HYDROLOGIC SYSTEMS: MONITORING, SENSING & MODELING

HYDROLOGIC SYSTEMS: MONITORING, SENSING & MODELING

​Project activities are focused on the observation and modeling of water resources in Saudi Arabia as well as characterizing the water use and productivity of agricultural systems, with a focus on improving food and water security. The projects make use of modeling, field work and greenhouse based experiments to better understand and characterize hydrologic systems. Results from these studies have been presented at international conferences and in journal publications.

PROJECT 1

WATER ACCOUNTING IN IRRIGATED REGIONS OF SAUDI ARABIA​

PI: Matthew McCabe
Accurate accounting of agricultural water use at high spatial and temporal scales is of considerable importance to better secure and manage water resources in arid and semi arid regions. This is especially true in Saudi Arabia, where agriculture consumes more than 80% of all freshwater and where much of this comes from unsustainable groundwater extractions. The project aims to exploit diverse satellite remote sensing datasets and combine these with sophisticated energy budget modeling frameworks to quantify the total crop water loss across a range of irrigated districts. Remote sensing data sets are being compiled to monitor rainfall, evaporation and soil moisture in these regions. At the same time, numerical weather prediction models are being employed to provide retrospective assessments of meteorological conditions, providing a capability for high-resolution hydrological modeling. To this end, a land surface hydrological model is employed to partition the hydrological variables for estimating water budget fluxes and storage changes as a result of abstracted groundwater in the irrigated agricultural sectors. Since direct measurements of groundwater extraction are mostly non-existent in many regions of the world, a novel approach is being employed to determine water use. Estimated crop water losses, calculated via knowledge of evapotranspiration, are combined with meteorological and other key hydrological inputs to infer the discharge rates of groundwater (since this is the predominant source of water for agriculture in Saudi Arabia). The developed water accounting framework will provide precise assessment of agricultural water consumption as a function of unmonitored groundwater discharge for recent years (2011-2015) and deliver an efficient and sustainable agricultural focused water resource management technique for Saudi Arabia and other arid and semi-arid regions of the world.​

PROJECT 2

HIGH RESOLUTION MAPPING AND MODELING CROP HEALTH AND FUNCTION IN SAUDI ARABIA​

PI: Matthew McCabe
The reflected satellite signal in the visible to shortwave infrared region of the electromagnetic spectrum has great utility for monitoring vegetation dynamics and plant physiological condition across a range of spatial and temporal scales. We are interested in retrieving metrics of plant health and condition, since these variables provide indirect information on water, energy and carbon exchange processes. Approaches for estimating leaf and canopy biophysical properties can be used to track changes in plant health, phenology, nutrient availability, water content, and photosynthetic function. However, developing versatile retrieval methodologies is challenging due to limitations in the radiometric information content and confounding influences from the atmosphere, canopy and soil. This project aims to advance the use of multi-, super- and hyper-spectral remote sensing data for retrieving key descriptors of vegetation function and health. An integrated radiative transfer modeling scheme is being developed for automated processing of at-sensor observations from various satellite systems and conversion of the satellite signal into physically meaningful vegetation characteristics such as leaf area index (LAI) and leaf chlorophyll (Chl). Novel regularization strategies are being applied to increase robustness and accuracy of retrieved properties and more reliably separate contributions from the soil, leaf and canopy. The retrieval scheme will be applied and validated over targeted agricultural sites within the Kingdom of Saudi Arabia, providing spatially distributed time-series information on vegetation density, vitality and functioning, which is useful for agricultural management purposes. We are also engaged in studies on retrieval relationship to plant functional traits, validation against in-situ measurements and integration into land surface models as further detailed below. A key aspect of this work involves the hyperspectral sensing and plant physiological monitoring of crops under salinity and fertilizer stress. This work is directly related to better understanding the impact of improved water or partially desalinated waters to agricultural systems, using remote sensing approaches to directly infer the crop condition.​​

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