Climate change is statistical variations over an extended period in the features of the climate system, such as variations in global temperatures and precipitation, caused by human and natural sources. In this study aimed to measure and examine how streamflow in the Dawa sub-basin, Genale Dawa River basin was affected by climate change. It used the average of five regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa, under two different scenarios of Representative Concentration Pathways: RCP4.5 and RCP8.5. The baseline scenario was based on the data from 1975 to 2005, while the future scenarios were based on the data from 2020s (2025–2054) and 2050s (2055–2084). The HBV hydrological model used to assess the impact on streamflow. The HBV model showed good statistical performance in simulating the impact of climate change on streamflow, with a coefficient of determination (R2) of 0.88 and Nash-Sutcliffe Efficiency (NSE) of 0.77 for monthly calibration, and R2 of 0.86 and NSE of 0.83 for monthly validation. The impacts quantified using the mean monthly changes in precipitation, maximum and minimum temperatures. The bias-corrected precipitation and temperature showed a reasonable increase in both future periods for both RCP 4.5 and RCP 8.5 scenarios. These changes in climate variables resulted in a decrease in mean annual streamflow by 1.6 and 3.5% for RCP 4.5 and by 4.6 and 4.9% for RCP 8.5 scenarios of the 2020s and 2050s, respectively. Based on the analysis that predicted a drop in precipitation during the months, and seasons and an increase in precipitation during the Belg season, with a corresponding decrease and rise in stream flow throughout the watershed. So to offset the variation in the watershed, community should adopt various; Soil and water conservation technologies, Using drought tolerant crops, Implementing various trees and appropriate design and applying a water harvesting structure like in-situ, internal or micro catchment, external or macro catchment water harvesting and Surface runoff harvesting. This result offers useful information for current and future water resource management in the basin and similar other watershed in the country.
Published in | American Journal of Water Science and Engineering (Volume 10, Issue 2) |
DOI | 10.11648/j.ajwse.20241002.12 |
Page(s) | 36-47 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Climate Change, CMhyd, Cordex, Ethiopia, HBV, Streamflow
Months | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Average |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RF (mm) | 17.1 | 24.7 | 74.8 | 147.2 | 109.2 | 22.6 | 14.0 | 15.7 | 37.6 | 119.6 | 61.1 | 0.7 | 644.2 |
Tmax (°C) | 27.7 | 28.4 | 27.6 | 24.7 | 24.4 | 23.2 | 23.0 | 23.9 | 25.3 | 24.7 | 32.6 | 27.0 | 26.0 |
Tmin (oC) | 12.9 | 14.2 | 14.9 | 15.5 | 14.9 | 14.0 | 13.9 | 13.9 | 14.2 | 14.7 | 13.8 | 12.7 | 14.1 |
Flow (M3/s) | 9.7 | 8.1 | 8.7 | 18.4 | 33.5 | 26.9 | 20.3 | 22.1 | 26 | 34.7 | 32.7 | 17.5 | 24.1 |
Institution | GCM | RCM | Resolution (Lat & Log) |
---|---|---|---|
Canadian Centre for Climate Modelling and Analysis | CCCmaa-CanESM2 | SMHI-RCA4 | 2.8*2.8 |
National Institute for Environmental Studies and Japan Agency for Marine-earth Science and Technology, Japan | MIROC-MIROC5 | SMHI-RCA4 | 1.4*1.4 |
Met Office Hadley Centre, UK | HadGEM2-ES | SMHI-RCA4 | 1.25*1.25 |
CSIRO-QCCCE-CSIRO-MK3-0 | CSIRO-MK3-6-0 | SMHI-RCA4 | 1.9*1.9 |
Max Planck Institute for Meteorology, Germany | MPI-M-MPI-ESM-LR | SMHI-RCA4 | 1*1 |
LULC class 2016 | Proportional area (%) | Soil type | Proportional area (%) | Slope classes (%) | Proportional area (%) |
---|---|---|---|---|---|
Tree cover areas | 4.29 | Chromic Cambisols | 41.5 | 0 - 4.9 | 47.58 |
Shrubs cover areas | 36.71 | Chromic Luvisols | 36.3 | 4.9 - 10.8 | 46.28 |
Grassland | 46.85 | Lithic Leptosols | 10.4 | 10.8 - 20.1 | 4.7 |
Cropland | 10.55 | Eutric Vertisols | 7.4 | 20.1 - 33.3 | 1.4 |
Flooded | 0.002 | Eutric Leptosols | 4.4 | >33.3 | 0.03 |
Sparse vegetation | 0.03 | ||||
Bare areas | 1.49 | ||||
Build up areas | 0.09 | ||||
Waterbody | 0.013 |
Data Set | Resolution | Parameters |
---|---|---|
DEM | 12.5m | Topographical data |
Soil map | 1Km | Soil class |
Land use map | 12.5m | Lad cover and use |
Climate | Daily (0.440) | Precipitation and Temperature |
Discharge | Daily | Stream flow data |
Name of indicator perfect | Formula | Simulation value |
---|---|---|
Coefficient of determination | R2 = 0.88 | 0.86 |
Nash-Sutcliffe Efficiency | NSE = 0.77 | 0.83 |
Time | Mean | SD | CV | Z | P | SS |
---|---|---|---|---|---|---|
Jan | 17.1 | 23.3 | 136.4 | -0.04 | 0.68 | |
Feb | 24.7 | 34.3 | 139.3 | -0.04 | 0.68 | |
Mar | 74.8 | 79.6 | 106.4 | 0.01 | 0.34 | 0.46 |
Apr | 147.2 | 72.7 | 49.4 | -0.13 | 0.23 | |
May | 109.2 | 74.1 | 67.8 | -0.28 | 0.01 | |
Jun | 22.6 | 26.8 | 118.9 | -0.13 | 0.20 | |
Jul | 14.0 | 14.8 | 105.4 | -0.27 | 0.01 | |
Aug | 15.7 | 21.5 | 136.9 | -0.05 | 0.61 | |
Sep | 37.6 | 33.7 | 89.6 | -0.28 | 0.01 | |
Oct | 119.6 | 87.7 | 73.3 | 0.04 | 0.69 | 0.30 |
Nov | 61.1 | 51.5 | 84.3 | 0.11 | 0.30 | 0.46 |
Dec | 0.7 | 2.7 | 387.5 | -0.41 | 0.00 | |
Kiremt | 89.9 | 55.3 | 61.4 | -0.19 | 0.07 | |
Bega | 198.4 | 121.5 | 61.2 | 0.08 | 0.02 | |
Belg | 355.9 | 151.9 | 42.7 | -0.25 | 0.33 | |
Annual | 644.2 | 222.2 | 34.5 | -0.1 | 0.33 |
CORDEX | Coordinated Regional Climate Downscaling Experiment |
RCP | Representative Concentration Pathways |
R2 | Coefficient of Determination |
NSE | Nash-Sutcliffe Efficiency |
HBV | Hydrologiska Byråns Vattenbalansavdelning |
CMhyd | Climate Model for Hydrology |
HWSD | Harmonized World Soil Database |
MoWE | Ministry of Water and Energy |
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APA Style
Bulti, A., Abegaz, F. (2024). Impacts of Climate Change on Streamflow on Dawa Sub-watershed, Genale-Dawa River Basin, Southern Ethiopia. American Journal of Water Science and Engineering, 10(2), 36-47. https://doi.org/10.11648/j.ajwse.20241002.12
ACS Style
Bulti, A.; Abegaz, F. Impacts of Climate Change on Streamflow on Dawa Sub-watershed, Genale-Dawa River Basin, Southern Ethiopia. Am. J. Water Sci. Eng. 2024, 10(2), 36-47. doi: 10.11648/j.ajwse.20241002.12
@article{10.11648/j.ajwse.20241002.12, author = {Ayana Bulti and Fentaw Abegaz}, title = {Impacts of Climate Change on Streamflow on Dawa Sub-watershed, Genale-Dawa River Basin, Southern Ethiopia }, journal = {American Journal of Water Science and Engineering}, volume = {10}, number = {2}, pages = {36-47}, doi = {10.11648/j.ajwse.20241002.12}, url = {https://doi.org/10.11648/j.ajwse.20241002.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajwse.20241002.12}, abstract = {Climate change is statistical variations over an extended period in the features of the climate system, such as variations in global temperatures and precipitation, caused by human and natural sources. In this study aimed to measure and examine how streamflow in the Dawa sub-basin, Genale Dawa River basin was affected by climate change. It used the average of five regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa, under two different scenarios of Representative Concentration Pathways: RCP4.5 and RCP8.5. The baseline scenario was based on the data from 1975 to 2005, while the future scenarios were based on the data from 2020s (2025–2054) and 2050s (2055–2084). The HBV hydrological model used to assess the impact on streamflow. The HBV model showed good statistical performance in simulating the impact of climate change on streamflow, with a coefficient of determination (R2) of 0.88 and Nash-Sutcliffe Efficiency (NSE) of 0.77 for monthly calibration, and R2 of 0.86 and NSE of 0.83 for monthly validation. The impacts quantified using the mean monthly changes in precipitation, maximum and minimum temperatures. The bias-corrected precipitation and temperature showed a reasonable increase in both future periods for both RCP 4.5 and RCP 8.5 scenarios. These changes in climate variables resulted in a decrease in mean annual streamflow by 1.6 and 3.5% for RCP 4.5 and by 4.6 and 4.9% for RCP 8.5 scenarios of the 2020s and 2050s, respectively. Based on the analysis that predicted a drop in precipitation during the months, and seasons and an increase in precipitation during the Belg season, with a corresponding decrease and rise in stream flow throughout the watershed. So to offset the variation in the watershed, community should adopt various; Soil and water conservation technologies, Using drought tolerant crops, Implementing various trees and appropriate design and applying a water harvesting structure like in-situ, internal or micro catchment, external or macro catchment water harvesting and Surface runoff harvesting. This result offers useful information for current and future water resource management in the basin and similar other watershed in the country. }, year = {2024} }
TY - JOUR T1 - Impacts of Climate Change on Streamflow on Dawa Sub-watershed, Genale-Dawa River Basin, Southern Ethiopia AU - Ayana Bulti AU - Fentaw Abegaz Y1 - 2024/08/20 PY - 2024 N1 - https://doi.org/10.11648/j.ajwse.20241002.12 DO - 10.11648/j.ajwse.20241002.12 T2 - American Journal of Water Science and Engineering JF - American Journal of Water Science and Engineering JO - American Journal of Water Science and Engineering SP - 36 EP - 47 PB - Science Publishing Group SN - 2575-1875 UR - https://doi.org/10.11648/j.ajwse.20241002.12 AB - Climate change is statistical variations over an extended period in the features of the climate system, such as variations in global temperatures and precipitation, caused by human and natural sources. In this study aimed to measure and examine how streamflow in the Dawa sub-basin, Genale Dawa River basin was affected by climate change. It used the average of five regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa, under two different scenarios of Representative Concentration Pathways: RCP4.5 and RCP8.5. The baseline scenario was based on the data from 1975 to 2005, while the future scenarios were based on the data from 2020s (2025–2054) and 2050s (2055–2084). The HBV hydrological model used to assess the impact on streamflow. The HBV model showed good statistical performance in simulating the impact of climate change on streamflow, with a coefficient of determination (R2) of 0.88 and Nash-Sutcliffe Efficiency (NSE) of 0.77 for monthly calibration, and R2 of 0.86 and NSE of 0.83 for monthly validation. The impacts quantified using the mean monthly changes in precipitation, maximum and minimum temperatures. The bias-corrected precipitation and temperature showed a reasonable increase in both future periods for both RCP 4.5 and RCP 8.5 scenarios. These changes in climate variables resulted in a decrease in mean annual streamflow by 1.6 and 3.5% for RCP 4.5 and by 4.6 and 4.9% for RCP 8.5 scenarios of the 2020s and 2050s, respectively. Based on the analysis that predicted a drop in precipitation during the months, and seasons and an increase in precipitation during the Belg season, with a corresponding decrease and rise in stream flow throughout the watershed. So to offset the variation in the watershed, community should adopt various; Soil and water conservation technologies, Using drought tolerant crops, Implementing various trees and appropriate design and applying a water harvesting structure like in-situ, internal or micro catchment, external or macro catchment water harvesting and Surface runoff harvesting. This result offers useful information for current and future water resource management in the basin and similar other watershed in the country. VL - 10 IS - 2 ER -