Modeling the impact of climate change on the hydrology of Andasa watershed

Tarekegn, Negusu; Abate, Brook; Muluneh, Alemayehu; Dile, Yihun T.

doi:10.1007/s40808-020-01063-7

Cited by 37 publications

(8 citation statements)

References 28 publications

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“…This result can be attributed to a decrease in precipitation in future climate change scenarios. The decreasing trend of precipitation in the 2020s has also been mentioned in previous studies [43][44][45].…”

Section: Shp Potential Predictionsupporting

confidence: 78%

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

et al. 2021

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As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

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Section: Shp Potential Predictionsupporting

confidence: 78%

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

et al. 2021

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“…Most regions are subject to variations in temperature and rainfall patterns because hydrological conditions vary from region to region [8]. Strong agreement was found between the 34 climate models used in Future Climate for Africa (FCFA) 2017 regarding Tanzania's continued warming in the range of 0.80 C to 1.80 C by the 2040s [9], which will cause an increase in annual evapotranspiration, unbalanced atmospheric moisture, and changes in the hydrological cycle of the ecosystems [10].…”

Section: Introductionmentioning

confidence: 87%

Assessment of Climate Change’s Impacts on River Flows in the Songwe Sub-Basin

Mwalwiba¹,

Kifanyi²,

Mutayoba³

et al. 2023

OJMH

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River flow in the Songwe sub-basin is predicted to alter due to climate change, which would have an impact on aquatic habitats, infrastructure, and people's way of life. Therefore, the influence of climate change should be taken into account when making decisions about the sustainable management of water resources in the sub-basin. This study looked into how river discharge would react to climate change in the future. By contrasting hydrological characteristics simulated under historical climate with projected climate (2011-2040, 2041-2070, and 2071-2100) under two emission scenarios, the effects of climate change on river flow were evaluated (RCP 4.5 and RCP 8.5). The ensemble average of four CORDEX regional climate models was built to address the issue of uncertainty introduced by the climate models. The SWAT model was force-calibrated using the results from the generated ensemble average for the RCP 4.5 and RCP 8.5 emission scenarios in order to mimic the river flow during past (1981-2010) and future (2011-2100) events. The increase in river flows for the Songwe sub-basin is predicted to be largest during the rainy season by both the RCP 4.5 and RCP 8.5 scenarios. Under RCP 8.5, the abrupt decrease in river flow is anticipated to reach its maximum in March 2037, when the discharge will be 44.84 m 3 /sec, and in March 2027, when the discharge will be 48 m 3 /sec. The extreme surge in river flow will peak, according to the RCA4, in February 2023, in April 2083 under RCP 4.5, and, according to the CCLM4 and RCA4, in November 2027 and November 2046, respectively. The expected decrease and increase in river flow throughout both the dry and wet seasons may have an impact on the management of the sub-water basin's resources, biodiversity, and hydraulic structures. The right adaptations and mitigation strategies should be adopted in order to lessen the negative consequences of climate change on precipita-

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“…The method significantly reduced the biases between the GCMs output and the observation data and minimized the errors in the projections. The data will help overcome limitations in climate change impact studies on crop growth 33 – 36 and hydrology 37 – 39 in Ethiopia resulting from the insufficient number of GCM used. Our evaluation allows a clear perspective on individual GCMs by means of correlations, biases, and temporal evolution.…”

Section: Background and Summarymentioning

confidence: 99%

High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method

et al. 2023

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High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.

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Modeling the impact of climate change on the hydrology of Andasa watershed

Cited by 37 publications

References 28 publications

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Assessment of Climate Change’s Impacts on River Flows in the Songwe Sub-Basin

High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method

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