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Vegetation restoration is an effective and important measure for controlling soil erosion in arid and -arid regions. Both its aboveground and underground parts play a crucial role in controlling surface runoff and soil detachment on slopes. But how much the parts of vegetation contribute to the runoff and sediment reducing benefits of rill erosion on slopes is unclear. We used grassland slopes at four successional stages for simulated scouring experiments to observe how successional vegetation community structures, root characteristics, and soil structures contribute to erosion and sand production. Initial flow production time increased, and total runoff decreased. Under the scour intensities, the 11-year slope had the lowest flood peak and volume and the greatest runoff reduction benefit. The 25-year slope had the lowest sand peak and volume and the greatest sediment reduction benefit. As scour intensity increased, runoff reduction effect of vegetation at the successional stages decreased; the sediment reduction benefit remained high. PLS-PM analysis showed that the indirect effects of the aboveground and underground parts of vegetation on sand production were -0.364 and -0.439, respectively. Aboveground parts mainly embodied the regulation of runoff, in which stem count, humus mass, and biomass were the main factors affecting runoff and sand production. Underground parts mainly reflected their soil structure improvement, in which root volume density, root surface area density, and root mass density are the main explanatory variables. The direct effects of runoff and soil structure on slope rill erosion were 0.330 and -0.616, respectively, suggesting the stability of soil structure is the primary factor affecting the sand production, not erosion energy. The results provide a reference for scientific assessment of the key role of natural vegetation restoration in regional soil erosion control and the development of biological measures for soil and water conservation on the slopes of the Loess Plateau.

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Soil erosion and sediment buildup are the factors that speed up the decline in capacity and function of reservoirs, agricultural products, and water resources. In order to simulate sediment and runoff and map high sediment-yielding sub-basins in the Gibe Gojeb catchment in southwest Ethiopia, this study used the Soil and Water Assessment Tool (SWAT) model. Using data on sediment and river flow, calibration and validation were carried out. Between 2003 and 2016, the catchment produced an average annual sediment loading of 62.5 tons ha-1 yr-1, with loading fluctuations ranging from 0.2 to 108.4 tons ha-1 yr-1. The acceptable sediment yield threshold value ranges from 12.3 to 108.4 tons ha-1 yr-1 for 56 sub-basins, and from 0.2 to 10 tons ha-1 yr-1 for 5 sub-basins. The most significant sub-basins with very high to extremely severe sediment yields were sub-basins 1 to 30, 32 to 44, 47, 48, 50, 51, and 53 to 61. After thirteen years of operation, the yearly amount of 58,802 tons of sediment transferred from the catchment and deposited into Gibe One reservoir has decreased the capacity by 5.7 %. The accumulation of sediment in a reservoir has an impact on its functionality, power production, and capacity, affecting the safety of dams and the environment. The study's findings enhanced our comprehension of sediment accumulation in reservoirs and furnished us with the necessary information regarding reservoir safety, integrated soil, and water management.

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The Jinsha River Basin (JRB) contributes a significant amount of sediment to the Yangtze River; however, an imbalance exists between runoff and sediment. The underlying mechanisms and primary factors driving this imbalance remain unclear. In this study, the Shapley Additive Explanation (SHAP) and Geographical Detector Model (GDM) were employed to quantify the importance of the driving factors for water yield (WYLD) and sediment yield (SYLD) using the Soil and Water Assessment Tool (SWAT) model in the JRB. The results indicated that the SWAT model performed well in simulating runoff and sediment, with R2 > 0.61 and NSE > 0.5. Based on the simulated data, SYLD exhibited strong spatiotemporal linkages with WYLD. Temporally, both sediment and runoff showed decreasing trends, with the sediment decrease being more pronounced. Spatially, WYLD and SYLD displayed similar distribution patterns, with low values in the southwest and high values in the northeast. By quantifying the driving factors, we found that climatic factors, including precipitation and potential evapotranspiration, were the main influencing factors for WYLD and SYLD across the entire region, though their contributions to the two variables differed. For WYLD, climatic factors accounted for 70 % of the total influencing factors, whereas their contribution to SYLD was 50 %. Furthermore, soil type and land-use type played significant roles in the SYLD, with importance values of 16 % and 12 %, respectively. Under the influence of surface conditions, the proportion of SYLD in the JRB to the total SYLD in the Yangtze River Basin was greater than that of WYLD. The findings of this study provide scientific evidence and technical support for local environmental impact assessments and the formulation of soil and water conservation plans.

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The study utilized the Modified Universal Soil Loss Equation (MUSLE) to predict sediment loss and evaluate the model's performance in the Agewmariam experimental watershed in order to support the planning, management, and appropriate use of the soil and water resources in the watershed. The natural resources conservation service (NRCS) curve number method was used to model runoff energy factor. By overlaying maps of runoff energy, soil erodibility, slope length and steepness, cover management, and support practice factors with assigned values, the cumulative effect of these parameters for the suspended sediment yield was calculated using the ArcGIS raster calculator. The runoff energy factor was the most sensitive parameter, followed by slope length and steepness factor. To improve the model's fit to the local conditions, the initial abstraction to storage ratio (λ) of the runoff energy factor was reduced to 0.023, and the MUSLE model coefficient and exponent were adjusted to 1 and 0.59, respectively. During calibration, the mean observed and estimated suspended sediment yields were 0.2 and 0.23 ton/ha, respectively, while during validation, they were 0.7 and 0.53 ton/ha, respectively. The model evaluation showed that the MUSLE model, without calibration, was not appropriate for estimating runoff and sediment yield. However, with appropriate calibration, the model showed good performance with a coefficient of determination (R2), coefficient of efficiency (E), and index of agreement (d) of 0.85, 0.85, and 0.96 respectively, during calibration and 0.84, 0.65, and 0.83 respectively, during validation. Based on these findings, this study suggests that the calibrated MUSLE model can be used to prioritize soil and water conservation interventions within the watershed or can be extrapolated to neighboring similar watersheds. Further refinement of model input parameters using more data from the watershed is recommended to increase the prediction accuracy of the model.

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The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

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The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The analysis was meticulously segmented into three distinct time spans: 2025-2049, 2050-2074, and 2075-2099. This innovative method integrates insights from multiple climate models under two Representative Concentration Pathways (RCP4.5 and RCP8.5), along with LULC projections generated through the Cellular Automata Markov model. By combining the strengths of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) techniques, the study aims to improve the accuracy of sediment yield simulations in response to changing environmental conditions. The non-linear autoregressive with external input (NARX) method was adopted for the ANN component of the hybrid model. The adoption of the hybrid SWAT-ANN approach appears to be particularly effective in improving the accuracy of sediment yield simulation compared to using the SWAT model alone, as evidenced by the higher coefficient of determination value of 0.74 for the hybrid model compared to 0.35 for the standalone SWAT model. In the context of the RCP4.5 scenario, during 2075-99, the study noted a 29.34% increase in sediment yield, accompanied by simultaneous rises of 42.74% in discharge and 27.43% in rainfall during the Indian monsoon season, spanning from June to September. In contrast, under the RCP8.5 scenario, for the same period, the increases in sediment yield, discharge, and rainfall for the monsoon season were determined to be 116.56%, 103.28%, and 64.72%, respectively. The present study's comprehensive analysis of the factors influencing sediment supply in the Puthimari River basin fills an important knowledge gap and provides valuable insights for designing proactive flood and erosion management strategies. The findings from this research are crucial for understanding the vulnerability of the Puthimari basin to climate and land use changes, and by incorporating these findings into policy and decision-making processes, stakeholders can work towards enhancing resilience and sustainability in the face of future hydrological and environmental challenges.

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Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7 %), water bodies (3.0 %), wetlands (1.5 %), and grassland (1.7 %), the analysis of LULC change (1991-2020) showed a yearly increase in the area of cultivated land (1.4 %), built-up land (0.8 %), and bare land (3.5 %). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed's economically productive topsoil was lost throughout the study's twenty-nine-year period (1991-2020). According to the degree of erosion, 16 % of the watershed was deemed seriously damaged, while 70 % was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52 % of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

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With the economic development, a large number of engineering accumulation bodies with Lou soil as the main soil type were produced in Guanzhong area, Northwest China. We examined the characteristics of runoff and sediment yield of Lou soil accumulation bodies with earth (gravel content 0%) and earth-rock (gravel content 30%) under different rainfall intensities (1.0, 1.5, 2.0, 2.5 mm·min-1) and different slope lengths (3, 5, 6.5, 12 m) by the simulating rainfall method. The results showed that runoff rate was relatively stable when rainfall intensity was 1.0-1.5 mm·min-1, while runoff rate fluctuated obviously when rainfall intensity was 2.0-2.5 mm·min-1. The average runoff rate varied significantly across different rainfall intensities on the same slopes, and the difference of average runoff rate of the two slopes was significantly increased with rainfall intensity. Under the same rainfall intensity, the difference in runoff rate between the slope lengths of the earth-rock slope was more obvious than that of the earth slope. When the slope length was 3-6.5 m, flow velocity increased rapidly at first and then increased slowly or tended to be stable. When the slope length was 12 m, flow velocity increased significantly. In general, with the increases of rainfall intensity, inhibition effect of gravel on the average flow velocity was enhanced. When rainfall intensity was 2.5 mm·min-1, the maximum reduction in the average flow velocity of earth-rock slope was 61.5% lower than that of earth slope. When rainfall intensity was less than 2.0 mm·min-1, sediment yield rate showed a trend of gradual decline or stable change, while that under the other rainfall intensities showed a trend of rapid decline and then fluctuated sharply. The greater the rainfall intensity, the more obvious the fluctuation. There was a significant positive correlation between the average sediment yield rate and runoff parameters, with the runoff rate showing the best fitting effect. Among the factors, slope length had the highest contribution to runoff velocity and rainfall erosion, which was 51.8% and 35.5%, respectively. This study can provide scientific basis for soil and water erosion control of engineering accumulation in Lou soil areas.

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The strategic reduction and remediation of degraded land is a global environmental priority. This is a particular priority in the Great Barrier Reef catchment area, Australia, where gully erosion a significant contributor to land degradation and water quality deterioration. Urgent action through the prioritisation and remediation of gully erosion sites is imperative to safeguard this UNESCO World Heritage site. In this study, we analyze a comprehensive dataset of 22,311 mapped gullies within a 3480 km2 portion of the lower Burdekin Basin, northeast Australia. Utilizing high-resolution lidar datasets, two independent methods - Minimum Contemporary Estimate (MCE) and Lifetime Average Estimate (LAE) - were developed to derive relative erosion rates. These methods, employing different data processing approaches and addressing different timeframes across the gully lifetime, yield erosion rates varying by up to several orders of magnitude. Despite some expected divergence, both methods exhibit strong, positive correlations with each other and additional validation data. There is a 43% agreement between the methods for the highest yielding 2% of gullies, although 80.5% of high-yielding gullies identified by either method are located within a 1 km proximity of each other. Importantly, distributions from both methods independently reveal that ∼80% of total volume of gully erosion in the study area is produced from only 20% of all gullies. Moreover, the top 2% of gullies generate 30% of the sediment loss and the majority of gullies do not significantly contribute to the overall catchment sediment yield. These results underscore the opportunity to achieve significant environmental outcomes through targeted gully management by prioritising a small cohort of high yielding gullies. Further insights and implications for management frameworks are discussed in the context of the characteristics of this cohort. Overall, this research provides a basis for informed decision-making in addressing gully erosion and advancing environmental conservation efforts.

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Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management.

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The increasing sediment yield in the watershed is caused by natural and human activities, which significantly shifts the hydro-meteorological in the watershed. The Modified Universal Soil Loss Equation (MUSLE) equation in the Soil and Water Assessment Tool (SWAT) was used to estimate sediment yields for each hydrological response unit (HRU) based on peak runoff, daily runoff volume, area of hydrological response unit, and other estimated and default hydrological model parameters. The amount of sediment yield from each HRU is then summed to give the total soil erosion for the watershed.The spatio-temporal variations of sediment yield in the Upper Gilo watershed was simulated to identify the hotspot area and select the effective management practices (BMPs) for reducing significant problems. Model calibration and validation were carried out using sediment yield data from 1990 to 2004 and 2005 to 2014. The results indicated that the watershed total sediment yield is 1021.8 tonnes/yr. Furthermore, 17 sub-basins (37.8% of total watershed area) are severely threatened by high soil erosion. According to the simulation results, the filter strips, terraces, and contours reduced the watershed sediment yield by up to 53.2%, 45.4%, and 48%. Overall, the selected BMPs are highly effective in reducing sediment yield in watershed-prone areas.

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Unaltered watersheds with natural vegetation cover (forest, grasslands, etc.) provide several ecological benefits in addition to providing freshwater, controlling water levels, and supporting flourishing streamside ecosystems. However, as in many watersheds in the World, the research area in this study, the Borcka Dam Watershed (BDW), has been affected by many human-induced disturbances affecting a wide area of forest and grassland areas as well as soil and water resources. Therefore, the objective of this study was to assess and evaluate the possible effects of anthropogenic disturbances, particularly on annual changes in water discharge, some water quality parameters, and total suspended sediment (TSS) amounts in the main streams of four sub-watersheds (Fabrika, Godrahav, Hatila, and Murgul) and the reservoir of the dam. In addition, we intend to confirm that land use change and/or transformation play a significant role in influencing stream water quality. The YSI/Professional-Plus, a portable water quality measurement device, was used to determine the amounts of pH, dissolved oxygen (DO), total dissolved substance (TDS), ammonium (NH4-N), nitrate (NO3-N), salinity, electrical conductivity (EC), and temperature besides measuring discharge and total suspended sediments (TSS) from a total of 27 sampling points in the field. Although the results revealed that the annual mean values of all water quality parameters for all four streams were mostly in good condition, for some time and points of the measurements, several parameters were found to be above the official water quality standards due to the intensive aforementioned anthropogenic activities, particularly in the stream waters of Murgul (e.g. pH and TSS being 10,84 and 236 mg/L, respectively) and Fabrika (e.g. EC of 412 µs/cm; DO of 4.44 mg/L; 14 ml of NO3-N) sub-watersheds. These outcomes indicate that these two sub-watersheds have been impacted more severely by the human-induced disturbances compared to Hatila and Godrahav sub-watersheds.

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The characterization of a rainfall simulator provides an excellent opportunity to study the potential of soil erosivity without waiting for natural rain. But, precise instrumentation is required to estimate the parameters, which is seldom available. To overcome this problem, the empirical and conceptual relationships obtained through physically-based modeling help to correlate the rain parameters contributing to soil erosion. The present laboratory study used five pressurized nozzles of different capacities and a Laser Precipitation Monitor (LPM) to generate different rain intensities (21.0-79.0 mm h-1) and to register drop size distribution, respectively. The sediment transportation induced by rain and runoff was measured with an erosion flume of 2.50 × 1.25 × 0.56 m with an adjustable longitudinal slope. The spatial uniformity, drop size distribution, drop velocity, and kinetic energy were used to evaluate the simulator's performance. The different rain erosivity parameters were correlated and tested statistically using linear and non-linear regression analysis. The rain simulation experiments of different intensities at different pressure ranges were performed on flat, 5, 10, and 15% slopes of the erosion flume to evaluate rain characteristics and record the surface runoff and sediment yield. The median drop sizes produced during the simulator ranged from 0.38 to 2.11 mm, coinciding with natural rain. The empirical relationships were developed to correlate surface discharge and sediment yield with rain intensity by optimizing the parameters for further study of experimental field plots of different slopes. The observed and estimated rain erosivity parameters showed a significant relationship (R2 = 0.75 to 0.93; P < 0.001) in multiple regression analysis, and the metrics used to test the developed regression equations showed lower MAE, MSE, and RMSE errors indicating the adequacy of the relationships. The results indicated that the simulator helps to understand the complex task of soil erosion with hydrologic and geomorphic processes in laboratory experimentation with sufficient accuracy in measuring sediment transport events.

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This study predicts sediment yield on various landuse surfaces within the Calabar River Catchment, Nigeria. Five experimental plots of 31 by 23 cm (representing urban, farm, grass, bare, and forest surfaces) were established on a convex slope series with a 20% gradient, oriented along the slope strike. Rainfall, morphological, and hydraulic stations were derived for each plot. Multiple regressions and Factor analysis were employed to analyse the collected data. The research identifies critical factors influencing sediment yield, such as rainfall amount, rainfall intensity, slope gradient, slope length, sand, silt, clay, vegetation cover, and infiltration capacity. The results (p < 0.05) indicate that slope length, sand, silt, clay, infiltration capacity, and vegetation cover significantly influence sediment yield for urban, farmland, grassland, and bare surfaces, respectively. Factor analysis revealed strong correlations between sediment yield, silt, rainfall amount, rainfall intensity, and slope gradient. Case-wise diagnostics predictions indicate sediment yields for urban, bare, farm, grass, and vegetation-covered surfaces as 14.95 kg, 33.91 kg, 28.78 kg, 33.50 kg, and 5.66 kg, respectively. The regression model, with case-wise diagnostic residual statistics and standard prediction coefficients, provides valuable insights. For example, the forest surface exhibited a minimum sediment yield of -1.413 kg/m2 with each unit decrease in forest area, emphasising the significance of vegetation cover in sediment retention. Conversely, bare surfaces showed a maximum sediment yield of 0.843 kg/m2 with each unit increase in bare surface area, highlighting their heightened vulnerability to sediment erosion. Considering the implications of these findings, the development of urban master plans that incorporate well-designed landscaping and drainage systems is crucial, particularly in high rainfall catchments like the study area. Such measures can effectively mitigate sediment yield and address the adverse effects of land use changes on different surfaces.

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This study aimed to assess the impact of climate change on the hydrological components of Gilgel Gibe-1 using the ensemble of Coordinated Regional Climate Downscaling Experiments (CORDEX) Africa Domain namely REMO2009, HIRAM5, CCLM4-8 and RCA4 Regional Climate Models (RCMs) simulations. The performance of these RCM models was evaluated using the observed data from 1985 to 2005 and the ensemble was shown to simulate rainfall and air temperature better than individual RCMs. Then the RCMs ensemble data for historical and future projections from 2026 to 2055 years under RCP4.5 and RCP8.5 were corrected for bias and used to evaluate the impact of climate change. A non-linear bias correction and the monthly mean biases corrections method is used to adjust precipitation and temperature respectively. The future projection shows that; rainfall is expected to increase from August to December with maximum values of 1.97-235.23% under RCP4.5. The maximum temperature is expected to increase with maximum value of 1.62 °C under RCP8.5 in the study area. The calibrated and validated Soil and Water Assessment Tool (SWAT) model was used to investigate the impact of climate change on hydrologic components such as surface runoff, lateral flow, water yield, evapotranspiration and sediment yield. The SWAT model was calibrated and validated using monthly stream flow with the statistical performance of R2 value of 0.82 and NSE value of 0.72 for calibration and R2 of 0.79 and NSE of 0.67 for validation. Surface runoff and sediment yield are expected to increase from August to December under RCP4.5 and from August to February under RCP8.5. Overall both surface runoff and sediment yield are expected to increase in the future.

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Changes in natural rainfall characterized by heavy precipitation and high rainfall intensity would increase the risks and uncertainty of nutrients losses. Losses of nitrogen (N) and phosphorus (P) with water erosion from agriculture-related activities has become the principal nutrients resulting the eutrophication of water bodies. However, a little attention has been paid to the loss characteristic of N and P responding to natural rainfall in widely used contour ridge systems. To explore the loss mechanism of N and P in contour ridge system, nutrient loss associated with runoff and sediment yield was observed in in situ runoff plots of sweet potato (SP) and peanut (PT) contour ridges under natural rainfall. Rainfall events were divided into light rain, moderate rain, heavy rain, rainstorm, large rainstorm, and extreme rainstorm level, and rainfall characteristics for each rainfall level were recorded. Results showed that rainstorm, accounting for 46.27% of the total precipitation, played a destructive role in inducing runoff, sediment yield, and nutrient loss. The average contribution of rainstorm to sediment yield (52.30%) was higher than that to runoff production (38.06%). Rainstorm respectively generated 43.65-44.05% of N loss and 40.71-52.42% of P loss, although light rain induced the greatest enrichment value for total nitrogen (TN, 2.44-4.08) and PO4-P (5.40). N and P losses were dominated by sediment, and up to 95.70% of the total phosphorus and 66.08% of TN occurred in sediment. Nutrient loss exhibited the highest sensitivity to sediment yield compared to runoff and rainfall variables, and a significant positive linear relationship was observed between nutrient loss and sediment yield. SP contour ridge presented higher nutrient loss than that in PT contour ridge, especially for P loss. Findings gained in this study provide references for the response strategies of nutrient loss control to natural rainfall change in contour ridge system.

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Soil erosion has become a worldwide problem that threatens the environment and the future of economic and social development. The purpose of this study is to investigate the contribution of steep slopes and gullies to erosion in high precipitation tropical areas of the Ethiopian highlands. A trapezoidal weir was installed at the head and tail of the gully to monitor the discharge and sediment concentration from 2017 to 2020. Sediment yield and runoff are heavily influenced by the amount and timing of precipitation. The coefficients of variation for total sediment loads ranged from 65.1 to 96.1% at the head and 17.1-78.1% at the tail; the lowest coefficients were found in 2018 and the highest in 2020. Furthermore, 85% of the sediment at the tail comes from the gully, according to the four-year sediment budget. Further, a hysteretic analysis of suspended sediment concentration and runoff revealed that hilly sediment sources are limited (clockwise), then sediment can be transported through the gully via bank failures (counterclockwise). Study findings contributed to a classification of runoff patterns and an investigation of suspended sediment dynamics. In the gully tail, sediment yield was higher than in the head, suggesting gully sediment contributed more to sediment yield than large upland catchments. As a result of the study, we have been able to develop practical recommendations for managing gully erosion in the future.

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Excess fine sediment delivery is a major contributor to the declining health of the Great Barrier Reef and identifying the dominant source areas of fine sediment has been critical to prioritising erosion remediation programs. The Bowen River catchment within the Burdekin Basin has been recognised as a major contributor and hence received considerable research investment over the last two decades. This study adopts a novel approach to integrate three independently derived sediment budgets produced from a catchment scale sediment budget model (Dynamic SedNet), targeted tributary water quality monitoring and geochemical sediment source tracing to refine and map the sediment source zones within the Bowen catchment. A four year study of water quality monitoring combined with modelled discharge estimates and geochemical source tracing both identified that the Little Bowen River and Rosella Creek were the largest sources of sediment in the Bowen River catchment. Both data sets contradicted initial synoptic sediment budget model predictions due to inadequate representation of hillslope and gully erosion. Recent improvements in model inputs have resulted in predictions that are consistent with the field data and are of finer resolution within the identified source areas. Priorities for further investigation of erosion processes are also revealed. Examining the benefits and limitations of each method indicates that these are complimentary methods which can effectively be used as multiple lines of evidence. An integrated dataset such as this provides a higher level of certainty in the prediction of fine sediment sources than a single line of evidence dataset or model. The use of high quality, integrated datasets to inform catchment management prioritisation will provide greater confidence for decision makers when investing in catchment management.

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Sediment yield estimation along with identification of soil erosion mechanisms is essential for developing sophisticated management approaches, assessing, and balancing different management scenarios and prioritizing better soil and water conservation planning and management. At the watershed scale, land management practices are commonly utilized to minimize sediment loads. The goal of this research was to estimate sediment yield and prioritize the spatial dispersion of sediment-producing hotspot areas in the Nashe catchment using the Soil and Water Assessment Tool (SWAT). Moreover, to reduce catchment sediment output, this study also aims to assess the effectiveness of certain management practices. For calibration and validation of the model, monthly stream flow and sediment data were utilized. The model performance indicators show good agreement between measured and simulated stream flow and sediment yields. The study examined four best management practice (BMP) scenarios for the catchment's designated sub-watersheds: S0 (baseline scenario), S1 (filter strip), S2 (stone/soil bunds), S3 (contouring), and S4 (terracing). According to the SWAT model result, the watershed's mean yearly sediment output was 25.96 t/ha. yr. under baseline circumstances. The model also revealed areas producing the maximum sediment quantities indicating the model's effectiveness for implementing and evaluating the sensitivity of sediment yield to various management strategies. At the watershed scale, treating the watershed with various management scenarios S1, S2, S3, and S4 decreased average annual sediment yield by 34.88%, 57.98%, 39.55%, and 54.77%, respectively. The implementations of the soil/stone bund and terracing scenarios resulted in the maximum sediment yield reduction. The findings of this study will help policymakers to make better and well-informed decisions regarding suitable land use activities and best management strategies.

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In the present study, suspended sediment load (SSL), sediment yield and erosion rates in Pindari Glacier basin (PGB) and Kafni Glacier basin (KGB) have been estimated using daily discharge and suspended sediment concentration (SSC) data for three ablation seasons (2017-2019). For this, one meteorological observatory and two gauging sites have been established at Dwali (confluence point), and water samples have been collected twice in a day for high flow period (July to September) and daily for lean period (May, June and October). An area-velocity method and stage-discharge relationship has been established to convert water level into discharge (m3 s-1). For estimating SSC (mg/l), collected water samples have been filtered, dried, analysed and confirmed with an automatic suspended solid indicator. Further, SSL, sediment yield and erosion rates have been computed using SSC data. The results reveal that mean annual discharge in PGB (35.06 m3 s-1) has been found approximately 1.7 times higher than KGB (20.47 m3 s-1). The average SSC and SSL in PGB have been observed about 396.07 mg/l and 1928.34 tonnes, and in KGB, it is about 359.67 mg/l and 1040.26 tonnes, respectively. The SSC and SSL have followed the pattern of discharge. A significant correlation of SSC and SSL has been found with discharge in both the glacierized basins (p < 0.01). Interestingly, average annual sediment yield in PGB (3196.53 t/km2/yr) and KGB (3087.23 t/km2/yr) have been found almost identical. Likewise, the erosion rates in PGB and KGB have been witnessed about 1.18 and 1.14 mm/yr, respectively. Sediment yield and erosion rates in PGB and KGB have been found in correspondence with other basins of Central Himalaya. These findings will be beneficial for engineers and water resource managers in the management of water resources and hydropower projects in high-altitude areas and in the planning and designing of water structures (dams, reservoirs etc.) in downstream areas.

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