ANALYSIS OF PRECIPITATION ESTIMATION MODELS USING TIME SERIES: A BIBLIOMETRIC ANALYSIS

Abstract

This study conducts a bibliometric analysis of precipitation estimation models using time series, aiming to identify trends, methods, and scientific contributions over the last four decades. The research is justified by the importance of precipitation as a primary variable in hydrological modeling and the scarcity of accurate rainfall data, especially in regions with high spatial and temporal variability. The work seeks to map the main precipitation estimation models, analyze their spatial and temporal resolution, and identify methodological trends and innovations in the field. The methodology adopted included a bibliometric approach, using the Scopus database to collect scientific articles, followed by statistical analyses and term co-occurrence networks using the VOSviewer software. Seventy-eight articles published between 1982 and 2023 were analyzed, focusing on the 40% most cited in each decade. The results reveal a significant evolution in precipitation modeling, with the transition from simple statistical methods to advanced techniques such as artificial neural networks (ANNs) and deep learning (LSTM). Daily temporal and regional spatial resolution predominated in the studies, and the integration of satellite data and artificial intelligence (AI) techniques has become a dominant trend in the last decade. The conclusion is that advances in precipitation modeling have significant practical impacts on water resource management and agriculture, contributing to the prediction of extreme events and adaptation to climate change.

DOI: https://doi.org/10.56238/sevened2025.036-019