Modeling and Practice of Erosion and Sediment Transport under Change

Aksoy, Hafzullah; Mahé, Gil; Meddi, Mohamed

doi:10.3390/w11081665

Cited by 13 publications

(8 citation statements)

References 49 publications

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“…Erosi adalah proses pelepasan dan pengangkutan material tanah oleh agen-agen erosif seperti angin, curah hujan, atau limpasan (Aksoy et al, 2019). Terjadinya erosi melalui tiga tahapan meliputi pelepasan partikel tanah kemudian terangkut oleh agen erosi (angin dan air) dan terjadi pengendapan sedimen yang dipengaruhi oleh alam maupun faktor antropogenik (Osuagwu et al, 2014).…”

Section: Das Garang Secara Administratif Termasuk Dalam Das Lintas Ka...unclassified

Identifikasi Kondisi Eksisting Dan Dampak Sedimentasi Pada Sungai Kreo Dan Kaligarang Kota Semarang

2023

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The Garang watershed which includes the Kreo River and the Kaligarang River is an ecosystem unit that is limited by topographical separators and functions as a collector, storage, and distributor of water, nutrients, and sediment through the river's hydrological system. Garang watershed plays an important role in achieving Sustainable Development Goals (SDGs) 6 related to clean water, namely supplying drinking water raw materials in the city of Semarang experiencing environmental damage due to sedimentation common in river systems in Indonesia. Sedimentation occurs due to the deposition of fragmental material by water caused by erosion. Erosion consists of three phases, namely the release of soil particles and then transport by erosion agents (wind and water). Sediment deposition occurs which is influenced by natural and anthropogenic factors. The impact of sedimentation causes siltation which affects the decreasing capacity of river flow and decreases river water quality. This study aims to identify and analyze the existing conditions of the Kreo River and Kaligarang River related to the impact of sedimentation with qualitative descriptive methods and literature studies of previous studies. The Kreo River and Kaligarang River experience sedimentation due to suspended solids in the form of soil and sand mixed with household waste, resulting in decreased river flow and water quality. This is in line with the results of literature studies from previous studies, in 2008 sedimentation in the Kaligarang River was 124.944.13 tons/year with an erosion rate of 1,064.260.08 tons/ha/year. In 2009-2014 sedimentation in the Kreo River increased from 2,781 to 4,083 tons/year with an erosion rate of 17,827 - 26,171 tons. It can be concluded that the Kaligarang River and the Kreo River are currently experiencing increased sedimentation, so they are threatened with silting and damage to the river's natural functions.

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Section: Das Garang Secara Administratif Termasuk Dalam Das Lintas Ka...unclassified

Identifikasi Kondisi Eksisting Dan Dampak Sedimentasi Pada Sungai Kreo Dan Kaligarang Kota Semarang

2023

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“…Natural processes, such as erosion, sediment transport, and its deposition, can shape the land surface and influence the structure and function of river catchment ecosystems worldwide (Guillén Ludeña et al 2017;Vercruysse et al 2017;Wu et al 2018;Stähly et al 2020;Hu et al 2021;Feng and Shen 2021). However, these processes are very sensitive to both climate change and land use, especially in mountainous areas (Hohmann et al 2018;Dibike et al 2018;Zhao et al 2018;Aksoy et al 2019;Zhang et al 2020). Already, many mountain catchments are struggling with an increase in soil loss due to intensification of rainfall, and consequently, surface runoff (Halecki et al 2018a, b;Borrelli et al 2018;Mostowik et al 2019;Berteni and Grossi 2020;Ciampalini et al 2020).…”

Section: Introductionmentioning

confidence: 99%

From the source to the reservoir and beyond—tracking sediment particles with modeling tools under climate change predictions (Carpathian Mts.)

et al. 2022

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Purpose The study tracks spatial and temporal distribution of sediment particles from their source to the deposition area in a dammed reservoir. This is particularly important due to the predicted future climate changes, which will increase the severity of problems with sediment transport, especially in catchments prone to erosion. Methods Analyses were performed with a monthly step for two mineral and one mineral/organic sediment fractions delivered from the Carpathian Mts. catchment (Raba River) to the drinking water reservoir (Dobczyce) by combining SWAT (Soil and Water Assessment Tool), and AdH/PTM (Adaptive Hydraulics Model/Particle Tracking Model) modules on the digital platform—Macromodel DNS (Discharge Nutrient Sea). To take into account future changes in this catchment, a variant scenario analysis including RCP (representative concentration pathways) 4.5 and 8.5, and land use change forecasts, was performed. Results The differences between the two analyzed hydrological units (catchment and reservoir) have been highlighted and showed a large variability of the sediment load between months. The predicted climate changes will cause a significant increase of mineral fraction loads (silt and clay) during months with high flows. Due to the location and natural arrangement of the reservoir, silt particles will mainly affect faster loss of the first two reservoir zones capacities. Conclusions The increased mobility of finer particles (clay) in the reservoir may be more problematic in the future, mainly due to their binding pollutant properties, and the possible negative impact on drinking water abstraction from the last reservoir zone. Moreover, the study shows that the monthly approach to forecasting the impact of climate change on sediment loads in the reservoir is recommended, instead of a seasonal one.

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“…To explain the primary hydrological and sediment transport-related processes of small watersheds, Nabi et al used SWAT watershed modeling (Nabi et al, 2020). Aksoy et al are regarded as making contributions to the problem of erosion and sediment transport in hydrological watersheds at various spatial and temporal scales as well as under any form of change (Aksoy et al, 2019). A better understanding of the process can be incorporated into strategic or numerical tools for reservoir operations, as demonstrated by Hauer et al (Hauer, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sediment load forecasting of Gobindsagar reservoir using machine learning techniques

et al. 2022

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With ever advancing computer technology in machine learning, sediment load prediction inside the reservoirs has been computed using various artificially intelligent techniques. The sediment load in the catchment region of Gobindsagar reservoir of India is forecasted in this study utilizing the data collected for years 1971–2003 using several models of intelligent algorithms. Firstly, multi-layered perceptron artificial neural network (MLP-ANN), basic recurrent neural network (RNN), and other RNN based models including long-short term memory (LSTM), and gated recurrent unit (GRU) are implemented to validate and predict the sediment load inside the reservoir. The proposed machine learning models are validated for Gobindsagar reservoir using three influencing factors on yearly basis [rainfall (Ra), water inflow (Iw), and the storage capacity (Cr)]. The results demonstrate that the suggested MLP-ANN, RNN, LSTM, and GRU models produce better results with maximum errors reduced from 24.6% to 8.05%, 7.52%, 1.77%, and 0.05% respectively. For future prediction of the sediment load for next 22 years, the influencing factors were first predicted for next 22 years using ETS forecasting model with the help of data collected for 33 years. Additionally, it was noted that each prediction’s error was lower than that of the reference model. Furthermore, it was concluded that the GRU model predicts better results than the reference model and its alternatives. Secondly, by comparing the prediction precision of all the machine learning models established in this study, it can be evidently shown that the LSTM and GRU models were superior to the MLP-ANN and RNN models. It is also observed that among all, the GRU took the best precision due to the highest R of 0.9654 and VAF of 91.7689%, and the lowest MAE of 0.7777, RMSE of 1.1522 and MAPE of 0.3786%. The superiority of GRU can also be ensured from Taylor’s diagram. Lastly, Garson’s algorithm and Olden’s algorithm for MLP-ANN, as well as the perturbation method for RNN, LSTM, and GRU models, are used to test the sensitivity analysis of each influencing factor in sediment load forecasting. The sediment load was discovered to be most sensitive to the annual rainfall.

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Modeling and Practice of Erosion and Sediment Transport under Change

Cited by 13 publications

References 49 publications

Identifikasi Kondisi Eksisting Dan Dampak Sedimentasi Pada Sungai Kreo Dan Kaligarang Kota Semarang

Identifikasi Kondisi Eksisting Dan Dampak Sedimentasi Pada Sungai Kreo Dan Kaligarang Kota Semarang

From the source to the reservoir and beyond—tracking sediment particles with modeling tools under climate change predictions (Carpathian Mts.)

Sediment load forecasting of Gobindsagar reservoir using machine learning techniques

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