1. 中国科学院 地理科学与资源研究所陆地水循环与地表过程 重点试验室,北京,100101
2. 中国科学院大学 资源与环境学院,北京,100049
纸质出版:2023
移动端阅览
翟钰钰, 方海燕. 基于文献计量学的EPIC模型应用综述[J]. 水土保持通报, 2023,43(1):263-271.
Zhai Yuyu, Fang Haiyan. A Review of EPIC Model Applications Based on Bibliometrix Analysis[J]. Bulletin of Soiland Water Conservation, 2023, 43(1): 263-271.
翟钰钰, 方海燕. 基于文献计量学的EPIC模型应用综述[J]. 水土保持通报, 2023,43(1):263-271. DOI: 10.13961/j.cnki.stbctb.2023.01.030.
Zhai Yuyu, Fang Haiyan. A Review of EPIC Model Applications Based on Bibliometrix Analysis[J]. Bulletin of Soiland Water Conservation, 2023, 43(1): 263-271. DOI: 10.13961/j.cnki.stbctb.2023.01.030.
[目的] 检索1991—2021年EPIC模型的应用情况,进行文献计量和聚类分析,为EPIC模型未来的应用和发展指出方向。[方法] 通过检索Web of Science核心数据库,基于检索到的1991—2021年与EPIC模型相关的261篇论文和R软件包bibliometrix文献计量和聚类分析法,分析了EPIC模型的研究热点和历史发展趋势。[结果] 1991—2021年,EPIC模型应用年发文量呈增加趋势,已有研究侧重于水文水资源与气候变化、土壤侵蚀与养分流失、农业干旱和作物生长等4个方面。该模型模拟精度高,能够很好地评价和预测过去、现在和未来的水、土、土壤养分流失和作物产量,但模型也存在参数多,输入数据制备难的缺点。[结论] EPIC模型可应用于多个研究领域,与其他模型及深度学习等方法的耦合,为深入开展“双碳”和水土流失研究、作物生产及对气候变化的响应模拟和预测等工作提供支撑。
[Objective] Previous studies regarding the use of the erosion-productivity impact calculator (EPIC) model from 1991 to 2021 were retrieved in order to conduct bibliometrics and cluster analysis so that the direction for the future application and development of the EPIC model could be determined. [Methods] The R-bibliometrix tool was used to analyze literature related to the EPIC model in the Web of Science Core Collection Database
and 261 papers were ultimately selected. The research hotspot and historical development trend of EPIC model were analyzed. [Results] During 1991—2021
the applications of the EPIC model were mainly focused on four aspects: water resources
crop growth
soil erosion and organic matter loss
and agricultural drought. The EPIC model had high simulation accuracy and was able to well evaluate water erosion
soil nutrient loss
and crop yield in the past
present
and future. However
disadvantages included too many parameters and extensive input data requirements. [Conclusion] The EPIC model has many modules and thus has potential to be applied across broadly different research fields in the future. It also has the potential to be coupled with other models and deep learning methods in the future in order to carry out in-depth simulations regarding “double carbon”
soil erosion
crop production
and climate change responses.
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