Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R _H in a river basin at monthly scale. Uncertainty in the future projections of R _H is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R _H are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978–2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978–2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R _H is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.     

Downscaling of precipitation for climate change scenarios: A support vector machine approach

The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.     

Solitary sound pulses via electron-vortex distribution in partially ionized plasmas

It is shown that nonlinear propagation of sound waves in partially ionized collisional plasmas is governed by a modified Korteweg-de Vries (mKdV) equation when the electron distribution function deviates from the Boltzmann distribution. The mKdV equation admits a sharply localized solitary sound pulse, the profile and width of which are different from that which involves the Boltzmann electron number density response. Since the electron-vortex distribution induced solitary sound pulses are more pronounced, it can easily be identified in partially ionized plasmas.     

Spherical harmonic synthesis and least squares computations in satellite gravity gradiometry

The computational requirements in the simulations of geopotential estimation from satellite gravity gradiometry are discussed. Fast algorithms for spherical harmonic synthesis and least squares accumulation on a vectorizing supercomputers are presented. Using these methods, in a test case estimation of 2595 coefficients of a degree and order 50 gravity field, sustained program execution speeds of 275 Mflops (87 % peak machine speed) on a single processor of a CRAY Y-MP were achieved, with spherical harmonics computation accounting for less than 1 % of total cost. From the results, it appears that brute-force estimation of a degree and order 180 field would require 537 Million Words of memory and 85 hours of CPU time, assuming mission duration of 1 month, and execution speed of 1 Gflops. Both memory size and execution speed requirements are within the capabilities of modern multi-processor supercomputers.     

OBSERVATIONS ON ESTUARINE FLUID MUD ENTRAINMENT

I. INTRODUCTIONPrediction of mud bed erosion by forcing due to tidal currents usually requires a numerical solution of the advection--dispersion equation for sediment mass transport. Key role is of course played inthis by the bottom boundary conditions defining erosion and deposition fluxes. The issue of erosion isbriefly considered here. noting that it is customary to calculate the rate of erosion as a function of thebed shear stress in excess of the erosion shear strength of the bed (Me…     

Far infrared photometry of planets: Saturn and Venus: Volume 38, Number 3 (1979), in the article,by R. Courtin,P. Léna,M. De Muizon,D. Rouan,C. Nicollier,and J. Wijnbergen,pp. 411–419

The global distribution of existing lunar topography suffers from a lack of measurements of far-side radii because of the sparsity of data types in the nonequatorial regions. This paper presents determinations of far-side lunar radii based on the reduction of photogrammetric measurements derived from selected Apollo 16 trans-Earth phase photographs. The regions covered in this analysis lie west of Mare Moscoviense between longitudes 90 and 130°E and latitudes 10 and 60°N. The determinations are made using control points appearing on both NASA topographic orthophoto maps and the Apollo 16 photographs. The estimated lunar radii are referred to these control points and determined with a relative accuracy of 500 m. The new lunar radii are used to generate a topographic map covering the area investigated. The map shows that, with the given spatial density of surface festures measured, basin-sized features can be resolved. In particular, the far-side craters Fabry, Riemann, and Szilard comprise a topographically depressed region about 500 km in diameter centered at 120°E and 38.5°N. The floor of this basin is 2.4 to 3.4 km below the reference sphere of 1738.0 km and 4.8 to 5.8 km below the northern rim of the basin. A comparison of the depth of the unfilled basin with the depths of maria-filled front-side basins leads to the conclusion that basalt fill of the near-side maria may be 2 km deep. The topographic map shows good correlation with geologic provinces of young plains and cratered terra in the far-side highland region investigated. Lack of correlation between sampled values of the state-of-the-art 16th-order and 16th-degree harmonic gravity field model and corresponding topographical values leads to the conclusion that the far-side region investigated is isostatically compensated.     

Lemniscate representation for inversion of gravity and magnetic data through nonlocal optimization

A method of representing surfaces and volumes by a set of geometric points and a small set of auxiliary parameters, based on generalization of Bernoulli’s notion of lemniscates is introduced. It provides for easy generation and modification of surfaces and volumes, which could be connected, disjoint or even with very irregular boundaries. This allows solving geophysical inversion problems, without constraining the anomalous volumes to some ideal or simple forms. This is illustrated by the example of joint inversion of gravity and magnetic data sets attributable to two-dimensional anomalous bodies. A nonlocal optimization algorithm calledsegmented Hamming scan is used for inversion. Comparison with nonlinear least-squares algorithm shows the advantages of the chosen approach. The concepts ofdesideratal andprocedural detours are illustrated.     

Evaluation of Ground Water Potential Zones Using Remote Sensing Data -A Case Study of Gaimukh Watershed, Bhandara District, Maharashtra

In the present study efforts have been made to evaluate ground water potential zones for ground water targeting using IRS-IC LISS-II1 geo-coded data on 1:50,000 scale. The drainage, geology, geomorpholgoy and lineament information has been generated and integrated to evaluate hydro-geomorphological characteristics of the Gairnukh watershed, Bhandara district, Maharashtra for delineation of ground water potential zones. The analysis reveals that the deep valley fills with thick alluvium have excellent, shallow valley tills and deeply weathered pediplains with thin alluvium have very good and moderately weathered pediplains in the geological formations of Tirodi Gneiss and Sausar Groups have god ground water potential and these units are highly favourable for ground water exploration and development. Shallow weathered pediments in geological formations of Tirodi Gnesis and Sausar Groups are marked under moderate ground water potential zone. Shallow weathered pediplains in geological formations of Tiridi Gneiss and Sausor Groups are grouped under limited ground water potential category, except along the fractures/lineaments. Structural hills in geological formations of Tirodi Gneiss and Sausar Groups have poor ground water prospects. Inselbergs and Linear ridges in geological formations of Tirodi Gneiss are grouped under very poor ground water prospects zone. The good inter-relationship was found among the geological units, geomorphological units, lineament density, hydro-geomorphological zones and ground water yield data.