Articles | Volume 29, issue 8
https://doi.org/10.5194/hess-29-2081-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2081-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Integration of the vegetation phenology module improves ecohydrological simulation by the SWAT-Carbon model
Mingwei Li
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Shouzhi Chen
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Fanghua Hao
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Nan Wang
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Zhaofei Wu
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yue Xu
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Jing Zhang
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yongqiang Zhang
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Water Sciences, Beijing Normal University, Beijing 100875, China
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Cited articles
Abbaspour, K. C.: SWAT‐CUP: SWAT Calibration and Uncertainty Programs – A User Manual, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Dubendorf, Switzerland, https://swat.tamu.edu/media/114860/usermanual_swatcup.pdf (last access: 10 April 2025), 2015.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large Area Hydrologic Modeling and Assessment Part I: Model Development, J. Am. Water Resour. As., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J. G., Moriasi, D. N., Gassman, P., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., Van Liew, M. W., Kannan, N., and Jha, M. K.: SWAT: Model Use, Calibration, and Validation, T. ASABE, 55, 1491–1508, https://doi.org/10.13031/2013.42256, 2012.
Bhatta, B., Shrestha, S., Shrestha, P. K., and Talchabhadel, R.: Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin, CATENA, 181, 104082, https://doi.org/10.1016/j.catena.2019.104082, 2019.
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008.
Buermann, W., Forkel, M., O'Sullivan, M., Sitch, S., Friedlingstein, P., Haverd, V., Jain, A. K., Kato, E., Kautz, M., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Tian, H., Wiltshire, A. J., Zhu, D., Smith, W. K., and Richardson, A. D.: Widespread seasonal compensation effects of spring warming on northern plant productivity, Nature, 562, 110–114, https://doi.org/10.1038/s41586-018-0555-7, 2018.
Chen, S., Fu, Y. H., Geng, X., Hao, Z., Tang, J., Zhang, X., Xu, Z., and Hao, F.: Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient, Front. Plant Sci., 12, 802664, https://doi.org/10.3389/fpls.2021.802664, 2022a.
Chen, S., Fu, Y. H., Hao, F., Li, X., Zhou, S., Liu, C., and Tang, J.: Vegetation phenology and its ecohydrological implications from individual to global scales, Geography and Sustainability, 3, 334–338, https://doi.org/10.1016/j.geosus.2022.10.002, 2022b.
Chen, S., Fu, Y. H., Wu, Z., Hao, F., Hao, Z., Guo, Y., Geng, X., Li, X., Zhang, X., Tang, J., Singh, V. P., and Zhang, X.: Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes, J. Hydrol., 616, 128817, https://doi.org/10.1016/j.jhydrol.2022.128817, 2023.
Chuine, I.: A Unified Model for Budburst of Trees, J. Theor. Biol., 207, 337–347, https://doi.org/10.1006/jtbi.2000.2178, 2000.
Chuine, I.: Why does phenology drive species distribution?, Philos. T. R. Soc. B., 365, 3149–3160, https://doi.org/10.1098/rstb.2010.0142, 2010.
Creed, I. F., Hwang, T., Lutz, B., and Way, D.: Climate warming causes intensification of the hydrological cycle, resulting in changes to the vernal and autumnal windows in a northern temperate forest, Hydrol. Process., 29, 3519–3534, https://doi.org/10.1002/hyp.10450, 2015.
Cui, T., Martz, L., and Guo, X.: Grassland Phenology Response to Drought in the Canadian Prairies, Remote Sens.-Basel, 9, 1258, https://doi.org/10.3390/rs9121258, 2017.
Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé J., and François, C.: Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agr. Forest Meteorol., 149, 938–948, https://doi.org/10.1016/j.agrformet.2008.11.014, 2009.
Fischer, G., Nachtergaele, F., Prieler, S., Van Velthuizen, H. T., Verelst, L., and Wiberg, D.: Global agro-ecological zones assessment for agriculture (GAEZ 2008), IIASA, Laxenburg, Austria and FAO, Rome, Italy [data set], https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/, last access: 10 April 2025, 2008.
Fu, Y., Li, X., Zhou, X., Geng, X., Guo, Y., and Zhang, Y.: Progress in plant phenology modeling under global climate change, Sci. China Earth Sci., 63, 1237–1247, https://doi.org/10.1007/s11430-019-9622-2, 2020.
Fu, Y. H., Campioli, M., Demarée, G., Deckmyn, A., Hamdi, R., Janssens, I. A., and Deckmyn, G.: Bayesian calibration of the Unified budburst model in six temperate tree species, Int. J. Biometeorol., 56, 153–164, https://doi.org/10.1007/s00484-011-0408-7, 2012.
Fu, Y. H., Piao, S., Op De Beeck, M., Cong, N., Zhao, H., Zhang, Y., Menzel, A., and Janssens, I. A.: Recent spring phenology shifts in western Central Europe based on multiscale observations, Global Ecol. Biogeogr., 23, 1255–1263, https://doi.org/10.1111/geb.12210, 2014.
Fu, Y. H., Zhao, H., Piao, S., Peaucelle, M., Peng, S., Zhou, G., Ciais, P., Huang, M., Menzel, A., Peñuelas, J., Song, Y., Vitasse, Y., Zeng, Z., and Janssens, I. A.: Declining global warming effects on the phenology of spring leaf unfolding, Nature, 526, 104–107, https://doi.org/10.1038/nature15402, 2015.
Fu, Y. H., Piao, S., Delpierre, N., Hao, F., Hänninen, H., Liu, Y., Sun, W., Janssens, I. A., and Campioli, M.: Larger temperature response of autumn leaf senescence than spring leaf-out phenology, Glob. Change Biol., 24, 2159–2168, https://doi.org/10.1111/gcb.14021, 2018.
Fu, Y. H., Zhou, X., Li, X., Zhang, Y., Geng, X., Hao, F., Zhang, X., Hanninen, H., Guo, Y., and De Boeck, H. J.: Decreasing control of precipitation on grassland spring phenology in temperate China, Global Ecol. Biogeogr., 30, 490–499, https://doi.org/10.1111/geb.13234, 2021.
Gaertner, B. A., Zegre, N., Warner, T., Fernandez, R., He, Y., and Merriam, E. R.: Climate, forest growing season, and evapotranspiration changes in the central Appalachian Mountains, USA, Sci. Total Environ., 650, 1371–1381, https://doi.org/10.1016/j.scitotenv.2018.09.129, 2019.
Garonna, I., de Jong, R., and Schaepman, M. E.: Variability and evolution of global land surface phenology over the past three decades (1982–2012), Glob. Change Biol., 22, 1456–1468, https://doi.org/10.1111/gcb.13168, 2016.
Ge, Q., Wang, H., Rutishauser, T., and Dai, J.: Phenological response to climate change in China: a meta-analysis, Glob. Change Biol., 21, 265–274, https://doi.org/10.1111/gcb.12648, 2015.
Geng, X., Zhou, X., Yin, G., Hao, F., Zhang, X., Hao, Z., Singh, V. P., and Fu, Y. H.: Extended growing season reduced river runoff in Luanhe River basin, J. Hydrol., 582, 124538, https://doi.org/10.1016/j.jhydrol.2019.124538, 2020.
GLASS LAI: Global Land Surface Satellite leaf area index [data set], https://www.glass.hku.hk/archive/LAI/AVHRR/, last access: 10 April 2025.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Haas, H., Kalin, L., and Srivastava, P.: Improved forest dynamics leads to better hydrological predictions in watershed modeling, Sci. Total Environ., 821, 153180, https://doi.org/10.1016/j.scitotenv.2022.153180, 2022.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
He, K., Chen, X., Zhou, J., Zhao, D., and Yu, X.: Compound successive dry-hot and wet extremes in China with global warming and urbanization, J. Hydrol., 636, 131332, https://doi.org/10.1016/j.jhydrol.2024.131332, 2024.
Huang, M., Piao, S., Ciais, P., Peñuelas, J., Wang, X., Keenan, T. F., Peng, S., Berry, J. A., Wang, K., Mao, J., Alkama, R., Cescatti, A., Cuntz, M., De Deurwaerder, H., Gao, M., He, Y., Liu, Y., Luo, Y., Myneni, R. B., Niu, S., Shi, X., Yuan, W., Verbeeck, H., Wang, T., Wu, J., and Janssens, I. A.: Air temperature optima of vegetation productivity across global biomes, Nat. Ecol. Evol., 3, 772–779, https://doi.org/10.1038/s41559-019-0838-x, 2019.
Hwang, T., Band, L. E., Miniat, C. F., Song, C., Bolstad, P. V., Vose, J. M., and Love, J. P.: Divergent phenological response to hydroclimate variability in forested mountain watersheds, Glob. Change Biol., 20, 2580–2595, https://doi.org/10.1111/gcb.12556, 2014.
Hwang, T., Martin, K. L., Vose, J. M., Wear, D., Miles, B., Kim, Y., and Band, L. E.: Nonstationary Hydrologic Behavior in Forested Watersheds Is Mediated by Climate-Induced Changes in Growing Season Length and Subsequent Vegetation Growth, Water Resour. Res., 54, 5359–5375, https://doi.org/10.1029/2017WR022279, 2018.
Hwang, T., Band, L. E., Oishi, A. C., and Kang, H.: Greenup Variability Impact on Seasonal Streamflow and Soil Moisture Dynamics in Humid, Temperate Forests, Water Resour. Res., 59, e2022WR034125, https://doi.org/10.1029/2022WR034125, 2023.
IPCC: Climate Change 2021: The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021.
Jiang, Q., Yuan, Z., Yin, J., Yao, M., Qin, T., Lü, X., and Wu, G.: Response of vegetation phenology to climate factors in the source region of the Yangtze and Yellow Rivers, J. Plant Ecol., 17, rtae046, https://doi.org/10.1093/jpe/rtae046, 2024.
Kim, J. H., Hwang, T., Yang, Y., Schaaf, C. L., Boose, E., and Munger, J. W.: Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment, J.-Geophys. Res.-Biogeo., 123, 1960–1975, https://doi.org/10.1029/2018JG004438, 2018.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2), Zenodo [data set], https://doi.org/10.5281/zenodo.8253971, 2023.
Li, X., Long, D., Scanlon, B. R., Mann, M. E., Li, X., Tian, F., Sun, Z., and Wang, G.: Climate change threatens terrestrial water storage over the Tibetan Plateau, Nat. Clim. Change, 12, 801–807, https://doi.org/10.1038/s41558-022-01443-0, 2022.
Lian, X., Piao, S., Li, L. Z. X., Li, Y., Huntingford, C., Ciais, P., Cescatti, A., Janssens, I. A., Peñuelas, J., Buermann, W., Chen, A., Li, X., Myneni, R. B., Wang, X., Wang, Y., Yang, Y., Zeng, Z., Zhang, Y., and McVicar, T. R.: Summer soil drying exacerbated by earlier spring greening of northern vegetation, Sci. Adv., 6, eaax0255, https://doi.org/10.1126/sciadv.aax0255, 2020.
Lu, J., Wang, G., Li, S., Feng, A., Zhan, M., Jiang, T., Su, B., and Wang, Y.: Projected Land Evaporation and Its Response to Vegetation Greening Over China Under Multiple Scenarios in the CMIP6 Models, J.-Geophys. Res.-Biogeo., 126, e2021JG006327, https://doi.org/10.1029/2021JG006327, 2021.
Luan, J., Miao, P., Tian, X., Li, X., Ma, N., Abrar Faiz, M., Xu, Z., and Zhang, Y.: Estimating hydrological consequences of vegetation greening, J. Hydrol., 611, 128018, https://doi.org/10.1016/j.jhydrol.2022.128018, 2022.
Ma, T., Duan, Z., Li, R., and Song, X.: Enhancing SWAT with remotely sensed LAI for improved modelling of ecohydrological process in subtropics, J. Hydrol., 570, 802–815, https://doi.org/10.1016/j.jhydrol.2019.01.024, 2019.
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Mukundan, R., Gelda, R. K., Moknatian, M., Zhang, X., and Steenhuis, T. S.: Watershed scale modeling of Dissolved organic carbon export from variable source areas, J. Hydrol., 625, 130052, https://doi.org/10.1016/j.jhydrol.2023.130052, 2023.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and water assessment tool theoretical documentation version 2009, Texas Water Resources Institute, College Station, Texas, USA, https://swat.tamu.edu/media/99192/swat2009-theory.pdf (last access: 10 April 2025), 2011.
Paiva, K., Rau, P., Montesinos, C., Lavado-Casimiro, W., Bourrel, L., and Frappart, F.: Hydrological Response Assessment of Land Cover Change in a Peruvian Amazonian Basin Impacted by Deforestation Using the SWAT Model, Remote Sens.-Basel, 15, 5774, https://doi.org/10.3390/rs15245774, 2023.
Peñuelas, J., Filella, I., Zhang, X., Llorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., and Terradas, J.: Complex spatiotemporal phenological shifts as a response to rainfall changes, New Phytol., 161, 837–846, https://doi.org/10.1111/j.1469-8137.2004.01003.x, 2004.
Piao, S., Tan, J., Chen, A., Fu, Y. H., Ciais, P., Liu, Q., Janssens, I. A., Vicca, S., Zeng, Z., Jeong, S.-J., Li, Y., Myneni, R. B., Peng, S., Shen, M., and Peñuelas, J.: Leaf onset in the northern hemisphere triggered by daytime temperature, Nat. Commun., 6, 6911, https://doi.org/10.1038/ncomms7911, 2015.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, https://doi.org/10.1111/gcb.14619, 2019.
Roberts, A. M. I., Tansey, C., Smithers, R. J., and Phillimore, A. B.: Predicting a change in the order of spring phenology in temperate forests, Glob. Change Biol., 21, 2603–2611, https://doi.org/10.1111/gcb.12896, 2015.
Shen, M., Wang, S., Jiang, N., Sun, J., Cao, R., Ling, X., Fang, B., Zhang, L., Zhang, L., Xu, X., Lv, W., Li, B., Sun, Q., Meng, F., Jiang, Y., Dorji, T., Fu, Y., Iler, A., Vitasse, Y., Steltzer, H., Ji, Z., Zhao, W., Piao, S., and Fu, B.: Plant phenology changes and drivers on the Qinghai–Tibetan Plateau, Nat. Rev. Earth Environ., 3, 633–651, https://doi.org/10.1038/s43017-022-00317-5, 2022.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
SRTMDEM 90M: Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences [data set], https://www.gscloud.cn/sources/details/305?pid=302, last access: 10 April 2025.
Stevens, B. and Bony, S.: What Are Climate Models Missing?, Science, 340, 1053–1054, https://doi.org/10.1126/science.1237554, 2013.
Strauch, M. and Volk, M.: SWAT plant growth modification for improved modeling of perennial vegetation in the tropics, Ecol. Model., 269, 98–112, https://doi.org/10.1016/j.ecolmodel.2013.08.013, 2013.
Tang, Z., Zhou, Z., Wang, D., Luo, F., Bai, J., and Fu, Y.: Impact of vegetation restoration on ecosystem services in the Loess plateau, a case study in the Jinghe Watershed, China, Ecol. Indic., 142, 109183, https://doi.org/10.1016/j.ecolind.2022.109183, 2022.
Thrasher, B., Maurer, E. P., McKellar, C., and Duffy, P. B.: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314, https://doi.org/10.5194/hess-16-3309-2012, 2012.
Tian, P., Lu, H., Feng, W., Guan, Y., and Xue, Y.: Large decrease in streamflow and sediment load of Qinghai–Tibetan Plateau driven by future climate change: A case study in Lhasa River Basin, CATENA, 187, 104340, https://doi.org/10.1016/j.catena.2019.104340, 2020.
Vitasse, Y., Baumgarten, F., Zohner, C. M., Rutishauser, T., Pietragalla, B., Gehrig, R., Dai, J., Wang, H., Aono, Y., and Sparks, T. H.: The great acceleration of plant phenological shifts, Nat. Clim. Change, 12, 300–302, https://doi.org/10.1038/s41558-022-01283-y, 2022.
Wang, K., Onodera, S., Saito, M., Shimizu, Y., and Iwata, T.: Effects of forest growth in different vegetation communities on forest catchment water balance, Sci. Total Environ., 809, 151159, https://doi.org/10.1016/j.scitotenv.2021.151159, 2022.
Wilcke, R. A. I., Mendlik, T., and Gobiet, A.: Multi-variable error correction of regional climate models, Climatic Change, 120, 871–887, https://doi.org/10.1007/s10584-013-0845-x, 2013.
Wu, Y., Fang, H., Huang, L., and Ouyang, W.: Changing runoff due to temperature and precipitation variations in the dammed Jinsha River, J. Hydrol., 582, 124500, https://doi.org/10.1016/j.jhydrol.2019.124500, 2020.
Wu, Z., Chen, S., De Boeck, H. J., Stenseth, N. C., Tang, J., Vitasse, Y., Wang, S., Zohner, C., and Fu, Y. H.: Atmospheric brightening counteracts warming-induced delays in autumn phenology of temperate trees in Europe, Global Ecol. Biogeogr., 30, 2477–2487, https://doi.org/10.1111/geb.13404, 2021.
Wu, Z., Fu, Y. H., Crowther, T. W., Wang, S., Gong, Y., Zhang, J., Zhao, Y.-P., Janssens, I., Penuelas, J., and Zohner, C. M.: Poleward shifts in the maximum of spring phenological responsiveness of Ginkgo biloba to temperature in China, New Phytol., 240, 1421–1432, https://doi.org/10.1111/nph.19229, 2023.
Xu, X., Liu, J., Zhang, S., Li, R., Yan, C., and Wu, S.: China Multiperiod Land Use Remote Sensing Monitoring Dataset (CNLUCC), Resources and Environmental Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, [data set], https://doi.org/10.12078/2018070201, 2018.
Yang, K., He, J., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: China meteorological forcing dataset (1979–2018), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file, 2019.
Yang, X., Mustard, J. F., Tang, J., and Xu, H.: Regional-scale phenology modeling based on meteorological records and remote sensing observations, J.-Geophys. Res.-Biogeo., 117, G03029, https://doi.org/10.1029/2012JG001977, 2012.
Yang, Y., Roderick, M. L., Guo, H., Miralles, D. G., Zhang, L., Fatichi, S., Luo, X., Zhang, Y., McVicar, T. R., Tu, Z., Keenan, T. F., Fisher, J. B., Gan, R., Zhang, X., Piao, S., Zhang, B., and Yang, D.: Evapotranspiration on a greening Earth, Nat. Rev. Earth Environ., 4, 626–641, https://doi.org/10.1038/s43017-023-00464-3, 2023.
Zhang, C., Sun, F., Sharma, S., Zeng, P., Mejia, A., Lyu, Y., Gao, J., Zhou, R., and Che, Y.: Projecting multi-attribute flood regime changes for the Yangtze River basin, J. Hydrol., 617, 128846, https://doi.org/10.1016/j.jhydrol.2022.128846, 2023.
Zhang, H., Wang, B., Liu, D. L., Zhang, M., Leslie, L. M., and Yu, Q.: Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia, J. Hydrol., 585, 124822, https://doi.org/10.1016/j.jhydrol.2020.124822, 2020.
Zhang, X., Izaurralde, R. C., Arnold, J. G., Williams, J. R., and Srinivasan, R.: Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux: Model development and initial evaluation, Sci. Total Environ., 463–464, 810–822, https://doi.org/10.1016/j.scitotenv.2013.06.056, 2013.
Zhang, X., Zhang, Y., Ma, N., Kong, D., Tian, J., Shao, X., and Tang, Q.: Greening-induced increase in evapotranspiration over Eurasia offset by CO2-induced vegetational stomatal closure, Environ. Res. Lett., 16, 124008, https://doi.org/10.1088/1748-9326/ac3532, 2021.
Zhang, Y., Chiew, F. H. S., Zhang, L., and Li, H.: Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast Australia, J. Hydrometeorol., 10, 969–980, https://doi.org/10.1175/2009JHM1061.1, 2009.
Zhao, J., Zhang, H., Zhang, Z., Guo, X., Li, X., and Chen, C.: Spatial and Temporal Changes in Vegetation Phenology at Middle and High Latitudes of the Northern Hemisphere over the Past Three Decades, Remote Sens.-Basel, 7, 10973–10995, https://doi.org/10.3390/rs70810973, 2015.
Zhou, S., Yu, B., Lintner, B. R., Findell, K. L., and Zhang, Y.: Projected increase in global runoff dominated by land surface changes, Nat. Clim. Change, 13, 442–449, https://doi.org/10.1038/s41558-023-01659-8, 2023.
Short summary
Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.
Climate-driven shifts in vegetation phenology have a significant impact on hydrological...