New Challenges in Rainfall Erosion

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 995

Special Issue Editors

Department of Applied Physics, University of Leon, Leon, Spain
Interests: rainfall characterization; measurements of rainfall; rainfall simulators; disdrometers; splash erosion; karstification; impacts of water on construction; fluid dynamics engineering; erosion; weather types
Special Issues, Collections and Topics in MDPI journals
School of Soil and Water Conservation, Jixian National Forest Ecosystem Observation and Research Station, CNERN, Beijing Forestry University, Beijing, China
Interests: soil and water conservation; surface runoff; watershed management; water erosion; rainfall
Special Issues, Collections and Topics in MDPI journals
Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
Interests: land management; natural hazards; rainfall simulators; soil erosion; soil conservation

Special Issue Information

Dear Colleagues,

Rainfall erosion is one of the most damaging factors in agricultural lands, as well as one of the most important concerns in fire areas. The erosive potential of rainfall can lead to loss of fertility of soils that are limited resources due to loss of surface area but also causes erosion damage to other vulnerable surfaces, such as weathered stone heritage, or forest roads and tracks or concrete structures. In addition, the movement of aggregates can lead to increased soil compaction and other phenomena such as flooding, decreased infiltration into the ground, and significant topographic changes. New tools such as more realistic rainfall simulators, the use of drones, satellite or lidar analysis, and new methodologies such as the measurement of the distance between certain parts of the vine and the soil can help us understand how we have evolved and what remains to be developed in order to contribute to the protection of vulnerable surfaces against water. In this Special Issue, we look forward to contributions with innovative methodologies that shed light on how to fight against water erosion.

Dr. María Fernández-Raga
Dr. Yang Yu
Dr. Ataollah Kavian
Guest Editors

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Keywords

  • rainfall erosion
  • splash erosion
  • agriculture soil loss
  • erosion of heritage
  • erosion in burnt areas
  • measurement of erosion
  • erosion in roads

Published Papers (1 paper)

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Research

20 pages, 5171 KiB  
Article
A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network
Water 2023, 15(20), 3559; https://doi.org/10.3390/w15203559 - 12 Oct 2023
Viewed by 560
Abstract
Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity [...] Read more.
Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest. Full article
(This article belongs to the Special Issue New Challenges in Rainfall Erosion)
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