Regional methods for trend detection: Assessing field significance and regional consistency

Abstract

[1] This paper describes regional methods for assessing field significance and regional consistency for trend detection in hydrological extremes. Four procedures for assessing field significance are compared on the basis of Monte Carlo simulations. Then three regional tests, based on a regional variable, on the regional average Mann-Kendall test, and a new semiparametric approach, are tested. The latter was found to be the most adequate to detect consistent changes within homogeneous hydro-climatic regions. Finally, these procedures are applied to France, using daily discharge data arising from 195 gauging stations. No generalized change was found at the national scale on the basis of the field significance assessment of at-site results. Hydro-climatic regions were then defined, and the semiparametric procedure applied. Most of the regions showed no consistent change, but three exceptions were found: in the northeast flood peaks were found to increase, in the Pyrenees high and low flows showed decreasing trends, and in the Alps, earlier snowmelt-related floods were detected, along with less severe drought and increasing runoff due to glacier melting. The trend affecting floods in the northeast was compared to changes in rainfall, using rainfall-runoff simulation. The results showed flood trends consistent with the observed rainfall.

The impact of ENSO and the NAO on extreme winter precipitation in North America in observations and regional climate models

“…the p values themselves. An alternative method is based on the false discovery rate (FDR), which is the expected proportion of falsely rejected null hypotheses from the set of local tests (Renard et al 2008;Wilks 2006;Ventura et al 2004). The FDR can be controlled at a global significance level (α = 0.05) and a FDR probability is defined for each p value in the set of K ordered p values,…”

“…Field significance is declared at the α significance level if at least one local null hypothesis has a p-value less than the P FDF and is thus rejected at the global significance level (Renard et al 2008;Wilks 2006). The FDR method assumes that all tests are independent, which is generally not true for spatial climate data, but Wilks (2006) showed that the method is robust to spatial dependence (Renard We evaluate the influence of spectral nudging by concatenating two simulations that differ only in their nesting strategy, CanRCM4_0.44 (uses spectral nudging) and Can-RCM4_NS (no spectral nudging).…”

Trends in Rainfall and Peak Flows for some River Basins in India

“…Several studies dealing with climate-driven changes for rivers in Europe covering countries like Germany, Swiss Alps, France, Spain and the United Kingdom have been reported in the literature [36][37][38][39] , but no trends have been found in flood magnitude and frequency on continental scale. Jiang et al 40 found increasing trend in annual maximum floods in the lower Yangtze River for the 40 years of data studied.…”

Frequency Analysis of Extreme Sub-Daily Precipitation under Stationary and Non-Stationary Conditions across Two Contrasting Hydroclimatic Environments

“…The RAMK test indicates that trends are 25 statistically positive for 15-, 30-, and 1440-minute durations. The lack of a clear signal in the direction of the trends has been previously reported for other regions (Sarhadi and Soulis, 2017) and might be linked to the weak power of statistical methods when the data are affected by high natural variability as is the case of precipitation (Renard et al, 2008).…”

“…For both extreme value models, the magnitudes of the trends observed in the study period are assumed to continue at the same rate in the next 100 years into the future. Estimated precipitation intensities (return levels) for different return periods, and the corresponding credibility interval (this term is 20 called confidence interval in Frequentist context (Renard et al, 2008)) are shown in Fig. 5.…”