Monocular depth estimation has experienced significant progress on terrestrial images in recent years thanks to deep learning advancements. But it remains inadequate for underwater scenes primarily due to data scarcity. Given the inherent challenges of light attenuation and backscatter in water, acquiring clear underwater images or precise depth is notably difficult and costly. To mitigate this issue, learning-based approaches often rely on synthetic data or turn to self- or unsupervised manners. Nonetheless, their performance is often hindered by domain gap and looser constraints. In this paper, we propose a novel pipeline for generating photorealistic underwater images using accurate terrestrial depth. This approach facilitates the supervised training of models for underwater depth estimation, effectively reducing the performance disparity between terrestrial and underwater environments. Contrary to previous synthetic datasets that merely apply style transfer to terrestrial images without scene content change, our approach uniquely creates vivid non-existent underwater scenes by leveraging terrestrial depth data through the innovative Stable Diffusion model. Specifically, we introduce a specialized Depth2Underwater ControlNet, trained on prepared {Underwater, Depth, Text} data triplets, for this generation task. Our newly developed dataset, Atlantis, enables terrestrial depth estimation models to achieve considerable improvements on unseen underwater scenes, surpassing their terrestrial pretrained counterparts both quantitatively and qualitatively. Moreover, we further show its practical utility by applying the improved depth in underwater image enhancement, and its smaller domain gap from the LLVM perspective. Code and dataset are publicly available at https://github.com/zkawfanx/Atlantis.