Abstract
The modeling and forecasting of short-duration and high-intensity floods are of importance for flood defenses and adaptations. One of the conventional ways to model or forecast such events is to utilize hydrological models driven by meteorological and hydrological station data. However, this suffers from complicated parameter specification and large uncertainties, particularly in regions with very few gauged stations. Based on the daily downscaled Gravity Recovery and Climate Experiment (GRACE) solutions, this study employed three different machine learning models and two hydrological models for flood modeling at the daily timescale by taking the Xijiang River Basin in China as a case study. The results show that: (1) the uncertainty of daily GRACE solutions alone governs the difference between GRACE data and hydrological simulations; (2) there is a strong correlation between the high-frequency components of runoff anomalies and terrestrial water storage anomaly (TWSA), and runoff plays a dominant role in TWSA variation during floods; (3) the developed machine learning models can model runoff during floods effectively and outperform the hydrological models. The proposed comprehensive method based on remote sensing satellites provides a potential new way for flood modeling, particularly for poorly gauged regions.
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Data availability
Data availability is given in Sect. 2.2.
References
Adler J, Parmryd I (2010) Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander’s overlap coefficient. Cytom A 77:733–742. https://doi.org/10.1002/cyto.a.20896
Afzaal H, Farooque A, Abbas F, Acharya B, Esau T (2020) Computation of evapotranspiration with artificial intelligence for precision water resource management. Appl Sci 10:1621. https://doi.org/10.3390/app10051621
Bergmann I, Dobslaw H (2012) Short-term transport variability of the Antarctic circumpolar current from satellite gravity observations: ACC variability from satellite gravity. J Geophys Res Oceans 117:C05044. https://doi.org/10.1029/2012JC007872
Bergmann WI, Forootan E, Klemann V, Kusche J, Dobslaw H (2015) Updating ESA’s Earth System Model for gravity mission simulation studies: 3. a realistically perturbed non-tidal atmosphere and ocean de-aliasing model, (Scientific Technical Report; 14/09), Potsdam: Deutsches Geo Forschungs Zentrum, GFZ, 62 p. doi:https://doi.org/10.2312/GFZ.b103-14091
Chen X, Jiang J, Li H (2018) 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
Chen L, He Q, Liu K, Li J, Jing C (2019) Downscaling of GRACE-derived groundwater storage based on the random forest model. Remote Sens 11(24):2979. https://doi.org/10.3390/rs11242979
Christiano LJ, Fitzgerald TJ (1999) The band pass filter. NBER Working Papers 7257. National Bureau of Economic Research, Inc.
Christiano LJ, Fitzgerald TJ (2003) The band pass filter. Int Econ Rev 44:435–465
Dill R (2008) Hydrological model LSDM for operational Earth rotation and gravity eld variations. Scientific Technical Report 0809.
Döll P, Müller S, Schuh C, Portmann FT, Eicker A (2014) Global-scale assessment of groundwater depletion and related groundwater abstractions: combining hydrological modeling with information from well observations and GRACE satellites. Water Resour Res 50:5698–5720. https://doi.org/10.1002/2014WR015595
Eicker A, Schumacher M, Kusche J, Döll P, Schmied HM (2014) Calibration/data assimilation approach for integrating GRACE data into the WaterGAP global hydrology model (WGHM) using an ensemble kalman filter: first results. Surv Geophys 35:1285–1309. https://doi.org/10.1007/s10712-014-9309-8
Ewing BT, Thompson MA (2007) Dynamic cyclical comovements of oil prices with industrial production, consumer prices, unemployment, and stock prices. Energ Policy 35:5535–5540. https://doi.org/10.1016/j.enpol.2007.05.018
Famiglietti JS, Rodell M (2013) Water in the balance. Science 340:1300–1301. https://doi.org/10.1126/science.1236460
Feng W, Shum C, Zhong M, Pan Y (2018) Groundwater storage changes in China from satellite gravity: an overview. Remote Sens 10:674. https://doi.org/10.3390/rs10050674
Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578. https://doi.org/10.1002/joc.1556
Fu R, Hu L, Gu G, Li Y (2008) A comparison study of summer-time synoptic-scale waves in South China and the Yangtze River basin using the TRMM Multi-Satellite Precipitation Analysis daily product. Sci China Ser D Earth Sci 51:114–122. https://doi.org/10.1007/s11430-007-0125-6
Geruo A, Wahr J, Zhong S (2013) Computations of the viscoelastic response of a 3-D compressible Earth to surface loading: an application to Glacial Isostatic Adjustment in Antarctica and Canada. Geophys J Int 192:557–572. https://doi.org/10.1093/gji/ggs030
Gessner MO, Hinkelmann R, Nützmann G, Jekel M, Singer G, Lewandowski J, Nehls T, Barjenbruch M (2014) Urban water interfaces. J Hydrol 514:226–232. https://doi.org/10.1016/j.jhydrol.2014.04.021
Ghorbani MA, Khatibi R, Hosseini B, Bilgili M (2013) Relative importance of parameters affecting wind speed prediction using artificial neural networks. Theor Appl Climatol 114:107–114
Gouweleeuw BT, Kvas A, Gruber C, Gain AK, Mayer-Guerr T, Flechtner F, Guentner A (2018) Daily GRACE gravity field solutions track major flood events in the Ganges-Brahmaputra Delta. Hydrol Earth Syst Sc 22:2867–2880. https://doi.org/10.5194/hess-22-2867-2018
Gruber C, Gouweleeuw B (2019) Short-latency monitoring of continental, ocean- and atmospheric mass variations using GRACE intersatellite accelerations. Geophys J Int 217:714–728
Gupta D, Dhanya CT (2021) Quantifying the effect of grace terrestrial water storage anomaly in the simulation of extreme flows. J Hydrol Eng 26:04021007. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002072
Gürr M, Behzadpur S, Ellmer M, Kvas A, Klinger B, Strasser S, Zehentner N (2018) ITSG-Grace2018-monthly, daily and static gravity field solutions from GRACE. GFZ Data Serv. https://doi.org/10.5880/ICGEM.2018.003
Hallegatte S, Green C, Nicholls RJ, Corfee-Morlot J (2013) Future flood losses in major coastal cities. Nat Clim Change 3:802–806. https://doi.org/10.1038/nclimate1979
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang Q, Qin G, Zhang Y, Tang Q, Liu C, Xia J, Chiew F, Post D (2020) Using remote sensing data-based hydrological model calibrations for predicting runoff in ungauged or poorly gauged catchments. Water Resour Res 56: e2020WR028205. doi:https://doi.org/10.1029/2020WR028205
Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2010) The TRMM multisatellite precipitation analysis (TMPA): Quasi-Global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55
Hulsman P, Winsemius HC, Michailovsky CI, Savenije HHG, Hrachowitz M (2020) Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region. Hydrol Earth Syst Sc 24:3331–3359. https://doi.org/10.5194/hess-24-3331-2020
Idowu D, Zhou W (2019) Performance evaluation of a potential component of an early flood warning system: a case study of the 2012 flood, Lower Niger River Basin. Nigeria Remote Sens 11:1970. https://doi.org/10.3390/rs11171970
Idowu D, Zhou W (2021) Spatiotemporal evaluation of flood potential indices for watershed flood prediction in the Mississippi River Basin, USA. Environ Eng Geosci 27:319–330
Klinger B, Mayer-Gürr T (2016) The role of accelerometer data calibration within GRACE gravity field recovery: results from ITSG-Grace2016. Adv Space Res 58:1597–1609. https://doi.org/10.1016/j.asr.2016.08.007
Kurtenbach E, Eicker A, Mayer-Guerr T, Holschneider M, Hayn M, Fuhrmann M, Kusche J (2012) Improved daily GRACE gravity field solutions using a Kalman smoother. J Geodyn 59–60:39–48. https://doi.org/10.1016/j.jog.2012.02.006
Kusche J, Schmidt R, Petrovic S, Rietbroek R (2009) Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model. J Geodesy 83:903–913. https://doi.org/10.1007/s00190-009-0308-3
Kvas A, Behzadpour S, Ellmer M, Klinger B, Strasser S, Zehentner N, Mayer-Gürr T (2019) ITSG-Grace2018: overview and evaluation of a new GRACE-Only gravity field time series. J Geophys Res Sol Ea 124:9332–9344. https://doi.org/10.1029/2019JB017415
Li J, Wang Z, Wu X, Xu CY, Guo S, Chen X (2020) Toward monitoring short-term droughts using a novel daily scale, standardized antecedent precipitation evapotranspiration index. J Hydrometeorol 21:891–908
Li J, Wang Z, Wu X, Zscheischler J, Guo S, Chen X (2021a) A standardized index for assessing sub-monthly compound dry and hot conditions with application in China. Hydrol Earth Syst Sc 25:1587–1601
Li J, Wang Z, Wu X, Xu C-Y, Guo S, Chen X, Zhang Z (2021b) Robust meteorological drought prediction using antecedent SST fluctuations and machine learning. Water Resour. Res 57: e2020WR029413. doi:https://doi.org/10.1029/2020WR029413
Long D, Shen Y, Sun A, Hong Y, Longuevergne L, Yang Y, Li B, Chen L (2014) 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
Lv N, Liang X, Chen C, Zhou Y, Li J, Wei H, Wang H (2020) A long Short-Term memory cyclic model with mutual information for hydrology forecasting: A case study in the xixian basin. Adv Water Resour 141:103622. https://doi.org/10.1016/j.advwatres.2020.103622
Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48: RG3003. doi:https://doi.org/10.1029/2009RG000314
Martens B, Miralles DG, Lievens H, van der Schalie R, de Jeu RAM, Fernandez-Prieto D, Beck HE, Dorigo WA, Verhoest NEC (2017) GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10:1903–1925. https://doi.org/10.5194/gmd-10-1903-2017
Milly PCD, Wetherald RT, Dunne KA, Delworth TL (2002) Increasing risk of great floods in a changing climate. Nature 415:514–517. https://doi.org/10.1038/415514a
Molodtsova T, Molodtsov S, Kirilenko A, Zhang X, VanLooy J (2016) Evaluating flood potential with GRACE in the United States. Nat Hazard Earth Sysy 16:1011–1018. https://doi.org/10.5194/nhess-16-1011-2016
Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Iny J Geogr Inf Sci 4:313–332. https://doi.org/10.1080/02693799008941549
Polk J (2013) Evolution of major environmental geological problems in karst areas of Southwestern China. Environ Earth Sci 69:2427–2435. https://doi.org/10.1007/s12665-012-2070-8
PRWRC (Pearl River Water Resources Committee): The Zhujiang Archive, vol 1, Guangdong Science and Technology Press, Guangzhou, 2005–2018 (in Chinese).
Reager JT, Famiglietti JS (2009) Global terrestrial water storage capacity and flood potential using GRACE. Geophys Res Lett 36:L23402. https://doi.org/10.1029/2009GL040826
Reager JT, Thomas BF, Famiglietti JS (2014) River basin flood potential inferred using GRACE gravity observations at several months lead time. Nat Geosci 7:589–593. https://doi.org/10.1038/NGEO2203
Sakumura C, Bettadpur S, Save H, Mccullough C (2016) High-frequency terrestrial water storage signal capture via a regularized sliding window mascon product from GRACE. J Geophys Res Sol Ea 121:4014–4030. https://doi.org/10.1002/2016JB012843
Specht DF (1991) A general regression neural network. IEEE Trans Neur Net Lear 2:568–576. https://doi.org/10.1109/72.97934
Sun A (2013) Predicting groundwater level changes using GRACE data. Water Resour Res 49:5900–5912. https://doi.org/10.1002/wrcr.20421
Sun A, Scanlon BR, AghaKouchak A, Zhang Z (2017) Using grace satellite gravimetry for assessing large-scale hydrologic extremes. Remote Sens 9:1287. https://doi.org/10.3390/rs9121287
Swenson S, Wahr J (2006) Post-processing removal of correlated errors in GRACE data. Geophys Res Lett 33:L08402. https://doi.org/10.1029/2005GL025285
Tang G, Ma Y, Long D, Zhong L, Hong Y (2016) Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland Chinaat multiple spatiotemporal scales. J Hydrol 533:152–167. https://doi.org/10.1016/j.jhydrol.2015.12.008
Tangdamrongsub N, Forgotson C, Gangodagamage C, Forgotson J (2021) The analysis of using satellite soil moisture observations for flood detection, evaluating over the Thailand’s Great Flood of 2011. Nat Hazards 108:2879–2904. https://doi.org/10.1007/s11069-021-04804-8
Tapley BD, Bettadpur S, Ries JC, Thompson PF, Watkins MM (2004) GRACE measurements of mass variability in the Earth system. Science 305:503–505. https://doi.org/10.1126/science.1099192
Wahr J, Swenson S, Zlotnicki V, Velicogna I (2004) Time-variable gravity from GRACE: First results. Geophys Res Lett 31:L11501. https://doi.org/10.1029/2004GL019779
Wang J, Chen Y (2021) Using NARX neural network to forecast droughts and floods over Yangtze River Basin. Nat Hazards. https://doi.org/10.1007/s11069-021-04944-x
Wang R, Chen J, Chen X, Wang Y (2017a) Variability of precipitation extremes and dryness/wetness over the southeast coastal region of China, 1960–2014. Int J Climatol 37:4656–4669. https://doi.org/10.1002/joc.5113
Wang Z, Zhong R, Lai C (2017b) Evaluation and hydrologic validation of TMPA satellite precipitation product downstream of the Pearl River Basin, China. Hydrol Process 31:4169–4182. https://doi.org/10.1002/hyp.11350
Wang R, Gentine P, Yin J, Chen L, Chen J, Li L (2021) Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces. Hydrol Earth Syst Sc 25:3805–3818. https://doi.org/10.5194/hess-25-3805-2021
Wiese DN, Landerer FW, Watkins MM (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour Res 52:7490–7502. https://doi.org/10.1002/2016WR019344
Wu X, Wang Z, Guo S, Liao W, Zeng Z, Chen X (2017) Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework: a case study in Dongguan City. China J Hydrol 547:428–442
Wu X, Wang Z, Guo S, Lai C, Chen X (2018a) A simplified approach for flood modeling in urban environments. Hydrol Res 49:1804–1816
Wu X, Guo S, Yin J, Yang G, Zhong Y, Liu D (2018b) On the event-based extreme precipitation across China: time distribution patterns, trends, and return levels. J Hydrol 562:305–317
Xie Z, Huete A, Cleverly J, Phinn S, McDonald-Madden E, Cao Y, Qin F (2019) Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia. Remote Sens Environ 231:111270. https://doi.org/10.1016/j.rse.2019.111270
Xiong J, Wang Z, Lai C, Liao Y, Wu X (2020) Spatiotemporal variability of sunshine duration and influential climatic factors in mainland China during 1959–2017. Inter J Climatol 40:6282–6300. https://doi.org/10.1002/joc.6580
Xiong J, Guo S, Yin J (2021a) Discharge estimation using integrated satellite data and hybrid model in the midstream Yangtze River. Remote Sens 13:2272. https://doi.org/10.3390/rs13122272
Xiong J, Yin J, Guo S, Slater L (2021b) Continuity of terrestrial water storage variability and trends across mainland China monitored by the GRACE and GRACE-Follow on satellites. J Hydrol 599:126308. https://doi.org/10.1016/j.jhydrol.2021.126308
Xiong J, Guo S, Yin J, Gu L, Xiong F (2021c) Using the global hydrodynamic model and grace follow-on data to access the 2020 catastrophic flood in Yangtze River basin. Remote Sens 13:3023. https://doi.org/10.3390/rs13153023
Xiong J, Yin J, Guo S, Gu L, Xiong F, Li N (2021d) Integrated flood potential index for flood monitoring in the GRACE era. J Hydrol 603:127115. https://doi.org/10.1016/j.jhydrol.2021.127115
Yang T, Shao Q, Hao Z, Chen X, Zhang Z, Xu C, Sun L (2010) Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River Basin, China. J Hydrol 380:386–405. https://doi.org/10.1016/j.jhydrol.2009.11.013
Yang P, Zhan C, Xia J, Han J, Hu S (2018) Analysis of the spatiotemporal changes in terrestrial water storage anomaly and impacting factors over the typical mountains in China. Int J Remote Sens 39:505–524. https://doi.org/10.1080/01431161.2017.1388939
Young C, Liu W (2013) Prediction and modelling of rainfall-runoff during typhoon events using a physically-based and artificial neural network hybrid model. Hydrolog Sci J 60:2102–2116
Yue M, Aihui W (2020) A daily 0.25° × 0.25° hydrologically based land surface flux dataset for conterminous China, 1961–2017. J Hydrol 590: 125413. doi:https://doi.org/10.1016/j.jhydrol.2020.125413
Zhang S, Hua D, Meng X, Zhang Y (2011) Climate change and its driving effect on the runoff in the “Three-River Headwaters” region. J Geogr Sci 21:963. https://doi.org/10.1007/s11442-011-0893-y
Zhong Y, Zhong M, Feng W, Zhang Z, Shen Y, Wu D (2018) Groundwater depletion in the West Liaohe River Basin, China and its implications revealed by grace and in situ measurements. Remote Sens 10:493. https://doi.org/10.3390/rs10040493
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The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.
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This work is supported by the Visiting Researcher Fund Program of State Key Laboratory of Water Resources and Hydropower Engineering Science (Wuhan University, 2019SWG03), the National Natural Science Foundation of China (52109019), the Guangdong Basic and Applied Basic Research Foundation (2021A1515010935), and the Science and Technology Planning Project of Guangdong Province in China (2020A0505100009).
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JH X designed the experiment and wrote the manuscript; ZL W, SL G, and XS W supervised the research. JB Y revised the manuscript; J W and CG L performed the experiment; QJ G collected the data.
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Xiong, J., Wang, Z., Guo, S. et al. High effectiveness of GRACE data in daily-scale flood modeling: case study in the Xijiang River Basin, China. Nat Hazards 113, 507–526 (2022). https://doi.org/10.1007/s11069-022-05312-z
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DOI: https://doi.org/10.1007/s11069-022-05312-z