Assessing the Impact of Man–Made Ponds on Soil Erosion and Sediment Transport in Limnological Basins

Sayah, Mario J. Al; Nedjaï, Rachid; Kaffas, Konstantinos; Abdallah, Chadi; Khouri, Michel

doi:10.3390/w11122526

Cited by 8 publications

(5 citation statements)

References 55 publications

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“…Landscape-scale conservation of urban wetlands is increasingly recognised as important, but this requires methods for spatial data acquisition and analysis suited to this scale. Limited attempts have been made to quantify pond and lake numbers at regional or national scales (Lacaux et al, 2007;Al Sayah et al, 2019), but high levels of uncertainty are evident, reflecting the spatial resolution of remote sensing data and the associated difficulty of identifying the smallest ponds. Pond numbers have been estimated to be between 547 million and 3.19 billion globally, with the wide range in these values demonstrating the uncertainty surrounding estimates (Holgerson and Raymond, 2016).…”

Section: Discussionmentioning

confidence: 99%

Landscape-scale Remote Sensing and Classification of Lentic Habitats in a Tropical City

et al. 2021

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Ponds and lakes may be common in urban landscapes and frequently have high biodiversity and conservation value. The importance of landscape-scale conservation of lentic habitat networks has recently been recognised, yet little research has been conducted at this scale. Approaches to inventorying lentic habitats at landscape-scale are needed to support science and conservation. This paper uses remote sensing to inventory lentic habitats across Greater Kuala Lumpur (GKL), Malaysia, characterising their distribution, abundance and type. Remote sensing images were employed to automatically identify and map the distribution of lentic habitats, capturing 1,013 individual ponds and lakes, which represent 74.3% of the likely total. Partitioning Around Medoids (PAM), a multivariate hierarchical clustering method, was used to develop a typology using physical characteristics extracted from the imagery, yielding six distinct types. The diversity of habitat types was greater in peri-urban and suburban areas (Shannon's H = 1.68) than in the urban core (Shannon's H = 1.48), suggesting that urban structure influences the spatial pattern of lentic habitat diversity. Physicochemical and vegetation field data were collected from a sample of each type (n=60). Comparisons of group membership in the remote sensing and field data indicated demonstrable heterogeneity in local conditions within each type, and a lack of consistency between local and broader scale characteristics. Our work shows how inventories produced from remote sensing can provide insights into landscape-scale biodiversity drivers, and contribute to larger-scale conservation of lentic habitat networks. This approach can complement local studies to improve understanding of factors operating at different scales.

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Section: Discussionmentioning

confidence: 99%

Landscape-scale Remote Sensing and Classification of Lentic Habitats in a Tropical City

et al. 2021

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“…Globally, pond numbers have been estimated to be between 547 million -3.19 billion globally, demonstrating the uncertainty surrounding pond numbers at national and international scales (Holgerson and Raymond, 2016). Limited attempts have also been made to quantify pond numbers at regional or national scales (Al Sayah et al, 2019;Lacaux et al, 2007), with similar high levels of uncertainty, reflecting the spatial resolution of remote sensing and identification of the smallest ponds.…”

Section: Discussionmentioning

confidence: 99%

Landscape-scale Remote Sensing and Classification of Pond and Lake Habitat Diversity in a Tropical City

Teo

Hill

Lechner

et al. 2021

Preprint

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Ponds and lakes are common freshwater habitats in urban landscapes, and often have a high biodiversity and conservation value. The importance of landscape-scale conservation of pond networks has recently been recognised, yet the categorisation and classification of pond network spatial structures is missing. Developing spatial methods and tools to characterise and understand pond networks is a critical first step to accurately conserve pond habitats and inhabiting species. This paper presents an inventory of ponds and lakes in Greater Kuala Lumpur, Malaysia, characterising their distribution, abundance and type. Remote sensing was first employed to map and characterise these habitats, followed by multivariate cluster analysis to classify and develop a typology of the ponds identified. Physicochemical data was collected from a sample (n=60) of ponds to compare with the remotely sensed pond classification. Results demonstrated that multi-source remote sensing can be highly accurate and effective in inventorying ponds and lakes of varying sizes. A total of 1013 ponds and lakes were identified within the Greater Kuala Lumpur region and were found to be highly environmentally heterogeneous. Typology clusters were driven by land cover rather than local physicochemical variables demonstrating that specific remotely-sensed variables may be sufficient proxies for certain chemical variables. Landscape-scale conservation and management of pond networks should utilise remote sensing tools, to establish pond network structure, and to maintain wide environmental heterogeneity among pond habitats. Incorporating remote sensing tools into pond conservation will ensure that effective pond conservation can be achieved and biodiversity protection can be maximised.

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“…Xin et al [36] observed simulated rainfall experiments to observe the infiltration processes of black soil slopes under bare and residue-covered situations. A study of the impact of ponds in limnologically rich basins on soil erosion and sediment transport has been reported by Al Sayah et al [37]. The construction of a debris-blocking dam has been studied by Wang et al [38] under various downstream riverbed slope conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches

Hassan

Shaukat

Ahmad

et al. 2022

Water

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Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (Ra), water inflow (Iw), minimum water reservoir level (Lr), and storage capacity of the reservoir (Cr) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of Ra, Iw, Lr, and Cr. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment.

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Assessing the Impact of Man–Made Ponds on Soil Erosion and Sediment Transport in Limnological Basins

Cited by 8 publications

References 55 publications

Landscape-scale Remote Sensing and Classification of Lentic Habitats in a Tropical City

Landscape-scale Remote Sensing and Classification of Lentic Habitats in a Tropical City

Landscape-scale Remote Sensing and Classification of Pond and Lake Habitat Diversity in a Tropical City

Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches

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