Uncertainties and risks associated with hydroclimatic variations pose a challenge to the management and planning of water resources systems. This study demonstrates the importance of understanding the changing hydrologic regime of the Feather River Basin (FRB) and its impacts on water resources decision variables (i.e., storage requirement and performance of a water supply reservoir). A simple storage–yield–reliability model (S–Y–R) is used to quantify the risk of the stationary-based designed reservoir under the temporal variation and nonstationarity in N-year blocks of the Feather River Inflow into Lake Oroville (FRI). Furthermore, the potential linkages of the long-term variability in the FRI to climate variations are investigated by applying wavelet spectrum and coherence analysis to the FRI and atmospheric–oceanic indices (e.g., ENSO and PDO). The results show substantial variations in the FRB hydrologic regime over different timescales with episodes of abrupt shifts toward significantly higher storage requirements, and decrease in the reservoir performance during historical periods of high FRI variance and lag-1 serial correlation. Although the mean inflows are high, the storage capacity is increased by (a) 38 and 48% due to the 5 and 20% increase in the FRI variance during the periods 1904–1953 and 1960–2009, respectively, and (b) 34% due to the increase in the serial correlation coefficient in the period of 1750–1799. Likewise, reservoir performance significantly decreased for the same reasons in the same critical periods. The reliability and resilience dropped to 74 and 29% (1904–1953) and to 76 and 50% (1960–2009 period) due to the increased variance of FRI, while vulnerability reached 70% during the high lag-1 correlations in 1532–1581 and 1564–1613, and 40% in 1904–1953 due to the high FRI variance. Furthermore, the wavelet coherence analysis observes strong associations between the streamflow and climate teleconnection patterns in specific periodic cycles during the same critical periods which link the variability in FRI and decision variables to the hydroclimatic variations. These linkages give a primary indication for the reservoir storage requirement characterization.

Temporal changes in the seasonality of extreme precipitation, and possible teleconnections between the seasonality of extreme precipitation and large‐scale climate patterns are not well understood. In this study, we investigated temporal changes in seasonality of annual daily maximum (ADM) and monthly maximum (MM) precipitation indices over the period 1951–2014 for 1,108 stations across the contiguous USA. We also examined seasonality of extreme precipitation during negative and positive phases of three major oscillations: the El Niño–Southern Oscillation, the Northern Atlantic Oscillation, and the Pacific Decadal Oscillation. Our results show that many climate regions within the contiguous USA display distinct seasonality for both ADM and MM. Comparison of seasonality between two historical records of equal length, that is, before and after 1981, shows great spatial variability across the contiguous USA. While a spatial coherence of change in the mean date of occurrence of extreme precipitation across a large area is not visible, a cluster of stations showing decrease in strength of seasonality for the recent period is concentrated in the eastern Gulf Coast and coastal sites of Northeast and Northwest regions. Extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large‐scale climate variabilities have a strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous USA. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality.

<p><strong>Abstract</strong></p> <p>The empirical probability distribution of extreme precipitation in the eastern United States comprises heavy rainfall events stemming from the moisture held by the Atmospheric Rivers (ARs). In many sites, ARs trajectories can have varying impacts on the extreme precipitation seasonality based on the moisture source and tracks. Consequently, a characterization of location specific and regional patterns of timing of extreme precipitation caused by ARs and their non-stationarity has salience for both scientific and engineering concerns. To this end, analysis of annual maximum daily precipitation (AMP) at 581 long-term stations across the eastern United States was pursued in this study to evaluate the role of moisture sources and tracks in the seasonality of extreme rainfall-AR related events (AMP-AR) and their temporal changes over the 1950–2015 period. The key results from this study include: (a) spatio-temporal variation in the fraction of annual maximum precipitation events linked to ARs, and (b) a marked influence of moisture sources on the seasonality of AMP-AR related events. Results from this study have important bearing on the flood risk management and preparedness.</p>

Global warming is likely to provoke extreme storms in the eastern United States (eUS), ultimately affecting the probabilistic distribution of the dates of daily maximum precipitation. In this study, probabilistic properties of timing of annual maximum precipitation (AMP) were studied using circular statistics at 583 sites in the eUS (1950–2019). A kernel circular density method was applied to examine distributional modes of timing of AMP. The results of circular median show that seasonality is pronounced across the eUS with many locations having their median date of occurrence in summer, and AMP seasonality is strong in the East North Central region. Similarly, results of circular density method applied to the distribution of AMP timing shows that around 90% of the sites have two or three modes of AMP seasonality in the eUS. Comparison of seasonality between two historical records of equal length (1950–1984 and 1985–2019) shows great spatial variability across the eUS. Temporal changes in seasonal modes for AMP dates revealed four different cases of seasonality changes: (i) weakening of seasonality, (ii) strengthening of seasonality, (iii) strong seasonality for both the old and recent periods, (iv) or uniform or no preferred seasonality for both periods. While a spatial coherence of seasonality changes was not observed, majority of sites showed strong seasonality (case iii) for old and recent periods mainly during summer and fall seasons.

<p>Despite the importance of seasonality of extreme precipitation events to stormwater management, there are limited number of studies examining seasonality of daily and monthly precipitation extremes over the contiguous United States. In this study, a circular statistical method was used for spatio-temporal assessment of seasonality of daily and monthly precipitation extremes and their teleconnections with large-scale climate patterns over the contiguous United States. Historic precipitation time series over the period of 64 years (1951–2014) for 1108 sites was used for the analysis. Calendar dates for extreme precipitation were used to characterize seasonality within a circular statistics framework which includes indices reflecting the mean date and variability of occurrence of extreme events. The rainfall seasonality during negative and positive phases of the El Niño–Southern Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were also investigated. Results showed that extreme precipitation seasonality varied across the contiguous United States with distinct spatial pattern of seasonality (strong seasonality) in the western and mid-western regions and mixed spatial pattern in the eastern region. In addition, extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large-scale climate variabilities have strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous United States. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality.</p><p> </p>