H54A-01 INVITED
Remote Sensing of Groundwater Storage Changes in Illinois Using GRACE (Gravity Recovery and Climate Experiment)
Recent research has demonstrated that GRACE can be used to estimate total water storage changes (TWSC) in large river basins, evapotranspiration, continental discharges, and snow storage. In this study we propose to estimate groundwater storage change by using remote-sensed GRACE data. Regional groundwater storage changes in Illinois are estimated from monthly GRACE total water storage change (TWSC) data and in situ measurements of soil moisture for the period 2002-2005. Groundwater storage change estimates are compared to those derived from the observed soil moisture and groundwater well level data. The seasonal pattern and amplitude of GRACE-estimated groundwater storage changes track those of the in situ measurements reasonably well, although substantial differences exist in month-to-month variations. The seasonal cycle of GRACE TWSC agrees well with observations (correlation coefficient = 0.83), while the seasonal cycle of GRACE- based estimates of groundwater storage changes beneath 2 meters depth agrees with observations with a correlation coefficient of 0.63. As another independent estimation, the combined atmospheric and terrestrial water balance computation is performed to estimate TWSC. The water-balance estimated TWSC from 1984-2005 agrees well with the in-situ observations in Illinois with a correlation coefficient of 0.69. We conclude that (1) the GRACE-based method of estimating monthly to seasonal groundwater storage changes performs reasonably well at the 200,000 km2 scale of Illinois, and (2) the combined water-balance computation can be used to estimate TWSC at the regional scale larger than Illinois.
H54A-02 INVITED
A First Look at ASCAT Soil Moisture Products
The Advanced Scatterometer (ASCAT) onboard the MetOp series of satellites is the newest addition to a series of
microwave remote sensing instruments proven to be capable to retrieve soil moisture globally and in a highly
efficient and consistent manner. ASCAT is a follow-up to the wind mode of the Active Microwave Instruments (AMI)
on the ERS-1 and ERS-2 platforms. MetOp-A, the first of the three satellites is in orbit since October 2006 and the
first ASCAT data has become available. In terms of design, the ASCAT instrument is very similar to the ERS
scatterometers, offering improved coverage (one extra set of antennae producing a second swath) and spatial
resolution (additional 25 km resolution product for research purposes).We used the new ASCAT data in
combination with a global geophysical parameter database derived from long-term ERS scatterometer time
series to obtain the first ASCAT-based soil moisture products. A preliminary intercomparison study between ERS-
2 and ASCAT acquisitions during the first half of 2007 has also been carried out. Compared to the nominal 50 km
product, we have found the 25 km resolution ASCAT-based soil moisture to display significantly more detail at
acceptable noise levels. Thanks to the common heritage of the ERS and ASCAT instruments, we expect a quick
and efficient integration of the latter sensor into soil moisture retrieval activities. This will secure the possibility for
near-real time applications based on a quasi-consistent and global soil moisture dataset stretching over three
decades.
http:www.ipf.tuwien.ac.at/radar
H54A-03
Land Surface Modeling and Satellite Passive Microwave Imagery: a Comparison of Top Soil Moisture and Surface Temperature Estimates
Improved accuracy in defining initial conditions for fully-coupled numerical weather prediction models (NWP) along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSMs) is expected to lead to improved short-term to long-range weather forecasting capability. Because land surface parameters are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores of these models, such as soil moisture and surface temperature. This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. For the regional subset of Oklahoma, USA, surface soil moisture and surface temperature estimates obtained with a recently improved retrieval algorithm from the Advanced Microwave Scanner Radiometer (AMSR) aboard NASA's Earth Observing System (EOS) Aqua satellite are evaluated against model output of the Community Noah Land Surface Model and Community Land Model (CLM2) operated within the Land Information System (LIS) forced with atmospheric data of a variety of sources, i.e. the NCEP Global Data Assimilation System (GDAS), the European Centre of Medium Range Weather Forecast (ECMWF) and the North American Data Assimilation System (NLDAS). The surface temperature retrievals and LSM output are further evaluated against local measurements from the Mesonet observational grid in Oklahoma.
H54A-04
Remotely Sensed Precipitation Estimation using Multi-Spectral IR and MW
Retrieving more accurate distribution and quantity of precipitation, which are the key parameters for the most of hydrologic applications such as real time precipitation forecasting, severe weather monitoring, water resources management, and flood forecasting, is a major area of emphasis within the hydrologic community. However, accurate high spatial and temporal resolution precipitation (both rainfall and snowfall) estimation is still a challenging problem. Rainfall intensity can be captured through different ground and remote observation sources. But, ground-based traditional techniques such as rain gauge and radar networks have limitations, particularly on spatial coverage. Although, satellite is the only possible source of collecting information with no spatial limitation, precipitation estimates from satellite imagery have greater uncertainties particularly on estimating precipitation intensity. Hence, application of remote sensing data for precipitation estimation, particularly over the remote and mountainous regions, where there is usually heavier precipitation and cannot completely be covered by ground-based rain gauge and radar networks, is a challenging research area. Currently, a number of research efforts are directed to estimate actual precipitation using remotely sensed infrared observations, which identify cloud-top temperature, from geostationary satellites. Using multi-sensor satellite-based observations can provide information from various cloud properties and as a result can improve precipitation estimates. Improving precipitation estimates using combination of satellite-based infrared (IR) with microwave (MW) information will be discussed in this presentation. Distribution and intensity of both rainfall and snowfall are enhanced using cloud-top IR from Geostationary Operational Environmental Satellite (GOES) in conjunction with multi frequency microwave information from Advanced Microwave Sounding Units (AMSU). Preliminary investigation indicates that the higher microwave frequency (89 GHz and 150 GHz) is more sensitive to precipitation. In addition to remotely sensed cloud information some ground surface as well as meteorological measurements, such as topography (DEM), temperature, relative humidity, and wind speed and direction, is also used to enhance snowfall/rainfall detection and estimation.
H54A-05
Surface Energy Balance Methods for Evapotranspiration - Some Enhancements and Applications
Satellite-received radiances and auxiliary ground-based information are routinely used to estimate the evapotranspiration rate (ET, or LE as a latent heat energy flux density) on landscape elements. Many methods compute LE as a residual, computing the terms Rn, G, and H in the full energy-balance equation, S = Rn - G ¬ H - LE, where S is surface (canopy) heat storage (often assumed near zero), Rn is net radiation, G is heat flux into the (soil) surface, and H is the sensible heat flux. Computation of H is prone to errors in obtaining accurate radiometric temperatures, TR, of the surface and in relating TR to the true kinetic temperature of the surface heat source. The Surface Energy BAlance Land (SEBAL) method avoids the offset errors by introducing an assumption of a linear relation of TR to the surface-to-air temperature difference. This assumption, and several others, can introduce distinct errors and operational problems, which will be discussed, along with several improvements under development. The latter include direct regression solutions for LE, correcting for advection of energy and for the lapse rate of the surface (not air) temperature, and the use of auxiliary radiance-based information on vegetation water stress. Also to be discussed are potential applications of enhanced ET methods to estimate hydrologic redistributions (runon, runoff), the consequent spatial patterning of vegetation, and the implications of both for ecological studies (equilibrium canopy development, long-term acclimation of stomatal control) and ecosystem management (estimating forest water stress and its relations to stand density, forest thinning exercises, and hazards of fire and insect outbreaks).
H54A-06
METRIC Estimated ET Evaluation on the Semiarid Southern High Plains
Declining groundwater levels in the Southern High Plains of the United States, and the fact that agriculture in this region uses more than 90% of groundwater withdrawals, combine to increase the demand for efficient agricultural water use. Accurate regional evapotranspiration (ET) maps would provide valuable information on crop water use. In this study, we applied METRIC (Mapping Evapotranspiration at High Resolution using Internalized Calibration), a remote sensing based ET algorithm, and micrometeorological data measured at a grass reference ET weather station maintained by the Texas High Plains Evapotranspiration Network (TXHPET). For this purpose, a Landsat Thematic Mapper image covering a major portion of the Southern High Plains (parts of Texas Panhandle and northeastern New Mexico) was acquired for 23 July 2006 at 11:26 AM CST. Comprehensive ground-truth data were collected to develop a detailed land use map showing major crops grown in the region. Performance of the METRIC model was evaluated using measured ET data on five weighing lysimeters at Bushland, TX [35 Deg. 11' N, 102 Deg. 06' W; 1,170 m elevation MSL] managed by the Conservation and Production Research Laboratory, USDA-ARS. Lysimeter-measured ET rates varied from 2.4 to 7.8 mm/d. Good agreement was found between the remote sensing based ET and measured ET. Comparison of estimated daily mapped ET values with lysimetric measurements had an accuracy within 9% of the measured ET (r2 = 0.89) with a mean square error of 0.9 mm/d. The use of METRIC for advective conditions of the Southern High Plains is promising; however, more evaluation is needed for different agroclimatological conditions.