Adapting the WEPP Hillslope Model and the TLS Technology to Predict Unpaved Road Soil Erosion

Wang, Yi; He, Wei; Zhang, Ting; Zhang, Yani; Cao, Longxi

doi:10.3390/ijerph19159213

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…However, it still follows the basic framework of USLE, and it is still an empirical statistical model and only applicable to plain and gently sloping areas. In addition, there is the Water Erosion Prediction Project (WEPP) [23], and the Revised Wind Erosion Equation (RWEQ) [24]. However, these models are mostly derived from specific scientific problems and may be valid for specific situations [6].…”

Section: Related Workmentioning

confidence: 99%

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Liu

et al. 2023

IJERPH

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The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

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Section: Related Workmentioning

confidence: 99%

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Liu

et al. 2023

IJERPH

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“…Human activities were used as the input data to describe the wind erosion process separately [49], and quantitatively evaluating the wind soil loss of the site and surrounding areas caused by residences and roads sheds light on spatial wind erosion modelling. Residences and roads can be included in such models as parameters that affect wind erosion in future concerns, similar to the water erosion prediction project for forest roads [50]. For example, the WEPP: Road model developed by the US Forest Service combines terrain and soil databases with GIS technology to provide a specific web-based interface for assessing forest road erosion at the watershed scale [50].…”

Section: The Significance Of Considering Residences and Roads In Wind...mentioning

confidence: 99%

“…Residences and roads can be included in such models as parameters that affect wind erosion in future concerns, similar to the water erosion prediction project for forest roads [50]. For example, the WEPP: Road model developed by the US Forest Service combines terrain and soil databases with GIS technology to provide a specific web-based interface for assessing forest road erosion at the watershed scale [50]. The landscape scale in wind erosion is similar to the watershed scale in water erosion, and like the WEPP: Road, erosion of residences and roads can be considered in wind erosion modelling, so that the impact of human pathways on wind erosion can be reasonably evaluated.…”

Section: The Significance Of Considering Residences and Roads In Wind...mentioning

confidence: 99%

The Impact of Residences and Roads on Wind Erosion in a Temperate Grassland Ecosystem: A Spatially Oriented Perspective

Zhou

Zhang

et al. 2022

IJERPH

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The existence of residences and roads is an important way in which human activity affects wind erosion in arid and semiarid environments. Studies assessing the impact of these elements on wind erosion have only focused on limited plots, and their threat of erosion to the surrounding environment has been ignored by many studies. This study was based on spatially overlayed analysis of independent wind erosion distribution simulated by the revised wind erosion equation (RWEQ) and remote-sensing-image-derived residence and road distribution data. Wind erosion at different distances from residences and roads was quantified at the landscape scale of a typical temperate grassland ecosystem, explicitly demonstrating the crucial impacts of both elements on wind erosion. The results showed that wind erosion weakened as the distance from residences and roads increased due to the priority pathways of human activities, and the wind erosion around the residence was more severe than around the road. Human activities in the buffer zones 0–200 m from the residences most frequently caused severe wind erosion, with a wind soil loss of 25 t ha−1 yr−1 and a wind soil loss of approximately 5.25 t ha−1 yr−1 for 0–60 m from the roads. The characteristics of wind erosion variation in the buffer zones were also affected by residence size and the environments in which the residences were located. The variation in wind erosion was closely related to the road levels. Human activities intensified wind erosion mainly by affecting the soil and vegetation around residences and roads. Ecological management should not be limited to residences and roads but should also protect the surrounding environments. The findings of this study are aimed towards a spatial perspective that can help implement rational and effective environmental management measures for the sustainability of wind-eroded ecosystems.

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“…For decades, research on morphosedimentary changes in channels was restricted to descriptive observations in situ or interpretations of aerial photographs from different dates; however, this methodology has been largely superseded by techniques that fall into two general categories: direct-measurement field techniques, using erosion and deposition meters, and remote sensing or non-contact techniques such as Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) [11,12]. There is extensive literature focusing on the use of each of these methods to detect and quantify geomorphic change at a variety of scales and settings [13][14][15][16][17][18][19], including morphological adjustments in dry channels [4,[20][21][22][23][24]. The multi-temporal application of high-resolution digital terrain models (HRDTMs) (pixel size < 5 cm), generated using SfM-MVS from UAV (unmanned aerial vehicles) and 3D point clouds (3DPC) (<3 cm per pixel) from TLS, offers higher performance to detect spatial differences in bed elevation and surface bed texture caused by specific events [4,5,25].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Retrospective Morphological Channel Adjustments Using High-Resolution Differential Digital Elevation Models versus Predicted Sediment Delivery and Stream Power Variations

Conesa-García

Martínez-Salvador

Puig-Mengual

et al. 2023

Water

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This work proposes a methodological approach applied to ephemeral gravel-bed streams to verify the change in the magnitude and frequency of hydrological events affecting the morphological dynamics and sediment budget in this type of channel. For the case study, the Azohía Rambla, located in southeastern Spain, was chosen, emphasizing the research on two reference riverbed sections (RCRs): an upper one, with a predominance of erosion, and a middle one, where processes of incision, transport, and deposition were involved. First, this approach focuses on relationships between peak discharges and sediment budgets during the period 2018–2022. For this purpose, water level measurements from pressure sensors, a One-Dimensional Hydrodynamic model, and findings from comparative analyses of high-resolution differential digital elevation models (HRDEM of Difference-HRDoD) based on SfM-MVS and LiDAR datasets were used. In a second phase, the GeoWEPP model was applied to the period 1996–2022 in order to simulate runoff and sediment yield at the event scale for the watersheds draining into both RCRs. During the calibration phase, a sensitivity analysis was carried out to detect the most influential parameters in the model and confirm its capacity to simulate peak flow and sediment delivery in the area described above. Values of NS (Nash–Sutcliffe efficiency) and PBIAS (percent bias) equal to 0.86 and 7.81%, respectively, were found in the calibration period, while these indices were 0.81 and −4.1% in the validation period. Finally, different event class patterns (ECPs) were established for the monitoring period (2018–2022), according to flow stage and morphological channel adjustments (overtopping, bankfull and sub-bankfull, and half-sub-bankfull), and then retrospectively extrapolated to stages of the prior simulated period (1996–2018) from their typical sequences (PECPs). The results revealed a significant increase in the number of events and PECPs leading to lower bed incision rates and higher vertical accretion, which denotes a progressive increase in bed armoring and bank erosion processes.

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Adapting the WEPP Hillslope Model and the TLS Technology to Predict Unpaved Road Soil Erosion

Cited by 5 publications

References 37 publications

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

The Impact of Residences and Roads on Wind Erosion in a Temperate Grassland Ecosystem: A Spatially Oriented Perspective

Simulation of Retrospective Morphological Channel Adjustments Using High-Resolution Differential Digital Elevation Models versus Predicted Sediment Delivery and Stream Power Variations

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