Slope Position Research Articles

Effects of vertical forest stratification on precipitation material redistribution and ecosystem health of Pinus massoniana in the Three Gorges Reservoir area of China.

Vertical stratification of forest plays important roles in the local material balance and in maintaining forest health by distributing and redistributing precipitation materials through adsorption, fixation, and release. Differences in runoff nutrient concentrations among vertical layers are closely related to vertical stratification (factors such as the trunk, canopy, forest litter, and soil physical and chemical properties). Long-term forest observations revealed significant spatial differences in Pinus massoniana (Pinus massoniana Lamb.) forests in the Three Gorges Reservoir area. Pinus massoniana forests on downslopes were characterized by a dense canopy, green needles, and rich forest vegetation, while those on upslopes were characterized by low vegetation cover, dead trees, and decreases in the tree height, diameter at breast height, and volume per plant with increasing slope. By analyzing the soil at different sites, we found that the pH of the forest land soil differed significantly among different slope positions. Soil on upper slopes was significantly more acidic than soil on lower slopes, indicating that acidic substances were intercepted by filtration through the broad litter layer and the soil surface layer. This filtration process resulted in a normal rhizosphere environment suitable for the absorption of nutrients by vegetation on the lower slopes. In this way, downhill sites provided a good microenvironment for the growth of Pinus massoniana and other vegetation. Our results show that direct contact between needles and acid rain was not the main cause of root death. Instead, the redistribution of rainfall substances by forest spatial stratification caused changes in the soil microenvironment, which inhibited the absorption of nutrients by the roots of Pinus massoniana and the growth of understory plants in Pinus massoniana forests on upper slopes. These findings emphasize that increasing land cover with forests with vertical structural stratification plays an important role in woodland material redistribution and forest conservation.

Habitat Associated with Ramps/Wild Leeks (Allium tricoccum Ait.) in Pennsylvania, USA: Guidance for Forest Farming Site Selection

Ramps or wild leeks (Allium tricoccum) are a popular foraged non-timber forest product in North America consumed for their edible bulbs and leaves. The agroforestry practice of forest farming is a possible solution to conservation challenges surrounding the wild exploitation of this species, but it requires proper site selection to be successful. In this study, maximum entropy (Maxent) modeling using 163 occurrence points and field data collected at 30 wild populations were combined to determine the characteristics of the ramp habitat in Pennsylvania. Both Maxent modeling and field measurements highlighted moist, lower slope positions with base-rich bedrock types as suitable for ramps. Sites shared 50% of their floristic associates on average, with 252 species documented in total. Forest communities associated with ramps included many species indicative of base-rich mesic soil conditions, but the relative abundance of some indicator species differed by region. The confirmation of model variables by field measurements and forest community types points to the usefulness of these characteristics in identifying suitable forest farming sites. When used in tandem, these results can help to guide site selection for forest farming and other conservation strategies.

Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy

Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping.

Influence of land-use types and topographic slopes on the physico-chemical characteristics of soils in Northwestern Ethiopia

AimsThis study examines the effects of land-use types and slope position on selected soil physico-chemical properties in the Ageza Watershed, Northwestern Ethiopia, focusing on soil fertility depletion and its impact on agricultural productivity.Materials and methodsThe experiment utilized a randomized complete block design (RCBD) with three replications, involving three land-use types and slope positions. A total of 27 soil samples (both disturbed and undisturbed) were collected, prepared, and analyzed for selected physico-chemical properties following standard laboratory procedures. Data were analyzed using two-way ANOVA with SAS software.ResultsSignificant variations in most soil physico-chemical properties were observed across land-use types and slope positions. Soil bulk density, organic carbon (OC), total nitrogen (TN), available phosphorus (Av. P), cation exchange capacity (CEC), and exchangeable bases significantly (P ≤ 0.001) differed among the land-use types and slope positions. The main effects showed significant (P ≤ 0.05) differences in sand, silt, and clay contents across the land-use types. However, pH did not show significant differences (P > 0.05). Soil bulk density, silt content, Av. P, exchangeable bases, and CEC all showed significant (P ≤ 0.01) differences among the slope positions. Forest land had the highest values for clay (46.11%), OC (6.08%), Av. P (20.60 mg/kg), CEC (33.89 cmol (+) kg-¹), and exchangeable cations: Ca²+ (23.72 cmol (+) kg-¹), Mg²+ (4.40 cmol (+) kg-¹), K+ (1.18 cmol (+) kg-¹), and Na+ (0.77 cmol (+) kg-¹). Grazing land exhibited higher silt and bulk density, while cultivated land had higher sand content. Lower slope positions recorded the highest values for clay (47.33%), silt (20.77%), pH (4.90), Av. P (16.61 mg/kg), Ca²+ (20.10 cmol (+) kg-¹), Mg²+ (3.73 cmol (+) kg-¹), K+ (1.00 cmol (+) kg-¹), Na+ (0.66 cmol (+) kg-¹), and CEC (28.71 cmol (+) kg-¹), while bulk density was higher in the upper slope position.ConclusionLand-use types and slope positions significantly influence variations in soil physico-chemical properties and overall soil fertility status. Forest preservation and management are essential to improve the soil fertility in this region.

Evaluating the impacts of area closure on soil properties in south central highland of Ethiopia

Soil degradation affects its quality and productivity of the land. To control soil degradation, various soil and water conservation (SWC) measures, including area closure are implemented. Understanding the impacts of SWC measures could support planning and implementation of land management practices. This study aimed to investigate impacts of area closure on soil properties in south central highland of Ethiopia. To acquire the necessary data, we conducted interviews with 80 households. Furthermore, nine years old area closure with physical soil and water conservation (AC-SWC), open grazing with physical SWC (OG-SWC), and open grazing without physical SWC (OG) were identified and each divided into upper, middle, and lower slope positions. In each slope position, 3 sample plots were established at 25–50 m intervals. A total of 27 composite samples and other 27 core samples for bulk density (3 treatments∗3 slope position∗3 replications) were collected and analyzed for soil properties. The data were analyzed using descriptive statistics and one-way ANOVA. The results showed that about 98 % of the respondents perceived that area closure would be an effective way to restore degraded land. Majority of the farmers (86 %) believed that area closure could reduce soil erosion and replenish soil fertility and thus, is beneficial in improving land productivity. Soil test results revealed that AC-SWC had significantly greater (P < 0.05) clay fractions, pH, available potassium, and available phosphorus than OG-SWC and OG. Variations of soil organic carbon (SOC) and total nitrogen (TN) were more pronounced. The SOC in AC-SWC was 83 % and 314 % greater than OG-SWC and OG, respectively. In the AC-SWC, TN was three and seven times higher than that of OG-SWC and OG, respectively. This could be due to vegetation restoration that reduces erosion, and enhances organic matter, moisture, and nutrient. The SOC, TN, pH, available potassium, and clay were significantly greater (P < 0.05) at lower slope position than upper in all forms of land management, which could be due to deposition at downslope. About 12 % and 11 % variations of clay fractions and SOC were, respectively, observed in between upper and lower slope positions at AC-SWC, but they varied by 25 % and 57 %, respectively, in OG. These imply area closure has had a beneficial effect on soil properties even on sloping land. AC-SWC is an important option to integrate in land management for rehabilitation of degraded soil.

Development of Polymorphic Index Model for Assessing Subtropical Secondary Natural Oak Forest Site Quality Under Complex Site and Climate Variables

Site and climate conditions are the key determinants controlling dominant height growth and forest productivity, both independently and interactively. Secondary natural oak forests are a typical forest type in China, especially in Hunan Province, but little is known about the site index of this forest under the complex site and climate variables in the subtropics. Based on survey data of dominant trees and site variables from 101 plots in Hunan oak natural secondary forests and climate data obtained using spatial interpolation, we used the random forest method, correlation analysis, and the analysis of variance to determine the main site and climate factors affecting oak forest dominant height and proposed a modeling method of an oak natural secondary forest site index based on the random effect of site–climate interaction type. Of the site variables, elevation affected stand dominant height the most, followed by slope direction and position. Winter precipitation and summer mean maximum temperature had the greatest impact on stand dominant height. To develop the modeling method, we created 10 popular base models but found low performance (R2 ranged from 0.1731 to 0.2030). The optimal base model was Mitscherlich form M3 (R2 = 0.1940) based on parameter significance tests. Since site and climate factors affect the site index curve, the dominant site and climate factors were combined into site types and climate types, respectively, and a nonlinear mixed-effects approach was used to simulate different site types, climate types, site–climate interaction types, and their combinations as random effects. Site–climate interaction type as a random factor enhanced model (M3.4) performance and prediction accuracy (R2 from 0.1940 to 0.8220) compared to the optimum base model. After clustering the 62 site–climate interaction types into three, five, and eight groups using hierarchical clustering, a mixed-effects model with the random effects of eight groups improved model performance (R2 = 0.8265) and applicability. The modeling method developed in this study could be used to assess a regional secondary natural oak forest site index under complex site and climate variables to evaluate the forest productivity.

Infiltration Characteristics and Hydrodynamic Parameters in Response to Topographic Factors in Bare Soil Surfaces, Laboratory Experiments Based on Cropland Fields of Purple Soil in Southwest China

Topography is an important factor that impacts the hydrological processes on sloping farmlands. Yet, few studies have reported the combined influences of slope gradient and slope position on infiltration characteristics and hydrodynamic parameters on sloping croplands in purple soil regions, an important area for agricultural productivity in Southwest China. Here, laboratory-simulated rainfall experiments were conducted in a steel trough (5 m long, 2 m wide, and 0.45 m deep), and rainfall lasted for 1 h at a rate of 90 mm h−1 to examine the variations in the infiltration rates and hydrodynamic parameters under varying slope gradients (i.e., 3°, 6°, 10°, 15°, 21°, and 27°) and slope positions (i.e., upper, middle, and lower), and explore the relationships between the infiltration rate and the soil detachment rate. The results showed that the infiltration rate decreased gradually with duration rainfall and ultimately approached a steady state in the six slope treatments. Cumulative infiltration ranged from 15.54 to 39.32 mm during rainfall, and gradually reduced with the increase of slope gradient. The Horton’s model outperforms other models for predicting the infiltration rate with an R2 value of 0.86. Factors such as Darcy–Weisbach friction, flow shear force, Manning friction coefficient, unit energy, and runoff depth varied in the following order: upper slope &gt; middle slope &gt; lower slope, whilst the Reynolds number and Froude number gradually increased along the slope transect from the upper to lower slope positions. A significant linear function was fitted between the soil detachment rate and the infiltration rate at the gentle slopes (3°, 6°, 10°), whereas an exponential relationship was observed at the steep slopes (15°, 21°, and 27°). Observation also suggested that 15° was the critical slope gradient of sediment detachment, infiltration characteristics, and hydrodynamic parameters. Our results provide theoretical insight for developing models that predict the impacts of topographic factors on hydrological characteristic and soil erosion in hilly agricultural landscapes of purple soil fields.

Transect approach for assessing major and trace elemental contamination in agricultural soils of the palamaner division, chittoor district, andhra pradesh, india.

Three soil transects located in the granitic regions of Palamaner mandal, Andhra Pradesh, India, were examined to assess the pollution levels of both primary and secondary metals (Si, Al, Fe, Ca, Mg, Na, K, Cu, Mn, P, and Zn) and to ascertain the degree of soil pollution in agricultural areas. The soils along these transects are slightly acid to neutral, with dark brown to red rubified argillic clay-rich B horizons alongside a moderate cation exchange capacity. The A horizon soils display low organic carbon levels with a moderate variability and contain over 70% SiO2, exhibiting low variability due to limited leaching in a semiarid climate. The findings suggest that iron (Fe) and manganese (Mn) oxides play a role in reducing contamination levels through oxidation and precipitation processes. Furthermore, the soils show low to moderate cation exchange capacity, which restricts the retention of absorbed heavy metals, thus lessening their negative impacts. A two-way ANOVA revealed significant differences in CEC, organic carbon, and total zinc content across different horizons and landscape positions. Si, Al, and Cu had small increases and negative geoaccumulation indices in all soil profiles, suggesting no pollution. However, the Nemerow and mean contamination degree over 6 point to slight to moderate pollution. The analysis identified three distinct clusters with significant variations in contamination factors for SiO2 and Cu. Five principal components were determined, explaining 76% of the total variance, primarily derived from geogenic sources and remaining within acceptable limits. This research on soil transects in granitic regions contributes to a better understanding of the distribution, movement, and concentration of elemental oxides based on slope position, which is essential for pollution assessment and soil quality enhancement.

Estimation of Carbon Stock in Chinese Fir Stands Based on Stacking Ensemble Learning and LSTM Models

Abstract Forest carbon sinks, a critical component of the global carbon cycle, constitute nearly half of the total terrestrial carbon pool. This study employed correlation analysis and factor effect analysis to quantify the influences of various factors on the volume of Chinese fir stands. A novel modeling framework was developed using the stacking ensemble learning and LSTM (LongShort-TermMemory) models. This framework incorporated diverse base learners, including XGBoost, Adaboost, KNN, and DT, which are intelligently ensemble via GBDT as a meta learner. The model was further optimized by comparing activation functions and optimizers, with ReLU selected as the activation function and Adam as the optimizer. Model accuracy was evaluated using RMSE, MAE, and R2 metrics, significantly enhancing its learning ability and generalization performance. The findings are as follows: (1) Stand stocking showed strong positive correlations with depression, age group, average diameter at breast height (ADBH), average tree height (ATH), slope, and elevation. Conversely, it exhibited significant negative correlations with origin, stand density, and slope position. In investigating Chinese fir growth on slopes, no significant growth differences were observed between downslopes and midslopes; however, both differed significantly from upper slopes. (2) The stacking ensemble learning method constructed here surpassed all existing single models in terms of estimation and assessment indices, demonstrating superior comprehensive performance. (3) Among the LSTM models, Adam-LSTM performed the best (R2=0.844), followed by Sigmoid-LSTM (R2=0.656), while the RMSprop-LSTM model performed the worst (R2=0.618). Combined with artificial intelligence methods, our optimized carbon stock estimation model can help to improve the ability of forest land management and provide a theoretical basis for the scientific management of forest areas.

Characterizing rapid infiltration processes on complex hillslopes: Insights from soil moisture response to rainfall events

Evaluating the soil moisture response characteristics to rainfall events is valuable for understanding eco-hydrological effects in the context of global climate change. However, the effects of soil thickness and rainfall characteristic indicators on soil moisture responses remain unclear, especially in the karst hillslopes characterized by highly complex topography. This study examined the soil moisture response to 35 rainfall events at 5-min intervals by deploying 20 sets of monitoring devices across two hillslope plots (5 × 20 m) with distinct mean soil thicknesses. The response metrics included the soil moisture response time (Tp2p) and wetting front velocity (Vwf), whereas indicator factors considered were soil thickness distribution, slope position, and rainfall characteristics. Overall, the results showed that Tp2p increased from downslope to upslope, as well as from the surface to deep layers. Surprisingly, the mean Vwf (1373 mm h−1) in this study site was significantly higher than that in non-karst regions (17–610 mm h−1). This suggests that rainwater can rapidly infiltrate the soil profile and contribute to subsurface runoff generation. Rainfall characteristics are the primary controlling factors influencing the soil moisture response to rainfall, with their contribution priority (44.3–63.9 %) higher than that of antecedent soil moisture conditions (26.1–35.2 %). The variation in soil moisture response metrics in shallow soil cover hillslopes was more affected by rainfall indicators (41–64 % vs. 31–47 %), thereby potentially weakening the potential influence of topography and soil properties. These findings highlight the effect of refined monitoring in characterizing soil moisture heterogeneity in response to rainfall and emphasize the potential impact of rapid responses on karst eco-hydrological processes.

Accuracy assessment of the geomorphon approach to detect ecological sites in the Dry Chaco region of Argentina

Ecological sites are a classification of local-land types based on differences in important environmental factors including soil properties, slope and landscape position within a geomorphologic and climatic zone. The concept is pivotal to conduct adaptative management in arid rangelands because it defines homogeneous areas over which specific soil-vegetation-disturbance dynamic occurs. Geomorphon is a computationally efficient method that uses Digital Elevation Models (DEMs) for the classification of landforms at multiple scales. The objective of this study was to evaluate the accuracy of the geomorphon approach to map ecological sites in a 11,259-ha study area within the Dry Chaco region of Argentina, utilizing local topographic positions along soil catena as a predictor of broader ecological sites classes. We used two DEM pixel resolutions (12.5 m and 30 m) and optimized the flatness threshold (t) and search radius (L) geomorphon parameters. As “ground truth” we used legacy data of 62 soil profiles descriptions. The geomorphon elements detected were reclassified into the three main ecological sites of the study area: highlands, midlands and lowlands. We used the overall disagreement (D) as the main metric for evaluating the accuracy of ecological site classifications. We found that: i) the lowest t value (0.05°) captured subtle topographical changes and thus more effectively reflected the soil-landscape relationship, and ii) larger L values paired with the lower pixel resolution (30 m) diminished the impact of minor landforms, improving the accuracy of ecological site detections. We determined that the geomorphon model with the pixel resolution of 30 m, t-value of 0.05°, and L-value of 12 produced the ecological site classification map with the highest accuracy, achieving a moderate-high accuracy (D of 33.9 %). Our study suggests that the geomorphon approach shows potential for consistent, reproducible and easily updatable ecological site mapping over larger areas.

Seasonal and interspecific variations in the water uptake depth of trees in a moist cool-temperate mixed forest in Japan

ABSTRACT The water uptake depth of trees affects their overall water use and the water cycle of forest ecosystems. However, empirical data on the depth from which water is drawn are lacking for many moist forests. In this study, we estimated the water uptake depths of 24 coexisting tree species in a moist cool-temperate mixed forest in Japan using stable oxygen isotopes. Our goal was to clarify the factors influencing water uptake depth including season, species, slope position, and tree height under moist conditions. Water uptake depth was determined during three periods in which temperatures differed but soil moisture levels remained similar. Season was shown as one of the factors affecting water uptake depth, which was shallowest in August, when temperatures reached maximum values, and deepest during May, when temperatures were lowest. This seasonal variation is likely to be related to physiological processes such as increasing transpiration which leads to water uptake shifting to the water-rich shallow soil layer and/or activation of fine root production at the soil surface, where temperatures are high. Water uptake depth also varied with tree species, suggesting the occurrence of belowground resource segregation among moist forest tree species. Slope position and tree height did not affect water uptake depth. This study highlights the importance of considering temperature-driven physiological processes when examining water uptake depth in moist forests, which could have implications for predicting the responses of these ecosystems to climate change.

Effects of Terracing on Soil Aggregate Stability and Erodibility in Sloped Farmland in Black Soil (Mollisols) Region of China

Soil aggregates are important indicators of soil structure stability and quality. The black soil region of northeast China, known for its high agricultural productivity, faces significant challenges due to soil erosion. This study investigates the impact of terracing on the stability and erodibility characteristics of soil aggregates in sloped farmlands, which is crucial for this important agricultural area. Three research sites with the same basic management modes were selected along a latitudinal gradient, from the mid-temperate zone to the cold temperate zone, in the black soil region of northeast China. The Savinov method was used to analyze the differences in soil aggregate size distribution, stability characteristics, and soil erodibility between terraced and non-terraced slopes at each research site. The results showed that terracing increased the content of large soil aggregates (&gt;0.25 mm) by 5.38–6.35%, with the increase becoming more pronounced from north to south. The improvement in soil structure varied by location and slope position, with the most significant improvement at the middle slope position. Terracing enhanced soil aggregate stability, reduced soil erodibility, and improved soil structure by increasing clay and soil organic matter (SOM) content and reducing soil bulk density (BD), promoting the conversion of small aggregates to large aggregates. Soil stability indicators such as water-stable aggregates (WSAs), mean weight diameter (MWD), and geometric mean diameter (GMD) were dominated by aggregates &gt; 5 mm, while erodibility indicators such as fractal dimensions (Ds) and the soil erodibility factor (K values) were mainly influenced by aggregates &lt; 0.25 mm. Terraces can improve the soil structure and stability of sloping farmland by increasing the content of large soil aggregates and enhancing overall soil quality. The benefits of these improvements increase with latitude. These findings provide critical insights for determining effective management practices for sloped farmlands in the black soil region under various site conditions. They offer scientific evidence for preventing soil erosion and improving soil quality, thus supporting the sustainable development strategy for protecting black soil and ensuring long-term agricultural productivity.

A dataset of plant community dynamics in the growing season at different slope positions at the National Field Research Station at the Northern Foot of Yinshan Mountain (2019&amp;ndash;2022)

A dataset of plant community dynamics in the growing season at different slope positions at the National Field Research Station at the Northern Foot of Yinshan Mountain (2019&amp;ndash;2022)

Quantifying spatially explicit uncertainty in empirically downscaled climate data

AbstractEcological simulations including forest and vegetation growth models require climate inputs that match the resolution and extent of the process being modelled. Climate inputs are often derived at resolutions coarser than the scale of many ecosystem processes. Machine learning models can be trained to spatially downscale climate data to fine (30 m) resolution using topographic variables such as elevation, aspect and other site‐specific factors. Statistically downscaled climate models will have spatially varying uncertainty that is not usually incorporated into downscaling techniques for error propagation into later models, are often applied on smaller areas, are not fine enough resolutions for many modelling techniques, or are not always scalable to large spatial extents. There remains opportunity to leverage machine learning advancements to downscale climate to very fine (30 m) resolutions with associated spatially explicit uncertainty to represent microclimatic variation in ecological models. In this study, we used quantile machine learning to produce 30 m downscaled temperature and precipitation data and associated model prediction uncertainty for the state of California. Temperature models were accurate at downscaling 4 km climate data to 30 m, performing better than the 4 km data at high and low slope positions and at high elevations, especially where there were fewer weather observations. Precipitation model predictions did not show global improvement over the 4 km scale, but were more accurate at high elevations, slopes with higher solar radiation and in valleys. For all climate variables, the added detail of spatial explicit uncertainty via 90% prediction intervals provides critical insight into the utility of empirically downscaled climate. The resulting 30 m spatially contiguous outputs can be used as ecological model inputs with uncertainty propagation, to illuminate climate trends over time as a function of fine‐scale spatial factors, and to highlight areas of spatially explicit uncertainty.

Evaluation of Slope Position for Variable‐Rate Fertilization in Southern Alberta

AbstractSoil properties and crop yield vary with slope position in hummocky landscapes. Slope position is also readily mapped and thus potentially useful for delineating management zones for variable‐rate fertilization (VRF). Slope position was evaluated as a basis for VRF in southern Alberta, Canada. Earn 0.5 CEUs in Nutrient Management by reading the article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses.

Diversity of Soil Physical Properties of Andisols in Toposequence Results from the Eruption of Mount Tangkuban Parahu in Sukawana Tea Plantation

Topography is one of the soil forming factors that affect soil properties and crop productivity. Soil properties that affect crop productivity include soil physical properties. The purpose of this research was to identify the characteristics of soil physical properties including permeability, bulk density, and soil porosity at various slope positions and identify the effect of slope position on soil physical properties. This research was conducted on a toposekuen located in Sukawana Tea Plantation which has an undulating to hilly topography with a slope of 25 - 45%. This research was conducted in May - July 2024. The research method used was comparative descriptive survey method based on direct observation in the field and continued with F-test to see the effect of slope position on soil physical properties with DMRT further test. Soil samples were taken as many as three replicates on each slope position (top, middle slope, lower slope). The results showed that the slope position did not significantly affect the permeability, bulk density, and porosity of the soil.

Integrated machine learning and geospatial analysis enhanced gully erosion susceptibility modeling in the Erer watershed in Eastern Ethiopia

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (&gt;1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (&lt;15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

Intelligent trapezoid and variable weight combination-based reconstructed GM model

The GM(1,1) model's prediction accuracy is significantly influenced by the accuracy of background value estimation. The traditional trapezoidal background value can only be applied to a specific data sequence. Therefore, this study proposes a GM(1,1) model background value reconstruction approach based on the combination of intelligent trapezoidal and variable weights in order to increase the model’s application as well as its prediction accuracy. The trapezoidal background value function with slope and point position parameters is called model I. Then, a set of point position parameter sequences, with a new background value function is constructed, called model II. A genetic algorithm is utilized to seek for the values of the parameters to be determined in both models I and II. The results showed that for the exponential growth data series, model I and II have higher prediction accuracy compared to traditional models. For data sequences, taking the traffic volume series of a road from 2014 to 2023, the prediction accuracy of this paper's model I method can be improved by 0.3643 % and 0.2725 % compared with Deng's and Wang's models. The prediction accuracy of this paper's model II method has been further improved by 0.1075 % compared with that of model I.