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Observations and modeling are used to assess potential impacts of sediment releases due to dam removals on the Hudson River estuary. Watershed sediment loads are calculated based on sediment-discharge rating curves for gauges covering 80% of the watershed area. The annual average sediment load to the estuary is 1.2 Mt, of which about 0.6 Mt comes from side tributaries. Sediment yield varies inversely with watershed area, with regional trends that are consistent with substrate erodibility. Geophysical and sedimentological surveys in seven subwatersheds of the Lower Hudson were conducted to estimate the mass and composition of sediment trapped behind dams. Impoundments were classified as (1) active sediment traps, (2) run-of-river sites not actively trapping sediment, and (3) dammed natural lakes and spring-fed ponds. Based on this categorization and impoundment attributes from a dam inventory database, the total mass of impounded sediment in the Lower Hudson watershed is estimated as 4.9 ± 1.9 Mt. This represents about 4 years of annual watershed supply, which is small compared with some individual dam removals and is not practically available given current dam removal rates. More than half of dams impound drainage areas less than 1 km², and play little role in downstream sediment supply. In modeling of a simulated dam removal, suspended sediment in the estuary increases modestly near the source during discharge events, but otherwise effects on suspended sediment are minimal. Fine-grained sediment deposits broadly along the estuary and coarser sediment deposits near the source, with transport distance inversely related to settling velocity.

Under the background of large-scale ecological restoration, China’s southwestern karst region has become a hotspot of global vegetation cover “greening” in the past 20 years. However, because of geological constraints, it is difficult to restore the forest landscapes in some areas. It is urgent to understand the impacts of human disturbances during the historical period on the difficult-to-forestation rocky-desertification areas of the karst region in order to guide future afforestation. In this study, we quantified the changes of specific sediment yield in typical karst depressions over the past 500 years by using 137 Cs, 210 Pb, and 14 C dating methods in karst depressions, and identified the main human disturbances related to historical erosion and sedimentation by combining with historical data. The results showed that the erosion and sedimentation of the three depressions in 1921–1963 were significantly higher than that in 1963–2021, and the sedimentation rate (0.64–1.33 cm a −1 ) and the specific sediment yield (2.51–13.11 t ha −1 a −1 ) during Ming and Qing dynasties (1504–1812) were higher than the sedimentation rate (0.26–0.95 cm a −1 ) and specific sediment yield (0.95–6.99 t ha −1 a −1 ) in the recent century (1921–2021). Reconstruction data and literature from the Ming and Qing dynasties show an empirical link between changes in population, arable land, food, forest area, and deforestation events during the same period. It was found that the population and arable land in Guangxi increased more than three-fold and the forest area decreased significantly in the 17th century after the migration of the Yao ethnic group and the introduction of maize to the region, which may be the main reason for intensifying the erosion of depressions. This study is of great significance to understanding the evolution history of rocky desertification in this region and to answer the potential of afforestation.

Watershed prioritization based on the natural resources and physical processes involves locating critical areas of erosion, which produce maximum sediment yield to take up conservation activities on priority basis. The present study was taken up with a specific objective of prioritization of micro-watersheds using Multi-Criteria Decision Approach – Analytic Hierarchy Process (AHP) based SYI model (AHPSYI) under GIS environment for a case study area of Mayurakshi watershed in India. This method basically uses information of Potential Erosion Index (PEI) and Sediment Delivery Ratio (SDR), indicative of transport capacity. In the present study, sediment delivery factors viz., topography, vegetation cover, proximity to water courses and soil were translated into GIS layers and integrated using Boolean conditions to create a data layer of spatially distributed SDIs’ across the watershed. For assessment of PEI, important watershed parameters viz., land use/land cover, soil, slope, and drainage density maps were integrated in the GIS environment using Weighted Linear Combination method (WLC) by assigning weights to themes and ranks to features of individual theme using AHP technique. A comparison between AHPSYI based sub watershed prioritization map with that of prioritization map based on the observed sediment yield data revealed that about 78 % of the area showed concurrence. Thus, it can be inferred that the watershed prioritization based on only thematic layers can be dependable to maximum extent. Subsequently, proposed approach was adopted for prioritization of the study area at micro watershed scale, where area under high and very high categories together constitutes around 33 % of the study area. Around 100 micro-watersheds out of 276 watersheds are under moderate to very high category respectively, signifying the need for watershed management.

The loess hilly area consists of a slope–gully system, which promotes erosion; as such. it is one of the most intensely eroded areas in the world. The construction of check dams can effectively control water and soil loss of slope gullies. However, existing studies focus on the benefits of intercepting runoff and sediments at dam sites, while ignoring the change law of hydrological processes with respect to progressing dam land sedimentation. Moreover, past studies focus on the “runoff–sediment” or “flood–sediment” relationships, but rarely consider the “hydrodynamics–runoff” and “hydrodynamics–sediment” dynamics. Therefore, in this study, we developed five physical models of slope–gully systems for dam land sedimentation depths of 0, 1, 2, 3, and 4 m, in order to explore the effects of sedimentation on runoff–sediment–hydrodynamic processes. The runoff and sediment yield of the slope–gully system decreased with increasing siltation depth. The spatial and temporal distributions of the hydrodynamic parameters were different. The Reynolds number (Re), runoff energy consumption (ΔE), and runoff power (P) increased with rainfall time, whereas runoff shear stress (τ) and Froude number (Fr) did not show a significant trend over time. Re and ΔE could better describe the runoff process of the slope–gully system, while P and ΔE could better simulate the sedimentation process. Notably, our study can provide a scientific basis for establishing effective erosion prediction models to estimate the water erosion process of slope–gully systems.

Anticipated changes in climate by the end of this century are likely to modify suspended‐sediment yields (Sy) in diverse ways. Past work has shown how hydrological non‐stationarity may alter water discharges and hence Sy, but less attention has been given to the impact of likely future changes in upland sediment‐detachment rates on downstream Sy. In certain environments, climatically driven changes in vegetation cover on upland hillslopes may more than counteract the effects of changing runoff on Sy. Changes in precipitation, temperature, and vegetation may, therefore, interact in nonlinear ways to produce unexpected changes. In this work, we simulated future changes to background Sy (i.e., changes unrelated to land‐use changes and dams) with climatological and vegetative data output from an ensemble of CMIP6 Earth System Model (ESM) simulations. Depending on the future scenario, the cumulative annual sediment flux of 780 globally distributed rivers increases by between 2.3% and 8.4%. Significant deviations from historical Sy are projected at high latitudes in response to each forcing variable, while low‐latitude responses are regionally varied. In regions where ensemble members agree on future changes in forcing variables, large Sy changes are forecast with high confidence (e.g., >200% Sy increase for several northeastern U.S. rivers at the 95% level). In contrast, ensemble variability in vegetation projections results in considerable uncertainty in the projected Sy of rivers in other regions. Further improvements to the vegetation components of ESMs will help to reduce regional uncertainties in projected changes to Sy. Plain Language Summary Rivers move sediment eroded from across the landscape downstream through mountains, valleys, and deltas, carrying small‐enough grains in suspension over long distances from source to sink. The amount of suspended sediment a river transports varies according to flow competence and upstream basin characteristics, but anthropogenic climate change could alter baseline sediment mobilization rates in complex ways that cascade through river networks and disturb both natural and human systems. In this study, we combined a multi‐scenario ensemble of earth system models with a global suspended‐sediment‐flux model calibrated to natural conditions in order to understand how future changes in temperature, precipitation, and vegetation will translate into adjustments to the sediment delivered to large river deltas. We find that across future scenarios, cumulative sediment transport will increase by 2%–8% by the year 2100, but many regions (especially at high latitudes) may see declines locally due to greater vegetative cover that reduces the erosive potential of rainfall. Altogether, our results indicate that rivers in the near future may experience large changes in sediment loading, and improvements to dynamic vegetation models will improve confidence in projections of suspended‐sediment fluxes. Key Points We provide spatially distributed ensemble projections of global suspended‐sediment‐yield responses to anthropogenic climatic changes by 2100 We project a median increase of 2.3%–8.4% in the cumulative suspended‐sediment flux of 780 globally distributed rivers Shifts in vegetation, temperature, and rainfall drive significant regional and latitudinal changes in hillslope sediment‐detachment rates

Soil and nutrient loss is a common natural phenomenon but it exhibits unclear understanding especially on bare loess soil with variable rainfall intensity and slope gradient, which makes it difficult to design control measures for agricultural diffuse pollution. We employ 30 artificial simulated rainfalls (six rainfall intensities and five slope gradients) to quantify the coupling loss correlation of runoff-sediment-adsorbed and dissolved nitrogen and phosphorus on bare loess slope. Here, we show that effects of rainfall intensity on runoff yield was stronger than slope gradient with prolongation of rainfall duration, and the effect of slope gradient on runoff yield reduced gradually with increased rainfall intensity. But the magnitude of initial sediment yield increased significantly from an average value of 6.98 g at 5° to 36.08 g at 25° with increased slope gradient. The main factor of sediment yield would be changed alternately with the dual increase of slope gradient and rainfall intensity. Dissolved total nitrogen (TN) and dissolved total phosphorus (TP) concentrations both showed significant fluctuations with rainfall intensity and slope gradient, and dissolved TP concentration was far less than dissolved TN. Under the double influences of rainfall intensity and slope gradient, adsorbed TN concentration accounted for 7–82% of TN loss concentration with an average of 58.6% which was the main loss form of soil nitrogen, adsorbed TP concentration accounted for 91.8–98.7% of TP loss concentration with an average of 96.6% which was also the predominant loss pathway of soil phosphorus. Nitrate nitrogen (NO 3 − -N) accounted for 14.59–73.92% of dissolved TN loss, and ammonia nitrogen (NH 4 + -N) accounted for 1.48–18.03%. NO 3 − -N was the main loss pattern of TN in runoff. Correlation between dissolved TN, runoff yield, and rainfall intensity was obvious, and a significant correlation was also found between adsorbed TP, sediment yield, and slope gradient. Our results provide the underlying insights needed to guide the control of nitrogen and phosphorus loss on loess hills.

Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN’s parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications.

Understanding the link between extreme precipitation and changes in runoff and sediment yield is of great significance for regional flood disaster response and soil and water conservation decision-making. This study investigated the spatial and temporal distribution of extreme precipitation (characterized by 10 extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices) in the Toudaoguai–Longmen section of the middle Yellow River from 1960 to 2021 and quantified the effects of extreme precipitation on runoff and sediment yield based on the method of partial least squares regression (PLSR). The extreme precipitation index showed an obvious upward trend in the last 20 years, with the increases in the central and northern regions (upstream) being stronger than the increase in the southern region (downstream). However, the runoff and sediment yield decreased significantly due to the implementation of large-scale soil and water conservation measures on the Loess Plateau, with average rates of 94.7 million m3/a and 13.3 million t/a during 1960–2021, respectively. The change points of runoff and sediment yield change occurred in 1979. Compared with those in the period from 1960 to 1979, the reductions in runoff and sediment yield in the years 1980–2021 were 52.7% and 70.6%, respectively. Moreover, extreme precipitation contributed 35.3% and 6.2% to the reduction in runoff in the 1980–1999 and 2000–2021 periods, respectively, and contributed 84.3% and 40.0% to the reduction in sediment yield, respectively. It indicated that other factors (such as large-scale soil and water conservation construction) played main roles in the decrease in runoff and sediment yield in the study area in recent 20 years.

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 PO 4 -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.

A study was undertaken to develop an appropriate plan of land use under suitable slope gradient to control soil erosion from a red soil hilly watershed of southern China by using the GeoWEPP (Geo-spatial Interface for the Water Erosion Prediction Project) model. The model was calibrated and validated using monitoring data of the outlet from 2010 to 2012, in which the 2010 and 2012 annual total runoff and sediment yield data were used for calibration, and the 2011 monthly runoff and sediment yield data for validation. The performance of the model in validation period were good with a high coefficient of determination values of 0.98 and 0.93 and Nash-Sutcliffe simulations of 0.96 and 0.91 while low root mean square error values of 6.91 mm and 0.35 t respectively for runoff and sediment yield. Subsequently, the model was used to simulate four typical land use (forest, farm, orchard, and fallow land) in the study area to evaluate their impacts on soil erosion production. The results showed that the runoff decreased by 44.7% and 61.1% for forest and orchard land compared to the current land use, as well as the sediment yield decreased by 43.7% and 68.6%. While the runoff and sediment yield increased by 52.2% and 42.6% for farm land, and 48.8% and 29.6% for fallow land. As the same time, soil erosion increased with increasing of the slope gradient of the quadratic regression equation for all land use. The critical slope gradient of 15° for returning the farmland to forest or others is suitable in the red soil region but is not accurate. The result of the study provides good scientific evidence for developing an appropriate plan of land use in the watershed and other similar areas.