Multi-site calibration of SWAT for the spatial distribution of sediment yield, Middle Awash Dam watershed, Ethiopia

2021 ◽  
Vol 17 (4) ◽  
Author(s):  
Tesfu Abebe Tesema ◽  
◽  
Kuok Choy Lam ◽  
Bogale GebreMariam ◽  
Wasu Manawko Tefere ◽  
...  
Author(s):  
Vito Ferro

Beyond damage to rainfed agricultural and forestry ecosystems, soil erosion due to water affects surrounding environments. Large amounts of eroded soil are deposited in streams, lakes, and other ecosystems. The most costly off-site damages occur when eroded particles, transported along the hillslopes of a basin, arrive at the river network or are deposited in lakes. The negative effects of soil erosion include water pollution and siltation, organic matter loss, nutrient loss, and reduction in water storage capacity. Sediment deposition raises the bottom of waterways, making them more prone to overflowing and flooding. Sediments contaminate water ecosystems with soil particles and the fertilizer and pesticide chemicals they contain. Siltation of reservoirs and dams reduces water storage, increases the maintenance cost of dams, and shortens the lifetime of reservoirs. Sediment yield is the quantity of transported sediments, in a given time interval, from eroding sources through the hillslopes and river network to a basin outlet. Chemicals can also be transported together with the eroded sediments. Sediment deposition inside a reservoir reduces the water storage of a dam. The prediction of sediment yield can be carried out by coupling an erosion model with a mathematical operator which expresses the sediment transport efficiency of the hillslopes and the channel network. The sediment lag between sediment yield and erosion can be simply represented by the sediment delivery ratio, which can be calculated at the outlet of the considered basin, or by using a distributed approach. The former procedure couples the evaluation of basin soil loss with an estimate of the sediment delivery ratio SDRW for the whole watershed. The latter procedure requires that the watershed be discretized into morphological units, areas having a constant steepness and a clearly defined length, for which the corresponding sediment delivery ratio is calculated. When rainfall reaches the surface horizon of the soil, some pollutants are desorbed and go into solution while others remain adsorbed and move with soil particles. The spatial distribution of the loading of nitrogen, phosphorous, and total organic carbon can be deduced using the spatial distribution of sediment yield and the pollutant content measured on soil samples. The enrichment concept is applied to clay, organic matter, and all pollutants adsorbed by soil particles, such as nitrogen and phosphorous. Knowledge of both the rate and pattern of sediment deposition in a reservoir is required to establish the remedial strategies which may be practicable. Repeated reservoir capacity surveys are used to determine the total volume occupied by sediment, the sedimentation pattern, and the shift in the stage-area and stage-storage curves. By converting the sedimentation volume to sediment mass, on the basis of estimated or measured bulk density, and correcting for trap efficiency, the sediment yield from the basin can be computed.


2012 ◽  
Vol 12 (5) ◽  
pp. 199-206 ◽  
Author(s):  
Sung Soo Im ◽  
Minseok Kim ◽  
Joong Hoon Kim ◽  
Kyungrock Paik

2021 ◽  
Author(s):  
Alice Dambroz ◽  
Jean Minella ◽  
Tales Tiecher ◽  
Jean Moura-Bueno ◽  
Felipe Bernardi ◽  
...  

<p>Although sediment yield reflects a catchment’s erosive processes, material transfer from hillslopes to rivers depends on a series of phenomena occurring on variable and continuous range of scales. Physically based, distributed models can be used to evaluate erosion’s spatial variability within a catchment and to identify hotspots. Sediment fingerprinting allows source type discrimination based on sediment and soil properties. The analysis of these dynamic systems could be coupled by addressing hillslope processes with modeling, while fingerprinting enlightens the connection between them and the drainage network. We aimed to evaluate the erosive susceptibility and its spatial distribution in three environmentally fragile paired headwater catchments, nested within Guarda Mor catchment, located in the border of the volcanic plateau in southern Brazil. This catchment is characterized by intense agricultural use, diverse geology, and complex terrain. WATERSED model was used as a dynamic method to evaluate the spatial distribution of hydrologic and erosive fragility during rainfall events. WATERSED was parameterized for modeling surface runoff volume, sediment yield and interrill erosion, based on monitored data from a zero-order no-till catchment and literature data. Modeling results were analyzed for each land use. For fingerprinting, two sediment sampling strategies and source groupings were considered. One considered spatial sources, and the endmembers were the sub catchments, the other considered land use source types within each sub catchment. Deposited bed sediment samples were collected at the outlets of each sub catchment and the main outlet. Soil source samples were collected in crop fields, grasslands, stream channels, forests, and unpaved roads. Crop fields and grasslands compose the source type topsoil. Samples were analyzed by near-infrared spectroscopy. Artificial mixtures were made to calibrate the prediction models. Fifteen Support Vector Machine (SVM) models were built and independently trained. Modeled erosion indicates that the steepest areas and those near the drainage network can be the most susceptible to erosion and runoff. The spatial distribution of runoff-prone areas shows the connectivity from upper segments of these catchments increases with higher magnitude events. In fingerprinting, calibration results’ predictors show good performance by the models, validation results vary from poor to good. SVM models for unpaved roads and forest had the best validation performance. For sourcing tributaries, results and poor validation statistical results indicate the need to use different tracers, and to consider unsampled sources associated to soil and geological differences found downstream from the sub catchment’s outlets. As for the sub catchments, there is a variation among the main sediment sources and a significantly constant contribution from unpaved roads in all of them. Other important sources are topsoil and stream channels, while forests did not show significant contribution. These methodologies were useful in seeking a more holistic process understanding, as physical processes were addressed and later integrated with the resulting sediment yield. Despite the results are modelled, the complementation of their insights indicates that there is a possibility for validating the sediment fingerprinting technique once modelling is validated by monitored and measured data.</p>


2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Ebenezer Boakye ◽  
F. O. K. Anyemedu ◽  
Emmanuel A. Donkor ◽  
Jonathan A. Quaye-Ballard

Author(s):  
Belayneh Yigez ◽  
Donghong Xiong ◽  
Baojun Zhang ◽  
Yong Yuan ◽  
Muhammad Aslam Baig ◽  
...  

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