Quality control and homogenization of daily meteorological data in the trans-boundary region of the Jhelum River basin

Many studies such as climate variability, climate change, trend analysis, hydrological designs, agriculture decision-making etc. require long-term homogeneous datasets. Since homogeneous climate data is not available for climate analysis in Pakistan and India, the present study emphases on an extensive quality control and homogenization of daily maximum temperature, minimum temperature and precipitation data in the Jhelum River basin, Pakistan and India. A combination of different quality control methods and relative homogeneity tests were applied to achieve the objective of the study. To check the improvement after homogenization, correlation coefficients between the test and reference series calculated before and after the homogenization process were compared with each other. It was found that about 0.59%, 0.78% and 0.023% of the total data values are detected as outliers in maximum temperature, minimum temperature and precipitation data, respectively. About 32% of maximum temperature, 50% of minimum temperature and 7% of precipitation time series were inhomogeneous, in the Jhelum River basin. After the quality control and homogenization, 1% to 11% improvement was observed in the infected climate variables. This study concludes that precipitation daily time series are fairly homogeneous, except two stations (Naran and Gulmarg), and of a good quality. However, maximum and minimum temperature datasets require an extensive quality control and homogeneity check before using them into climate analysis in the Jhelum River basin.     

Spatial and temporal variations of aridity indices in Iraq

In this paper, a nonhydrostatic Weather Research and Forecasting (WRF) model has been used to simulate the extreme precipitation event of 25 November 2009, over Jeddah, Saudi Arabia. The model is integrated in three nested (27, 9, and 3 km) domains with the initial and boundary forcing derived from the NCEP reanalysis datasets. As a control experiment, the model integrated for 48 h initiated at 0000 UTC on 24 November 2009. The simulated rainfall in the control experiment depicts in well agreement with Tropical Rainfall Measurement Mission rainfall estimates in terms of intensity as well as spatio-temporal distribution. Results indicate that a strong low-level (850 hPa) wind over Jeddah and surrounding regions enhanced the moisture and temperature gradient and created a conditionally unstable atmosphere that favored the development of the mesoscale system. The influences of topography and heat exchange process in the atmosphere were investigated on the development of extreme precipitation event; two sensitivity experiments are carried out: one without topography and another without exchange of surface heating to the atmosphere. The results depict that both surface heating and topography played crucial role in determining the spatial distribution and intensity of the extreme rainfall over Jeddah. The topography favored enhanced uplift motion that further strengthened the low-level jet and hence the rainfall over Jeddah and adjacent areas. On the other hand, the absence of surface heating considerably reduced the simulated rainfall by 30% as compared to the observations.     

Bela oceanic lithosphere assemblage and its relation to the Réunion hotspot

The Bela ophiolite of Pakistan contains a complete ophiolite-accretionary wedge-trench sequence emplaced onto the Indian continental margin during the northward drift of India-Seychelles over the active Réunion hotspot. A structurally higher ophiolite overlies an accretionary prism, which is thrust over a foreland basin. Shear-sense determinations in peridotite mylonites in the ophiolite footwall and imbrication structures in the underlying accretionary wedge indicate an ESE emplacement. Sedimentary rocks in the accretionary wedge indicate Aptian-Albian pillow lavas, initially deep water conditions, and increasing influence from the continent until the Maastrichtian. The ophiolite emplacement was predated and accompanied by Fe-tholeiitic and alkaline magmatism related to the Réunion hotspot and continuous incorporation of trench sediments into the accretionary wedge. ³⁹Ar/⁴⁰Ar dating shows that the ophiolite formed around 70 Ma. Intraoceanic subduction initiated between 70 and 65 Ma, obduction onto the Indian passive margin occurred during the formation of the Deccan traps at ≈ 66 Ma, and final thrusting onto the continental margin ended in the early Eocene (≈ 50 Ma). The ophiolite emplacement occurred during the counterclockwise separation of Madagascar and India-Seychelles which caused shortening and consumption of oceanic lithosphere between the African-Arabian and the Indian-Seychelles plates.     

Clasts in the CM2 carbonaceous chondrite Lonewolf Nunataks 94101: Evidence for aqueous alteration prior to complex mixing

Clasts in the CM2 carbonaceous chondrite Lonewolf Nunataks (LON) 94101 have been characterized using scanning and transmission electron microscopy and electron microprobe analysis to determine their degrees of aqueous alteration, and the timing of alteration relative to incorporation of clasts into the host. The provenance of the clasts, and the mechanism by which they were incorporated and mixed with their host material are also considered. Results show that at least five distinct types of clasts occur in LON 94101, of which four have been aqueously altered to various degrees and one is largely anhydrous. The fact that they have had different alteration histories implies that the main part of aqueous activity occurred prior to the mixing and assimilation of the clasts with their host. Further, the presence of such a variety of clasts suggests complex mixing in a dynamic environment involving material from various sources. Two of the clasts, one containing approximately 46 vol% carbonate and the other featuring crystals of pyrrhotite up to approximately 1 mm in size, are examples of unusual lithologies and indicate concentration of chemical elements in discrete areas of the parent body(ies), possibly by flow of aqueous solutions.     

Seismic triggering of atmospheric variables prior to the major earthquakes in the Middle East within a 12-year time-period of 2002–2013

Several studies have reported the increased values of surface-latent heat flux (SLHF) and rainfall events prior to the earthquakes as the seismic precursors. In order to investigate the variation of two mentioned atmospheric variables, we analyzed 39 major earthquakes in the Middle East based on the time series of NASA remote sensing data. On this basis, we observed that accumulated rainfall and SLHF of about 29 earthquakes were higher than 10 mm and 50 W/m², respectively (~75 %), over 3–23 days prior to the main shock of major earthquakes. We believed that the records of atmospheric variables are the consequence of a seismic-triggered chain including of air ionization, surface-latent heat exhalation, water vapor condensation and subordinate rainfall as the atmospheric responses to lithospheric motions. This seismic triggering in the Middle East has averagely caused to accumulated rainfall of ~35 mm and maximum SLHF of ~115 W/m² over the 3–23 days prior to 39 major earthquakes. To investigate of spatial correlation between earthquakes and atmospheric variations, we clustered 39 major earthquakes in eight seismological regions. Then, we estimated the moderate and strong correlation (R ²) between preceding times of earthquakes with magnitude of major earthquakes and their hypocenter depth equal to 0.48 and 0.68, respectively.     

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.