Prediction models for urban flood evolution for satellite remote sensing

Roderick Lammers, Alan Li, Sreeja Nag, Vinay Ravindra

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Accurate and timely flood forecasts are critical for protecting people and infrastructure in a changing climate. Satellite remote sensing provides the necessary wide area coverage and period revisits to measure episodic heavy precipitation and resultant urban floods. We propose two methods to assimilate satellite-observed precipitation into hydrologic models in real time to update flood forecasts, bypassing two previous barriers in this technology: infrequent satellite overpasses and long model run times. Constellations of small satellites overcomes the first barrier by providing frequent flights over an area of interest; however, these constellations require coordination and planning to capture precipitation data where it is most needed to inform flood forecasts. The primary purpose of this paper is to address the second barrier – that is high computational costs that make it infeasible to run flood forecast models on-board these satellites so that they can re-orient to measure where most needed. We develop a simple regression-based approach and a machine learning framework (Long Short-Term Memory (LSTM) models) to provide reliable flood forecasts using satellite-observed precipitation at a fraction of the computational cost of physics-based hydrologic models. We apply these approaches to a test case for the Atlanta metropolitan region using the Weather Research and Forecasting model hydrologic modeling system (WRF-Hydro) to simulate flooding across the model domain for several precipitation events. We compare the accuracy of our proposed approaches to the WRF-Hydro model using different spatial extents and temporal frequencies of precipitation observations to examine different plausible satellite constellation scenarios. The LSTM approach trades performance accuracy and adaptability for computational efficiency, which can be important in a time and resource constrained scenario. The LSTM model reduces total error up to 38% from an initial flood forecast. Additionally, this approach correctly classified flooding to within one flood magnitude category in ∼90% of cases. These new forecasting algorithms can be used onboard constellations of small satellites to observe ongoing flood events, update short term predictions, and schedule observations to maximize useful measurements and thereby improve flood warning systems for protecting residents and properties.

Original languageEnglish
Article number127175
JournalJournal of Hydrology
StatePublished - Dec 2021
Externally publishedYes


  • Adaptive sensing
  • LSTM
  • Remote sensing
  • Urban flooding
  • WRF-Hydro


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