Soil Erosion Conditions Research Articles

Analysis of the coupling and coordination between soil erosion and land use in the Northeastern black soil region of China: a case study of Lishu County

Lishu County, which is located in the black soil region of Northeast China, represents a key site for the analysis of soil erosion intensity. This study offers a scientific foundation for the development of targeted soil and water conservation strategies within the region. The Revised Universal Soil Loss Equation (RUSLE) was employed to compute the soil erosion modulus in Lishu County, with the objective of conducting a quantitative analysis of the temporal and spatial distribution patterns of soil erosion. Additionally, the changing characteristics of soil erosion were examined from the perspectives of land use types and slope variations. The Generalized Connectivity Causality Model (GCCM) was utilized to identify the causal relationship between soil erosion and land use types through the reconstruction of state space and cross-mapping predictions. (1) Soil erosion in Lishu County between 2000 and 2020 predominantly exhibited mild to moderate levels, characterized by patchy and sporadic erosion, with relatively severe occurrences in the northern and central regions. (2) Soil erosion was correlated with land use and slope variations, with more than 90% of erosion incidents transpiring in cultivated land areas. The 3°-5° slope range in Lishu County emerged as a focal point for erosion, necessitating targeted prevention and control measures. (3) The GCCM model illustrated a discernible causal relationship between soil erosion and land use, revealing mutual influences between the two factors. Between 2000 and 2020, both the area and intensity of soil erosion in Lishu County exhibited an initial increase, followed by a subsequent decrease. This suggests an overall trend of amelioration in soil erosion conditions. However, notable spatial disparities persist in the erosion distribution across the region.

Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators

Climate change and reduced rainfall in the past decade have resulted in an alarming increase in soil erosion within arid and semi-arid regions, including southern Iran. Given the significance of this issue, our research aimed to predict soil erosion in the southwest of Fars province, Iran, by utilizing climatic indicators and remote sensing. To determine the key indicators of drought influencing soil erosion, the principal component analysis (PCA) method was employed. Subsequently, Markov chains and Cellular Automata Markov chain (CA-Markov) were utilized to forecast soil erosion and drought conditions in 2040. The findings revealed a steady rise in drought frequency within the region from 2000 to 2020. Notably, meteorological indices and meteorological drought exhibited strong correlations with the Standard Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI) (RSPEI = 0.98 and RSPI = 0.97). The correlation analysis between soil erodibility indices and drought identified Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) as significantly linked to erosion rates. The soil erosion predictions using Markov and CA-Markov chains in 2040 indicated that approximately 38% of the region faces erosion risk based on the NDVI index, while around 47% face risk based on the EVI index. Moreover, the southern and eastern regions are projected to experience increased erosion due to the combination of low rainfall and high drought in the upcoming years. These results underscore the profound impact of drought on critical soil properties, including soil moisture, texture, and the adhesion of soil particles. Consequently, drought-induced soil erosion may lead to greater wastage and ecological repercussions. By leveraging remote sensing drought indicators and their influence on soil erosion, effective land management practices can be implemented to prevent soil erosion and mitigate its adverse effects on the ecological potential of watersheds.

Effect of rock exposure on runoff and sediment on karst slopes under erosive rainfall conditions

Study regionGuizhou Province, Southwest China Study focusKarst slopes are highly heterogeneous, with a large number of rocks exposed on the surface. Empirical soil erosion models (such as the revised universal soil loss equation (RUSLE)) based on homogeneous soil areas have encountered significant challenges in their application to karst areas. Currently, a quantitative relationship between the exposed rock and soil erosion has not been systematically established, although the establishment of this relationship could provide opportunities for empirical soil erosion model optimisation in karst areas. In view of this, we combined a field natural rainfall test with an indoor simulated rainfall test and artificially simulated exposed rock to investigate rock exposure rate (RER) effects on soil loss and runoff processes on karst slopes and attempted to build a coupled RER and soil loss model. New hydrological insights for the regionAs the RER increased, the runoff and sediment content decreased under both simulated and natural rainfall conditions. Soil loss decreased exponentially with an increase in RER under natural rainfall tests in the field. There was a relationship between the soil loss and the RER quadratic function under simulated rainfall tests indoors. The exponential relationship based on natural rainfall more accurately reflected karst slope soil erosion conditions. These results contribute to a deeper understanding of soil erosion mechanisms on slopes in karst areas and provide confidence in our subsequent introduction of soil erosion sensitivity-exposed rock relationships to optimise soil erosion models such as RUSLE.

The response of geographical processes to landscape restoration: China’s research progress

The UN Decade of Ecosystem Restoration (2021–2030) provides a new momentum for scaling up ecosystem restoration efforts to landscape restoration. China’s recent experience with transformative investment in landscape restoration provides invaluable guidance for the world. We retrospectively reviewed the scientific evidence on the responses of physical, ecological, and social processes to China’s landscape restoration under geographic heterogeneity and obtained four experiences and lessons. First, China’s forest landscape restoration has successfully promoted vegetation growth and enlarged the carbon sink. Second, landscape restoration has reduced the local water yield, while the regional responses of rainfall are still not clear. Third, the local conditions of soil erosion and habitat quality were largely improved by landscape restoration, while the decreases in soil moisture and streamflow demonstrated significant trade-offs among ecosystem services. Last, geographical differentiation existed in the local responses of livelihoods to landscape restoration strategies, and the win‒win solutions between human development and nature improvement under different landscape contexts were still uncertain. We summarize three additional questions as future prospects: what is the scale of the thresholds to prevent overshoot and cascading negative ecological effects? what are people’s prior needs from nature? considering that there may be no universal win‒win pathways, how to promote co-benefits based on regional human–nature relationships?

Structure-from-Motion Photogrammetry and Rare Earth Oxides can quantify diffuse and convergent soil loss and source apportionment

Accurately quantifying rates of soil erosion requires capturing both the volumetric nature of the visible, convergent fluvial pathways (also known as rills) and the subtle nature of the less-visible, diffuse pathways (interrill areas). The aim of this study was to use Rare Earth Oxide (REO) tracers and Structure-from-Motion (SfM) photogrammetry to elucidate retrospective information about soil erosion rates and sediment sources during different soil erosion conditions, within a controlled laboratory environment. The experimental conditions created erosion events consistent with diffuse and convergent erosion processes. REO tracers allowed the sediment transport distances of over 2 m to be described, and helped resolved the relative contribution of diffuse and convergent soil erosion; interrill areas were also identified as a significant sediment sources soil loss under convergent erosion conditions. While the potential for SfM photogrammetry to resolve sub-millimetre elevations changes was demonstrated, under some conditions non-erosional changes in surface elevation, such as compaction, exceeded volumes of soil loss via diffuse erosion. The discrepancies between SfM Photogrammetry calculations and REO tagged sediment export were beneficial, identifying that during soil erosion events sediment in both aggregate and particle form is deposited within the convergent features, even when the rill extended the full length of the soil surface. The combination of SfM photogrammetry and REO tracers has provided a novel platform for building a spatial understanding of patterns of soil loss and source apportionment between rill and interrill erosion.

An Erosion-Based Approach Using Multi-Source Remote Sensing Imagery for Grassland Restoration Patterns in a Plateau Mountainous Region, SW China

Satellite remote sensing of grassland ecosystem restoration requires considering both the above-ground biomass and soil information, and the latter is even more crucial due to the value and restoration difficulty of soil productivity. In this study, we proposed an approach to support the restoration pattern for mountainous grasslands at regional scale. The approach integrates different aspects and key processes, including degradation status, restoration potential and recovery capability, compared to a reference state. Specifically, we illustrated the method with the case of grasslands in southwestern China from a conservation perspective. Soil erosion conditions, net primary productivity and regrowth rate of grasslands were selected as indicators to reveal restoration possibilities. The results showed that the method proposed for remote sensing identification of grassland distribution has an overall accuracy of 88.21% at the regional scale. 59.54% of grasslands in Zhaotong are being eroded with an unsustainable erosion rate greater than the tolerant soil loss, and the average annual soil erosion rate is 952.17 t/(km2·a). Meanwhile, there is obvious spatial heterogeneity in soil erosion factors, vegetation restoration potential and regrowth rate, and the dry–hot valley of Jinsha River in the southwest is much more sensitive to climate change and vulnerable than other regions. The grassland vegetation cover revealed a fluctuating trend and protection of grassland vegetation on soil from erosion has an obvious lag, restoration efforts should be focused on the months before the arrival of the rainy season. In light of various grassland types, the overlay zoning results suggest various restoration patterns of natural repair and manual intervention should be employed for different grasslands. Urgent action is needed to face the challenge and process of grassland degradation and restore its sustainability with shared understanding by taking the stakeholders, collaborations and mutual relationships among different roles into account (e.g., scientist, government and herdsman).

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas.

The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

Dynamics of spatial and ontogenetic structure of Cephalanthera rubra (Orchidaceae) populations in the east of European Russia (Middle Volga Region)

The assessment of the spatial and functional features of rare species populations without considering the ontogenetic groups, which are being rarely distinguished at international literature, does not give a complete understanding of the current status of populations and prospects of their development under various management conditions. This paper is aimed to determine the status of a threatened orchid species, Cephalanthera rubra, at the eastern border of its range (Republic of Tatarstan, European Russia). For this purpose, a complex of various population parameters was used. Ontogenetic groups of C. rubra individuals have been reliably identified on the basis of morphometric traits of reproductive and vegetative organs. The obtained results showed that the fruit set is low, ranging at average from 24% for young reproductive individuals (g1) to 39% for mid-mature reproductive individuals (g2); it reflects prospects for seed reproduction of this species at the eastern edge of its range. The abundance dynamics of populations has a fluctuation type; it is related to climatic factors of the growing season. So, we found its significant positive correlations with air humidity (from r = 0.6 to r = 0.7) and precipitation (from r = 0.5 to r = 0.6), and a negative correlation with temperature (from r = -0.5 to r = -0.6). In the Republic of Tatarstan, the base spectrum of C. rubra populations is of the centred type, 1:10:51:38 (j:im:v:g); it corresponds to the general ontogenetic spectrum of rhizomatous orchids. The spatial-ontogenetic structure of populations, and especially its dynamics, reflects the intraspecific relationships of various ontogenetic groups involved in maintaining the stability of C. rubra population in space and time. Under optimal conditions, the spatial structure of all individuals and reproductive groups is characterised by a spatial randomness, which probably reduces intraspecific competition. In contrary, pre-reproductive groups form aggregations with 0.5–0.9-m radius in microloci, favourable for seed germination. A characteristic feature of the spatial structure is the formation of aggregations of reproductive and pre-reproductive individuals with a 0.7–1.0-m radius with a 0.2–0.4-m zone of the random spatial positioning of individuals, which aims to reduce intraspecific competition between them. The probability of meeting an individual of another ontogenetic group increases towards the periphery of the formed aggregation. In C. rubra populations, the abundance and density of individuals, and the fruit set decrease in pessimal conditions of landslides, soil erosion, and habitat shading. Under these conditions, pre-reproductive individuals do not form aggregations, nor aggregations with reproductive individuals. In general, the spatial structure of a population depends on the life-form type of the species, the mechanism of spatial growth of underground organs; it is considered a diagnostic sign of the population status.

THE ROLE OF COMMUNITIES LIVING IN WATERSHEDS TOWARDS FLOOD DISASTER MANAGEMENT : SYSTEMATIC REVIEW

Introduction. Disaster is a series of events that can threaten and disrupt human life, caused by natural and non-natural factors. A flood disaster is a natural disaster that can occur due to extreme storms in the upper watersheds, climate change, and geomorphological conditions of soil slopes, erosion, and sediment material. Flood disasters will have an impact on the social situation, people's economy, health status and death. Method. A systematic review was conducted through several stages, namely making questions, identifying, eligibility, selecting article inclusions, screening, and appraisal. The selection process was listed in the Systematic review framework and 10 articles are obtained. Later, articles were summarized and rated according to The Joanna Briggs Institute (JBI) in each article. Result&Analysis. The data shows that the results vary according to the system, and the conditions in which the study was conducted. Increasing the role of the community in flood disaster management efforts is done by increasing community knowledge, skills and experience, related to an efficient disaster management system, with several methods, namely by giving questionnaires and conducting interviews with the community or family, special training and simulations. Discussion. To increase the awareness and role of the community in managing flood disasters, several methods are applied. The simulation method is an educational effort that can be used to improve people's knowledge, skills, and experience in conducting flood management activities.

Hydrological simulation and prediction of soil erosion using the SWAT model in a mountainous watershed: a case study of Murat River Basin, Turkey

Abstract The Euphrates-Tigris basin in Turkey suffers from sedimentation and erosion problems. The purpose of this study was to estimate streamflow, sediment yield, and identification of soil erosion-prone areas for the Murat River Basin which is the headwater of Euphrates River, with Soil and Water Assessment Tool (SWAT). The model was calibrated and validated with observed streamflow and sediment yield data obtained from two gauging stations. The statistical model performance was evaluated by using NSE, R2, and PBIAS. NSE values were calculated as 0.57, 0.50 and 0.77, 0.75 for the stations E21A074 and E21A077, respectively, in the monthly streamflow calibration and validation periods. Monthly sediment yield generated the NSE values for stations E21A074 and E21A077 were 0.54, 0.69 and 0.62, 0.35 in the calibration and validation periods, respectively. Spatial analysis indicates that 3.9% of the basin is under very severe (>20 t/ha/y), and 21.3% is under severe (>10 t/ha/y) soil erosion conditions. Most of the soil erosion occurs at the barren land use and in the sub-watersheds with a slope greater than 25%. Overall, the SWAT model satisfactorily simulated the streamflow and sediment yield, and identified the erosion-prone areas at the sub-watershed scale at the Murat River Basin for water resource management.

Probabilistic spatial susceptibility modeling of carbonate karst sinkhole

Sinkhole is one of major geohazards in karst area where soluble carbonate bedrock is underlain by cover soils (e.g., sandy or clayey soils) and active groundwater circulation is present. Groundwater recharge easily erode the overburden soils away into cavities in the bedrock, and ultimately structural collapse in ground. The induced ground collapse/subsidence may pose a great threat to human safety, environment, and buildings and infrastructure. The goal of this study is to develop a method/tool to spatially assess the possibility of sinkhole occurrence. The paper presents two main taks: (1) development of a probabilistic spatial prediction model of sinkhole susceptibility and (2) a geographical information system (GIS)-based regional-scale sinkhole susceptibility map. The study area is the east central Florida (ECF) region that has been experiencing more abrupt and larger cover collapse sinkholes. The research methodology employs two statistical methods for the spatial prediction model, frequency ratio (FR) and logistic regression (LR), accounting for six criteria that are soil erosion, mechanical stability, distance, geology, human activity, and climate condition. The final section of factors includes head difference, soil permeability, thicknesses of aquifer systems (overburden, surficial/intermediate aquifer), distance to karst features, surficial geology, lithology, land use/land cover, and rainfall. Once the models were constructed, both models were validated and compared by using receiver operating characteristics (ROC) analysis. The results indicate that the LR model better delineates high susceptibility areas of sinkhole and hydrological factors (recharge and head difference) are more accurately reflected on the model of the LR.

Hydraulic Tests and Interpretation of Test Data for Reno Mattresses in Open Channel Flow

The efficacy of erosion control systems depends on preventing soil loss underneath and maintaining its integrity under the effects of the water flow. The paper presents the research results at the Colorado State University on the performance of double twisted wire mesh products, known as Reno Mattresses, used as soil erosion control systems. Mattresses were subjected to various flow conditions on a 10 m long flume placed on a soil layer. The performance against erosion was evaluated by assessing the effect of the stone motion inside the mattress combined with the condition of incipient soil erosion underneath, in relationship to the mattress thickness, the filling stone properties, and under variable hydraulic flow regimes. At the same time, confirming the stability obtained using the conventional tractive force design approach, the research results allowed to introduce a new performance limit based on incipient soil erosion underneath the revetment. Based on the research results, the authors propose to express the shear resistance of mattresses used as soil erosion control systems as a function of the filling stones’ size, uniformity, unit weight, mattress thickness, and the presence of vertical strengthening elements.

Spatially distributed assessment and forecast of soil erosion losses as a basis for optimization the use of erosion-hazardous agricultural lands


 
 
 The article is devoted to the problem of sustainable use of agricultural lands in conditions of active manifestation of water soil erosion, which is typical for all natural zones of Ukraine, but to the greatest extent – for the Forest-Steppe and Steppe zone. The aim of the article is a spatially distributed quantitative assessment and forecast of soil erosion losses and the development on this basis of recommendations to optimize the use of agricultural land on the example of the Pidhayetskyi district of the Ternopil region. The area is located within the Western region of Ukraine with a fairly high intensity of water erosion, where in accordance with the long-term climate forecast a further increase in erosion hazard of land is expected. A quantitative assessment of soil erosion losses for the basic period (1961–1990) and forecast periods (2031–2050 and 2081–2100) was carried out using a spatially distributed physical-statistical GIS-model of soil erosion-sedimentation developed at the Department of Physical Geography, Nature Management and Geoinformation Technologies of Odesa I. I. Mechnikov National University. Spatial realization of the model performed using the analytical capabilities of the Software for Environmental Modeling PCRaster (University of Utrecht, Netherlands). The forecast of the changes in the hydrometeorological conditions of water soil erosion was made using the forecast of monthly average air temperatures and monthly average precipitation, developed at the Ukrainian Hydrometeorological Institute. It established that most of the arable land (about 52 %) of Pidhayetskyi district is erosively dangerous. Due to the projected increase in summer rainfall in the middle and at the end of this century, further intensification of soil erosion is expected, especially in 2031–2050, when soil losses on arable land will increase to 16.9 t/ha/year, and only due to rainstorm erosion. Soil losses by meltwater will be insignificant. Based on the calculations, it was concluded that it is impossible to protect the agricultural lands of the region from erosional degradation without withdrawal from the arable land about 8.15 thousand hectares (28) of the most erosion-hazardous lands (with annual soil losses exceeding 20 tons per a hectare), changing the structure of sown areas and introduction a soil-protective adaptive-landscape farming system over a significant area.
 
 

Analysis of the Critical Hydraulic Condition of Subterranean Soil Erosion and Its Factors

Subterranean soil erosion is a common form of soil failure that is caused by groundwater seepage. In this paper, we present a subterranean soil erosion model ideally supposed as pipe flow with four conditions. By using the theories of pipe flow, its critical expressions are derived, including the critical flow quantity (Qcr), the critical velocity (Vcr), and the hydraulic gradient (Icr). A simulating experiment is designed to test Qcr to calculate the other critical values. In a case study, 14 clay samples from Guiyang, Guizhou, China, are tested via a simulation experiment. As a result, the critical shear stress of samples is found to be 32.01 Pa. Furthermore, three critical values of suffusion, including Qcr, Vcr and Icr, are calculated with different diameters of soil pores by using the critical expressions. Analyzing the relationship among the values shows that the pore diameter and groundwater temperature influence the critical hydraulic condition of subterranean soil erosion.

Self-Organizing Map Network-Based Soil and Water Conservation Partitioning for Small Watersheds: Case Study Conducted in Xiaoyang Watershed, China

Soil and water conservation partitioning (SWCP) considers complex environmental statutes and development demands and serves as a scientific basis for conducting soil erosion management and practice. However, few studies have researched partitioning in small watersheds (< 50 km2), and guidelines for enabling region-specific measures are lacking. In this study, the Xiaoyang watershed located in the red soil region of southern China was selected as a representative small watershed in which to conduct partitioning. The pressure–state–response (PSR) model was used as a framework for establishing an indicator system that included soil erosion sensitivity, the soil erosion condition, and ecosystem services. With three soil and water conservation variables as the input layer, a one-dimensional self-organizing map was applied to identify clusters in the small watershed. The silhouette width was evaluated to determine the optimal number of regions. Based on the associated results, the Xiaoyang watershed was divided into five regions accounting for 82%, 9%, 8%, 2%, and 1% of the total area, respectively. This study provides a framework on which region-specific soil erosion measures can be planned, and it also provides a partitioning method that can be employed in other areas.

Using SWAT to Evaluate Streamflow and Lake Sediment Loading in the Xinjiang River Basin with Limited Data

Soil erosion and lake sediment loading are primary concerns of watershed managers around the world. In the Xinjiang River Basin of China, severe soil erosion occurs primarily during monsoon periods, resulting in sediment flow into Poyang Lake and subsequently causing lake water quality deterioration. Here, we identified high-risk soil erosion areas and conditions that drive sediment yield in a watershed system with limited available data to guide localized soil erosion control measures intended to support reduced sediment load into Poyang Lake. We used the Soil and Water Assessment Tool (SWAT) model to simulate monthly and annual sediment yield based on a calibrated SWAT streamflow model, identified where sediment originated, and determined what geographic factors drove the loading within the watershed. We applied monthly and daily streamflow discharge (1985–2009) and monthly suspended sediment load data (1985–2001) to Meigang station to conduct parameter sensitivity analysis, calibration, validation, and uncertainty analysis of the model. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and RMSE -observation’s standard deviation ratio (RSR) values of the monthly sediment load were 0.63, 0.62, 3.8%, and 0.61 during calibration, respectively. Spatially, the annual sediment yield rate ranged from 3 ton ha−1year−1 on riparian lowlands of the Xinjiang main channel to 33 ton ha−1year−1 on mountain highlands, with a basin-wide mean of 19 ton ha−1year−1. The study showed that 99.9% of the total land area suffered soil loss (greater than 5 ton ha−1year−1). More sediment originated from the southern mountain highlands than from the northern mountain highlands of the Xinjiang river channel. These results suggest that specific land use types and geographic conditions can be identified as hotspots of sediment source with relatively scarce data; in this case, orchards, barren lands, and mountain highlands with slopes greater than 25° were the primary sediment source areas. This study developed a reliable, physically-based streamflow model and illustrates critical source areas and conditions that influence sediment yield.

Studying evidence of land degradation in the Indian Ganga River Basin—a Geoinformatics approach

Land degradation is a long-term loss of ecosystem function and productivity which takes place due to a wide variety of land processes, namely soil erosion, soil sodification, green-cover loss, and soil conditions such as soil infertility that leads to productivity loss. About 41% of the land in India is under different forms of land degradation in which a major part lies in the Indian Ganga River Basin (IGRB). In this work, we evaluated the evidence of land degradation in the IGRB by analyzing (i) the changes in the forest cover and land use (FCLU) between 1975 and 2010, (ii) forest fragmentation status for the same time period, and (iii) decline in rain-use efficiency (RUE) during 2000-2010. The FCLU-type mapping for the year 1975 and 2010 was carried out using 216 Landsat satellite scenes that derived 40 vegetation and 7 non-vegetation classes. The highest negative change (loss) was observed in the dry deciduous forest of mixed forest formation (4699.9 km2) and gregarious formation (1337.6 km2), and a major gain in settlement (5396.3 km2) and managed lands (3408.4 km2). An increase in forest fragmentation was observed in all the forest classes with the highest rise in the deciduous forest of the central basin. A consistent decline in RUE was observed highest in the South-Western semi-arid IGRB (0.02-0.15) that stretched up to the central basin. All the three analyses showed evidence of active land degradation in the form of green-cover loss, fragmented forests, and declined primary productivity with visual evidence for some of the severely degraded areas. The use of Geoinformatics to analyze land degradation using surface indicators is promising and provide possibilities of further improvements using better resolution data.

Investigating rainfall duration effects on transport of chemicals from soil to surface runoff on a loess slope under artificial rainfall conditions

The release and transport of soil chemicals in water erosion conditions are important for the local environment, soil and water resources conservation. According to the artificial rainfall experiments with a constant rainfall intensity of 90 mm/h and different rainfall duration (30, 60, 90, 120 and 150 min), the traits of soil PO<sub>4</sub><sup>3–</sup>, K<sup>+</sup>, and Br<sup>–</sup> release and transport from soil to surface runoff on the loess slope were analysed, and a model describing the chemical concentration change in surface runoff under soil erosion conditions was developed. The runoff coefficient quickly increased in 15 min or so, and then it was stable in the range of 0.60–0.85. The sediment intensity decreased in 30 min and soon increased after severe sheet erosion occurred on the slope. The concentration curve of Br<sup>–</sup> in surface runoff can be divided into two stages, quickly decreasing in the initial 30 min after the surface runoff occurred, and then stable. The concentration curve of PO<sub>4</sub><sup>3–</sup> and K<sup>+</sup> in surface runoff can be divided into three stages, quickly decreasing like Br<sup>– </sup>was decreasing, then stable, and increasing after severe sheet erosion began. Compared with the exponential function, the power function was found more suitable for fitting the change in chemicals in runoff with unsaturated soil; while neither of them could well fit the PO<sub>4</sub><sup>3–</sup> and K<sup>+</sup> concentration change after severe erosion occurred. The transport of chemicals under complex soil erosion conditions seems to be a dynamic release process between surface runoff and sediment. Based on the convection-dispersion mechanism and desorption kinetics, the polynomial model under soil erosion conditions was created. For adsorbed PO<sub>4</sub><sup>3–</sup> and K<sup>+</sup>, it is more suitable to simulate that process than the power function, while it is not so good for mobile Br<sup>–</sup>.

Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling

A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial computing cannot fully meet the computational requirements of massive data sets. Therefore, it is necessary to perform soil erosion modelling under a parallel computing framework. This paper focuses on a parallel computing framework for soil erosion modelling based on the Hadoop platform. The framework includes three layers: the methodology, algorithm, and application layers. In the methodology layer, two types of parallel strategies for data splitting are defined as row-oriented and sub-basin-oriented methods. The algorithms for six parallel calculation operators for local, focal and zonal computing tasks are designed in detail. These operators can be called to calculate the model factors and perform model calculations. We defined the key-value data structure of GeoCSV format for vector, row-based and cell-based rasters as the inputs for the algorithms. A geoprocessing toolbox is developed and integrated with the geographic information system (GIS) platform in the application layer. The performance of the framework is examined by taking the Gushanchuan basin as an example. The results show that the framework can perform calculations involving large data sets with high computational efficiency and GIS integration. This approach is easy to extend and use and provides essential support for applying high-precision data to refine soil erosion modelling.

The importance of mycorrhiza in agroforestry systems for the stability of ecosystems in semi-arid zones and simple quantification of soil fungi - A study at the Mollesnejta research center for Andean Agroforestry in the valley of Cochabamba, Bolivia

Soil erosion and poor production conditions in developing countries are a major problem for local primary care. It is therefore even more important to ensure a functioning and stable ecosystem from which agricultural plants profit, too. Trees have found a brilliant way to gather enough nutrients for their survival. They enter a symbiosis with special types of fungi, the so-called mycorrhiza. This leads to more resistance of the crops, especially against drought. There is a method that can be used with the simplest means to prove that mycorrhizae are found in tree roots and thus assure improvement of soil fertility.