A coupling of two probability measures $P,\tilde P$ on a Borel space $X$ is any probability measure on $X^2$ whose one-dimensional marginals are $P$ and $\tilde P$. In particular, for any such coupling $\Bbb P$ we have
$$
 2\cdot \Bbb P(X^2\setminus\Delta\_X)\geq\|P - \tilde P\| \tag{1}
$$
where $\Delta\_X = \{(x,x):x\in X\}$ is the diagonal, and $\|\cdot\|$ denotes the total variation norm. At the same time, there always exists a maximal coupling $\Bbb P^\*$ such that the equality holds in $(1)$.


Extension of these results to the case when $P$ and $\tilde P$ are non-probability measures, or at least sub-probabilities does not seem to be complicated, however I wonder whether it has been already addressed somewhere. So far a search on google did not help.



Coupling of non-probability/sub-probability measures
I thought, I could turn the comments into an answer…


The approach by couplings does not work without modifications and the reason is that couplings do not exist if the measures have different total mass: If $P$ and $Q$ are two measures on $X$ with different total masses which were coupled by $\mu$, then
$$
\int\_x\int\_y d\mu(x,y) = \int\_x dP(x) \neq \int\_y d\tilde P(y) = \int\_y\int\_xd\mu(x,y).
$$ 
However, for a given metric $d$ for probability measures one can build a metric for measures with different masses as follows: If $P$ has total mass $P(X) = p$ and $Q$ has total mass $Q(X) = q$, define
$$
D(P,Q) = |p-q| + d(\tfrac{P}{p},\tfrac{Q}{q})
$$
(see Gromov's "Metric structures on Riemanian and Non-Riemannian Spaces", Chapter $3\tfrac12$.B).


There are also other approaches to metrics on measure spaces such as the Kantorovich-Rubinstein norm
$$
W(P,Q) = \sup\bigg\{\int f\, dP - \int f\, dQ\ :\ f\ \text{Lipschitz with constant}\ \leq 1\bigg\}
$$
and others (cf. Villani's "Optimal Transport - Old and New", Chapter 6).


