Abstract
Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998–2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years (2014–2015, 2009–2010, 1994–1995 and 1983–1984) were successfully indicated. Moreover, severe drought conditions were observed in the drought prone parts of the basin (i.e., central, eastern and southeastern). Hence, the CHIRPS rainfall product can be used as an alternative source of information in developing the grid-based drought monitoring tools for the basin that could help in developing early warning systems.
Highlights
Drought is one of the most costly weather extremes that occurs in different parts of the world every year
An excellent score of Bias and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations
There are more than 94 operational weather stations in the study region; most of them are used in the blending process in the majority of satellite rainfall products
Summary
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 Remote Sens. Ethiopia has been impacted by recurrent and prolonged drought events that have affected a large proportion of the population, destroyed crops and killed livestock. 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. Drought monitoring and early warning systems are crucial to mitigate drought’s adverse impacts in the country [8]
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