, 1998, 20141987, 1988, 1992, 1997–2000, 2013, 2016
Mankush1982, 1991, 1995, 2001, 20101983, 1986, 1987, 1990, 1995, 2002, 2003, 2011, 2015–2016, 20221981, 1990–1992, 19981995, 1996, 2006, 2015, 2019
Mendi1995, 19961982, 1986, 1990, 1992, 2001, 2005, 2009, 2012–20131997, 2016, 20171983, 1985, 1988–1989, 1996, 2008, 2014, 2019, 2022
Table 3. Spatial coverage of drought severity classes based on VCI values.
Table 3. Spatial coverage of drought severity classes based on VCI values.
YearDrought Severity ClassArea (km2)Area (%)
2003Extreme drought70,723.7435.3
Severe drought60,632.5430.3
Moderate drought43,245.9821.6
No drought25,550.6212.8
Total200,152.88100
2009Extreme drought83,098.6241.5
Severe drought62,504.3831.2
Moderate drought31,729.3415.9
No drought22,820.5411.4
Total200,152.88100
2012Extreme drought130,253.1865.1
Severe drought36,114.6218.0
Moderate drought20,772.0610.4
No drought13,013.026.5
Total200,152.88100
2013Extreme drought98,461.1849.2
Severe drought45,741.8222.9
Moderate drought31,408.715.7
No drought24,541.1812.3
Total200,152.88100
2018Extreme drought86,708.8643.3
Severe drought48,635.924.3
Moderate drought34,470.317.2
No drought30,337.8215.2
Total200,152.88100
2022Extreme drought98,147.149.0
Severe drought33,209.0216.6
Moderate drought28,849.1814.4
No drought39,947.5820.0
Total200,152.88100
Table 4. Drought conditions based on SPEI 12 month timescales.
Table 4. Drought conditions based on SPEI 12 month timescales.
StationExtremely DrySeverely DryExtremely WetSeverely Wet
Alibo2003, 20151986, 2002, 2012, 201820071996, 1997, 1998, 2008
Bahirdar20032003, 2004, 200920141993, 1998, 2000, 2006, 2013, 2014, 2017, 2020
Bedele2003, 20041984, 1986, 19951998, 20071988, 1993, 2009, 2019, 2020, 2021
Chewahit 1982, 2003, 2004, 200919981987, 1998, 2000, 2001, 2007, 2014, 2017, 2021
Dibate 2002–2005, 2009, 20121998, 1985, 1988, 1989, 20141991, 1999, 2016
Fitche2002, 20151992,1998, 20041989, 20201993, 1996
Kelem-Meda19841988, 1991, 2003, 2008, 20152016, 20201993, 1998, 2001, 2019
Keranio20031998, 1992, 2002, 2003, 2004, 20051998, 20142020
Mankush19831986, 1992, 1995, 1997, 2002, 2010 1981, 1989, 1991, 1996, 1999, 2008
Mendi 1992, 1995, 2002, 2003, 2006, 201020171989, 2016, 2020–2022
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gemeda, D.O.; Bejaoui, B.; Farhat, N.; Dejene, I.N.; Fufa Eticha, S.; Girma, T.; Ejeta, T.M.; Jabana, G.B.; Tufa, G.E.; Mamo, M.H.; et al. Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia. Water 2024, 16, 3143. https://doi.org/10.3390/w16213143

AMA Style

Gemeda DO, Bejaoui B, Farhat N, Dejene IN, Fufa Eticha S, Girma T, Ejeta TM, Jabana GB, Tufa GE, Mamo MH, et al. Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia. Water. 2024; 16(21):3143. https://doi.org/10.3390/w16213143

Chicago/Turabian Style

Gemeda, Dessalegn Obsi, Béchir Bejaoui, Nasser Farhat, Indale Niguse Dejene, Soreti Fufa Eticha, Tadelu Girma, Tadesse Mosissa Ejeta, Gamachu Biftu Jabana, Gadise Edilu Tufa, Marta Hailemariam Mamo, and et al. 2024. "Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia" Water 16, no. 21: 3143. https://doi.org/10.3390/w16213143

APA Style

Gemeda, D. O., Bejaoui, B., Farhat, N., Dejene, I. N., Fufa Eticha, S., Girma, T., Ejeta, T. M., Jabana, G. B., Tufa, G. E., Mamo, M. H., Alo, Z. K., Chalchisa, F. B., Amanuel, J., Disassa, G. A., Kumsa, D. M., Mekonen, L. D., Beyene, E. M., Bortola, G. W., Wagari, M., ... Moisa, M. B. (2024). Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia. Water, 16(21), 3143. https://doi.org/10.3390/w16213143

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop