Several model assumptions and practical constraints were adopted when designing a sampling strategy for the Dutch water supply companies. First, a predominant horizontal layering of the geochemical, reactive properties was assumed, which relates to the main horizontal layering of the unconsolidated deposits that is common for most Dutch aquifers (for example Hartog et al. in press). This simplification is sensible, because current modelling practice requires a considerable simplification of the hydrogeological and hydrogeochemical situation in order to use complex hydrogeochemical transport models (Griffioen et al. 1998, Stuyfzand 1998b). Lateral variations in hydraulic and geochemical properties within the model layers are currently neglected in the model schematization. In deep-well recharge studies, a 1D simulation is often carried out (Brun et al. 1998, Saaltink et al. 1998). Because the presented sampling strategy should yield input data for the simplified transport models, a basic framework of geochemical and hydraulic layering was used to define the sampling objectives.
The second constraint was that data collection is restricted to existing drilling programs for planned observation wells and pumping wells. This implies that the selection of well locations cannot be tuned to the purpose of the sampling of reactivity data and predefined locations should be used. The two constraints imply that the sampling design is mainly directed to assess vertical variations in the reactive properties. Horizontal variations in sediment reactivity can only be assessed when sufficient boreholes become available.
The transport models are normally used for two types of problems: 1. the prediction and monitoring of quality evolution at phreatic well fields and 2. the prediction and process control at deep-well recharge systems. Groundwater flow patterns, the movement of reaction fronts and the input of solutes are significantly different for the two kinds of problems, which is discussed below.
Phreatic well fields
For a phreatic well field (PWF) the aim is to be able to predict the propagation of reaction fronts from diffuse pollution sources, such as pesticides and nutrients. These are introduced as solutes in infiltrating groundwater. Figure 7.3 provides an overview of the groundwater flow patterns at a phreatic well field for a situation where drawdown is negligible (see Chapter 6). In this situation, the groundwater travel times and the vertical position of advective pollution fronts are determined by the groundwater recharge rates, the aquifer thickness and the porosity (Raats 1981, Chapter 6). Assuming spatially averaged inputs of solutes in recharging ground- water, the pollution fronts move vertically, perpendicular to the reactive geochemical layering (Figure 7.3). The assumption of a homogeneous input is reasonable because a spatially heterogeneous input averages out in the breakthrough of the pumping well when the correlation scale is less than 10% of the radius of the contributing area of the well (Duffy & Lee 1992, Beltman 1995, see Chapter 6). The shifts of the pollution fronts are to be measured in the vertical. Figures 7.3 b-d show how an infiltrating pollution front becomes retarded when a reactive layer is encountered. This situation is similar to the one shown in Figure 7.2c and the estimation of the average content of the reactive component will suffice to predict the front movement. Therefore, the chosen sampling objective is to determine the average content of the
reactive components and to determine the depth and thickness of the reactive subsurface layers.
Deep-well recharge systems
Deep-well recharge systems (DWR) are generally used to improve water quality using subsurface passage (Peters 1998). Surface water or groundwater is injected into the aquifer and abstracted downstream (Figure 7.4). The injected water normally has different redox, pH or dissolved solid content than the native water in the aquifer. For example, water with dissolved oxygen may be pumped into an anoxic aquifer.
In a deep-well recharge system, groundwater transport is merely horizontal, parallel to the assumed reactive geochemical layering (Figure 7.4). Transit times in the deep-well recharge systems are short compared with transit times at phreatic well fields (months versus years or tens of years). The breakthrough of solutes will proceed differently at different depths, due to the vertical variations in the reactive aquifer properties (Hartog et al. 2002). Figure 7.4b shows the position of the reactive fronts in a hypothetical case with two reactive layers. The situation is similar to the one of Figure 7.2b and comparable breakthrough is likely to be observed.
For deep-well recharge systems, sampling objectives should therefore include the quantification of the vertical variability of the reactive properties. Average values of reactive
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very reactive layer position of 10 yearblock front contamination streamline isochrone (years) 0.1 0.2 0.3 0.4 0.5 0.6 Q Q geochemical layering 2000 4000 6000 8000 m 0.1 0.2 0.3 0.4 0.5 0.6 Q Q
Deep well recharge system
reactive layer 2000 4000 6000 8000 m 0 10 20 30 40 m 0 10 20 30 40 m
Figure 7.4 - Groundwater flow patterns and movement of a pollution front at a deep-well recharge system. (A): streamlines and isochrones of equal residence time, (B): propagation of a block front input with retardation in two reactive layers.
contents are not sufficient to predict solute breakthrough. Preferably, also the vertical variability of the hydraulic conductivity is quantified.
In summary, different objectives may be adopted for phreatic well fields and deep-well recharge systems, to account for the different directions of groundwater transport relative to the geochemical stratification. Table 7.1 lists the information goals for phreatic well fields and deep- well recharge systems. Two subsequent sampling stages are foreseen with different information goals. The reconnaissance stage is used to obtain first indications about average contents (PWF) and percentiles (DWR). The quantification stage is subsequently used to improve the precision of the estimates and to quantify the uncertainty using confidence intervals.
7.5 Sampling stages