Flood mapping and soil moisture retrieval for improved water management


CALL: 2012

DOMAIN: SR - Environmental and Earth Sciences







START: 2012-07-01

END: 2014-04-30

WEBSITE: https://www.list.lu/

Submitted Abstract

Flood prediction systems are of key importance for properly managing the event and organizing rescue operations. Unfortunately, the models which are used make errors with respect to timing, flood extent or stage height. In this spin-off project we will investigate how remote sensing observations of soil moisture and flood extent can be jointly assimilated into flood prediction systems. For this, microwave remote sensing holds a large potential, or as stated by prof. Wolfgang Wagner from TU Vienna (see http://www.esa.int/esaEO/SEMMP9BE8JG_index_0.html) “… SAR data … can be used in two important ways for flood monitoring. Firstly, the data can be used to continuously monitor how much water is stored in the soil as this determines the amount of runoff resulting from rain and secondly, by observing inundated areas during a flood because radar can penetrate through clouds and even rain.” HYDRASENS (the mother project) indeed demonstrated the interesting complementary of using soil moisture and flood extent observations in these models. In this project, it will be shown that the joint assimilation of both data sets in a coupled hydrologic-hydraulic model greatly benefits flood predictions.The overall goals of this spin-off project are 1) to explore new strategies to extract hydrology-related information from microwave remote sensing and 2) to demonstrate the merit of jointly assimilating soil moisture and flood extent information into coupled hydrologic-hydraulic models. This project will thus focus on the remote sensing of the two most important variables that govern the hydrologic and hydraulic models: soil moisture and flood extent or stage height. The outcome of this project will demonstrate that operational water management would greatly benefit from updating the models using both types of remotely sensed information simultaneously.Soil moisture retrieval from SAR has been hampered by the infeasibility of accurately characterizing soil roughness. In the mother project, a methodology was developed that circumvents the need for in situ soil roughness measurements. In this project, this technique will be tested upon its robustness using SAR imagery and, given the similarities, will be extended to radiometer data (more specifically SMOS data). Assimilating SMOS data will have the advantage that soil moisture information can be assimilated with a high temporal frequency.Flood area/edge height mapping from SAR imagery is characterized by uncertainty, and different mapping techniques result in as many different flood maps. This project will focus on the uncertainty inherent to remotely sensed flood maps and techniques will be developed that (1) describe this uncertainty and (2) apply these uncertainty maps within a data assimilation framework. As preliminary results already demonstrated that including uncertainty in flood maps improves the calibration of hydraulic models, it is hypothesized that similar uncertain maps will also be more beneficial than binary maps when used in a data assimilation framework.Finally, the merit of jointly assimilated remotely sensed soil moisture and flood extent into a coupled hydrologic-hydraulic model will be demonstrated. Therefore, an existing data assimilation framework will be used, and if necessary adapted according to new type of data generated within the project. Through this exercise, the potential added value of new and enhanced data sets such as soil moisture estimates retrieved from SMOS data, as well as spatially distributed flood extent and water stage data originating from very high resolution SAR data will be investigated.Although several datasets will be used to test the newly developed retrieval schemes, one unique data set will be used that allows the integration of all research. In January 2011, a high magnitude flood in the Alzette River was observed from nine different remot

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