• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.140-147
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• 2004

Agrometeorological information is essential in many agricultural decisions if it reaches the user in a timely and appropriate manner. Agriculture is the backbone to local, regional, and global economic development. Thus, strengthening agrometeorological application to diverse agricultural sectors will benefit economic development. This paper discusses three distinct organizational minions that all share the same need for improved information technology. The World Meteorological Organization's (WMOs) Commission for Agricultural Meteorology (CAgM) has global responsibility for improved agrometeorological services of Members to aid agricultural production and to conserve natural resources. The United States Department of Agriculture, World Agricultural Outlook Board, publishes monthly World Agricultural Supply and Demand Estimates, considered to be a benchmark for both government and industry in production and trade decisions. The National Drought Policy Commission (NDPC), created by an act of the United States Congress, formulated a national drought policy based on preparedness rather than on crisis management. All three organizations recognize the need for IT applications in agricultural meteorology and have been active in implementing this technology. The development of information technology offers new means of dissemination of agrometeorological products. World Agrometeorological Information Service (WAMIS) has taken advantage of the global Internet application to offer WMO Members a dedicated web server to host agrometeorological bulletins and training modules.

• Current Research on Agriculture and Life Sciences
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• v.16
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• pp.37-44
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• 1998

The estimation of PMP (Probable Maximum Precipitation) and the analysis of characteristics of PMF (Probable Maximum Flood) according to the types of time distribution of rainfall and variations of base flow for the determination of design flood of major hydraulic structures in the watershed area of Wi stream were analysed. The PMP was estimated by the hydro-meteorological method suggested by the guideline of the World Meteorological Organization(WMO). The Blocking method was cited to transpose from PMP to PMS (Probable Maximum Storm) with time distribution. The unit hydrograph, applied for the estimation of PMF was derived by Clark's method. The summaryzed results : (1) The 72 hrs duration PMP in the area is 477.3mm which is 80mm less than the PMP map in Korea and 134 mm lager than the maximum precipitation of 342.9mm in Taegu, near the Wi stream watershed. (2) According to the types of time distribution and variations of base flow, the ranges of PMF for advanced type, central type and delayed type are 3,145.3~3,348.3cms, 3,774.6~3,977.7cms and 3,814.6~4,017.3cms, respectively. Those mean that peak discharge of advanced type is 600cms less than the central type and delayed type. (3) Delayed type among three types by Blocking method has been estimated the largest PMF of 4,017.3cms, and the advanced type has been estimated the smallest PMF of 3,145.3cms. The mean value of the peak PMF of 3,653.6cms may probably be resonable PMF in the Wi stream watershed. The mean PMF could probably be 1.7 times lager than the result of Gajiyama's equation. It is equivalent to the flood of return period 1,000 to 10,000 yrs.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.19-32
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• 2014

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.