Urban heat environmental quality(UHEQ) is affected by the interacting of weather condition and underlying surface framework of urban area .In the last two decades,many researchers from domestic and overseas have studied many problems at the aspect of urban heat environment such as urban heat islands ,urban air temperature and their rela-tion with urban land cover,city population,air pollution etc,In the recent years,Hangzhou,acting as a center city of Zhejiang Province in China,its urbanization quantum and quantity have both changed greatly,in particular ,representing as business affairs building,resident real property and all kins of specialty market having arisen in built-up zone,Based on Landsat TM images data in 1991 and 1999,urban underlying surface temperature value and Normalized Difference Vegetation Index (NDVI) were calculated using image interpreting and supervised classification technique by remote sensing software ERDAS image 8.4,The relation model between urban underlying surface temperature (UUST )and urban air temperature was setup according to the certain correlation patten .Reference to the relational standard of assessing human comfort and other meteorology data of Hangzhou City in summer,the spatial distribution characteristic and the spatial varia-tion degree of human comfort of heat environmental quality are estimated and mapped on a middle scale,that is ,in six districts of Hangzhou City .Then the paper reveals the main characteristic of spatial variation from 1991 to 1999.Lastly,the change mechanism is analyzed and discussed from the viewpoint of city planning,construction and environmental protec-tion. 相似文献
We designed a new seismic source model for Italy to be used as an input for country-wide probabilistic seismic hazard assessment (PSHA) in the frame of the compilation of a new national reference map.
We started off by reviewing existing models available for Italy and for other European countries, then discussed the main open issues in the current practice of seismogenic zoning.
The new model, termed ZS9, is largely based on data collected in the past 10 years, including historical earthquakes and instrumental seismicity, active faults and their seismogenic potential, and seismotectonic evidence from recent earthquakes. This information allowed us to propose new interpretations for poorly understood areas where the new data are in conflict with assumptions made in designing the previous and widely used model ZS4.
ZS9 is made out of 36 zones where earthquakes with Mw > = 5 are expected. It also assumes that earthquakes with Mw up to 5 may occur anywhere outside the seismogenic zones, although the associated probability is rather low. Special care was taken to ensure that each zone sampled a large enough number of earthquakes so that we could compute reliable earthquake production rates.
Although it was drawn following criteria that are standard practice in PSHA, ZS9 is also innovative in that every zone is characterised also by its mean seismogenic depth (the depth of the crustal volume that will presumably release future earthquakes) and predominant focal mechanism (their most likely rupture mechanism). These properties were determined using instrumental data, and only in a limited number of cases we resorted to geologic constraints and expert judgment to cope with lack of data or conflicting indications. These attributes allow ZS9 to be used with more accurate regionalized depth-dependent attenuation relations, and are ultimately expected to increase significantly the reliability of seismic hazard estimates. 相似文献
Doppler radar derived wind speed and direction profiles showed a well developed sea breeze circulation over the Chennai, India
region on 28 June, 2003. Rainfall totals in excess of 100 mm resulted from convection along the sea breeze front. Inland propagation
of the sea breeze front was observed in radar reflectivity imagery. High-resolution MM5 simulations were used to investigate
the influence of Chennai urban land use on sea breeze initiated convection and precipitation. A comparison of observed and
simulated 10m wind speed and direction over Chennai showed that the model was able to simulate the timing and strength of
the sea breeze. Urban effects are shown to increase the near surface air temperature over Chennai by 3.0K during the early
morning hours. The larger surface temperature gradient along the coast due to urban effects increased onshore flow by 4.0m
s−1. Model sensitivity study revealed that precipitation totals were enhanced by 25mm over a large region 150 km west of Chennai
due to urban effects. Deficiency in model physics related to night-time forecasts are addressed. 相似文献
