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%===============================================
\section{Introduction}
%===============================================

The Hydrogeology module of \CS is a numerical model for water flow and solute
transport in continuous porous media,
based on the Darcy law for flow calculation, and on the classical
convection-diffusion equation for transport.
It allows to simulate steady or unsteady flows, saturated or not, with scalar or
tensorial permeabilities, and transport with dispersion, sorption and radioactive decay.
Any law, even unlinear, is acceptable for dependances between moisture content,
permeability and hydraulic head.

For the solving of the flow, the Richards equation is used, derived from the Darcy law and the conservation of mass.
In the general case, this equation is non-linear and must be solved by a Newton scheme.

From this flow, the transport equation is solved, taking into account convection and diffusion,
both slightly modified due to the specificities of underground transport.

Physical concepts and equations developed in this module are detailed hereafter.

%===============================================
\section{Groundwater flows}
%===============================================

\subsection{Continuity Equation}
%===============================================

The expression of the mass conservation for the water contained in a volume $\Omega$ of
the subsurface, delimited by a surface boundary $\partial \Omega$,
can be written:

\begin{equation}\label{eq:groundwater:int_mass_cons}
\int_\Omega{\rho \frac{\partial{\theta}}{\partial{t}} \dd \Omega} + \int_{\partial \Omega}{ \rho \vect{u}\cdot  \dd \vect{S}}=
\int_\Omega{\rho\,Q_s \dd \Omega}
\end{equation}
with:
\begin {itemize}
 \item[$\bullet$] $\theta$ is the moisture content (also called saturation) [$L^3.L^{-3}$];
 \item[$\bullet$] $\rho$ is the density of water [$M.L^{-3}$];
 \item[$\bullet$] $\vect{u}$ is the water velocity [$L.T^{-1}$];
 \item[$\bullet$] $Q_s$ is a volumetric source term [$L^3.T^{-1}$].
\end{itemize}

By assuming a constant density of water $\rho$ and using Gauss theorem, the equation simplifies to the following local expression:
\begin{equation}\label{eq:groundwater:mass_cons}
\frac{\partial{\theta}}{\partial{t}} + \dive \left( \vect{q} \right) = Q_s
\end{equation}
As seen in section \ref{sec:groundwater:soil_water_laws}, the moisture content can be determined from the pressure head.

\subsection{Darcy Law}\label{sec:groundwater:darcy_law}
%===============================================

The momentum conservation equation in a continuous porous medium is expressed through Darcy law,
an empirical relationship which shows the proportionality between the velocity of the water $\vect{u}$ and the gradient of the soil water potential.
This means that motion of water in a porous medium is due to both the gradient of water pressure and gravity.
The following equation describes the pressure head $h$, which is equivalent to the water pressure but expressed in a length unit [L]:
\begin{equation}
\label{eq:groundwater:definition_h}
h=\dfrac{p}{\rho g} + A,
\end{equation}
with $A$ a constant such that $h = 0$ at the atmospheric pressure, as a convention.
We also introduce the hydraulic head $H$:
\begin{equation}
\label{eq:groundwater:definition_h_2}
H = h + z.
\end{equation}
%
Darcy law was initially established for mono-dimensional flows in saturated isotropic conditions.
It binds darcian velocity $\vect{q}$, which is the product of the real flow velocity
$\vect{u}$ and the moisture content $\theta$ of the medium, to the gradient of hydraulic head.
To represent anisotropic conditions and multidimensional flows, it has to be extended to a tensorial relationship:
\begin{equation}
\label{eq:groundwater:darcy_3d}
\vect{q} = \theta \vect{ u} = -K \grad H = -K \grad (h + z)
\end{equation}
where $K$ can be scalar or tensoriel. It is called hydraulic conductivity. By abuse of langage, we also call it permeability, but in a rigorous sense the hydraulic conductivity
is deduced from the permeability of the soil and the properties of the water.
It varies in unsaturated conditions, and depends on the nature of the soil and the pressure head $h$ (see section \ref{sec:groundwater:soil_water_laws}).
Notice that the constant $A$ has no importance in the Darcy equation, neither in the following of the development.

\subsection{Richards equation}
%===============================================

Richards equation is obtained by substitution of the velocity expression given in equation \eqref{eq:groundwater:darcy_3d} directly into
the continuity equation \ref{eq:groundwater:mass_cons}:
\begin{equation}
\label{eq:groundwater:richards}
\frac{\partial{\theta(h)}}{\partial{t}} = \dive \left( K(h) \grad H \right) + Q_s
\end{equation}

\section{Soil-water relationships}\label{sec:groundwater:soil_water_laws}
%===============================================
To close Richards equation, two extra relationships have to be given to link the hydraulic conductivity and the moisture content to the pressure head.
The relationship between the moisture content and the pressure head is usually derived from the \textit{soil water retention} curve,
which is determined experimentally.
The Hydrogeology module permits to define any model of that kind.
In the following we denote $\theta_s$ the porosity of the soil and $\theta_r$ the residual moisture content, which is fraction of water that cannot be
removed from the soil.

In saturated conditions, we have $\theta = \theta_s$ everywhere, thus $\theta$ only depends on the nature of the soil.
As for the permeability, it usually does not depend on the pressure head in this case, and is also constant for a given soil.

In unsaturated conditions, the laws used to analytically derive soil hydraulic properties are usually highly non-linear.
The Richards' equation is thus a non-linear second order partial differential equation in unsaturated conditions.
The method chosen to solve it involves an iterative process used to linearise the equation as described in section \ref{sec:groundwater:solving_richards}.
Let us give the example of the Van Genuchten model with Mualem condition, wich is the most commonly used:
\begin{equation}
  S_e = \frac{\theta - \theta_r}{\theta_s - \theta_r} =
  \begin{cases}
     \left[1 + {\left|\alpha h\right|}^n \right]^{-m} &\text{if $h < 0$} \\
    1 \quad &\text{if $h \geq 0$}
  \end{cases}
\end{equation}
with $n$ and $m$ two constant parameters.
\begin{equation}
  K =
  \begin{cases}
    K_0 \ S_e^L \left( 1 - \left( 1 - S_e^{1/m} \right)^m\right)^2 \quad &\text{if $h < 0$} \\
    K_0 \hspace{2cm} \quad &\text{if $h \geq 0$}
  \end{cases}
\end{equation}
with $K_0$ a constant not depending on the moisture content. Notice that if $h > 0$, then we have a saturated soil.
That is because we chose, as a convention (and as explained in section \ref{sec:groundwater:darcy_law}), to define the pressure head such that it vanishes
at the atmospheric pressure.
When the soil is saturated,
the permeability is
equal to $K_0$, depending only on the soil.

\section{Solving of the Richards equation}\label{sec:groundwater:solving_richards}
%===============================================

In the general case, the laws connecting the hydraulic properties are non-linear.
We will have to implement an iterative process for solving the Richards equation.
First, we define the soil capacity $C$:
\begin{equation}
 \label{eq:groundwater:c_definition}
  C(h) = \frac{\partial \theta}{\partial h},
\end{equation}
which can be derived from the soil law linking $\theta$ and $h$.
A classical way to solve Richard's equation \eqref{eq:groundwater:richards} is
to first transform it with the approximation:
\begin{equation}
\label{approximation}
\frac{\partial \theta}{\partial t} \simeq C(h) \frac{\partial h}{\partial t},
\end{equation}
so that it becomes:
\begin{equation}
\label{eq:groundwater:richards_bis}
 C(h) \ \dfrac{\partial{h}}{\partial{t}} \simeq \dive \left( K(h) \ \grad(h + z) \right) + Q_s,
\end{equation}
this last formulation being called the \textit{h-based} formulation.
The equation (\ref{eq:groundwater:richards_bis}) can be written (recalling that $H = h + z$):
\begin{equation}
\label{eq:groundwater:richards_ter}
 C(H-z) \ \der{H}{t} \simeq \dive \left( K(H-z) \grad(H) \right) + Q_s,
\end{equation}
and then discretized in time:
\begin{equation}
\label{eq:groundwater:richards_discretized}
 C(H^{n}-z) \ \frac{ H^{n+1} - H^n }{\Delta t} \simeq \dive \left( K(H^{n+1}-z) \grad(H^{n+1}) \right) + Q_s.
\end{equation}
The complete implicitation of the right hand term is made for ensuring stability. The explicitation of the capacity $C$
is chosen after testing different options and noticing that implicitation does not improve the results.
We will now linearize the equation (\ref{eq:groundwater:richards_discretized}) and define sub-iterations to solve it.
Suppose that we seek unknown variable $H^{n+1}$ at the sub-iteration $k+1$ from its value at sub-iteration $k$.
We write:
\begin{equation}
\label{eq:groundwater:richards_linearized}
 C(H^{n}-z) \ \frac{H^{n+1, \ k+1} - H^n}{\Delta t} \simeq \dive \left( K(H^{n+1, \ k} - z) \ \grad(H^{n+1, \ k+1}) \right) + Q_s.
\end{equation}
The equation (\ref{eq:groundwater:richards_linearized}), whose unknown is $H^{n+1, k+1}$,
is a transport equation without convection, which can be solved by the usual routines of \CS.

But the approximation (\ref{approximation}) does not ensure a rigorous conservation of mass after discretization, because we do \textbf{not} have:
$$C(h^{n}) \ \frac{h^{n+1} - h^n}{\Delta t} = \frac{\theta(h^{n+1}) - \theta(h^n)}{\Delta t}.$$
Anyway, it is still possible to define the exact residual:
\begin{equation}
\label{residual}
  R(h^n, \ h^{n+1}) = C(h^{n}) \ \frac{h^{n+1} - h^n}{\Delta t} - \frac{\theta(h^{n+1}) - \theta(h^n)}{\Delta t}
\end{equation}
and to mix it into the discretized and linearized formulation (\ref{eq:groundwater:richards_linearized}) at the sub-iteration $k$, to obtain:
\begin{multline}
\label{eq:groundwater:richards_final}
 C(H^{n}-z) \ \frac{H^{ n+1, \ k+1} - H^n}{\Delta t} \simeq \\ \dive \left( K(H^{ n+1, \ k} - z) \grad(H^{ n+1, \ k+1}) \right) + Q_s + R(H^n - z, \ H^{ n+1, \ k} - z).
\end{multline}
As equation \eqref{eq:groundwater:richards_linearized}, this can be solved by the usual routines of \CS.
Then the sub-iterations, if they converge, lead to the exact solution of:
\begin{equation}
 \label{eq:groundwater:richards_final_bis}
\frac{ \theta(h^{n+1}) - \theta(h^n) }{\Delta t} = \left[ \dive \left( K(h^{n+1}) \grad(H^{n+1}) \right) \right]^D + Q_s,
\end{equation}
where exponent $D$ represents the spatial discretization of gradient - divergent operator in \CS.
This discrete operator
is rigorously conservative, thus the global volume of water:
$$\int_D{{\theta} \dd \Omega},$$
where $D$ denotes the entire domain, is conserved in a discrete sense (provided that there is no physical loss at the boundaries).

\subsection{Finite Volume method in \CS for the operator-diffusive terms}\label{sec:groundwater:operator_diffusive}
%===============================================

In \CS, the integral on a cell $\Omega$ of the term $\dive \left( K \grad \varia \right)$ is discretized this way:
\begin{equation}
 \label{discrete_diffusive}
\int_{\Omega} \dive \left( K \grad \varia \right) \dd \Omega \simeq \sum_{\face \in \Face{\celli}} K_\face \grad_\face \varia \cdot \vect{S}_\face,
\end{equation}
where $\Face{\celli}$ is the set of the faces of cell $\celli$. For each face $\face$,
$K_\face$ is the face diffusivity (calculated from the diffusivities at centers of
the adjacent cells, with an harmonic or arithmetic mean),
$\grad_\face \varia$ is the gradient of $\varia$ at the face center,
and $\vect{S}_\face$ is a vector normal to the face, whose size is the surface of the face, directed towards the outside of the cell $\Omega$.
There are two ways of calculating the term $\grad_\face Y \cdot \vect{S}_\face$: a simple one (\emph{i.e.} without reconstruction)
and an accuracy one (\emph{i.e.} with reconstruction). In the general case, two adjacent cells of \CS can be represented like in the picture below,
where two cells $\celli$ and $\cellj$ are separated by a face denoted $\fij$:
\begin{figure}[!h]
%FIXME \includegraphics[scale=0.6]{saturne_1.png}
\end{figure}
The variables are known at centers of the cells, \emph{i.e.} at points $\centi$ and $\centj$. But to get a rigorous estimation of the normal gradient at
the center of the face $\fij$, we need values at points $\centip$ and $\centjp$:
\begin{equation}
 \label{eq:groundwater:101}
\grad_{\fij}\varia \cdot \vect{S}_{\ij} = \frac{\varia_{\centjp}-\varia_{\centip}}{\centip\centjp}.
\end{equation}
The face gradient without reconstruction is calculated by the approximation:
\begin{equation}
 \label{eq:groundwater:102}
\grad_{\fij}\varia \cdot \vect{S}_{\ij} = \frac{\varia_{\centj}-\varia_{\centi}}{\centip\centjp}.
\end{equation}
The less the vector $\vect{\centi\centj}$ is orthogonal to the face $\fij$, the more this approximation is wrong. But it has the advantage to be easy and quick
to deduce from the variables at cell centers, with a linear relation, and to depend only on the variables of the adjacent cells.
The face gradient with reconstruction is calculated following the relation \eqref{eq:groundwater:101}, thanks to the relations:
\begin{equation}
 \label{103}
\varia_{\centip} = \varia_{\centi} + \grad \varia_\centi  \cdot \vect{\centi\centip}.
\end{equation}
\begin{equation}
 \label{104}
\varia_{\centjp} = \varia_{\centj} + \grad \varia_\centj \cdot \vect{\centj\centjp}.
\end{equation}
Thus, the calculation of the face gradient with reconstruction requires a calculation of the gradients of $\varia$ at cell centers, which can be done
by several methods. Depending on the choosen method, the relation between the values of $\varia$ at cell centers and the gradient at cell centers can
be nonlinear and require a large stencil of cells. We will see in section \ref{sec:groundwater:solving} how the laplacian is solved in \CS, in order to get the solution
with the accurate definition of the face gradients but to keep the simple definition for matrix inversions.

\subsection{Solving of the linear sub-iteration in \CS} \label{sec:groundwater:solving}
%===============================================
The sub-iteration \eqref{eq:groundwater:richards_final} can be rewritten:
\begin{equation}
 \label{eq:groundwater:solving_1}
\face_s \ \delta_H - \dive \left( \mu \grad \delta_H \right) = Q_H,
\end{equation}
where:
\begin{itemize}
\item[$\bullet$] $\delta_H = H^{ n+1, \ k+1} - H^{ n+1, \ k}$ is the unknown;
\item[$\bullet$] $f_s$ = $\frac{C(H^n-z)}{\Delta t}$, not depending on the unknown;
\item[$\bullet$] $\mu = K(H^{ n+1, \ k} - z)$ is the diffusion coefficient, tensorial if the permeability is tensorial. It does not depend on the unknown;
\item[$\bullet$] $Q_H$ is the second member, not depending on the unknown.
\end{itemize}
We have:
\begin{equation}
\label{qh}
Q_H = Q_s + R(H^n - z, \ H^{ n+1, \ k} - z) - \dive \left( K(H^{ n+1, \ k} - z) \
\grad(H^{ n+1, \ k}) \right).
\end{equation}
Now, let us denote $E_n$ the following operator, that applies on any discrete field $x$:
\begin{equation}
\label{epsilon}
E_n(x) = f_s \ x - \left[ \dive \left( \mu \grad x \right) \right]^D,
\end{equation}
where exponent $D$ represents the spatial discretization of gradient - divergent operator in \CS. This operator is linear but, when using
the reconstruction of the non-orthogonalities, it is difficult to invert (see section \ref{sec:groundwater:operator_diffusive}).
Thus, we also define $EM_n$, that is the equivalent of $E_n$ but without taking into account the reconstruction of non-orthogonalities.
Now, we write the discretisation of the equation (\ref{eq:groundwater:solving_1}) in the form:
\begin{equation}
\label{eq:groundwater:solving_2}
E_n(x) = Q_H,
\end{equation}
where $x$ is the unknown. In order to solve it, we define the sequence $(x^m)_{m \in \mathbb{N}}$
that is calculated following these iterations:
\begin{itemize}
 \item[$\bullet$] $EM_n(\delta x^{m+1}) = - E_n(\delta x^{m}) + Q_H$;
 \item[$\bullet$] $x^{m+1} = x^m + \delta x^{m+1}$;
 \item[$\bullet$] $x^0 =$ \textit{initial guess}.
\end{itemize}
With that method, we only invert the simple matrix $EM_n$. If the iterations converge, we get the solution of \eqref{eq:groundwater:solving_2}
with an arbitrary precision, and with a precise definition of the discrete diffusive operator. This is the standard way
of dealing with the diffusion problem in \CS.
%FIXME
See documentation on routine \textit{codits}, for example, for further details.

\subsection{Determination of the velocity field}
%===============================================
\label{velocity_field}
Theoretically, the darcy velocity field $\vect{q}$ of the flow just has to be calculated from the pressure head field, thanks to the Darcy relation
\eqref{eq:groundwater:darcy_3d}. This can be done with the routine of \CS that calculates the gradient of a variable at cell centers from the values of this
variable at cell centers.
However, this simple way of getting the velocity field is only used for posttreatment purpose,
and not used in the transport equation, for the reasons given below.

In \CS, the integral on a cell $\Omega$ of the convection term $\dive \left( \varia \vect{q} \right)$, where $\vect{q}$ is a velocity field
and $\varia$ a transported variable, is discretized this way:
\begin{equation}
 \label{discrete_convective}
\int_{\Omega} \dive \left( \varia \ \vect{q} \right) \dd \Omega \simeq \sum_{\face \in \Face{\celli}} \varia_\face \ \vect{q}_\face \cdot \vect{S}_\face,
\end{equation}
where $\Face{\celli}$ is the set of faces for cell $\celli$. For each face $\face$,
$\varia_\face$ is the value of $\varia$ at the center of face $\Face{\celli}$ (calculated from the values of $\varia$ at centers of the adjacent cells,
with an arithmetic mean), $\vect{q}_\face$ is the velocity at face center, and $\vect{S}_\face$ is a vector normal to the face,
whose size is the surface of the face, directed towards the outside of the cell $\Omega$.
Thus the term $\vect{q}_\face \cdot \vect{S}_\face$ is the mass flux at face center.

These mass fluxes at face centers could be deducted from the velocity values at cell centers, but this method would not ensure the exact
coherence of these mass fluxes with the continuity equation \eqref{eq:groundwater:mass_cons}.
To ensure this coherence, let us write the discrete contnuity equation:
\begin{equation}
 \label{eq:groundwater:mass_cons_discrete}
\frac{\theta^{n+1}-\theta^n}{\partial t} + \left[ \dive \left( \vect{q} \right) \right]^D = Q_s,
\end{equation}
where exponent $D$ corresponds to the discrete operator for convection, described above.
Mixing the discrete Richards equation \eqref{eq:groundwater:richards_final_bis} and the discrete continuity equation \eqref{eq:groundwater:mass_cons_discrete}, we want to have:
\begin{equation}
 \label{discrete_egality}
\left[ \dive \left( K(h^{n+1}) \grad(H^{n+1}) \right) \right]^D = \left[ \dive \left( \vect{q} \right) \right]^D.
\end{equation}
Exponent $D$ still represents discretisation of operators.
Taking into account equation (\ref{discrete_diffusive}) and equation (\ref{discrete_convective}), this leads for each face $\face$ of the domain to:
\begin{equation}
 \label{verification}
K(h^{n+1})_\face \grad_f H^{n+1}\cdot \vect{S}_\face = \vect{q}_\face \cdot \vect{S}_\face.
\end{equation}
This gives the good value for $\vect{q}_\face\cdot \vect{S}_\face$, available from the solving of Richards equation.

So, for the purpose of discrete coherence between flow and transport equations (which is important for the precision of the computation and coherence of the results), we deduct the mass fluxes
used in the transport equation from the normal gradients of the pressure head $H$ calculated in the solving of
Richards equation, instead of deducting them from the velocity values at cell centers.

\subsection{Convergence criterion}
%===============================================
Two choices are available for the convergence criterion of the loop over sub-iterations $k$ (from section \ref{sec:groundwater:solving_richards}).
The first possibility is to continue the loop until two successive pressure head fields are close enough, \emph{i.e.}
$$\Vert h^{n + 1, \ k + 1} - h^{n + 1, \ k } \Vert^{L2} \leq \epsilon,$$
where the value of $\epsilon$ is given by the user.
The second possibility is to impose such a condition on the velocity field of the flow, \emph{i.e.}
$$\Vert u_x^{n + 1, \ k + 1} - u_x^{n + 1, \ k } \Vert^{L2}
+ \Vert u_y^{n + 1, \ k + 1} - u_y^{n + 1, \ k } \Vert^{L2}
+ \Vert u_z^{n + 1, \ k + 1} - u_z^{n + 1, \ k } \Vert^{L2} \leq \epsilon,$$
where we denoted $u_x$, $u_y$ and $u_z$ the components of $\vect{u}$ over the three spatial directions.
This last choice imposes to calculate the velocity field at the end of each sub-iteration.
Both of these options are available in the module.

\subsection{Cases without gravity}
%===============================================
If we don't want to take into account the gravity, then the Darcy law writes:
\begin{equation}
\label{darcy_without_gravity}
 \vect{u} = - K \grad h,
\end{equation}
and the Richards equation becomes:
\begin{equation}
 \frac{\partial{\theta(h)}}{\partial{t}} = \dive \left( K(h) \ \grad(h) \right) + Q_s,
\end{equation}
which is solved exactly the same way, except that the solved variable is $h$ instead of $H$. The user of the module
must be careful to adapt the initial conditions, boundary conditions and soil-laws accordingly.

%===============================================
\section{Groundwater Transferts}
%===============================================

\subsection{Introduction}
%===============================================
The transport of
a contaminant in variably saturated porous media is treated by the Hydrogeology module, based on the transport solver already present in \CS, with few
specific developments.

\subsection{Advection/dispersion/diffusion/decay equation}
%===============================================
We assume hereafter that the contaminant only exists in the liquid phase and is potentially sorbed on the solid matrix.
We also assume that the transport phenomena in the liquid phase are advection, kinematic dispersion and molecular diffusion.
The classical transport equation in variably saturated flow is:
\begin{equation}
\label{sec:groundwater:edv_disp_diff_ini}
\frac{\partial(R \theta c)}{\partial{t}}=\frac{\partial}{\partial x_j}(D_{ij} \frac{\partial c}{\partial x_i})-\frac{\partial \vect{q}_i c}{\partial x_i} + Q_s c_r - \lambda R \theta c
\end{equation}
where:
\begin {itemize}
 \item[$\bullet$] $R$ is the delay factor, representing sorption phenomena [-];
 \item[$\bullet$] $\theta$ is the moisture content [$L^3.L^{-3}$];
 \item[$\bullet$] $c$ is the contaminant concentration in the liquid phase [$M.L^{-3}$];
 \item[$\bullet$] $\vect{q}$ refers to the darcian velocity, which is a result of the solving of the Richards equation (see section \ref{velocity_field}) [$L.T^{-1}$];
 \item[$\bullet$] $\lambda$ is a first-order decay coefficient [$M.L^{-3}.T^{-1}$];
 \item[$\bullet$] $Q_s$ refers to the volumetric flow source/sink, from the Richards equation (\ref{eq:groundwater:richards}) [$M^3.T^{-1}$];
 \item[$\bullet$] $c_r$ is the source/sink concentration [$M.L^{-3}$];
 \item[$\bullet$] $D_{ij}$ is the dispersion tensor. It contains both the kinematic dispersion and the molecular diffusion [$L^2.T^{-1}$].
\end{itemize}
We note the following differences with the standard formulation of the transport equation in \CS:
\begin {itemize}
 \item[$\bullet$] the presence of the delay factor $R$ and the moisture content $\theta$ in the unsteady term;
 \item[$\bullet$] the tensorial diffusivity $D_{ij}$.
\end{itemize}
% Moreover, in the unsaturated case, the water velocity has a non-zero divergence, which cannot be the case in the standard transport algorithm of \CS.
% This constrains to artificially cancel a term of the equation solved by \CS.
The delay factor $R$ is given by:
\begin{equation}\label{k_d}
R = 1 + \frac{\rho k_d}{\theta}
\end{equation}
\begin {itemize}
 \item[$\bullet$] $\theta$ is the moisture content [$L^3.L^{-3}$];
 \item[$\bullet$] $\rho$ is the \textit{bulk} density [$M.L^{-3}$]
 \item[$\bullet$] $K_d$ is the contaminant distribution coefficient [$L^3.M^{-1}$]
\end{itemize}

\subsection{Kinematic dispersion}
%===============================================
%
Kinematic dispersion results from the existence of a very complex and unknown velocity field which is
not taken into account for advection (the average Darcy velocity is considered instead).
It results in a kinematic dispersion tensor denoted $D_k$, whose main directions of anisotropy are the direction of the flow and
two directions orthogonal to the flow. This tensor can be inferred from two parameters named longitudinal and transversal dispersion coefficients (m.s-1),
denoted $\alpha_l$ and $\alpha_t$, and from the amplitude of the velocity darcy field.
In an orthonormal frame such that the first direction is the direction of the flow, this
kinematic dispersion tensor writes:
\begin{equation}
\label{eq:groundwater:dispersion_tensor}
D_k = \ \mid \vect{q} \mid
\begin{pmatrix}
\alpha_l & 0 & 0 \\
0 & \alpha_t & 0 \\
0 & 0 & \alpha_t
\end{pmatrix}
\end{equation}
Physically, The coefficients $\alpha_l$ and $\alpha_t$ are representative of the size of the biggest heterogeneities on the solute path. Their
determination is empirical.

\subsection{Molecular diffusion}
%===============================================
Molecular diffusion is due to Brownian motion of solute molecules that tends to reduce the differences
of concentration in different points of a continuous medium.
It is already taken into account in the standard transport equation of \CS.
In porous media, molecular diffusion occurs in the whole fluid phase but not in the solid medium. Hence, the diffusion coefficient
is, in the general case, proportional to the moisture content $\theta$. It is denoted $d_m$.

\subsection{Dispersion tensor}
%===============================================
Finally, the dispersion tensor $D_{ij}$ is the cumulation of the kimematic dispersion tensor $D_k$ and the molecular diffusion scalar.
In a frame independant of the flow, it can be written:
\begin{equation}\label{formul_disp}
 D_{ij}= \alpha_t \mid \vect{q} \mid \delta_{ij} + (\alpha_l - \alpha_t) \frac{\vect{q}_i \vect{q}_j}{\mid \vect{q} \mid} + d_m \delta_{ij},
\end{equation}
where:
\begin{itemize}
 \item[$\bullet$] $\delta_{ij}$ refers to the Kronecker symbol [$-$];
 \item[$\bullet$] $\alpha_l$ is the longitudinal dispersivity [$L$];
 \item[$\bullet$] $\alpha_t$ is the transversal dispersivity [$L$];
 \item[$\bullet$] $d_m$ is the water molecular diffusion [$L^2.T^{-1}$];
 \item[$\bullet$] $\vect{q}_i$ refers to the darcian velocity in the direction $i$ [$L.T^{-1}$];
 \item[$\bullet$] $\mid \vect{q} \mid$ is the norm of the darcian velocity [$-$].
\end{itemize}
Finally, the tensor is symetric (\emph{i.e.} $D_{ij}=D_{ji}$) and can be expressed as:
\begin{itemize}
 \item[$\bullet$] $D_{xx}=\alpha_l\frac{\vect{q}_x^2}{\mid \vect{q} \mid}+\alpha_t\frac{\vect{q}_y^2}{\mid \vect{q} \mid}+\alpha_t \frac{\vect{q}_z^2}{\mid \vect{q} \mid}+d_m$;
 \item[$\bullet$] $D_{yy}=\alpha_t\frac{\vect{q}_x^2}{\mid \vect{q} \mid}+\alpha_l\frac{\vect{q}_y^2}{\mid \vect{q} \mid}+\alpha_t \frac{\vect{q}_z^2}{\mid \vect{q} \mid}+d_m$;
 \item[$\bullet$] $D_{yy}=\alpha_t\frac{\vect{q}_x^2}{\mid \vect{q} \mid}+\alpha_t\frac{\vect{q}_y^2}{\mid \vect{q} \mid}+\alpha_l \frac{\vect{q}_z^2}{\mid \vect{q} \mid}+d_m$;
 \item[$\bullet$] $D_{xy}=(\alpha_l - \alpha_t) \frac{\vect{q}_x \vect{q}_y}{\mid \vect{q} \mid}$;
 \item[$\bullet$] $D_{xz}=(\alpha_l - \alpha_t) \frac{\vect{q}_x \vect{q}_z}{\mid \vect{q} \mid}$;
 \item[$\bullet$] $D_{yz}=(\alpha_l - \alpha_t) \frac{\vect{q}_y \vect{q}_z}{\mid \vect{q} \mid}$.
\end{itemize}

\subsection{Specificities of the groundwater transport equation in relation to the standard transport equation}
%===============================================
The general method developed in \CS for the treatment of the transport equation is kept; just a few changes in the definition
of the general matrix to invert and of the right hand side have been done, in order to take into account the presence of the
moisture content and delay in the unsteady term,
and to give to the user the opportunity to define a dispersion tensor.
More specifically, as values at iterations $n$ and $n+1$ of moisture content and delay are available for the transport calculation at iteration $n$,
we can discretize the unsteady term this way:
\begin{equation}
\label{unsteady}
\frac{\partial(R \theta c)}{\partial{t}} \simeq \frac{R^{n+1} \ \theta^{n+1} \ c^{n+1} - R^{n} \ \theta^{n} \ c^{n}}{\Delta t},
\end{equation}
which ensures global discrete mass conservation of the tracer.

