Riparian erosion vulnerability model based on environmental features.

J Environ Manage

USDA-ARS Grazinglands Research Laboratory, Research Hydrologist, 7207 W. Cheyenne Street, El Reno, OK 73036, USA. Electronic address:

Published: December 2017

Riparian erosion is one of the major causes of sediment and contaminant load to streams, degradation of riparian wildlife habitats, and land loss hazards. Land and soil management practices are implemented as conservation and restoration measures to mitigate the environmental problems brought about by riparian erosion. This, however, requires the identification of vulnerable areas to soil erosion. Because of the complex interactions between the different mechanisms that govern soil erosion and the inherent uncertainties involved in quantifying these processes, assessing erosion vulnerability at the watershed scale is challenging. The main objective of this study was to develop a methodology to identify areas along the riparian zone that are susceptible to erosion. The methodology was developed by integrating the physically-based watershed model MIKE-SHE, to simulate water movement, and a habitat suitability model, MaxEnt, to quantify the probability of presences of elevation changes (i.e., erosion) across the watershed. The presences of elevation changes were estimated based on two LiDAR-based elevation datasets taken in 2009 and 2012. The changes in elevation were grouped into four categories: low (0.5 - 0.7 m), medium (0.7 - 1.0 m), high (1.0 - 1.7 m) and very high (1.7 - 5.9 m), considering each category as a studied "species". The categories' locations were then used as "species location" map in MaxEnt. The environmental features used as constraints to the presence of erosion were land cover, soil, stream power index, overland flow, lateral inflow, and discharge. The modeling framework was evaluated in the Fort Cobb Reservoir Experimental watershed in southcentral Oklahoma. Results showed that the most vulnerable areas for erosion were located at the upper riparian zones of the Cobb and Lake sub-watersheds. The main waterways of these sub-watersheds were also found to be prone to streambank erosion. Approximatively 80% of the riparian zone (streambank included) has up to 30% probability to experience erosion greater than 1.0 m. By being able to identify the most vulnerable areas for stream and riparian sediment mobilization, conservation and management practices can be focused on areas needing the most attention and resources.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jenvman.2017.02.045DOI Listing

Publication Analysis

Top Keywords

riparian erosion
12
vulnerable areas
12
erosion
11
riparian
8
erosion vulnerability
8
environmental features
8
management practices
8
soil erosion
8
riparian zone
8
presences elevation
8

Similar Publications

elevated concentrations of soil-bound heavy metals and magnetic particles in a typical urban plateau lake wetland, China.

Heliyon

January 2025

Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region, Collaborative Innovation Center for Mountain Ecology & Agro-Bioengineering, College of Life Sciences, Guizhou University, Guiyang, 550025, China.

Vegetation change significantly altered the hydrological processes and soil erosion within riparian ecosystems. It is unclear how change in managed vegetation types affect the geochemical behavior of heavy metals (HMs) and magnetic particles in karst riparian areas. Two soil depths of 0-20 cm and 20-40 cm were taken in alien species (), native species and in a typical urban plateau Lake wetland, Caohai lake, China.

View Article and Find Full Text PDF

In fluvial environments, the shifting of river channels and bank erosion are frequently caused by both natural and anthropogenic factors. Riverine hazards like bank erosion and course alterations offer severe issues to the riparian villages along the lower basin of the Tista River in India, which substantially influence the livelihoods of inhabitants living there. This research addressed river channel shifting tendency and identified major bank erosion-prone villages along the lower course of the Tista River and challenges to the livelihoods of the riparian people.

View Article and Find Full Text PDF

Optimal allocation of technical reclamation and ecological restoration for a cost-effective solution in Pingshuo Opencast Coal Mine area of China.

J Environ Manage

January 2025

School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, 100083, Beijing, People's Republic of China.

Limiting adverse consequences of mining activities requires ecosystem restoration efforts, whose arrangement around mining areas is poorly designed. It is unclear, however, where best to locate ecological projects to enhance ecosystem services cost-effectively. To answer this question, we conducted an optimized ecological restoration project planning by the Resource Investment Optimization System (RIOS) model to identify the restoration priority areas in the Pingshuo Opencast Coal Mine region in Shanxi Province.

View Article and Find Full Text PDF

Long term conservation practice effects on agricultural soil loss from concentrated and distributed sources.

J Environ Manage

December 2024

National Sedimentation Laboratory, USDA-Agricultural Research Service, MS, USA.

Conservation practices have been recognized as an important mitigation tool to reduce soil loss and sediment transport from agricultural fields. Multiple conservation structures and farming practices have been proposed to target erosional processes with varying results of sediment trapping efficiency. The quantification of their performance at the watershed scale when multiple integrated and spatiotemporal varying processes occur, remains a challenge.

View Article and Find Full Text PDF

This study aimed to explore the relationship between land use landscape pattern and water quality in the upstream of the Gansu water conservation, water and soil erosion, and ecological fragile areas. Based on the land use data and water quality monitoring section in 2020 in the 200 m, 500 m, 1 km, 2 km, 50 km, and 10 km riparian buffer area, the single-factor index evaluation method, random forest regression model, and BP neural network were used to quantify the response relationship between land use and landscape pattern of the upper Yellow River in Gansu province and water quality index and to carry out the basin water quality prediction based on land use landscape index data. The results showed that: ① through the single-factor index method, the major indicators of the total nitrogen (TN) in July and September, dissolved oxygen (DO), permanganate index, ammonia nitrogen (NH -N), total phosphorus (TP), and other surface indexes met the surface water environment class Ⅲ water quality standard.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!