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Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data. / by Delroy Peters. / Thesis (M.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Radar rainfall estimates have become a decision making tool for scientists, engineers and water managers in their tasks for developing hydrologic models, water supply planning, restoration of ecosystems, and flood control. In the present study, the utility of a power function for linking the rain gauge and radar estimates has been assessed. Mean daily rainfall data from 163 rain gauges installed within the South Florida Water Management District network have been used and their records from January 1st, 2002 to October 31st, 2007 analyzed. Results indicate that the power function coefficients and exponents obtained by using a non-linear optimization formulation, show spatial variability mostly affected by type of rainfall events occurring in the dry or wet seasons, and that the linear distance from the radar location to the rain gauge has a significant effect on the computed values of the coefficients and exponents. / by Mario Mayes-Fernandez. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Accuracy in estimation of precipitation can be achieved by utilizing the combination of spatial radar reflectivity data (Z) and the high resolution temporal rain gage based rainfall data (R). The study proposes the use of optimization models for optimizing the Z-R coefficients and exponents for different storm types and seasons. Precipitation data based on reflectivity, collected from National Climatic Data Center (NCDC) and rain gage data from Southwest Florida Water Management District (SWFWMD) over same temporal resolutions were analyzed using the Rain-Radar- Retrieval (R3) system developed as a part of the study. Optimization formulations are proposed to obtain optimal coefficients and exponents in the Z-R relationships for different seasons and objective selection of storm-type specific Z-R relationships. Different approaches in selection of rain gage stations and selection of events for optimization are proposed using gradient based solver and genetic algorithms. / Kandarp Pattani. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.\

Three major teleconnections, Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO), in warm and cool phases, effect precipitation in Florida. The effects of the oscillation phases on the precipitation characteristics are analyzed by using long-term daily precipitation data, on different temporal (annual, monthly, and daily) and spatial scales, utilizing numerous indices, and techniques. Long-term extreme precipitation data for 9 different durations is used to examine the effects of the oscillation phases on the rainfall extremes, by employing different parametric and non-parametric statistical tests, along with Depth-Duration- Frequency analysis. Results show that Florida will experience higher rainfall when AMO is in the warm phase, except in the panhandle and south Florida, while PDO cool phase is positively correlated with precipitation, except for the southern part of the peninsula. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2013.

Estimates of average annual precipitation (AAP) are-needed for hydrologic modeling at Yucca Mtn., Nevada, site of a proposed, high-level nuclear waste repository. Historical precipitation data and station elevation were obtained for stations in southern Nevada and southeastern California. Elevations for 1,531 additional locations were obtained from topographic maps. The sample direct-variogram for the transformed variable TAAP = ln(AAP) * 1000 was fit with an isotropic, spherical model with a small nugget and a range of 190,000 ft. The sample direct-variogram for elevation was fit with an isotropic model with four nested structures (nugget, Gaussian, spherical, and linear) with ranges between 0 and 270,000 ft. There was a significant (p = 0.05, r = 0.75) linear correlation between TAAP and station elevation. The sample cross-variogram for TAAP and elevation was fit with two nested structures (Gaussian, spherical) with ranges from 55,000 to 355,000 ft. Alternate model structures and parameters were compared using cross-validation. Isohyetal maps for average annual precipitation (AAP) were prepared from estimates obtained by kriging and cokriging using the selected models. Isohyets based on the kriging estimates were very smooth, increasing gradually from the southwest to the northeast. Isohyets based on the cokriging estimates and the spatial correlation between AAP and elevation were more irregular and displayed known orographic effects. Indirect confirmation of the cokriging estimates were obtained by comparing isohyets prepared with the cokriging estimates to the boundaries of more densely vegetated and/or forested zones. Estimates for AAP at the repository site were 145 and 165 mm for kriging and cokriging, respectively. Cokriging reduced estimation variances at the repository site by 55% relative to kriging. The effectiveness of an existing network of stations for measuring AAP is evaluated and recommendations are made for optimal locations for additional stations. / Graduation date: 1991

Typescript. / Bibliography: leaves 64-69. / ix, 75 leaves ill., maps

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The lagged responses of tree-ring indices to annual climatic or hydrologic series are examined in this study. The objectives are to develop methods to analyze the lagged responses of individual tree-ring indices, and to improve upon conventional methods of adjusting for the lag in response in regression models to reconstruct annual climatic or hydrologic series. The proposed methods are described and applied to test data from Oregon and Southern California. Transfer-function modeling is used to estimate the dependence of the current ring on past years' climate and to select negative lags for reconstruction models. A linear system is assumed; the input is an annual climatic variable, and the output is a tree-ring index. The estimated impulse response function weights the importance of past and current years' climate on the current year's ring. The identified transfer function model indicates how many past years' rings are necessary to account for the effects of past years' climate. Autoregressive-moving-average (ARMA) modeling is used to screen out climatically insensitive tree-ring indices, and to estimate the lag in response to climate unmasked from the effects of autocorrelation in the tree-ring and climatic series. The climatic and tree-ring series are each prewhitened by ARMA models, and crosscorrelation between the ARMA residuals are estimated. The absence of significant crosscorrelations Implies low sensitivity. Significant crosscorrelations at lags other than zero indicate lag in response. This analysis can also aid in selecting positive lags for reconstruction models. An alternative reconstruction method that makes use of the ARMA residuals is also proposed. The basic concept is that random (uncorrelated in time) shocks of climate induce annual random shocks of tree growth, with autocorrelation in the tree-ring index resulting from inertia in the system. The steps in the method are (1) fit ARMA models to the tree-ring index and the climatic variable, (2) regress the ARMA residuals of the climatic variable on the ARMA residuals of the treering index, (3) substitute the long-term prewhitened tree-ring index into the regression equation to reconstruct the prewhitened climatic variable, and (4) build autocorrelation back into the reconstruction with the ARMA model originally fit to the climatic variable. The trial applications on test data from Oregon and Southern California showed that the lagged response of tree rings to climate varies greatly from site to site. Sensitive tree-ring series commonly depend significantly only on one past year's climate (regional rainfall index). Other series depend on three or more past years' climate. Comparison of reconstructions by conventional lagging of predictors with reconstructions by the random-shock method indicate that while the lagged models may reconstruct the amplitude of severe, long-lasting droughts better than the random-shock model, the random-shock model generally has a flatter frequency response. The random-shock model may therefore be more appropriate where the persistence structure is of prime interest. For the most sensitive series with small lag in response, the choice of reconstruction method makes little difference in properties of the reconstruction. The greatest divergence is for series whose impulse response weights from the transfer function analysis do not die off rapidly with time.

The effect of altitude on individual storm precipitation in some of the San Dimas experimental watersheds is investigated. It is found that there is a well- defined increase of storm precipitation with altitude for storms greater than one inch. This increase is a linear function of storm depth. Using 41 storms of different magnitudes, a precipitation -altitude relationship is derived for a small area in the San Dimas Experimental Forest. The regionalization of this relationship and its transferability are tested by analyzing differences (errors) between computed and observed storm precipitation values in each case. In testing the regionalization of the precipitation- altitude relationship by computing mean areal storm precipitation over a larger area the standard error of estimate is around 11 percent. In transfering the same relationship the results are not as good and give a standard error of 16 percent. For individual points, however, the error is much higher. A rainfall- runoff model is used as a tool for evaluating the effect of precipitation errors, on simulated streamflow, in a watershed of 4.5 square miles. For annual flows, errors range between 3.4 and 12.3 percent while errors in simulated monthly flows are as high as 22 percent. It is also evident that there is a strong dependence of the error magnitude on the state (wet, dry, etc.) of the preceding year or months, whichever is applicable. An error propagation is observed as a result of consistently over -estimating the precipitation input to the model. This evaluation is more of a qualitative nature and the values of error given should be viewed in this sense.