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Runoff–sediment process modeling is highly variable and nonlinear in nature. For sediment yield prediction, the difficulty of rainfall–runoff–sediment yield hydrological processes remains challenging. The present study uses a simple nonlinear dynamic (NLD) model to predict daily sediment yields, taking into account the degree of daily–sediment yield in catchment areas, and its findings were compared to three widely used models including artificial neural networks (ANN), support vector machine (SVM), and gene expression programming (GEP). The daily measured discharge–sediment data for 25 years were obtained from Shakkar Watershed; Central India as in the current study. The coefficient of correlation (CC), Nash-Sutcliff (NS), and root-mean-square error (RMSE) were employed to assess the performance of the models. The results show that the NLD model was found better than ANN, SVM, and GEP model. These models had correlation coefficient (CC = 0.975, 0.887, 0.843, and 0.901), root-mean-square error (RMSE = 0.748, 1.751, 1.961, and 1.545), and Nash–Sutcliffe efficiency (0.952, 0.784, 0.673, and 0.814) correspondingly. Hence, the NLD model can be used for predicting sediment. In order to implement appropriate measures of soil conservation in the watershed to reduce the sediment load in the river, predicting the sediment yield is very necessary to maximize the life of the structure. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R2 = 0.54; p < 0.0075). A significant statistical relationship (r = −0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management. [ABSTRACT FROM AUTHOR]

Identification of the sensitive regions to soil erosion can be the foremost step in controlling the vigorous pace of soil degradation in agricultural lands. This study employs individual and ensemble models including compound factor (CF), analytical hierarchy process (AHP), and technique for order preference by similarity to ideal solution-analytical hierarchy process (TOPSIS-AHP) for identifying the regions susceptible to soil erosion in Dnyanganga watershed of India using morphometric attributes (MAs). Thirteen MAs were computed using the newly released TanDEM-X data, which is a bi-static X-band interferometric Synthetic Aperture Radar (SAR) acquisition. Weights for each of the MAs were calculated using AHP, which was employed in both AHP and TOPSIS-AHP models. The outcome of the AHP model displays that watershed slope (Sw), stream frequency (Fu), ruggedness number (Rn), and relative relief (Rr) greatly influences soil erosion in the study area. The priority ranking outcome of individual and ensemble models are compared using the Spearman correlation coefficient test (SCCT) and Kendall tau correlation coefficient test (KTCCT) to select the best model for soil erosion susceptibility mapping. The result of the non-parametric tests reveals that TOPSIS-AHP ensemble model is better in ranking alternatives than the individual models. According to TOPSIS-AHP ensemble model, the sub-watershed DNY16 is highly susceptibility to erosion, whereas DNY1 is least susceptible. Based on the ensemble model, 14.54%, 41.02%, and 44.43% of the area in Dnyanganga watershed fall under high, medium and low soil erosion susceptible classes, respectively. The results from this study will aid decision makers in better conservation planning of soil and water resources. [ABSTRACT FROM AUTHOR]