以海河流域为研究区域，通过 InVEST 模型对 1990—2020 年碳储量状况进行分阶段定量反演，并结合 Markov-PLUS 模型从数量与空间角度进行未来模拟，最终明确流域碳储量发展趋势及关键驱动因素。结果表明：1990— 2020 年海河流域碳储量共减少 1.30 亿 t，耕地碳储量损失最大；碳储量高值主要集中在西部与北部山地地区，低值 分布于东南部平原地区。人口密度在各阶段解释力占比均超过 20%，且呈现增加趋势；年均降水对碳储量空间分 异影响最小，最低占 0.21%。各因子相互作用对碳储量空间分异解释力显著提高，人口密度与其他因子交互作用 对碳储量变化的解释力最强。Markov-PLUS-InVEST 预测结果显示，在城市快速发展情景下，2030 年碳储量与 2020 年基本持平，但生态保护情景增加 0.88 亿 t。相关结论可为海河流域进行土地类型间的生态调控及未来规划 发展提供理论支撑。

The?change?of?regional?land?use?affects?carbon?emission?and?carbon?sequestration?processes,?which?in turn?causes?changes?in?the?carbon?cycle?and?carbon?storage?in?terrestrial?ecosystems.?Based?on?the?China?land?cover dataset,?the?carbon?storage?and?sequestration?module?of?the?integrated?valuation?of?ecosystem?services?and?trade-offs model?was?used?to?estimate?the?carbon?storage?and?changes?in?terrestrial?ecosystems?from?1990?to?2020?in?the?Haihe River?basin.?The?patch-generating?land?use?simulation?model?was?combined?to?predict?future?land?use?and?carbon storage.?The?integrated?valuation?of?ecosystem?services?and?trade-offs?model?could?quantify?regional?carbon?storage based?on?regional?carbon?density.?The?carbon?storage?and?sequestration?module?mainly?depended?on?land?cover types ?and ?a ?basic ?carbon ?pool ?model. ?The ?basic ?carbon ?pool ?model ?divided ?carbon ?storage ?into ?four ?types: aboveground?biomass?carbon,?belowground?biomass?carbon,?soil?carbon,?and?dead?organic?matter?carbon.?The?patch- generating?land?use?simulation?model?is?a?cellular?automata?model?based?on?raster?data?that?could?simulate?land?use changes?at?the?patch?scale.?It?integrated?the?land?expansion?analysis?strategy?and?a?cellular?automata?model?based?on multiple?random?patch?seeds,?which?could?be?used?to?explore?the?driving?factors?of?land?expansion?and?predict?the patch-level?evolution?of?land?use?landscapes.?Additionally,?geodetector?was?utilized?to?quantitatively?explain?the driving?mechanisms?of?carbon?storage?in?the?Haihe?River?basin.?Geodetector?is?a?method?for?detecting?spatial variations ?of ?geographic ?features ?and ?revealing ?their ?driving ?factors. ?This ?method ?allows ?for ?direct ?correlation analysis ?between ?the ?dependent ?variable ?and ?the ?independent ?variables ?without ?considering ?collinearity ?among factors. ??????The?results?showed?that:?(1)?the?carbon?storage?decreased?by?a?total?of?4.98%?from?1990?to?2020,?with?the?year 2003?as?the?turning?point.?Carbon?storage?exhibited?a?fluctuating?declining?trend,?followed?by?a?decrease?in?the magnitude?of?fluctuations,?fluctuating?around?2.05?billion?tons.?(2)?The?spatial?distribution?of?carbon?density?in?the basin?exhibited?significant?heterogeneity.?High?carbon?density?values?were?mainly?concentrated?in?the?eastern?and northern?forested?areas?of?the?basin,?while?low?values?were?primarily?distributed?in?cities?and?the?Bohai?Bay?region. (3)?In?2030,?apart?from?the?scenario?of?rapid?urban?development,?other?scenarios?show?varying?degrees?of?carbon stock?increase.?The?scenario?with?the?highest?increase?is?the?ecological?conservation?scenario,?which?has?a?carbon stock ?of ?0.77 ?million ?tons ?higher ?than ?the?scenario ?of ?rapid ?urban ?development. ?This ?indicates ?that?ecological improvement?is?beneficial?for?carbon?sequestration?in?regional?terrestrial?ecosystems.?In?the?scenario?of?rapid?urban development,?the?expansion?of?impervious?surfaces?encroached?upon?cropland,?leading?to?a?significant?reduction?in carbon?storage?in?the?eastern?plain?area?of?the?Haihe?River?basin.?(4)?Natural?factors?had?a?higher?explanatory?power than?socio-economic?factors,?and?the?interaction?between?population?density,?DEM,?and?other?climatic?factors?has the?strongest?explanatory?power?for?changes?in?carbon?stock.?The?results?will?provide?certain?theoretical?support?for land?regulation?and?future?low-carbon?development?in?the?Haihe?River?basin?and?also?serve?as?a?reference?for?better implementation?of?the?carbon?peaking?and?carbon?neutrality?goals.