In the new global era, determining trends can play an important role in guiding researchers, scientists, and agencies. The main faced challenge is to track the emerging topics among the stacked publications. Therefore, any study done to propose the trend topics in a field to foresee upcoming subjects is crucial. In the current study, the trend topics in the field of "Hydrology" have been attempted to evaluate. To do so, the model is composed of three key components: a gathering of data, preprocessing of the article's significant features, and determining trend topics. Various topic models including Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Latent Semantic Analysis (LSA) have been implemented. Comparing the obtained results with respect to the $C_V$ coherence score, in 2022, the topics of "Climate change", "River basin", "Water management", "Natural hazards/erosion", and "Hydrologic cycle" have been obtained. According to a further analysis, it is shown that these topics keep their impact on the field in 2023, as well.

翻译：在全球新时代背景下，趋势识别对于指导研究人员、科学家及机构具有重要作用。当前面临的主要挑战在于从大量累积出版物中追踪新兴主题。因此，任何旨在预测领域未来研究方向的趋势主题研究都至关重要。本研究尝试评估"水文学"领域的趋势主题。为此，构建了包含三大核心组件的模型：数据采集、论文关键特征预处理及趋势主题识别。本研究实现了包括潜在狄利克雷分配（LDA）、非负矩阵分解（NMF）和潜在语义分析（LSA）在内的多种主题模型。通过$C_V$一致性分数比较结果表明，2022年获得的主题包括"气候变化"、"流域"、"水资源管理"、"自然灾害/侵蚀"及"水文循环"。进一步分析显示，这些主题在2023年仍将持续影响该领域。