Vulnerability and adaptability of wheat production in different climatic zones of Pakistan under climate change scenarios

Ten wheat production sites of Pakistan were categorized into four climatic zones i.e. arid, semi-arid, sub-humid and humid to explore the vulnerability of wheat production in these zones to climate change using CSM-Cropsim-CERES-Wheat model. The analysis was based on multi-year (1971–2000) crop model simulation runs using daily weather series under scenarios of increased temperature and atmospheric carbon dioxide concentration (CO₂) along with two scenarios of water management. Apart from this, sowing date as an adaptation option to offset the likely impacts of climate change was also considered. Increase in temperature resulted in yield declines in arid, semi-arid and sub-humid zone. But the humid zone followed a positive trend of gain in yield with rise in temperature up to 4°C. Within a water regime, increase in CO₂ concentration from 375 to 550 and 700 ppm will exert positive effect on gain in wheat yield but this positive effect is significantly variable in different climatic zones under rainfed conditions than the full irrigation. The highest response was shown by arid zone followed by semi-arid, sub-humid and humid zones. But if the current baseline water regimes (i.e. full irrigation in arid and semi-arid zones and rainfed in sub-humid and humid zones) persist in future, the sub-humid zone will be most benefited in terms of significantly higher percent gain in yield by increasing CO₂ level, mainly because of its rainfed water regime. Within a CO₂ level the changes in water supply from rainfed to full irrigation shows an intense degree of responsiveness in terms of yield gain at 375 ppm CO₂ level compared to 550 and 700 ppm. Arid and semi-arid zones were more responsive compared to sub-humid and humid zones. Rise in temperature reduced the length of crop life cycle in all areas, though at an accelerated rate in the humid zone. These results revealed that the climatic zones have shown a variable intensity of vulnerability to different scenarios of climate change and water management due to their inherent specific and spatial climatic features. In order to cope with the negative effects of climate change, alteration in sowing date towards cooler months will be an appropriate response by the farmers.     

SHRIMP zircon dating and Sm/Nd isotopic investigations of Neoproterozoic granitoids, Eastern Desert, Egypt

There is an increasing evidence for the involvement of pre-Neoproterozoic zircons in the Arabian–Nubian Shield, a Neoproterozoic crustal tract that is generally regarded to be juvenile. The source and significance of these xenocrystic zircons are not clear. In an effort to better understand this problem, older and younger granitoids from the Egyptian basement complex were analyzed for chemical composition, SHRIMP U–Pb zircon ages, and Sm–Nd isotopic compositions. Geochemically, the older granitoids are metaluminous and exhibit characteristics of I-type granites and most likely formed in a convergent margin (arc) tectonic environment. On the other hand, the younger granites are peraluminous and exhibit the characteristics of A-type granites; these are post-collisional granites. The U–Pb SHRIMP dating of zircons revealed the ages of magmatic crystallization as well as the presence of slightly older, presumably inherited zircon grains. The age determined for the older granodiorite is 652.5 ± 2.6 Ma, whereas the younger granitoids are 595–605 Ma. Xenocrystic zircons are found in most of the younger granitoid samples; the xenocrystic grains are all Neoproterozoic, but fall into three age ranges that correspond to the ages of other Eastern Desert igneous rocks, viz. 710–690, 675–650 and 635–610 Ma. The analyzed granitoids have (+3.8 to +6.5) and crystallization ages, which confirm previous indications that the Arabian–Nubian Shield is juvenile Neoproterozoic crust. These results nevertheless indicate that older Neoproterozoic crust contributed to the formation of especially the younger granite magmas.     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

Automatic identification of fake patterns caused by short-width wavelets in seismic data

Despite the popularity of using the Haar wavelet filter in many applications, it sometimes introduces fake patterns into the multi resolution analysis (MRA) of seismic data. In this work, we compared different wavelet filters to demonstrate that these patterns are fake and not part of the original waveforms and to show that they are a result of using the Haar wavelet filter as a short-width wavelet. To achieve this, many seismic waveforms from two different sources: the Egyptian National Seismic Network (ENSN) and the High Sensitivity Seismograph Network Japan (Hi-net) are used with different wavelet filters. We propose an algorithm based on an autoregressive (AR) model to detect these patterns automatically and fully.     

Time series analysis of daily rainfall and streamflow in a volcanic dike-intruded aquifer system, O��ahu, Hawai��i, USA

A conceptual model of groundwater occurrence was developed for a dike-intruded aquifer system in M??kaha valley, O??ahu, Hawai??i, USA, and used to explain the impacts of water resource development on groundwater levels and streamflow. Time-series analyses were applied to two subsets of daily rainfall, total flow, and baseflow??from the third (1946?C1968) and fourth (1968?C1991) periods of development??to evaluate changes in streamflow response. Autocorrelation, cross-correlation, squared coherency and phase functions were used to estimate the decorrelation lag time, and the correlation length, linearity with frequency, and frequency response delay between rainfall and streamflow. The decorrelation lag time for total flow and baseflow declined by 16?C20?days (d). The correlation length between rainfall and streamflow declined 4?C7?d for total flow and 10?C13?d for baseflow. The squared coherency function indicates an overall decline in linearity between rainfall and total flow across most frequencies. The changes in hydrologic response following increased water-resource development is consistent with a model of groundwater depletion resulting in less groundwater discharge and more induced groundwater recharge. Changes in streamflow response are inconsistent with observed changes in rainfall and imply that streamflow decline is more likely to be the result of groundwater pumping.     

Cement Deep Soil Mixing (CDSM) for Solidification of Soft Estuarine Sediments

A preliminary study was conducted to determine the potential for cement deep soil mixing (CDSM) technology as a method for in-situ solidification of contaminated river and estuarine sediments. The study was conducted in Newark Bay, near the mouth of the Passaic River, New Jersey. The primary objective of the study was to evaluate the viability of CDSM for the in-situ S/S with a focus on: 1) determining the correct mix of the cement slurry, which provides rapid stabilization of the sediment matrix, 2) potential resuspension of solids during CSDM operations, 3) the effects of high organic content on the solidification process, and 4) the feasibility of using conventional dredging/extraction methods once the sediments have been stabilized and allowed to cure. The results of the study show CDSM slurry mixtures, as low as 7% in cement content, result in significant solidification and strength gain of in-situ sediments under ambient conditions. In sediments with very high organic contents (> 20%), the slurry mix would need to be adjusted to account for retardation effects of organics on cement hydration. Sediment resuspension during application was shown to be minimal at a distance of as little as 75 feet from the mixing head. Strength gains were considerable, effectively consolidating the sediment particles in a secure matrix, but not so high as to preclude extraction of solidified sediments with conventional dredging equipment. Dredged solidified sediment exhibited characteristics of a stiff glacial clay, and as such was easier to handle and transport than untreated dredged sediments. This technique has high potential to be used as an interim remedial measure prior to either extraction and decontamination/disposal or proper capping.     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Is the age problem resolved?

The cosmological, astrophysical, and nucleocosmochronological methods for estimating the age of the universe and the corresponding uncertainties are comparatively studied in the present paper. We are led to the conclusion that the new measurements of cosmological parameters, and the recent estimates of the age of globular clusters have led to the gradual disappearance of the age problem from the arena of modern cosmology.     

APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.