Abstract
The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.
Highlights
The Qinghai-Tibetan Plateau is acknowledged as the “Third Pole” and has received an increasing attention toward ecological and environmental concerns (Yao et al, 2012; Madsen, 2016)
Among the 33 environmental variables, the number of variables selected by the genetic algorithm (GA), recursive features elimination (RFE), Boruta all-relevant (BOR), univariate filters (UF), and simulated annealing (SA) algorithms was 29, 26, 22, 22, and 13 variables, respectively (Appendix Table 2)
The crossvalidation results of GA boosting tree (BST) model that selected 29 variables showed slightly inferior performance to the UF cubist model. This indicated that the 22 variables selected by the UF cubist model contained better predictive power to that of 29 variable selected by the GA BST model
Summary
The Qinghai-Tibetan Plateau is acknowledged as the “Third Pole” and has received an increasing attention toward ecological and environmental concerns (Yao et al, 2012; Madsen, 2016). The alpine grasslands in the SPYR plays an exceptional role in ecological functions such as water conservation, biodiversity protection, and carbon fixation (Harris, 2010), and play critical roles in livestock production, representing the main sources of income for local pastoralists. Portions of the grasslands are experiencing moderate and severe degradation, 40.8 and 17.58%, respectively (Miehe et al, 2019) and a direct soil erosion is resulted in recent decades (Yao et al, 2016). This erosion reduces soil fertility and pollutes water resources and responsible for sediment accumulation, river obstructions, downstream flooding and flow patterns (Evans et al, 2017). Exploring the mechanism, influencing factors, and current situation of soil erosion in the alpine grassland ecosystems of the SPYR is significant for protecting the ecological and productive functions
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