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Sediment source fingerprinting provides an effective and reliable way for targeted control of soil erosion. However, the accuracy of source contribution predictions of different fingerprinting methods has not been well verified, which affects the further application of these methods. Here, compared with the real sediment sources measured by indoor simulated scouring experiments, we verified the accuracy of two tracers (geochemical elements and n‐alkanes) and three mixing models (Walling model, MixSIAR, and Landwehr model). The results showed that geochemical properties have high accuracy in identifying sediment sources, with the mean absolute error (MAE) ranging from 3.5%–5.9%, while poor results were shown for n‐alkanes (MAE values 7.5%–45.7%). The Walling model and MixSIAR showed higher accuracy of contribution prediction, and the MAE was 3.5%–5.9% and 0.7%–3.5%, respectively. Although Landwehr also presented high accuracy of model output, it was relatively less robust compared with the former two mixing models (the MAE range was 3.8%–9.7%). Furthermore, the mixing model with three correction factors (particle size, organic matter (OM), and particle size with OM) showed poor output results for scouring experiments. This study provides a reference for the selection and determination of tracers, mixing models, and correction factors in field experiments. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accurate quantification of soil erosion sub-process by remote sensing and check dam. • Vegetation restoration significantly reduced soil erosion modulus. • Check dams effectively reduce sediment delivery on the watershed scale. • The variation of erosion and deposition changes the sediment delivery ratio. China has implemented a series of ambitious soil and water conservation (SWC) projects on the Chinese Loess Plateau, which have significantly changed the erosion, transport, and deposition of sediment. As a result, the sediment flux of the Yellow River, once the largest carrier of fluvial sediment worldwide, has been reduced by approximately 85 % in the past 60 years. However, the effects of SWC measures on erosion, transport, and deposition of sediment are still difficult to quantify, which greatly limits the further planning and adjustment of SWC measures. Here, we determined soil erosion, sediment deposition, and sediment yield at different historical stages in a 187 km2 hilly and gully watershed on the Chinese Loess Plateau using the unmanned aerial vehicle (UAV) photogrammetry technology combined with check dam surveying and hydrologic monitoring. The results show that the construction of check dams and vegetation restoration significantly reduced the soil erosion rate, from 21,144 t km−2 yr−1 at Stage-1 (1960–1969) to 13,819 ± 3,622 t km−2 yr−1 at Stage-2 (1970–1999) and further to 4,723 ± 1,278 t km−2 yr−1 at Stage-3 (2000–2018). The sediment deposition rate was estimated to be 5,989 and 2,582 ± 351 t km−2 yr−1 at Stage-2 and Stage-3, respectively. The variation in the erosion and deposition of sediment led to a significant change in the sediment delivery ratio, from 1 at Stage-1 to 0.57 at Stage-2 and 0.45 at Stage-3. Our research results provide an important reference for further SWC planning. [ABSTRACT FROM AUTHOR]

• A UAV-based method estimating sediment deposited behind check dams was developed. • Errors of the optimal model for single and regional check dams are 12–13% and 2–3%. • This method avoids limitations of tedious field surveys and historical records. • This method can evaluate sediment retained by check dams in areas as large as 211,800 km2. The estimation of sediment deposited behind dams and reservoirs is of great significance for quantifying soil erosion rate and sediment flux. However, existing methods of sediment volume estimation often require detailed field measurements and historical topographic data, limiting the broader application of these methods. In this paper, focusing on the region that suffers the world's most severe soil erosion, we combine unmanned aerial vehicle (UAV) photogrammetry and simulate submerging analysis to propose a novel method for estimating sediment silted by check dams under complex topography. We obtained 1339 groups of topographic factors and ascertained the corresponding volumes of sediment deposited behind check dams in the loess hilly region of China (LHRC), and established five different models combined with regression analysis. Two different sets of data were used for method validation and optimal model determination. The results showed that the error of the optimal model in the volume estimation of single check dam and regional check dams is 12–13% and 2–3%, respectively. Additionally, the area-volume model has the potential to evaluate the sediment retention capacity of check dams (in the order of billions of cubic meters) in the whole LHRC, because the variables are easy to obtain and the model accuracy is relatively high. The further application of this method will help to evaluate the watershed sediment yield and regional soil and water conservation benefits, and is of great significance for explaining the future changes in water and sediment in the Yellow River basin. [ABSTRACT FROM AUTHOR]