Utilizing Empirical Orthogonal Function on Hydrogeological Parameter Estimation

• Main Authors: Guan-Wei Li, 李冠緯
• Other Authors: 徐年盛
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/08636940709193168263

碩士 === 國立臺灣大學 === 土木工程學研究所 === 101 === The underground flow numerical model of the spatial distribution between recharging and pumping volume in the past few years still has a great space to improve, especially the artificial pumping volume which has the most serious bias. Due to the unchecked spreading of private wells, the accuracy of the field estimation and the spatial distribution is low. As for the recharging distribution, it is set by the river module, soil texture, land use patterns and the boundary conditions while the guess value is still the majority. As result, the accuracy of the model calibration at the unsure hydrological spatial and temporal distribution is hard to evaluate cause the complex hydrological space distribution which can’t be fully proved to be right. This study is based on the empirical orthogonal function method and takes Ping-Tung Plain as an example. The virtual case is designed at the very beginning to analyze the hydraulic value spatial distribution of the unconfined aquifer in Ping-Tung Plain by the empirical orthogonal function method. In this virtual case, the hydrogeological parameters are known. The modeling observation level can calculate the water storage changes hydrograph and be used to do the hydraulic net value space distribution by the empirical orthogonal function method. Then, at the appropriate hydraulic net value space distribution, the best hydrogeological parameters and the true value deviation can be evaluated after the parameter calibration of this virtual case. The result can be divided into two parts: First, the hydraulic net value space distribution. The root mean square error of simulated water levels and observation water level is 1.97 cm. The hydraulic net value RMSE is 9,310,648 tons which is 4.59% of the average value that compares with the daily average storage equals to 2.03 hundred million tons. From the above data, the empirical orthogonal function method can be proved to be the most effective way to do the hydraulic net value space distribution while the overall error is in the acceptable range; Second, the test of hydrogeological parameters. The water level RMSE after the model calibration equals to 1.11 m which is 0.86 m lower than the previous one and the hydraulic net value RMSE is 5,222,030 tons which is 4,088,618 tons lower than the previous one. Therefore, the calibration value of hydrogeological parameters and the initial value are different in two power orders. The virtual case shows that this study can set the hydraulic net value spatial distribution effective. And also, when the distribution error is low, the hydrogeological parameters can also be controlled effectively. This study takes further steps to apply this virtual case into the real case of numerical modeling in Ping-Tung Plain to evaluate the applicability. The real case is to verify the research method of the virtual case whether to find the better initial hydraulic spatial distribution then the previous methods or not. Therefore, three models are built to compare the advantages and disadvantages, which are (1) the recharging and pumping spatial distribution with the empirical orthogonal function, (2) the average recharging and pumping spatial distribution, (3) recharging spatial distribution with soil texture analysis. The result can also be divided into two parts: Fist, the hydraulic net value spatial distribution. The water levels RMSE after the distribution are (1) 6.31 m, (2) 10.67 m, (3) 10.43 m. The hydraulic net values RMSE are (1) 27,931,944 tons, (2) 47,484,304 tons, (3) 44,691,110 tons. The average values comparing with the daily average storage volume which equals to 2.19 hundred million tons are (1) 12.75%, (2) 21.68%, (3) 20.41%. As a result, the hydraulic spatial distribution by using the empirical orthogonal function method do effectively reduce the overall error; Second, the test of hydrogeological parameters. The water level RMSE after the model (1) calibration is 4.20 m which is 2.11 m lower than the previous one. And the hydraulic net value RMSE is 19,552,360 tons which is 8,379,584 tons lower than the previous one. As a result, the calibration value of hydrogeological parameters and the initial value are different in two power orders. This study shows that the research method has practical feasibility. But, in the field conditions which contain the confined aquifer and flooded areas, the water level has a significant change with only slight volume of water which shows that the water level deviation is larger.