Accurate 3D reconstruction in degraded imaging conditions remains a key challenge in photogrammetry and neural rendering. In underwater environments, spatially varying visibility caused by scattering, attenuation, and sparse observations leads to highly non-uniform information quality. Existing 3D Gaussian Splatting (3DGS) methods typically optimize primitives based on photometric signals alone, resulting in imbalanced representation, with overfitting in well-observed regions and insufficient reconstruction in degraded areas. In this paper, we propose SWAGSplatting (Semantic-guided Water-scene Augmented Gaussian Splatting), a multimodal framework that integrates semantic priors into 3DGS for robust, high-fidelity underwater reconstruction. Each Gaussian primitive is augmented with a learnable semantic feature, supervised by CLIP-based embeddings derived from region-level cues. A semantic consistency loss is introduced to align geometric reconstruction with high-level semantics, improving structural coherence and preserving salient object boundaries under challenging conditions. Furthermore, we propose an adaptive Gaussian primitive reallocation strategy that redistributes representation capacity based on both primitive importance and reconstruction error, mitigating the imbalance introduced by conventional densification. This enables more effective modeling of low-visibility regions without increasing computational cost. Extensive experiments on real-world datasets, including SeaThru-NeRF, Submerged3D, and S-UW, demonstrate that the proposed method consistently outperforms state-of-the-art approaches in terms of average PSNR, SSIM, and LPIPS. The results validate the effectiveness of integrating semantic priors for high-fidelity underwater scene reconstruction. Code is available at https://github.com/theflash987/SWAGSplatting.