Integrating Satellite Imagery and Coupled Land-Atmospheric Modeling for the Monitoring of Hydrological Processes in the UAE and the Region

  • Youssef R. Wehbe

Student thesis: Master's Thesis

Abstract

Reliable and timely monitoring and forecasting of extreme weather events is key, particularly in arid regions, where the spatiotemporal distribution of rainfall is highly sporadic and stochastic by nature. The United Arab Emirates (UAE) has been subject to recurring flash flood events reaping countless casualties and millions of dollars in damage with the absence of flash flood guidance systems over the nation. Similar to other dryland environments lacking adequate hydrologic monitoring, the UAE presents a unique area to evaluate satellite remote sensing products and the erratic variability of precipitation in diverse environments. The methodology developed in this thesis relies on both modeling and remote sensing-based assessments of key hydrological parameters over the Arabian Peninsula, with a main focus on the UAE. In-situ measurements from 39 groundwater observation wells (Fujairah, northeastern UAE) and 53 weather stations across the country were used for validation, along with soil moisture, precipitation, and cloud fraction retrievals from ongoing satellite missions. Within the UAE, statistical analyses indicate that the Version 7 of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA V7) products record the highest overall agreement with the observational network, and areas that receive high rainfall within the vegetated highlands (e.g., >250 m), provide the most promise for incorporating satellite precipitation into hydrologic monitoring, modeling, or water resource management. The low agreement between precipitation and both surface and groundwater components (PCC
Date of AwardMay 2017
Original languageAmerican English
SupervisorMarouane Temimi (Supervisor)

Keywords

  • Satellite imagery
  • Atmospheric Modeling
  • Hydrological processes
  • Extreme Weather
  • Flash floods
  • Remote sensors
  • Atmospheric Processes
  • Water resource management.

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