Techniques for water demand analysis and forecasting: Puerto Rico, a case study

Abstract

The rapid economic growth of the Commonwealth of Puerto Rico since 1947 has brought public pressure on Government agencies for rapid development of public water supply and waste treatment facilities. Since 1945 the Puerto Rico Aqueduct and Sewer Authority has had the responsibility for planning, developing and operating water supply and waste treatment facilities on a municipio basis. The purpose of this study was to develop operational techniques whereby a planning agency, such as the Puerto Rico Aqueduct and Sewer Authority, could project the temporal and "spatial distribution of future water demands. This report is part of a 2-year cooperative study between the U.S. Geological Survey and the Environmental Quality Board of the Commonwealth of Puerto Rico, for the development of systems analysis techniques for use in water resources planning. While the Commonwealth was assisted in the development of techniques to facilitate ongoing planning, the U.S. Geological Survey attempted to gain insights in order to better interface its data collection efforts with the planning process. The report reviews the institutional structure associated with water resources planning for the Commonwealth. A brief description of alternative water demand forecasting procedures is presented and specific techniques and analyses of Puerto Rico demand data are discussed. Water demand models for a specific area of Puerto Rico are then developed. These models provide a framework for making several sets of water demand forecasts based on alternative economic and demographic assumptions. In the second part of this report, the historical impact of water resources investment on regional economic development is analyzed and related to water demand forecasting. Conclusions and future data needs are in.the last section.

An analysis of residential demand for water using micro time‐series data

“…Income elasticity is somewhat lower than studies based upon cross-sectional data and more in line with those based upon time-series data ( Table l). The estimate obtained by Attanasi et al [1975], which was based upon pooled data and random error components methodology (to the author's knowledge, the only other study of that type available), was considerably below that of this study.…”