Remote sensing applications to hydrology: runoff

Abstract

Abstract Since no remote sensing (RS) devices have been developed allowing the measurement of river runoff directly, information from RS sources is used to compute runoff values indirectly. This is done with the aid of hydrological models, where RS data are used in two different ways: (1) in the form of model input data; and (2) for model parameter estimation. Three types of models are discussed, the parameters of which are estimated—at least partially—with the aid of RS information. A mathematical model is demonstrated, which reconstructs monthly river runoff volumes on the basis of IR data obtained by the Meteosat geostationary satellite. The second model computes flood hydrographs with the aid of a distributed system rainfall/runoff model. A major model parameter, viz. the soil water storage capacity, which varies in space, is determined on the basis of Landsat imagery and digital soil maps. The third model discussed is a water balance model which computes all relevant variables of the water balance equation including runoff on a daily basis. Parameters used in the model components for interception, evapotranspiration and soil storage are estimated with the aid of RS information originating from Landsat and NOAA data. Examples of the performance of all three models are presented. Input to hydrological models computing runoff is usually either rainfall or snowmelt or both. An example for model input estimation on the basis of satellite data is presented as well as the use of ground-based weather radar rainfall measurements for real time flood forecasting. An example of snowmelt runoff modelling is mentioned, followed by a brief discussion of future perspectives of runoff computations with the aid of RS data.