ABSTRACT
Increasingly frequent extreme rainfall as a result of climate change is strongly damaging the global soil and water environment. However, few studies have focused on daily extreme sediment events (DESE) in heterogeneous karst watersheds based on long-term in-situ observations. This study quantitatively assessed the time effect of DESE on rainfall response, decoupled the impact of environmental factors on DESE by using structural equation modelling, and finally explored the modelling scheme of DESE based on the hybrid model. The results showed that DESE had the highest frequency of occurrence in May-July, with dispersed distribution in the value domain. Rainfall with a time lag of 1 day and a time accumulation of 2 or 3 days was an important contribution to DESE (P < 0.01, R = 0.47-0.68). Combined effects of environmental factors explained 53.6 %-64.1 % of the variation in DESE. Runoff and vegetation exerted the strongest direct and indirect effects on DESE, respectively (ß = 0.66/-0.727). Vegetation was the dominant driver of DESE in Dabanghe and Yejihe (ß = -0.725/-0.758), while the dominant driver in Tongzhihe was climate (ß = 0.743). In the future, the risk of extreme sediments should be prevented and resolved through the comprehensive regulation of multiple paths, such as runoff and vegetation. Hybrid models significantly improved the modelling performance of machine learning models. Generalized additive model-Extreme gradient boost had the best performance, while Partial least squares regression-Extreme gradient boost was the most valuable when considering performance and input data cost. Two methods can be used as recommended solutions for DESE modelling. This study provides new and in-depth insights into DESE in karst watersheds and helps the region develop forward-looking soil and water management models to cope with future extreme erosion hazards.
ABSTRACT
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.
