Statistics – Computation
Scientific paper
May 1993
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993adspr..13r.149s&link_type=abstract
Advances in Space Research, Volume 13, Issue 5, p. 149-166.
Statistics
Computation
4
Scientific paper
Starting with the water balance equation the various terms of the equation are briefly discussed and the question, how far these parameters can be estimated with the aid of remote sensing data. The difference in use of RS data for the estimation of parameters of hydrological models and the use of such data as model input is discussed. For the modeling of hydrological processes single processes are discussed, i.e. the values of a hydrological variable are determined from RS data of one source only, while in combined processes the values of a desired hydrological variable are determined indirectly with the aid of some other information. Examples for both conditions are given. A major part of the paper is devoted to the question of modeling at different scales. Problems in hydrological modeling at micro-scale, meso-scale and macro-scale are discussed. A model for the micro-scale is presented which uses remote sensing data as well as digital elevation model data for the estimation of model parameters. The structure of the model is such that the hydrologic computations are done for all the Landsat pixels within a catchment area and the flow is routed downhill and eventually down the river by mathematical procedures. The technique is presented for rainfall runoff modeling in the Volme river in Germany. Also for the meso-scale hydrological modeling an approach is presented which uses the principle of overlays and generates aggregated Hydrologically Similar Units, for which the relevant flow computations are carried out. The chapter closes with a few remarks on hydrological macro-scale modeling within the context of coupled Atmospheric General Circulation models with ocean and hydrological models.
A more detailed presentation follows dealing with the question of mathematical structures of hydrological models using remote sensing data either as input or for model parameter estimation. Conventional concepts like lumped vs. distributed models, linear vs. nonlinear models and deterministic vs. stochastic models are discussed with the focus on their suitability for use of RS data. The paper concludes with a discussion of expected future developments, both in the field of hydrological modeling as well as in the field of new remote sensing platforms and sensors.
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