Building causal graphs for complex environmental problems such as water quality management often requires extensive analysis and discussion by researchers and experts in the field. Recently, it has been demonstrated that Large Language Models (LLMs) are able to infer correct causal arguments across different contexts, allowing experts to reduce the effort in building complex causal analyses. However, the different failure models that an LLM may present (e.g. hallucinations) make it necessary to statistically assess their performance in terms of correctness or semantic similarity. Furthermore, new model capabilities such as reasoning need to be assessed to offer up-to-date insights on the state of the art. In this study, a state-of-the-art LLM reasoning proprietary model (Gemini 2.5-Flash) is employed to analyze 26 causal relationships among 10 key variables involved in the water eutrophication control of the As Conchas Reservoir, within the Miño-Sil river basin district (NW Spain). The output of the LLM is compared against a ground truth written by experts, analyzing its capabilities for pairwise causal discovery. A statistical analysis of the performance metrics assesses model variability, showing 89.6% overall accuracy across all experimental factors, and a promising 100% F1-score for a low, non-zero model temperature setting.