Drought is one of the most costly weather extremes that occurs in different parts of the world every year. It affects many sectors, causes large economic losses and threatens human life and the environment [1
]. Ethiopia has been impacted by recurrent and prolonged drought events that have affected a large proportion of the population, destroyed crops and killed livestock. Severe historic drought events have occurred in the country in the past few decades [5
]. Some of the notable droughts (such as those in 2014–2015, 2009–2010, 1994–1995 and 1983–1984) covered the majority of the country [5
]. Based on data obtained from Centre for Research on the Epidemiology of Disasters (CRED) (http://www.emdat.be/database
, accessed on March 2017), the 1983–1984 drought was one of the most severe drought years, with 22% of the total population (~35 million) affected [9
]. Drought affects the agricultural sector, which relies on adequate and timely seasonal rainfall. More than 85% of the population in Ethiopia depend on income generated from agricultural products [10
]. However, agricultural practices are very primitive and predominantly rain-fed [11
]. In addition, the high variability of rainfall in both space and time leads to high variability of the available water at the root zone [12
]. Drought monitoring and early warning systems are crucial to mitigate drought’s adverse impacts in the country [8
In general, drought monitoring includes the wide application of drought indices that measure the deficit of hydrologic cycle components, as compared to the long-term mean [14
]. The long-term mean is used as a reference to measure the deviation of a particular event. Accordingly, meteorological drought is defined based on the degree of dryness or deviation from normal or average amount of rainfall for a prolonged period [16
]. The deficit in meteorological parameters (mainly rainfall) can be considered as a precursor for the deficit of other hydrological water cycle components (river flow, ground water flow, reservoir storage, etc.), known as hydrological drought. The deficit of readily available water for plants to use to satisfy water demand is often defined as agricultural drought [17
]. Meteorological drought can be considered as an early indication of drought before it affects the agricultural and hydrological water components [20
]. The meteorological drought monitoring system depends on the availability of rainfall data at adequate spatial and temporal scales. However, getting a reliable climatic record of rainfall amounts at weather observing stations that are evenly distributed over the area is one of the main challenges, particularly in developing countries such as Ethiopia [21
]. To address these challenges, satellite-based rainfall estimates/products are becoming increasingly available for use at global and regional scales. The main advantage of the remote sensing based rainfall products is that they are reasonably good in terms of spatial and temporal coverage and have proved their applicability to climate and hydrological studies [23
]. However, their accuracy has to be evaluated and compared with ground truth rainfall measurement before using it for further application in drought and other natural hazards studies [25
Several studies have attempted to compare and evaluate satellite rainfall products with ground observations across many watersheds. For example, evaluation of the satellite rainfall products was undertaken through hydrologic simulation in semi-distributed (Soil and Water assessment tool—SWAT) [27
] and fully distributed (MIKESHI) hydrologic models [28
] in small watersheds in the Upper Blue Nile Basin. Hydrological models use many input parameters together with rainfall to represent the complex nature of the hydrological processes in a given catchment and hence the contribution of rainfall might sometimes be dominated by other input parameters (e.g., land use change, water holding capacity). Other studies have evaluated satellite rainfall products such as the Climate Prediction Center’s morphing technique (CMORPH), the Tropical Rainfall Measuring Mission (TRMM), the Multi-satellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), and the TMPA method post-real-time research version product (3B42) in the UBN basin [23
]. Seven satellite rainfall products—Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Time-series (TARCAT), and Multi-satellite Precipitation Analysis (TMPA)—were evaluated and compared with ground weather station data for Burkina Faso, West Africa [31
]. The study indicated that RFE performed best and TARCAT was the weakest [31
]. Similar studies (e.g., study [32
]) carried out for Mozambique showed TARCAT outperforming based on the majority of statistical measures of skill. However, limited studies have been conducted on assessing the applicability of satellite rainfall for meteorological drought monitoring, particularly for the UBN basin in Ethiopia.
These satellite products have long-term recorded data, which is an advantage for drought study. Moreover, they represent and capture the spatial variability of rainfall, which is useful for studying the spatial patterns of drought in the UBN basin. This study focus on the spatial and temporal assessment of meteorological drought in the Ethiopian highlands (UBN basin) using better satellite rainfall products. For this purpose, five satellite rainfall products (ARC 2.0, CHIRPS, PERSIANN, TARCAT, and TMPA) were first evaluated with respect to weather station data to identify and recommend the best satellite-derived rainfall product for drought monitoring in the basin.
2. Study Area
The Ethiopian part of the UBN, also known as the Abbay Basin, is located in the northwestern region of the country between 7°40′N and 12°51′N latitudes and 34°25′E and 39°49′E longitudes (Figure 1
). The UBN basin ranks as the largest river basin of the country by its volume of discharge and the second largest by its area [33
]. It is also the largest tributary of the Nile River (60% of the Nile total flow), covering a total drainage area of 176,000 km2
]. The UBN basin is the most important water resource for Ethiopia, Sudan and Egypt. Lake Tana, the largest lake in Ethiopia (about 3000 km2
) is located in the north part of the basin. The topography of the UBN basin signifies two distinct features: the highlands with rugged mountainous areas in the central and eastern part of the basin, and the lowlands in the western part of the basin. The altitude in the basin ranges from 492 m in the lowlands to 4261 m in the highlands. While the highlands are the main source of water, the lowlands have expanses of flat lands through which the accumulated flows travel from the highlands to the lower riparian countries (i.e., Sudan and Egypt). The annual rainfall ranges from 787 to 2200 mm, with the highlands having the highest rainfall (ranging from 1500 to 2200 mm) and the lowlands receiving less than 1500 mm [33
]. The basin experiences bimodal rainfall seasons locally called Belg and Kiremit. Belg is a short rainfall season from March to May whereas Kiremit is the main rainfall season, from June to September, and its failure often causes drought and resulting famine in the basin [33
6. Summary and Conclusions
The availability of satellite-derived rainfall products at local and global scales has proved to be beneficial in filling the data gap, particularly in developing countries that have data scarcity. However, evaluating these rainfall products is essential for any application that includes studying drought and water resources problems. In this study, the performances of five satellite rainfall products (CHIRPS, PERSIANN, TARCAT, TMPA and ARC 2.0) were first investigated by comparing them with gauged rainfall data from ten independent weather stations across the UBN basin. The statistical approach was used for the performance evaluation at multiple time scales (i.e., dekadal, monthly and seasonal time scales). The evaluation process was undertaken to identify the best satellite rainfall product for spatial and temporal assessment of meteorological drought in the basin. After analyzing the results, the following conclusions were drawn.
In general, the performance of the five satellite rainfall products were reasonably good in detecting the occurrence of rainfall and in estimating the amount of dekadal, monthly and seasonal rainfall in the basin. Comparison of the five-satellite rainfall products have shown that CHIRPS and TARCAT are the best products, whereas PERSSIAN exhibited the poorest performance when evaluated in all statistical measures considered in this study at the dekadal time scale. This shows that both CHIRPS and TARCAT products can be used to develop operational drought or flood monitoring and early warning system since dekadal time scale better identify periods of low or heavy rainfall events in the study area. Even though TMPA showed an average performance on other criteria, it scored the perfect bias that shows its performance to capture the total volume of dekadal rainfall. Hence, TMPA can be used in the water resource applications that involve the potential use of the total volume of rainfall. ARC showed an average performance under all the evaluation criteria.
The performances of all the satellite rainfall products were increased as the aggregation period increases. CHIRPS performed very well under all the evaluation criteria considered during the monthly and seasonal time scales. Relatively higher correlation coefficients (r > 0.80) and mean Bias (0.98) were scored when the CHIRPS rainfall product was compared with gauged rainfall on monthly time scale. In addition, CHIRPS scored the best ME and RMSE in 50% and 80% of the total validation weather stations. TARCAT scored the next highest performance whereas PERSIANN showed relatively weak performance. In general, the CHIRPS rainfall product outperformed the other four satellite-derived rainfall products at monthly and seasonal time scales. Thus, CHIRPS rainfall was selected and used for further application to study the spatial and temporal patterns of meteorological drought in the UBN basin. The good performance of CHIRPS and TARCAT may depend on the reduced effect of pixel-to-point comparison associated with the smaller the grid size. Moreover, the number of the ground observation stations data used in each satellite rainfall product may have a great contribution in terms of reducing the errors induced due to the orographic effect of rainfall in the highlands in the case of the UBN basin.
The temporal assessment of meteorological drought showed the occurrence of mild to severe historic drought events in the UBN basin. The severity of the known drought years, such as 2014–2015, 2009–2010, 1994–1995 and 1983–1984, was indicated in more than 50% of the weather stations. The spatial assessment of drought in the UBN basin also showed the occurrence of the extreme drought event that covered mainly the central, eastern and southeastern parts of the basin. The 2015 drought was remarkable and clearly indicated in the drought-prone region of the UBN basin (i.e., eastern and northeastern parts). Generally, the results indicated that the CHIRPS rainfall product could be used as an alternative source of information to develop the drought monitoring tools for an early warning system that could help in making better decisions in the UBN basin.