Remote Sensing For An Improved Geospatial Flash Flood Susceptibility Modeling Over An Arid Environment

Khadra, Mohamed Shawky Mohamed Ali

Remote Sensing For An Improved Geospatial Flash Flood Susceptibility Modeling Over An Arid Environment

dc.contributor.advisor	El-Sheimy, Naser
dc.contributor.advisor	Hassan, Quazi K.
dc.contributor.author	Khadra, Mohamed Shawky Mohamed Ali
dc.contributor.committeemember	Zhang, Yun
dc.contributor.committeemember	Kattan, Lina
dc.contributor.committeemember	Wang, Xin
dc.contributor.committeemember	Charif, Omar
dc.date	2020-06
dc.date.accessioned	2020-04-02T18:38:29Z
dc.date.available	2020-04-02T18:38:29Z
dc.date.issued	2020-03-31
dc.description.abstract	Flash floods are the foremost cause of irretrievable environmental damage in the arid Arabian Peninsula. The better understanding of the geomorphologic, topographic, climatic, and hydrologic characteristics of a selected watershed, and determining their geospatial relationships with respect to the flood extent are the core steps for mitigating and minimizing negative impacts of flooding. Therefore, the overall aim of the current study was to employ different remote sensing datasets in predicting prone areas to flash floods in the 'wilayats' (i.e., cities) of El Hamra, Bahla, and Nizwa, Ad Dakhiliyah Governate, the Sultanate of Oman as an example of the arid areas. Precipitation is a crucial variable for studying various climate-related research such as flash flood monitoring and prediction. The performance of five global satellite precipitation estimates (GSPEs) was evaluated using the available sub-daily and daily ground rainfall records. Generally, the five sub-daily and daily GSPEs showed good performance compared to the in-situ measurements. Moreover, statistical error models were employed to quantify the uncertainties in the daily GSPEs. Accurate digital terrain model (DTM) and channel network/orders with fine spatial details are mandatory for flood extent modeling. The DTM and its derived channel network have been employed successfully in many studies to extract various geomorphometric, topographic and hydrologic attributes. Therefore, a new pixel-based method was developed to quantify the horizontal accuracy of channel networks/orders-based three global DEMs using those extracted from LiDAR datasets as references. The vertical accuracy of global DEMs were also evaluated utilizing reference LiDAR elevation data. PALSAR DTM (12.5 m) and its derived channel network/orders were found to be the optimal candidates to derive geospatial layers required for flood susceptibility modeling. Flood susceptibility models were developed to define the likelihood of future flash flooding in the study area. The spatial relationships between flood triggering factors and flood inventory map were quantified. The integrated bivariate and multivariate statistical methods-based flood susceptibility models provided precise maps to predict future flood-prone areas under a rainfall intensity close to that which prevailed during the past flood event, at both high- and low- lands. The developed flood susceptibility models can contribute to mitigating the negative impacts of flash floods.	en_US
dc.identifier.citation	Khadra, K. S. M. A. (2020). Remote Sensing For An Improved Geospatial Flash Flood Susceptibility Modeling Over An Arid Environment (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/37663
dc.identifier.uri	http://hdl.handle.net/1880/111773
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Flash Flood, Satellite Precipitation Estimates, DEM, Susceptibility Modeling, Arid Areas.	en_US
dc.subject.classification	Geology	en_US
dc.subject.classification	Environmental Sciences	en_US
dc.subject.classification	Engineering	en_US
dc.title	Remote Sensing For An Improved Geospatial Flash Flood Susceptibility Modeling Over An Arid Environment	en_US
dc.type	doctoral thesis	en_US
thesis.degree.discipline	Engineering – Geomatics	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Doctor of Philosophy (PhD)	en_US
ucalgary.item.requestcopy	true	en_US