Estimating the spatial variations of aquifer parameters

9 Estimating transmissivity or hydraulic conductivity field for characterizing the heterogeneity 10 of a crystalline aquifer is particularly difficult because of the wide variations of such 11 parameters. We developed a new approach based on the analysis of a dense network of water12 table data. It is based on the concept that large-scale variations in hydraulic head may give 13 information on large-scale aquifer parameters. The method assumes that flux into the aquifer 14 is mainly sub-horizontal and that the water table is mostly controlled by topography, rather 15 than recharge. It is based on an empirical statistical relationship between field data on 16 transmissivity and the inverse slope values of a topography-reduced water-table map. This 17 relationship is used to establish a computed transmissivity map that must be validated with 18 field measurements. A hydraulic-conductivity map is then computed with data on the 19 saturated aquifer thickness. The proposed approach can provide a general pattern of 20 transmissivity, or hydraulic conductivity, but cannot correctly reproduce strong variations at 21 very local scale (<10 metres). 22 The method was tested on a peridotite (ultramafic rock) aquifer of 3.5 km 2 in area located in 23 New Caledonia. The resulting map shows transmissivity variations over about 5 orders of 24 magnitude (average -5.2±0.7). Comparison with a map based on measured water-level data 25 (n=475) shows that the comparison between LogT-computed values and LogT data deduced 26 from 28 hydraulic tests is estimated with an error <20% in 71% of cases (LogT±0.4), and with 27 an error <10% (LogT±0.2 on average) in 39% of cases. From this map a hydraulic28 conductivity map has been computed showing values ranging over 8 orders of magnitude. The 29 repeatability of the approach was tested on a second data set of hydraulic-head measurements 30 (n=543); the mean deviation between both LogT maps is about 11%. These encouraging 31 results show that the method can give valuable parameter estimates, and can characterize 32 aquifer heterogeneity. The computed LogT and LogK maps highlight the spatial distribution 33 of parameters that show a pattern clearly controlled by the fault network of this ultramafic 34 massif. However, the faults are mainly characterized by low-permeability zones; this differs 35 from results on other crystalline aquifers and may be due to the fact that weathering products 36 of peridotite are clay-like materials. 37