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• The soil erosion performances of frozen, nonfrozen and thawed soil were evaluated. • Four models were implemented to simulate the key parameters for thawed soil erosion. • The performance of SR and machine learning models were compared. • Influencing factors of straw returning on thawed soil were analyzed. Snow and ice‐melted water flow over thawed soil cause severe soil erosion. The application of straw mulch has long been considered as an effective measure to reduce soil erosion, but the effects of straw mulch and straw incorporation on thawed soil erosion remain uncertain. This study conducted detailed laboratory experiments to investigate the effects of straw mulch and straw incorporation on soil erosion and sediment transport of thawed soil. The median particle size (D 50) of transported sediment, flow velocity and sediment concentration for thawed soil were compared with that of unfrozen and frozen soil. Models based on Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BP) and stepwise regression (SR) were implemented to simulate the key parameters of thawed soil erosion, and the accuracy of the developed models was evaluated. The results showed that the sediment yield increased significantly in thawed soil compared with that in unfrozen soil. The maximum flow velocity over thawed soil decreased by 20.7 % compared with that over frozen soil. The D 50 of transported sediment decreased by 29.4 % after the soil suffered the effects of freeze–thaw, and the finer sizes were more likely to be entrained. The straw mulch effectively reduced the D 50 of transported sediment, flow velocity and sediment concentration. In addition, the RF model showed a better result to predict the sediment concentration, flow velocity and D 50 of transported sediment (NSE > 0.849, RRMSE < 0.493) than the BP, SVM and SR models. Comparing the performance of the RF model with the models reported in previous studies, the RF model showed the best performance. In general, the results of this study provide important information for soil erosion in thawed soil. [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]

• Seasonal and diel CO 2 emissions in a hard-water reservoir show temporal variability. • Inputs of dissolved inorganic carbon play a central role in sustaining the p CO 2. • Nocturnal fluxes are higher than daytime fluxes due largely to aquatic metabolism. • Episodic weather events reverse the diel pattern and cause higher daytime fluxes. • Using daytime measurements alone would underestimate the daily efflux by 9–25%. Reservoirs represent a key component of the global carbon cycle. However, estimates of carbon dioxide (CO 2) emissions from reservoirs remain poorly constrained due to the absence of spatially and temporally resolved measurements. We performed high-resolution monitoring of CO 2 emissions (F CO2) in a semiarid hard-water reservoir to examine its seasonal and diel variability. Our results suggest that dissolved inorganic carbon input plays a central role in sustaining the surface water CO 2 partial pressure (p CO 2), which varies from 1076 to 4587 μatm. Although the reservoir is moderately to highly productive throughout the year, it is a net CO 2 source with F CO2 values in the range of 308–1753 mg C m−2 d–1. This high CO 2 efflux indicates that productive waters are not necessarily CO 2 sinks. Both p CO 2 and F CO2 exhibit clear seasonal and diel patterns. Surface water p CO 2 is highest in March and presents a consistent diurnal/nocturnal pattern with the daytime p CO 2 6–13% lower than the nighttime p CO 2. High CO 2 efflux is observed during the ice-thaw period, indicating the release of CO 2 that was accumulated during the winter. CO 2 effluxes are typically higher during the nighttime driven by aquatic metabolism, but episodic weather events (e.g., rainfall and strong winds) can significantly enhance CO 2 emissions and even reverse the diel pattern. Our study also shows that using only daytime measurements to estimate daily CO 2 emissions would underestimate it by 9–25%. Hence, future global assessments should incorporate CO 2 emissions from hard-water reservoirs and account for their seasonal and diel variability. [ABSTRACT FROM AUTHOR]