Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5933-2022
© Author(s) 2022. 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-26-5933-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2022
Research article |
| 25 Nov 2022
| 25 Nov 2022
Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data
Jingkai Xie
Institute of Hydrology and Water Resources, Zhejiang University,
Hangzhou, 310058, China
Institute of Hydrology and Water Resources, Zhejiang University,
Hangzhou, 310058, China
Hongjie Yu
Institute of Hydrology and Water Resources, Zhejiang University,
Hangzhou, 310058, China
Yan Huang
Changjiang Water Resources Commission of the Ministry of Water
Resources, Wuhan, 43000, China
Yuxue Guo
Institute of Hydrology and Water Resources, Zhejiang University,
Hangzhou, 310058, China
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Cited articles
Abhishek, Kinouchi, T., and Sayama, T.: A comprehensive assessment of water
storage dynamics and hydroclimatic extremes in the Chao Phraya River Basin
during 2002–2020, J. Hydrol., 603, 126868, https://doi.org/10.1016/j.jhydrol.2021.126868, 2021.
Abhishek, Kinouchi, T., Abolafia-Rosenzweig, R., and Ito, M.: Water Budget
Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource
Data, Remote Sens., 14, 173, https://doi.org/10.3390/rs14010173, 2022.
Ahmed, M., Aqnouy, M., and Messari, J. S. E.: Sustainability of Morocco's
groundwater resources in response to natural and anthropogenic forces, J.
Hydrol., 603, 126866, https://doi.org/10.1016/j.jhydrol.2021.126866, 2021.
Bai, P., Liu, X., and Xie, J.: Simulating runoff under changing climatic
conditions: A comparison of the long short-term memory network with two
conceptual hydrologic models, J. Hydrol., 592, 125779, https://doi.org/10.1016/j.jhydrol.2020.125779, 2021.
Beaudoing, H. and Rodell, M.: NASA/GSFC/HSL, GLDAS Noah Land Surface Model L4 3 hourly 0.25 × 0.25 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Bomers, A., van der Meulen, B., Schielen, R. M. J., and Hulscher, S. J. M.
H.: Historic flood reconstruction with the use of an Artificial Neural
Network, Water Resour. Res., 55, 9673–9688,
https://doi.org/10.1029/2019WR025656, 2019.
Boucher, M. A., Quilty, J., and Adamowski, J.: Data assimilation for
streamflow forecasting using extreme learning machines and multilayer
perceptrons, Water Resour. Res., 56, e2019WR026226, https://doi.org/10.1029/2019WR026226, 2020.
Chao, N., Jin, T., Cai, Z., Chen, G., Liu, X., Wang, Z., and Yeh, P. J. F.:
Estimation of component contributions to total terrestrial water storage
change in the Yangtze river basin, J. Hydrol., 595, 125661,
https://doi.org/10.1016/j.jhydrol.2020.125661, 2021.
Chen, J. L., Wilson, C. R., and Tapley, B. D.: The 2009 exceptional Amazon
flood and interannual terrestrial water storage change observed by GRACE,
Water Resour. Res., 46, W12526, https://doi.org/10.1029/2010WR009383, 2010.
Chen, X., Jiang, J., and Li, H.: Drought and flood monitoring of the Liao
River Basin in northeast China using extended GRACE Data, Remote Sens.,
10, 1168, https://doi.org/10.3390/rs10081168, 2018.
Dottori, F., Szewczyk, W., Ciscar, J., Zhao, F., Alfieri, L., Hirabayashi,
Y., Bianchi, A., Mongelli, I., Frieler, K., Betts, R. A., and Feyen, L.:
Increased human and economic losses from river flooding with anthropogenic
warming, Nat. Clim. Change, 8, 781–786,
https://doi.org/10.1038/s41558-018-0257-z, 2018.
Döll, P., Hoffmann-Dobrev, H., Portmann, F. T., Siebert, S., Eicker, A.,
Rodell, M., Strassberg, G., and Scanlon, B. R.: Impact of water withdrawals
from groundwater and surface water on continental water storage variations,
J. Geodyn., 59–60, 143–156, https://doi.org/10.1016/j.jog.2011.05.001, 2012.
Fang, H., Han, D., He, G., and Chen, M.: Flood management selections for the
Yangtze River midstream after the Three Gorges Project operation, J.
Hydrol., 432-433, 1–11, https://doi.org/10.1016/j.jhydrol.2012.01.042, 2012.
Fatolazadeh, F. and Goïta, K.: Reconstructing groundwater storage
variations from GRACE observations using a new Gaussian-Han-Fan (GHF)
smoothing approach, J. Hydrol., 604, 127234, https://doi.org/10.1016/j.jhydrol.2021.127234, 2022.
Felfelani, F., Wada, Y., Longuevergne, L., and Pokhrel, Y. N.: Natural and
human-induced terrestrial water storage change: A global analysis using
hydrological models and GRACE, J. Hydrol., 553, 105–118,
https://doi.org/10.1016/j.jhydrol.2017.07.048, 2017.
Fischer, S., Schumann, A., and Bühler, P.: A statistics-based automated
flood event separation, J. Hydrol. X, 10, 100070, https://doi.org/10.1016/j.hydroa.2020.100070, 2021.
Giani, G., Tarasova, L., Woods, R. A., and Rico-Ramirez, M. A.: An objective
time-series-analysis method for rainfall-runoff event identification, Water
Resour. Res., 58, e2021WR031283, https://doi.org/10.1029/2021WR031283, 2022.
Gouweleeuw, B. T., Kvas, A., Gruber, C., Gain, A. K., Mayer-Gürr, T., Flechtner, F., and Güntner, A.: Daily GRACE gravity field solutions track major flood events in the Ganges–Brahmaputra Delta, Hydrol. Earth Syst. Sci., 22, 2867–2880, https://doi.org/10.5194/hess-22-2867-2018, 2018.
GRACE: CSR GRACE/GRACE-FO RL06 Mascon Solutions (version 02), GRACE [data set], https://www2.csr.utexas.edu/grace/RL06_mascons.html, last access: 17 November 2022.
Guo, Y., Yu, X., Xu, Y.-P., Chen, H., Gu, H., and Xie, J.: AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment, Hydrol. Earth Syst. Sci., 25, 5951–5979, https://doi.org/10.5194/hess-25-5951-2021, 2021.
Herath, S. M., Sarukkalige, P. R., and Nguyen, V. T. V.: A spatial temporal
downscaling approach to development of IDF relations for Perth airport
region in the context of climate change, Hydrolog. Sci. J.,
61, 2061–2070, https://doi.org/10.1080/02626667.2015.1083103, 2016.
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735,
1997.
Huang, S., Zhang, X., Chen, N., Li, B., Ma, H., Xu, L., Li, R., and Niyogi,
D.: Drought propagation modification after the construction of the Three
Gorges Dam in the Yangtze River Basin, J. Hydrol., 603, 127138,
https://doi.org/10.1016/j.jhydrol.2021.127138, 2021.
Huang, Y., Salama, M. S., Krol, M. S., Su, Z., Hoekstra, A. Y., Zeng, Y.,
and Zhou, Y.: Estimation of human-induced changes in terrestrial water
storage through integration of GRACE satellite detection and hydrological
modeling: A case study of the Yangtze River basin, Water Resour. Res., 51, 8494–8516, https://doi.org/10.1002/2015WR016923, 2015.
Humphrey, V. and Gudmundsson, L.: GRACE-REC: a reconstruction of climate-driven water storage changes over the last century, Earth Syst. Sci. Data, 11, 1153–1170, https://doi.org/10.5194/essd-11-1153-2019, 2019.
Jia, H., Chen, F., Pan, D., Du, E., Wang, L., Wang, N., and Yang, A.: Flood
risk management in the Yangtze River basin – Comparison of 1998 and 2020
events, Int. J. Disast. Risk Re., 68, 102724,
https://doi.org/10.1016/j.ijdrr.2021.102724, 2021.
Jing, W., Di, L., Zhao, X., Yao, L., Xia, X., Liu, Y., Yang, J., Li, Y., and
Zhou, C.: A data-driven approach to generate past GRACE-like terrestrial
water storage solution by calibrating the land surface model simulations,
Adv. Water Resour. Res., 143, 103683,
https://doi.org/10.1016/j.advwatres.2020.103683, 2020.
Khorrami, B. and Gunduz, O.: An enhanced water storage deficit index (EWSDI)
for drought detection using GRACE gravity estimates, J. Hydrol., 603,
126812, https://doi.org/10.1016/j.jhydrol.2021.126812, 2021.
Kong, R., Zhang, Z., Zhang, F., Tian, J., Chang, J., Jiang, S., Zhu, B., and
Chen, X.: Increasing carbon storage in subtropical forests over the Yangtze
River basin and its relations to the major ecological projects, Sci. Total
Environ., 709, 136163, https://doi.org/10.1016/j.scitotenv.2019.136163,
2020.
Kumar, J., Brooks, B.-G. J., Thornton, P. E., and Dietze, M. C.: Sub-daily
statistical downscaling of meteorological variables using neural networks,
Proc. Comput. Sci., 9, 887–896,
https://doi.org/10.1016/j.procs.2012.04.095, 2012.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial
water storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453, 2012.
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T.,
Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H.,
Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D.,
McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S.,
Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H. Y., Wiese,
D. N., and Yuan, D. N.: Extending the Global Mass Change Data Record: GRACE
Follow-On Instrument and Science Data Performance, Geophys. Res. Lett., 47,
1–10, https://doi.org/10.1029/2020GL088306, 2020.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521,
436–444, https://doi.org/10.1038/nature14539, 2015.
Li, X., 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.
Liu, B., Zou, X., Yi, S., Sneeuw, N., Cai, J., and Li, J.: Identifying and
separating climate- and human-driven water storage anomalies using GRACE
satellite data, Remote Sens. Environ., 263, 112559,
https://doi.org/10.1016/j.rse.2021.112559, 2021.
Liu, L., Jiang, L., Wang, H., Ding, X., and Xu, H.: Estimation of glacier
mass loss and its contribution to river runoff in the source region of the
Yangtze River during 2000–2018, J. Hydrol., 589, 125207,
https://doi.org/10.1016/j.jhydrol.2020.125207, 2020.
Liu, X. J., Min, F. Y., and Kettner, A. J.: The impact of large to extreme
flood events on floodplain evolution of the middle and lower reaches of the
Yangtze River, China, Catena, 176, 394–409,
https://doi.org/10.1016/j.catena.2019.01.027, 2019.
Long, D., Shen, Y., Sun, A., Hong, Y., Longuevergne, L., Yang, Y., Li, B.,
and Chen, L.: Drought and flood monitoring for a large karst plateau in
Southwest China using extended GRACE data, Remote Sens. Environ., 155,
145–160, https://doi.org/10.1016/j.rse.2014.08.006, 2014.
Long, D., Yang, Y., Wada, Y., Hong, Y., Liang, W., Chen, Y., Yong, B., Hou,
A., Wei, J., and Chen, L.: Deriving scaling factors using a global
hydrological model to restore GRACE total water storage changes for China's
Yangtze River Basin, Remote Sens. Environ., 168, 177–193,
https://doi.org/10.1016/j.rse.2015.07.003, 2015.
Long, D., Yang, W., Scanlon, B.R., Zhao, J., Liu, D., Burek, P., Pan, Y.,
You, L., and Wada, Y.: South-to-North Water Diversion stabilizing Beijing's
groundwater levels, Nat. Commun., 11, 3665,
https://doi.org/10.1038/s41467-020-17428-6, 2020.
Loomis, B. D., Luthcke, S. B., and Sabaka, T. J.: Regularization and error
characterization of GRACE mascons, J. Geodesy, 93, 1381–1398, https://doi.org/10.1007/s00190-019-01252-y, 2019.
Lu, W., Lei, H., Yang, W., Yang, J., and Yang, D.: Comparison of floods
driven by tropical cyclones and monsoons in the Southeastern Coastal Region
of China, J. Hydrometeorol., 21, 1589–1603,
https://doi.org/10.1175/JHM-D-20-0002.1, 2020.
Lv, M., Ma, Z., Yuan, X., Lv, M., Li, M., and Zheng, Z.: Water budget
closure based on GRACE measurements and reconstructed evapotranspiration
using GLDAS and water use data for two large densely-populated mid-latitude
basins, J. Hydrol., 547, 585–599, https://doi.org/10.1016/j.jhydrol.2017.02.027, 2017.
Lyu, K., Zhang, X., and Church, J. A.: Projected ocean warming constrained
by the ocean observational record, Nat. Clim. Change, 11, 834–839,
https://doi.org/10.1038/s41558-021-01151-1, 2021.
Mulder, G., Olsthoorn, T. N., Al-Manmi, D. A. M. A., Schrama, E. J. O., and Smidt, E. H.: Identifying water mass depletion in northern Iraq observed by GRACE, Hydrol. Earth Syst. Sci., 19, 1487–1500, https://doi.org/10.5194/hess-19-1487-2015, 2015.
Mohanasundaram, S., Mekonen, M. M., Haacker, E., Ray, C., Lim, S., and
Shrestha, S.: An application of GRACE mission datasets for streamflow and
baseflow estimation in the Conterminous United States basins, J. Hydrol.,
601, 126622, https://doi.org/10.1016/j.jhydrol.2021.126622,
2021.
Mo, S., Zhong, Y., Forootan, E., Mehrnegar, N., Yin, X., Wu, J., Feng, W.,
and Shi, X.: Bayesian convolutional neural networks for predicting the
terrestrial water storage anomalies during GRACE and GRACE-FO gap, J.
Hydrol., 604, 127244, https://doi.org/10.1016/j.jhydrol.2021.127244, 2022.
NASA: Gravity Recovery and Climate Experiment (GRACE), NASA [data set], https://podaac.jpl.nasa.gov/GRACE?tab=mission-objectives§ions=about+data, last access: 17 November 2022.
Nourani, V., Baghanam, A. H., and Gokcekus, H.: Data-driven ensemble model
to statistically downscale rainfall using nonlinear predictor screening
approach, J. Hydrol., 565, 538–551, https://doi.org/10.1016/j.jhydrol.2018.08.049, 2018.
Ramesh, K., Smith, A. K., Garcia, R. R., Marsh, D. R., Sridharan, S., and
Kumar, K. K.: Long-term variability and tendencies in middle atmosphere
temperature and zonal wind from WACCM6 simulations during 1850–2014, J.
Geophys. Res.-Atmos., 125, e2020JD033579, https://doi.org/10.1029/2020JD033579, 2020.
Reager, J. T. and Famiglietti, J. S.: Global terrestrial water storage
capacity and flood potential using GRACE, Geophys. Res. Lett., 36, L23402,
https://doi.org/10.1029/2009GL040826, 2009.
Reager, J. T., Thomas, B. F., and Famiglietti, J. S.: River basin flood
potential inferred using GRACE gravity observations at several months lead
time, Nat. Geosci., 7, 588–592, https://doi.org/10.1038/ngeo2203, 2014.
Requena, A. I., Nguyen, T. H., Burn, D. H., and Coulibaly, P.: A temporal
downscaling approach for sub-daily gridded extreme rainfall intensity
estimation under climate change, J. Hydrol. Reg. Stud., 35, 100811,
https://doi.org/10.1016/j.ejrh.2021.100811, 2021.
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H.
K., Landerer, F. W., and Lo, M. H.: Emerging trends in global freshwater
availability, Nature, 557, 6510659,
https://doi.org/10.1038/s41586-018-0123-1, 2018.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning
representations by backpropagating errors, Nature, 323, 533–536, 1986.
Save, H., Bettadpur, S., and Tapley, B. D.: High-resolution CSR GRACE RL05
mascons, J. Geophys. Res.-Sol. Ea., 121, 7547–7569,
https://doi.org/10.1002/2016JB013007, 2016.
Scanlon, B. R., Zhang, Z., Save, H., Wiese, D. N., Landerer, F. W., Long,
D., Longuevergne, L., and Chen, J. L.: Global evaluation of new GRACE mascon
products for hydrologic applications, Water Resour. Res., 52,
9412–9429, https://doi.org/10.1002/2016WR019494, 2016.
Shah, D. and Mishra, V.: Strong influence of changes in terrestrial water
storage on flood potential in India, J. Geophys. Res.-Atmos., 126,
e2020JD033566, https://doi.org/10.1029/2020JD033566, 2021.
Sharifi, E., Saghafian, B., and Steinacker, R.: Downscaling satellite
precipitation estimates with multiple linear regression, artificial neural
networks, and spline interpolation techniques, J. Geophys. Res.-Atmos., 124,
789–805, https://doi.org/10.1029/2018JD028795, 2019.
Shi, R., Yang, H., and Yang, D.: Spatiotemporal variations in frozen ground
and their impacts on hydrological components in the source region of the
Yangtze River, J. Hydrol., 590, 125237, https://doi.org/10.1016/j.jhydrol.2020.125237, 2020.
Shu, C. and Ouarda, T. B.: Flood frequency analysis at ungauged sites using
artificial neural networks in canonical correlation analysis physiographic
space, Water Resour. Res., 43, W07438, https://doi.org/10.1029/2006WR005142,
2007.
Sinha, D., Syed, T. H., and Reager, J. T.: Utilizing combined deviations of
precipitation and GRACE-based terrestrial water storage as a metric for
drought characterization: A case study over major Indian river basins, J.
Hydrol., 572, 294–307, https://doi.org/10.1016/j.jhydrol.2019.02.053, 2019.
Slater, L. J. and Villarini, G.: Recent trends in U.S. flood risk, Geophys.
Res. Lett., 43, 12428–12436, https://doi.org/10.1002/2016GL071199, 2016.
Sousa, S. I. V., Martins, F. G., Alvim-Ferraz, M. C. M., and Pereira, M. C.:
Multiple linear regression and artificial neural networks based on principal
components to predict ozone concentrations, Environ. Modell. Softw., 22, 97–103, https://doi.org/10.1016/j.envsoft.2005.12.002,
2007.
Sun, A. Y., Scanlon, B. R., Zhang, Z., Walling, D., Bhanja, S. N.,
Mukherjee, A., and Zhong, Z.: Combining physically based modeling and deep
learning for fusing GRACE satellite data: can we learn from mismatch?, Water
Resour. Res., 55, 1179–1195, https://doi.org/10.1029/2018WR023333, 2019.
Sun, Z., Long, D., Yang, W., Li, X., and Pan, Y.: Reconstruction of GRACE
data on changes in total water storage over the global land surface and 60
basins, Water Resour. Res., 56, e2019WR026250, https://doi.org/10.1029/2019WR026250, 2020.
Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., and Wilson, C. R.:
Analysis of Terrestrial Water Storage Changes from GRACE and GLDAS, Water
Resour. Res., 44, W02433, https://doi.org/10.1029/2006WR005779, 2008.
Tangdamrongsub, N., Ditmar, P. G., Steele-Dunne, S. C., Gunter, B. C., and
Sutanudjaja, E. H.: Assessing total water storage and identifying flood
events over Tonlé Sap basin in Cambodia using GRACE and MODIS satellite
observations combined with hydrological models, Remote Sens. Environ., 181,
162–173, https://doi.org/10.1016/j.rse.2016.03.030, 2016.
Tanoue, M., Taguchi, R., Nakata, S., Watanabe, S., Fujimori, S., and
Hirabayashi, Y.: Estimation of direct and indirect economic losses caused by
a flood with long-lasting inundation: Application to the 2011 Thailand
flood, Water Resour. Res., 56, e2019WR026092, https://doi.org/10.1029/2019WR026092, 2020.
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M.
M.: GRACE measurements of mass variability in the earth system, Science,
305, 503, https://doi.org/10.1126/science.1099192, 2004.
Tarasova, L., Basso, S., Zink, M., and Merz, R.: Exploring controls on
rainfall-runoff events: 1. Time series-based event separation and temporal
dynamics of event runoff response in Germany, Water Resour. Res., 54,
7711–7732, https://doi.org/10.1029/2018WR022588, 2018.
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S.,
Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging
reveals increased proportion of population exposed to floods, Nature, 596,
80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Velicogna, I., Tong, J., Zhang, T., and Kimball, J. S.: Increasing subsurface
water storage in discontinuous permafrost areas of the Lena River basin,
Eurasia, detected from GRACE, Geophys. Res. Lett., 39, L09403, https://doi.org/10.1029/2012GL051623, 2012.
Vu, M. T., Jarhani, A., Massei, N., and Fournier, M.: Reconstruction of
missing groundwater level data by using Long Short-Term Memory (LSTM) deep
neural network, J. Hydrol., 597, 125776,
https://doi.org/10.1016/j.jhydrol.2020.125776, 2021.
Wang, L., Chen, C., Na, X., Fu, Z., Zheng, Y., and Peng, Z.: Evaluation of
GRACE mascon solutions using in-situ geodetic data: The case of
hydrologic-induced crust displacement in the Yangtze River Basin, Sci. Total
Environ., 707, 135606, https://doi.org/10.1016/j.scitotenv.2019.135606, 2020.
Wang, M., Zheng, H., Xie, X., Fan, D., Yang, S., Zhao, Q., and Wang, K.: A
600-year flood history in the Yangtze River drainage: comparison between a
subaqueous delta and historical records, Chin. Sci. Bull., 58, 188–195,
https://doi.org/10.1007/s11434-010-4212-2, 2011.
Wang, Q., Huang, J., Liu, R., Men, C., Guo, L., Miao, Y., Jiao, L., Wang,
Y., Shoaib, M., and Xia, X.: Sequence-based statistical downscaling and its
application to hydrologic simulations based on machine learning and big
data, J. Hydrol., 586, 124875, https://doi.org/10.1016/j.jhydrol.2020.124875, 2020.
Wasko, C. and Natthan, R.: Influence of changes in rainfall and soil
moisture on trends in flooding, J. Hydrol., 575, 432–441, https://doi.org/10.1016/j.jhydrol.2019.05.054, 2019.
Wei, L., Jiang, S., Ren, L., Tan, H., Ta, W., Liu, Y., Yang, X., Zhang, L.,
and Duan, Z.: Spatiotemporal changes of terrestrial water storage and
possible causes in the closed Qaidam Basin, China using GRACE and GRACE
Follow-On data, J. Hydrol., 598, 126274, https://doi.org/10.1016/j.jhydrol.2021.126274, 2021.
Winter, C., Tarasova, L., Lutz, S. R., Musolff, A., Kumar, R., and
Fleckenstein, J. H.: Explaining the Variability in High-Frequency Nitrate
Export Patterns Using Long-Term Hydrological Event Classification, Water
Resour. Res., 58, e2021WR030938, https://doi.org/10.1029/2021WR030938, 2022.
Wu, H., Yang, Q., Liu, J., and Wang, G.: A spatiotemporal deep fusion model
for merging satellite and gauge precipitation in China, J. Hydrol., 584,
124664, https://doi.org/10.1016/j.jhydrol.2020.124664, 2020.
Wu, Y., Long, D., Lall, U., Scanlon, B. R., Tian, F., Fu, X., Zhao, J.,
Zhang, J., Wang, H., and Hu, C.: Reconstructed eight-century streamflow in
the Tibetan Plateau reveals contrasting regional variability and strong
nonstationarity, Nat. Commun., 13, 6416, https://doi.org/10.1038/s41467-022-34221-9, 2022.
Xie, J., Xu, Y. P., Wang, Y., Gu, H., Wang, F., and Pan, S.: Influences of
climatic variability and human activities on terrestrial water storage
variations across the Yellow River basin in the recent decade, J. Hydrol.,
579, 124218, https://doi.org/10.1016/j.jhydrol.2019.124218,
2019a.
Xie, J., Xu, Y. P., Gao, C., Xuan, W., and Bai, Z.: Total basin discharge
from GRACE and Water balance method for the Yarlung Tsangpo River basin,
Southwestern China, J. Geophys. Res.-Atmos., 124, 7617–7632,
https://doi.org/10.1029/2018JD030025, 2019b.
Xie, J., Xu, Y. P., Guo, Y., and Wang, Y.: Detecting the dominant
contributions of runoff variance across the source region of the Yellow
River using a new decomposition framework, Hydrol. Res., 52, 1015–1032,
https://doi.org/10.2166/nh.2021.179, 2021.
Xie, J., Xu, Y. P., Guo, Y., Wang, Y., and Chen, H.: Understanding the
impact of climatic variability on terrestrial water storage in the
Qinghai-Tibet Plateau of China, Hydrolog. Sci. J., 67, 1–16,
https://doi.org/10.1080/02626667.2022.2044482, 2022.
Xiong, J., Yin, J., Guo, S., Gu, L., Xiong, F., and Li, N.: Integrated flood
potential index for flood monitoring in the GRACE era, J. Hydrol., 603,
127115, https://doi.org/10.1016/j.jhydrol.2021.127115, 2021a.
Xiong, J., Guo, S., and Yin, J.: Discharge Estimation Using Integrated
Satellite Data and Hybrid Model in the Midstream Yangtze River, Remote
Sens., 13, 2272, https://doi.org/10.3390/rs13122272, 2021b.
Yan, X., Zhang, B., Yao, Y., Yang, Y., Li, J., and Ran, Q.: GRACE and land
surface models reveal severe drought in eastern China in 2019, J. Hydrol.,
601, 126640, https://doi.org/10.1016/j.jhydrol.2021.126640,
2021.
Yang, L., Zeng, S., Xia, J., Wang, Y., Huang, R., and Chen, M.: Effects of
the Three Gorges Dam on the downstream streamflow based on a large-scale
hydrological and hydrodynamics coupled model, J. Hydrol. Reg. Stud., 40, 101039, https://doi.org/10.1016/j.ejrh.2022.101039, 2022.
Yang, P., Xia, J., Luo, X., Meng, L., Zhang, S., Cai, W., and Wang, W.:
Impacts of climate change-related flood events in the Yangtze River Basin
based on multi-source data, Atmos. Res, 263, 105819, https://doi.org/10.1016/j.atmosres.2021.105819, 2021.
Yang, S., Liu, Z., Dai, S., Gao, Z., Zhang, J., Wang, H., Luo, X., Wu, C.,
and Zhang, Z.: Temporal variations in water resources in the yangtze river
(Changjiang) over the industrial period based on reconstruction of missing
monthly discharges, Water Resour. Res., 46, W10516, https://doi.org/10.1029/2009WR008589, 2010.
Yao, L., Li, Y., and Chen, X.: A robust water-food-land nexus optimization
model for sustainable agricultural development in the Yangtze River Basin,
Agr. Water Manage., 256, 107103,
https://doi.org/10.1016/j.agwat.2021.107103, 2021.
Yin, G. and Park, J.: The use of triple collocation approach to merge
satellite- and model-based terrestrial water storage for flood potential
analysis, J. Hydrol., 603, 127197, https://doi.org/10.1016/j.jhydrol.2021.127197, 2021.
Yin, H., Wang, F., Zhang, X., Zhang, Y., Chen, J., Xia, R., and Jin, J.:
Rainfall-runoff modeling using long short-term memory based step-sequence
framework, J. Hydrol., 610, 127901,
https://doi.org/10.1016/j.jhydrol.2022.127901, 2022.
Yue, Y., Yan, D., Yue, Q., Ji, G., and Wang, Z.: Future changes in
precipitation and temperature over the Yangtze River Basin in China based on
CMIP6 GCMs, Atmos. Res., 264, 105828,
https://doi.org/10.1016/j.atmosres.2021.105828, 2021.
Zhang, D., Lindholm, G., and Ratnaweera, H.: Use long short-term memory to
enhance Internet of Things for combined sewer overflow monitoring, J.
Hydrol., 556, 409–418, https://doi.org/10.1016/j.jhydrol.2017.11.018, 2018.
Zhang, Q., Xu, C. Y., Zhang, Z. X., Chen, Y. D., Liu, C. L., and Lin, H.:
Spatial and temporal variability of precipitation maxima during 1960–2005 in
the Yangtze River basin and possible association with large-scale
circulation, J. Hydrol., 353, 215–227,
https://doi.org/10.1016/j.jhydrol.2007.11.023, 2008.
Zhang, X., Zhang, G., Long, X., Zhang, Q., Liu, D., Wu, H., and Li, S.:
Identifying the drivers of water yield ecosystem service: A case study in
the Yangtze River Basin, China, Ecol. Indicat., 132, 108304, https://doi.org/10.1016/j.ecolind.2021.108304, 2021.
Short summary
Monitoring extreme flood events has long been a hot topic for hydrologists and decision makers around the world. In this study, we propose a new index incorporating satellite observations combined with meteorological data to monitor extreme flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The conclusions drawn from this study provide important implications for flood hazard prevention and water resource management over this region.
Monitoring extreme flood events has long been a hot topic for hydrologists and decision makers...
