Half-space depth (also called Tukey depth or location depth) is one of the most commonly studied data depth measures because it possesses many desirable properties for data depth functions. The data depth contours bound regions of increasing depth. For the sample case, there are two competing definitions of contours: the rank-based contours and the cover-based contours. In this paper, we present three dynamic algorithms for maintaining the half-space depth of points and contours: The first maintains the half-space depth of a single point in a data set in $O(\log n)$ time per update (insertion/deletion) and overall linear space. By maintaining such a data structure for each data point, we present an algorithm for dynamically maintaining the rank-based contours in $O(n\cdot\log n)$ time per update and overall quadratic space. The third dynamic algorithm maintains the cover-based contours in $O(n\cdot \log^2 n)$ time per update and overall quadratic space. We also augment our first algorithm to maintain the local cover-based contours at data points while maintaining the same complexities. A corollary of this discussion is a strong structural result of independent interest describing the behavior of dynamic cover-based contours near data points.