Full text: Mesures physiques et signatures en télédétection

INTEGRATION OF REMOTE SENSING AND HYDROLOGIC MODELING 
THROUGH MULTI-DISCIPLINARY SEMIARID FIELD CAMPAIGNS: 
MONSOON’90, WALNUT GULCH’92, AND SALSA-MEX 
M.S. MORAN 1 , D.C. GOODRICH 2 and W.P. KUSTAS 3 
1 USDA-ARS U.S. Water Conservation Laboratory, 4331 E. Broadway, Phoenix, Arizona 85040 USA 
2 USDA-ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, Arizona 85719 USA 
3 USDA-ARS Hydrology Laboratory, Bldg. 007, BARC-West, Beltsville, Maryland 20705 USA 
ABSTRACT: 
A research and modeling strategy is presented for development of distributed hydrologic models driven by 
a combination of remotely-sensed and ground-based data. In support of this strategy, two experiments — 
Monsoon’90 and Walnut Gulch’92 - have been conducted in a semiarid rangeland southeast of Tucson, 
Arizona, and a third experiment — SALSA-MEX — has been proposed. Results from the Monsoon’90 
experiment have substantially advanced our understanding of the hydrologic and atmospheric fluxes in an arid 
environment and provided insight into the use of remote sensing data for hydrologic modeling. The Walnut 
Gulch’92 Experiment [designed as a follow-up to Monsoon’90] addressed the seasonal hydrologic dynamics 
of the region and the potential of combined optical-microwave remote sensing for hydrologic applications. 
The Semi-Arid Land-Surface-Atmospheric Mountain Experiment (SALSA-MEX) [proposed for the late 
1990s] will combine measurements and modeling to study hydrologic processes influenced by surrounding 
mountains, such as enhanced precipitation, snowmelt and recharge to ground water aquifers. The results from 
these experiments, along with the extensive experimental data bases, should aid the research community in 
large-scale modeling of mass and energy exchanges across the soil-plant-atmosphere interface. 
KEY WORDS: Hydrology, Modeling, Remote Sensing, Energy Flux, Semiarid, Watershed, Water Balance 
1 - INTRODUCTION 
A major objective of hydrology is to accurately forecast the effects of climatic variations on watershed 
behavior over a range of scales. Existing hydrologic models do not perform this task well, primarily due to 
1) the difficulty and expense of obtaining adequate information regarding watershed parameters, states and 
fluxes, and 2) our lack of understanding of the critical surface/atmosphere processes. These problems have 
served to bias the modeling approach towards simplistic solutions and utilization of easily available 
information. Further, it has retarded research aimed at uncovering the appropriate hydrologic principles and 
physical laws that operate at different basin space-time scales. 
The next decade (when Eos, AWIPS and NEXRAD 1 remotely sensed data bases become available) will 
initiate a revolution in the science of watershed hydrology. For the first time, it should be possible to utilize 
an approach to hydrologic modeling that reflects the temporal and spatial distribution of the various 
components involved. Such an approach requires the development of a theoretical framework that identifies 
the factors which control and dominate the hydrologic cycle as a function of basin and climatic scales. 
Figure 1 presents an overview of a research and modeling strategy designed to develop distributed 
hydrologic models driven by parameters estimated from a combination of remotely-sensed and ground-based 
data (Kerr and Sorooshian, 1990). Collectively, these models will monitor and describe the water and energy 
balance for a given basin or region continuously as a function of time. Because different hydrologic 
processes (with significantly different temporal dynamics) control the water and energy balance during the 
storm events and between storm events, we have distinguished between "storm event" (box 1) and "interstorm 
event" (box 2) hydrologic model components. As natural conditions toggle between these two models, these 
1 Eos refers to Earth Observing System; AWIPS refers to Advanced Weather Interactive Processing 
System; NEXRAD refers to NEXt generation RADar.
	        
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