SatVITS-Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

Omer Burstein, Tamir Grodek, Yehouda Enzel, David Helman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS-Flood). SatVITS-Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time-series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing-season integral anomaly (TFA-GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil-adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High-Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS-Flood in three regions on different continents with 40-year-long, systematic, reliable gauge data. Our results indicate that SatVITS-Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS-Flood proved useful for detecting historical floods and may provide valuable long-term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Original languageEnglish
Article numbere2023WR035164
JournalWater Resources Research
Volume59
Issue number9
DOIs
StatePublished - Sep 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Authors.

Funding

The authors thank Israel Water Authority for sharing the hydrological data for Nahal Zin and USGS for making their data open and accessible, from which the hydrological data for Mojave River was downloaded. This study is part of the M.Sc. conducted by O.B. and supervised by D.H. The authors thank the Ring Foundation and the Israel Women League Foundation for partly supporting O.B. through his studies. The authors also thank the Associate Editor and two anonymous reviewers for insightful comments that helped improve the quality of this manuscript. The authors thank Israel Water Authority for sharing the hydrological data for Nahal Zin and USGS for making their data open and accessible, from which the hydrological data for Mojave River was downloaded. This study is part of the M.Sc. conducted by O.B. and supervised by D.H. The authors thank the Ring Foundation and the Israel Women League Foundation for partly supporting O.B. through his studies. The authors also thank the Associate Editor and two anonymous reviewers for insightful comments that helped improve the quality of this manuscript.

FundersFunder number
Israel Water Authority
Israel Women League Foundation
Ring Foundation
U.S. Geological Survey

    Keywords

    • BFAST
    • NDVI
    • flood
    • hyperarid
    • satellite
    • vegetation index

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