Sununtha Kingpaiboon

Determination of the dates of the southwest monsoon in northeastern Thailand from the data on precipitable water vapor obtained by GPS

This paper outlines a new method for determining the onset and withdrawal of the southwest monsoon using Precipitable Water Vapor (PWV) estimated from GPS data: GPS-PWV. In this study, PWV is computed from GPS data collected from four ground-based GPS receivers in northeastern Thailand. Initially, we study the temporal and geographical distributions of the GPS-PWV data. Their statistical characteristics show that GPS-PWV can be used as a tool for determining the onset and withdrawal of the southwest monsoon in northeastern Thailand. The GPS-PWV data were processed using a decision tree analysis to obtain a new set of criteria for determining the onset and withdrawal of the monsoon. The new criterion specifies the onset as a period when the daily averaged GPS-PWV exceeds 52.1 mm for eight consecutive days. The withdrawal is a period when the daily averaged GPS-PWV is 52.1 mm or less for six consecutive days. The dates of onset and withdrawal of monsoons calculated from this method was compared with those obtained with other methods. The difference equaled 1–2 day except years 2010 and 2012 when the errors were more than two days.

Possibility of GPS precipitable water vapour for reservoir inflow forecasting

Abstract We investigated the possibility of using GPS precipitable water vapour (GPS-PWV) for forecasting reservoir inflow. The correlations between monthly GPS-PWV and the inflow of two reservoirs were examined and the relationship tested, using a group method of data handling (GMDH) type neural network algorithm. The daily and monthly reservoir inflows were directly proportional to daily and monthly GPS-PWV trends. Peak reservoir inflow, however, shifted from the peak averages for GPS-PWV. A strong relationship between GPS-PWV and inflow was confirmed by high R2 values, high coefficients of correlation, and acceptable mean absolute errors (MAE) of both the daily and monthly models. The Ubon Ratana reservoir model had a monthly MAE of 54.19·106 m3 and a daily MAE of 5.40·106 m3. By comparison, the Lumpow reservoir model had a monthly MAE of 25.65·106 m3 and a daily MAE of 2.62·106 m3. The models using GPS-PWV as input data responded to extreme inflow better than traditional variables such that reservoir inflow could be predicted using GPS-PWV without using actual inflow and rainfall data. GPS-PWV, thus, represents a helpful tool for regional and national water management. Further research including more reservoirs is needed to confirm this preliminary finding.

Field-scale Spatial Variability of Electrical Conductivity of the Inland, Salt-affected Soils of Northeast Thailand

This article goes beyond the historical narratives to apply scientific methods to the archaeological studies of the ancient kingdom of Tambralinga in Southern Thailand. Its objectives are (1) to examine the geography related to agriculture and settlement pattern using data from ground surveys, aerial photographs, and Geographical Information System, (2) to determine the dates of some important brick shrines using data from the themoluminescence dating technique, and (3) to reveal the layout of the ancient Khao Kha religious complex using the data from the Structure-from-Motion technology in the area of Sichon District, Nakhon Si Thammarat Province. It proposes that the ancient communities of approximately the 6 th to 11 th centuries CE lived in the areas suitable for wet-rice cultivation, with sufficient supply of water and alluvial soils. It may also be hypothesized, based on the calculation of the amount of rice produced in the floodplain, the density and dates of brick shrines, and the complicated layout and massiveness of the Khao Kha religious complex, that this area between the Tha Khwai, Tha Chieo, and Tha Thon rivers in Sichon District was one of the most significant centers in the Tambralinga Kingdom.Dengue is a viral disease, transmitted by Aedes aegypti , and is still a big problem in tropical areas, including Indonesia, where the temperatures are relatively warm and suitable for vector mosquito life. In the dry season, the day and night temperature differences are quite sharp and, at that time, the number of dengue cases is low. In this study, the difference between day and night temperature is referred to as daily temperature fluctuation and represented by the maximum and minimum temperature difference in each month. The research was conducted in Sleman Regency, Yogyakarta Province, Indonesia, as an endemic area, and the data were collected from 4 endemic areas in Sleman; Gamping, Godean, Sleman, and Depok districts. The data collected were quantitative with serial data retrospective. Secondary data of monthly dengue incidence in the years 2008 - 2013 were obtained from the Regency Health Office and used as a dependent variable. Monthly minimum and maximum temperatures in the same periods were obtained from the Agency of Meteorology, Climatology, and Geophysics. The differences between the minimum and maximum temperatures were calculated, to be used as independent variable data, and represented the different day and night temperatures of the month. Data were analyzed by using linear regressions to determine the influence of fluctuating temperature on the incidence of dengue. Results show that fluctuating temperature affected dengue incidence in the districts of Godean (p = 0.000; R 2 = 0.207) and Gamping (p = 0.006; R 2 = 0.125), but did not affect it in Sleman (p = 0.164) or Depok (p = 0.075). The data suggests that fluctuating temperature affected dengue incidence with powers of 20.7 % in Godean and 12.5 % in Gamping.

The development of Atmospheric Crop Moisture Index for irrigated agriculture

This paper develops the Atmospheric Crop Moisture Index (ACMI), an indicator of atmospheric drought. Levels in the index represent the possibility of rainfall or the lack of moisture needed at various stages of crop growth. The lack of moisture is determined with the ratio between water supply and demand, where the supply indicator is Precipitable Water Vapor (PWV). The ACMI was calculated from data collected between February 2010 and September 2014. The example of calculation of 10-day values of ACMI10 for rice is provided. The comparison of ACMI and other indices shows poor correlation with the SPI, the SPEI, and the scPDSI; however, it displays high correlation with precipitation, the PE, and the MAI. The ACMI is a parameter affected by surface temperature, relative humidity, wind speed, atmospheric pressure, and solar irradiation; all these parameters are included in the study of drought. The ACMI is an effective tool for agricultural water management.

Estimation of Missing GPS Precipitable Water Vapor Data by Zenith Wet Delay and Meteorological Data

This article presents a statistical correlation between GPS precipitable water vapor and meteorological data, i.e., surface temperature, air pressure, relative humidity, dew point temperature, and water vapor pressure by using linear regression. The data, recorded over a 4-year period, was used as an estimation of missing GPS precipitable water vapor data from discontinuous recordings. A multiple linear regression equation showed a correlation among zenith wet delay (ZWD), water vapor pressure (e) and surface temperature (T) was ZWD(e,T) = 17.4952e-0.8281T-93.164, with a coefficient of determination (R2) of 0.725, a mean absolute error of 8.71 mm, a root mean square error of 10.39 mm, and a mean absolute percentage error of 18.63%. The equation obtained can be used to estimate GPS precipitable water vapor data which is missing from recordings due to accident or technological error.

Spatial Variability of Sodium Adsorption Ratio and Sodicity in Salt-Affected Soils of Northeast Thailand

Information on spatial variability of Sodium Adsorption Ratio (SAR) is useful for implementation of appropriate control measures for the salt-affected soils. The major objective of this study was to use geostatistics to describe the spatial variability of (i) the SAR and consequently (ii) the soil sodicity, in areas of different classes of salt-affected soils. Attention was on areas of very severely salt-affected soils (class 1), severely salt-affected soils (class 2), and moderately salt-affected soils (class 3). For each class, 2 study sites were chosen, totally 6 sites were taken into consideration. In each site, 100 soil samples were collected at 0-30 cm depth according to the stratified systematic unaligned sampling method in the dry season of 2012, and analyzed for the SAR in the laboratory. Descriptive statistics and Geostatistics were applied to describe the variability and spatial variability of SAR and soil sodicity, respectively. The result revealed very high variability of SAR. Descriptive statistics showed the CV values of ≥ 35% for every site of every class. When using semivariogram to describe the spatial correlation of SAR, it was found that in 3 study sites, the semivariogram models fitted well with the corresponding semivariogram samples indicating spatial correlation of SAR in the areas. In these cases, the Ordinary Kriging was applied to generate soil sodicity map. The relatively short range values especially for class 1 indicated very high variation of SAR. However, for the other 3 study sites, the linear models were fitted indicating no spatial correlation. Consequently, Trend Surface Analysis was applied instead. According to the soil sodicity maps generated in this study, the areas of class 1 were entirely occupied by strongly sodic soils. For classes 2 and 3, the soils in all study sites belonging to these classes included normal and slightly sodic soils of different proportions. Furthermore, inconsistency of the spatial variability patterns of SAR was found even in areas within the same class of salt-affected soils. As a result, prior to the intensive management of this problem soil in a particular area, investigation on the spatial variability pattern should be performed