Climate Change and Flooding: Bibliometric Analysis to Identify Future Research

Davronjon Allayorov (Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent Tashkent State Transport University, 100167, Tashkent)
Kudrat Rakhimov (Tashkent State Transport University, 100167, Tashkent)
Uchqun Umarov (Tashkent State Transport University, 100167, Tashkent)
Dinislam Atakulov (Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent)
Dilbar Allayorova (Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent Research Institute of Irrigation and Water Problems, 100187, Tashkent)
Samandar Shaymardonov (Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent)

Forum Geografi
Vol. 40 No. 1 (2026): April 2026

Among the various hydroclimatic phenomena, floods occupy a special place due to their environmental and socio-economic impact. However, identifying the main focus of research related to climate change and floods requires the examination of a large number of publications. This article presents a bibliometric analysis of publications on climate change and floods from the Web of Science database during the period 2015 to 2024. The analysis indicates that climate change-induced flood research is a rapidly expanding and increasingly popular field. The majority of publications (94%) were scientific articles, and in the 5 years up to 2024 there has been an improvement in their quality. Much of the research was conducted in China, the US, and EU. The Chinese Academy of Sciences is a leader in this field, and journals such as Science of the Total Environment, Water, and the Journal of Hydrology are active in disseminating research results. Bibliometric analysis using VOSviewer and Bibliometrix found that in the climate change context, the major institutions are studying precipitation changes and water management in catchments. Flood susceptibility and streamflow forecasting are the central topics that need to be studied in-depth in order to develop highly accurate early warning systems. During the process, physically-based hydrological modelling must also be incorporated using advanced soft computing techniques, particularly machine learning, artificial neural networks, and geospatial tools such as geographic information systems and remote sensing.