Land-water Boundary Research Articles

LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2

Abstract. The transport mechanisms of floating marine debris in coastal zones remain poorly understood due to complex geometries and the influence of coastal processes, posing difficulties in incorporating them into Lagrangian numerical models. The numerical model LOCATE overcomes these challenges by coupling Eulerian hydrodynamic data at varying resolutions within nested grids using Parcels, a Lagrangian particle solver, to accurately simulate the motion of plastic particles where a high spatial coverage and resolution are required to resolve coastal processes. Nested grids performed better than a coarse-resolution grid when analysing the model's dispersion skill by comparing drifter data and simulated trajectories. A sensitivity analysis of different beaching conditions comparing spatiotemporal beaching patterns demonstrated notable differences in the land–water boundary detection between nested hydrodynamic grids and high-resolution shoreline data. The latter formed the basis for a beaching module that parameterised beaching by calculating the particle distance to the shore during the simulation. A realistic debris discharge scenario comparison around the Barcelona coastline using the distance-based beaching module in conjunction with nested grids or a coarse-resolution grid revealed very high levels of particle beaching (>91.5%) in each case, demonstrating the importance of appropriately parameterising beaching at coastal scales. In this scenario, high variability in particle residence times and beaching patterns was observed between simulations. These differences derived from how each option resolved the shoreline, with particle residence times being much higher in areas of intricate shoreline configurations when using nested grids, thus resolving complex structures that were undetectable using the coarse-resolution grid. LOCATE can effectively integrate high-resolution hydrodynamic data within nested grids to model the dispersion and deposition patterns of particles at coastal scales using high-resolution shoreline data for shoreline detection uniformity.

Spatio-temporal analysis of shoreline changes and future forecast using remote sensing, GIS and kalman filter model: A case study of Rio de Janeiro, Brazil

Shoreline changes embrace alterations due to the physical features and location of the land-water boundary, which can arise from natural or human causes. Natural processes, like sea level fluctuations, erosion, and sediment deposition, impact coasts. Human activities also significantly influence shoreline changes. The study area's shoreline extends 59.80 km from Reduto de Sao Teodosio to Barra de Guaratiba. The study analyzed shoreline changes along a 60 km stretch of the southern coast of Rio de Janeiro (Reduto de Sao Teodosio to Barra de Guaratiba) from 1986 to 2018. This study examines historical shoreline positions, calculating change rates and predicting 2032 and 2042 positions using satellite imagery spanning 1972–2022. The historical shoreline was reconstructed using earlier satellite data. Digital Shoreline Analysis System (DSAS) tools (EPR, LRR, NSM) in ArcMap were then used to quantify change rates statistically. The EPR model reports a maximum annual accretion rate of 19.33 m and a maximum erosion rate of −4.91 m, while the LRR model indicates a maximum accretion rate of 21.31 m and a maximum erosion rate of −5.04 m. In contrast, the NSM model calculates a notably higher maximum accretion rate of 945.96 m and a much more substantial maximum erosion rate of −240.34 m. These findings informed the mapping of anticipated 2032 and 2042 shorelines, helping identify areas of gain and loss, highlighting the vulnerability of certain regions. It's important to note that human activities, such as alterations in beach topography, can influence these models. Protective measures, like those in Rio de Janeiro, Barra Da Tijuca, and Recreio Dos Bandeirantes, may affect erosion and accretion patterns. Additionally, coastal development activities, including seawall dredging, can disrupt natural processes. Effective management of shoreline changes is essential to safeguard ecosystems and communities, with strategies encompassing beach nourishment, vegetation restoration, and sustainable coastal development. Resilient, adaptive coastal management balances human needs and environmental protection amid ongoing changes.

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide.

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

Landfast ice is an integral component of the coastal ecosystem. Extracting the edge and mapping the extent of landfast ice are one of the main methods for studying ice changes. In this work, a standardized process for extracting landfast ice edge in the Baltic Sea using the InSAR coherence image is established with Sentinel-1 radar data and InSAR technology. A modified approach combining multiscale segmentation and morphological erosion is then proposed to provide a reliable way to extract landfast ice edge. Firstly, the coherence image is obtained using InSAR technology. Then, the edge is separated and extracted with the modified approach. The modified approach is essentially a four-step procedure involving image segmentation, median filter, morphological erosion, and rejection of small patches. Finally, the full extent of landfast ice can be obtained using floodfill algorithm. Multiple InSAR image pairs of Sentinel-1A acquired from 2018 to 2019 are utilized to successfully extract the landfast ice edge in the Gulf of Bothnia. The results show that the landfast ice edge and the extents obtained by the proposed approach are visually consistent with those shown in the ice chart issued by the Swedish Meteorological and Hydrological Institute (SMHI) over a coastline length of 345 km. The mean distance between land–water boundary and the coastline issued by the National Oceanic and Atmospheric Administration (NOAA) is 109.1 m. The modified approach obviously preserves more details in local edge than the reference method. The experimental results show that the modified approach proposed in this paper can extract the edge and map the extent of landfast ice more accurately and quickly, and is therefore expected to contribute to the further understanding and analyzing the changes of landfast ice in the future.

Technical and Social Approaches to Study Shoreline Change of Kuakata, Bangladesh

In recent years, shoreline determination has become an issue of increasing importance and concern, especially at the local level, as sea level continues to rise. This study identifies the rates of absolute and net erosion, accretion, and shoreline stabilization along the coast of Kuakata, a vulnerable coastal region in south-central Bangladesh. Shoreline change was detected by applying remote sensing and geographic information system (RS-GIS)-based techniques by using Landsat Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) satellite images at 30-m resolution from 1989, 2003, 2010, and 2020. The band combination (BC) method was used to extract the shoreline (i.e., land-water boundary) due to its improved accuracy over other methods for matching with the existing shoreline position. This study also used participatory rural appraisal (PRA) tools which revealed the societal impacts caused by the shoreline changes. Coupling RS-GIS and PRA techniques provides an enhanced understanding of shoreline change and its impacts because PRA enriches the RS-GIS outcomes by contextualizing the findings. Results show that from 1989 to 2020, a total of 13.59 km2 of coastal land was eroded, and 3.27 km2 of land was accreted, suggesting that land is retreating at about 0.32 km2 yr–1. Results from the PRA tools support this finding and demonstrate that fisheries and tourism are affected by the shoreline change. These results are important in Kuakata, a major tourist spot in Bangladesh, because of the impacts on fisheries, recreation, resource extraction, land use planning, and coastal risk management.

Mapping inter- and intra-annual dynamics in water surface area of the Tonle Sap Lake with Landsat time-series and water level data

Surface water monitoring is the foundation of water resources management. However, large-scale surface water monitoring based on long-sequence Landsat images is easily affected by cloud cover. In this study, taking the Tonle Sap Lake in the lower Mekong as an example, a series of water-land boundaries with height attributes based on water level data were extracted from multi-temporal Landsat images without cloud cover using the optimal water index and the lakeshore elevation model (LEM) of Tonle Sap Lake was constructed. On this basis, with the help of the limited land-water boundary provided by the Landsat image over a longer period, long-term monitoring of the changes in the water area of the entire lake area was realized. The results show that the Landsat image data in conjunction with the LEM can effectively remove the effect of cloud cover in monitoring the change of long time series water bodies. Reconstruction of historical data on changes in the waters of Tonle Sap Lake with the proposed method shows that in the past 30 years, there has been no significant change in the water areas of Tonle Sap Lake in either the dry or rainy seasons. Water area changes of the lake have a delayed response of 1-3 months to changes in the water level of the Mekong River, which is more affected by the mainstream recharge. The water area of the lake is unaffected in the dry season; however, it is weakly affected in the rainy season by the Xiaowan and Nuozhadu hydropower stations in China.

Photogrammetry and image processing techniques for beach monitoring

<p>The land-water boundary varies according to the sea level and the shape of a beach profile that is continuously modelled by incident waves. Attempting to model the response of a landscape as geomorphologically volatile as beaches requires multiple precise measurements to recognize responses to the actions of various geomorphic agents. It is therefore essential to have monitoring systems capable of systematically recording the shoreline accurately and effectively. New methods and tools are required to efficiently capture, characterize, and analyze information – and so obtain geomorphologically significant indicators. This is the aim of the doctoral thesis, focusing on the development of tools and procedures for coastal monitoring using satellite images and terrestrial photographs. The work brings satellite image processing and photogrammetric solutions to scientists, engineers, and coastal managers by providing results that demonstrate the usefulness of these viable and lowcost techniques. Existing and freely accessible public information (satellite images, video-derived data, or crowdsourced photographs) can be converted into high quality data for monitoring morphological changes on beaches and thus help achieve a sustainable management of coastal resources.</p>

Lines in the Mud: Tank Eco-restoration and Boundary Contestations in Chennai

This article analyses the politics of environmentalism revealed in struggles over the land–water boundary of an urbanising tank in Chennai. In contesting this boundary, property-less settlers on its banks called into question the tank’s ‘nature’ and functions in its urban milieu, and demanded a redrawing of boundaries to reflect the socio-natural transformations that had turned parts of it into land. Simultaneously, propertied residents, in concert with state eco-restoration schemes and court rulings, fought to restore the tank to its ‘original’ dimensions. In foregrounding the liminalities of the urbanising tank, this article suggests the limits of the contemporary property-determined eco-restoration discourse, premised on a return to pristine pasts and original boundaries. It proposes that the stand-off between water body restoration and the defence of working-class housing rights necessitates recognising the tank as an artefact assembled over time by socio-technical and natural processes.

Accuracy Assessment and Normalisation of Water Spread Area Estimate from Multi-sensor Satellite Data

Water spread area is one of the few parameters that can be easily derived using multispectral satellite data. However, due to limitation in land–water boundary pixel delineation and availability of area estimate from simultaneous images of different spatial resolutions, it poses serious challenges for accurate estimation of water spread area. The area estimated from data of different resolutions is not the same owing to many factors like shape and size of the water body and the efficiency of the approaches to delineate land–water boundary pixel. In addition to this, multiple coverage of same water body from sensors of different resolutions will lead to varying water spread area estimates. In order to assess the effect of these factors on water spread area estimate, a study was undertaken to assess the accuracy of water spread area estimate using the field-based measurement and to establish relationship for normalisation of water spread area estimate from different resolution sensors. In this study, water boundary was traced by collecting continuous measurements of location with the help of satellite-based positioning system. The study showed that water spread area estimated through linear spectral unmixing of images from LISS IV, LISS III, AWiFS and MSI sensors resulted in better estimate compared to the index-based method. The error in estimate was less than 5% in all the cases except for AWiFS-based estimate for the smaller water body. The study also concluded that either simple linear regression equation or multiple linear regression equation can be used to normalise the water spread area estimated from different sensors.

Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

The applicability of optical satellite data to quantify coastal erosion across the Arctic is limited due to frequent cloud cover. Synthetic Aperture Radar (SAR) may provide an alternative. The interpretation of SAR data for coastal erosion monitoring in Arctic regions is, however, challenging due to issues of viewing geometry, ambiguities in scattering behaviour and inconsistencies in acquisition strategies. In order to assess SAR applicability, we have investigated data acquired at three different wavelengths (X-, C-, L-band; TerraSAR-X,Sentinel-1,ALOS PALSAR 1/2). In a first step we developed a pre-processing workflow which considers viewing geometry issues (shoreline orientation, incidence angle relationships with respect to different landcover types). We distinguish between areas with foreshortening along cliffs facing the sensor, radar shadow along cliffs facing away and traditional land-water boundary discrimination. Results are compared to retrievals from Landsat trends. Four regions which feature high erosion rates have been selected. All three wavelengths have been investigated for Kay Point (Canadian Beaufort Sea Coast). C- and L-band have been studied at all sites, including also Herschel Island (Canadian Beaufort Sea Coast), Varandai (Barents Sea Coast, Russia), and Bykovsky Peninsula (Laptev Sea coast, Russia). Erosion rates have been derived for a one-year period (2017-2018) and in case of L-band also over 11 years (2007-2018). Results indicate applicability of all wavelengths, but acquisitions need to be selected with care to deal with potential ambiguities in scattering behaviour. Furthermore, incidence angle dependencies need to be considered for discrimination of the land-water boundary in case of L- and C-band. However, L-band has the lowest sensitivity to wave action and relevant future missions are expected to be of value for coastal erosion monitoring. The utilization of trends derived from Landsat is also promising for efficient long-term trend retrieval. The high spatial resolution of TerraSAR-X staring spot light mode (<1m) also allows the use of radar shadow for cliff-top monitoring in all seasons. Derived retreat rates agree with rates available from other data sources, but the applicability for automatic retrieval is partially limited. The derived rates suggest an increase of erosion at all four sites in recent years, but uncertainties are also high.

Macrohabitat studies in large Brazilian floodplains to support sustainable development in the face of climate change

Large floodplains are complex ecosystems at the land–water boundary. Because of rainy and dry seasons, most tropical floodplains are subjected to annual water-level fluctuations. During typical low-water periods, extensive areas of the floodplains are dry. However, relatively small geomorphological differences within the boundaries of the floodplains result in comparatively large differences in the length and depth of inundation and, in turn, the establishment of distinct plant communities, called macrohabitats. The specific ecological conditions of these macrohabitats have to be taken into consideration in the development of sustainable management methods and protection measures. Extreme floods and droughts have very strong effects on flora and fauna. The low hydrological buffer capacity of floodplains makes them vulnerable to anthropogenic changes in hydrology and to the impacts of global climate change. This study presents a macrohabitat classification system that has been applied to three large Brazilian floodplains: the forested Amazonian várzeas and igapós, and the savanna floodplains of the upper Paraguai River (Pantanal of Mato Grosso). Examples are provided to demonstrate the applicability of this system in comparative inter- and intra-wetland studies and therefore its utility in assessing environmental changes, including those arising from global climate change. Our approach can also be used to guide the implementation of sustainable management methods and as a focal point for the development of environmental legislation.

An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries

ABSTRACTThe United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

Shoreline is a very important element to identify exact boundary at the coastal areas of a country. However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming. Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors. The trend of extracting shoreline has been shifted from image processing to machine learning and data mining techniques. By using machine learning technique, the satellite images could be classified into land and water classes in order to extract shoreline. However, the result is meaningless if it has cloud and shadow on the water-land boundary. In this study, we compare the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image. A case study for this research is done alongside Tumpat beach, located at the Northeast Coast of Peninsular Malaysia. All the machine learning techniques have been tested on the three types of images. The experimental results show that classification using SVM on single multispectral image has the highest accuracy among all. However, the classified of fused image using SVM is considered much more accurate because it can cater the cloud and shadow problem. Additionally, the classification on 5 and 10 m fused images also tested and the result shows that with the increase of spatial resolution of fused image, the classification accuracy also increases.

Tide-Induced Geometrical Changes in Chilika Lagoon Using Remote Sensing

This article presents hourly data from four tide gauge locations in the Chilika lagoon vis-a-vis satellite data, to study the tidal influences on the geometrical changes in the lagoon. The satellite-derived highest area and perimeter are 864.76 km 2 and 1512 km respectively, consistent with the field-observed occurrences of the low and high tides. The geometrical changes of 5.4% (44.36 km 2 ) in area, and 24.8% (300.83 km) in the perimeter, in one month duration, solely because of the tides are considerable changes. High radiometric resolution of the sensor provided improved delineation of land-water boundary of the lagoon shores.

Shifting shoreline of Sagar Island Delta, India

Shoreline mapping is extremely important in order to determine the dynamic nature of coastal areas. This paper presents shoreline mapping of the Sagar Island delta, Sundarban region, India. The island is part of mangrove ecosystem and is facing constant erosion and deposition from tidal action and cyclonic storms which have made this an area of unique importance. Mapping of shoreline has been performed 1951 to 2011 and change in the land-water boundary of the island calculated. Further shoreline prediction is performed on the basis of the extracted shorelines using the End point Rate model with a micro-level grid-based approach. The predicted maps have been validated using ground control points. Three images from 1951, 1990 and 2011 have been used for the mapping and detection of changes in the island area and shoreline over 60 years.

Land–Water Boundary Treatment for a Tsunami Model With Dimensional Splitting

A numerical model for tsunami simulations Cliffs is exercised with the complete set of NTHMP-selected benchmark problems focused on inundation, such as simulating runup of a non-breaking solitary wave onto a sloping beach, runup on a conical island, a lab experiment with a scaled model of Monai Valley, and the 1993 Hokkiado tsunami and inundation of the Okushiri Island.

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision

A high precision geometric method for automated shoreline detection from Landsat TM and ETM+ imagery is presented. The methodology is based on the application of an algorithm that ensures accurate image geometric registration and the use of a new algorithm for sub-pixel shoreline extraction, both at the sub-pixel level. The analysis of the initial errors shows the influence that differences in reflectance of land cover types have over shoreline detection, allowing us to create a model to substantially reduce these errors. Three correction models were defined according to the type of gain used in the acquisition of the original Landsat images. Error assessment tests were applied on three artificially stabilised coastal segments that have a constant and well-defined land-water boundary. A testing set of 45 images (28 TM, 10 ETM high-gain and 7 ETM low-gain) was used. The mean error obtained in shoreline location ranges from 1.22 to 1.63m, and the RMSE from 4.69 to 5.47m. Since the errors follow a normal distribution, then the maximum error at a given probability can be estimated. The results confirm that the use of Landsat imagery for detection of instantaneous coastlines yields accuracy comparable to high-resolution techniques, showing the potential of Landsat TM and ETM images in those applications where the instantaneous lines are a good geomorphological descriptor.

Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image

Coastline identification is important for surveying and mapping reasons. Coastline serves as the basic point of reference and is used on nautical charts for navigation purposes. Its delineation has become crucial and more important in the wake of the many recent earthquakes and tsunamis resulting in complete change and redraw of some shorelines. In a tropical country like Malaysia, presence of cloud cover hinders the application of optical remote sensing data. In this study a semi-automated technique and procedures are presented for shoreline delineation from RADARSAT-1 image. A scene of RADARSAT-1 satellite image was processed using enhanced filtering technique to identify and extract the shoreline coast of Kuala Terengganu, Malaysia. RADSARSAT image has many advantages over the optical data because of its ability to penetrate cloud cover and its night sensing capabilities. At first, speckles were removed from the image by using Lee sigma filter which was used to reduce random noise and to enhance the image and discriminate the boundary between land and water. The results showed an accurate and improved extraction and delineation of the entire coastline of Kuala Terrenganu. The study demonstrated the reliability of the image averaging filter in reducing random noise over the sea surface especially near the shoreline. It enhanced land-water boundary differentiation, enabling better delineation of the shoreline. Overall, the developed techniques showed the potential of radar imagery for accurate shoreline mapping and will be useful for monitoring shoreline changes during high and low tides as well as shoreline erosion in a tropical country like Malaysia.

Optimized design of natural ecological wastewater treatment system based on water environment model of dynamic mesh technique

A Natural Ecological Wastewater Treatment System (NEWTS) is usually built on natural terrain with necessary topography modification to improve water flowing route and pattern, and then the topography modified NEWTS should also have a reasonable water storage volume and hydraulic retention time so as to achieve the anticipated water purification effect. In this study, the dynamic mesh technique based on the finite element method and element storativity coefficients was presented to develop a two-dimensional hydrodynamic and water quality model, which was used to optimize the design of NEWTS under the dynamic land-water boundary due to various water storage volume. The models were employed in the optimized design of NEWTS from a large abandoned coal mine, which purifies the polluted water flowing into a large water storage lake, as part of the East Route South-to-North Water Transfer Project in China. Specifically, the natural topography modification scheme was presented, and further, a reasonable water storage volume and hydraulic residence time was obtained, based on the reasonable estimation of roughness coefficient and pollutant removal rate of the NEWTS with phragmites communis.