In areas with extensive, nomadic, or transhumant livestock farming, it is important to access regular information on the location of ephemeral surface water bodies. Existing near-real time methods for high-resolution surface water mapping are mainly based on the use of optical satellite imagery. However, the use of optical data restricts the water detection to cloud-free conditions. To overcome this limitation SAR data are used for water bodies mapping. Nevertheless, the implemented techniques are usually not fully automated or are not applicable in hilly landscapes. Indeed, surface roughness, hill shadows, and presence of vegetation are known to affect the backscatter and lead to false alarms. In this study, a SAR-based method was used to map surface water from a set of Sentinel-1 images using the Otsu Valley Emphasis method to automatically detect a threshold for water in the histogram of backscatter. In order to reduce the false alarm rate in the steep areas, five different water masks using terrain and drainage information with different thresholds are compared in the mountainous province of KwaZulu-Natal (KZN) in South-Africa. The quantitative assessment shows that the overall accuracy ranged between 0.865 and 0.958 with the highest value obtained with the HAND (Height Above the Nearest Drainage)-based mask with a threshold of 10m. This mask also minimized the false detection of water with the lowest specificity of 0.037. The visual inspection over two reservoirs (Midmar Dam and Wagendrift Dam) shows that there is high agreement between the produced map and the reference data despite differences in their spatial and temporal coverage. Besides, radiometrically terrain corrected SAR data, which could be advantageous in such landscapes were recently made available by the ASF vertex platform. Even though they are not available in NRT, the potential of using such data for water detection is investigated.