Soil-Water Conservation, Erosion, and Landslide

Edited by
March 2022
392 pages
  • ISBN978-3-0365-3432-9 (Hardback)
  • ISBN978-3-0365-3431-2 (PDF)

This book is a reprint of the Special Issue Soil–Water Conservation, Erosion, and Landslide that was published in

Biology & Life Sciences
Chemistry & Materials Science
Environmental & Earth Sciences
Public Health & Healthcare

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
landslide; image classification; spectrum similarity analysis; extreme rainfall-induced landslide susceptibility model; landslide ratio-based logistic regression; landslide evolution; Typhoon Morakot; Taiwan; vegetation community; vegetation importance value; root system; soil erosion; grey correlation analysis; soil erosion; sediment yield; RUSLE; Lancang–Mekong River basin; landslide; rainfall threshold; landslide probability model; Taiwan; debris flow; Zechawa Gully; mitigation countermeasures; Jiuzhaigou Valley; water erosion; susceptibility; Gaussian process; climate change; radial basis function kernel; weighted subspace random forest; extreme events; extreme weather; naive Bayes; feature selection; machine learning; hydrologic model; simulated annealing; earth system science; climate change; soil erosion; sediment yield; PSED Model; loess; ICU; static liquefaction; mechanical behavior; pore structure; alpine swamp meadow; alpine meadow; degradation of riparian vegetation; root distribution; tensile strength; tensile crack; soil management; land cover changes; Syria; soil erosion; hillslopes; soil erosion; gully erosion; vegetation restoration; soil erodibility; land use; bridge pier; overfall; scour; landform change impact on pier; shallow water equations; wet-dry front; outburst flood; TVD-scheme; MUSCL-Hancock method; laboratory model test; extreme rainfall; rill erosion; shallow landslides; deep lip surface; safety factor; rainfall erosivity factor; USLE R; machine learning; Deep Neural Network; tree ring; dendrogeomorphology; landslide; landslide activity; deciduous broadleaved tree; Shirakami Mountains; landslide evolution; spatiotemporal cluster analysis; landslide hotspots; dam breach; seepage; overtopping; seismic signal; flume test; breach model; n/a