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Individual fractures (micro to meso)
Individual fractures have a limited spatial extent and are discontinuous in their own plane. They are characterized by their orientation (dip direction and amount), genetic nature (shear/tensile), trace lengths and fracture aperture and asperity. Flow within an individual fracture is defined by the Boussinesq equation (1868) (Witherspoon et al., 1980), also termed the Cubic Law where flow in fractures is proportional to fracture ap-erture and fracture length. Aperture refers to the perpendicular distance separating the adjacent rockwalls, in which the intervening space is air or water-filled (Singhal & Gupta, 2010). Aperture may increase by dissolution, particularly in the weathered zone.
To account for width variations within the fracture the term ‘equivalent aperture’ may also be used. Tsang (1992) introduced the term and ‘hydraulic aperture’, wherein he was able to estimate fracture aperture from measured transmissivities, which, based on the Cubic Law, give: ∝ (1.1).
where is the transmissivity of the formation, a measure of how much water can be transmitted [L2/T]. These equations are generally applicable in cases where the fracture is relatively open. Clogging (biological or mineral) and the asperity of the walls may also affect the fracture hydraulic properties and lead to local channeling effects (Sausse, 2002; Singhal & Gupta, 2010) (FIGURE 1.10). This reduces the effective porosity and makes flow velocities irregular, which may lead to an overestimation of flow by the Cubic Law (Zhang et al., 2014), and has strong implications on transport mechanisms inferred from tracer experiments (Becker & Shapiro, 2000; Guihéneuf et al., 2017).
Fracture networks (meso to macro)
By mutual intersection, various fractures form interconnected networks which allow for greater hydraulic conductivities. The way fractures are arranged (i.e. the fracture patterns and orientation) and their density will have an impact on the values and spatial distribution (heterogeneity, degree of anisotropy) of the aquifer’s hydrodynamic parame-ters. Greater fracture densities overall lead to higher hydraulic conductivities (FIGURE 1.11). However, as was noted in 2.3 fracture density decreases with depth, which explains in part why yields decrease with depth in crystalline environments. Further, parallel fractures lead to strong anisotropies in the rock mass, whereas large numbers of more interconnected fractures tends to reduce anisotropy (Singhal & Gupta, 2010). For exam-ple, in areas where weathering processes (mostly defined by sub-horizontal fracturing) dominate over tectonic processes (where fracture directions depend on the tectonic stress field), horizontal permeabilities have found to be 2 to 30 times stronger than vertical permeability, leading to a strong anisotropy (Maréchal et al., 2004).
Another important element to consider is the scale dependence (Hsieh, 1998), which results from the presence of heterogeneity over broad ranges of spatial scales. For exam-ple, at centimeter scale, a random rock volume may appear in a number of ways: with no fractures, with one of more fractures, very highly fractured, or even as a void within an enlarged channel. As the scale of observation increases, features that are correlated over larges distances (e.g. the highly fractured saprock) tend to merge together to become a single large feature, while smaller isolated features (e.g. few interconnected fractures) lose their prominence (Hsieh, 1998). This means that using different exploration methods might lead to different conceptualizations of the hydraulic conductivity field (Hsieh, 1998) depending on the study scale. Practically speaking, different scales may be investi-gated using different methods: laboratory tests, for small scales, single borehole tests and cross borehole tests, for small to medium scales, and pumping tests and calibration of regional or basin scale models for medium to large scales (FIGURE 1.12).
FIGURE 1.12: Illustration of some techniques used to estimate transmissivity and storage coefficient (T and S): (a) single borehole flowmeter test, (b) cross-borehole flowmeter test, and (c) long term pumping test with observation wells (modified from Le Borgne et al., 2006).
Many authors have undertaken this type of comprehensive study where the depend-ence of hydraulic conductivity to scale is evaluated by comparing the outputs of several different methods which operate at different scales (e.g. Hsieh, 1998 shown in FIGURE 1.13; Le Borgne et al., 2006; Maréchal et al., 2004; Martinez-Landa & Carrera, 2005; Rovey & Cherkauer, 1995). As an example we can cite the study by Hsieh (1998) in Mir-ror Lake, New Hampshire which was aimed at comparing the hydraulic conductivity at three scales (meter scale, 100 meter scale, and kilometer scale). The resulting distribu-tion of hydraulic conductivity values spreads out over six orders of magnitude, although no significant scale effect was found. Contrariwise, Maréchal et al. (2004) showed a de-pendence of hydraulic conductivity to scale, where hydraulic conductivity increased with scale. They identified two scales of fracture networks, the primary fracture network (PFN), which affects the matrix at a decimeter scale by increasing the permeability and storage capacity of the blocks, and the secondary fracture network (SFN) which is re-sponsible for the larger scale permeability of the weathered-fractured layer. The permea-bility of the PFN was found to be about 3 orders of magnitude weaker than that of the SFN. In sum, these studies show that scale dependence is linked to the connectivity of preferential flow paths (De Dreuzy et al., 2012), although this is still debated by some authors (e.g. Illman, 2006; Le Borgne et al., 2006; Martinez-Landa & Carrera, 2005), who have highlighted the shortcomings of applying such methodology. For example, there is no straight-forward way to estimate the true scale of influence of a given test. Further, the models often used to interpret data assume medium homogeneity, while the medium that needs to be described might be quite heterogeneous (Le Borgne et al., 2006). To overcome the latter, one option is to build large-scale models that incorporate point values and geological information concerning connectivities (Martinez-Landa & Carrera, 2005). To do so, some authors have opted for Discrete Fracture Network (DFN) models, which account explicitly for fracture network connectivity (e.g. Davy et al., 2010; De Dreuzy et al., 2012; Long & Witherspoon, 1985). Fortunately, numerical simu-lations have also found that permeability is susceptible to vary with scale, and that these scale effects are more significant if there are transmissive zones that are long enough to bridge fracture intersections within a fracture plane.
Scaling up fracture properties
It follows from the above paragraphs that fractures exist over a broad range of scales, ranging from the rock grain to the scale of tectonic plates. Interestingly, it has been found that the characteristics that determine groundwater flow in crystalline rock (e.g. fracture density, aperture, connectivity) scale as fractals (Bonnet et al., 2001), which means that fracture patterns at one scale are in many cases relatively similar to patterns at an entirely different scale. These similarities provide a rationale for extrapolating pat-tern data from one scale (preferably at which fractures may be easily described) to an-other (such as a larger scale where fracture networks are difficult to characterize). Many studies have examined how to quantitatively scale fracture data (e.g. Bour et al., 2002; Gillespie et al., 1993; National Research Council, 1996 and references therein). The exist-ing fractal methods are however still regarded as experimental for several reasons, such as the difficulty to test it rigorously, as cataloging all fractures at all scales is not feasi-ble, or because a single technique can yield significantly different spatial distributions of fractures. So far, stochastic methods which blend aspects of deterministic methods and fractal scale methods (e.g. Cacas et al., 1990; Darcel et al., 2003; Molz, 2004) are the most promising (National Research Council, 1996).
Landscape scale heterogeneities (macro)
Regional and catchment-scale conceptualizations of crystalline aquifers pose a signifi-cant challenge, particularly in the assessment of the structural controls on groundwater flow related to the multi-scale heterogeneity of this type of medium (Comte et al., 2012). To do so, two aspects need to be taken into account: the geological setting that defines the geometry of the hydrogeological environment (i.e. structure, the static properties), and the spatial and time variations of available water (i.e. recharge, the dynamic proper-ties) (Krásný, 2002). This part will focus on the former.
As we have seen, the main sources and reservoirs of groundwater in crystalline rock are the weathered layer and fractures. In view of this, the large-scale geometry of the aquifer can be dissected as, on one part, the heterogeneity and anisotropy of fracture systems at the meso- and macro-scale (see above), and, on the other, the heterogeneity of the geological structure and weathering patterns at the macro-scale (Comte et al., 2012). Weathering patterns at the macro-scale shape the landscape. They determine the landform sequences, where each landform type has a general groundwater potential, and they determine the thickness, extent and characteristics of the weathered layer. These, in turn, depend on several factors (i) climate (both past and present), where significant rainfall contributes to thicker weathered layers, (ii) topography, where weathered layers are mainly formed in erosional peneplain areas of low relief, (iii) the lithology and tex-ture of the parent rock, and (iv) the time span involved in weathering.
Landforms and drainage network
The most commonly developed landforms in crystalline rock are structural hills, in-selbergs, pediments, buried pediments, and valley fills. Their characteristic features are [see Singhal, 2010] (FIGURE 1.14):
Structural hills These large-scale structures outcrop after combined processes of tectonism and denudation. These rocks are hard and compact, and thus act as run-off zones and have negligible groundwater potential. Some infiltration may take place in fractures and joints, which may then either discharge as springs or as seepages along the valley portions. Inselbergs are small residual hills which stand above the general level of the surrounding erosional plains.
Pediments Formed due to the process of denudation, these broad, flat, or gently sloping areas develop at the base of mountain fronts or plateau escarpments. Groundwater potential of these units is limited due to the thinness of the weathered material. Buried pediments form when the sloping surface of the pediment gets gradually covered with soil and colluvial material. These areas form better groundwa-ter potential zones as they have a better retention capacity and storage volume.
Valley Fills As pedimentation progresses, channel deposits may develop. These areas are characterized by gentler slopes, and better water retention. They are the most important landforms for groundwater development.
Table of contents :
PART I: THEORETICAL DESCRIPTION OF NATURAL AND ARTIFICIAL RECHARGE IN FRACTURED CRYSTALLINE ROCK
Introduction
Groundwater resources in fractured crystalline rock
1. Geography of fractured crystalline rocks
2. Geology of fractured crystalline rocks
2.1. Definition
2.2. Crystalline rock fracturing
2.3. Weathering profile
3. Multi-scale heterogeneity
3.1. Fracture heterogeneity (micro to macro)
3.2. Landscape scale heterogeneities (macro)
4. Aquifer conceptualization
Chapter 2 Aquifer recharge and the water cycle
1. The water cycle
1.1. Generalities
1.2. The “Water Budget Myth”
2. Components of the water cycle
2.1. Precipitation
2.2. Evapotranspiration
2.3. Runoff
2.4. Infiltration and the vadose zone
2.5. The role of aquifers in the water cycle
3. Aquifer recharge
3.1. Definition
3.2. Types of recharge
3.3. Recharge controls
3.4. Global recharge distribution
Chapter 3 Quantitative evaluation of recharge processes
1. The basic water-balance equation
2. Recharge estimation methods
2.1. Recharge from infiltration RI (direct recharge)
2.2. Recharge from surface-water RSW (indirect recharge)
2.3. Total recharge (RI +RSW)
2.4. Choosing the appropriate technique
3. Remaining challenges in estimating recharge
3.1. The variability of recharge in time and space
3.2. The assessment of localized and indirect recharge
Humid versus arid regions
3.4. Recharge in fractured rock
Research problem
Chapter 4 Managed Aquifer Recharge
1. Generalities
1.1. Definition
1.2. Key issues
2. MAR methods
2.1. Types of methods
2.2. Types of structures
3. Factors determining the effectiveness of MAR
3.1. Hydrological criteria
3.2. Hydrogeological criteria
4. MAR challenges and risks
4.1. Decrease in infiltration potential
4.2. Contamination of groundwater
4.3. Watershed scale impacts
5. Predicting and assessing MAR efficacy
5.1. Soil maps and hydrogeological reports
5.2. Field methods
5.3. Modeling the aquifer response to MAR
5.4. Monitoring infiltration and groundwater mounding
6. MAR in fractured crystalline rock
7. Watershed development and MAR in India
7.1. National scale
7.2. Telangana state-scale: Mission Kakatiya
Research problem
PART II: EXPERIMENTAL AND NUMERICAL INVESTIGATIONS OF NATURAL AND ARTIFICIAL RECHARGE IN FRACTURED CRYSTALLINE ROCK
Chapter 5 Natural recharge heterogeneity in weathered fractured crystalline rock
1. Introduction
2. Study site
2.1. General
2.2. Geological setting
2.3. Hydrological setting
2.4. Soil types
2.5. Land use
2.6. Reference recharge
3. Methodology
3.1. Hydraulic model
3.2. Basin discretization
3.3. Sensitivity analysis
4. Results and discussion
4.1. Threshold runoff
4.2. Diffuse recharge distribution
4.3. Diffuse recharge/rainfall relationship
Annual groundwater budget
4.5. Focused recharge
4.6. Sensitivity analysis of the diffuse recharge model
4.7. Conclusion
Chapter 6 Managed Aquifer Recharge in fractured crystalline rock aquifers
1. Introduction
2. Study site
2.1. Geological setting
2.2. Hydrological setting
2.3. Water level variations in response to recharge
3. Methods
3.1. Estimating infiltration rates and vertical hydraulic conductivity
3.2. Modeling the aquifer response to infiltration
3.2.1. Analytical solutions
3.2.2. Numerical modeling for connected basin
4. Results
4.1. Infiltration estimation and relative contributions
4.2. Vertical hydraulic conductivity
4.3. Horizontal hydraulic conductivity and storativity
4.4. Effect of bedrock relief (numerical model)
4.4.1. Synthetic scenarios
4.4.2. Application to the present field case
5. Discussion
5.1. Representativity of hydraulic parameters
5.2. Comparison to inferred interface relief
5.3. Artificial recharge modeling
6. Conclusions
Acknowledgments
Chapter 7 Conclusions & perspectives
1. Conclusions
1.1. Catchment-scale natural recharge processes
1.2. Site-scale artificial recharge processes
2. Perspectives
2.1. Remote sensing and airborne geophysics
2.2. Recharge quantification
2.3. On the representativity of site-scale analysis
2.4. Effects of MAR on groundwater quality
References
