[1] |
Zhen T, Xu Z, Cheng L, et al. Spatiotemporal distributions of blue and green water resources: A case study on the Lushi watershed. Resources Science, 2010, 32 (6): 1177–1183. (in Chinese)
|
[2] |
Falkenmark M. Land and Water Integration and River Basin Management. Rome, Italy: FAO, 1995.
|
[3] |
Rost S, Gerten D, Bondeau A, et al. Agricultural green and blue water consumption and its influence on the global water system. Water Resources Research, 2008, 44 (9): W09405. doi: 10.1029/2007wr006331
|
[4] |
Liu J, Wang Y, Yu Z, et al. A comprehensive analysis of blue water scarcity from the production, consumption, and water transfer perspectives. Ecological Indicators, 2017, 72: 870–880. doi: 10.1016/j.ecolind.2016.09.021
|
[5] |
Falkenmark M, Rockström J. The new blue and green water paradigm: Breaking new ground for water resources planning and management. Journal of Water Resources Planning & Management, 2006, 132 (3): 129–132. doi: 10.1061/(asce)0733-9496(2006)132:3(129
|
[6] |
Xu J. Increasing trend of green water coefficient in the middle Yellow River basin and the eco-environmental implications. Acta Ecologica Sinica, 2015, 35 (22): 7298–7307. doi: 10.5846/stxb201404040646
|
[7] |
Veettil A V, Mishra A K. Water security assessment using blue and green water footprint concepts. Journal of Hydrology, 2016, 542: 589–602. doi: 10.1016/j.jhydrol.2016.09.032
|
[8] |
Badou D F, Diekkrüger B, Kapangaziwiri E, et al. Modelling blue and green water availability under climate change in the Beninese Basin of the Niger River Basin, West Africa. Hydrological Processes, 2018, 32 (16): 2526–2542. doi: 10.1002/hyp.13153
|
[9] |
He X, Wang G, Bao Z. Progress and prospective of climate and vegetation coverage change as well as responses of hydrological cycle. Journal of Water Resources and Water Engineering, 2016, 27 (2): 1–5. doi: 10.11705/j.issn.1672-643x.2016.02.01
|
[10] |
Yang D, Lei H, Cong Z. Overview of the research status in interaction between hydrological processes and vegetation in catchment. Journal of Hydraulic Engineering, 2008, 39 (Z2): 1142–1149. doi: 10.13243/j.cnki.slxb.2010.10.001
|
[11] |
Liu J, Gao G, Wang S, et al. The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 2018, 28 (1): 59–78. doi: 10.1007/s11442-018-1459-z
|
[12] |
Zhao A, Zhao Y, Liu X, et al. Impact of human activities and climate variability on green and blue water resources in the Weihe River Basin of Northwest China. Scientia Geographica Sinica, 2016, 36 (4): 571–579. doi: 10.13249/j.cnki.sgs.2016.04.011
|
[13] |
Du L, Rajib A, Merwade V. Large scale spatially explicit modeling of blue and green water dynamics in a temperate mid-latitude basin. Journal of Hydrology, 2018, 562: 84–102. doi: 10.1016/j.jhydrol.2018.02.071
|
[14] |
Tadesse A, Ann V G, Taddesse W B, et al. An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems. Hydrology and Earth System Sciences, 2017, 21 (9): 4449–4467. doi: 10.5194/hess-21-4449-2017
|
[15] |
Strauch M, Volk M. SWAT plant growth modification for improved modeling of perennial vegetation in the tropics. Ecological Modelling, 2013, 269 (1771): 98–112. doi: 10.1016/j.ecolmodel.2013.08.013
|
[16] |
Kiniry J R, Macdonald J D, Kemanian A, et al. Plant growth simulation for landscape-scale hydrological modelling. International Association of Scientific Hydrology, 2008, 53 (5): 1030–1042. doi: 10.1623/hysj.53.5.1030
|
[17] |
Arnold J G, Kiniry J R, Srinivasan R, et al. Soil and water assessment tool input/output file documentation: Version 2009. Temple, TX: Texas Water Resources Institute, 2011: No. 365.
|
[18] |
Ma T, Duan Z, Li R, et al. Enhancing SWAT with remotely sensed LAI for improved modelling of ecohydrological process in subtropics. Journal of Hydrology, 2019, 570: 802–815. doi: 10.1016/j.jhydrol.2019.01.024
|
[19] |
Wagner P D, Kumar S, Fiener P, et al. Hydrological modeling with SWAT in a monsoon-driven environment: experience from the Western Ghats, India. Transactions of the ASABE, 2011, 54 (5): 1783–1790. doi: 10.13031/2013.39846
|
[20] |
Lamparter G, Nobrega R L B, Kovacs K, et al. Modelling hydrological impacts of agricultural expansion in two macro-catchments in Southern Amazonia, Brazil. Regional Environmental Change, 2018, 18 (1): 91–103. doi: 10.1007/s10113-016-1015-2
|
[21] |
Lai G, Qiu L, Zhang Z, et al. Modification and efficiency of SWAT model based on multi-plant growth mode. Journal of Lake Sciences, 2018, 30 (2): 472–487. doi: 10.18307/2018.0219
|
[22] |
Huang M, Ji J. The spatial-temporal distribution of leaf area index in China: A comparison between ecosystem modeling and remote sensing reversion. Acta Ecologica Sinica, 2010, 30 (11): 3057–3064. (in Chinese)
|
[23] |
Xiao Z, Wang J, Wang Z. Improvement of MODIS LAI product in China. Journal of Remote Sensing, 2008, 12 (6): 993–1000. (in Chinese)
|
[24] |
Zhang H, Gao W, Shi R. Reconstruction of high-quality LAI time-series product based on long-term historical database. Journal of Remote Sensing, 2012, 16 (5): 986–999. (in Chinese)
|
[25] |
Yuan H, Dai Y, Xiao Z, et al. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sensing of Environment, 2011, 115 (5): 1171–1187. doi: 10.1016/j.rse.2011.01.001
|
[26] |
Li R, Zhu A, Li B, et al. Response of simulated stream flow to soil data spatial detail across different routing areas. Process in Geography, 2011, 30 (1): 80–86. (in Chinese)
|
[27] |
Emelyanova I V, Mcvicar T R, Van Niel T G, et al. Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection. Remote Sensing of Environment, 2013, 133 (12): 193–209. doi: 10.1016/j.rse.2013.02.007
|
[28] |
Houborg R, Mccabe M F, Gao F. A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI). International Journal of Applied Earth Observation and Geoinformation, 2016, 47: 15–29. doi: 10.1016/j.jag.2015.11.013
|
[29] |
Abbaspour K C, Rouholahnejad E, Vaghefi S, et al. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 2015, 524: 733–752. doi: 10.1016/j.jhydrol.2015.03.027
|
[30] |
Zhao K, Su B, Shen M, et al. An improved method for parameter identification of SWAT model. South-to-North Water Transfers and Water Science & Technology, 2017, 15 (4): 49–53. doi: 10.13476/j.cnki.nsbdqk.2017.04.009
|
[31] |
Luo K, Tao F. Hydrological modeling based on SWAT in arid northwest China: A case study in Linze County. Acta Ecologica Sinica, 2018, 38 (23): 8593–8603. doi: 10.5846/stxb201801200159
|
[32] |
Nan Z, Zhao Y, Li S. Improvement of snowmelt implementation in the SWAT hydrologic model. Acta Ecologica Sinica, 2013, 33 (21): 6992–7001. doi: 10.5846/stxb201207110977
|
[33] |
Moriasi D N, Arnold J G, Liew M W V, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007, 50 (3): 885–900. doi: 10.13031/2013.23153
|
[1] |
Zhen T, Xu Z, Cheng L, et al. Spatiotemporal distributions of blue and green water resources: A case study on the Lushi watershed. Resources Science, 2010, 32 (6): 1177–1183. (in Chinese)
|
[2] |
Falkenmark M. Land and Water Integration and River Basin Management. Rome, Italy: FAO, 1995.
|
[3] |
Rost S, Gerten D, Bondeau A, et al. Agricultural green and blue water consumption and its influence on the global water system. Water Resources Research, 2008, 44 (9): W09405. doi: 10.1029/2007wr006331
|
[4] |
Liu J, Wang Y, Yu Z, et al. A comprehensive analysis of blue water scarcity from the production, consumption, and water transfer perspectives. Ecological Indicators, 2017, 72: 870–880. doi: 10.1016/j.ecolind.2016.09.021
|
[5] |
Falkenmark M, Rockström J. The new blue and green water paradigm: Breaking new ground for water resources planning and management. Journal of Water Resources Planning & Management, 2006, 132 (3): 129–132. doi: 10.1061/(asce)0733-9496(2006)132:3(129
|
[6] |
Xu J. Increasing trend of green water coefficient in the middle Yellow River basin and the eco-environmental implications. Acta Ecologica Sinica, 2015, 35 (22): 7298–7307. doi: 10.5846/stxb201404040646
|
[7] |
Veettil A V, Mishra A K. Water security assessment using blue and green water footprint concepts. Journal of Hydrology, 2016, 542: 589–602. doi: 10.1016/j.jhydrol.2016.09.032
|
[8] |
Badou D F, Diekkrüger B, Kapangaziwiri E, et al. Modelling blue and green water availability under climate change in the Beninese Basin of the Niger River Basin, West Africa. Hydrological Processes, 2018, 32 (16): 2526–2542. doi: 10.1002/hyp.13153
|
[9] |
He X, Wang G, Bao Z. Progress and prospective of climate and vegetation coverage change as well as responses of hydrological cycle. Journal of Water Resources and Water Engineering, 2016, 27 (2): 1–5. doi: 10.11705/j.issn.1672-643x.2016.02.01
|
[10] |
Yang D, Lei H, Cong Z. Overview of the research status in interaction between hydrological processes and vegetation in catchment. Journal of Hydraulic Engineering, 2008, 39 (Z2): 1142–1149. doi: 10.13243/j.cnki.slxb.2010.10.001
|
[11] |
Liu J, Gao G, Wang S, et al. The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 2018, 28 (1): 59–78. doi: 10.1007/s11442-018-1459-z
|
[12] |
Zhao A, Zhao Y, Liu X, et al. Impact of human activities and climate variability on green and blue water resources in the Weihe River Basin of Northwest China. Scientia Geographica Sinica, 2016, 36 (4): 571–579. doi: 10.13249/j.cnki.sgs.2016.04.011
|
[13] |
Du L, Rajib A, Merwade V. Large scale spatially explicit modeling of blue and green water dynamics in a temperate mid-latitude basin. Journal of Hydrology, 2018, 562: 84–102. doi: 10.1016/j.jhydrol.2018.02.071
|
[14] |
Tadesse A, Ann V G, Taddesse W B, et al. An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems. Hydrology and Earth System Sciences, 2017, 21 (9): 4449–4467. doi: 10.5194/hess-21-4449-2017
|
[15] |
Strauch M, Volk M. SWAT plant growth modification for improved modeling of perennial vegetation in the tropics. Ecological Modelling, 2013, 269 (1771): 98–112. doi: 10.1016/j.ecolmodel.2013.08.013
|
[16] |
Kiniry J R, Macdonald J D, Kemanian A, et al. Plant growth simulation for landscape-scale hydrological modelling. International Association of Scientific Hydrology, 2008, 53 (5): 1030–1042. doi: 10.1623/hysj.53.5.1030
|
[17] |
Arnold J G, Kiniry J R, Srinivasan R, et al. Soil and water assessment tool input/output file documentation: Version 2009. Temple, TX: Texas Water Resources Institute, 2011: No. 365.
|
[18] |
Ma T, Duan Z, Li R, et al. Enhancing SWAT with remotely sensed LAI for improved modelling of ecohydrological process in subtropics. Journal of Hydrology, 2019, 570: 802–815. doi: 10.1016/j.jhydrol.2019.01.024
|
[19] |
Wagner P D, Kumar S, Fiener P, et al. Hydrological modeling with SWAT in a monsoon-driven environment: experience from the Western Ghats, India. Transactions of the ASABE, 2011, 54 (5): 1783–1790. doi: 10.13031/2013.39846
|
[20] |
Lamparter G, Nobrega R L B, Kovacs K, et al. Modelling hydrological impacts of agricultural expansion in two macro-catchments in Southern Amazonia, Brazil. Regional Environmental Change, 2018, 18 (1): 91–103. doi: 10.1007/s10113-016-1015-2
|
[21] |
Lai G, Qiu L, Zhang Z, et al. Modification and efficiency of SWAT model based on multi-plant growth mode. Journal of Lake Sciences, 2018, 30 (2): 472–487. doi: 10.18307/2018.0219
|
[22] |
Huang M, Ji J. The spatial-temporal distribution of leaf area index in China: A comparison between ecosystem modeling and remote sensing reversion. Acta Ecologica Sinica, 2010, 30 (11): 3057–3064. (in Chinese)
|
[23] |
Xiao Z, Wang J, Wang Z. Improvement of MODIS LAI product in China. Journal of Remote Sensing, 2008, 12 (6): 993–1000. (in Chinese)
|
[24] |
Zhang H, Gao W, Shi R. Reconstruction of high-quality LAI time-series product based on long-term historical database. Journal of Remote Sensing, 2012, 16 (5): 986–999. (in Chinese)
|
[25] |
Yuan H, Dai Y, Xiao Z, et al. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sensing of Environment, 2011, 115 (5): 1171–1187. doi: 10.1016/j.rse.2011.01.001
|
[26] |
Li R, Zhu A, Li B, et al. Response of simulated stream flow to soil data spatial detail across different routing areas. Process in Geography, 2011, 30 (1): 80–86. (in Chinese)
|
[27] |
Emelyanova I V, Mcvicar T R, Van Niel T G, et al. Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection. Remote Sensing of Environment, 2013, 133 (12): 193–209. doi: 10.1016/j.rse.2013.02.007
|
[28] |
Houborg R, Mccabe M F, Gao F. A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI). International Journal of Applied Earth Observation and Geoinformation, 2016, 47: 15–29. doi: 10.1016/j.jag.2015.11.013
|
[29] |
Abbaspour K C, Rouholahnejad E, Vaghefi S, et al. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 2015, 524: 733–752. doi: 10.1016/j.jhydrol.2015.03.027
|
[30] |
Zhao K, Su B, Shen M, et al. An improved method for parameter identification of SWAT model. South-to-North Water Transfers and Water Science & Technology, 2017, 15 (4): 49–53. doi: 10.13476/j.cnki.nsbdqk.2017.04.009
|
[31] |
Luo K, Tao F. Hydrological modeling based on SWAT in arid northwest China: A case study in Linze County. Acta Ecologica Sinica, 2018, 38 (23): 8593–8603. doi: 10.5846/stxb201801200159
|
[32] |
Nan Z, Zhao Y, Li S. Improvement of snowmelt implementation in the SWAT hydrologic model. Acta Ecologica Sinica, 2013, 33 (21): 6992–7001. doi: 10.5846/stxb201207110977
|
[33] |
Moriasi D N, Arnold J G, Liew M W V, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007, 50 (3): 885–900. doi: 10.13031/2013.23153
|