The Impact of Extreme Weather on Land Degradation and Conservation

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land–Climate Interactions".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4293

Special Issue Editors

Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: agricultural soil; agrohydrology; soil conservation; groundwater pollution; irrigation management
Special Issues, Collections and Topics in MDPI journals
Faculty of Civil and Geodetic Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: hydrology; sediment transport; soil erosion; rainfall; runoff; modelling; engineering applications; floods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land degradation, particularly soil erosion, is a major environmental problem involving the degradation of topsoil and a reduced ability of soil to provide ecosystem services. Several forms of land degradation have been recorded worldwide, from mountainous regions to floodplains. In recent years, floods and droughts have become major drivers of this phenomenon due to their increasing frequency, highly erosive runoff and the changing nature of sediments, which now are now contaminated by an abundance of pollutants (e.g., metals, plastics, organic compounds) from upstream watersheds. Studies on land degradation processes are critical for evaluating the success of prevention efforts and subsequent planning of control and prevention measures to ensure land degradation neutrality targets. This Special Issue will present priority actions and issues for sustainable land management and soil erosion control strategies.

This Special Issue is aimed at gathering contributions in the form of case studies and review studies on methods and modelling applications for land degradation reduction and soil conservation in the context of extreme weather events, such as floods and droughts.

Topics of interest include:

  • Soil conservation in agriculture;
  • Integrating soil erosion prevention and flood mitigation measures into system-based solutions;
  • Scaling soil erosion prevention and flood mitigation measures;
  • Societal approaches to land erosion prevention measures, practices of landowners, farmers and indigenous people;
  • Land degradation under climate change and adaptation measures;
  • Coupled nature-based solutions to protect against soil erosion and measures to mitigate flooding.

Dr. Vesna Zupanc
Dr. Nejc Bezak
Dr. Carla Ferreira
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soil degradation
  • soil conservation
  • agricultural land
  • climate change
  • weather extremes
  • land degradation

Published Papers (2 papers)

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Research

19 pages, 4265 KiB  
Article
Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya
by Kennedy Were, Syphyline Kebeney, Harrison Churu, James Mumo Mutio, Ruth Njoroge, Denis Mugaa, Boniface Alkamoi, Wilson Ng’etich and Bal Ram Singh
Land 2023, 12(4), 890; https://doi.org/10.3390/land12040890 - 15 Apr 2023
Cited by 3 | Viewed by 1492
Abstract
This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) [...] Read more.
This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) in a Kenyan semi-arid landscape. A total of 431 geo-referenced gully erosion points were gathered through a field survey and visual interpretation of high-resolution satellite imagery on Google Earth, while 24 raster-based GECFs were retrieved from the existing geodatabases for spatial modeling and prediction. The resultant models exhibited excellent performance, although the machine learners outperformed the benchmark LR technique. Specifically, the RF and BRT models returned the highest area under the receiver operating characteristic curve (AUC = 0.89 each) and overall accuracy (OA = 80.2%; 79.7%, respectively), followed by the SVM and LR models (AUC = 0.86; 0.85 & OA = 79.1%; 79.6%, respectively). In addition, the importance of the GECFs varied among the models. The best-performing RF model ranked the distance to a stream, drainage density and valley depth as the three most important GECFs in the region. The output gully erosion susceptibility maps can support the efficient allocation of resources for sustainable land management in the area. Full article
(This article belongs to the Special Issue The Impact of Extreme Weather on Land Degradation and Conservation)
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26 pages, 37954 KiB  
Article
Shifting Sands: Assessing Bankline Shift Using an Automated Approach in the Jia Bharali River, India
by Jatan Debnath, Dhrubajyoti Sahariah, Anup Saikia, Gowhar Meraj, Nityaranjan Nath, Durlov Lahon, Wajahat Annayat, Pankaj Kumar, Kesar Chand, Suraj Kumar Singh and Shruti Kanga
Land 2023, 12(3), 703; https://doi.org/10.3390/land12030703 - 17 Mar 2023
Cited by 10 | Viewed by 1935
Abstract
Bank erosion hazard is a frequent occurrence that poses threats to floodplain ecosystems. This analysis examined changes to the Jia Bharali River channel in India using the GIS-based Digital Shoreline Analysis System [DSAS]. The Jia Bharali’s future channel was predicted so as to [...] Read more.
Bank erosion hazard is a frequent occurrence that poses threats to floodplain ecosystems. This analysis examined changes to the Jia Bharali River channel in India using the GIS-based Digital Shoreline Analysis System [DSAS]. The Jia Bharali’s future channel was predicted so as to identify the most erosion-susceptible zones. The rate of bankline movement was calculated using remotely sensed data collected over a period of 45 years (1976–2021). The results show that the river’s erosion and deposition rates were higher in the early years than towards the later part of the period under analysis. On the right and left banks of the river, the average shift rate was −9.22 and 5.8 m/y, respectively, which is comparatively high. The chosen portion of the river was evenly divided into three zones, A, B, and C. The most positively affected zone was zone A. The left bank of zone B exhibited a higher rate of erosion than the right bank, indicating that the river was moving to the left [eastward] in this zone. At the same time, the right bank was being eroded faster than the left, indicating a westward thrust at zone C. The predicted result demonstrates that the left bank of zone B and the right bank of zone C would have a higher average migration rate. Therefore, these banks were identified as being the most susceptible to bank erosion. The study evaluates the spatio-temporal change of the river in sensitive regions where neighboring settlements and infrastructure were at risk of changing channel dynamics. Using the actual and forecasted bankline, the degree of accuracy was confirmed. The results of the automated prediction approach could be useful for river hazard management in the Jia Bharali and in similar environmental settings with tropical high precipitation zones. Full article
(This article belongs to the Special Issue The Impact of Extreme Weather on Land Degradation and Conservation)
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