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Natural Hazards - We present a Risk Atlas of Mexico City based on a Geographical Information System (RA-GIS). We identified the prevalent social risk to the more relevant hazards in Mexico City...  相似文献   
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There is growing concern that increasing concentrations of greenhouse gases in the atmosphere have been responsible for global warming through their effect on radiation balance and temperature. The magnitude of emissions and the relative importance of different sources vary widely, regionally and locally. The Indus Basin of Pakistan is the food basket of the country and agricultural activities are vulnerable to the effects of global warming due to accelerated emissions of GHGs. Many developments have taken place in the agricultural sector of Pakistan in recent decades in the background of the changing role of the government and the encouragement of the private sector for investment in new ventures. These interventions have considerable GHG emission potential. Unfortunately, no published information is currently available on GHG concentrations in the Indus Basin to assess their magnitude and emission trends. The present study is an attempt to estimate GHG (CO2, CH4 and N2O) emissions arising from different agro-ecosystems of Indus Basin. The GHGs were estimated mostly using the IPCC Guidelines and data from the published literature. The results showed that CH4 emissions were the highest (4.126 Tg yr^-1) followed by N20 (0.265 Tg yr^-1) and CO2 (52.6 Tg yr^-1). The sources of CH4 are enteric fermentation, rice cultivation and cultivation of other crops. N2O is formed by microbial denitrification of NO3 produced from applied fertilizer-N on cropped soils or by mineralization of native organic matter on fallow soils. CO2 is formed by the burning of plant residue and by soil respiration due to the decomposition of soil organic matter.  相似文献   
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Weak lensing by large-scale mass inhomogeneities in the Universe induces correlations in the observed ellipticities of distant sources. We first review the harmonic analysis and statistics required of these correlations and discuss calculations for the predicted signal. We consider the ellipticity correlation function, the mean-square ellipticity, the ellipticity power spectrum and a global maximum-likelihood analysis to isolate a weak-lensing signal from the data. Estimates for the sensitivity of a survey of a given area, surface density, and mean intrinsic source ellipticity are presented. We then apply our results to the FIRST radio-source survey. We predict an rms ellipticity of roughly 0.011 in 1 × 1 deg2 pixels and 0.018 in 20 × 20 arcmin2 pixels if the power spectrum is normalized to σ8 Ω0.53 = 0.6, as indicated by the cluster abundance. The signal is significantly larger in some models if the power spectrum is normalized instead to the COBE anisotropy. The uncertainty in the predictions from imprecise knowledge of the FIRST redshift distribution is about 25 per cent in the rms ellipticity. We show that FIRST should be able to make a statistically significant detection of a weak-lensing signal for cluster-abundance-normalized power spectra.  相似文献   
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Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.  相似文献   
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Dursun  Arif Emre 《Natural Hazards》2020,100(1):329-343
Natural Hazards - There is an increased risk in post-fire debris flow (DF) occurrences in the western USA with recent increase in wildfire frequencies. DFs are destructive, causing high loss to...  相似文献   
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Theoretical and Applied Climatology - In the present study, we aimed to understand the current condition of land suitability and how climate change will affect its suitability for rice paddy...  相似文献   
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