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中国农学通报

中国农学通报 ›› 2024, Vol. 40 ›› Issue (6): 75-83.doi: 10.11924/j.issn.1000-6850.casb2023-0249

• 资源·环境·生态·土壤 • 上一篇    下一篇

黄三角濒海区土壤盐渍化的驱动力分析及预测模拟

赵铭(), 常春艳(), 王卓然, 赵庚星()   

  1. 山东农业大学资源与环境学院 土肥高效利用国家工程研究中心，山东泰安 271000
  • 收稿日期:2023-03-22 修回日期:2023-05-08 出版日期:2024-02-22 发布日期:2024-02-22
  • 通讯作者:
    常春艳，女，1985年出生，山东东营人，讲师，博士，主要从事土地资源利用研究。通信地址：271000 山东省泰安市泰山区岱宗大街61号 山东农业大学，Tel：0538-8243939，E-mail：。
    赵庚星，男，1964年出生，山东东营人，教授，博士生导师，博士，主要从事土地资源、遥感及信息技术应用研究。通信地址：271000 山东省泰安市泰山区岱宗大街61号 山东农业大学，Tel：0538-8243939，E-mail：。
  • 作者简介:

    赵铭，女，1996年出生，山东菏泽人，硕士研究生，主要从事农业工程与信息技术研究。通信地址：271000 山东省泰安市泰山区岱宗大街61号 山东农业大学，Tel：15315403689，E-mail：。

  • 基金资助:
    国家自然科学基金“黄三角濒海区土壤盐渍化的尺度特征、变异机制及预测预警”(41877003); 山东省重大科技创新工程项目“盐碱地“根际微域改良”土壤修复菌剂开发应用”(2019JZZY010724); 山东省“双一流”奖补资金“主要农作物田间自动监测与精准管理”(SYL2017XTTD02)

Driving Force Analysis and Prediction Simulation of Soil Salinization in Coastal Area of Yellow River Delta

ZHAO Ming(), CHANG Chunyan(), WANG Zhuoran, ZHAO Gengxing()   

  1. National Engineering Research Center for Efficient Utilization of Soil and Fertilizer, College of Resources and Environment, Shandong Agricultural University, Tai’an, Shandong 271000
  • Received:2023-03-22 Revised:2023-05-08 Published-:2024-02-22 Online:2024-02-22

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摘要：

摸清土壤盐渍化的发生机制是盐渍土改良利用的重要基础。本文选取了黄河三角洲濒海区-垦利区为研究区，首先通过地理探测器分析蒸发量、降水量、地下水埋深、地下水矿化度、土壤粘粒含量、相对高程、植被覆盖度、距海远近8个因子对土壤盐分的影响力大小，进而进行主要驱动力的筛选；接着分别构建了MLR、PLSR、BPNN和SVM模型，并选取精度最高的模型实现土壤盐分预测模型的构建；最后在地下水变化情形下分别设定对照组、地下水位下降0.5 m和上升0.5 m 3种情景，进行了土壤盐分的情景模拟。地理探测器中因子探测结果显示，不同影响因子对土壤盐分的影响力大小为地下水矿化度>植被覆盖度>地下水埋深>距海远近>粘粒含量>地表高程>降水量>蒸发量；交互作用探测结果显示，地下水因素、植被覆盖度、距海远近之间的交互作用影响力较强，确定地下水矿化度、地下水埋深、植被覆盖度、距海远近为土壤盐分主要驱动因子；精度最高的预测模型为BP神经网络模型，模型建模集R2为0.8847，RMSE为1.1350，验证集R2为0.7999，RMSE为1.1204；地下水情景模拟结果显示，适当降低地下水位可以改善土壤盐渍化状况，水位下降时轻度、中度、重度盐渍土和盐土的变化率分别为0.22%、 -5.46%、15.28%、-10.04%；水位升高时轻度、中度、重度盐渍土和盐土的变化率分别为-0.02%、-14.77%、22.51%、-8.02%。本文筛选出土壤盐分的主要驱动因子，构建了最佳预测模型，并根据设定的情景对土壤盐分进行模拟分析，为黄河三角洲土壤盐渍化的调控及防治提供了依据。

关键词: 黄河三角洲, 土壤盐渍化, 驱动力, 预测模拟

Abstract:

Understanding the mechanism of soil salinization is an important foundation for the improvement and utilization of saline soil. This paper selects Kenli District, the coastal area of Yellow River Delta, as the study area. First, the influences of 8 factors on soil salinity, including evaporation, precipitation, groundwater burial depth, groundwater mineralization, soil clay content, relative elevation, vegetation coverage and distance from the sea, were analyzed through geographical detectors, and then the main driving forces were screened; subsequently, MLR, PLSR, BPNN and SVM models were constructed, and the most accurate model was selected to construct the soil salt prediction model; finally, under the situation of groundwater changes, three scenarios (a control group, a 0.5 m decrease in groundwater level and a 0.5 m increase in groundwater level) were set up to simulate soil salinity. The factor detection results by the geographical detector showed that the influence of different factors on soil salinity was groundwater salinity>vegetation coverage>groundwater burial depth>distance from the sea>clay content>surface elevation>precipitation>evaporation; the interaction detection results showed that the interaction between groundwater factors, vegetation coverage and distance from the sea had a strong influence. It determined that groundwater mineralization, groundwater depth, vegetation coverage and distance from the sea were the main driving factors for soil salinity; the most accurate prediction model was the BP neural network model, with a modeling set R2 of 0.8847 and RMSE of 1.1350, a validation set R2 of 0.7999 and RMSE of 1.1204; the simulation results of groundwater scenarios showed that appropriately lowering the groundwater level could improve soil salinization. When the water level dropped, the change rates of mild, moderate, and severe saline soil and saline soil were 0.22%, -5.46%, 15.28% and -10.04%, respectively; when the water level rose, the change rates of mild, moderate and severe saline soil and saline soil were -0.02%, -14.77%, 22.51% and -8.02%, respectively. This paper screened out the main driving factors of soil salinity, constructed the best prediction model, and simulated and analyzed the soil salinity according to the set scenarios, providing a basis for the control and prevention of soil salinization in Yellow River Delta.

Key words: the Yellow River Delta, soil salinization, driving force, predictive simulation