Paul Pilon

Bio: Paul Pilon is an academic researcher from Meteorological Service of Canada. The author has contributed to research in topics: Trend analysis & Sample size determination. The author has an hindex of 7, co-authored 7 publications receiving 3310 citations.

The influence of autocorrelation on the ability to detect trend in hydrological series

Abstract: This study investigated using Monte Carlo simulation the interaction between a linear trend and a lag-one autoregressive (AR(1)) process when both exist in a time series. Simulation experiments demonstrated that the existence of serial correlation alters the variance of the estimate of the Mann–Kendall (MK) statistic; and the presence of a trend alters the estimate of the magnitude of serial correlation. Furthermore, it was shown that removal of a positive serial correlation component from time series by pre-whitening resulted in a reduction in the magnitude of the existing trend; and the removal of a trend component from a time series as a first step prior to pre-whitening eliminates the influence of the trend on the serial correlation and does not seriously affect the estimate of the true AR(1). These results indicate that the commonly used pre-whitening procedure for eliminating the effect of serial correlation on the MK test leads to potentially inaccurate assessments of the significance of a trend; and certain procedures will be more appropriate for eliminating the impact of serial correlation on the MK test. In essence, it was advocated that a trend first be removed in a series prior to ascertaining the magnitude of serial correlation. This alternative approach and the previously existing approaches were employed to assess the significance of a trend in serially correlated annual mean and annual minimum streamflow data of some pristine river basins in Ontario, Canada. Results indicate that, with the previously existing procedures, researchers and practitioners may have incorrectly identified the possibility of significant trends. Copyright  2002 Environment Canada. Published by John Wiley & Sons, Ltd.

Canadian streamflow trend detection: impacts of serial and cross-correlation

Abstract: The nonparametric Mann-Kendall (MK) statistical test has been widely applied to assess the significance of trends in hydrological time series. It is known that the existence of serial correlation in a time series will affect the ability of the test to assess the site significance of a trend; and the presence of cross-correlation among sites in a network will influence the ability of the test to evaluate the field significance of trends over the network. This study proposes to use a trend-free pre-whitening (TFPW) procedure to remove serial correlation from time series, and hence to eliminate the effect of serial correlation on the MK test. An additional bootstrap test with preserving the cross-correlation structure of a network is proposed to assess the field significance of upward and downward trends over the network separately. At the significance level of 0.05, the site significance of trends in Canadian annual minimum, mean, and maximum daily streamflows with 30-, 40- and 50-year records was ...

Probability distribution type of Canadian annual minimum streamflow

Abstract: La methode des diagrammes des rapports des L-moments est appliquee, en association avec la distance moyenne ponderee, pour identifier une distribution de probabilite du debit minimum annuel, a partir du debit journalier minimum annuel dans 11 regions climatiques du Canada. Pour l'ensemble du pays, la distribution de probabilite Pearson type III est acceptable pour decrire le debit minimum annuel, les distributions log-normale a 3 parametres et log Pearson type III etant des alternatives potentielles. Quelques differences mineures dans le type de distribution de probabilite sont observees selon les differentes regions climatiques, dont il peut etre tenu compte lors de la selection du type de distribution du debit annuel minimum.

Climate-induced variations in global wildfire danger from 1979 to 2013

Abstract: Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km 2 (25.3%) of the Earth’s vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (41.0 s above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km 2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.

The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series

Abstract: The non-parametric Mann-Kendall (MK) statistical test has been popularly used to assess the significance of trend in hydrological time series The test requires sample data to be serially independent When sample data are serially correlated, the presence of serial correlation in time series will affect the ability of the test to correctly assess the significance of trend To eliminate the effect of serial correlation on the MK test, effective sample size (ESS) has been proposed to modify the MK statistic This study investigates the ability of ESS to eliminate the influence of serial correlation on the MK test by Monte Carlo simulation Simulation demonstrates that when no trend exists within time series, ESS can effectively limit the effect of serial correlation on the MK test When trend exists within time series, the existence of trend will contaminate the estimate of the magnitude of sample serial correlation, and ESS computed from the contaminated serial correlation cannot properly eliminate the effect of serial correlation on the MK test However, if ESS is computed from the sample serial correlation that is estimated from the detrended series, ESS can still effectively reduce the influence of serial correlation on the MK test