Overall and Partial Trend Identification of High Resolution Gridded Rainfall Data over Vietnam by Applying Innovative-Şen Trend Analysis (ITA) Method

Abstract

Abstract Trend detection in rainfall time series is a prime task to provide reliable outcomes for better planning and management of water resources under climate change. This study investigated the rainfall trends in seasonal and monthly rainfall over Vietnam, using high-resolution gridded datasets from 1980 to 2010. Possible trends in rainfall values were detected by a recently proposed Innovative-Şen Analysis (ITA) method, which allows identifying the trends of the low, medium, and high values of a series. The outcomes showed high domination of significantly increasing trends in annual rainfall. Moreover, the analysis of partial trends in the time series identified a sequence of alternating decreasing, increasing, and trendless seasonal and monthly rainfall. On the other hand, opposite trends were found for extreme rainfall, in which the high values in the rainy season showed negative trends, while the positive trends of low values in the dry season were indicated. Besides, based on categorization, the low, medium, and high monthly rainfall values of the seven sub-regions were mainly evaluated, which occurred in different trends. The high values in monthly of the rainy season had also detected an upward trend, and the low values in monthly dry season existed downward trends that are expected that these results will be associate with rainfall trends with floods and droughts. Besides, the study showed that the ITA method could be successfully used in trends analysis for gridded datasets of rainfall variables.