[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.
Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.
A sensitivity and uncertainty analysis of the Penman‐Brutsaert evapotranspiration (ET) model was conducted using meteorological data collected from the humid South Florida region. Since net radiation and relative humidity were found to be correlated (ρ = −0.74) in this region, a method was developed to analyze the sensitivity with the correlation effect of these two independent variables. After conducting sensitivity analyses with and without the correlation effect, the results were evaluated using conditional probability density functions. Both theoretical and computed results show that the conditional probability with the correlation effect increases in proportion to the increasing absolute correlation coefficient of two independent variables. This finding suggests that the sensitivity analysis with the correlation effect represents more adequately a given set of meteorological data in South Florida than that without the correlation effect. After defining the random errors of the independent variables based on multiple measurements, the ET model error as well as the propagated error caused by erroneous independent variables are presented.
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