VFSMOD-W a graphical Windows system for the evaluation and design of vegetative filter strips for sediment trapping

2013 ◽  
Author(s):  
John E. Parsons ◽  
Rafael Muñoz-Carpena
Keyword(s):  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips

scholarly journals Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

2019 ◽  
Vol 11 (24) ◽  
pp. 7212 ◽  
Author(s):  
Joo Hyun Bae ◽  
Jeongho Han ◽  
Dongjun Lee ◽  
Jae E Yang ◽  
Jonggun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Trapping Efficiency ◽  
Learning Models ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Physically Based ◽  
Physically Based Models ◽  
Machine Learning Models

The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process.

Vegetative filter strips to control sediment movement in forest plantations: validation of a simple model using field data

Soil Research ◽  
1999 ◽  
Vol 37 (5) ◽  
pp. 929 ◽  
Author(s):  
R. J. Loch ◽  
T. Espigares ◽  
A. Costantini ◽  
R. Garthe ◽  
K. Bubb
Keyword(s):  
Conceptual Approach ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Sediment Movement ◽  
Paired Samples ◽  
Vegetative Barrier ◽  
Velocity Flow ◽  
Total Sediment ◽  
Sampling Times

A field study of sediment movement through vegetative barriers was carried out to assess the sediment-trapping effectiveness of vegetative barrier types typically used in forest forest plantation management in south-east Queensland, Australia, and to develop a simple methodology for predicting sediment movement through these barriers. For sites at the centre of Queensland's 110 000 ha Pinus plantation and 45 000 ha Araucaria plantation program, small field flumes (plots) were established on a range of vegetation types and slope gradients, and sediment-laden flows passed through them. Sediment trapping in the plots was assessed by comparing paired samples taken from the inlet and outlet of the plots at pre-determined sampling times. Measurements included total sediment and equivalent size distributions of sediment particles (the latter measurements being based on settling velocities). For plots that did not erode, the degree of sediment trapping, if based on total sediment only, was quite variable. However, if rates of transport were considered in terms of the various size fractions, results were very consistent. A simple conceptual approach equating the vegetated area to a sedimentation pond allowed deposition to be calculated on the basis of settling velocity, flow depth, and residence time within the vegetated area. Estimated transport rates of sediment through the vegetated areas were in close agreement with measured transport rates, confirming the eciency of this approach. The results highlight a number of issues for management of sediment movement from forest estates.

An improved method for the parameterization of sediment trapping in VFSMOD

2021 ◽  
Author(s):  
Stefan Reichenberger ◽  
Robin Sur ◽  
Stephan Sittig ◽  
Sebastian Multsch ◽  
Rafael Muñoz-Carpena
Keyword(s):  
Surface Runoff ◽  
Particle Diameter ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Data Set ◽  
Filter Strips ◽  
Explanatory Variables ◽  
Data Points ◽  
Pesticide Load ◽  
Event Based

<p>The most widely implemented mitigation measure to reduce transfer of pesticides to surface water bodies via surface runoff are vegetative filter strips (VFS). To reliably model the reduction of surface runoff, eroded sediment and pesticide load by VFS an event-based model is needed. The most commonly used model for this purpose is VFSMOD. VFSMOD simulates reduction of total inflow (∆Q) and reduction of incoming eroded sediment load (∆E) mechanistically. These variables are subsequently used to calculate the reduction of pesticide load (∆P). While ∆P can be relatively well predicted from ∆Q, ∆E and some other variables, errors in ∆Q and ∆E will propagate to ∆P. Hence, for strongly sorbing compounds, an accurate prediction of ∆E is crucial. The most important parameter characterizing the incoming sediment in VFSMOD is the median particle diameter d50. The objective of this study was to derive a generic d50 parameterization methodology for sediment trapping in VFSMOD that can be readily used for regulatory VFS scenarios.</p><p>Four studies with 16 hydrological events were selected for modelling. A first set of VFSMOD simulations, following the SWAN-VFSMOD sediment parameterization with d50 = 20 µm yielded a general overestimation of ∆E. Consequently, a maximum-likelihood-based calibration and uncertainty analysis with the DREAM-ZS algorithm was performed for the 16 events. The resulting d50 values were all in the low range (1.3-5.4 µm) and did not allow to establish a robust relationship to predict a wider range of d50 from the available explanatory variables. To increase the sample size and the range of d50 values, the comprehensive Kinston dataset for a loamy sand in North Carolina was calibrated with DREAM-ZS. Calibration was performed separately for each hydrological event. Further data points with measured particle size distributions in run-on were assimilated from the literature. The extended test data set of d50 values and explanatory variables was analysed using an extended multiple linear regression (MLR) approach and Classification and Regression Trees (CART).</p><p>A good calibration of event totals and outflow hydrographs could be achieved for most events and VFS treatments of the Kinston site. The calibrated d50 values yielded a wider range (2-16 µm) than the initial 16 events.</p><p>The improved d50 parameterization method derived with MLR/CART will be adopted in the next version of SWAN-VFSMOD to provide more realistic quantitative mitigation within FOCUS STEP4.</p>

An Empirical Model of Sediment Trapping Efficiency by Vegetative Filter Strips in Chinese Northwest

2014 ◽  
Vol 998-999 ◽  
pp. 1405-1409
Author(s):  
Na Deng ◽  
Huai En Li
Keyword(s):  
Empirical Model ◽  
Reduction Rate ◽  
Trapping Efficiency ◽  
Inflow Rate ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Vegetative Filter Strip ◽  
Content Factor ◽  
Northwest Region

Vegetative filter strip (VFS) is be defined as areas of vegetation designed to remove sediment and other pollutants from surface runoff. Many factors affect the effectiveness of VFS. So the quantitative analysis on relation between effectiveness and influencing factors had been conducted based on the plot experiment data in this paper. Result reveals that the order in impact degree of its factors is: inflow rate factor > width factor > vegetation condition > pollutants concentration in inflow > initial soil water content factor, the relation equation of purification effect and VFS width is the form of logarithm, and the relation equation of concentration reduction rate and inflow rate is the form of power function. Furthermore, a simple empirical model had been developed to predict sediment trapping efficiency in allusion to Chinese northwest region, which can provide computational basis for design of VFS in northwest region and other similar areas.

Vegetative filter strips-Effect of vegetation type and shape of strip on run-off and sediment trapping

2018 ◽  
Vol 29 (11) ◽  
pp. 3917-3927 ◽  
Author(s):  
Daili Pan ◽  
Xiaodong Gao ◽  
Juan Wang ◽  
Min Yang ◽  
Pute Wu ◽  
...  
Keyword(s):  
Vegetation Type ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Run Off

Dynamics of runoff and sediment trapping performance of vegetative filter strips: Run-on experiments and modeling

2017 ◽  
Vol 593-594 ◽  
pp. 54-64 ◽  
Author(s):  
Daili Pan ◽  
Xiaodong Gao ◽  
Miles Dyck ◽  
Yaqian Song ◽  
Pute Wu ◽  
...  
Keyword(s):  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Trapping Performance

Effect of vegetative filter strips on herbicide runoff under various types of rainfall

Chemosphere ◽  
2012 ◽  
Vol 88 (1) ◽  
pp. 113-119 ◽  
Author(s):  
Stefan Otto ◽  
Alessandra Cardinali ◽  
Ester Marotta ◽  
Cristina Paradisi ◽  
Giuseppe Zanin
Keyword(s):  
Vegetative Filter Strips ◽  
Filter Strips
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