Multiscale Analysis for Identifying the Impact of Human and Natural Factors on Water-Related Ecosystem Services
Multiscale Analysis for Identifying the Impact of Human and Natural Factors on Water-Related Ecosystem Services
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DOI：
10.3390/su16051738
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发表时间：
2024-02
期刊：
影响因子：
3.9
通讯作者：
Yuncheng Jiang;Bin Ouyang;Zhigang Yan
中科院分区：
文献类型：
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作者：
Yuncheng Jiang;Bin Ouyang;Zhigang Yan
准确识别和获取生态系统驱动因素的变化及其对生态系统服务影响的空间异质性，可以为生态治理提供全面的支撑信息。本研究从单因素效应、非线性效应、交互效应和空间特征四个方面探讨了不同时期不同子流域的人与自然因素与水相关生态系统服务（WES）关系的变化。以地形、气候、经济特征复杂的南部盆地为研究区，将研究区划分为四个各具特色的子流域。利用InVEST模型对2000年至2020年5个时期的产水量、土壤保持和水净化的WES进行量化，并结合OPGD和MGWR模型评估15个因素对WES的影响及其空间特征。结果表明：（1）通过多时段数据对比，降水等气候因素（0.4033）是影响南部盆地WES的主要因素，建筑面积（0.0688）等人为因素影响较弱。人为因素对 WES 的直接影响在短期内并不显着，但随着时间的推移而增加。 (2)不同子流域对WES的影响不同。例如，人类活动强度（0.3518）是影响内流区WES的关键因素，而降水是影响其他子流域WES的主要因素。 (3)影响因素与WES变化往往呈非线性相关；然而，一旦超过一定的阈值，它们可能会对WES产生不利影响。 (4)当单一因素与其他因素相互作用时，其解释力往往会增强。 (5)与传统方法相比，MGWR的估计精度更高。强烈的人类活动会对WES产生不利影响，而丰富的降水则为WES的形成创造了有利条件。因此，将长时间序列多遥感数据与OPGD和MGWR模型相结合适合识别和分析影响WES变化的人为和自然因素的驱动机制。在全球变化的背景下，阐明生态系统服务的驱动因素可以为制定实际政策和土地管理应用提供重要见解。
Accurately identifying and obtaining changes in ecosystem drivers and the spatial heterogeneity of their impacts on ecosystem services can provide comprehensive support information for ecological governance. In this study, we investigate the changes in the relationship between human and natural factors and water-related ecosystem services (WESs) in different sub-watersheds across various time periods, focusing on four aspects: single-factor effect, nonlinear effect, interactive effects, and spatial characteristics. Taking the southern basins, which have complex topographic, climatic, and economic characteristics, as a study area, the study area was divided into four sub-basins with different characteristics. WESs of water yield, soil conservation, and water purification were quantified using the InVEST model for five periods from 2000 to 2020, and the OPGD and MGWR models were integrated to assess the impacts of 15 factors on WESs and their spatial characteristics. The results show the following: (1) After comparing the data over multiple time periods, climate factors such as precipitation (0.4033) are the primary factors affecting WESs in the southern basins, and human factors such as construction area (0.0688) have a weaker influence. The direct impact of human factors on WESs is not significant in the short term but increases over time. (2) Different sub-watersheds have different impacts on WESs. For instance, human activity intensity (0.3518) is a key factor affecting WESs in the Inward Flowing Area, while precipitation is the primary factor influencing WESs in other sub-watersheds. (3) Influencing factors and WES changes are often nonlinearly correlated; however, once a certain threshold is exceeded, they may have adverse impacts on WESs. (4) When a single factor interacts with other factors, its explanatory power tends to increase. (5) Compared to traditional methods, the estimation accuracy of MGWR is higher. Intense human activities can adversely affect WESs, while abundant precipitation creates favorable conditions for the formation of WESs. Therefore, integrating long-time-series multi-remote sensing data with OPGD and MGWR models is suitable for identifying and analyzing the driving mechanisms of human and natural factors that influence changes in WESs. Against the backdrop of global change, elucidating the driving factors of ecosystem services can provide crucial insights for developing practical policies and land management applications.