ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Earth and Space 04 June 2024

Impact of modified SWAT plant growth module on modeling green and blue water resources in subtropics

Cite this:
https://doi.org/10.52396/JUSTC-2023-0023
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  • Author Bio:

    Tianming Ma is currently a postdoctoral fellow at the School of Earth and Space Sciences, University of Science and Technology of China. He received his Ph.D. degree in Marine Science from Tongji University in 2020. His research mainly focuses on ice core studies and paleoclimate

    Tianxiao Ma is currently an assistant research fellow at the Institute of Applied Ecology, Chinese Academy of Sciences. He received his Ph.D. degree in Geographic Information and Systems from the University of Chinese Academy of Sciences in 2019. His research mainly focuses on landscape ecology

  • Corresponding author: E-mail:matianxiao@iae.ac.cn
  • Received Date: 19 February 2023
  • Accepted Date: 04 May 2023
  • Available Online: 04 June 2024
  • The dynamics of water availability within a region can be quantitatively analyzed by partitioning the water into blue and green water resources. It is widely recognized that vegetation is one of the key factors that affect the assessment and modeling of blue and green water in hydrological models. However, SWAT-EPIC has limitations in simulating vegetation growth cycles in subtropics because it was originally designed for temperate regions and naturally based on temperature. To perform a correct and realistic assessment of changing vegetation impacts on modeling blue and water resources in the SWAT model, an approach was proposed in this study to modify the SWAT plant growth module with the remotely sensed leaf area index (LAI) to finally solve problems in simulating subtropical vegetation growth, such as controlling factors and dormancy. Comparisons between the original and modified model were performed on the model outputs to summarize the spatiotemporal changes in hydrological processes (including rainfall, runoff, evapotranspiration and soil water content) under six different plant types in a representative subtropical watershed of the Meichuan Basin, Jiangxi Province. Meanwhile, detailed analysis was conducted to discuss the effectiveness of the modified SWAT model and the impacts of vegetation changes on blue and green water modeling. The results showed that (1) the modified SWAT produced more reasonable seasonal curves of plants than the original model. ENS (Nash-Sutcliffe efficiency) and R2 increased by 0.02 during the calibration period and accounted for an increase of 0.09 and 0.03, respectively, during the validation period. (2) The comparison of model outputs between the original and modified SWAT suggested that evapotranspiration was more sensitive to vegetation changes than other components of green water. In addition, vegetation presented conservation capability in the blue water. (3) The variation in blue and green water resources with different plant types after modifying the SWAT model showed that seasonal changes in vegetation led to a significant difference between forest and non-forest areas.
    The different steps adopted to Modified SWAT Plant Growth Module on Modelling Green and Blue Water Resources in Subtropics.
    The dynamics of water availability within a region can be quantitatively analyzed by partitioning the water into blue and green water resources. It is widely recognized that vegetation is one of the key factors that affect the assessment and modeling of blue and green water in hydrological models. However, SWAT-EPIC has limitations in simulating vegetation growth cycles in subtropics because it was originally designed for temperate regions and naturally based on temperature. To perform a correct and realistic assessment of changing vegetation impacts on modeling blue and water resources in the SWAT model, an approach was proposed in this study to modify the SWAT plant growth module with the remotely sensed leaf area index (LAI) to finally solve problems in simulating subtropical vegetation growth, such as controlling factors and dormancy. Comparisons between the original and modified model were performed on the model outputs to summarize the spatiotemporal changes in hydrological processes (including rainfall, runoff, evapotranspiration and soil water content) under six different plant types in a representative subtropical watershed of the Meichuan Basin, Jiangxi Province. Meanwhile, detailed analysis was conducted to discuss the effectiveness of the modified SWAT model and the impacts of vegetation changes on blue and green water modeling. The results showed that (1) the modified SWAT produced more reasonable seasonal curves of plants than the original model. ENS (Nash-Sutcliffe efficiency) and R2 increased by 0.02 during the calibration period and accounted for an increase of 0.09 and 0.03, respectively, during the validation period. (2) The comparison of model outputs between the original and modified SWAT suggested that evapotranspiration was more sensitive to vegetation changes than other components of green water. In addition, vegetation presented conservation capability in the blue water. (3) The variation in blue and green water resources with different plant types after modifying the SWAT model showed that seasonal changes in vegetation led to a significant difference between forest and non-forest areas.
    • SWAT Plant Growth Module (SWAT-EPIC) modified with remotely sensed leaf area index (LAI) has a better performance in simulations of subtropical vegetation growth.
    • The modeled results using modified SWAT show that evapotranspiration is more sensitive to vegetation changes than other components of green water in a representative subtropical watershed.
    • Seasonal changes of vegetation can cause a different response of blue and green water resources between forest and non-forest plant type.

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  • [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
  • 加载中

Catalog

    Figure  1.  (a) Locations of rain gauge stations, domain rivers, sub-basins, (b) landcover and (c) soil type in the Meichuan Basin.

    Figure  2.  Flowchart illustrating the different steps adopted to improve the SWAT plant growth module.

    Figure  3.  MCD15A2 (a) and downscaled (b) maps of LAI on August 20, 2008. The right plot (c) shows the normalized frequency distributions of these two LAI maps.

    Figure  4.  Temporal variability of rainfall, observed and estimated monthly runoff from original (a) and modified (b) SWAT with MODIS LAI at Fenkeng station.

    Figure  5.  Spatial distribution of annual mean water resource components in the Meichuan Basin during 2003-2014: (a) precipitation, (b) green water flow, (c) green water resources, (d) surface runoff, (e) base flow and (f) blue water resources.

    Figure  6.  Estimated components of water resources from the original and modified SWAT models during 2003–2014.

    Figure  7.  Comparison of blue and green water resource components between the original and modified SWAT models during 2003–2014.

    Figure  8.  Simulated monthly changes (%) in rainfall, LAI, blue and green water resources in the Meichuan Basin by original and modified SWAT models.

    Figure  9.  The time series of LAI as simulated by the original SWAT and modified SWAT with MODIS LAI.

    [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

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