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A new framework for monitoring flood inundation using readily available satellite data
Author(s) -
Parinussa Robert M.,
Lakshmi Venkat,
Johnson Fiona M.,
Sharma Ashish
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2016gl068192
Subject(s) - flood myth , environmental science , flood forecasting , warning system , flooding (psychology) , flood warning , precipitation , satellite , early warning system , water content , topographic wetness index , natural disaster , antecedent moisture , structural basin , hydrology (agriculture) , drainage basin , meteorology , remote sensing , geology , geography , cartography , computer science , runoff curve number , psychotherapist , aerospace engineering , archaeology , engineering , psychology , telecommunications , paleontology , geotechnical engineering , digital elevation model
Floods are deadly natural disasters that have large social and economic impact. Their impact can be reduced through near real‐time warning systems utilizing information from satellite remote sensing for flood tracking and forecasting. In this study we formulate that differences in day and night land surface temperature (ΔLST) are a skillful predictor for inundation and can serve parallel to soil moisture in warning systems. Satellite measurements of ΔLST and soil moisture revealed distinct spatial patterns for the extreme hydrological conditions that Australia has encountered since 2002. A significant flood revealed large negative ΔLST anomalies whereas droughts corresponded to positive anomalies. ΔLST and soil moisture showed distinct behavior prior to flooding as anomalies displayed gradual build up, suggesting signals could be valuable in flood warning systems. Strong agreement was found between ΔLST, antecedent precipitation index, and soil moisture anomalies over Australia and the Murray Darling Basin. This indicates their skills to represent wetness state, an important input additional to precipitation in flood warning systems.