Developing a Probability-based Model of Aquifer Vulnerability Assessment in Choushui River alluvial fan

• Main Authors: Yi-Huei Peng, 彭怡惠
• Other Authors: 陳世楷
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/p8b4m6

碩士 === 國立臺北科技大學 === 土木與防災研究所 === 100 === Groundwater is one of the most important water resources. To compare with surface water, groundwater has the advantage of cheap cost, stable temperature and yield, excellent water quality and convenient acquisition. Nowadays, the substantial amount of groundwater has been still extracted to supply agriculture, aquaculture, household and industry needs in many southwestern Taiwan. If groundwater is polluted, the balance of regional water resources supply may be broken and polluted groundwater poses a threat to human health. Therefore, groundwater resources conservation is a very critical issue. The aquifer vulnerability model, DRASTIC, was frequently applied to assess the contamination potential of groundwater. However, because of few observation data on assessed parameters, this model typically involves high levels of prediction uncertainty. This study uses indicator-based geostatistics to develop a probability-based DRASTIC model which is adopted to determine extents of contamination potential and discuss the performance of model prediction. A case study is performed in Cho-shui River alluvial fan. The developed probability-based DRASTIC model includes three methods of parameter estimation – selecting a maximum estimation probability, calculating an expected value and using traditional parameter estimation. The study results reveal that the proximal-fan, which is an agricultural region, is the high contamination potential region, the mid-fan is the medium contamination potential region, and the western coastal and southern areas are the low or no contamination potential region. For selecting a maximum estimation probability, calculating an expected value, and using traditional parameter estimation, the Pearson correlations between the DRASTIC scores and observed nitrate-N concentrations are 0.32, 0.39, and 0.29, respectively, and the Spearman correlations between the DRASTIC scores and observed nitrate-N concentrations are 0.42, 0.39, and 0.25, respectively. To test the predicting performance of high nitrate-N pollution of more than 0.5 mg/L, the accurate prediction rates are 91%, 82%, and 64% for selecting a maximum estimation probability, calculating an expected value, and using traditional parameter estimation, respectively. The analyzed results show that the probability-based DRASTIC model is superior to the traditional one for assessing groundwater vulnerability. The results of this research can provide government administrators with establishing groundwater protection zones and land-use management strategies.