Sediment connectivity reflects potential linkages between sediment sources and sink areas and identifies priority regions for implementing sediment control measures. However, the impact of soil and water conservation measures on catchment topography and geomorphological development, as well as their potential effects on sediment connectivity, are not yet fully understood. This study aims to quantify the spatial variations in sediment connectivity induced by the combined effects of terraces and check dams in a representative small catchment on the Loess Plateau. We used the landscape evolution model (LAPSUS) to simulate the spatial distribution of erosion and deposition within the watershed and to determine the spatial coupling patterns between erosion and connectivity. The results indicate that: (i) Soil and water conservation measures effectively reduced sediment connectivity within the catchment, with terraces, check dams, and their combined effect contributing to a 19.55%, 4.82%, and 31.99% reduction in sediment connectivity, respectively. (ii) Soil and water conservation measures altered the erosion-deposition spatial distribution patterns within the catchment. Terraces reduced the area of soil erosion on slopes by 33.44%, while check dams increased the area of sediment deposition in channels by 90%. (iii) Low erosion-high connectivity and high erosion-high connectivity zones were key areas for soil erosion and sediment loss within the catchment, located on steep slopes and highly erodible channels. The research findings contribute to the development of more effective soil and water conservation programmable for erosive small catchments in the Loess Plateau, enhancing the sustainability of catchment management.

Understanding sedimentation and runoff variations caused by land use change have emerged as important research areas, due to the ecological functions of landscape patterns. The aims of this study were to determine the relationship between landscape metrics (LMs), runoff, and sedimentation and explore the crucial LMs in the watersheds on the Loess Plateau. From 1985 to 2010, Grassland (GRA) was the dominant landscape in the Tuweihe (TU) and Gushanchuan (GU) watersheds. Unused land (UNL) and farmland (FAR), respectively, experienced the greatest transformations. The landscape in the study area tended to become regular, connected, and aggregated. The landscape stability of the TU watershed was higher than that of the GU watershed. Annual runoff and sedimentation gradually decreased and significant relationship was found between them (P <0.01). Due to larger FAR area and lower landscape stability in the GU watershed, the sedimentation of the two watersheds were similar, even though the runoff in the TU watershed was greater. The LMs had more significant effects on runoff than that on sedimentation yield. Shannon’s evenness index and the patch cohesion index had the greatest effects on runoff and sedimentation, making them the key factors of influencing water and soil loss. which could provide a scientific basis for the prevention and treatment of soil erosion.

A probability prediction using conditional distribution function derived from copula provides a great deal of flexibility in the suspended sediment concentration as well as other hydrological variable estimations, but the influencing variables of the probability prediction model capability are necessary to be investigated. The bivariate conditional distribution function of suspended sediment concentration with runoff as its only influencing variable is firstly derived to assess the sensitivity of the probability prediction to the choice of copula and marginal distribution, and the probability prediction is further extended to the trivariate conditional distribution function with runoff and precipitation as its influencing variables. The approach is exemplified using stationary mean daily precipitation, runoff and suspended sediment concentration data sets from six hydrological stations in the central Yellow River located in the Loess Plateau, which is characterized by heavy sediment transport. The results of the bivariate conditional distribution functions indicate that the probability prediction is mainly influenced by the choice of copula function, and the tail dependence of the copula function determines the shape of the estimated suspended sediment concentration curve. The comparison between the bivariate conditional distribution function, trivariate conditional distribution function, and traditional sediment rating curve demonstrates the uncertainty bands from trivariate conditional distribution function are always smallest, and those from the sediment rating curve are usually largest, while the difference between different models become larger at hydrological stations with smaller sample size.