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Priya Narayanan Ashoke Basistha Sumana Sarkar Sachdeva Kamna 《Comptes Rendus Geoscience》2013,345(1):22-27
Spatial and temporal variability of rainfall over different seasons influence physical, social and economic parameters. Pre-monsoon (March, April and May – MAM) rainfall over the country is highly variable. Since heat lows and convective rainfall in MAM have an impact on the intensity of the ensuing monsoons, hence the pre-monsoon period was chosen for the study. The pre-whitened Mann Kendall test was used to explore presence of rainfall trend during MAM. The results indicate presence of significant (at 10% level) increasing trend in two stations (Ajmer, Bikaner). The practical significance of the change in rainfall was also explored as percentage changes over long term mean, using Theil and Sen's median slope estimator. Forecast using univariate ARIMA model for pre-monsoon months indicates that there is a significant rise in the pre-monsoon rainfall over the northwest part of the country. 相似文献
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Chitra Arora B. Prasad Kumar Indu Jain Ashoke Bhar A. C. Narayana 《Marine Geodesy》2013,36(2-3):261-281
The development of a theoretical model for estimating bottom boundary layer characteristics in the Hooghly estuary, located in the east coast of India, under combined effects of waves and currents is reported. Three numerical models, viz a depth averaged hydrodynamic model, SWAN wave model, and bottom boundary layer model, were integrated. In the bottom boundary layer parameters, maximum bottom stress, effective friction factor, and near-bed velocity both during ebb and flood phases of the tidal forcing are investigated and validated for the Haldia channel. The close match seen from results signifies applicability of this model for entire Hooghly basin. 相似文献
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Madhuri?Kumari Chander?Kumar?SinghEmail author Oinam?Bakimchandra Ashoke?Basistha 《Theoretical and Applied Climatology》2017,130(1-2):51-58
In mountainous region with heterogeneous topography, the geostatistical modeling of the rainfall using global data set may not confirm to the intrinsic hypothesis of stationarity. This study was focused on improving the precision of the interpolated rainfall maps by spatial stratification in complex terrain. Predictions of the normal annual rainfall data were carried out by ordinary kriging, universal kriging, and co-kriging, using 80-point observations in the Indian Himalayas extending over an area of 53,484 km2. A two-step spatial clustering approach is proposed. In the first step, the study area was delineated into two regions namely lowland and upland based on the elevation derived from the digital elevation model. The delineation was based on the natural break classification method. In the next step, the rainfall data was clustered into two groups based on its spatial location in lowland or upland. The terrain ruggedness index (TRI) was incorporated as a co-variable in co-kriging interpolation algorithm. The precision of the kriged and co-kriged maps was assessed by two accuracy measures, root mean square error and Chatfield’s percent better. It was observed that the stratification of rainfall data resulted in 5–20 % of increase in the performance efficiency of interpolation methods. Co-kriging outperformed the kriging models at annual and seasonal scale. The result illustrates that the stratification of the study area improves the stationarity characteristic of the point data, thus enhancing the precision of the interpolated rainfall maps derived using geostatistical methods. 相似文献
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