TY  - JOUR
AU  - Yoshe, Agegnehu Kitanbo
PY  - 2025
DA  - 2025/01/06
TI  - Sustainable Water Management Using Rainfall-Runoff Modelling in Rift Valley Basin, East Africa
JO  - Advances in Environmental and Engineering Research
SP  - 003
VL  - 06
IS  - 01
AB  - Managing water resources offers crucial information about the availability of water supplied from catchments into water bodies, which plays a vital role in water resource engineering. However, due to changes in the global climate, hydrological modeling of river catchments is critically crucial for socio-economic development and livelihoods. Numerous models evaluate runoff from precipitation, but the SCS-CN method is fundamental and the most widely recognized for calculating runoff. This research evaluates runoff depth in the Rift Valley River basin using the SCS-CN model and remote sensing techniques from 1991 to 2022 based on precipitation data availability. The 37861 km² (65.75%) of the study area was covered by the hydrological soil group “C,” and 19729 km² (34.26%) was by the hydrological soil group “D.” The land use classification shows that approximately 2556.65 km² (4.44%) is water, 9003.72 km² (15.63%) is tree cover, 144.3 km² (0.25%) is flooded vegetation, 19012.21 km² (33.01%) is cropland, 3122.07 km² (5.42%) is built-up area, 984.29 km² (1.71%) is bare land, and 22763.82 km² (39.53%) is rangeland, which covers the largest area in the study region. The evaluated curve numbers for the study area were 74.71 for normal soil moisture conditions (AMC-II), 55.37 for dry soil moisture conditions (AMC-I), and 87.20 for wet soil moisture conditions (AMC-III). The evaluated probable maximum retention capacity (S) was 213.73 for AMC-I, 94.98 for AMC-II, and 46.38 for AMC-III. The preliminary abstraction loss (Ia) was 42.75 for AMC-I, 19.00 for AMC-II, and 9.280 for AMC-III. The higher the value of maximum retention (S) and Ia, the more maximum retention and maximum abstraction loss, which leads to low runoff depth, whereas the smallest value of S and Ia represents less retention and less abstraction loss, demonstrating high runoff depth. As a result, the average annual surface runoff calculated for the Rift Valley River Basin from 1991 to 2022 was observed to be 787.425 mm per year, with a total volume of approximately 45347805750 m³/year. The maximum rainfall recorded was 1047.11 mm in 2020, while the minimum was 673.22 mm in 2021. From the evaluated results, the estimated average rainfall runoff varies between 562.70 and 1047.1 mm, and the average volume of rainfall-runoff ranges from 32403589400 to 60303064900 Cubic Meters. The spatial distribution of runoff shows a significant variation in the study period between 2011 and 2022, which was essential to identify hotspot areas for water resource management. This data is valuable for watershed development, effective planning of water resources, sustainable ecological practices, and groundwater recharge initiatives. Moreover, the SCS-CN and GIS techniques have proven effective, requiring less time and resources to manage large datasets across broader environmental regions for identifying potential sites for artificial recharge structures.KeywordsWater resource management; rainfall-runoff modeling; spatiotemporal variation of runoff; SCS-CN model and GIS techniques
SN  - 2766-6190
UR  - https://doi.org/10.21926/aeer.2501003
DO  - 10.21926/aeer.2501003
ID  - Yoshe2025
ER  -