Modelling the impacts of changing land use and climate on sediment and nutrient retention in Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

Getachew, Birhan; Gill, Laurence; Rusnak, Milos; Manjunatha, B.R.

doi:10.1016/j.ecolmodel.2023.110383

Cited by 10 publications

(5 citation statements)

References 51 publications

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“…In addition, watersheds have the potential for growth in different sectors, such as agroindustry, ecotourism, agroforestry, livestock, and energy [48]. The watershed under study was selected because it is prone to soil erosion and flooding as a result of intensive agricultural cultivation, as shown in previous studies [49,50]. watershed is approximately 20.5 °C.…”

Section: Study Areamentioning

confidence: 99%

“…As the study watershed is the main flow contributor to Lake Tana, sediments and contaminated surface runoff from cultivated lands pollute the lake water. Recent studies have indicated that the deterioration of the water quality of Lake Tana is the result of soil erosion and contaminant flows from the contributing watersheds [50,85].…”

Section: Impacts Of Lulc Change On Socioeconomic and Environmental Co...mentioning

confidence: 99%

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Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Belay,

Melesse,

Tegegne

2024

Land

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Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies.

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Section: Study Areamentioning

confidence: 99%

Section: Impacts Of Lulc Change On Socioeconomic and Environmental Co...mentioning

confidence: 99%

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Belay,

Melesse,

Tegegne

2024

Land

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“…To define the possible influence of land use on the content of heavy metals and organic carbon [56], it was necessary to derive for both reservoirs, in a GIS environment, the subbasins underlying each of the sampling stations, assumed to be the closing section. The land use characterization of all sub-basins was obtained based on the 2018 Corine Land Cover, prepared by the European Environment Agency, again through GIS analysis.…”

Section: Data Analysesmentioning

confidence: 99%

A Relevant Characterization and Compatibility for Reuse the Sediments from Reservoirs in Southern Italy

Martellotta,

Levacher,

Gentile

et al. 2024

Applied Sciences

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The damming of watercourses results in sediment accumulation and, therefore, in the reduction of useful storage capacity. The storage capacity can be recovered through dredging, but this process generates large volumes of sediments that require proper management. To avoid landfilling and promote recovery operations, sediment characterization is the preliminary step to any assessment and decision. This paper presents the results of tests on sediments sampled at two reservoirs in southern Italy, the Camastra and the San Giuliano, in Basilicata. These investigations include testing of organic matter, heavy metals grain size distribution, and the assessment of the pollution degree. A lack of correlation between the sampling point and the heavy metal content was observed in sediments, except Be, Cr and Ni for the San Giuliano reservoir. This may be attributed to the presence of agricultural activities and fertilizer use in its watershed. Similarly, there is no dependence between the organic carbon and the grain size distribution, the former being scarcely found in both reservoirs (on average 0.91% for the Camastra sediments and 0.38% for the San Giuliano sediments), the latter being predominantly characterized by sandy matrices downstream of the reservoirs (on average 64.3% ± 32.9%) and by silty-clayey matrices in the upstream areas (on average 65% ± 14.3%). Finally, the determination of the single pollution index Pi and the Nemerow integrated pollution index PN highlights that sediments are not contaminated with heavy metals. Most of them show values of the indices above between 0 and 1 (“unpolluted”) and, in a few cases, values between 1 and 2 (“poorly polluted”). The findings suggest that these sediments can be reused for environmental and material recovery, using them as secondary raw materials for sub-bases and embankments, for filling in disused quarries, for reprofiling and reconstructing the morphology of coastlines or riverbeds, for beach nourishment and in the agronomic and construction industry fields.

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“…The model calibration was performed by adjusting the parameters of retention efficiency, threshold flow accumulation, and hydrological connectivity to achieve the best consistency of calculated values with observed data. The utilization of modelprovided parameters to simulate nutrient delivery was similarly applied in some other studies [50][51][52]. According to the available data on monthly water quality monitoring conducted by JICA [32] in 2009 and 2010 in the study area, the monitoring points were overlaid on the LULC map to identify the corresponding point for each LULC class.…”

Section: Analysis Of Water Availabilitymentioning

confidence: 99%

Impact of Land Use Change on the Water Environment of a Key Marsh Area in Vientiane Capital, Laos

Phanmala,

Lai,

Xiao

2023

Water

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The water environment is critical to maintaining ecosystem balance and human well-being globally. It is essential to comprehend the effects of land use change on water quantity and quality for sustainable development of the urban environment. Expansion of urban areas leads to intensified human activity and increased pollution loads in natural waterbodies. This study aimed to monitor changes in land use over a span of two decades to evaluate the condition of the water environment in That Luang Marsh (TLM). The land use and land cover (LULC) classes, including agricultural land, bare land, built-up land, vegetation, waterbody, and wetland, were categorized via Landsat images utilizing the maximum likelihood algorithm. A digital elevation model was used to estimate the water surface area and volume, and the nutrient delivery ratio model was employed to analyze nutrient distribution across the LULC classes. The results showed that from 2001 to 2020, the bare land, built-up, waterbody, and wetland areas increased by 29.92, 18.64, 0.87, and 0.16 times, respectively, while the agricultural and vegetation land decreased by 0.80 and 0.76 times, respectively. A binary logistic regression model for influential factors implies that road network expansion within the special economic zone in TLM could result in an increase in residential areas, thereby impacting the LULC classes. The increase in water volume showed a robust correlation with the expansion of built-up land, bare land, and waterbody. TLM had an average nitrate-nitrogen export of 317 tons/year with a 95% confidence interval of (56, 770) tons/year in 2020. The distribution over LULC classes affected the export, which varied dynamically. Vegetation land had the highest nitrate-nitrogen load of 0.57 tons/ha/year, probably due to poorly managed use of fertilizers. The developed land surface for an artificial lake could lead to an increase in the water volume, which could be involved in the dilution of nutrient concentration. Therefore, it is crucial to prioritize policies that aim to establish sustainable urban water environments through rational urban planning and by making LULC management a primary consideration, especially for developing countries undergoing similar processes of urbanization along the Mekong River in Southeast Asia.

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Modelling the impacts of changing land use and climate on sediment and nutrient retention in Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

Cited by 10 publications

References 51 publications

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

A Relevant Characterization and Compatibility for Reuse the Sediments from Reservoirs in Southern Italy

Impact of Land Use Change on the Water Environment of a Key Marsh Area in Vientiane Capital, Laos

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