A behavioural approach for household outdoor water use modelling

Reliable predictions of household outdoor water use are important inputs for effective design and management of urban water systems. This paper evaluates and enhances the behavioural approach (BA) for modelling outdoor water use. The underlying premise in the BA is that outdoor water use is governed by people's probabilistic behavioural response to recent weather conditions (rainfall and temperature).The BA models used in this paper were evaluated using a 12 year dataset of monthly outdoor water use for 135 homes in the Newcastle region of New South Wales, Australia. The BA model of Coombes et al (2000) was found to found to outperform traditional linear regression techniques, after calibration using a new simulated likelihood calibration approach. However, it was found to be over-parameterised and underestimated observed variability by 22%. An enhanced BA model was more parsimonious and better simulated the observed variability (only 9% underestimation). Conditioning behavioural response on daily rainfall and maximum temperature did not provide good model performance. Rather, the major drivers of household outdoor water use variability were found to be long dry periods (for 80% of homes), while a smaller number (20%) additionally responded to the long hot periods (characterised by the degree day concept).T. Micevski, M. Thyer, George Kuczer

Household characteristics that influence household water use in the Hunter Region

Up to 3 years of monthly per capita water use data (both indoor and outdoor) for 225 houses from the Hunter Region in NSW was analysed in this preliminary study. The consumption figures were found to be broadly consistent with recent Australian and New Zealand studies. The distributions of indoor and outdoor water use were highly skewed, with the highest 20% of water users consuming 30% of mains water and over 50% of outdoor water. The water use data was then stratified by various available household characteristics. Weather-related factors (eg. increasing temperature and decreasing rainfall) increased outdoor water use. The biggest intervention factor was plumbing of a rainwater tank into the toilet or laundry with indoor savings of 50-75 L/capita/day, while rainwater tanks did not significantly affect outdoor water use. Front- versus top-loading washing machines save 40 L/capita/day. Presence of an irrigation system or a swimming pool led to a non-significant increase in outdoor water. Observed water savings achieved with rainwater tanks were found to be broadly consistent with recent Australian prediction studies, although the comparison was limited by lack of household characteristics. This highlights the need for detailed household information collection to enable both proper analysis and comparison to model predictions.D. Orr, T. Micevski, M. Thye

Evaluation of a behavioural approach and a regression approach for the modelling of household-scale outdoor water use

Invalid ISBN as printed on item: 97808258259461http://trove.nla.gov.au/work/3665096

A behavioural approach to stochastic end use modelling

Mark Thyer, T. Micevski, George Kuczera, P. Coombe

Making Nursing Work: Breaking Through the Role Confusion of Advanced Practice Nursing

The Aim of this study was to develop a research-informed model of the service parameters and an analysis framework for advanced practice nursing roles. Background: Changing patterns of health care are forcing service planners to examine new service delivery models. Apparent is the call for nursing service that incorporates expanded levels of autonomy, skill and decision making. A number of nursing roles conform to this description under the generic title of advanced practice nurse. However, there is confusion in the health service community internationally about nomenclature, role and scope of practice for advanced nursing roles. An emerging priority in response to recent developments in the nurse practitioner role is to establish service parameters for advanced practice nursing and to operationally differentiate between advanced practice and practitioner nursing roles. Design: We conducted an interpretive, qualitative examination of the practice of a random sample of nine advanced practice nurses working in three acute care hospitals in south east Queensland, Australia. Methods: Data collection involved individual in-depth interviews. The interview data were deductively analysed and tested against published advanced practice nursing models. Results: This analysis identified the Strong Model of Advanced Practice as most comprehensively supporting the practice experiences of the research participants. The Strong Model supports definition of the service parameters and the design of an operational framework for implementation and evaluation of APN roles. Conclusions: This exploratory study has addressed some of the confusion that surrounds advanced practice nursing roles. The findings provide a description of the service parameters of the APN role; differentiate advanced practice nurse and nurse practitioner roles; and provides an operational framework to identify, establish and evaluate advanced and extended nursing positions. Subject to further validation, this research outcome can provide operational information for implementing innovative nursing roles appropriate to consumer needs and specific health service models

Exploring the utility of multi-response calibration in river system modelling

Water allocation models can be used to compare water sharing scenarios in regulated catchments to evaluate the effects on both the water users and the environment. These models include a representation of the physical system with modules such as flow routing, rainfall-runoff modelling or groundwater/surface water interactions, as well as management components to take into account infrastructure such as dams, canals or extraction points. Water allocation models can be complex modelling structures with a large number of parameters to be calibrated on limited datasets, especially regarding the management aspects. Additionally, these models are used as a tool in the making of long-term decisions with important social and environmental impacts. As a result, the assessment of uncertainty becomes a critical task to inform the decision-makers about the likely robustness of the model analysis and predictions. Calibration of these models is currently problematic. In particular, the errors affecting system observations are often not properly accounted for, which is a concern since these errors may be quite large. Furthermore, calibration is often performed separately on various components of the system, resulting in inconsistencies when the components are linked. These deficiencies make it difficult to quantify the uncertainty in the predictions of the entire system performance. The Bayesian approach provides a platform to directly address the sources of uncertainty (input, output, and model error) in the model calibration and prediction process. This study seeks to develop a Bayesian multiresponse method for use with river system models, allowing joint calibration to all sources of information available in a particular application. Unlike the traditional approach, joint calibration forces consistency in performance across the entire system. Moreover, the Bayesian approach provides a framework for a proper accounting of uncertainty both in the inferred parameters and in the model predictions. This study illustrates the application of the Bayesian multi-response calibration approach to the STICKMAN model, a simplified river system model which describes key aspects of complex river basin models such as IQQM but is computationally less demanding. The model was calibrated using a Weighted Least Squares method in a synthetic data study. Model calibration used both single and multiple response data (eg. streamflow at the outlet and at internal system nodes, reservoir time series, etc.) to investigate the improvements in parameter estimation associated with the inclusion of additional responses. The use of multiple response data during model calibration was generally found to reduce parameter uncertainty. However, the extent of reductions in uncertainty depended on which responses were included, highlighting that some sources of data are more informative than others. This supports the findings of Kuczera and Mroczkowski's (1998), who conclude that the value of new sources of response data should be assessed a priori before embarking on (potentially expensive) field campaigns. This study reports the first findings in this project. Future work will explore the effects of multiple response data on model predictive performance, further develop the STICKMAN model to better represent processes and errors, and finally consider IQQM case studies.T. Micevski, J. Lerat, D. Kavetski, M. Thyer, and G. Kuczerahttp://www.mssanz.org.au/modsim2011/index.ht

A probabilistic behavioural approach for the dynamic modelling of indoor household water use

Invalid ISBN as printed on item: 97808258259461The emergence of the Integrated Urban Water Management (IUWM) design paradigm which utilises interventions at the household scale to reduce the demand on large scale water supply infrastructure has driven the need for a greater understanding and ability to simulate urban water use dynamics at the household scale. Urban water use at the household scale is a probabilistic behavioural response by individuals to a set of drivers. This paper outlines a hierarchical urban water use modelling framework for the probabilistic behavioural modelling of urban water use. The top level consists of the main drivers of urban water use, the second level simulates the spatial variability between houses (varying number of people, and varying water use appliances) and the third level simulates the temporal variability of an individual house. The methodology for simulating household indoor water use within this framework is described in this study. This consists of probabilistically simulating water use occurrence and event volumes for different end use categories (shower, washing machine, toilet etc) at minute time steps. The results showed the simulations provided a good match to the observed statistics provided by a detailed end use measurement study (Roberts, 2005) for individual water use events. The simulated aggregated statistics (total daily water use per capita for each end use and the distribution of daily total water use per capita) also provide a good match to the observations. A generic modelling approach has been adopted by separating out the behavioural processes which govern the occurrence of the water use event and the appliance that is used for that event. This has two advantages (1) Provides the potential for the model to be transferred to other regions and utilise local information and (2) Water use scenarios with different rates of uptake of water efficient appliance can be easily simulated. This capability was demonstrated with by providing simulations of water use for two scenarios with varying rates of uptake of water efficient appliances.M. A. Thyer, H. Duncan, P. Coombes, G. Kuczera and T. Micevskihttp://trove.nla.gov.au/work/36650960http://search.informit.com.au/documentSummary;dn=753853732312615;res=IELEN