Impacts of Land‐Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

Getachew, Birhan; Manjunatha, B.R.

doi:10.1002/gch2.202200041

Cited by 22 publications

(15 citation statements)

References 64 publications

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“…Much literature published on LU conducted in the past years reported that changing natural lands to urban areas, agriculture, or bare ground will lead to frequent environmental issues and flooding due to increased runoff and peak discharge. For example, in the hydrology process (Son et al, 2022;Getachew & Manjunatha, 2022), soil quality (Vanacker et al, 2019;Padbhushan et al, 2022) and climate change (Şen, 2020); these variables are essential to the mitigation of water risk…”

Section: Methodsmentioning

confidence: 99%

The Impacts of Land Use Changes in Urban Hydrology, Runoff and Flooding: A Review

Alshammari¹,

Rahman²,

Rainis³

et al. 2023

CUS

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The issue of Land Use (LU) change has received considerable critical attention because it is one of the most significant factors caused by human activities worldwide. Recent critical changes in this direction have affected urban hydrology. LU change affects water resources and hydrological characteristics such as runoff and urban flooding. The development of LU causes a rapid increase in impermeable surfaces, increasing the flooding rate. LU also plays a vital role in extending water drainage, groundwater intrusion, and flooding during and after rainfall. This paper aims to investigate LU change impacts on runoff and urban flooding. This review focused on most articles, conference papers, and book chapters published in SCOPUS, including Google Scholar. The study was limitation to the last published from 2017-2021 and also extended some published theories for different years published. In addition, in the short and long term, the development of LU affects the environment, and most factors are involved at a catchment level. However, there is a strong relationship between human activities at the catchment level and runoff. The study concluded that LU strongly influences topography and the landscape in arid, semi-arid, and humid zones. This is why runoff and water distribution happen in urban areas. Furthermore, this study found that builtup area is a critical factor that increases urban flood risk, especially in lowlands along floodplains. It is common for the frequency of floods to become more severe due to a rapid increase in impervious surfaces brought on by urbanisation, increasing runoff. The review concludes that runoff affects by catchment size and its condition. Finally, humans can be reduced runoff and flood risk with a sensibility strategy.

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Section: Methodsmentioning

confidence: 99%

The Impacts of Land Use Changes in Urban Hydrology, Runoff and Flooding: A Review

Alshammari¹,

Rahman²,

Rainis³

et al. 2023

CUS

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“…The lake water quality has degraded slowly with time [15]. The explanation for this is that the amount of cultivated land in the Lake Tana area has increased by 20% and covers 68% of the basin in the last 30 years [63]. The soil erosion from these areas ranges from 5 tons per hectare per year to 50 tons per hectare per year, indicating a doubling of the sediment transport to the lake from 1980s to 2020s [64].…”

Section: Significance Of ML In Lake Water Quality Managementmentioning

confidence: 99%

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

et al. 2023

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Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.

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“…[ 52 , 53 ] Therefore, this study is designed to use recently developed data mining methods for multispectral image classification in the BNRB. Hence, many of the previous studies [ 54 , 55 , 56 , 57 ] have employed traditional parametric statistics for the classification of multispectral imagery for land use mapping. In addition, they have applied classified satellite data to many environmental and socioeconomic applications.…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia

Tikuye,

Rusnak,

Manjunatha

et al. 2023

Global Challenges

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Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built‐ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built‐up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large‐scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision‐making for sustainable development.

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Impacts of Land‐Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

Cited by 22 publications

References 64 publications

The Impacts of Land Use Changes in Urban Hydrology, Runoff and Flooding: A Review

The Impacts of Land Use Changes in Urban Hydrology, Runoff and Flooding: A Review

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia

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