氣候與土地利用變遷下的生態系服務時空熱點與權衡關係—以陳有蘭溪流域為例

Abstract

生態系服務(Ecosystem Service, ES)為大自然中的生態系統環境所提供給人類社會之資源與服務，為人類生存與生活的一切基礎。聯合國政府間氣候變遷專門委員會(IPCC)於2022年的氣候報告指出，不當的開發所帶來的土地利用變遷及氣候變遷的壓力之下正影響著碳循環、水循環等多個生態過程，加劇對生態系服務的破壞，進而對人類社會造成威脅。本研究基於土地利用變遷以及氣候變遷，探討陳有蘭溪流域在2007年、2014年、2020年三個歷史年份，以及2050年代表濃度路徑(Representative Concentration Path, RCP)為RCP8.5，大氣環流模式(General Circulation Models, GCM): CanESM2、CESM1-CAM5、MIROC5情境下的生態系服務熱點以及權衡關係，以生態系服務管理的角度提供流域內優先進行保護及治理之建議。 本研究使用InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs)模式量化流域內歷史年份的生態系服務:碳儲存、棲地品質、產水量、氮磷營養鹽留存、沉積物留存。並採區域空間自相關(Local Indicators of Spatial Association)的High-High聚集區域與前25%高值區的交集做各生態系服務的熱點分析，視可提供四種以上熱點的區域作為生態系服務綜合熱點，再將綜合熱點聯集政府所劃設之國有林地分區作為未來土地發展之限制保護區，使用CLUE-s (Conversion of Land Use and its Effects at Small regional extent)模式模擬流域內2050年土地利用分佈，其中土地利用變遷驅動因子的溫度及雨量分別輸入三種GCM的預測資料，以探討不同氣候情境的土地利用分佈的差異，接著再量化2050年的生態系服務，最後計算Pearson相關係數及雙變量區域空間自相關分析(Bivariate local indicator of spatial association)以探討兩兩生態系服務間的權衡關係與空間聚集情形。 研究結果表示陳有蘭溪流域內有14%的土地為生態系服務綜合熱點，綜合熱點土地類型以林地為主(94%)，其次為草地(3%)，65%的綜合熱點已位於政府劃設之國有林地。在本研究對未來土地變化的假設情境下，不同氣候GCM的土地利用分佈差異不大。所有生態系服務中僅產水量受該年降雨空間分佈影響而具明顯的時空變異性，受氣候變遷的影響大於土地利用變遷，而棲地品質、氮磷營養鹽留存、沉積物留存、碳儲存則是主要受土地利用影響。除了產水量以外的生態系服務不論在歷史年份或是未來2050年三種GCM情境下均維持穩定的協同關係，具有競合關係的生態系服務均與產水量相關，但在CESM1-CAM5情境下無競合關係。生態系服務雙變量空間自相關的結果表示在所有研究年份中，流域內的山區林地皆為碳儲存-棲地品質的High-High熱點，中下游地勢平緩處農地、建地則為為多組生態系服務Low-Low聚集的熱點，可見人類活動越密集的區域，對生態系服務產生的負面影響越大。最後，作為未來政策規劃之參考，本研究將未包含在國有林地內的綜合熱點所缺少的生態系服務項目進行分類，建議主要位於信義鄉神木村、豐丘村、自強村的生態系服務綜合熱點區域可新增納入國土保安區，以加強流域內的生態系服務管理，實踐生態系服務永續經營的目標。

Ecosystem services (ES) refer to the resources and benefits provided by natural ecosystems to human societies, forming the foundation for human survival and well-being. In its 2022 climate report, the Intergovernmental Panel on Climate Change (IPCC) highlighted that improper land use change resulting from development, under the pressures of climate change, are impacting various ecological processes such as carbon and water cycling, exacerbating the degradation of ecosystem services, and thereby posing threats to human society. This study examines the Chenyoulan watershed at historical years 2007, 2014, 2020, and 2050under the Representative Concentration Path (RCP)8.5 and General Circulation Model(GCM): CanESM2, CESM1-CAM5, MIROC5. It aims to identify ES hotspots and trade-offs, considering land use changes and climate change, and provide recommendations for prioritized conservation and management from an ecosystem service management perspective. This study employs the InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) to quantify historical-year ecosystem services within the watershed: carbon storage, habitat quality, water yield, nitrogen and phosphorus retention, sediment retention. The hotspot analysis for each ES is based on the intersection of Local Indicators of Spatial Association (LISA) High-High clusters and the top 25% high-value areas. Regions offering hotspots for four or more ES are considered comprehensive ES hotspots. These areas are then intersected with government-designated state-owned forest zones, serving as restricted conservation zones for future land development. The CLUE-s model (Conversion of Land Use and its Effects at Small regional extent) is employed to simulate land use distribution for the year 2050 within the watershed. Temperature and precipitation, as drivers of land use change, are inputted from three GCMs'' predictive data to explore variations in land use distribution under different climate scenarios. The study quantifies 2050 ecosystem services and concludes with Pearson correlation coefficients and Bivariate Local Indicators of Spatial Association analysis to investigate trade-offs and spatial clustering between pairs of ES. The results reveal that 14% of the Chenyoulan watershed comprises comprehensive ES hotspots, predominantly consisting of forests (94%) followed by grasslands (3%), with 65% already located within government-designated state-owned forest areas. Under assumed future land change scenarios, differences in land use distribution among different climate GCMs are negligible. Among all ecosystem services, only water yield exhibits significant spatiotemporal variability due to rainfall distribution, with climate change exerting a greater influence than land use change. Habitat quality, nitrogen and phosphorus retention, sediment retention, and carbon storage are primarily influenced by land use. Except for water yield, the synergistic relationships between ES remain stable across historical years and the three GCM scenarios for 2050, with competing relationships observed primarily involving water yield, except for the CESM1-CAM5 scenario. Bivariate spatial autocorrelation of ecosystem services indicates that mountainous forests within the watershed are consistently high-high hotspots for carbon storage and habitat quality across all study years, while agricultural and built-up areas in the middle and lower reaches are hotspots for co-occurring low values of multiple ecosystem services. This underscores that regions with intensified human activities experience greater negative impacts on ecosystem services. Finally, the study classifies the missing ecosystem service components in comprehensive hotspots not encompassed within state-owned forest areas, suggesting the addition of hotspot regions located primarily in the Shengmu, Fengqiu, and Ziqiang villages of Xinyi Township to protected areas, strengthening ecosystem service management within the watershed and serving as a reference for future policy planning.