A Nonparametric Trend Test for Seasonal Data With Serial Dependence

摘要: Statistical tests for monotonic trend in seasonal (e.g., monthly) hydrologic time series are commonly confounded by some of the following problems: nonnormal data, missing values, seasonality, censoring (detection limits), and serial dependence. An extension Mann-Kendall test (designed such data) is presented here. Because based entirely on ranks, it robust against nonnormality censoring. Seasonality values present no theoretical or computational obstacles to its application. Monte Carlo experiments show that, terms type I error, correlation except when data have strong long-term persistence ARMA (1, 1) monthly processes with ϕ > 0.6) short records (∼ 5 years). When there correlation, less powerful than a related simpler which not correlation.