Dissertation/Thesis Abstract

On the statistical nature of daily rainfall and the Storage-Reliability-Yield behavior of rainwater harvesting systems in the United States
by Hanson, Lars S., M.S., Tufts University, 2008, 132; 1449722
Abstract (Summary)

Part I. The probability distribution of daily rainfall in the United States . Choosing a probability distribution to represent the precipitation depth at various durations has long been a topic of interest in hydrology. Early study into the distribution of wet-day daily rainfall has identified the 2-parameter Gamma (G2) distribution as the most likely candidate distribution based on traditional goodness of fit tests. This paper uses probability plot correlation coefficient test statistics and L-moment diagrams to examine the complete series and wet-day series of daily precipitation records at 237 U.S. stations. The analysis indicates that the Pearson Type-III (P3) distribution fits the full record of daily precipitation data remarkably well, while the Kappa (KAP) distribution best describes the observed distribution of wet-day daily rainfall. We also show that the G2 distribution performs poorly in comparison to either the P3 or KAP distributions.

Part II. Generalized storage-reliability-yield equations for designing rainwater harvesting systems in the United States . Rainwater harvesting (RWH) systems have been used for centuries to provide or augment water supplies. Although rainwater harvesting is gaining popularity as a sustainable water supply source in urban as well as rural areas, there are few commonly practiced methods for calculating the required storage volume of a proposed system. This paper attempts to generate a robust, computationally simple equation for calculating required storage capacity for a RWH system, which is generally applicable in the United States (U.S.). A simulation model with a daily time step employs a yield-after-spill algorithm to generate empirical Storage – Reliability – Yield (SRY) relationships for a RWH system at 232 U.S., first-order precipitation gaging stations with long daily precipitation records. A regional regression modeling approach demonstrates that by combining system parameters (daily yield, collection area, reliability) with climatic variables, a generalized regression model can be developed to predict required storage capacity. Nationwide regression models built for useful fixed reliability cases (80, 90, 95, 98%) demonstrate good fits between model predicted and simulated storage capacities. The fits improve when the nation is broken down into smaller, more climatically homogeneous regions. Further research using smaller geographic regions and considering a broader range of climatic, geographic, and system variables may result in further improvements in the predictive capability of regional regression models.

Indexing (document details)
Advisor: Kirshen, Paul
Commitee: Kirshen, Paul, Shanahan, Peter, Vogel, Richard M.
School: Tufts University
Department: Civil Engineering
School Location: United States -- Massachusetts
Source: MAI 46/04M, Masters Abstracts International
Source Type: DISSERTATION
Subjects: Hydrology, Civil engineering, Environmental engineering
Keywords: Daily precipitation, L-moments, Pearson-III, Rainwater harvesting, Regional regression, Storage-Reliability-Yield
Publication Number: 1449722
ISBN: 9780549355366