Changing River Flood Timing in the Northeastern and Upper Midwest United States: Weakening of Seasonality over Time?

Abstract

<jats:p>Climate change is likely to impact precipitation as well as snow accumulation and melt in the Northeastern and Upper Midwest United States, ultimately affecting the quantity and seasonal distribution of streamflow. The objective of this study is to analyze seasonality of long-term daily annual maximum streamflow (AMF) records and its changes for 158 sites in Northeastern and Upper Midwest Unites States. A comprehensive circular statistical approach comprising a kernel density method was used to assess the seasonality of AMF. Temporal changes were analyzed by separating the AMF records into two 30-year sub-periods (1951–1980 and 1981–2010). Results for temporal change in seasonality showed mixed pattern/trend across the stations. While for majority of stations, the distribution of AMF timing is strongly unimodal (concentrated around spring season) for the period 1951–1980, the seasonal modes have weakened during the period 1981–2010 for several stations along the coastal region with simultaneous emergence of multiple modes indicating changes of seasonality therein. The fresh statistical approach based on non-parametric circular density estimates reduces some of the limitations of previous studies to detect and model event timing distributions with multiple seasons and addresses issues of non-stationarity in the data records of extreme events.</jats:p>

Changing seasonality of daily and monthly precipitation extremes for the contiguous USA and possible connections with large‐scale climate patterns

“…The stations showing increase in mean resultant length of MM are concentrated in central area of Northeast region and few coastal sites in West region. As reported by Dhakal and Palmer (2020), decrease in ρ values indicates the weakening of seasonality and increase in ρ values indicates the strengthening of seasonality for the recent period. Based on regional boxplots, strength of seasonality (median ρ values) appears to wane in the recent period for both ADM and MM in almost every climate region except Northeast and Southwest regions (Figure S5).…”

“…The mean resultant length ρ is a statistic between 0 and 1, where ρ = 0 indicates that dates of extreme precipitation events are uniformly dispersed around the year (no preferred seasonality) and ρ = 1 indicates all the extreme precipitation events occurred on the same day of the year (strong seasonality). Several published studies have used circular mean and mean resultant length to analyse seasonality of streamflow records (Bayliss and Jones, 1993;Burn, 1997;Parajka et al, 2009Parajka et al, , 2010Villarini, 2016;Bloschl et al, 2017;Ye et al, 2017;Dhakal and Palmer, 2020). Similarly, limited studies have used circular mean and mean resultant length to analyse seasonality of precipitation records (Dhakal et al, 2015;Gu et al, 2017;Ye et al, 2017).…”

“…The circular mean direction representing mean date of occurrence of n extreme precipitation events is obtained by using (Jammalamadaka and SenGupta, 2001): Here S and C represent the polar coordinates of the mean date on a unit circle given by: The mean resultant length ρ measures the strength of seasonality by representing the spread of n extreme precipitation event occurrences about the mean date is then computed as: The mean resultant length ρ is a statistic between 0 and 1, where ρ = 0 indicates that dates of extreme precipitation events are uniformly dispersed around the year (no preferred seasonality) and ρ = 1 indicates all the extreme precipitation events occurred on the same day of the year (strong seasonality). Several published studies have used circular mean and mean resultant length to analyse seasonality of streamflow records (Bayliss and Jones, 1993; Burn, 1997; Parajka et al ., 2009, 2010; Villarini, 2016; Bloschl et al ., 2017; Ye et al ., 2017; Dhakal and Palmer, 2020). Similarly, limited studies have used circular mean and mean resultant length to analyse seasonality of precipitation records (Dhakal et al ., 2015; Gu et al ., 2017; Ye et al ., 2017).…”

“…To be spatially consistent, randomly selected years are kept the same across all the stations during each resampling step. Dhakal and Palmer (2020) used a similar approach for analysis of non‐stationarity in seasonality of streamflow in the Northeastern and Upper Midwest United States.…”

Iso-severity flood hazard patterns in diverse relief and climatic regions to support flood management

“…Numerous studies have been conducted on oods and their spatial distribution patterns, including regionalization of ood estimation historical ood events (Barriendos and Rodrigo 2006;Arshia et al 2018); statistical distribution to analyze and estimate ood frequency with different return periods (Lin et al 2016); seasonal ood peak (Chen et al 2010;Fischer et al 2016; Bartiko et al 2019); ood seasonality and its temporal shifts (Ye et al 2017); seasonal distribution of ooding (Diakakis 2017); climate change impacts on seasonal oods (Muttarak and Dimitrova 2019); river ood timing and seasonality (Dhakal and Palmer 2020); ood frequency analysis (Engeland et al 2020); taking account of seasonality in a regional ood frequency (Ding and Arnaud 2022) and ood seasonality assessment (Ruiz et al 2014;Al eri et al 2020;Couasnon et al 2022).…”