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Depth to wet front is generally considered as the amount of water that penetrates into soil and wets the internal soil layer. This is an important variable especially in applications such as runoff generation and sediment yield estimation. This variable in some cases is used in the hydrological science, in the form of a proxy for infiltration as an important factor in soil erosion processes. This work deals with estimating event-based suspended sediment yield in relation to depth to wet front to capture the significance of the depth to wet front in Modified Universal Soil Loss Equation (MUSLE). In this field study, a rainfall simulator equipped with drip systems installed over an experimental hillslope plot was used to generate rainfall with intensities of 45, 60, and 70 mm/h over three slopes 10, 20, and 30% with three repetitions. The coefficient of determination (R2) and the Nash–Sutcliffe efficiency (ECNS) were applied as performance metrics for the model. The results revealed that storm rainfall energy or Erosivity Index (EI30) itself was not suitable for estimating the sediment yield. On the other hand, incorporating both EI30 and rainfall-runoff resulted in higher model efficiency. Upon addition of the depth to wet factor into MUSLE model, a greater efficiency was obtained. Further, the results of event-based simulated and observed sediment indicated that they closely corresponded to a straight line (푅2 = 0.94 and NSE = 0.93). Thus, the depth to wet front was a useful variable in sediment yield simulation. The Nash efficiency coefficient and correlation factor for the estimation total sediment yield were obtained (R2 = 0.93; NSE : 0.92) for both calibration and (R2 = 0.93; NSE : 0.86) validation stages. The result suggests that consideration of the depth to wet front in MUSLE model may lead to improved hydrological and sediment applications.

Gully erosion is the most active hydro-geomorphological phenomenon in the continental areas due to the high erosion rates triggered by the gully system. Monitoring and modelling gully development and gully distribution will contribute to understand landforms evolution and risk assessment. The purpose of the current research is to model head-cut gully erosion susceptibility (HCGES) using support vector machine (SVM), random forest (RF) and novel ensemble model of random forest with four Oblique methods (Logistic Regression, Ridge Regression, Partial least squares (PLS) and Support vector machine (SVM)) (hereafter called ensemble ORF) data mining models in Fareghan watershed, Hormozghan province, Iran. For this purpose, 14 variables influencing the gully development, were prepared and 145 head-cut gully erosion locations were identified in the study area. The efficiency of SVM, RF, ensemble ORF were evaluated based on receiver operating characteristic (ROC), the results have shown that all these three models are highly accurate and robust in predicting the head-cut gully erosion susceptibility zones. The results of the models were evaluated based on the area under the receiver operatic characteristic curve (AUC) in the validation stage presented that the efficiency of these models are 0.91, 0.94, and 0.96, respectively. Altitude and distance from the road in all three models were more important than other variables. The findings of this research will contribute to develop gully control strategies and to prevent the gully initiation where gully erosion is more susceptible.

Cultural heritage sites, particularly those in mountainous regions face serious threats as mountains are hazardous places and many of them are located on shifting tectonic plates and live under the threat of earthquakes and related activities. Alongside, mountains are also exposed to atmospheric interactions and rainfall plays an important role in land movement activities through seepage of rainwater in fragile structures. Moreover, gravity pushing along with land use and climate change induced changing rainfall patterns and modification of slope lead to several hazards in mountainous regions. Environmental degradation in terms of soil erosion, loss of forests, and agricultural products are common phenomena in mountainous regions due to various multi-hazard threats. Therefore, it is necessary to conserve and management of our mountain environments as it is essential to the survival of the global ecosystem. Thus, the current research article focused on multi-hazard susceptibility mapping and evaluation of its risk assessment in some of the famous cultural heritage sites in the eastern Himalayan region of Sikkim state, India. Multi-hazard susceptibility mapping was carried out using boosted regression tree (BRT), Bayesian additive regression tree (BART) and Bayesian generalized linear model (BGLM) considering twenty-two conditioning factors and seismic activity, as this region is highly susceptible to earthquakes. The future climate and land use change were estimated using four representative concentration pathway (RCP) scenarios and Dynamic Conversion of Land-Use and its Effects (Dyna-CLUE) model respectively to identify future multi-hazard susceptibility areas and vulnerable cultural heritage sites. The novelty of this study is to a combination of machine learning, RCPs derived future climate and Dyna-CLUE induced future land use change estimation for multi-hazard modelling and identification of vulnerable cultural heritage sites. The result of this study will help land use planners and archaeologists to adopt proper management strategies for protecting the cultural heritage sites and maintaining environmental sustainability for the proper management of mountain resources.

This study was aimed (i) to examine using diffuse reflectance spectra within VNIR region to estimate soil heavy metals concentrations at large scale, (ii) to compare the influence of different pre-processing models on predictive model accuracy, and (iii) to explore the best predictive models. A number of 325 topsoil samples were collected and their spectral data, pH, clay content, organic matter, Ni, and Cu concentrations were determined. To improve spectral data, various pre-processing methods including Savitzky-Golay smoothing filter, Savitzky-Golay smoothing filter with first and second derivatives, and standard normal variant (SNV) were used. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) models were employed to build calibration models for estimating soil heavy metals concentration followed by evaluation of provided predictive models. Results indicated that Cu had stronger correlation coefficients with spectral bands compared to Ni. Cu and Ni demonstrated strongest correlations at wavelengths 1925 and 1393 nm, respectively. Based on RMSE, R2, and RPD statistics, the PLSR model with Savitzky-Golay filter pretreatment provided the most accurate predictions for both Cu and Ni (R2 = 0.905, RMSE = 0.00123, RPD = 2.80 for Ni; R2 = 0.825, RMSE = 0.00467, RPD = 2.04 for Cu) where such prediction was much better for Ni than for Cu. Reasonable results with lower accuracy and stability were obtained for PCR (R2 = 0.742, RMSE = 0.00181, RPD = 1.91 for Ni; R2 = 0.731, RMSE = 0.00578, RPD = 1.65 for Cu) and SVMR (R2 = 0.643, RMSE = 0.00091, RPD = 3.80 for Ni; R2 = 0.505, RMSE = 0.00296, RPD = 3.22 for Cu). We concluded that reflectance spectroscopy technique could be applied as a reliable tool for detection and prediction of soil heavy metals.

Drought is a climatic anomaly, characterized by shortage (lack) of rainfall, high evaporation and unsuitable distribution of rainfall. This study investigated and compared the aggregated drought index (ADI) and standardized precipitation index (SPI) for drought monitoring in Minab watershed in Hormozgan province in south of Iran. Precipitation data were used for SPI calculation. Four variables including precipitation, potential evaporation, temperature and peak discharge (4 available recorded data) were used for ADI calculations using principal component analysis (PCA). Results of SPI show that watershed has normal drought situation from 1980 to 2009. On the other hand, ADI shows capability to detect dry and wet years. Moreover, it has capability to detect historical drought. Overall, by considering the results, the ADI is more reliable than SPI for drought monitoring in the study area. Key words: Drought, standardized precipitation index (SPI), aggregated drought index (ADI), Brentin, Minab, Hormozgan, Iran.

Soil organic carbon (SOC) concentration is a useful soil property with which to guide agricultural applications of chemical inputs. To enable this, simple, accurate, rapid and inexpensive methods are needed to produce maps of surface SOC concentrations. Researchers have investigated estimates of soil surface properties from remotely sensed information as a means of rapidly quantifying and monitoring some surface soil properties, such as SOC. The objective of this paper is to review the potential and limitations of remotely sensed data for mapping and evaluating SOC. Several statistical methods including simple regression models, the ‘soil line’ approach, principal component analysis and geostatistics have been applied to data to investigate the accuracy of such estimates. A review of the literature shows that predictive equations are not universal and require new regression models for every scene. An important benefit of remotely sensed data is to suggest a sampling strategy that can lead to improved representation of spatial heterogeneity in SOC.

In this study, we developed spectrotransfer functions (STFs) that relate soil hydraulic properties (SHPs) to spectral reflectance values to estimate hydraulic parameters of the Mualem–van Genuchten (MvG) model. We investigated the general potential of airborne as well as space-borne remote sensors to retrieve MvG hydraulic parameters of a bare soil agricultural field. Based on the ASD full spectrum (Scenario I), simple spectral signatures were generated mimicking the hyperspectral EnMAP sensor (Scenario II), and the multispectral Sentinel-2 sensor (Scenario III). A stepwise multiple linear regression method was used for each scenario to derive STFs. We further tested laboratory- and soil-map-based HYPRES and Rosetta pedotransfer functions (PTFs) to parameterize MvG parameters and thus provide soil water characteristics and hydraulic conductivity functions in the region. The best results were obtained for Scenarios I and II, with similar R 2 values for shape parameters α* and n and the lognormal saturated hydraulic conductivity ( K s*). The R 2 values were highest for K s* in Scenarios I and II (0.58 and 0.57, respectively). The R 2 values for α* and n were 0.30 and 0.34 in Scenario I and 0.39 and 0.31 in Scenario II, respectively. In all scenarios, the lowest R 2 values were obtained for saturated water content (θs), with values around 0.10 for Scenarios I and II and almost zero in Scenario III. Compared with HYPRES and Rosetta PTFs, the spectral approach performed reasonably well in terms of predicting soil water retention characteristics and unsaturated hydraulic conductivity. These findings suggest that spectral reflectance data provide a promising indirect and quick method for large-scale parameter estimation.