Comparison of ecosystem health in different geomorphic regions of Chishui River Basin, Southwest China.

Ecosystem health of the Chishui River Basin (CRB, a crucial ecological barrier in the upper reaches of the Yangtze River) is vital for the ecological security and sustainability of the Yangtze River Basin. We used RUSLE model, SWAT model, Fragstats and geographic detectors to construct a theoretical framework of ecosystem health assessment for CRB, and examined the spatiotemporal variations and driving factors of ecosystem health in CRB under ecological restoration from 2010 to 2020. The results showed that ecosystem service in the CRB decreased and then increased during 2010-2020 and the overall trend was downward. The overall ecosystem service function was higher in the Danxia (non-karst) area than that in the karst area. The ecosystem health was generally subhealthy, with the Danxia area being mostly extremely healthy and healthy, whereas the karst area mostly subhealthy and unhealthy. There were differences in the dominant drivers of ecosystem health between karst and Danxia areas. Vegetation, precipitation, and bedrock bareness rate were the dominant drivers in the karst area, while vegetation, land use, and precipitation were the dominant factors in Danxia area. After interaction detection, the explanatory power of impact factors increased, and the dominant interaction factor combinations in different geomorphological type regions had shown great differences. Among them, precipitation∩normalized difference vegetation index (NDVI), precipitation∩digital elevation model (DEM) and precipitation ∩ bedrock bareness rate were the dominant interaction factor combinations in the karst area, and NDVI∩precipitation, NDVI∩land use and NDVI∩DEM were the dominant interaction factor combinations in Danxia area. These results would provide scientific support for health maintenance and conservation of CRB ecosystem.

Rainstorm sediment events in heterogeneous karst small watersheds: Process characteristics, prediction modeling and management enlightenment.

Frequent rainstorms caused by climate change are causing significant stresses and impacts on karst zones and even global hydrological systems. However, few reports have focused on rainstorm sediment events (RSE) based on long series, high-frequency signals in karst small watersheds. Present study assessed the process characteristics of RSE and analyzed the response of specific sediment yield (SSY) to environmental variables using random forest and correlation coefficients. Management strategies are then provided based on revised index of sediment connectivity (RIC) visualizations, sediment dynamics and landscape patterns, and modeling solutions for SSY are explored through the innovative use of multiple models. The results showed that the sediment process showed high variability (CV > 0.36), and the same index had obvious watershed differences. Landscape pattern and RIC show highly significant correlation with mean or maximum suspended sediment concentration (p<0.01, |r|>0.235). Early rainfall depth was the dominant factor affecting SSY (Contribution = 48.15 %). The hysteresis loop and RIC infer that the sediment of Mahuangtian and Maolike mainly comes from downstream farmland and riverbeds, while Yangjichong comes from remote hillsides. The watershed landscape is centralized and simplified. In the future, patches of shrubs or herbaceous plants should be added around the cultivated land and at the bottom of the sparse forest to increase the sediment collection capacity. The backpropagation neural network (BPNN) is optimal for modeling SSY, particularly for running the variables preferred by the generalized additive model (GAM). This study provides insight into understanding RSE in karst small watersheds. It will help the region to cope with future extreme climate change and develop sediment management models that are consistent with regional realities.