Gamage, Nilantha, Agrawal, R, Smakhtin, V and Perera, B. J. C (2011) An Artificial Neural Network Model for Simulating Streamflow Using Remote Sensing Data. In: Proceedings of the 34th World Congress of the International Association for Hydro-Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water Engineering. Valentine, E. M, Apelt, C. J, Ball, J. E, Chanson, H, Cox, R, Ettema, R, Kuczera, G, Lambert, M, Melville, B. W and Sargison, J. E, eds. Engineers Australia, Barton, A.C.T., pp. 1371-1378.

Abstract

Streamflow data play a key role in water resources management; however these data are not often available. One of the alternatives then is to use the rainfall-runoff models, but in most cases the required inputs such as rainfall and evapotranspiration are not available to use these models. Freely available remote sensing data, which represent features of the above input variables, can be used to generate streamflow data as an alternative. This project uses daily Moderate Resolution Imaging Spectrometer (MODIS) data to generate daily streamflow for the Thomson catchment in Victoria in Australia through an Artificial Neural Network (ANN) model. Daily MODIS reflectance and radiance data were first converted to Normalized Difference Vegetation Index (NDVI) and cloud top temperature (CTT) respectively. Several ANN models with one hidden layer were then developed using combinations of present day NDVI and CTT variables, and several daily lags of these variables. Results showed that a seasonally stratified model with five inputs had given predictions comparable to observed streamflow. Five inputs were present day NDVI and CTT, and three past days of CTT

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