Pyroclastic deposits of the Mt. Pinatubo eruption in 1991 have been transported as Lahar (Mud flow) by rainfall and inflicted damage to the area surrounding the Mt. Pinatubo. changes in the quality and quantity of groundwater have affected the regional community, which mainly depends on underground water for water resources. The purpose of this study is to understand groundwater flow and recharge in the Lahar disaster area around Pasig-Potrero and Porac river basins, Pampanga province, Philippines. Resistivity sounding was applied to measure underground water level, and a water analysis was conducted to understand the regional distribution of ion concentration. A landform classification was carried out to consider the influence of landforms on underground water flow and regional distribution of water quality. The former landforms (before 1991 eruption) were classified into mountain, alluvial fan at volcanic foot, alluvial fan (Porac, Pasig, Abacan), delta, and flood plain. Underground water flow and regional distribution of water quality were prescribed by these former landforms, except in the area surrounding Sand-Pocket. From the fact that the deposition of lahar in Sand-Pocket has raised the bed of Pasig-Potrero River, it is considered that river water has intended to infiltrate into the underground water in the area surrounding Sand-Pocket. The influences offormer landform and artificial landform (Mega Dike) were pointed out as factors regulating characteristic underground water flow and distribution of water quality in this area. Spatial distribution and seasonal changes of underground water flow and water quality were obtained from observed results of resistivity soundings and analytical results of water quality. Underground water flow, which had small seasonal changes, was classified into 3 regional groups (IIII). The existence of a peculiar regulating factor for underground water flow was suggested. In addition, monthly changes of water level indicate a translocation of the recharge area. The propagation speed of recharge area was related to the true speed of the translocation of recharge area, which was estimated from the results of a pumping test and from calculations of the apparent velocity of underground water flow. Furthermore, the distribution of SO42- concentration showed a good correspondence with underground water flow. In particular, the high concentration area of SO42- was distributed inside the Mega-Dike. From the results obtained, a model was suggested to explain the formation of water quality in this area characterized by the underground water flow from Pasig-Potrero River. From above results, groundwater flow and recharge in Lahar disaster area around Pasig-Potrero and Porac river basins were understood.
Urban Heat Island, a meteorological phenomenon by which the air temperature in an urban area increases beyond that in the suburbs, grows with the progress of urbanization. The difference of air temperatures between city center and suburbs is called Urban Heat Island Intensity (UHII). UHII is usually calculated as a fluctuation of the highest and lowest values of ground-observed air temperatures. However, the magnitude of UHII may vary with the locations of observations. Satellite remote sensing has been expected to be effective for obtaining thermal information of the earth's surface with a high resolution. However, the possibility and the technique for evaluating UHII with brightness temperature (BTUHII) from satellite images have not been verified yet. This paper, taking Tokyo as the study area, aims to clarify a method for calculating BTUHII with Landsat/TM thermal images and verifying its usability with in situ ATUHII. Based on the principle of ATUHII, we consideredthat BTUHII must be estimated with the same spatial resolution as the spatial scale at which air temperatures are formed. This is called the resolution condition. Moreover, the highest and lowest temperatures for BTUHII must come from similar land uses as those for ATUHII. This is called the land use condition. Our previous research has shown that the brightnesstemperatures reach a maximal correlation with air temperatures at a distance of about 600 m in Tokyo. Invoking this result, we improved the spatial resolution of brightness temperature by applying a low path filter to the Landsat/TM night thermal image dated March 1, 1999. To match the land use condition, we regrouped the 10 m high-resolution land use data into 8 land use categories according to the brightness temperature and NDVI. In addition, we divided the study area from the city center to the suburbs into 6 zones : CBD, SubCBD, within 10 km, 10-20 km, 20-30 km, and beyond 30 km to take account of the impact of land uses in differentdistance spheres. The following facts have been successfully confirmed. (1) In an urban context like Tokyo, resampling brightness temperature image into a resolution of 500 m can effectively remove the influence of cooling or heating facilities, and make the spatial structure of UHII much clearer. (2) BTUHII is a little smaller than ATUHII, but they are still comparable. (3) Although BTUHII can not substitute for ATUHII completely, the trend of BTUHII decreasing from the city center to the suburbs is coincident with ATUHII so BTUHII would be an effective indicator for a comparison of thermal attributes between different urban districts or land use categories. We believe that these conclusions have great significance for using satellite sensed thermal images in research on urban climatology and the practice of urban planning.
The thermal infrared images observed by satellites represent integral of radiations from both surface and atmosphere. This has been pointed out qualitatively, however, it has not been clarified quantitatively. Using Landsat-5 TM images (Kanto scene, Path107, Row35), this study quantitatively investigated the ratio of the radiant flux densities of surface temperature and those of air temperature. A multiple regression analysis was applied in this investigation. Four daytime scenes of the thermal infrared images of Landsat-5 TM (28 Feb1992, 25 Feb 1997, 13 Dec 1998, 30 Jan 1999, all were fine), and meteorological data in meteorological observatories, AMeDAS stations and Terrestrial Environment Research Center, University of Tsukuba were used for the analysis. Generally, surface temperature around10 : 00 JST is not observed when Landsat passes the study area, so the diurnal variation of thesurface temperature and energy budget at each site was calculated by the method of Kondo (1992) who set exchange coefficient constant throughout a day. It was clarified that the radiant flux densities of surface temperature and those of air temperature equally contribute to the radiant flux densities of brightness temperature observed by Landsat-5 TM, except for a case of strong wind since the constant value of exchange coefficient was not appropriate in this case. In the case of 13 Dec 1998, correlation between brightness temperature and air temperature, obtained in this study (r=0.71) was better than that of Yan and Mikami (2002) (r=0.53) who analyzed the same thermal infrared images. This was due to the difference of the area studied. In this case, correlation betweenradiant flux densities of brightness temperature and those of air temperature were also 0.71. Moreover, the multiple correlation coefficient among brightness temperature, surface temperature and air temperature (r=0.76), and radiant flux densities of brightness temperature, that of both surface temperature and air temperature (r=0.76) was better than the single correlation coefficients between brightness temperature and air temperature, and radiant flux densities of them. Since AIC (Akaike's Information Criterion) of the multiple correlation analysis was smaller than that of the single correlation analysis, this study statistically showed that the radiant flux densities of brightness temperature observed byLandsat-5 TM represented equal contribution of both surface temperature and air temperature.
This paper analyzes the characteristics of daily precipitation during the monsoon season from June to September for 21 years from 1976 to 1996 in Nepal, which is situated in the southern Himalaya. The average monsoon precipitation, and the number of rainy days in Nepal are 1, 410 mm, and 73 days, respectively. On the basis of the total monsoon precipitationand the number of rainy days, 1978, 1984, and 1985 are classified as wet years, and 1977, 1979, and 1992 are classified as dry years. Nepal is divided into six regions on the basis of the statistical characteristics of total monsoon precipitation and number of rainy days. On the other hand, the distribution pattern of 5-year probable rainfall in Nepal, is considerably different from that of average monsoon precipitation. Five-year probable rainfall is small in northern Nepal, and becomes large toward the south. On the basis of 5-year probable rainfall, which is regarded to be the threshold value of heavy rainfall in Nepal, heavy rainfall occurred more frequently in 1981 and 1987. These years did not agree with the wet years based on total precipitation and number of rainy days, indicating that the occurrence of heavy rainfall that causes water hazards in Nepal does not synchronize with wet years.