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Data scarcity poses difficulties to rainfall-runoff modelling in a semi/un-gauged river basin. There are certain alternative precipitation data sources, including satellite derived and gauge data derived interpolated products. However, their efficiency in estimating runoff is questioned and proved to be condition specific. In this article, an attempt has been made to compare two mostly used gridded precipitation data products, i.e. TRMM and Aphrodites in predicting runoff for semigauged Kangshabati river basin of tropical part of India during three consecutive years using a simplistic NRCS-CN approach. Considering the purpose of comparing the results in an uncalibrated model setting was used and performance of the outputs of runoff simulations based on both the precipitation datasets were tested using coefficient of determination, RMSE and Nash–Sutcliffe methods. Catchment wise model performance was compared to understand the relationship pattern of the model and input data. The accuracy level of the model outputs for upper, middle and lower catchments using two different types of input precipitation data is distinguishable. Comparing the discharge magnitude as predicted by two simulations using statistical parameters, it is apparent that the accuracy level of TRMM based discharge prediction is more in upper catchment and Aphrodite based discharge prediction is more in lower catchment. In other words, TRMM is more effective than Aphrodite's in recording higher precipitation or high intensity discharge than that of lower precipitation or low intensity discharge and vice versa. [ABSTRACT FROM AUTHOR]

In this paper, six image-based Relative Radiometric Normalization (RRN) techniques were applied to normalize the bi-temporal Landsat 5 TM data-set. RRN techniques do not require any atmospheric and ground information at the time of image acquisition. The target image for the year 2009 was normalized in such a way that it resembled the atmospheric and sensor conditions similar to those under which the reference image of the same season for the year 1990 was acquired. Among the selected methods applied, it was found that the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) method performed better, based on the error statistic. The IR-MAD technique was found to be advantageous as it identified a large set of true time-invariant pixels automatically from the change background using iterative canonical component analysis. The technique also stretches the values of Normalized Difference Vegetation Index and Normalized Difference Water Index and may help to distinguish different vegetation and water bodies better. [ABSTRACT FROM AUTHOR]