Integration of satellite imagery, ground-based measurements and numerical models for the monitoring of hydrological processes over the UAE

Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE), characterized by complex land-atmosphere dynamics and inadequate hydrological monitoring networks. The proposed work is divided into three work packages aiming to (i) improve the representation of hydrologic and (ii) atmospheric processes in the Weather Research and Forecasting (WRF) model, and (iii) develop a multi-source merged precipitation product over the UAE. The methodology incorporates numerical modeling, remote sensing, and both ground-based and in-situ airborne measurements of key hydro-meteorological variables. The WRF model, along with its enhanced hydrological component (WRF-Hydro), are fully-coupled in a nested configuration over the UAE and compared to standalone WRF simulations during an extreme weather event. In addition to the physics-based WRF model assessments, two multivariate regressive approaches, namely, Geographically Weighted Regression (GWR) and Artificial Neural Networks (ANNs), are employed to improve the accuracy of the latest GPM IMERG V6B locally over the UAE. The datasets used include daily observations from a network of 65 rain gauges, 4 ground-based weather radars, topography, and soil moisture retrievals from ongoing satellite missions.

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