Spatio-temporal variability and trends of hydroclimatic variables at Zarima Sub-Basin North Western Ethiopia

Abstract

Globally, hydroclimatic variables have been changed due to human activities and have an impact on agricultural production and water resource management. This study investigated the spatio-temporal variability of hydroclimatic variables in Zarima subbasin using rainfall and temperature data from 1984 to 2018 and stream flow data from 1990 to 2014. The data were analyzed using the coefficient of variation, standardized anomaly index, Mann–Kendall trend and Sen’s slope estimator test. The results showed that rainfall, maximum and minimum temperatures of the Zarima subbasin varied spatially and temporally. The annual and Kiremit season rainfall and stream flow showed low to moderate variability (CV < 30%) whereas Bega and Belg season rain fall and stream flow showed high degree of variability (CV < 40%). The annual and Kiremit season rainfall showed relatively higher variability along the northern border of the subbasin. However, maximum temperature showed less degree of variability (CV < 10%) over the subbasin, whereas the annual and seasonal minimum temperatures showed less to moderate variability (CV = 2–28%). The minimum temperature showed moderate variability around the south east lower border of the subbasin on the annual time scale and the north east part of the subbasin during the Kiremit season. The Bega and Belg seasons mean minimum temperatures showed less to high degree of variability (CV = 0–40%). The south east lower part of the subbasin showed moderate to high variability during the Bega and Belg seasons. Results of a standardized anomaly index of the rainfall, temperature and stream flow confirmed that the sub basin experienced fluctuations between dry, wet, cool and warm years. The spatial representation of annual rainfall showed a statically significant increasing trend in some parts of the subbasin and an insignificant increasing trend in the majority of the subbasin up to 300 mm per decade, while the Bega and Belg seasons showed statistically insignificant increasing and decreasing trends. Kiremit season rainfall showed heterogenous results in both statistics and trend direction. The mean annual maximum showed an increasing trend from 0.35 to 0.9 °C. The Bega, Belg and Kiremit seasons' maximum temperatures increased by a range of 0.35 to 0.95 °C; 0.4 from 1.2 °C and 0.1 to 0.55 °C pre decade respectively. While the annual minimum temperature increased by 0.05 to 0.5 °C and decreasing trend by 0.1 to 0.3 °C around the south eastern part of the subbasin. The Bega and Belg season minimum temperatures increased up to 0.5 °C and 1 °C and temperatures decreased in the range of 0.1 to 0.5 oC per decade. The Kiremit season minimum temperature increased by a range of 0.05 to 0.5 °C. The annual, Bega, Belg and Kiremit season stream flow increased insignificantly by 0.32, 0.31, 0.02 and 0.68 mm3/s respectively. In general, the subbasin experienced hydroclimatic variability which affects the life of the people, agricultural production and the economy of the area. This needs further investigation for planning and management of water resource management for the success of future water resources projects.

Grid-based climate variability analysis of Addis Ababa, Ethiopia

“…In connection with this, the proximity of Ethiopia to the equator as well as the intricacy nature of the country's topography have a significant influence Ethiopia's climate, particularly the temperature [ 174 , 175 ]. Similarly, physical features such as terrain and sub-basin characteristics typically influence the spatial variability of maximum and minimum temperatures [ 120 ].…”

“…Concerning the change in average annual maximum temperature across seasons, the highest temperature change occurred during the Belg season ( Table 1 ). In this regard, previous studies reported that the Belg season, which includes the months of March, April, and May, are the warmest months where the maximum temperature is recorded during this season [ 120 ].…”

“…One essential statistical measure employed in this analysis is the coefficient of variation (CV). CV serves as a valuable tool for assessing the year-to-year variability within temperature datasets, presenting the degree of variation as a percentage relative to the mean temperature [ 25 , 120 ]. The CV (Eq.…”

“…In this study, the number of cooling (-ve) and warming (+ve) years, as well as the frequency and severity of dry and wet years, were all determined using the standardized anomalies index [ 120 , 122 , 124 ]. The standardized anomaly index (Eq.…”

Historical Trend Analysis and Future Projections of Rainfall in Amhara, Ethiopia

“…Like most African countries, developing countries are vulnerable to adverse impacts of climate change and variability because they have low adaptive capacity and high exposure as their economies rely mainly on agriculture. Among African regions, East Africa is the most vulnerable (Gebrechorkos et al, 2018;Zegeye et al, 2022;Gedefaw, 2023). In East Africa, agriculture contributes 30-50% of gross domestic product (GDP) and >80% of livelihood (FAO, 2014).…”