Episodic interannual climate oscillations and their influence on seasonal rainfall in the Everglades National Park

Abstract

[1] The restoration of the Everglades in Florida is an exemplary ecosystem project. A basic challenge of the restoration project is to operate the hydrologic control structures in a manner that allows the right quantity and quality of water to be delivered at the right times to the right locations. An understanding of long-term variations in seasonal rainfall as well as prospects for the upcoming season are of interest for operational planning. This paper aims to characterize the interannual variability in seasonal rainfall in the Everglades and to identify regions of Pacific and Atlantic oceans whose sea surface temperatures (SSTs) may be the carriers of the low-frequency information associated with Everglades rainfall. It is now known that interannual and interdecadal quasi-oscillatory phenomena modulate continental rainfall in many places. The amplitudes of these ''oscillations'' vary with time, and they conform to activity in specific frequency bands. The dominant low-frequency modes also vary by season. Identifying the climate modes that influence specific low-frequency aspects of rainfall is a challenge that is addressed here using wavelet analysis to diagnose the time-varying low-frequency structure and independent component analysis to identify the spatial modes of variation of the low-frequency signals. The combined approach is termed wavelet-independent component analysis (WICA). In addition to identifying dominant timescales of quasi-oscillatory phenomena that modulate interannual rainfall in the Everglades National Park, we investigate how the amplitude (power) associated with these interannual modes varies at decadal or longer timescales. The analyses presented motivate the need for the development of methods for the analysis and simulation of nonstationary hydroclimatic phenomena. The connection between the resulting low-frequency rainfall modes and sea surface temperatures (SSTs) is then established using correlation analysis using concurrent and preceding season SSTs. The results provide the motivation for the development of a new generation of simulation and forecasting models for rainfall that could directly use such low-frequency information.

Effect of interannual and interdecadal climate oscillations on groundwater in North Carolina

“…The past decade has seen a preponderance of research on the link between interannual and interdecadal climate oscillations such as ENSO and PDO, respectively, and a wide variety of hydrological processes. These studies range from an examination of the correlation between these signals and the magnitude and timing of snowmelt runoff in Oregon [ Beebee and Manga , 2004] and assessment of the correlation between rainfall erosivity and ENSO [ D'Odorico et al , 2001] to direct examination of interannual climate oscillation‐precipitation relationships [ Rajagopalan and Lall , 1998; Kwon et al , 2006], interdecadal climate oscillation‐precipitation relationships [ Lucero and Rodríquez , 1999], and interdecadal climate oscillation‐streamflow relationships [ Gutiérrez and Dracup , 2001]. Little research, however, investigates the relationship between climate oscillations and groundwater resources hypothesized by Rodell and Famiglietti [2001].…”

Predicting foraging wading bird populations in Everglades National Park from seasonal hydrologic statistics under different management scenarios

“…We suggest it is therefore important to consider the relative performance of these scenarios in a larger context determined by longer-term rainfall amounts and timing. Optimizing foraging conditions for wading bird species over longer time periods is likely to require adaptive release schedules which incorporate real-time information on water levels, rainfall, and climatic drivers [e.g., Kwon et al, 2006]. Of the four analyzed here, the management scenario most closely tied to climatic conditions is the rainfall plan, and while it did not produce the highest numbers of Great Egret or White Ibis during either the low or high rainfall periods, this scenario did produce the highest median number of foraging birds when the species were combined over the full period of record (2450 birds yr À1 ).…”

Evaluation of typhoon-induced rainfall using nonparametric Monte Carlo simulation and locally weighted polynomial regression

“…Nonparametric kernel density estimation has been widely used in the water resources field, and several promising nonparametric approaches to estimate the probability density function of hydrologic variables have been introduced (Moon et al , 1993; Moon and Lall, 1994; Kwon et al , 2006). The NMCS approach is applied to 45 rainfall events induced by typhoons.…”