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two model components continuously update the states of the basin. Values of soil moisture and temperature 
are passed from one to the other model to be used as initial conditions for the upcoming storm or interstorm 
interval. 
The three other major model components involve the determination of controlling parameters (from 
remotely-sensed data, ground data or estimated quantities) that will drive the two hydrologic components. 
Some parameters are spatially distributed but change relatively slowly with time (i.e., seasonally or less 
frequently) such as soil and vegetation parameters. Algorithms for determining these parameters, which 
control processes in both the storm and interstorm components, are included in the component labelled 
"distributed basin characteristics" (box 3). The interstorm model component requires input parameters that 
are both spatially and temporally variable such as surface temperature, moisture and parameters that control 
the atmospheric forcings on the surface fluxes. Algorithms for deriving those quantities from remotely sensed 
data are included in the surface characteristics and interstorm inputs components (boxes 3 and 4). The last 
model component (box 5) involves algorithms for providing the spatially and temporally variable precipitation 
input for the storm-event model, from a combination of space and earth-based remote sensing. 
This strategy will enable the investigation of watershed space-time thresholds and dominant hydrologic 
subprocesses, thereby greatly enhancing our conceptual understanding and modeling ability of hydrologic 
events. It will also provide the key to conducting more efficient data collection and deriving the maximum 
possible benefit from remotely-sensed data. 
/A 
Soil paramalara, Vegetation, Topography 
// 
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TIME 
Figure 1. Graphic illustration of a strategy designed to develop distributed hydrologic models 
driven by parameters estimated from remotely-sensed and ground-based data. Taken from 
Research Proposal to NASA Eos A.O. No. OSSA-1/88, Utilization of Eos Data in Quantifying 
the Processes Controlling the Hydrologic Cycle in Arid/Semiarid Regions. Principal 
Investigators: Dr. Y. Kerr (LERTS, Toulouse, France) and Dr. S. Sorooshian (Univ. of Ariz., 
Tucson, Arizona).