Showing papers in "Catena in 2021"

GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods

Abstract: Globally, but especially in China, landslides are considered to be one of the most severe and significant natural hazards. In this study, bivariate statistical-based kernel logistic regression (KLR) models with different kernel functions (Polynomial, PUK, and Radial Basis Function), named the PLKLR, PUKLR, and RBFKLR models, were proposed for landslide susceptibility evaluation in Zichang City, China. Meanwhile, the present study aims to build landslide susceptibility maps based on bivariate statistical correlation analysis, optimization of different kernel functions, comparison of three landslide susceptibility maps and systematic analysis of spatial patterns. The steps of this article are organized as follows: Firstly, a landslide inventory containing 263 historical landslide locations was constructed. For the purpose of training and validation of models, 263 landslide locations were randomly divided into two parts with a ratio of 70/30. Secondly, 14 landslide conditioning factors were extracted from the spatial database. Subsequently, correlation analysis between the conditioning factors and the occurrence of landslides was conducted using frequency ratios. Then, the conditioning factors with normalized frequency ratios values were used as inputs to build the landslide susceptibility maps using the three models. A multicollinearity analysis was performed using collinearity statistics. Finally, the area under the receiver operating characteristic curve (AUC) was used for comparison and validation of models for recognizing the prediction capability. By further quantitative comparing mapped susceptibility values on a pixel-by-pixel basis, which can acquire underestimations and overestimations of factors (distance to river and slope) and susceptibility area. The results indicated that the PUKLR model had superior performance in landslide susceptibility assessment, with the highest AUC values of 0.884 and 0.766 for training and validation datasets, respectively. This model was followed by the RBFKLR model and the PLKLR model for the training datasets (AUC values of 0.879 and 0.797, respectively), and the PLKLR model and the RBFKLR model for the validation datasets (AUC values of 0.758 and 0.752, respectively). The landslide susceptibility map could help government agencies and decision-makers make wise decisions for future natural hazards prevention in Zichang region.

The effects of biochar addition on soil physicochemical properties: A review

Abstract: Excessive land use has a series consequences on the degradation of land function and exerts tremendous pressure on the ecological environment. Farming, mining, and heavy metal pollution have resulted in many negative effects on soils. Biochar has become a hot research topic in the fields of agriculture, environment, and energy as an environmentally friendly soil improver in recent years. The application of biochar for both agricultural and environmental benefits has been studied and reviewed extensively. However, there are limited reviews on the structures of biochar and other biochar applications. This paper provides an overview of recent advances in the effects of the various physicochemical properties of biochar and biochar utilizations including its use as catalyst, soil amendment, water retention, contaminant adsorbent, gas storage, ion exchange, and soil microbial activity. Discussions on biochar on the physical, chemical, biological properties after amendment to the soil and preparation condition. However, the negative effects of biochar in preparations and applications need to be recognized through scientific observation and research. It is anticipated that further research on biochar amendment will increase the understanding on the interactions of biochar with soils, review the negative effects of biochar and it should be alleviated as much as possible.

Effects of chemical contamination on microscale structural characteristics of intact loess and resultant macroscale mechanical properties

Abstract: Soil contamination not only can cause environmental problems but also lead to a notable change in the mechanical properties of soil. Loess widely distributed over North-West (NW) China is featured with the metastable structure, and chemical contaminants produced especially during the rapid development of NW China in recent years seriously threaten the fragile loess environments. When exposed to chemical contaminants, the impacts on the microstructural characteristics of the loess and the resultant mechanical properties are deemed critical for land reclamation in NW China. In light of this, the microscale structural characteristics of the loess exposed to acetic acid, phosphoric acid, sodium hydroxide, and sodium sulfate respectively are studied using scanning electron microscopy, X-ray diffraction, and energy dispersive spectroscopy tests. Additionally, their resultant macroscale mechanical properties are determined by direct shear tests. The deterioration mechanism regarding the microscale structural characteristics when exposed to the contaminants is revealed, and the resultant macroscale mechanical properties present a good correspondence with the deteriorated microscale structural characteristics. The findings of this work provide some guideposts for contaminated land reclamation in NW China.

Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation

Abstract: River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study, a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then, integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance, several techniques including multi-layer perceptron (MLP), multi-layer perceptron integrated with particle swarm optimization (MLP-PSO), radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose, five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ton·day−1, MAPE = 41.50% and RRMSE = 107.09%, which were much lower than those of MLP based model with RMSE = 3133.7 ton·day−1, MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.

A comprehensive exploration of risk assessment and source quantification of potentially toxic elements in road dust: A case study from a large Cu smelter in central China

Abstract: Large-scale industrial activities emit large amounts of potentially toxic elements (PTEs) in the form of particulate pollutants. Most of these pollutants settle on the road and eventually mix into the road dust. In the long-term, they will have adverse effects on ecosystems and human health. In this research, various analytical methods were used to assess the pollution characteristics of the 12 PTEs (Mn, Ni, Cu, Zn, Hg, Cd, As, Cr, Pb, Tl, Co, and Sb) in the study area, and geo-statistics served to analyze the spatial distribution characteristics of PTEs. Combining the potential ecological risk method and health risk assessment with positive matrix factorization (PMF) from different sources entailed a quantitative evaluation. Taking a large Cu smelter in central China as an example, three sources were quantitatively apportioned, these being smelting emissions and transportation activities (54.04%), daily human activities and natural existence (33.36%), as well as coal-fired activities of industrial production (12.60%) separately. The results showed that in more than 90% of the samples, Cd, Cu, Hg, Pb, As, Zn, Tl and Sb indicated high levels of contamination. In terms of spatial distribution, the high-value PTEs were mainly distributed near the smelter. Regarding ecological risk, smelting emissions and transportation activities were the greatest source, followed by coal-fired activities. As for human health risk, adults did not have significant non-carcinogenic risk, but children had obvious non-carcinogenic risk. Among different sources, the risk of smelting emissions and transportation activities were the highest. After calculating the value of carcinogenic risks, it emerged there was no obvious carcinogenic risk for children and adults in the study area. The comprehensive approach combining risk assessments with source identification is very effective in identifying prior pollutants and important sources of pollution. They can provide a good theoretical reference for effective prevention and control of pollution.

Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale

Abstract: Rainfall-induced landslides represent an important threat in mountainous areas. Therefore, a physically-based model called “Fast Shallow Landslide Assessment Model” (FSLAM) was developed to calculate large areas (>100 km2) with a high-resolution topography in a very short computational time. FSLAM applies a simplified hydrological model and the infinite slope theory, while the two most sensitive soil properties regarding slope stability (cohesion and friction angle) can be stochastically included. The model has five necessary input raster files including information of soil properties, vegetation, elevation and rainfall. The principal output is the probability of failure (PoF) map. The Principality of Andorra was selected as case study, where FSLAM was successfully applied and validated using the existing landslide inventory. The PoF raster file of Andorra (including 19 million cells) was calculated in only 2 min. Therefore, an accurate calibration of the input parameters was easy, which strongly improved the final outcomes.

Sediment connectivity concepts and approaches

Abstract: In recent years the concept of connectivity has emerged in sediment management to describe transfer of sediment from different sections of landscapes at various spatial and temporal scales. The sediment connectivity concept has two distinct components: structural and functional. Structural and functional sediment connectivities have a hard and soft nature, respectively, in which the former relates to physical characteristics and the latter to soil erosion and sediment transport processes. Although there has been an increase in sediment connectivity studies over the last decade, disagreement and confusion seems to remain about conceptual and especially methodological approaches, related indices, methods of quantifying sediment connectivity at a range of spatial and temporal scales, and the current situation in sediment connectivity research. A review of the relevant literature is needed to address these issues and provide a holistic overview of different aspects of sediment connectivity which would enlighten the directions of future research. In this review, 117 papers have been classified into five different categories, namely (i) developing conceptual frameworks; (ii) depicting spatial and temporal distribution of sediment source and sink areas; (iii) developing sediment connectivity indices; (iv) using and developing models; or (v) investigating sediment delivery likelihood through a network analysis approach. The reviewed contributions within each category were 8%, 23%, 55%, 10% and 4% papers respectively. The results indicated that more studies at both global and local scales are necessary to achieve a comprehensive conceptualization of sediment yield and related processes using sediment connectivity. Most studies have concentrated on static characteristics rather than dynamic aspects of connectivity, leading to mainly structural-based methods and indices, resulting in less attention being paid to functional connectivity. Process-based sediment connectivity studies, by focusing on spatio-temporal variations of sediment transport throughout a watershed, will therefore be a crucial aspect in future studies.

Patterns of nitrogen and phosphorus stoichiometry among leaf, stem and root of desert plants and responses to climate and soil factors in Xinjiang, China

Abstract: Nitrogen (N) and phosphorus (P) play essential roles in plant growth and deserve more attention in desert ecosystems. Nutrients stoichiometry patterns across various plant organs can reflect the plants' trade-offs to obtain resources and their growth strategy. However, it is still unclear how these nutrients are allocated among desert plant organs and how they are related to the arid climate conditions. This study aimed to examine how plant N and P stoichiometry varies among the organs of desert plants, and how they respond to climate and soil factors. Therefore, we analyzed N and P stoichiometry of leaves, stems, and roots collected from 29 desert sites in Xinjiang, China, to achieve this goal. Our studies indicated that the mean N and P concentrations in the stems (17.5 ± 0.2 and 1.0 ± 0.02 mg g−1, respectively) and roots (10.3 ± 0.2 and 0.7 ± 0.01 mg g−1, respectively) were significantly lower than those in leaves (21.4 ± 0.3 and 1.2 ± 0.02 mg g−1, respectively); the N:P ratio in stems (19.1 ± 0.3) was significantly higher than those in roots (17.6 ± 0.4), but N:P in leaves (18.2 ± 0.3) was not significantly different from those in stems and roots. Across plant life forms, N and N:P of both leaves and roots were respectively higher in shrubs than those in trees and herbs, P in three organs were significantly lower in trees than those in shrubs and herbs. Moreover, our results demonstrated that most soil factors had direct influences on N and P stoichiometry among different organs, and climate factors had indirect effect on N and P stoichiometry by affecting soil factors. This study provided the N and P stoichiometric characteristics of desert plant organs and explored their relationships with environmental variables, which can help understand nutrient stoichiometry patterns and utilization strategy of N and P and their potential responses to global climate changes in the desert ecosystems of central Asia.

Forest soil nutrient stocks along altitudinal range of Uttarakhand Himalayas: An aid to Nature Based Climate Solutions

Abstract: Understating of forest functioning is crucial for ensuring the sustainable flow of forest ecosystem services. Climate regulation service of a forest ecosystem can be ensured through emission reduction by increasing carbon sequestration in forests. However, understanding about the functioning of forests for carbon sequestration is constrained due to lack of information on nutrient stocks and stoichiometry of soils of forests of India. Present study focuses to examine the stoichiometry of major nutrients; nitrogen (N), phosphorus (P), carbon (C) of forest soil to understand the dynamics of the forests of Uttarakhand, India. The study also attempted to supplement the information about the soil carbon sequestration potential of important tree species of the forest. Soil samples were collected randomly for the evaluation of physico-chemical characteristics and stoichiometry of forest soil at four altitudinal ranges i.e., 2000 m a.s.l in the Himalayan region of Uttarakhand, India. The analysis shows that total nitrogen, total phosphorous, and soil organic carbon contents in forest soil were 0.35 ± 0.11%, 0.10 ± 0.04% and 3.36 ± 0.84%, respectively, which increases with altitude. The stoichiometric ratios viz., C:N:P, N:P, C:N, and C:P, and N:P were reported of 51.6:5.4:1, 4.30 ± 2.39, 9.60 ± 1.48, and 41.94 ± 23.35, respectively which were invariant with altitude. The low C:N ratio may be attributed to either increase in the nitrous oxide (N2O) emissions with an increase in nitrogen, or low in carbon stock leading to decrease in carbon dioxide (CO2) and methane (CH4) emissions. Moreover, the soil C sequestration potential in the forest tree species follow the order of Abies pindrow > Cedrus deodara > Quercus leucotrichophora > Pinus roxburghii. The information of the study would facilitate for broadening the understanding about the soil properties and stoichiometry of forest ecosystem and would provide an aid to forest management besides contributing to the mitigations strategies of the forests.

Spatio-temporal variation characteristics of NDVI and its response to climate on the Loess Plateau from 1985 to 2015

Abstract: The response between NDVI and climate has been a hot topic in recent research. Based on the data of GIMMS NDVI 3g and five climatic factors (precipitation, humidity, atmospheric pressure, air temperature, and sunshine hours), we evaluate spatial-temporal variation characteristics of the Normalized Difference Vegetation Index (NDVI) on the Loess Plateau and its response to climate by Sen+Mann-Kendall, redundancy analysis (RDA) variance decomposition and correlation analysis methods. The results showed that: (1) At the annual scale, the change of vegetation manifested an overall upward trend. However, there was severe degradation (2.49%) in the region, which was mainly distributed in Hohhot City, eastern Yan'an City, eastern Qinghai Province, and along Baoji-Xianyang and Xi'an-Weinan linear district. (2) The correlation between NDVI and atmosphere pressure was negative, while the correlation between NDVI and other four climatic factors was positive. (3) The independent impact and the combined impact of climatic elements, topographical elements and geographical elements were different under different land uses. The uneven spatial distribution of NDVI under different land uses was driven by climatic elements groups. (4) The bi-direction lag effects between NDVI and climatic factors showed the characteristics of short (1–3 months) or long (3–6 months) term under different land uses. Except for atmospheric pressure, the effects were positive in the short-term and negative in the long-term, while atmospheric pressure exhibited the opposite. This study can be conducive to forecast and evaluate the vegetation dynamics under the background of global climate change and provide a theoretical basis for the protection of the regional ecological environment.

Predicting soil physical and chemical properties using vis-NIR in Australian cotton areas

Abstract: Management of Vertosols in southeast Australia, requires information about soil physical (e.g. particle size fractions) and chemical (e.g. cation exchange capacity [CEC – cmol(+) kg−1], exchangeable sodium percentage [ESP - %] and pH) properties. While visible and near-infrared (vis-NIR) spectroscopy calibration models have been developed, little has been done in Vertosols. The performance of multi-depth or depth-specific (i.e. topsoil [0–0.3 m], subsurface [0.3–0.6 m] and subsoil [0.9–1.2 m]) calibration models has also seldom been discussed. In this paper, using a spiking approach across seven cotton growing areas, our first aim was to determine which model (e.g. machine learning algorithm (Cubist) or partial least square regression with bootstrap aggregation [bagging-PLSR]) produced better calibrations using multi-depth data. The second aim was to see how these calibrations predict depth-specific soil properties using independent validation. Our third aim was to investigate whether depth-specific calibrations could produce better predictions. In terms of multi-depth calibration, exemplified by CEC, Cubist (R2 = 0.86) was stronger than bagging-PLSR (0.72). However, in terms of prediction agreement for independent validation, bagging-PLSR was superior to Cubist in the topsoil (LCCC = 0.84) and subsoil (0.83) and equivalent in the subsurface (0.74). Moreover, the depth-specific bagging-PLSR achieved equivelent prediction agreement for the independent validation of CEC to the multi-depth bagging-PLSR in the topsoil (LCCC = 0.85), subsoil (0.85) and subsurface (0.76). In terms of the other soil properties (i.e. clay, silt and sand), multi-depth bagging-PLSR was superior and overall a multi-depth spectral library is recommended for Vertosols. This has implications for acquiring a vis-NIR library more quickly and prediction efficiency with multi-depth calibrations.

Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models

Abstract: This paper aims to explore the influences of different attribute interval numbers (AINs) in the frequency ratio (FR) analysis of continuous environmental factors and the influences of different data-based models on the uncertainties of landslide susceptibility prediction (LSP). Taking Ningdu County of China as study area, 446 landslides and nine environmental factors are first acquired. Then the FR values of environmental factors under 6 different AINs (4, 6, 8, 12, 16 and 20) and 6 different data-based models (FR model, grey relational degree (GRD), logistic regression (LR), multilayer perceptron (MLP), C5.0 decision tree (C5.0 DT) and random forest (RF)) are set to 36 different conditions. Finally, the LSP results with uncertainties under all conditions are discussed. Results show that: 1) For a certain model, the LSP accuracy gradually increases with the AINs increasing from 4 to 8, and then the increase rate decreases until the accuracy is stable with the AINs increasing from 8 to 20; 2) For a certain AIN, the LSP accuracy of RF is higher than that of C5.0 DT, followed by the MLP, LR, FR and GRD; 3) The LSP accuracy is highest under an AIN of 20 and RF and is satisfied under an AIN of 8 and RF, while is the lowest under an AIN of 4 and GRD; 4) The landslide susceptibility indexes (LSIs) under AINs of 4, 6 and 12 are significantly different from the other AINs, and the LSIs calculated by the C5.0 DT and RF are significantly different compared to the other models; 5) The mean values and standard deviations of LSIs calculated by the MLP, C5.0 DT and RF models are relatively smaller and larger, respectively, than those of the other models, indicating that the LSIs calculated by these models are more consistent with the actual landslide distribution features.

Assessment of climate change impacts on the streamflow for the Mun River in the Mekong Basin, Southeast Asia: Using SWAT model

Abstract: The typical warm and wet regions of Southeast Asia have significant water resource issues Deep insight of the future streamflow in the region is therefore necessary for effective water resource management and prediction We coupled the Soil and Water Assessment Tool (SWAT) with a downscaling method (Delta) and global circulation models (GCMs) in the Mun River Basin (MRB), in Thailand under three Representative Concentration Pathways (RCPs) The results show that the calibrated SWAT model can accurately characterize the hydrological process on the daily, monthly, and yearly terms The future monthly minimum temperature would rise by >15 °C, >2 °C, and >3 °C in the 2030s, 2060s, and 2080s respectively, under all RCPs (26, 45, and 85), which would also occur at the maximum temperature The temperature increase in dry season was more significant than that of the wet season The average annual precipitation decreased in the 2030s, and increased by 89%, 128%, and 139% in the 2060s under the three climate scenarios, respectively Moreover, precipitation from June to September in wet season markedly increased The streamflow was projected to increase by 105%, 201%, and 232% during 2020–2093 under three climate scenarios, respectively Monthly average streamflow increased from June to September and decreased from February to May, and the dry seasonal streamflow decreased by 11%-372% These changes in flow were closely related to climate change Monthly flow changes were negatively related to temperature (p

Splash erosion affected by initial soil moisture and surface conditions under simulated rainfall

Abstract: Soil erosion by water is one of the most severe soil degradation processes. Splash erosion is the initial stage of soil erosion by water, resulting from the destructive force of rain drops acting on soil surface aggregates. Apart from rainfall properties, constant soil physical properties (texture and soil organic matter) are crucial in understanding the splash erosion. However, there is lack of information about the effect of variable soil properties such as soil initial water content and surface condition (seal formation) on splash erosion. The objective of the present study was to determine how initial water content and surface condition affected soil splash erosion under simulated rainfall. The changes in soil surface condition were characterized by hydraulic variability (saturated hydraulic conductivity) due to surface seal formation. Slit loam and loamy sand soil textures were used in the experiment. The soil samples were collected from the top layer; air dried, and filled into modified Morgan splash cups for splash erosion measurements. Rainfall was created in the laboratory using two types of rainfall simulators covering intensity range from 28 to 54 mm h−1 and from 35 to 81 mm h−1. The soil samples were exposed to three consecutive rainfall simulations with different time intervals between simulations and different initial water content and surface conditions (air-dried, wet-sealed, and dry-crusted). Wet-sealed soil samples had up to 70% lower splash erosion rate compared to air-dried samples, due to surface ponding followed by seal formation. A significant decrease in soil saturated hydraulic conductivity indicated the formation of surface seal for silt loam soils. A non-significant decrease in saturated hydraulic conductivity for loamy sand soil was attributed to earlier formation of stable seals. Two different rainfall simulators produced different amount of splash erosion rates; however, the splash erosion development for increasing rainfall intensity was almost equal considering same initial surface condition. These results provide insight into dynamic changes of individual soil parameters affected by rainfall, and could find wider application for more complex soil erosion prediction models.

Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques

Abstract: This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements, processes whose intermittent activity makes challenging any risk analysis worldwide. MLAs has been trained on datasets including Interferometric Synthetic Aperture Radar (InSAR) and additional remote sensing datasets such as aerial stereo photographs and LiDAR and tested in the Termini-Nerano landslides system (southern Apennines, Italy). The availability of such a wealth of materials allows also an unprecedented spatio-temporal reconstruction of the volume and the kinematic of the landslides system through which we could generate and validate the hazard map. Our analysis identifies fifteen slow-moving phenomena, traceable since 1955, whose total area amounts to 4.1 × 105 m2 and volume to ~1.4 × 106 m3. InSAR results prove that seven out of the fifteen slow-moving landslides are currently active and characterized by seasonal velocity patterns. These new insights on the dynamic of the landslides system have been selected as the main independent variables to train three MLAs (Artificial Neural Network, Generalized Boosting Model and Maximum Entropy) and derive the landslide hazard for the area. Finally, official population and buildings census data have been used to assess the landslide risk whose highest values are located in the crown area, south of Termini village, and nearby Nerano. This new methodology provides a different landslide risk scenario compared to the existing official documents for the study area and overall new insights on how to develop landslide risk management strategies worldwide based on a better understanding of slope processes thanks to the latest satellite technologies available.

Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model

Abstract: Hyperspectral remote sensing technology has considerable research value in monitoring and evaluating soil heavy metal pollution. In this study, the Three-River Source Region was taken as the study area. The occurrence relationship of six heavy metals in soil, such as Mn, Cu, Zn, Pb, Cr, Ni, with soil organic matter, clay minerals, and iron-manganese oxides, was studied through the determination and analysis of soil samples and the collection of soil reflectance spectrum. Spectral transformation was carried out by first derivative, second derivative, inverse-log, continuum removal and multiple scattering correction of the spectrum. The correlation between soil heavy metal content and soil spectrum was analyzed to select the characteristic band, and partial least squares (PLS) method, support vector machine (SVM) method and random forest (RF) model were used to build inversion model based on characteristic band. Then the best combination of spectral transformation and inversion model were explored. The results showed that Pb contents were the twice of the background in Qinghai province. The combination spectrum processing method can improve the correlation between spectrum and heavy metals. The location and quantity of characteristic bands of six heavy metals are different. The accuracy of RF was significantly better than that of SVM and PLS for all six heavy metal (i.e. pb: R2RF = 0.83, R2SVM = 0.62, R2PLS = 0.18), and the model effective of soil properties in non-polluted sites were reliable (i.e. clay: R2RF = 0.93, R2SVM = 0.87, R2PLS = 0.74). This study can provide technical support for the larger-scale monitoring of soil heavy metal content and heavy metal pollution assessment.

Impact of evaporation on groundwater salinity in the arid coastal aquifer, Western Saudi Arabia

Abstract: Understanding groundwater salinization and pollution in the arid coastal aquifer is crucial due to complex geochemical processes and sources. This study intends to evaluate the impact of evaporation on groundwater salinity in the arid coastal aquifer, Al Lusub basin, Saudi Arabia using geochemical and multivariate statistical tools. Groundwater samples were collected (n = 52) and analysed for major and minor ions. Groundwater is brackish and shallow wells have higher salinity compared to deeper ones. Hierarchical cluster analysis (HCA) classified the wells into three groups (CG1, CG2, CG3). In these cluster groups, salinity is in the order of CG1(1448 mg/l)

Responses of soil enzyme activity and soil organic carbon stability over time after cropland abandonment in different vegetation zones of the Loess Plateau of China

Abstract: The effects of cropland abandonment on soil enzyme activity and soil organic carbon (SOC) stability, along with the driving factors, are poorly understood. Here, we aimed to systematically and comprehensively evaluate soil enzyme activity, SOC stability, and the associated driving factors in different vegetation zones after cropland abandonment on the Loess Plateau, China. We selected grasslands with different recovery times along a rainfall gradient encompassing the steppe zone (SZ), forest-steppe zone (FSZ), and forest zone (FZ). We measured and compared the changes in soil enzyme activity (saccharase, polyphenol oxidase, urease, phosphatase, and catalase) and SOC stability as a function of recovery time; we also evaluated the relationships between these two parameters. In SZ and FSZ, soil enzyme activity, fractions of oxidizable carbon (including very labile [C1], labile [C2], and less labile [C3]), and the carbon management index (CMI) increased with recovery time, whereas the SOC stability index (SI) decreased. Conversely, in FZ, polyphenol oxidase activity increased linearly, urease and catalase activities decreased linearly, and the change in saccharase activity was represented by a cubic equation regression. SI showed no obvious changes with recovery time, whereas C1, C2, and C3 initially decreased and then increased. Redundancy analysis showed that, in FSZ and FZ, soil enzyme activity, C1, C2, C3, and SI were influenced by vegetation diversity, coverage, and soil nutrient levels. In comparison, in SZ, these parameters were mainly influenced by soil nutrient levels. Soil enzyme activity was strongly correlated with C1 and C3 in SZ and FSZ, but not in FZ. Overall, in SZ and FSZ, soil enzyme activity increased with recovery time, whereas SOC stability decreased. In contrast, both parameters were relatively stable in FZ, which had higher mean annual precipitation and mean annual temperature.

Assessment of the impact of LUCC on NPP and its influencing factors in the Yangtze River basin, China

Abstract: Land use and land cover change (LUCC) will directly affect the types, structures and functions of ecosystems, and then have important impact on vegetation net primary productivity (NPP). The evaluation of the spatio-temporal dynamic changes of LUCC and regional NPP, and assessing the impact of LUCC on NPP can facilitate the corresponding management of different environmental types of regions and provide scientific guidance for scientific development of ecological resources, and better restore and control the ecological environment, thus promoting rapid economic development. Using MODIS normalized difference vegetation index (NDVI) data and meteorological data from 2001 to 2018, combined with Carnegie-Ames-Stanford model (CASA), the regional NPP was estimated, and the influence of LUCC on NPP in Yangtze River basin from 2001 to 2018 and its causes were analyzed. The results showed that the area with a land cover and land use change account for 10.98% of the total study area in the past 18 years, and the most important change is the transformation from grassland to forest and grassland to crop/natural vegetation mosaic (NVM). Forests, wetlands, crop/natural vegetation mosaic, low vegetation cover (LVC) land, urban, and water bodies are growing. The forest land area increased the most and that of grassland decreased the most. Total NPP has increased by 53887.51GgC in the past 18 years, which was due to the combination effect of LUCC change and climate change.

Contrasting patterns of microbial community and enzyme activity between rhizosphere and bulk soil along an elevation gradient

Abstract: The distribution patterns of the microbial community and enzyme activity in soil systems along an elevation gradient have attracted considerable attention; however, the differences in microbial diversity and enzyme activity between the rhizosphere and bulk soil and their drivers are still unclear. Here, we used an elevation gradient that covered six elevation levels and ranged from 1308 to 2600 m above sea level. Illumina MiSeq sequencing of the 16S rRNA gene and ITS-1 gene was used to analyze the community of bacteria, total fungi, ectomycorrhizal (EcM) fungi, and saprotrophic fungi in both rhizosphere and bulk soil; in addition, the soil enzyme activity (β-glucosidase, N-acetyl-glucosaminidase, leucine aminopeptidase, and acid phosphatase) was investigated. The results revealed that the elevation significantly affected the diversity of the bacterial, total fungal, EcM, and saprotrophic fungal community, as well as the enzyme activity dynamics. The difference in the microbial diversity and enzyme activity between rhizosphere and bulk soil diminished as the elevation increased, except for the saprotrophic fungal diversity. Similarly, the dominant phyla from the compositions of bacteria, fungi, EcM fungi, and saprotrophic fungi, such as Proteobacteria, Acidobacteria, Actinobacteria, Basidiomycota, and Ascomycota, also changed with elevation and rhizosphere. In addition, the elevation-dependent differences in the microbial community and enzyme activity between the rhizosphere and bulk soil were affected mainly by climatic factors (mean annual temperature and precipitation) and soil properties, such as the bulk density, ammonium nitrogen, and total phosphorus. The effects of the climatic factors were greater than those of the soil properties along the elevation gradient. These results suggest that changes in climatic factors, such as temperature, with elevation may affect the microbial interaction between roots and the soil. The result highlight the importance of the ecological roles of the microbial community in climate change.

Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets

Abstract: The effects of landslides have been exponentially increasing due to the rapid growth of urbanization and global climate change. The information gained from predictive models and landslide susceptibility analyses can be used to develop warning systems and mitigation measures. A comparative study was conducted to evaluate the effectiveness of landslide susceptibility analyses in a given area using three decision tree algorithms including Random Forest (RF), C4.5, and C5.0. Two sets of imagery datasets (raster and vector) were used and three combinations of 13 conditioning factors (including seven geotechnical properties of the soil) were determined by Information Gain, Gain Ratio, Chi-Squared Test, and Random Forest Importance. Datasets for the landslide conditioning factors were created based on the outcomes from the feature selection methods, in three different scenarios. In Scenario 1 the least important factors/features (as identified by information gain, chi-square, and gain ratio measures) were eliminated. In Scenario 2 only the most important factors (as identified by RF feature selection method evaluation) were kept. In Scenario 3, no factor was eliminated, using the data directly obtained from the sources without applying any feature selection method. The performances of the models were evaluated using statistical verification scores. C4.5 was found to have the highest performance when all 13 conditioning parameters (Scenario 3) were used for both the raster and vector data set. The RF model was the least effective in predicting the landslides in all three scenarios. However, the use of the balance vector dataset significantly increased the performance of the RF model. C4.5 and C5.0 had significantly better performance in handling extremely unbalance data in comparison to RF. Density, silt and clay content, and Atterberg’s limits (LL and PI) were the most important geotechnical conditioning factors in the performed landslide susceptibility analyses.

Nitrogen cycling processes in sediments of the Pearl River Estuary: Spatial variations, controlling factors, and environmental implications

Abstract: Numerous studies have unveiled the importance of nitrogen (N) transformation processes over the past decades, but comprehensive studies of key N-cycling processes are still rare in estuarine and coastal ecosystems. Here, we used isotope pairing, isotope-tracing, and isotope dilution techniques combined with quantitative polymerase chain reaction to investigate microbial N-cycling processes in surface sediments (0–5 cm) of the Pearl River Estuary. The average rates of denitrification, anammox, DNRA, N2 fixation, N mineralization, and NH4+ immobilization were 1.41 ± 0.89, 0.067 ± 0.033, 0.47 ± 0.28, 0.31 ± 0.30, 1.86 ± 1.09, and 1.30 ± 0.83 μg N g−1 dry d−1, respectively. Sediment grain size, organic matter, nutrients, and Fe2+/Fe3+ rather than gene abundances controlled these rates. The abundances of bacterial 16S rRNA, anammox 16S rRNA, nirS, nrfA, nifH, bacteria-amoA, and archaea-amoA genes were significantly correlated with organic matter, nutrients, and sediment grain size. In general, higher rates and gene abundance was occurred in outer than inner estuary. Among these pathways, denitrification contributed 41.83–90.13% of the total nitrate reduction, as compared to 0.94–8.58% for anammox and 8.55–54.56% for DNRA. The sediment N-loss fluxes caused by denitrification and anammox in our study area (1.5 × 1010 m2) was about 6.2 × 107 mol N d−1, accounting for ~42.1% of the riverine dissolved inorganic N fluxes, suggesting that the sediment of the Pearl River Estuary has great significance to the mitigation and controlling of N pollution in this ecosystem. Additionally, the net NH4+ production via sediment microbial pathways (N mineralization, N2 fixation, DNRA, NH4+ immobilization, and anammox) was estimated at ~ 5.5 × 107 mol N d−1, while the net NO3− consumption (denitrification, anammox, and DNRA) was ~8.3 × 107 mol N d−1. Overall, these results highlight the importance of complicated N-cycling processes in controlling the N budget in the Pearl River Estuary and improve the understanding of both the processes and associated controlling factors in estuarine and coastal ecosystems.

Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling

Abstract: Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.

Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine

Abstract: Vegetation is essential in maintaining terrestrial ecosystem functions, and understanding why and how vegetation evolves is necessary for regional ecological management. On the Tibetan Plateau, the effects of various factors and their interactions in directing vegetation change remain unclear. Supported by the Google Earth Engine (GEE) platform, we quantified spatiotemporal vegetation variation in Gannan Prefecture on the northeastern Tibetan Plateau for 2000–2018 using trend analysis and spatial autocorrelation and explored its driving factors with a geographic detector method. The normalized difference vegetation index (NDVI) illustrated fluctuating growth from 2000 to 2018, and the overall growth rate was 0.002/year. The spatial distributions of NDVI were variable, and areas with NDVI greater than 0.8 accounted for more than 69% of Gannan Prefecture area. From 2000 to 2018, the global Moran’s index of vegetation NDVI was greater than 0.52 and showed a fluctuating upward trend, indicating that the vegetation NDVI tended to be distributed with a high spatial concentration. The local Moran’s index of NDVI was mainly characterized by “High-High” and “Low-Low” clustering types, and the former increased significantly, but the latter decreased. The annual mean temperature, soil type, and elevation were the dominant factors driving vegetation NDVI change in Gannan Prefecture, explaining more than 15% of the variability. Furthermore, the influence of the interaction between any two factors on NDVI was an almost nonlinear enhancement. These results could help us improve our understanding of the underlying mechanism of NDVI changes and provide a scientific basis for ecological protection in alpine regions.

Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models

Abstract: The stability evaluation of rainfall-induced landslides using a physical determination model supports disaster prevention, but it is mostly applied to the area with few landslides, and there is a lack of quantitative study on rainfall and landslide stability. This paper combined the Scoops3D model with the TRIGRS model (3D) to predict the shallow landslide spatiotemporal distribution and compared the simulation results with those of the TRIGRS model alone (1D), aiming to obtain more accurate assessment results. At the same time, the relationship between landslide stability and accumulative rainfall was quantitatively fitted to improve the real-time landslide prediction system. We applied the 1D and 3D models to the July 21, 2013 group-occurring landslide event (976 shallow landslides) in the Niangniangba basin, China. The required geotechnical parameters of both models were acquired by field and laboratory tests. We calculated the pressure head over time using the TRIGRS model based on practical rainfall data and predicted the shallow landslide stability using the Scoops3D model according to the resulting piezometric surface. We compared the landslide stability spatial distributions of the two models under initial and saturated conditions with the landslide catalogue. The success rate of landslides predicted by 3D model is 4.20% higher than 1D model. A composite index to quantitatively evaluate both models’ performances indicates over-prediction by the 1D model in the stable region, while the 3D model more effectively predicts shallow landslides with a smaller unstable region. The relationship between instability proportion and accumulative rainfall in the 1D and 3D model can be represented by y = 24.57 x 0.18 and y = 11.59 x 0.33 , respectively. The 3D model shows more conservative result, and the rainfall threshold analysis proposed in this paper can provide reference for the time of most landslides in the case of insufficient data, which is an important indicator for disaster early warning and prediction.

Multi-time scale variability of rainfall erosivity and erosivity density in the karst region of southern China, 1960–2017

Abstract: Rainfall erosivity, a measure of the potential for soil erosion by water, is an important factor for estimating soil loss. Understanding the variation tendency of rainfall erosivity is especially critical for soil and water conservation in the fragile ecological environment of the karst region in southern China. This study analysed the rainfall erosivity at multiple spatial and temporal scales based on daily rainfall data observed at 223 meteorological stations during the period of 1960–2017. A daily rainfall erosivity model, co-kring interpolation, regression analysis, gravity model and the Mann-Kendall test were applied in the analysis process. The results indicate the following: (i) The mean annual rainfall erosivity is 5130.00 MJ·mm·ha−1·h−1 in southern China, with a range of 3964.24 to 6425.87 MJ·mm·ha−1·h−1, and varies greatly among different provinces. (ii) The magnitude of rainfall erosivity varies unevenly among seasons, with the mean rainfall erosivity in summer being almost 15 times higher than that in winter. (iii) The annual and seasonal rainfall erosivity has increased in the karst region of southern China over the past 58 years, whereas at the province scale, the seasonal trend in rainfall erosivity is more complex, and the trends are not necessarily linear and positive. Furthermore, at the interdecadal scale, there is no regular trend, and the data exhibit considerable variation. (iv) The temporal variation characteristics of erosivity density are basically consistent with those of rainfall erosivity, and the two show a significant high correlation. (v) The gravity centre of annual rainfall erosivity is located in Tongdao County, while the monthly gravity centre has shifted across Guangxi, Hunan and Guizhou. In summary, knowledge of rainfall erosivity patterns is valuable for assessing the risk of soil erosion and formulating countermeasures.

Role of biochar from corn straw in influencing crack propagation and evaporation in sodic soils

Abstract: Soil degradation has become one of the serious global environmental problems, biochar is a recyclable natural material which has been widely used in soil remediation. Soil cracking can significantly change the migration path of water and nutrients in the soil, which affects the remediation performance of biochar and plant growth. The effects of different amounts of corn straw biochar on the evaporation and drying shrinkage characteristics of sodic soil are investigated in this paper. Different amounts (by mass) of biochar: 0%, 1%, 2%, 4% and 8% are added to the soil. A customized experimental device is used to measure the rate of evaporation of water and record the images of surface crack development of the samples during drying. Using image processing technology, the crack rate, crack entropy and fractal dimension of the cracks are obtained. The experimental results show that: (1) biochar can change the drying and cracking characteristics of sodic soil by changing the evaporation process of the soil. With biochar contents of 1%, 2%, and 4%, the residual water contents of soil samples are decreased by 4.83%, 43.94% and 85.71%, respectively. With an 8% biochar content, the residual water content of soil sample is increased by 64.49%, (2) the addition of biochar reduces the rate of cracking and fractal dimension of sodic soil. With an 8% biochar content, the rate of cracking and fracture fractal dimension are reduced by 46.39% and 13.74%, respectively, (3) the addition of biochar can effectively reduce the degree of the disorderly arrangement of surface cracks. At biochar contents of 2%, 4% and 8%, the crack entropies are decreased by 2.22%, 6.46% and 7.37%, respectively, and (4) the mechanisms in which biochar changes crack development in sodic soil are: (a) improving the soil texture and reducing the bulk density of soil and (b) increasing the number of water migration channels, soil aggregates and the water retention capacity in the soil.

Ensemble learning-based classification models for slope stability analysis

Abstract: In this study, ensemble learning was applied to develop a classification model capable of accurately estimating slope stability. Two prominent ensemble techniques—parallel learning and sequential learning—were applied to implement the ensemble classifiers. Additionally, for comparison, eight versatile machine learning algorithms were utilized to formulate the single-learning classification models. These classification models were trained and evaluated on the well-established global database of slope documented from 1930 to 2005. The performance of these classification models was measured by considering the F1 score, accuracy, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Furthermore, K-fold cross-validation was employed to fairly assess the generalization capacity of these models. The obtained results demonstrated the advantage of ensemble classifiers over single-learning classification models. When ensemble learning was used instead of the single learning, the average F1 score, accuracy, and AUC of the models increased by 2.17%, 1.66%, and 6.27%, respectively. In particular, the ensemble classifiers with sequential learning exhibited better performance than those with parallel learning. The ensemble classifiers on the extreme gradient boosting (XGB-CM) framework clearly provided the best performance on the test set, with the highest F1 score, accuracy, and AUC of 0.914, 0.903, and 0.95, respectively. The excellent performance on the spatially well-distributed database along with its capacity to distribute computing indicates the significant potential applicability of the presented ensemble classifiers, particularly the XGB-CM, for landslide risk assessment and management on a global scale.

Carbon sequestration under different organic amendments in saline-alkaline soils

Abstract: Organic amendments are a recyclable resource and can improve soil quality and carbon (C) sequestration, however, the difference in their effects on soil aggregate formation and C sequestration in saline-alkaline soils are less studied. We examined the effects of organic amendments to saline rice paddy fields with 4 treatments: (i) basal paddy soil (CK) lacking amendments (ii) organic fertilizer (OF) treatment at 910 kg C ha−1 from soybean litter and beans (iii) rice straw (RS) treatment at 3600 kg C ha−1 and (iv) rice straw-derived biochar (RS-biochar) treatment at 3600 kg C ha−1. All organic amendments significantly increased the soil organic C (SOC) stock in the 0–30 cm soil layer compared to CK. The SOC stock in the biochar treatment increased by 6.2 and 40.4% over the OF and RS amended treatments, respectively. This was most likely due to the greater levels of aromatic C in RS-biochar at 1.22 and 1.24 times greater than for the other organic amendments, respectively. Soil aggregates of 2–8 mm at 0–15 cm in the OF and RS amended soils were 38.3 and 58.2%, respectively, higher than that from the RS-biochar treatment. The mean weight diameter (MWD) in the OF and RS treatments were 35.4% and 45.8% higher than for the RS-biochar, respectively. MWD was significantly negatively correlated with soil exchangeable Na (p

Aboveground vegetation and soil physicochemical properties jointly drive the shift of soil microbial community during subalpine secondary succession in southwest China

Abstract: Based on long-term ecological research, microorganisms are sensitive to environmental changes in the process of ecosystem succession. However, few works have focused on the response of different groups of soil microorganisms to subalpine secondary succession and the driving forces behind the stepwise community development. To explore the changes in the microbial community structure and the driving mechanisms, we selected three replicated secondary succession gradients consisting of grass, shrub, different secondary forests (picea, pine and birch) and primary forest in a subalpine area in southwestern China. A high-throughput sequencing method was used to compare differences in the soil fungal and bacterial community structures. In combination with aboveground plant diversity and soil properties, the different mechanisms controlling the soil microbial community were revealed by the Mantel test. A two-way correlation network was also used to explore the connections among stages. The results show that the change in bacterial and fungal community structure was obvious, and these changes of bacteria were significantly correlated with soil acid phosphatase, LAP, soil moisture and aboveground vegetation communities during succession development in subalpine forests. Moreover, the fungal community was related to soil organic carbon, nitrogen, vegetation diversity, abundance and community structure. We also found that different dominant fungi play crucial roles in the succession sequence, and the relationship between soil microbes and vegetation is gradually simplified and stabilized. Our work suggests that both the relatively stable bacterial communities and the significantly changing fungal communities are notably associated with abiotic and biotic factors in secondary succession. As the ecosystem evolves, the dominant fungi obviously respond to succession and can successively establish close relationships with plants. Consequently, our results have important implications for understanding the driving mechanisms that control the soil microbial community during subalpine forest secondary succession.