**Contents**



## **About the Editor**

**Csaba Centeri** is an Associate Professor at the Dept. of Nature Conservation and Landscape Management at the Szent Istvan Campus of the Hungarian University of Agriculture and Life ´ Sciences in God¨ oll ¨ o. He has published more than 18 papers in Q1 and Q2 journals and has served ˝ as a Chief Editor in the Hungarian Journal of Landscape Ecology, Guest Editor of Special Issues in the Journal of Water, Sustainability, and Remote Sensing. His main research interest is soil water erosion; soil erosion modelling; land-use change; ecosystem services (related to the other research interests); and soil–plant, soil–wildlife, and soil–animal interactions, with special emphasis on nature conservation related issues.

## *Editorial* **Soil Water Erosion**

**Csaba Centeri**

Department of Nature Conservation and Landscape Management, Institute for Wildlife Management and Nature Conservation, Szent István Campus, Hungarian University of Agriculture and Life Sciences, 2100 Gödöll˝o, Hungary; centeri.csaba@uni-mate.hu

**Abstract:** The purpose of this Special Issue is to provide an opportunity for researchers to publish novel results that could help landowners, land-users, farmers, politicians, and other representatives of our global society to protect, and, if possible, improve the quality and quantity of our precious soil resources. Authors were encouraged to submit papers related to new ways of mapping, showing more detailed input data, new modeling results, areas that had never been mapped before, etc. The Special Issue provides novel results on the state of soil water erosion mapping and offers insight into new or easier ways to mitigate and reverse soil degradation. Papers from this Special Issue cover a good range of the world, from India through to Pakistan, Russia, China, Syria, Iran, Ethiopia, Italy, the Czech Republic, Germany, and Hungary. Several models or parts of them were analyzed, including USLE, MUSLE, USLE-M, RUSLE, EPIC, WEPP, WaTEM/SEDEM, GULTEM, and GYNDUL. Besides soil erosion modelling, machine learning was also used in one of the articles for the evaluation of gully development. One of the main subjects of the published research was sediments, which are related to one of the most interesting questions in the topic of soil erosion: "Where is the huge amount of soil being lost going?"

**Keywords:** soil degradation; soil erosion modeling; gully erosion; sedimentation; erosion models; freeze–thaw effects; infiltration; preferential flow; badland; rainfall simulation

## **1. Introduction**

Soil erosion by water is considered to be one of the major forms of soil degradation (other than soil erosion by wind, acidification, salinization, desertification, etc.) and causes the majority of problems related to the degradation of soil resources, leads to the largest amount of soil loss, and covers the greatest extent of areas affected worldwide [1]. This subject is very wide, and there are even many forms of soil water erosion, from splash erosion to gully erosion and sedimentation. It would be worth running a Special Issue just one the numerous soil erosion models now available. As the final product of soil water erosion, sedimentation has been investigated by many interested parties. Each one of these related issues would be worth a separate Special Issue, as all keywords listed have been investigated by many researchers and have been the subject of many papers published in various journals. There have been efforts to provide a wider view of problems related to erosion, such as non-agricultural uses—e.g., industrial pollution and contamination; the disposal of wastes; the restoration of polluted and degraded areas; and recreational uses [2]. The IPCC Report [3] also describes numerous issues relating to desertification, land degradation, sustainable land management, and their interlinkages in some of its chapters—e.g., land–climate interactions, land degradation, etc.

This is the reason why this Special Issue does not aim to serve as a collection on one very specific topic but rather to provide insight into a number of related topics, including soil erosion modelling using the classic USLE as well as more recent models; field research, including rainfall simulations; and mitigation technics and their related problems—e.g., the use of dams as sediment traps.

**Citation:** Centeri, C. Soil Water Erosion. *Water* **2022**, *14*, 447. https://doi.org/10.3390/ w14030447

Received: 19 January 2022 Accepted: 20 January 2022 Published: 1 February 2022

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This Special Issue was also a subject of finding interested researchers. Who is submitting manuscripts and what types of papers are submitted and from which countries? On the one hand, the time of researchers is limited and, on the other hand, most researchers wish to reach the widest possible audience, so it was rather difficult for someone to decide if they wanted to publish in this Special Issue or another. We feel that the purpose of this Special Issue was achieved, as various publications submitted showed a good variety of information on soil water erosion and novel analyses.

I hope that this Special Issue will provide new and useful information on the mitigation of soil water erosion-induced problems across the entire world. I also believe that, regardless of the localities used (which is an aspect that is often criticized when a paper is under review), many other countries under different climatic conditions as well as people in other geographical regions can learn from the presented results and methodologies and thus move closer to achieving degradation-free land use, as this it is the core interest of the population of Earth.

#### **2. Summary of this Special Issue**

Nowadays, as resources such as time and research personnel are limited, computerbased methodologies are receiving more attention and greater popularity. Perhaps this is also the reason why the first paper submitted to this Special Issue used machine learning techniques [4]. These techniques were used to investigate gullies—more precisely, gully head-cuts. A total of 119 gully head-cuts were identified and mapped in N.W. Iran. The authors found that, based on goodness-of-fit and AUROC (Area Under the Receiver Operating Characteristic is a performance metric that we can use to evaluate classification models) of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance.

Following this approach, another gully erosion investigation was performed for land managemen<sup>t</sup> purposes by another author from Russia [5]. Four models were used: two models calculated gully depth and width along with its longitudinal profile, while another two models were used for the novel modification of the area-slope approach, which gives the most probable position of possible gullies. The author suggested the use of the dynamic gully erosion model GULTEM (GULly erosion and Thermoerosion Model) was recommended for calculations of gully geometry transformation in time and space for the most detailed projects of land management.

The third article related to gully erosion identified and assessed the areal and temporal changes of badlands in Italy [6]. The case study area was in the Modena Province (Emilia Apennines), which was important because there was no previous detailed investigation. The authors revealed a general stabilization trend of the badlands in the study area due to an intensified revegetation process around the badlands. This trend is mainly the result of intensive land-use changes—namely the increase in forest cover and the reduction in agricultural land (occurring in the study area from the 1970s onwards). There was another article related to gullies, but its main purpose was to handle sediment-related issues [7] which is also an important part of the whole issue.

Soil erosion models were used by various authors and some of the model details were also analyzed. Kreklow et al. [8] compared rainfall erosivity (R-factor) estimation methods with the utilization of weather radar data in Germany. Their results showed that R-factors have increased significantly due to climate change and that the current R-factor maps need to be updated. One of the possible options to accomplish this is using more recent and spatially distributed rainfall data.

Almohamad [9] used the RUSLE model to show the impact of armed conflicts on soil erosion through the land cover change in Syria. Land cover was decreased due to the increase in forest fires as a result of armed conflict. Damage to coniferous forest and transitional woodland and scrub, especially on steep slopes, had the biggest impact on factor C after the fire. The soil loss was 200% to 800% higher than it had been in the pre-fire situation.

Keller et al. [10] performed a rainfall simulation for soil erodibility calculation using various models, such as USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE Modified), and EPIC (Erosion-Productivity Impact Calculator). Based on the soil loss measured during the rainfall simulation, the authors found that the RUSLE model resulted in the best performance in event soil loss estimation.

Another type of field experiment was conducted by Li et al. [11], who were interested in the effects of infiltration on preferential flow characteristics and solute transport in a Chinese area. The impact of three precipitation amounts of 20, 40, and 60 mm was investigated. Solute concentration was found to peak around the end of the preferential flow path and when preferential flow underwent lateral movement. The results indicated that the infiltration volume and transport capacity of preferential flow had important effects on the distribution of Br¯ and NO3¯ concentrations. The results can help in the managemen<sup>t</sup> of protected forests in China.

Su et al. [12] analyzed the effects of thawing on the slope erosion and hydraulics in the sand-covered Loess Plateau in China. The authors conducted laboratory experiments on meltwater flow to quantify the temporal and spatial distribution of the hydraulic parameters of sandy soils concerning runoff and sediment yield under a constant flow on unfrozen and frozen slopes of variable sand thickness. Their results showed that sand can prolong the initial runoff time, and that unfrozen and frozen slopes have significantly different initial runoff times. A significant linear relationship was found between the cumulative runoff and the cumulative sediment yield.

Lu et al. [13] went further than Su et al. [10] by investigating the effects of freeze–thaw cycling on the soil detachment capacities of three loamy soils on the Loess Plateau of China. After 20 freeze–thaw cycles, the degree of decline of silt loam was the greatest (77.72%), while sandy loam (63.18%) and clay loam (39.77%) showed smaller degrees of decline. The soil detachment capacity of silt loam and sandy loam was positively correlated with the freeze–thaw cycle and negatively with that of clay loam. The authors believe that their results can provide references for further studies on the mechanism of soil erosion in seasonal freeze–thaw regions.

The RUSLE model was used in numerous articles for estimating the sediment yield of certain areas. One of these estimations was conducted in the watershed of the Chenab River at the border of Pakistan and India [14]. The 30-year average annual sediment yield was estimated as 4.086, 6.163, and 7.502 million tons based on different approaches.

Gurmu et al. [15] simulated sediment influx using the RUSLE model and compared it to the amount of sediment removed during desilting campaigns. They found the sediment deposition rate to be 308 m3/km and 1087 m3/km, respectively, for the Arata-Chufa and Ketar schemes. The spatial soil losses amounted to up to 18 t/ha/yr for the Arata-Chufa scheme and 41 t/ha/yr for the Ketar scheme. The authors concluded that overland sediment inflow is not considered properly related to canal sedimentation, and this is a major cause of excessive sedimentation problems (among others) in Sub-Saharan Africa.

Jáchymová et al. [16] used the RUSLE-based WATEM/SEDEM model in their research. This modelling approach allowed for the modelling of sediment fluxes at 127,484 < risk points, meaning that a good coverage for the entire Czech Republic could be achieved. Risk points were defined for the study as follows: outlets of contributing areas 1 < ha, wherein the surface runoff enters residential areas or vulnerable bodies of water. The authors found that the most important factor for risk definition is a combination of morphometric characteristics (specific width and stream power index), followed by the watershed area, the proportion of grassland, the soil erodibility, and the rain erosivity (described by PC2).

Szabó et al. [17] analyzed the use of various rainfall simulators in the determination of the driving forces of changes in sediment concentration and clay enrichment. The authors compared a field and a laboratory rainfall simulator. They found that the slope gradient is an effective regulator only in the laboratory. Rainfall intensity was also more effective in the laboratory than in the field simulations. These findings sugges<sup>t</sup> that soil-related properties play a prominent role in driving sediment concentration in the field, whereas, in the laboratory, slope and rainfall intensity were found to be driving factors independent of soil-regulated sediment concentrations.

As we can see, on the one hand, there is a grea<sup>t</sup> deal of information on soil water erosion-related issues; on the other hand, as we go into more detail, there are always new findings. After all, we can conclude that a lot more research is still needed [18] in order to find the relations between the factors leading to water erosion. These findings can help in mitigation. We highly encourage the creation of more Special Issues in this same field of research.

**Funding:** This research received no external funding.

**Acknowledgments:** I wish to thank all of the authors who contributed to this Special Issue. The other group of people who contributed a huge amount of time and effort were the anonymous reviewers and the editorial managers—many thanks for them. I was very satisfied with the review process and managemen<sup>t</sup> of the Special Issue, and all those involved are very much appreciated.

**Conflicts of Interest:** The author declares no conflict of interest.
