ESR 5: Managing urban water demand across multiple spatio-temporal scales
Early Stage Researcher: Wenjin Hao
Host institution: Politecnico di Milano, Italy
Principal supervisor: Prof. Andrea Castelletti
Co-supervisor: Prof. Margreet Zwarteveen
Non-academic co-supervisor: Mrs. Nynke Schaap & Ms. Jolijn Posma
Changing water demands in urban centers are considerably raising the stress on finite supplies of available freshwater, as per the combined effect of climate change, population growth, and urbanization. In response to such changes, planners and policymakers need to improve urban water management plans and develop demand management strategies that complement supply-side interventions to enhance the resilience of urban water systems. The need to identify water demand patterns and drivers to inform such management strategies has fostered the development of several recent works and smart metering programs in a number of medium to large cities worldwide, providing new consumption data at high spatial and temporal resolution. The availability of such high-resolution data enables the development of advanced data-driven models of water demand at different scales. The aim of this project is to develop data-driven models of residential water demand at different spatial scales (e.g., buildings, districts, and cities). We will identify the main patterns of urban water demand, along with their economic, social, climatic, and environmental drivers at different spatial and temporal scales across heterogeneous urban contexts in European countries. Data-driven demand models will be developed to inform regional, national, and European water demand management policies and governance decisions.
This research project will:
- Identify the main patterns of urban water demand and use and their economic, social, climatic, and environmental drivers at different spatial and temporal scales across heterogeneous urban contexts in European countries;
- Enhance models describing how water governance decisions propagate across sectors and scales, under diverse social, economic, technological, and climate scenarios;
- Understand how fine-scale urban water use, demand, and management data and projective scenarios can be scaled up and integrated with regional, national, and European models;
- Develop tools ultimately supporting policy formulations, local and regional water management investments, and for discovering which factors most strongly influence water futures of interest.
- Informed description and comparative analysis of the spatial and temporal heterogeneity of urban water demand and use patterns and drivers across the main European urban centres, usually masked by current modelling approaches at coarse scales;
- Descriptive and predictive multi-scale mathematical models of urban water use and management implications that bridge the gap between state/European level modelling and the finer regional/urban/sub-urban scale;
- Scale-flexible assessments that better account for how decisions propagate across spatial scales and short- to long-term time horizons. These innovations are critical for supporting strategic decision making under uncertain futures at local, state, and European levels;
- Release of a set of open-source tools to mine water use data at various spatial and temporal scales and sectors, and ultimately support the design of demand management options.
About Wenjin Hao
Wenjin was born in 1995, in China. She graduated from Southeast University one of the leading universities in China for her bachelor's degree in Water and Wastewater Engineering in 2017. Then she obtained her master's degree in Environmental Engineering in ETH Zurich in 2020. Currently, she is a Ph.D. student in the Environmental Intelligence lab in Politecnico di Milano. Her research interests include urban water demand modelling, machine learning applications in water management, smart water, strategy development for sustainable water demand management. As a member of the NEWAVE project, she will contribute to identify the major urban water demand across multiple spatio-temporal scales in European countries, build enhanced models to find key drivers of specific demand and support policy making.
LinkedIn profile: Wenjin Hao
NEWAVE Early Stage Researcher, Ph.D. Candidate
Politecnico di Milano, Italy
Prof. Andrea Castelletti
Professor at Politecnico di Milano
Environmental Intelligence Lab at Politecnico di Milano, Italy
Prof. Margreet Zwarteveen
Professor of Water Governance at IHE-Delft and the University of Amsterdam
Universiteit van Amsterdam, The Netherlands
Mrs. Nynke Schaap
Project Leader and Consultant
ARCADIS, The Netherlands
Ms. Jolijn Posma
Project Leader and Team Manager
ARCADIS, The Netherlands