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Mapping of Climatic Data in Northeast Thailand: Rainfall

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Mapping of Climatic Data in Northeast Thailand: Rainfall TROPICS Vol. 14 (2) Issued Feburary 28, 2005 Mapping of climatic data in Northeast Thailand: Rainfall 1) 2) 1) 3) 1) Eiji NAWATA , Yoshikatsu NAGATA , Arimichi SASAKI , Kenji IWAMA and Tetsuo SAKURATANI 1) Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan 2) Graduate School for Creative Cities, Osaka City University, Osaka, 558-8585, Japan 3) School of Environmental Science, The University of Shiga Prefecture, Hikone, Shiga, 522-8533, Japan ABSTRACT Interpolation of rainfall databases of Northeast Thailand of daily measurements at rain stations over 20 years were mapped with GIS tools. Various maps clearly visualized the characteristics of rainfall in this area. Annual and mid-rainy season rainfall showed a descending trend from the northeastern area to the southwestern area, probably due to the southwest monsoon and mountains in Laos and between Central and Northeast Thailand. Yearly variation in annual rainfall in this area was not particularly high, suggesting that its general image as a drought-prone area may be caused by the undulating topography and predominance of sandy soils in this area, which contribute to its vulnerability to flooding and drought. Both mean rainfall amount per rainy day and the number of rainy days in the rainy season showed large regional and yearly variation, but mean rainfall per rainy day was correlated to annual rainfall, whereas the number of rainy days was not strongly correlated to annual rainfall. Large regional variation in the number of rainy days in the rainy season suggests large regional variations in agricultural productivity in this area. The fact that areas near many of the provincial capitals have a high number of rainy days in the rainy season with small yearly variation may indicate relatively high and stable agricultural productivity in such areas that probably supported the establishment of those cities in early days. The duration, onset and end of the rainy season showed small yearly variation, suggesting that these parameters are not solely responsible for the unstable and erratic rainfall in Northeast Thailand. Key words: agricultural production, GIS, monsoon, vegetation INTRODUCTION Natural ecosystems and agricultural production systems are significantly affected by climate, such as air temperature, radiation, rainfall, wind direction and speed, to name a few. Among these, the influence of rainfall is very large in both natural and agro-ecosystems. Generally, rainfall in the tropics is known to be unstable in both amount and distribution, especially under tropical savanna and monsoon climates (Jackson, 1977). Regional variation is also large, and it is not uncommon that even places nearby each other show very different rainfall characteristics (Yanagisawa and Nawata, 1996). Thus, the influence of rainfall characteristics on natural vegetation may appear in a rather complicated in tropical savanna and monsoon areas. Agricultural production is naturally unsteady under such unstable and erratic rainfall conditions, unless irrigation systems are supplied. Northeast Thailand is notorious for its low agricultural productivity. Here agriculture is mainly practiced under rain-fed conditions. The predominance of sandy soils with low water and nutrient holding capacities and low fertility, along with unstable and erratic rainfall, is considered to cause low agricultural productivity (Fukui, 1996). The MAPNET (Modeling agricultural productivity in Northeast Thailand) project was implemented in order to evaluate land productivity appropriately as a basis for the sustainable development of agriculture in Northeast Thailand, using a simulation model that has been described in detail elsewhere (Kono, 2001). In the process of implementing this project, we constructed climatic databases using the measured data at the meteorological stations and interpolation, for imputing climatic data into the model for the estimation of crop yields. These climatic databases could be mapped with GIS tools, and are useful in analyzing various characteristics of climatic factors in this area. In our previous study, we mapped the databases of air temperature and solar radiation in Northeast Thailand over the past 20 years, and discussed general characteristics of these climatic factors and their implications for natural ecosystems and agriculture in this area (Nawata et al., 2005). Although a clear descending trend in annual rainfall from the northeastern to the southwestern area is known to exist in Northeast Thailand (Takagaki, 1987), detailed analysis of the characteristics of rainfall in this area and their influences on vegetation and agriculture has not yet been conducted. In this study, we mapped the databases of rainfall and analyzed the relationship between rainfall characteristics and natural vegetation and agricultural production. 192 Eiji NAWATA, Yoshikatsu NAGATA, Arimichi SASAKI, Kenji IWAMA and Tetsuo SAKURATANI MATERIALS AND METHODS Just like the data on air temperature and sunshine duration described in our previous work (Nawata et al., 2005), rainfall data were collected daily at the Meteorological Department in Bangkok. There are approximately 300 rain-measuring stations in Northeast Thailand, among which 16 stations measured not only rainfall but also other climatic factors, under the direct control of the Meteorological Department. Among the approximately 300 rain stations, some started measurements in 1951, most of which are directly controlled by the Meteorological Department. However, most of the stations started the measurements only relatively recently. In addition, at some stations the measurements were not always reliable, with lack of data for some periods and inconsistent data. For the construction of a rainfall database over the past 20 years (from 1979 to 1998), 104 rain stations were selected. For the selection of reliable stations, figures showing the accumulated rain amount for each station with five neighboring stations for each year were drawn and the stations showing an extremely different accumulated rainfall pattern from neighboring stations were excluded from the analysis. Fig. 1 shows an example of the figures used to choose stations. In this case, although Station 407002 had less rainfall, the pattern was similar to that of the other stations. Thus, considering the results in the other years this station was accepted. This analysis was carried out for all stations, and 104 stations were retained for their high reliability and sufficient data. The location of the selected stations is shown in Fig. 2, along with the topography and province names of Northeast Thailand.(Fig.1,Fig.2) As with the air temperature and solar radiation study, the whole area of Northeast Thailand was divided into about 6,000 grids, each with the size of 3 minutes (about 5 km square). In grids with rain stations, daily rainfall data measured at the stations were used as daily rainfall of the grid. In grids without rain stations, daily rainfall was interpolated from the measured values of the nearest three stations. The average of the measured values of the nearest three stations, weighted 2000 1500 407009 407501 407002 407006 407005 1000 407008 Accumulated rainf all (mm) Accumulated 500 0 -Jul -Jan -Jun -Feb -Sep -Oct -Dec 1 -Apr -Aug -Mar -Nov 1 1 -May 1 1 1 1 1 1 1 1 Date 1 Fig. 1. Accumulated annual rainfall atPhibun Mangsahan station (407009) and the nearest 5 stations (407501, 407002, 407005, 407006 and 407008) in 1980. Mapping rainfall in Northeast Thailand 193 The Mekong River 1 2 5 3 4 6 Phu Phan Mountains 7 8 9 10 11 13 14 The Chi River 12 19 The Mun RRiveriver 15 16 17 18 Fig. 2. Topography and provinces of Northeast Thailand. Elevation > 250 m ASL ○ : Rain stations Province : 1. Nong Khai, 2. Loei, 3. Nong Bua Lamphu, 4. Udon Thani, 5. Sakhon Nakhon, 6. Nakhon Panom, 7. Khon Kaen, 8. Kalasin, 9. Muk Dahan, 10. Chaiyaphum, 11. Maha Sarakam, 12. Roi et, 13. Yasothon, 14, Amnat Charoen, 15. Nakhon Rachasima, 16. Buri Ram, 17. Surin, 18. Si Saket and 19. Ubon Rachathani. by the reciprocal of the distance between the grid and the station, was used as the daily rainfall data of the grid. Database construction was carried out using VBA of Microsoft Excel (Microsoft Co. Ltd.), as was done in air temperatures and solar radiation study. Based on the constructed database for daily rainfall, the annual rainfall, average rainfall amount per rainy day and the number of rainy days with more than 0.1 mm of rain in the rainy season were calculated. In addition, the onset and end of the rainy season were determined according to the criterion of Stern et al. (1982) after modification. The criterion of the onset of the rainy season was rainfall of at least 15 mm for 2 continuous days, but if more than 14 non-rain days followed within one month after the onset, the determined onset date was rejected and another date was sought. If it did not rain for 15 days after September 20, the final date of rainfall was defined as the end of rainy season. September 20, as the date defining the end of the rainy season, was chosen because conspicuous dry spells (non-rain period during the rainy season) of longer than 15 days, appear in some years before that date. Coefficient of variation was also calculated for each parameter. Obtained databases on the above parameters were then mapped using VBA of Microsoft Access (Microsoft Co. Ltd.). RESULTS Validation of interpolation method Validation of the interpolated data was conducted by comparing the measured values of 16 stations under the direct control of Meteorological Department, with the estimated values from the measured data of near stations from 1989 to 1998. Similar to the air temperature and solar radiation study, the measured values of the nearest station and averages of the measured values of three nearest stations weighted by the reciprocal of the distance between the stations were compared. In addition, considering general characteristics of rainfall in the tropical savannas (e.g. strong intensity for a short period within a limited range), averages of the measured values of three nearest stations weighted by the reciprocal of the square of the distance between the stations were also used for the comparison.
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