It is an interesting question.

Actually your guess about dependence on measurement parameter $p$ is correct. The minimal Wasserstein distance between two copulas (In your case the collection of all probability distributions with same uniform marginals is a copula, which also includes the 2-d uniform distribution. Let us denote the 2-d uniform distribution by $\pi_0$ in following discussion.) actually depends on the measurement parameter $p$ of the Wasserstein distance. See Prop 1.1 of Alfonsi&Jourdain. So it is not hard to see that the maximal Wasserstein distance will also depend on $p$ using the "coarest" Fréchet–Hoeffding copula bounds on each dimension of marginals and hence the calculation of Wasserstein distance. A concrete example where $p=2$ can be found in [Cuesta-Albertos et.al].

Now come to the other part of your question that what is the maximal Wasserstein distance to $\pi_0$. Then it is equivalent to find geodesics on the submanifold determined by copula $C_{unif}$ on the probability space metricized by Wasserstein distance. This problem is generally unsolved, if you do not restrict the family of probability distributions under consideration, to my best knowledge.

One noticeable attempt is [Ambrosio et.al] whose work is also on $p=2$. If you metricized this copula, then I think you only need to find the complementary geodesic in a circular neighborhood of $\pi_0$(geodesics in a circular neighborhood of $pi_0$ correspond to the distributions possessing the minimal Wasserstein ($L^2$) distances to $\pi_0$ ) Again for general case $p\neq 2$ I am also interested in knowing more.

One more comment is that Wasserstein distance is a measure of dissimilarity, and thus we usually talk about its minimization instead of maximization. OP seems asking a bound on Wasserstein distance for a general family. As you said in the comment, if the motivation is only a convex optimization problem, I was wondering if it could be re-phrase into a minimization problem by some sort of duality.

Reference

[Alfonsi&Jourdain]Alfonsi, Aurélien, and Benjamin Jourdain. "A remark on the optimal transport between two probability measures sharing the same copula." Statistics & Probability Letters 84 (2014): 131-134.

[Cuesta-Albertos et.al]Cuesta-Albertos, Juan A., Carlos Matrán Bea, and Jesús M. Rodríguez Rodríguez. "Shape of a distribution through the L2-Wasserstein distance." Distributions With Given Marginals and Statistical Modelling. Springer Netherlands, 2002. 51-61.

[Ambrosio et.al]Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. "Gradient flows with metric and differentiable structures, and applications to the Wasserstein space." Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni 15.3-4 (2004): 327-343.

It is an interesting question.

Actually your guess about dependence on measurement parameter $p$ is correct. The minimal Wasserstein distance between two copulas (In your case the collection of all probability distributions with same uniform marginals is a copula, which also includes the 2-d uniform distribution. Let us denote the 2-d uniform distribution by $\pi_0$ in following discussion.) actually depends on the measurement parameter $p$ of the Wasserstein distance. See Prop 1.1 of Alfonsi&Jourdain. So it is not hard to see that the maximal Wasserstein distance will also depend on $p$ using the "coarest" Fréchet–Hoeffding copula bounds on each dimension of marginals and hence the calculation of Wasserstein distance. A concrete example where $p=2$ can be found in [Cuesta-Albertos et.al].

Now come to the other part of your question that what is the maximal Wasserstein distance to $\pi_0$. Then it is equivalent to find geodesics on the submanifold determined by copula $C_{unif}$ on the probability space metricized by Wasserstein distance. This problem is generally unsolved, if you do not restrict the family of probability distributions under consideration, to my best knowledge.

One noticeable attempt is [Ambrosio et.al] whose work is also on $p=2$. If you metricized this copula, then I think you only need to find the complementary geodesic in a circular neighborhood of $\pi_0$(geodesics in a circular neighborhood of $pi_0$ correspond to the distributions possessing the minimal Wasserstein ($L^2$) distances to $\pi_0$ ) Again for general case $p\neq 2$ I am also interested in knowing more.

One more comment is that Wasserstein distance is a measure of dissimilarity, and thus we usually talk about its minimization instead of maximization. OP seems asking a bound on Wasserstein distance for a general family. As you said in the comment, if the motivation is only a convex optimization problem, I was wondering if it could be re-phrase into a minimization problem by some sort of duality.

Reference

[Alfonsi&Jourdain]Alfonsi, Aurélien, and Benjamin Jourdain. "A remark on the optimal transport between two probability measures sharing the same copula." Statistics & Probability Letters 84 (2014): 131-134.

[Cuesta-Albertos et.al]Cuesta-Albertos, Juan A., Carlos Matrán Bea, and Jesús M. Rodríguez Rodríguez. "Shape of a distribution through the L2-Wasserstein distance." Distributions With Given Marginals and Statistical Modelling. Springer Netherlands, 2002. 51-61.

[Ambrosio et.al]Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. "Gradient flows with metric and differentiable structures, and applications to the Wasserstein space." Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni 15.3-4 (2004): 327-343.

It is an interesting question.

Actually your guess about dependence on dimension of the support of $\mu$ and $\nu$ is correct. The minimal Wasserstein distance between two copulas (In your case the collection of all probability distributions with same uniform marginals is a copula, which also includes the 2-d uniform distribution. Let us denote the 2-d uniform distribution by $\pi_0$ in following discussion.) actually depends on the measurement parameter $p$ of the Wasserstein distance. See Prop 1.1 of Alfonsi&Jourdain. So it is not hard to see that the maximal Wasserstein distance will also depend on $p$ using the "coarest" Fréchet–Hoeffding copula bounds on each dimension of marginals and hence the calculation of Wasserstein distance. A concrete example where $p=2$ can be found in [Cuesta-Albertos et.al].

Now come to the other part of your question that what is the maximal Wasserstein distance to $\pi_0$. Then it is equivalent to find geodesics on the submanifold determined by copula $C_{unif}$ on the probability space metricized by Wasserstein distance. This problem is generally unsolved, if you do not restrict the family of probability distributions under consideration, to my best knowledge.

One noticeable attempt is [Ambrosio et.al] whose work is also on $p=2$. If you metricized this copula, then I think you only need to find the complementary geodesic in a circular neighborhood of $\pi_0$(geodesics in a circular neighborhood of $pi_0$ correspond to the distributions possessing the minimal Wasserstein ($L^2$) distances to $\pi_0$ ) Again for general case $p\neq 2$ I am also interested in knowing more.

As you said in the comment, if the motivation is only a convex optimization problem, I was wondering if it could be re-phrase into a minimization problem by some sort of duality.

Reference

[Alfonsi&Jourdain]Alfonsi, Aurélien, and Benjamin Jourdain. "A remark on the optimal transport between two probability measures sharing the same copula." Statistics & Probability Letters 84 (2014): 131-134.

[Cuesta-Albertos et.al]Cuesta-Albertos, Juan A., Carlos Matrán Bea, and Jesús M. Rodríguez Rodríguez. "Shape of a distribution through the L2-Wasserstein distance." Distributions With Given Marginals and Statistical Modelling. Springer Netherlands, 2002. 51-61.

[Ambrosio et.al]Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. "Gradient flows with metric and differentiable structures, and applications to the Wasserstein space." Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni 15.3-4 (2004): 327-343.

It is an interesting question.

Actually your guess about dependence on measurement parameter $p$ is correct. The minimal Wasserstein distance between two copulas (In your case the collection of all probability distributions with same uniform marginals is a copula, which also includes the 2-d uniform distribution. Let us denote the 2-d uniform distribution by $\pi_0$ in following discussion.) actually depends on the measurement parameter $p$ of the Wasserstein distance. See Prop 1.1 of Alfonsi&Jourdain. So it is not hard to see that the maximal Wasserstein distance will also depend on $p$ using the "coarest" Fréchet–Hoeffding copula bounds on each dimension of marginals and hence the calculation of Wasserstein distance. A concrete example where $p=2$ can be found in [Cuesta-Albertos et.al].

Now come to the other part of your question that what is the maximal Wasserstein distance to $\pi_0$. Then it is equivalent to find geodesics on the submanifold determined by copula $C_{unif}$ on the probability space metricized by Wasserstein distance. This problem is generally unsolved, if you do not restrict the family of probability distributions under consideration, to my best knowledge.

One noticeable attempt is [Ambrosio et.al] whose work is also on $p=2$. If you metricized this copula, then I think you only need to find the complementary geodesic in a circular neighborhood of $\pi_0$(geodesics in a circular neighborhood of $pi_0$ correspond to the distributions possessing the minimal Wasserstein ($L^2$) distances to $\pi_0$ ) Again for general case $p\neq 2$ I am also interested in knowing more.

One more comment is that Wasserstein distance is a measure of dissimilarity, and thus we usually talk about its minimization instead of maximization. OP seems asking a bound on Wasserstein distance for a general family. As you said in the comment, if the motivation is only a convex optimization problem, I was wondering if it could be re-phrase into a minimization problem by some sort of duality.

Reference

[Alfonsi&Jourdain]Alfonsi, Aurélien, and Benjamin Jourdain. "A remark on the optimal transport between two probability measures sharing the same copula." Statistics & Probability Letters 84 (2014): 131-134.

[Cuesta-Albertos et.al]Cuesta-Albertos, Juan A., Carlos Matrán Bea, and Jesús M. Rodríguez Rodríguez. "Shape of a distribution through the L2-Wasserstein distance." Distributions With Given Marginals and Statistical Modelling. Springer Netherlands, 2002. 51-61.

[Ambrosio et.al]Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. "Gradient flows with metric and differentiable structures, and applications to the Wasserstein space." Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni 15.3-4 (2004): 327-343.