AQUA Satellite Data and Imputation of Geopotential Height: A Case Study for Pakistan

In current study an attempt is carried out by filling missing data of geopotiential height over Pakistan and identifying the optimum method for interpolation. In last thirteen years geopotential height values over were missing over Pakistan. These gaps are tried to be filled by interpolation Techniques. The techniques for interpolations included Bilinear interpolations [BI], Nearest Neighbor [NN], Natural [NI] and Inverse distance weighting [IDW]. These imputations were judged on the basis of performance parameters which include Root Mean Square Error [RMSE], Mean Absolute Error [MAE], Correlation Coefficient [Corr] and Coefficient of Determination [R2]. The NN and IDW interpolation Imputations were not precise and accurate. The Natural Neighbors and Bilinear interpolations immaculately fitted to the data set. A good correlation was found for Natural Neighbor interpolation imputations and perfectly fit to the surface of geopotential height. The root mean square error [maximum and minimum] values were ranges from ±5.10 to ±2.28 m respectively. However mean absolute error was near to 1. The validation of imputation revealed that NN interpolation produced more accurate results than BI. It can be concluded that Natural Interpolation was the best suited interpolation technique for filling missing data sets from AQUA satellite for geopotential height.

Accurate Imputation for Relative Humidity over Pakistan Gathered from AQUA Satellite

The relative humidity in the atmosphere captured by AQUA satellite contains missing matrices. In order to fill such missing values four very popular imputation techniques: Bilinear, Inverse Distance Weighting, Natural Neighbor and Nearest Interpolations were tested. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Correlation Coefficient (Corr), were used to check the accuracy of these interpolations. It was found that the Inverse Distance Weighting and Nearest Interpolation were proved not to be suited. Natural interpolation gave accurate results than the aforementioned two interpolations. Missing values of relative humidity were accurately refilled with Bilinear Interpolation. This interpolation produced RMSE of ±0.543 for relative humidity over 100, 150, 200, 250, 300, 400, 500 hPa while for 600, 700, 850 and 925 hPa RMSE remainnear to 1. A perfect fit to the surface and very strong correlation (value near to 0.99) was found between actual and imputed relative humidity data through Bilinear Interpolation. Therefore it was concluded that the Bilinear Interpolation is the most accurate and best imputation for missing values of relative humidity form 100 to 1000 hPa levels.

Variation in Expression of <i>Sub</i>1 Gene and Association with Submergence Stress Related Traits in Advance Rice (<i>Oryza sativa</i>L.) Genotypes as a Tool of Climate Change Adaptation

Floods have now become most detrimental natural catastrophe worldwide due to radical climatic fluxes. Therefore, there is a dire necessity to develop a high yielding rice lines to deal with this scenario. For this purpose, a large scale experiment was conducted including one hundred and fifteen (115) rice genotypes having SUB1 gene imported from International Rice Research Institute (IRRI) Philippines, six local cultivars/approved varieties and three high yielding rice varieties i.e. Sabitri, IR6 and NSICRC222 being used as potential varieties in different countries of Asia as susceptible check and IR64-SUB1 as tolerant check. The genotypic screening was performed using two PCR-based DNA markers i.e. ART5 and SC3. Phenotypic screening was conducted in a natural pond to assess the interaction of SUB1 gene in natural stagnant flood water as well as the suitability of introgression of SUB1 gene into approved varieties and elite rice lines. The genotypes were assessed in terms of plant survival percentage, submergence tolerance index, physical condition, stem elongation, number of grains per panicle, thousand grain weight, grain yields and deviations in these traits after submergence stress. The PCR results suggested that both the primers ART5 and SC3 may be used as potential PCR-based markers for molecular screening of rice genotypes for SUB1 QTL. Furthermore, it confirmed the presence of SUB1 gene in all the lines imported from IRRI, while it was absent in all the local cultivars studied. All the genotypes with submergence tolerant gene (SUB1) showed significantly greater tolerance level in submergence stress of 14 days, as compared to other local cultivars/varieties, authenticating the effectiveness of SUB1QTL in conferring submergence tolerance. Significantly different performances of all the SUB1 genotypes in terms of all the studied traits indicate high Genotypic and Genotypic Environment Interaction (GEI) of SUB1QTL. Employment of SUB1 lines such as R105479:149-18, IR64-SUB1 and Rl05469:81-22-3 in breeding programs for developing flood tolerant rice varieties might further upsurge rice yields in flash flood areas. Correlation analysis revealed that plant survival percentage after submergence, reduced stem elongation during submergence and submergence tolerance index are very important traits for developing submergence tolerant lines.