Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics

A statistical model has been developed to forecast domestic water demand by considering climate change, population growth, urbanization, lifestyle changes and technological advances. The developed model is used to forecast future domestic water demand in different sub-basins of Huaihe River Basin of...

Full description

Saved in:
Bibliographic Details
Main Authors: Wang, Xiao-Jun, Zhang, Jian-Yun, Shahid, Shamsuddin, Xie, Wei, Du, Chao-Yang, Shang, Xiao-Chuan, Zhang, Xu
Format: Article
Published: Springer Netherlands 2018
Subjects:
Online Access:http://eprints.utm.my/id/eprint/85654/
http://dx.doi.org/10.1007/s10668-017-9919-7
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.85654
record_format eprints
spelling my.utm.856542020-07-07T05:16:21Z http://eprints.utm.my/id/eprint/85654/ Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics Wang, Xiao-Jun Zhang, Jian-Yun Shahid, Shamsuddin Xie, Wei Du, Chao-Yang Shang, Xiao-Chuan Zhang, Xu TA Engineering (General). Civil engineering (General) A statistical model has been developed to forecast domestic water demand by considering climate change, population growth, urbanization, lifestyle changes and technological advances. The developed model is used to forecast future domestic water demand in different sub-basins of Huaihe River Basin of China. The study reveals that mean temperature in Huaihe River Basin will increase by 0.7–1.6 °C, population will reach to 230 million, and 61.2% of the basin area will be urbanized by the year 2030, which will cause a sharp increase in domestic water demand. The increase in domestic water demand for 1 °C increase in mean temperature is found to vary between 0.549 × 108 and 5.759 × 108 m3 for different sub-basins of Huaihe River. The forecasted change in domestic water demand is also found to vary widely for different general circulation models (GCMs) used. The GCM BCC-CSM1-1 projected the highest increase in domestic water demand, 168.44 × 108 m3 in 2020, and the GISS-E2-R the lowest, 119.21 × 108 m3. On the other hand, the BNU-ESM projected the highest increase, 196.03 × 108 m3, and the CNRM-CM5 the lowest, 161.05 × 108 m3 in year 2030. Among the different sub-basins, the highest increase in water demand is projected in Middlestream of Huaihe River in the range of 46.9 × 108–65.5 × 108 m3 in 2020, and 61.3 × 108–76.1 × 108 m3 in 2030, which is supposed to cause serious water shortage and an increase in competition among water-using sectors. Springer Netherlands 2018-04 Article PeerReviewed Wang, Xiao-Jun and Zhang, Jian-Yun and Shahid, Shamsuddin and Xie, Wei and Du, Chao-Yang and Shang, Xiao-Chuan and Zhang, Xu (2018) Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics. Environment, Development and Sustainability, 20 (2). pp. 911-924. ISSN 1387-585X http://dx.doi.org/10.1007/s10668-017-9919-7 , Issue , 1 April , Pages -
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Wang, Xiao-Jun
Zhang, Jian-Yun
Shahid, Shamsuddin
Xie, Wei
Du, Chao-Yang
Shang, Xiao-Chuan
Zhang, Xu
Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics
description A statistical model has been developed to forecast domestic water demand by considering climate change, population growth, urbanization, lifestyle changes and technological advances. The developed model is used to forecast future domestic water demand in different sub-basins of Huaihe River Basin of China. The study reveals that mean temperature in Huaihe River Basin will increase by 0.7–1.6 °C, population will reach to 230 million, and 61.2% of the basin area will be urbanized by the year 2030, which will cause a sharp increase in domestic water demand. The increase in domestic water demand for 1 °C increase in mean temperature is found to vary between 0.549 × 108 and 5.759 × 108 m3 for different sub-basins of Huaihe River. The forecasted change in domestic water demand is also found to vary widely for different general circulation models (GCMs) used. The GCM BCC-CSM1-1 projected the highest increase in domestic water demand, 168.44 × 108 m3 in 2020, and the GISS-E2-R the lowest, 119.21 × 108 m3. On the other hand, the BNU-ESM projected the highest increase, 196.03 × 108 m3, and the CNRM-CM5 the lowest, 161.05 × 108 m3 in year 2030. Among the different sub-basins, the highest increase in water demand is projected in Middlestream of Huaihe River in the range of 46.9 × 108–65.5 × 108 m3 in 2020, and 61.3 × 108–76.1 × 108 m3 in 2030, which is supposed to cause serious water shortage and an increase in competition among water-using sectors.
format Article
author Wang, Xiao-Jun
Zhang, Jian-Yun
Shahid, Shamsuddin
Xie, Wei
Du, Chao-Yang
Shang, Xiao-Chuan
Zhang, Xu
author_facet Wang, Xiao-Jun
Zhang, Jian-Yun
Shahid, Shamsuddin
Xie, Wei
Du, Chao-Yang
Shang, Xiao-Chuan
Zhang, Xu
author_sort Wang, Xiao-Jun
title Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics
title_short Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics
title_full Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics
title_fullStr Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics
title_full_unstemmed Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics
title_sort modeling domestic water demand in huaihe river basin of china under climate change and population dynamics
publisher Springer Netherlands
publishDate 2018
url http://eprints.utm.my/id/eprint/85654/
http://dx.doi.org/10.1007/s10668-017-9919-7
_version_ 1672610564900126720
score 13.211869