Estimating Future Peak Water Demand with a Regression Model Considering Climate Indices

Anika Stelzl*, Michael Karl Pointl, Daniela Fuchs-Hanusch

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Although Austria is a water-rich country, impacts of climate change on water supply are already noticeable. Some regions were affected by water scarcity in recent years. Due to climate change, an increase in peak water demand is expected in the future. Therefore, water demand prediction models that include climate indices are of interest. In this paper, we present a general multiple linear regression (GMLR) model that can be applied to selected study sites. We compared the performance of the GMLR model with different modeling approaches, i.e., stepwise multiple linear regression, support vector regression, random forest regression and a neural network approach. All models were trained with water demand and weather data reaching back several years and tested with the last available observation year. The applied modeling approaches achieved a similar performance. As a second step, the GMLR model was used to estimate the peak water demands for the time period 2025–2050. For the future water demand estimate, 16 different climate projections were used. These climate projections represent the worst-case climate change scenario (RCP 8.5). The expected increase in peak water demand could be confirmed with the modeling approach. An increase in peak water demand by 3.5% compared to the reference period was estimated
Original languageEnglish
Article number1912
JournalWater
Volume13
Issue number14
DOIs
Publication statusPublished - 2 Jul 2021

Keywords

  • ANN
  • Climate change
  • Long-term daily water demand forecasting
  • MLR
  • Peak water demand
  • RF
  • SVR

ASJC Scopus subject areas

  • Water Science and Technology
  • Geography, Planning and Development
  • Aquatic Science
  • Biochemistry

Fingerprint

Dive into the research topics of 'Estimating Future Peak Water Demand with a Regression Model Considering Climate Indices'. Together they form a unique fingerprint.

Cite this