海河流域生态水分利用效率时空变化及其与气候因子的相关性分析
|
摘要
生态水分利用效率(water use efficiency,WUE)是碳-水循环的重要参数之一,明晰其时空演变特征对水资源短缺地区生态系统的健康发展具有重要的意义。海河流域水资源短缺是区域农业发展的重要制约因素,基于遥感、气象数据,利用趋势分析、相关分析等方法分析了海河流域2000—2014年总初级生产力(gross primary productivity,GPP)、蒸散量(evapotranspiration,ET)及WUE的时空分布特征,并识别WUE对降水、气温及干旱的响应。研究结果表明:(1)时间上,GPP和ET的变化趋势不显著,WUE呈现显著的增加趋势,增速为0.0185 gC/kg H_2O a~(-1)(R~2=0.6299,P<0.01);(2)空间上,WUE和GPP均呈现从东南向西北减小的趋势,高值区主要分布在华北平原农业生态区和京津唐城镇与城郊农业生态区。从变化趋势来看,黄土高原农业与草原生态区的GPP和WUE上升趋势最大;(3)植被类型中,农田的WUE值最高,草地的WUE最低,农田、有林草原和草地均呈现显著的增加趋势(P<0.05);(4)影响因素上,降水对WUE的影响最大,WUE由降水、干旱和气温控制的区域分别占整个流域植被面积的44.44%、39.23%和16.01%。
Water-use efficiency(WUE) is a key indicator of the interactions between the water and carbon cycles of terrestrial ecosystems. Understanding the spatiotemporal characteristics of WUE and the climatic factors that affect it could guide sustainable management of water resources and ecosystem services in areas lacking water. The Haihe River basin is located in a region sensitive to climate change and human activities, and water shortages have constrained sustainable development of regional agricultural and ecological environments. Using 2000—2014 Moderate Resolution Imaging Spectroradiometer(MODIS) and meteorology data, we investigated the spatiotemporal changes in gross primary productivity(GPP), evapotranspiration(ET), and WUE in the Haihe River basin using linear trend analysis, correlation analysis, Mann-Kendall(M-K) tests, and other statistical methods, revealing four key results.(1) The change in annual GPP trended upward non-significantly(R~2 = 0.1784, P > 0.1), and ET trended downward non-significantly(R~2 = 0.0269, P > 0.1). Annual WUE significantly increased over 2000—2014 at a rate of 0.0185 gC/kg H_2O a~(-1)(R~2 = 0.6299, P < 0.01).(2) The spatial patterns of average annual WUE and GPP in the Haihe River basin′s vegetated areas varied widely, and the annual average WUE and GPP values decreased from the southeast to the northwest. High WUE and GGP values were mainly found in the North China Plain agricultural ecoregion and the Tianjin-Beijing-Tangshan urban and suburban agricultural ecoregion. The lowest WUE and GGP values were mainly found in the East Central Inner Mongolian Plateau typical steppe ecoregion, Loess Plateau agricultural and steppe ecoregion, and the deciduous forest ecoregion northwest of the Yanshan-Taihang Mountains. The WUE of the Haihe River basin increased, with WUE values of 91.11% and 60.17% of the Haihe River basin′s vegetated areas increasing and increasing significantly(P < 0.05), respectively. That of the Loess Plateau agricultural and steppe ecoregion increased the most significantly.(3) The WUE means and trends among the different land use types also varied significantly. The mean WUE of croplands(CRO) was highest, and the WUE of grasslands(GRA) was lowest. The WUE of CRO, woody savannas(WSA), and GRA each increased significantly(P < 0.05).(4) Annual WUE was mainly affected by precipitation, and 41.44%, 33.23%, and 16.01% of WUE estimates in the Haihe River basin′s vegetated areas were dominated by inter-annual variation in precipitation, drought, and temperature, respectively. The vegetated areas in which WUE was primarily shaped by precipitation were mainly found in the northern portion of the Yanshan-Taihang Mountains deciduous forest ecoregion, the North China Plain agricultural ecoregion, and the Tianjin-Beijing-Tangshan urban and suburban agricultural ecoregion. The vegetated areas in which WUE was primarily shaped by drought were mainly found in the northeast of the North China Plain agricultural ecoregion and the southern portion of the Yanshan-Taihang Mountains deciduous forest ecoregion. The vegetated areas in which WUE was primarily shaped by temperature were mainly found in the western portion of the Yanshan-Taihang Mountains deciduous forest ecoregion and the East Central Inner Mongolian Plateau typical steppe ecoregion.
Water-use efficiency(WUE) is a key indicator of the interactions between the water and carbon cycles of terrestrial ecosystems. Understanding the spatiotemporal characteristics of WUE and the climatic factors that affect it could guide sustainable management of water resources and ecosystem services in areas lacking water. The Haihe River basin is located in a region sensitive to climate change and human activities, and water shortages have constrained sustainable development of regional agricultural and ecological environments. Using 2000—2014 Moderate Resolution Imaging Spectroradiometer(MODIS) and meteorology data, we investigated the spatiotemporal changes in gross primary productivity(GPP), evapotranspiration(ET), and WUE in the Haihe River basin using linear trend analysis, correlation analysis, Mann-Kendall(M-K) tests, and other statistical methods, revealing four key results.(1) The change in annual GPP trended upward non-significantly(R~2 = 0.1784, P > 0.1), and ET trended downward non-significantly(R~2 = 0.0269, P > 0.1). Annual WUE significantly increased over 2000—2014 at a rate of 0.0185 gC/kg H_2O a~(-1)(R~2 = 0.6299, P < 0.01).(2) The spatial patterns of average annual WUE and GPP in the Haihe River basin′s vegetated areas varied widely, and the annual average WUE and GPP values decreased from the southeast to the northwest. High WUE and GGP values were mainly found in the North China Plain agricultural ecoregion and the Tianjin-Beijing-Tangshan urban and suburban agricultural ecoregion. The lowest WUE and GGP values were mainly found in the East Central Inner Mongolian Plateau typical steppe ecoregion, Loess Plateau agricultural and steppe ecoregion, and the deciduous forest ecoregion northwest of the Yanshan-Taihang Mountains. The WUE of the Haihe River basin increased, with WUE values of 91.11% and 60.17% of the Haihe River basin′s vegetated areas increasing and increasing significantly(P < 0.05), respectively. That of the Loess Plateau agricultural and steppe ecoregion increased the most significantly.(3) The WUE means and trends among the different land use types also varied significantly. The mean WUE of croplands(CRO) was highest, and the WUE of grasslands(GRA) was lowest. The WUE of CRO, woody savannas(WSA), and GRA each increased significantly(P < 0.05).(4) Annual WUE was mainly affected by precipitation, and 41.44%, 33.23%, and 16.01% of WUE estimates in the Haihe River basin′s vegetated areas were dominated by inter-annual variation in precipitation, drought, and temperature, respectively. The vegetated areas in which WUE was primarily shaped by precipitation were mainly found in the northern portion of the Yanshan-Taihang Mountains deciduous forest ecoregion, the North China Plain agricultural ecoregion, and the Tianjin-Beijing-Tangshan urban and suburban agricultural ecoregion. The vegetated areas in which WUE was primarily shaped by drought were mainly found in the northeast of the North China Plain agricultural ecoregion and the southern portion of the Yanshan-Taihang Mountains deciduous forest ecoregion. The vegetated areas in which WUE was primarily shaped by temperature were mainly found in the western portion of the Yanshan-Taihang Mountains deciduous forest ecoregion and the East Central Inner Mongolian Plateau typical steppe ecoregion.
引文
[1] Feng X M, Fu B J, Piao S L Wang S, Ciais P, Zeng Z Z, Lv Y H, Zeng Y, Li Y, Jiang X H, Wu B F. Revegetation in China′s Loess Plateau is approaching sustainable water resource limits. Nature Climate Change, 2016, 6(11): 1019- 1022.
[2] 刘宪锋, 胡宝怡, 任志远. 黄土高原植被生态系统水分利用效率时空变化及驱动因素. 中国农业科学, 2018, 51(2): 302- 314.
[3] 邹杰, 丁建丽, 杨胜天. 近 15 年中亚及新疆生态系统水分利用效率时空变化分析. 地理研究, 2017, 36 (9): 1742- 1754.
[4] Fischer R A, Turner N C. Plant productivity in the arid and semiarid zones. Annual Review of Plant Physiology, 1978, 29(1): 277- 317.
[5] Yu G R, Qiu-Feng W, Zhuang J. Modeling the water use efficiency of soybean and maize plants under environmental stresses: application of a synthetic model of photosynthesis-transpiration based on stomatal behavior. Journal of Plant Physiology, 2004, 161(3): 303- 318.
[6] 邹杰, 丁建丽, 秦艳, 王飞. 遥感分析中亚地区生态系统水分利用效率对干旱的响应. 农业工程学报, 2018, 34(9): 145- 152.
[7] 王庆伟, 于大炮, 代力民, 周莉, 周旺明, 齐光, 齐麟, 叶雨静. 全球气候变化下植物水分利用效率研究进展. 应用生态学报, 2010, 21(12): 3255- 3265.
[8] Ito A, Inatomi M. Water-use efficiency of the terrestrial biosphere: a model analysis focusing on interactions between the global carbon and water cycles. Journal of Hydrometeorology, 2012, 13(2): 681- 694.
[9] Zhang T, Peng J, Liang W, Yang Y T, Liu Y X. Spatial-temporal patterns of water use efficiency and climate controls in China′s Loess Plateau during 2000- 2010. Science of the Total Environment, 2016, 565: 105- 122.
[10] Sun S B, Song Z L, Wu X C, Wang T J, Wu Y T, Du W L, Che T, Huang C L, Zhang X J, Ping B, Lin X F, Li P, Yang Y X, Chen B Z. Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades. Ecological Indicators, 2018, 94: 292- 304.
[11] Xiao J F, Sun G, Chen J Q, Chen H, Chen S P, Dong G, Gao S H, Guo H Q, Guo J X, Hai S J, Kato T, Li Y L, Lin G H, Lu W Z, Ma M G, McNulty S, Shao C L, Wang X F, Xie X, Zhang X D, Zhang Z Q, Zhao B, Zhou G S, Zhou J. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China. Agricultural and Forest Meteorology, 2013, 182: 76- 90.
[12] 黄小涛, 罗格平. 新疆草地蒸散与水分利用效率的时空特征. 植物生态学报, 2017, 41(5): 506- 518.
[13] 仇宽彪, 成军锋, 贾宝全. 中国中东部农田作物水分利用效率时空分布及影响因子分析. 农业工程学报, 2015, 31(11): 103- 109.
[14] Yang Y, Guan H, Batelaan O, McVicar T R, Long D, Piao S L, Liang W, Liu B, Zhao J, Simmons C T. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Scientific Reports, 2016, 6: 23284.
[15] Liu Y B, Xiao J F, Ju W M, Zhou Y L, Wang S J, Wu X C. Water use efficiency of China′s terrestrial ecosystems and responses to drought. Scientific Reports, 2015, 5: 13799.
[16] Sharma A, Goyal M K. Assessment of ecosystem resilience to hydroclimatic disturbances in India. Global Change Biology, 2018, 24(2): e432-e441.
[17] Sharma A, Goyal M K. District-level assessment of the ecohydrological resilience to hydroclimatic disturbances and its controlling factors in India. Journal of Hydrology, 2018, 564: 1048- 1057.
[18] Huang M T, Piao S L, Zeng Z Z, Peng S S, Ciais P, Cheng L, Mao J F, Poulter B, Shi X Y, Yao Y T, Yang H, Wang Y P. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Global Change Biology, 2016, 22(6): 2165- 2177.
[19] 曹永强, 张亭亭, 徐丹, 杨春祥. 海河流域蒸散发时空演变规律分析. 资源科学, 2014, 36 (7): 1489- 1500.
[20] 莫兴国, 刘苏峡, 林忠辉, 邱建秀. 华北平原蒸散和 GPP 格局及其对气候波动的响应. 地理学报, 2011, 66(5): 589- 598.
[21] 吴云, 曾源, 赵炎, 吴炳方, 武文波. 基于 MODIS 数据的海河流域植被覆盖度估算及动态变化分析. 资源科学, 2010, 32(7): 1417- 1424.
[22] He J, Yang X H, Li J Q, Jin J L, Wei Y M, Chen X J. Spatiotemporal variation of meteorological droughts based on the daily comprehensive drought index in the Haihe River basin, China. Natural Hazards, 2015, 75(2): 199- 217.
[23] 中国科学院生态环境研究中心. 中国生态系统评估与生态安全数据库. http://www.ecosystem.csdb.cn/.
[24] Zhao M, Heinsch F A, Nemani R R, Running S W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote sensing of Environment, 2005, 95(2): 164- 176.
[25] Mu Q, Zhao M, Running S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 2011, 115(8): 1781- 1800.
[26] Chen X J, Mo X G, Hu S, Liu S X. Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014. Journal of Geographical Sciences, 2017, 27(6): 661- 680.
[27] Jia Z Z, Liu S M, Xu Z W, Chen Y J Zhu M J. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research: Atmospheres, 2012, 117: D13113.
[28] Mu Q Z, Zhao M S, Kimball J S, McDowell N G, Running S W. A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society, 2013, 94(1): 83- 98
[29] Dorjsuren M, Liou Y A, Cheng C H. Time series MODIS and in situ data analysis for Mongolia drought. Remote Sensing, 2016, 8(6): 509.
[30] Kendall M G. Rank Correlation Methods. London: C. Griffin, 1948: 108.
[31] 汤旭光, 李恒鹏, 聂小飞, 李鹏程. 应用 MODIS 数据监测千岛湖流域植被覆盖动态(2001—2013年). 湖泊科学, 2015, 27(3): 511- 518.
[32] Planque C, Carrer D, Roujean J L. Analysis of MODIS albedo changes over steady woody covers in France during the period of 2001—2013. Remote Sensing of Environment, 2017, 191: 13- 29.
[33] 杜晓铮, 赵祥, 王昊宇, 何斌. 陆地生态系统水分利用效率对气候变化的响应研究进展. 生态学报, 2018, 38(23) [2018- 10- 24].
[34] Xue B L, Guo Q H, Otto A, Xiao J F, Tao S L, Li L. Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere, 2015, 6(10): 1- 18.
[2] 刘宪锋, 胡宝怡, 任志远. 黄土高原植被生态系统水分利用效率时空变化及驱动因素. 中国农业科学, 2018, 51(2): 302- 314.
[3] 邹杰, 丁建丽, 杨胜天. 近 15 年中亚及新疆生态系统水分利用效率时空变化分析. 地理研究, 2017, 36 (9): 1742- 1754.
[4] Fischer R A, Turner N C. Plant productivity in the arid and semiarid zones. Annual Review of Plant Physiology, 1978, 29(1): 277- 317.
[5] Yu G R, Qiu-Feng W, Zhuang J. Modeling the water use efficiency of soybean and maize plants under environmental stresses: application of a synthetic model of photosynthesis-transpiration based on stomatal behavior. Journal of Plant Physiology, 2004, 161(3): 303- 318.
[6] 邹杰, 丁建丽, 秦艳, 王飞. 遥感分析中亚地区生态系统水分利用效率对干旱的响应. 农业工程学报, 2018, 34(9): 145- 152.
[7] 王庆伟, 于大炮, 代力民, 周莉, 周旺明, 齐光, 齐麟, 叶雨静. 全球气候变化下植物水分利用效率研究进展. 应用生态学报, 2010, 21(12): 3255- 3265.
[8] Ito A, Inatomi M. Water-use efficiency of the terrestrial biosphere: a model analysis focusing on interactions between the global carbon and water cycles. Journal of Hydrometeorology, 2012, 13(2): 681- 694.
[9] Zhang T, Peng J, Liang W, Yang Y T, Liu Y X. Spatial-temporal patterns of water use efficiency and climate controls in China′s Loess Plateau during 2000- 2010. Science of the Total Environment, 2016, 565: 105- 122.
[10] Sun S B, Song Z L, Wu X C, Wang T J, Wu Y T, Du W L, Che T, Huang C L, Zhang X J, Ping B, Lin X F, Li P, Yang Y X, Chen B Z. Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades. Ecological Indicators, 2018, 94: 292- 304.
[11] Xiao J F, Sun G, Chen J Q, Chen H, Chen S P, Dong G, Gao S H, Guo H Q, Guo J X, Hai S J, Kato T, Li Y L, Lin G H, Lu W Z, Ma M G, McNulty S, Shao C L, Wang X F, Xie X, Zhang X D, Zhang Z Q, Zhao B, Zhou G S, Zhou J. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China. Agricultural and Forest Meteorology, 2013, 182: 76- 90.
[12] 黄小涛, 罗格平. 新疆草地蒸散与水分利用效率的时空特征. 植物生态学报, 2017, 41(5): 506- 518.
[13] 仇宽彪, 成军锋, 贾宝全. 中国中东部农田作物水分利用效率时空分布及影响因子分析. 农业工程学报, 2015, 31(11): 103- 109.
[14] Yang Y, Guan H, Batelaan O, McVicar T R, Long D, Piao S L, Liang W, Liu B, Zhao J, Simmons C T. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Scientific Reports, 2016, 6: 23284.
[15] Liu Y B, Xiao J F, Ju W M, Zhou Y L, Wang S J, Wu X C. Water use efficiency of China′s terrestrial ecosystems and responses to drought. Scientific Reports, 2015, 5: 13799.
[16] Sharma A, Goyal M K. Assessment of ecosystem resilience to hydroclimatic disturbances in India. Global Change Biology, 2018, 24(2): e432-e441.
[17] Sharma A, Goyal M K. District-level assessment of the ecohydrological resilience to hydroclimatic disturbances and its controlling factors in India. Journal of Hydrology, 2018, 564: 1048- 1057.
[18] Huang M T, Piao S L, Zeng Z Z, Peng S S, Ciais P, Cheng L, Mao J F, Poulter B, Shi X Y, Yao Y T, Yang H, Wang Y P. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Global Change Biology, 2016, 22(6): 2165- 2177.
[19] 曹永强, 张亭亭, 徐丹, 杨春祥. 海河流域蒸散发时空演变规律分析. 资源科学, 2014, 36 (7): 1489- 1500.
[20] 莫兴国, 刘苏峡, 林忠辉, 邱建秀. 华北平原蒸散和 GPP 格局及其对气候波动的响应. 地理学报, 2011, 66(5): 589- 598.
[21] 吴云, 曾源, 赵炎, 吴炳方, 武文波. 基于 MODIS 数据的海河流域植被覆盖度估算及动态变化分析. 资源科学, 2010, 32(7): 1417- 1424.
[22] He J, Yang X H, Li J Q, Jin J L, Wei Y M, Chen X J. Spatiotemporal variation of meteorological droughts based on the daily comprehensive drought index in the Haihe River basin, China. Natural Hazards, 2015, 75(2): 199- 217.
[23] 中国科学院生态环境研究中心. 中国生态系统评估与生态安全数据库. http://www.ecosystem.csdb.cn/.
[24] Zhao M, Heinsch F A, Nemani R R, Running S W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote sensing of Environment, 2005, 95(2): 164- 176.
[25] Mu Q, Zhao M, Running S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 2011, 115(8): 1781- 1800.
[26] Chen X J, Mo X G, Hu S, Liu S X. Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014. Journal of Geographical Sciences, 2017, 27(6): 661- 680.
[27] Jia Z Z, Liu S M, Xu Z W, Chen Y J Zhu M J. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research: Atmospheres, 2012, 117: D13113.
[28] Mu Q Z, Zhao M S, Kimball J S, McDowell N G, Running S W. A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society, 2013, 94(1): 83- 98
[29] Dorjsuren M, Liou Y A, Cheng C H. Time series MODIS and in situ data analysis for Mongolia drought. Remote Sensing, 2016, 8(6): 509.
[30] Kendall M G. Rank Correlation Methods. London: C. Griffin, 1948: 108.
[31] 汤旭光, 李恒鹏, 聂小飞, 李鹏程. 应用 MODIS 数据监测千岛湖流域植被覆盖动态(2001—2013年). 湖泊科学, 2015, 27(3): 511- 518.
[32] Planque C, Carrer D, Roujean J L. Analysis of MODIS albedo changes over steady woody covers in France during the period of 2001—2013. Remote Sensing of Environment, 2017, 191: 13- 29.
[33] 杜晓铮, 赵祥, 王昊宇, 何斌. 陆地生态系统水分利用效率对气候变化的响应研究进展. 生态学报, 2018, 38(23) [2018- 10- 24].
[34] Xue B L, Guo Q H, Otto A, Xiao J F, Tao S L, Li L. Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere, 2015, 6(10): 1- 18.