Remote sensing and associated image-processing technology provide access to spatial and temporal hydrologie information from watershed to global scales. Advances in sensor and imaging technology are increasing the capability of remote sensing for specific hydrologie application.

There are two general areas where remote sensing can be used in hydrologie modeling: (l) determining watershed geometry, drainage network, and other maptype information for distributed hydrologie models and for empirical flood peak, annual runoff, or low-flow equations; and (2) providing input data such as snow cover or precipitation, diagnostic variables such as soil moisture or surface temperature, or model parameters such as delineated land-use classes used to define runoff coefficients. In this review, the latter is addressed. The various uses of remote sensing to provide input data and diagnostic variables for hydrologie models are treated as they are used to measure the different hydrologie variables or processes, e.g., precipitation, snow, or evaporation. Each of these hydrologie variables or processes are discussed individually with the emphasis on how remote sensing is being used, and not on the technology as far as sensor details and specific instruments are concerned. More details can be found in two recent books on this general subject (Engman and Gurney, 1982; Schultz and Engman, 2000).

Handbook of Weather, Climate, and Water: Atmospheric Chemistry, Hydrology, and Societal Impacts, Edited by Thomas D. Potter and Bradley R. Colman. ISBN 0-471-21489-2 © 2003 John Wiley & Sons, Inc.

Finally, the current developments and hydrologic applications of integrated geographical information systems (GIS) technology are presented. Management and efficient utilization of large spatial data volumes is going to be one of the major challenges of the coming decades. GIS have the capability to efficiently store, manipulate, retrieve, and analyze spatially referenced data. This is the primary reason why GIS are becoming popular among the hydrological community to develop new types of hydrological models and to modify existing models to incorporate widely available spatial data.


Recognizing the practical limitations of rain gages for measuring spatially averaged rainfall over large areas and inaccessible areas, hydrologists have increasingly turned to remote sensing as a means for quantifying the precipitation input, especially in areas where there are few surface gages. Because the fundamental approach to measuring rainfall and snow are different with respect to remote sensing, snow is discussed separately.

Direct measurement of rainfall from satellites for operational purposes has not been generally feasible because the presence of clouds prevents observation of the precipitation directly with visible, near-infrared and thermal-infrared sensors. However, improved analysis of rainfall can be achieved by combining satellite and conventional gage data. Satellite data are most useful in providing information on the spatial distribution of potential rain-producing clouds, and gage data are most useful for accurate point measurements. Although ground-based radar, which is a remote-sensing technique, has advanced to an operational stage for locating regions of heavy rain and for estimating rainfall rates, it will not be discussed in this chapter.

Useful data can be derived from satellites used primarily for meteorological purposes, including polar orbiters such as the NOAA-N series and the Defense Meteorological Satellite Program, and from geostationary satellites such as GOES (Geostationary Orbiting Environmental Satellite), GMS, and Meteosat. However, their visible and infrared images can provide information only about the cloud tops rather than cloud bases or interiors. Since these satellites provide frequent observations (even at night with thermal sensors), the characteristics of potentially precipitating clouds and the rates of changes in cloud area and shape can be observed. From these observations, estimates of rainfall can be made that relate cloud characteristics to instantaneous rainfall rates and cumulative rainfall over time. For example, Strubing and Schultz (1983) have developed a runoff regression model that is based on Barrett's (1970) indexing technique. The cloud area and temperature are the satellite variables used to develop a temperature-weighted cloud cover index. This index is then transformed linearly to mean monthly runoff. Rott et al. (1986) also developed a daily runoff model using Meteosat data for a cloud index. Schultz (1994) has demonstrated the use of the infrared channel from Meteosat to estimate monthly rainfall volumes using a modified Arkin approach (Papadakis et al., 1992). The monthly rainfall data were transformed into monthly runoff volumes for the 16,000 km2 Tano basin in West Africa using a model based on a series of nonlinear reservoirs. The results were reasonably good and certainly adequate for water resources planning. For the practicing hydrologist, satellite rainfall methods are most valuable when there are no or very few surface gages for measuring rainfall.

Tsonis et al. (1996) investigated the ability of visible and infrared satellite data to produce rainfall estimates for input to the National Weather Service river forecast model that had been calibrated with rain gage data. They found good correlations with gage data for areas over 10,000km2. In a companion study, Guetter et al. (1996), using the satellite-derived rainfall estimates produced streamflow and soil moisture estimates using the river forecast model. They concluded that flow simulation accuracy is sensitive to basin scale with better results being produced from larger basins. Derived soil moisture estimates were similar to those simulated with gaged data for the surface layer but lower for the deep soil moisture.

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