A Review on the Possibilities and Challenges of Today’s Soil and Soil Surface Assessment Techniques in the Context of Process-Based Soil Erosion Models

Author:

Epple Lea,Kaiser Andreas,Schindewolf Marcus,Bienert Anne,Lenz JonasORCID,Eltner AnetteORCID

Abstract

To investigate relevant processes as well as to predict the possible impact of soil erosion, many soil erosion modelling tools have been developed. The most productive development of process-based models took place at the end of the 20th century. Since then, the methods available to observe and measure soil erosion features as well as methods to inter- and extrapolate such data have undergone rapid development, e.g., photogrammetry, light detection and ranging (LiDAR) and sediment tracing are now readily available methods, which can be applied by a broader community with lower effort. This review takes 13 process-based soil erosion models and different assessment techniques into account. It shows where and how such methods were already implemented in soil erosion modelling approaches. Several areas were found in which the models miss the capability to fully implement the information, which can be drawn from the now-available observation and data preparation methods. So far, most process-based models are not capable of implementing cross-scale erosional processes and can only in parts profit from the available resolution on a temporal and spatial scale. We conclude that the models’ process description, adaptability to scale, parameterization, and calibration need further development. The main challenge is to enhance the models, so they are able to simulate soil erosion processes as complex as they need to be. Thanks to the progress made in data acquisition techniques, achieving this aim is closer than ever, if models are able to reap the benefit.

Funder

Deutsche Forschungsgemeinschaft

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A review of soil erosion modeling in Nigeria using the Revised Universal Soil Loss Equation model;Agrosystems, Geosciences & Environment;2024-02-06

2. USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES;The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2023-12-13

3. A fully coupled superficial runoff and soil erosion basin scale model with efficient time stepping;Computers & Geosciences;2023-08

4. Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas;International Journal of Environmental Research and Public Health;2023-01-31

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