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Open AccessArticle

Integrating Water Observation from Space Product and Time-Series Flow Data for Modeling Spatio-Temporal Flood Inundation Dynamics

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Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
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College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
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CSIRO Land & Water, Canberra 2601, Australia
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2535; https://doi.org/10.3390/rs11212535
Received: 14 September 2019 / Revised: 9 October 2019 / Accepted: 27 October 2019 / Published: 29 October 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Periodic inundation of floodplains and wetlands is critical for the well being of ecosystems. This study proposes a simple but efficient model that integrates time series daily flow data and the Landsat-derived Water Observation from Space (WOfS) product to model the spatio-temporal flood inundation dynamics of the Murray-Darling Basin. A zone-gauge framework is adopted in order to reduce the hydrologic complexity of the large river basin. Under this framework, flood frequency analysis was conducted at each gauge station to identify historical peak flows and their annual exceedance probabilities. The results were then linked with the WOfS dataset through date to model the inundation probability in each zone. Inundation frequency was derived by simply overlaying the yearly inundation extent from 1988 to 2015 and counting the inundation times. Both the resultant inundation frequency map and inundation probability map are of ecological significance for the survival and prosperity of riparian ecosystems. The assumptions of the model were validated carefully to enhance its theoretical basis. The WOfS dataset was also compared with another independent water observation dataset to cross-validate its reliability. It is hoped that with the development of more and more global high-resolution surface water datasets, this study could inspire more studies that integrate surface water datasets with hydrological observations for flood inundation modeling. View Full-Text
Keywords: surface water; flood inundation; water observation product; time-series flow; Global Surface Water Dataset surface water; flood inundation; water observation product; time-series flow; Global Surface Water Dataset
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MDPI and ACS Style

Huang, C.; Chen, Y.; Zhang, S.; Li, L.; Shui, J.; Liu, Q. Integrating Water Observation from Space Product and Time-Series Flow Data for Modeling Spatio-Temporal Flood Inundation Dynamics. Remote Sens. 2019, 11, 2535.

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