Channel roughness is a sensitive parameter in development of hydraulic model for flood forecasting and flood inundation mapping. The requirement of multiple channel roughness coefficient Mannnig's 'n' values along the river has been spelled out through simulation of floods, using HEC-RAS, for years 1998 and 2003, supported with the photographs of river reaches collected during the field visit of the lower Tapi River. The calibrated model, in terms of channel roughness, has been used to simulate the flood for year 2006 in the river. The performance of the calibrated HEC-RAS based model has been accessed by capturing the flood peaks of observed and simulated floods; and computation of root mean squared error (RMSE) for the intermediated gauging stations on the lower Tapi River.

No abstract

No abstract

Estimates of sediment yield are needed for studies of reservoir sedimentation, river morphology and planning of soil and water conservation measures. The Soil and Water Assessment Tool model, a physically based distributed hydrological model, is utilized for sediment yield estimation in the Burhanpur subbasin measuring an area of 8487 km 2 in Upper Tapi catchment. The basin shows large heterogeneity in terms of hydrological parameters, land use -land cover and soil features. The model has been calibrated and validated using observed run-off and sediment yield data of 12 years at the basin outlet. The average values for Nash-Sutcliffe efficiency (NSE) and RSR for sediment yield are found to be 0.85 and 0.36, respectively, which are within satisfactory limits.

Today convincing digital forgery can be created without master learning of image editing software. These fake pictures over exceptionally quick media may cause extreme results in the public arena. Passive digital image forensic is an area which uncovers these problems. Since JPEG compression deals with 8 × 8 DCT matrix it makes its own fingerprint which can be utilized to distinguish further forgeries in the picture. In this paper, we have proposed a technique which automatically locates forgery in the image based on histogram of DCT coefficient factors, called as factor histogram. When image undergoes aligned double compression this factor histogram shows peak at current quantization step as well as primary quantization step. Our algorithm searches for absence of such double maxima in block-wise factor histogram to identify tampered region. This method can find copy-move, copy-paste as well as pre-processed forgeries such as rotation and scaling.