A catastrophe identification method for rainfall time series coupled sequential Mann-Kendall algorithm and Bernaola Galvan algorithm: a case study of the Qinglong River watershed, China

Abstract The identification of rainfall catastrophe characteristics is important for consistency testing in hydrostatistical analysis. In this study, a new classification framework method (trend, mean and change-rate catastrophe) coupled Sequential Mann-Kendall (SQ-MK) algorithm Bernaola-Galvan heuristic segmentation (B-G) was proposed, scheme carried out the Qinglong River watershed, Northern China. Meanwhile, accuracy improved by robustness parameter optimization B-G algorithm. Results revealed that (1) most significant point trend watershed 1997. based on SQ-MK sensitive to length time series. (2) sensitivity P0 (Range value (R) = 2.889) greater than l0 (R 0.333). points were 1997, 2002 2009. (3) insensitive (4) There no watershed. Trend did not necessarily lead catastrophe.