Slow-onset events such as droughts lack a clearly defined onset, and systematic databases do not capture their impacts at fine scales. News reports can help fill this gap by providing information on how drought-driven water shortages affect communities and how authorities respond. Yet, extracting structured information from text remains challenging. Here, we use news articles and official public communications on water-saving recommendations and water-use restrictions in Sweden as a case study to benchmark LLMs for drought-related information extraction. We compare the proprietary GPT-5 series against three large open-access models (Llama3.3, Deepseek, and GPT-OSS) in extracting variables such as the date, location, and causes of water-saving measures. Using prompt engineering, structured outputs, and minimal post-processing, we evaluate performance using a test set of 210 manually annotated articles. The GPT-5 model series outperforms all open-access models across all variables. We find that, without task-specific fine-tuning, open-source models are sometimes outperformed by a keyword-based baseline, suggesting that general-purpose capabilities alone are insufficient for reliably extracting structured data. These findings underscore the challenges of monitoring water-shortage risks based on texts, particularly when extracting complex variables.