Tracking the weight of Hurricane Harvey’s stormwater using GPS data

GPS can track terrestrial water storage following extreme precipitation events, with potential to improve flood planning. On 26 August 2017, Hurricane Harvey struck the Gulf Coast as a category four cyclone depositing ~95 km3 of water, making it the wettest cyclone in U.S. history. Water left in Harvey’s wake should cause elastic loading and subsidence of Earth’s crust, and uplift as it drains into the ocean and evaporates. To track daily changes of transient water storage, we use Global Positioning System (GPS) measurements, finding a clear migration of subsidence (up to 21 mm) and horizontal motion (up to 4 mm) across the Gulf Coast, followed by gradual uplift over a 5-week period. Inversion of these data shows that a third of Harvey’s total stormwater was captured on land (25.7 ± 3.0 km3), indicating that the rest drained rapidly into the ocean at a rate of 8.2 km3/day, with the remaining stored water gradually lost over the following 5 weeks at ~1 km3/day, primarily by evapotranspiration. These results indicate that GPS networks can remotely track the spatial extent and daily evolution of terrestrial water storage following transient, extreme precipitation events, with implications for improving operational flood forecasts and understanding the response of drainage systems to large influxes of water.

[1]  J. Zumberge,et al.  Precise point positioning for the efficient and robust analysis of GPS data from large networks , 1997 .

[2]  J. R.,et al.  Quantitative analysis , 1892, Nature.

[3]  Clifford H. Thurber,et al.  Parameter estimation and inverse problems , 2005 .

[4]  Enrico Serpelloni,et al.  Vertical GPS ground motion rates in the Euro‐Mediterranean region: New evidence of velocity gradients at different spatial scales along the Nubia‐Eurasia plate boundary , 2013 .

[5]  Ahmad Al Bitar,et al.  SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia , 2015 .

[6]  Robert F. Cahalan,et al.  Sampling Errors in the Estimation of Empirical Orthogonal Functions , 1982 .

[7]  R. Dill,et al.  Numerical simulations of global‐scale high‐resolution hydrological crustal deformations , 2013 .

[8]  M. Rodell,et al.  Water in the Balance , 2013, Science.

[9]  Yehuda Bock,et al.  Error analysis of continuous GPS position time series , 2004 .

[10]  Yehuda Bock,et al.  Spatiotemporal filtering using principal component analysis and Karhunen-Loeve expansion approaches for regional GPS network analysis , 2006 .

[11]  D. Baldocchi,et al.  Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .

[12]  D. L. Anderson,et al.  Preliminary reference earth model , 1981 .

[13]  George Alan Blackburn,et al.  A real-time hydrological model for flood prediction using GIS and the WWW , 2003, Comput. Environ. Urban Syst..

[14]  E. Ivins,et al.  ISSM-SESAW v1.0: mesh-based computation of gravitationally consistent sea-level and geodetic signatures caused by cryosphere and climate driven mass change , 2015 .

[15]  Brian F. Thomas,et al.  River basin flood potential inferred using GRACE gravity observations at several months lead time , 2014 .

[16]  Friederike E. L. Otto,et al.  Attribution of extreme rainfall from Hurricane Harvey, August 2017 , 2017 .

[17]  J. Famiglietti,et al.  Global terrestrial water storage capacity and flood potential using GRACE , 2009 .

[18]  W. Farrell.  Deformation of the Earth by surface loads , 1972 .

[19]  O. Francis,et al.  Modelling the global ocean tides: modern insights from FES2004 , 2006 .

[20]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[21]  P. Younger.  Water in the balance , 2013 .

[22]  W. Wagner,et al.  Improving runoff prediction through the assimilation of the ASCAT soil moisture product , 2010 .

[23]  H. Schuh,et al.  Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium‐Range Weather Forecasts operational analysis data , 2006 .

[24]  A. Moore,et al.  Sustained Water Loss in California's Mountain Ranges During Severe Drought From 2012 to 2015 Inferred From GPS , 2017 .

[25]  L. Rivera,et al.  LoadDef: A Python‐Based Toolkit to Model Elastic Deformation Caused by Surface Mass Loading on Spherically Symmetric Bodies , 2019, Earth and space science.

[26]  A. Pearce,et al.  Storm runoff generation in humid headwater catchments 1 , 1986 .

[27]  J. Kouba.  Implementation and testing of the gridded Vienna Mapping Function 1 (VMF1) , 2008 .

[28]  Yuanxi Yang,et al.  Spatiotemporal filtering for regional GPS network in China using independent component analysis , 2017, Journal of Geodesy.

[29]  Hydrogeology and Simulation of Ground-Water Flow and Land-Surface Subsidence in the Chicot and Evangeline Aquifers, Houston Area, Texas , 2002 .

[30]  Kuolin Hsu,et al.  Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood , 2015 .

[31]  Felix W. Landerer,et al.  GPS as an independent measurement to estimate terrestrial water storage variations in Washington and Oregon , 2015 .

[32]  Pedro M. Valero-Mora,et al.  Determining the Number of Factors to Retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis , 2007 .

[33]  Yehuda Bock,et al.  Southern California permanent GPS geodetic array: Spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake , 1997 .

[34]  David H. Bailey,et al.  Algorithms and applications , 1988 .

[35]  Wujiao Dai,et al.  Extracting seasonal deformations of the Nepal Himalaya region from vertical GPS position time series using Independent Component Analysis , 2017 .

[36]  Duncan Carr Agnew,et al.  Ongoing drought-induced uplift in the western United States , 2014, Science.

[37]  Eric S. Blake,et al.  National Hurricane Center Tropical Cyclone Report: Hurricane Harvey (17 August - 1 September 2017) , 2018 .

[38]  K. N. Dollman,et al.  - 1 , 1743 .

[39]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[40]  Quoc V. Le,et al.  ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning , 2011, NIPS.

[41]  K. Eckhardt.  How to construct recursive digital filters for baseflow separation , 2005 .

[42]  Jeffrey T. Freymueller,et al.  Seasonal and long-term vertical deformation in the Nepal Himalaya constrained by GPS and GRACE measurements , 2012 .

[43]  K. Beven,et al.  Reproducing an extreme flood with uncertain post-event information , 2016 .