WaSSI-C模型在焉耆盆地的适用性改进与应用
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摘要
【目的】为缓解干旱区日益突出的水碳矛盾,进而为其水碳资源综合管理提供有效的工具和决策支持.【方法】选取典型干旱区焉耆盆地为研究对象,基于WaSSI-C模型(water supply stress index-carbon model)的理论框架和运行机理,针对研究区的自然地理和环境特征,增加冰川融化计算模块,对WaSSI-C模型进行适用性改进.以2000～2009年作为率定期,2010～2015年作为验证期,对研究区实测径流、MODIS蒸散(ET)和总生态系统生产力(GEP)数据进行统计分析,利用决定系数(R~2)和效率系数(NS)评价模型的模拟精度,应用GIS空间分析技术探讨研究区水碳资源的空间分布特征.【结果】1)研究区率定期和验证期总径流对比验证的R~2分别为0.80和0.77,NS为0.77和0.69;ET对比验证R~2分别为0.82和0.79,NS为0.80和0.76;GEP对比验证R~2分别为0.88和0.84,NS分别为0.87和0.82;2)径流空间分布呈现"西高东低、北高南低"的特点;ET和GEP均呈现"中间高,四周低"的特点.【结论】改进后的WaSSI-C模型在焉耆盆地具有良好的适用性,且模拟结果显示焉耆盆地的水碳资源具有明显的空间异质分布特征.
【Objective】 In order to alleviate the increasingly prominent contradiction between water and carbon in arid areas,and provide effective tools and decision support for integrated management of water and carbon resources.【Method】 Yanqi Basin,a typical arid region,was selected as the research site.Based on the theoretical framework and operating mechanism of the WaSSI-C(water supply stress index-carbon model) model,the module for calculating the melt of glaciers was added to improve the applicability of the model according to the characters of natural geography and the environment in the study area.The data of measured runoff,evapotranspiration(ET) and total ecosystem productivity(GEP) of MODIS in the study area from 2000 to 2009 were analyzed as the calibration period and the data from 2010 to 2015 were used as the validation period.The precision of the simulation of the model was evaluated by using the coefficient of determination(R~2) and the efficiency coefficient(NS).The spatial distribution characteristics of resources of water and carbon were analyzed by using the technique of GIS Spatial Analysis.【Result】 The R~2 of calibration and verification period for the total runoff in the study area were 0.80 and 0.77 respectively,and NS was 0.77 and 0.69 respectively;The R~2 for ET were 0.82 and 0.79 respectively,NS was 0.80 and 0.76 respectively;The R~2 for GEP were 0.88 and 0.84,NS were 0.87 and 0.82.The characteristics of the spatial distribution of runoff were higher in west and north parts than in east and south parts.The characteristics of the spatial distribution of ET and GEP were higher in middle area than the around area.【Conclusion】 It could be concluded that the improved WaSSI-C model had an excellent applicability in Yanqi Basin.And the simulation results showed that the water and carbon resources in Yanqi basin were obvious spatial heterogeneity.
【Objective】 In order to alleviate the increasingly prominent contradiction between water and carbon in arid areas,and provide effective tools and decision support for integrated management of water and carbon resources.【Method】 Yanqi Basin,a typical arid region,was selected as the research site.Based on the theoretical framework and operating mechanism of the WaSSI-C(water supply stress index-carbon model) model,the module for calculating the melt of glaciers was added to improve the applicability of the model according to the characters of natural geography and the environment in the study area.The data of measured runoff,evapotranspiration(ET) and total ecosystem productivity(GEP) of MODIS in the study area from 2000 to 2009 were analyzed as the calibration period and the data from 2010 to 2015 were used as the validation period.The precision of the simulation of the model was evaluated by using the coefficient of determination(R~2) and the efficiency coefficient(NS).The spatial distribution characteristics of resources of water and carbon were analyzed by using the technique of GIS Spatial Analysis.【Result】 The R~2 of calibration and verification period for the total runoff in the study area were 0.80 and 0.77 respectively,and NS was 0.77 and 0.69 respectively;The R~2 for ET were 0.82 and 0.79 respectively,NS was 0.80 and 0.76 respectively;The R~2 for GEP were 0.88 and 0.84,NS were 0.87 and 0.82.The characteristics of the spatial distribution of runoff were higher in west and north parts than in east and south parts.The characteristics of the spatial distribution of ET and GEP were higher in middle area than the around area.【Conclusion】 It could be concluded that the improved WaSSI-C model had an excellent applicability in Yanqi Basin.And the simulation results showed that the water and carbon resources in Yanqi basin were obvious spatial heterogeneity.
引文
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[8] 苏培玺,赵爱芬,张立新,等.荒漠植物梭梭和沙拐枣光合作用、蒸腾作用及水分利用效率特征[J].西北植物学报,2003,23(1):11-17.
[9] 王冠依,丁洁,吴雨,等.节水灌溉稻田水碳通量日变化特征[J].节水灌溉,2016(11):1-4,10.
[10] Baldocchi D,Falge E,Gu L,et al.Fluxnet:A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide,water vapor,and energy flux densities[J].Bulletin of the American Meteorological Society,2001,82:2415-2434.
[11] Beer C,Reichstein M,Tomelleri E,et al.Terrestrial gross carbon dioxide uptake:global distribution and covariation with climate[J].Science,2010,329(5993):834.
[12] Zhao M,Running S W.Drought-induced reduction in global terrestrial net primary production from 2000 through 2009.[J].Science,2010,329(5994):940.
[13] 赵江涛,周金龙,梁川,等.新疆焉耆盆地平原区地下水反向水文地球化学模拟[J].干旱区资源与环境,2017,31(10):65-70.
[14] 麦麦提吐尔逊·艾则孜,海米提·依米提,马蓉.1956-2010年新疆焉耆盆地径流变化特征及驱动力分析[J].冰川冻土,2014,36(3):670-677.
[15] 宋春林,孙向阳,王根绪.森林生态系统碳水关系及其影响因子研究进展[J].应用生态学报,2015,26(9):2891-2902.
[16] 徐晓梧,余新晓,贾国栋,等.基于稳定同位素的SPAC水碳拆分及耦合研究进展[J].应用生态学报,2017,28(7):2369-2378.
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[20] 李佳,陈岩,秦淑静,等.涡度相关系统不同平均周期对干旱区玉米水热通量的影响[J].灌溉排水学报,2018,37(9):69-72.
[21] Cao M,Woodward F I.Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change[J].Global Change Biology,1998,4(2):185-198.
[22] Liu J,Chen J M,Cihlar J,et al.A process-based boreal ecosystem productivity simulator using remote sensing inputs[J].Remote Sensing of Environment,1997,62(2):158-175.
[23] Foley J A,Prentice I C,Ramankutty N,et al.An integrated biosphere model of land surface processes,terrestrial carbon balance,and vegetation dynamics.[J].Global Biogeochemical Cycles,1996,10(4):603-628.
[24] 刘宁,孙鹏森,刘世荣.陆地水-碳耦合模拟研究进展[J].应用生态学报,2012,23(11):3187-3196.
[25] Tague C L,Band L E.Rhessys:Regional hydro-ecologic simulation system-an object-oriented approach to spatially distributed modeling of carbon,water,and nutrient cycling[J].Earth Interactions,2004,8:1.
[26] Tian H,Liu M,Zhang C,et al.The dynamic land ecosystem model(DLEM) for simulating terrestrial processes and interactions in the context of multifactor global change[J].Acta Geographica Sinica,2010,65(9):1027-1047.
[27] Sun G,Caldwell P,Noormets A,et al.Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model[J].Journal of Geophysical Research Biogeo sciences,2015,116(G3):G00J05.
[28] 张晓琳,熊立华,林琳,等.五种潜在蒸散发公式在汉江流域的应用[J].干旱区地理,2012,35(2):229-237.
[29] Mccabe G J,Wolock D M.General-circulation-model simulations of future snowpack in the western unted states[J].Jawra Journal of the American Water Resources Association,1999,35(6):1473-1484.
[30] 刘金平,乐嘉祥.萨克拉门托模型参数初值分析方法研究[J].水科学进展,1996,7(3):69 -76.
[31] 刘宁,孙鹏森,刘世荣,等.流域水碳过程耦合模拟-WaSSI-C模型的率定与检验[J].植物生态学报,2013,37(6):492-502.
[32] Sun G,Mcnulty S G,Myers J A M,et al.Impacts of multiple stresses on water demand and supply across the Southeastern United States[J].Jawra Journal of the American Water Resources Association,2008,44(6):1441-1457.
[33] McCuen R H,Knight Z,Cutter A G.Evaluation of the Nash-Sutcliffe efficiency index[J].Journal of Hydrologic Engineering,2006,11(6),597-602.
[34] Sun G,Alstad K,Chen J,et al.A general predictive model for estimating monthly ecosystem evapotranspiration[J].Ecohydrology,2011,4(2):245-255.
[35] 卿文武,陈仁升,刘时银.冰川水文模型研究进展[J].水科学进展,2008,19(6):893-902.
[36] 张小咏,刘耕年,鞠远江,等.冰川径流模型研究进展[J].水土保持研究,2005,12(4):58-62.
[37] Turner D P,Ritts W D,Cohen W B,et al.Evaluation of MODIS NPP and GPP products across multiple biomes[J].Remote Sensing of Environment,2006,102(3/4):282-292.
[38] Mu Q,Zhao M,Running S W.Improvements to a MODIS global terrestrial evapotranspiration algorithm[J].Remote Sensing of Environment,2011,115(8):1781-1800.
[39] 毕雪丽.新疆博斯腾湖流域气候变化及地表径流响应[D].上海:华东师范大学,2012.
[40] 徐永明,赵巧华,巴雅尔,等.基于MODIS数据的博斯腾湖流域地表蒸散时空变化[J].地理科学,2012,32(11):1353-1357.
[41] 郭玉川,何英,董新光.基于MODIS数据的区域蒸散发估算研究[J].节水灌溉,2008,(1):44-47.
[42] Zhao M,Heinsch F A,Nemani R R,et al.Improvements of the MODIS terrestrial gross and net primary production global data set[J].Remote Sensing of Environment,2005,95(2):164-176.
[43] 陈福军,沈彦俊,李倩,等.中国陆地生态系统近30年NPP时空变化研究[J].地理科学,2011,31(11):1409-1414.
[44] 王辉,刘海隆,包安明,等.2001-2013年开孔河流域净初级生产力遥感估算及其时空分布特征[J].水土保持通报,2016,36(5):220-224,230.
[45] 王永兴.绿洲生态系统及其环境特征[J].干旱区地理,2000,23(1):7-12.
[2] 孙文,范昊明.全球变暖背景下松花江流域气温最新变化特征[J].水土保持研究,2018,25(3):97-104.
[3] 方精云,朱江玲,石岳.生态系统对全球变暖的响应[J].科学通报,2018,63(2):136-140.
[4] 于贵瑞,王秋凤,于振良.陆地生态系统水-碳耦合循环与过程管理研究[J].地球科学进展,2004,19(5):831-839.
[5] 张永强,沈彦俊,刘昌明,等.华北平原典型农田水热与CO2通量的测定[J].地理学报,2002,57(3):333-342.
[6] 朱治林,孙晓敏,张仁华,等.作物群体CO2通量和水分利用效率的快速测定[J].应用生态学报,2004,15(9):1684-1686.
[7] 郭维华,李思恩.西北旱区葡萄园水碳通量耦合的初步研究[J].灌溉排水学报,2010,29(5):61-63.
[8] 苏培玺,赵爱芬,张立新,等.荒漠植物梭梭和沙拐枣光合作用、蒸腾作用及水分利用效率特征[J].西北植物学报,2003,23(1):11-17.
[9] 王冠依,丁洁,吴雨,等.节水灌溉稻田水碳通量日变化特征[J].节水灌溉,2016(11):1-4,10.
[10] Baldocchi D,Falge E,Gu L,et al.Fluxnet:A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide,water vapor,and energy flux densities[J].Bulletin of the American Meteorological Society,2001,82:2415-2434.
[11] Beer C,Reichstein M,Tomelleri E,et al.Terrestrial gross carbon dioxide uptake:global distribution and covariation with climate[J].Science,2010,329(5993):834.
[12] Zhao M,Running S W.Drought-induced reduction in global terrestrial net primary production from 2000 through 2009.[J].Science,2010,329(5994):940.
[13] 赵江涛,周金龙,梁川,等.新疆焉耆盆地平原区地下水反向水文地球化学模拟[J].干旱区资源与环境,2017,31(10):65-70.
[14] 麦麦提吐尔逊·艾则孜,海米提·依米提,马蓉.1956-2010年新疆焉耆盆地径流变化特征及驱动力分析[J].冰川冻土,2014,36(3):670-677.
[15] 宋春林,孙向阳,王根绪.森林生态系统碳水关系及其影响因子研究进展[J].应用生态学报,2015,26(9):2891-2902.
[16] 徐晓梧,余新晓,贾国栋,等.基于稳定同位素的SPAC水碳拆分及耦合研究进展[J].应用生态学报,2017,28(7):2369-2378.
[17] Farquhar G D,Caemmerer S V,Berry J A.A biochemical model of photosynthetic CO2assimilation in leaves of C3 species[J].Planta,1980,149(1):78-90.
[18] Ball J T,Woodrow I E,Berry J A.A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions[C].Boston:Prog Photosynthesis Res Proc Int Congress,1987:221-224.
[19] Baldocchi D D.Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems:past,present and future[J].Global Change Biology,2003,9(4):479-492.
[20] 李佳,陈岩,秦淑静,等.涡度相关系统不同平均周期对干旱区玉米水热通量的影响[J].灌溉排水学报,2018,37(9):69-72.
[21] Cao M,Woodward F I.Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change[J].Global Change Biology,1998,4(2):185-198.
[22] Liu J,Chen J M,Cihlar J,et al.A process-based boreal ecosystem productivity simulator using remote sensing inputs[J].Remote Sensing of Environment,1997,62(2):158-175.
[23] Foley J A,Prentice I C,Ramankutty N,et al.An integrated biosphere model of land surface processes,terrestrial carbon balance,and vegetation dynamics.[J].Global Biogeochemical Cycles,1996,10(4):603-628.
[24] 刘宁,孙鹏森,刘世荣.陆地水-碳耦合模拟研究进展[J].应用生态学报,2012,23(11):3187-3196.
[25] Tague C L,Band L E.Rhessys:Regional hydro-ecologic simulation system-an object-oriented approach to spatially distributed modeling of carbon,water,and nutrient cycling[J].Earth Interactions,2004,8:1.
[26] Tian H,Liu M,Zhang C,et al.The dynamic land ecosystem model(DLEM) for simulating terrestrial processes and interactions in the context of multifactor global change[J].Acta Geographica Sinica,2010,65(9):1027-1047.
[27] Sun G,Caldwell P,Noormets A,et al.Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model[J].Journal of Geophysical Research Biogeo sciences,2015,116(G3):G00J05.
[28] 张晓琳,熊立华,林琳,等.五种潜在蒸散发公式在汉江流域的应用[J].干旱区地理,2012,35(2):229-237.
[29] Mccabe G J,Wolock D M.General-circulation-model simulations of future snowpack in the western unted states[J].Jawra Journal of the American Water Resources Association,1999,35(6):1473-1484.
[30] 刘金平,乐嘉祥.萨克拉门托模型参数初值分析方法研究[J].水科学进展,1996,7(3):69 -76.
[31] 刘宁,孙鹏森,刘世荣,等.流域水碳过程耦合模拟-WaSSI-C模型的率定与检验[J].植物生态学报,2013,37(6):492-502.
[32] Sun G,Mcnulty S G,Myers J A M,et al.Impacts of multiple stresses on water demand and supply across the Southeastern United States[J].Jawra Journal of the American Water Resources Association,2008,44(6):1441-1457.
[33] McCuen R H,Knight Z,Cutter A G.Evaluation of the Nash-Sutcliffe efficiency index[J].Journal of Hydrologic Engineering,2006,11(6),597-602.
[34] Sun G,Alstad K,Chen J,et al.A general predictive model for estimating monthly ecosystem evapotranspiration[J].Ecohydrology,2011,4(2):245-255.
[35] 卿文武,陈仁升,刘时银.冰川水文模型研究进展[J].水科学进展,2008,19(6):893-902.
[36] 张小咏,刘耕年,鞠远江,等.冰川径流模型研究进展[J].水土保持研究,2005,12(4):58-62.
[37] Turner D P,Ritts W D,Cohen W B,et al.Evaluation of MODIS NPP and GPP products across multiple biomes[J].Remote Sensing of Environment,2006,102(3/4):282-292.
[38] Mu Q,Zhao M,Running S W.Improvements to a MODIS global terrestrial evapotranspiration algorithm[J].Remote Sensing of Environment,2011,115(8):1781-1800.
[39] 毕雪丽.新疆博斯腾湖流域气候变化及地表径流响应[D].上海:华东师范大学,2012.
[40] 徐永明,赵巧华,巴雅尔,等.基于MODIS数据的博斯腾湖流域地表蒸散时空变化[J].地理科学,2012,32(11):1353-1357.
[41] 郭玉川,何英,董新光.基于MODIS数据的区域蒸散发估算研究[J].节水灌溉,2008,(1):44-47.
[42] Zhao M,Heinsch F A,Nemani R R,et al.Improvements of the MODIS terrestrial gross and net primary production global data set[J].Remote Sensing of Environment,2005,95(2):164-176.
[43] 陈福军,沈彦俊,李倩,等.中国陆地生态系统近30年NPP时空变化研究[J].地理科学,2011,31(11):1409-1414.
[44] 王辉,刘海隆,包安明,等.2001-2013年开孔河流域净初级生产力遥感估算及其时空分布特征[J].水土保持通报,2016,36(5):220-224,230.
[45] 王永兴.绿洲生态系统及其环境特征[J].干旱区地理,2000,23(1):7-12.