Numerical simulation of damage–Permeability relationship in brittle geomaterials

Finding a coupling model between a hydraulic parameter such as permeability and a mechanical parameter such as damage is the key element for several recent engineering problems. A review of the technical literature reveals that several mechanical constitutive laws exist which allow determining a damage tensor for a damageable porous material under loading. But the present work develops a method to deduce the permeability change due to the damage propagation.     

Temporal change of glaciers area and geomorphometric parameters in Parbati valley,Himachal Pradesh,India

Temporal change in the glacier coverage is analyzed for the period between 1962 and 2003 in Parbati valley, Himachal Pradesh. It is observed that the total glacier cover has been decreased by 17% ranging between 8 and 100% for individual glacier. The pattern of de-glaciation shows a high degree of shrinkage in outer zone of Parbati valley, while least shrinkage is observed in the inner valley. The present study is conducted to establish relationship between glacio-geomorphic parameters and glaciers shrinkage pattern to predict the future glacier cover in warming scenario. A systematic change is observed for glacio-geomorphic parameters associated with temporal change in glacier cover. It is observed that mean and minimum elevation, slope, relief and duration of insolation have changed substantially. Maximum elevation, plan/profile curvatures and aspect have shown less change from 1962 to 2003. A correlation matrix between glacio-geomorphic parameters for glaciers between 1962 and 2003 shows that the recent glaciers are much more controlled by terrain characteristics than that in the recent past.     

Peculiar motions of galaxy clusters: correlation function approach

The correlation function theory on the basis of prescribed boundary conditions provides a deeper understanding in studying the dynamical parameters of galaxy clusters. The approach approximates that the moderate dense systems discussed by a two point correlation function is helpful for describing the dynamical nature of galaxy clusters. The projected theory of two point correlation function for point mass and extended mass structures can be used an alternative tool in measuring the average peculiar motion and temperature profile of galaxy clusters.     

An Experiment Using the High Resolution Eta and WRF Models to Forecast Heavy Precipitation over India

In the present study using the Weather Research and Forecasting (WRF) and Eta models, recent heavy rainfall events that occurred (i) over parts of Maharastra during 26 to 27 July, 2005, (ii) over coastal Tamilnadu and south coastal Andhra Pradesh during 24 to 28 October, 2005, and (iii) the tropical cyclone of 30 September to 3 October, 2004/Monsoon Depression of 2 to 5 October 2004, that developed during the withdrawal phase of the southwest monsoon season of 2004 have been investigated. Also sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather events. The results show that the WRF model with the microphysical process and cumulus parameterization schemes of Ferrier et al. and Betts-Miller-Janjic was able to capture the heavy rainfall events better than the other schemes. It is also observed that the WRF model was able to predict mesoscale rainfall more realistically in comparison to the Eta model of the same resolution.     

On‐Site Vapor‐Phase Analysis as a Novel Approach for Monitoring Groundwater Wells

The results of comprehensive field testing of on‐site vapor‐phase‐based groundwater monitoring methods are presented to demonstrate their utility as a robust and cost‐effective approach for rapidly obtaining volatile organic compounds (VOCs) concentration data from a monitoring well. These methods—which rely on sensitive, commercially available field equipment to analyze vapor in equilibrium with groundwater—proved easy to implement and can be tailored to site‐specific needs, including multilevel sampling. During field testing, low‐flow groundwater concentrations could be reasonably estimated using submerged passive vapor diffusion samplers or field equilibration of collected groundwater (R² = 0.85 to 0.96). These two methods are not as reliant on in‐well mixing to overcome vertical stratification within wells as simpler headspace methods. The importance of well and aquifer‐specific factors on concentration data (and therefore method selection) is highlighted, including the effect of changing in‐well patterns due to seasonal temperature gradients. Results indicated that vertical stratification was relatively limited within the set of wells included in these studies, resulting in similar performance for short depth‐discrete passive vapor diffusion samplers (constructed from 40‐mL vials) and longer samplers (2.5 to 5 feet in length) designed to cover a larger portion of the screened interval. A year‐long, multi‐event evaluation demonstrated that vapor‐phase‐based monitoring methods are no more variable than conventional groundwater monitoring methods, with both types subject to similar spatial and temporal variability that can be difficult to reduce. Vapor sampling methods represent a promising approach for estimation of groundwater concentrations by reducing the cost liabilities associated with monitoring while providing a more sustainable approach.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Agrobiodiversity of Muda Rice Agroecosystem:A case Study in Largest Granary Area of Malaysia

A survey was carried out at the largest rice cultivation area in Peninsular Malaysia,the Muda rice agroecosystem.The main objective of this study was to document the overall biodiversity associated with this unique agroecosystem by using a combination of sampling techniques in order to record different groups of fauna and flora.The total number of biota recorded and identified from the rice field ecosystem during the study period consisted of 46 species of zooplankton,81 species of aquatic insects,5 species of rodents,7 species of bats,87 species of birds,11 species of fishes and 58 species of weeds.A long-term study should be carried out as more species are expected to be recorded when more of the Muda rice agroecosystem area has been sampled to obtain sufficient information on the Muda rice agrobiodiversity.