Recent advances in remote sensing in hydrology

1. Recent advances in remote sensing in hydrology

2. Introduction • Remote sensing has held a great deal of promise for hydrology, mainly because of the potential to observe areas and entire river basins rather than merely points. • Most of the advances in using remote sensing for hydrology have come from new areas of hydrologic analysis; areas where existing methods were unsatisfactory or limiting and areas where sufficient data were sparse or nonexistent. • In this review, the various applications of remote sensing to hydrology are treated as they are used to measure the different hydrologic variables or processes related to the water and energy cycle; i.e. precipitation, snow, evaporation, etc. Each of these hydrologic variables or processes is 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 is concerned

3. Remote Sensing • Remote sensing uses measurements of the electromagnetic spectrum to characterize the landscape, or infer properties of it, or in some cases, actually measure hydrologic state variables. • Different sensors can provide unique information about properties of the surface or shallow layers of the Earth. For example, Measurements of the reflected solar radiation give information on albedo, thermal sensors measure surface temperature, and microwave sensors measure the dielectric properties and hence, the moisture content, of surface soil or of snow .

4. Precipitation • Hydrologists have increasingly turned to remote sensing as a possible means for quantifying the precipitation input, especially in areas where there are few surface gauges. • Direct measurement of rainfall from satellites for operational purposes has not been generally feasible. • Satellites provide frequent observations, the characteristics of potentially precipitating clouds and the rates of changes in cloud area and shape can be observed.

5. Precipitation (Contd..) • The availability of meteorological and Landsat satellite data has produced a number of techniques for inferring precipitation from the visible and/or infrared (VIS/IR) imagery of clouds. • The GOES Precipitation Index [ Arkin, 1979], derived from thresholding the infrared brightness temperature of cloud tops has been used to study the distribution of tropical rainfall. • The university of Bristol has led the development of a cloud indexing approach. • A life-history approach, developed by Scofield and Oliver, [1977] considers the rates of change in individual convective clouds or clusters of convective clouds. This approach is the basis of a flash flood system that assimilates GOES data with ground based and atmospheric data to forecast precipitation amounts for use in a flood forecast model [ Clark and Morris, 1986].

6. Snow Hydrology • Snow is a form of precipitation; • Nearly all regions of the electromagnetic spectrum provide useful information about the snow pack. • The water equivalent of snow can be measured from low elevation aircraft carrying sensitive gamma radiation detectors [ Carroll and Vadnais, 1980] • Snow can readily be identified and mapped with the visible bands of satellite imagery because of its high reflectance in comparison to non-snow areas.

7. Snow Hydrology (Contd..) • Microwave remote sensing offers great promise for future applications to snow hydrology. • Active microwave remote sensing also has the potential to provide important information about the snow pack and at very high resolution with Synthetic Aperture Radar (SAR), [ Stiles et al, 1981, and Rott, 1986]. • The Snowmelt Runoff Model (SRM) [ Martinec et al, 1983] was specifically developed for using remote sensing of snow cover by elevation zone as the primary input variable.

8. Runoff and Hydrologic Modeling • Runoff cannot be directly measured by remote sensing techniques. (1) determining watershed geometry, drainage network, and other map-type information for distributed hydrologic models and for empirical flood peak, annual runoff or low flow equations; and (2) providing input data such as soil moisture or delineated land use classes that are used to define runoff coefficients. • Drainage basin areas and the stream network are easily obtained from good imagery, even in remote regions. • Empirical flood formulae are useful for making quick estimates of peak flow when there is very little other information available. • Landsat data have been used to improve empirical regression equations of various runoff characteristics.