Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-521-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-521-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data
Jianxin Zhang
School of National Safety and Emergency Management, Beijing Normal
University, 100875 Beijing, China
School of Systems Science, Beijing Normal University, 100875 Beijing,
China
Kai Liu
CORRESPONDING AUTHOR
School of National Safety and Emergency Management, Beijing Normal
University, 100875 Beijing, China
Ming Wang
School of National Safety and Emergency Management, Beijing Normal
University, 100875 Beijing, China
Related authors
No articles found.
Jinqi Wang, Hao Fang, Kai Liu, Yi Yue, Ming Wang, Bohao Li, and Xiaoyi Miao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3834, https://doi.org/10.5194/egusphere-2025-3834, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study assesses the future trends of rainfall-induced landslides across China by integrating a national landslide inventory with high-resolution precipitation data. Our findings reveal increasing susceptibility in China under climate change, highlighting the need for targeted disaster prevention strategies. The results can help improve disaster risk management and policy planning in the context of future climate scenarios.
Yuting Zhang, Kai Liu, Xiaoyong Ni, Ming Wang, Jianchun Zheng, Mengting Liu, and Dapeng Yu
Nat. Hazards Earth Syst. Sci., 24, 63–77, https://doi.org/10.5194/nhess-24-63-2024, https://doi.org/10.5194/nhess-24-63-2024, 2024
Short summary
Short summary
This article is aimed at developing a method to quantify the influence of inclement weather on the accessibility of emergency medical services (EMSs) in Beijing, China, and identifying the vulnerable areas that could not get timely EMSs under inclement weather. We found that inclement weather could reduce the accessibility of EMSs by up to 40%. Furthermore, towns with lower baseline EMSs accessibility are more vulnerable when inclement weather occurs.
Di Wang, Ming Wang, Kai Liu, and Jun Xie
Nat. Hazards Earth Syst. Sci., 23, 1409–1423, https://doi.org/10.5194/nhess-23-1409-2023, https://doi.org/10.5194/nhess-23-1409-2023, 2023
Short summary
Short summary
The short–medium-term intervention effect on the post-earthquake area was analysed by simulations in different scenarios. The sediment transport patterns varied in different sub-regions, and the relative effectiveness in different scenarios changed over time with a general downward trend, where the steady stage implicated the scenario with more facilities performing better in controlling sediment output. Therefore, the simulation methods could support optimal rehabilitation strategies.
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022, https://doi.org/10.5194/essd-14-3273-2022, 2022
Short summary
Short summary
We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to the interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing data sets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature data set with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
Weihua Zhu, Kai Liu, Ming Wang, Philip J. Ward, and Elco E. Koks
Nat. Hazards Earth Syst. Sci., 22, 1519–1540, https://doi.org/10.5194/nhess-22-1519-2022, https://doi.org/10.5194/nhess-22-1519-2022, 2022
Short summary
Short summary
We present a simulation framework to analyse the system vulnerability and risk of the Chinese railway system to floods. To do so, we develop a method for generating flood events at both the national and river basin scale. Results show flood system vulnerability and risk of the railway system are spatially heterogeneous. The event-based approach shows how we can identify critical hotspots, taking the first steps in developing climate-resilient infrastructure.
Weihua Zhu, Kai Liu, Ming Wang, Sadhana Nirandjan, and Elco Koks
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-277, https://doi.org/10.5194/nhess-2021-277, 2021
Manuscript not accepted for further review
Short summary
Short summary
We use multi-source empirical damage data to generate vulnerability curves and assess the risk of transportation infrastructure to rainfall-induced hazards. The results show large variations in the shape of the vulnerability curves and risk of railway infrastructure in China across the different regions. The usage of multi-source empirical data offer opportunities to perform risk assessments that include spatial detail among regions.
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-267, https://doi.org/10.5194/essd-2021-267, 2021
Manuscript not accepted for further review
Short summary
Short summary
We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing datasets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature dataset with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
Cited articles
Aggarwal, C. C.: Outlier ensembles: position paper, ACM SIGKDD Explorations
Newsletter, 14, 49–58, 2013.
Anderson, B., Mackintosh, A., Stumm, D., George, L., Kerr, T.,
Winter-Billington, A., and Fitzsimons, S.: Climate sensitivity of a
high-precipitation glacier in New Zealand, J. Glaciol., 56,
114–128, 2010.
Bergmann-Wolf, I., Forootan, E., Klemann, V., Kusche, J., and Dobslaw, H.:
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 GeoForschungsZentrum GFZ, 62 pp., https://doi.org/10.2312/GFZ.b103-14091, 2015.
Brakenridge, G. R.: Global Active Archive of Large Flood Events. Dartmouth
Flood Observatory, University of Colorado, USA, http://floodobservatory.colorado.edu/Archives/, last access: 1 January 2022.
Cazenave, A. and Chen, J.: Time-variable gravity from space and present-day
mass redistribution in theEarth system, Earth Planet. Sc. Lett.,
298, 263–274, 2010.
Chandola, V., Banerjee, A., and Kumar, V.: Anomaly detection: A survey, ACM
Comput. Surv., 41, 1–58, 2009.
Clark, E. V. and Zipper, C. E.: Vegetation influences near-surface
hydrological characteristics on a surface coal mine in eastern USA, Catena,
139, 241–249, 2016.
de Bruijn, J. A., de Moel, H., Jongman, B., de Ruiter, M. C., Wagemaker, J.,
and Aerts, J. C.: A global database of historic and real-time flood events
based on social media, Sci. Data, 6, 1–12, 2019.
Dill, R.: Hydrological model LSDM for operational Earth rotation and gravity field variations, Scientific Technical Report STR; 08/09, Potsdam: Deutsches GeoForschungsZentrum GFZ, 35 pp.,
https://doi.org/10.2312/GFZ.b103-08095, 2008.
Fischer, S., Schumann, A., and Bühler, P.: A statistics-based automated
flood event separation, J. Hydrol., 10, 100070, https://doi.org/10.1016/j.hydroa.2020.100070, 2021.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, 2017.
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.
Guha-Sapir, D., Below, R., and Hoyois, Ph.: EM-DAT: The CRED/OFDA International Disaster Database [data set], Université Catholique de Louvain, Brussels, Belgium, https://www.emdat.be, last access: 6 November 2021.
Gupta, D. and Dhanya, C.: The potential of GRACE in assessing the flood
potential of Peninsular Indian River basins, Int. J. Remote
Sens., 41, 9009–9038, 2020.
Hagen, E., Shroder Jr., J., Lu, X., and Teufert, J. F.: Reverse engineered
flood hazard mapping in Afghanistan: A parsimonious flood map model for
developing countries, Quatern. Int., 226, 82–91, 2010.
Hostache, R., Chini, M., Giustarini, L., Neal, J., Kavetski, D., Wood, M.,
Corato, G., Pelich, R. M., and Matgen, P.: Near-real-time assimilation of
SAR-derived flood maps for improving flood forecasts, Water Resour.
Res., 54, 5516–5535, 2018.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Huggel, C., Raissig, A., Rohrer, M., Romero, G., Diaz, A., and Salzmann, N.: How useful and reliable are disaster databases in the context of climate and global change? A comparative case study analysis in Peru, Nat. Hazards Earth Syst. Sci., 15, 475–485, https://doi.org/10.5194/nhess-15-475-2015, 2015.
Idowu, D. and Zhou, W.: 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, 2019.
Kron, W., Steuer, M., Löw, P., and Wirtz, A.: How to deal properly with a natural catastrophe database – analysis of flood losses, Nat. Hazards Earth Syst. Sci., 12, 535–550, https://doi.org/10.5194/nhess-12-535-2012, 2012.
Kussul, N., Shelestov, A., and Skakun, S.: Flood monitoring from SAR data,
in: Use of satellite and in-situ data to improve sustainability, Springer,
19–29, https://doi.org/10.1007/978-90-481-9618-0_3, 2011.
Kvas, A., Behzadpour, S., Ellmer, M., Klinger, B., Strasser, S., Zehentner,
N., and Mayer-Gürr, T.: ITSG-Grace2018: Overview and evaluation of a new
GRACE-only gravity field time series, J. Geophys. Res.-Sol. Ea., 124, 9332–9344, 2019.
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, 2013.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from
spaceborne elevation data, Eos T. Am. Geophys. Un., 89,
93–94, 2008.
Manavalan, R.: SAR image analysis techniques for flood area
mapping-literature survey, Earth Sci. Inform., 10, 1–14, 2017.
Mayer-Gürr, T., Behzadpour, S., Kvas, A., Ellmer, M., Klinger, B.,
Strasser, S., and Zehentner, N.: ITSG-Grace2018 – Monthly, Daily and Static
Gravity Field Solutions from GRACE, GFZ Data Services [data set], https://doi.org/10.5880/ICGEM.2018.003, 2018.
Molodtsova, T., Molodtsov, S., Kirilenko, A., Zhang, X., and VanLooy, J.: Evaluating flood potential with GRACE in the United States, Nat. Hazards Earth Syst. Sci., 16, 1011–1018, https://doi.org/10.5194/nhess-16-1011-2016, 2016.
Moriyama, K., Sasaki, D., and Ono, Y.: Comparison of global databases for
disaster loss and damage data, J. Disaster Res., 13, 1007–1014,
2018.
Myhre, G., Alterskjær, K., Stjern, C. W., Hodnebrog, Ø., Marelle, L.,
Samset, B. H., Sillmann, J., Schaller, N., Fischer, E., and Schulz, M.:
Frequency of extreme precipitation increases extensively with event rareness
under global warming, Sci. Rep., 9, 1–10, 2019.
Nandargi, S. and Dhar, O.: Extreme rainfall events over the Himalayas
between 1871 and 2007, Hydrolog. Sci. J., 56, 930–945, 2011.
Pendergrass, A. G.: What precipitation is extreme?, Science, 360, 1072–1073,
2018.
Rättich, M., Martinis, S., and Wieland, M.: Automatic flood duration
estimation based on multi-sensor satellite data, Remote Sens., 12, 643, https://doi.org/10.3390/rs12040643,
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, 2014.
Robert, C., William, C., and Irma, T.: STL: A seasonal-trend decomposition
procedure based on loess, J. Off. Stat., 6, 3–73, 1990.
Rosner, B.: Percentage points for a generalized ESD many-outlier procedure,
Technometrics, 25, 165–172, 1983.
Saghafian, B., Golian, S., and Ghasemi, A.: Flood frequency analysis based
on simulated peak discharges, Nat. Hazards, 71, 403–417, 2014.
Schinko, T., Mechler, R., and Hochrainer-Stigler, S.: A methodological
framework to operationalize climate risk management: managing sovereign
climate-related extreme event risk in Austria, Mitig. Adapt.
Strat. Gl., 22, 1063–1086, 2017.
Shi, X., Chen, J., Gu, L., Xu, C.-Y., Chen, H., and Zhang, L.: Impacts and
socioeconomic exposures of global extreme precipitation events in 1.5 and
2.0 ∘C warmer climates, Sci. Total Environ., 766,
142665, https://doi.org/10.1016/j.scitotenv.2020.142665, 2021.
Swiss Re: Sigma: Insurance research,
http://www.swissre.com/sigma/, last access: 18 June 2022.
Tellman, B., Sullivan, J., Kuhn, C., Kettner, A., Doyle, C., Brakenridge,
G., Erickson, T., and Slayback, D.: Satellite imaging reveals increased
proportion of population exposed to floods, Nature, 596, 80–86, 2021.
Tong, X., Luo, X., Liu, S., Xie, H., Chao, W., Liu, S., Liu, S., Makhinov,
A., Makhinova, A., and Jiang, Y.: An approach for flood monitoring by the
combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery,
ISPRS J. Photogramm., 136, 144–153, 2018.
Vallis, O., Hochenbaum, J., and Kejariwal, A.: A Novel Technique for Long-Term Anomaly Detection in the Cloud, 6th USENIX workshop on hot topics in cloud computing (HotCloud 14), 15, 2014.
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the Earth's
gravity field: Hydrological and oceanic effects and their possible detection
using GRACE, J. Geophys. Res.-Sol. Ea., 103, 30205–30229,
1998.
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013.
Wu, H., Kimball, J. S., Mantua, N., and Stanford, J.: Automated upscaling of
river networks for macroscale hydrological modeling, Water Resour.
Res., 47, W03517, https://doi.org/10.1029/2009WR008871,
2011.
Wu, H., Adler, R. F., Hong, Y., Tian, Y., and Policelli, F.: Evaluation of
global flood detection using satellite-based rainfall and a hydrologic
model, J. Hydrometeorol., 13, 1268–1284, 2012a.
Wu, H., Kimball, J. S., Li, H., Huang, M., Leung, L. R., and Adler, R. F.: A
new global river network database for macroscale hydrologic modeling, Water
Resour. Res., 48, W09701, https://doi.org/10.1029/2012WR012313, 2012b.
Wu, H., Adler, R. F., Tian, Y., Huffman, G. J., Li, H., and Wang, J.:
Real-time global flood estimation using satellite-based precipitation and a
coupled land surface and routing model, Water Resour. Res., 50,
2693–2717, 2014.
Xiong, J., Wang, Z., Guo, S., Wu, X., Yin, J., Wang, J., Lai, C., and Gong,
Q.: High effectiveness of GRACE data in daily-scale flood modeling: case
study in the Xijiang River Basin, China, Nat. Hazards, 113, 507–526, https://doi.org/10.1007/s11069-022-05312-z, 2022.
Yang, Y., Lin, P., Fisher, C. K., Turmon, M., Hobbs, J., Emery, C. M.,
Reager, J. T., David, C. H., Lu, H., and Yang, K.: Enhancing SWOT discharge
assimilation through spatiotemporal correlations, Remote Sens.
Environ., 234, 111450, https://doi.org/10.1016/j.rse.2019.111450, 2019.
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C.
H., Lu, H., Yang, K., and Hong, Y.: Global Reach-Level 3-Hourly River Flood
Reanalysis (1980–2019), B. Am. Meteorol. Soc.,
102, E2086–E2105, 2021.
Zhang, J., Liu, K., and Wang, M.: Flood detection using GRACE Terrestrial
Water Storage and Extreme Precipitation (1.0.0), Zenodo [code],
https://doi.org/10.5281/zenodo.6831105, 2022a.
Zhang, J., Liu, K., and Wang, M.: Flood Detection Using GRACE Terrestrial
Water Storage and Extreme Precipitation (1.0.0), Zenodo [data set],
https://doi.org/10.5281/zenodo.6831384, 2022b.
Short summary
This study successfully extracted global flood days based on gravity satellite and precipitation data between 60° S and 60° N from 1 April 2002 to 31 August 2016. Our flood days data performed well compared with current available observations. This provides an important data foundation for analyzing the spatiotemporal distribution of large-scale floods and exploring the impact of ocean–atmosphere oscillations on floods in different regions.
This study successfully extracted global flood days based on gravity satellite and precipitation...
Altmetrics
Final-revised paper
Preprint