Quantifying the spatiotemporal evolution and driving factors of soil erosion in the source region of the Yellow River using 137Cs and machine learning models

IF 5.7 1区 农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Jinxi Su, Zhenying Zhou, Juncheng Li, Mengyao Long, Huilong Lin
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引用次数: 0

Abstract

The source region of the Yellow River (SRYR), known as the “Water Tower of China”, is not only crucial for the sustainability of alpine grassland and wetland ecosystems, but has significant implications for water quality and sediment deposition in the river’s middle and lower reaches. However, complex topography, climate, and diverse soil erosion types limit the applicability of traditional estimation methods. This study developed a tailored compound erosion prediction model for the SRYR to quantify erosion rates and identify their driving mechanisms. A dataset of 537 soil erosion rates from 137Cs and 33 conditioning variables were used to construct an optimal model. Two variable selection methods—genetic algorithm (GA) and least absolute shrinkage and selection operator (LASSO)—were applied alongside three machine learning algorithms: categorical boosting (CatBoost), random forest (RF), and k-nearest neighbors (KNN). Geographical detectors were used to identify erosion drivers. Key findings include: (1) The GA-CatBoost model outperformed others (Rtest2 = 0.51). Based on optimal model, the estimated annual soil erosion rate (2001–2022) in the SRYR was 20.21 t·ha−1·a-1, with total annual erosion of 238.80 × 106 t·a-1. (2) Spatial analysis revealed high erosion in the northwest, low in the southeast, and mixed patterns in the central region, with 78.28 % of the SRYR exhibiting improvement over 2001–2022. (3) Precipitation and NDVI were identified as the dominant driving factors mitigating soil erosion in the SRYR. These findings demonstrate the effectiveness of ecological restoration efforts in the SRYR, providing empirical evidence for targeted soil and water conservation strategies. Future studies could enhance model accuracy by diversifying sampling, measuring erosion rates during shorter time windows and using higher resolution input data.

Abstract Image

基于137Cs和机器学习模型的黄河源区土壤侵蚀时空演变及驱动因素量化研究
被称为“中国水塔”的黄河源区不仅对高寒草地和湿地生态系统的可持续性至关重要,而且对黄河中下游的水质和泥沙沉积具有重要影响。然而,复杂的地形、气候和多样的土壤侵蚀类型限制了传统估算方法的适用性。本研究为SRYR开发了定制的复合侵蚀预测模型,以量化侵蚀速率并确定其驱动机制。利用137Cs的537个土壤侵蚀速率数据集和33个调节变量构建了最优模型。两种变量选择方法-遗传算法(GA)和最小绝对收缩和选择算子(LASSO) -与三种机器学习算法:分类增强(CatBoost),随机森林(RF)和k近邻(KNN)一起应用。地理探测器被用来识别侵蚀驱动因素。主要发现包括:(1)GA-CatBoost模型优于其他模型(Rtest2 = 0.51)。基于最优模型估算的2001-2022年SRYR年土壤侵蚀速率为20.21 t·ha−1·a-1,年侵蚀总量为238.80 × 106 t·a-1。(2)空间分析显示,2001-2022年,西北侵蚀程度高,东南侵蚀程度低,中部地区呈混合型分布,78.28%的区域土壤侵蚀程度有所改善。(3)降水和NDVI是缓解SRYR土壤侵蚀的主导驱动因子。这些发现证明了长江流域生态修复工作的有效性,为制定有针对性的水土保持战略提供了经验依据。未来的研究可以通过多样化采样、在更短的时间窗内测量侵蚀速率和使用更高分辨率的输入数据来提高模型的准确性。
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来源期刊
Catena
Catena 环境科学-地球科学综合
CiteScore
10.50
自引率
9.70%
发文量
816
审稿时长
54 days
期刊介绍: Catena publishes papers describing original field and laboratory investigations and reviews on geoecology and landscape evolution with emphasis on interdisciplinary aspects of soil science, hydrology and geomorphology. It aims to disseminate new knowledge and foster better understanding of the physical environment, of evolutionary sequences that have resulted in past and current landscapes, and of the natural processes that are likely to determine the fate of our terrestrial environment. Papers within any one of the above topics are welcome provided they are of sufficiently wide interest and relevance.
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