Quantifying Household Water Demand: A Review of Theory and Practice in the UK
Quantifying Household Water Demand: A Review of Theory and Practice in the UK
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DOI：
10.1007/s11269-012-0190-2
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发表时间：
2013-03
影响因子：
4.3
通讯作者：
J. Parker;R. Wilby
中科院分区：
文献类型：
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作者：
J. Parker;R. Wilby
社会经济变化、严重干旱和环境问题使人们关注供水的可持续性和水务公司满足服务水平的能力。传统上，水管理一直是供给侧主导的，长期需求预测相对较少受到关注。然而，越来越多的人认识到，鉴于未来非气候和气候压力的巨大不确定性，水需求管理可能是一种“低遗憾”的适应措施(在财务和环境方面都是如此)。本文首先简要回顾了英国家庭用水需求管理的历史。然后，我们回顾了短期(每日到季节)和长期(几年到十年)的水需求估计和预测方法，并注意到关于天气和气候的研究很少。我们讨论了从计量试验、基于微型成分日记的研究和长期需求预测的统计技术中确定的家庭用水高峰行为。我们引用盎格鲁水务公司(AWS)的“黄金100”数据来说明在挖掘家庭用水数据以获取天气信号时所面临的重大实践和概念问题，特别是当数据噪声较大且原本打算用于其他应用时。需要进一步研究气候变量和家庭微成分用水量之间的关系，特别是在高峰需求时。
Socio-economic change, severe droughts, and environmental concerns focus attention on sustainability of water supplies and the ability of water utilities to meet levels of service. Traditionally, water management has been supply-side dominated and long-term demand forecasting has received relatively little attention. However, it is increasingly recognised that water demand management could be a ‘low regret’ adaptation measure (both financially and environmentally) given large uncertainties about future non-climate and climate pressures. This paper begins with a brief history of household water demand management in the UK. We then review approaches to water demand estimation and forecasting over the short- (daily to season) and long-term (years to decade) and note the paucity of studies on weather and climate. We discuss peak household water use behaviours identified from metering trials, micro-component diary-based studies, and statistical techniques for long-term demand forecasting. We refer to the Anglian Water Services (AWS) ‘Golden 100’ data to illustrate the significant practical and conceptual issues faced when mining household water use data for weather signals, especially when the data are noisy and originally intended for other applications. Further research is needed into the relationships between climate variables and household micro-component water use, especially for peak demands.