A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin, Western Australia

Jones, Stuart; Lacey, P; Walshe, Terry

doi:10.1016/j.jenvman.2008.11.027

Cited by 18 publications

(13 citation statements)

References 32 publications

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“…Further detailed descriptions for parameter settings are presented in Section 3. Two-dimensional Monte Carlo analysis (also named 2-D MCA, second-order, double loop, two-phase, two-stage, or nested Monte Carlo) has been widely used to propagate both aleatory and epistemic uncertainty (Swiler and Giunta 2007;Frey et al 2004;Wu and Tsang 2004;Rao et al 2007;Jones et al 2009). This method is the most straightforward manner to separate epistemic uncertainty and aleatory uncertainty (Vose 2008;Bruns et al 2006).…”

Section: Two-dimensional Monte Carlo Analysismentioning

confidence: 99%

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Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change

Wilde

2009

Build. Simul.

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There is growing concern about the potential impact of climate change on the thermal performance of buildings. Building simulation is well-suited to predict the behaviour of buildings in the future, and to quantify the risks for prime building functions like occupant productivity, occupant health, or energy use. However, on the time scales that are involved with climate change, different factors introduce uncertainties into the predictions: apart from uncertainties in the climate conditions forecast, factors like change of use, trends in electronic equipment and lighting, as well as building refurbishment / renovation and HVAC (heating, ventilation, and air conditioning) system upgrades need to be taken into account. This article presents the application of two-dimensional Monte Carlo analysis to an EnergyPlus model of an office building to identify the key factors for uncertainty in the prediction of overheating and energy use for the time horizons of 2020, 2050 and 2080. The office has mixed-mode ventilation and indirect evaporative cooling, and is studied using the UKCIP02 climate change scenarios. The results show that regarding the uncertainty in predicted heating energy, the dominant input factors are infiltration, lighting gain and equipment gain. For cooling energy and overheating the dominant factors for 2020 and 2050 are lighting gain and equipment gain, but with climate prediction becoming the one dominant factor for 2080. These factors will be the subject of further research by means of expert panel sessions, which will be used to gain a higher resolution of critical building simulation input.

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Section: Two-dimensional Monte Carlo Analysismentioning

confidence: 99%

“…Illustration of two-dimensional Monte Carlo analysis to propagate epistemic and aleatory uncertainty (n, iterations of epistemic uncertainty; m, iterations of aleatory uncertainty; k, number of input variables; CDF, cumulative distribution function; (x, y) number of rows and columns in data set) (adapted fromJones et al (2009)) …”

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confidence: 99%

Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change

Wilde

2009

Build. Simul.

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“…For instance, capillary rise of salt driven by shallow saline groundwater could cause salinization of root zones and further decline in plant productivity (Kobryn et al, 2015). Comparably, groundwater salinization is a more severe problem in arid and semi-arid regions where the groundwater plays an essential role in supporting ecosystems and providing invaluable water resource (Alaghmand et al, 2013;Jones et al, 2009). In the southwest Australia, Caccetta et al (2010) evaluated that the land salinity driven by saline groundwater has covered almost one million hectares of land in 1996 and a further 5.4 million hectares are at risk of future salinization.…”

Section: Introductionmentioning

confidence: 99%

Cl/Br ratios and chlorine isotope evidences for groundwater salinization and its impact on groundwater arsenic, fluoride and iodine enrichment in the Datong basin, China

Wang

Xie

2016

Science of The Total Environment

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“…In the south-west of Australia (area known as the 'wheatbelt') dryland salinity is caused by increased recharge of water into the semi-confined aquifer bringing saline groundwater close to the surface (Clarke et al, 2002;George, 2004). As groundwater reaches within 2 m of the soil surface, capillary rise of salts causes salinisation of root zones and general decline in plant productivity (Nulsen, 1981), damage to buildings and infrastructure (Beresford et al, 2001) and has negative effects on natural ecosystems (Cale et al, 2004;Cramer and Hobbs, 2002;Jones et al, 2009). In this region almost one million hectares of land were mapped as saline in 1996 and a further 5.4 million hectares are at risk of future salinisation (Caccetta et al, 2010;McFarlane et al, 2004;McFarlane et al, 1992a).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater

Kobryn

Lantzke

Bell

et al. 2015

Journal of Environmental Management

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Kobryn, H.T., Lantzke, R., Bell, R. and Admiraal, R. (2015)Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater. AbstractThe installation of deep drains is an engineering approach to remediate land salinised by the influence of shallow groundwater. It is a costly treatment and its economic viability is, in part, dependent on the lateral extent to which the drain increases biological productivity by lowering water tables and soil salinity (referred to as the drains' zone of benefit). Such zones may be determined by assessing the biological productivity response of adjacent vegetation over time. We tested a multi-temporal satellite remote sensing method to analyse temporal and spatial changes in vegetation condition surrounding deep drainage sites at five locations in the Western Australian wheatbelt affected by dryland salinity-Morawa, Pithara, Beacon, Narembeen and Dumbleyung. Vegetation condition as a surrogate for biological productivity was assessed by Normalised Difference Vegetation Index (NDVI) during the peak growing season. Analysis was at the site scale within a 1000 m buffer zone from the drains. There was clear evidence of NDVI increasing with elevation, slope and distance from the drain. After accounting for elevation, slope and distance from the drain, there was a significant increase in NDVI across the five locations after installation of deep drains. Changes in NDVI after drainage were broadly consistent with measured changes at each site in groundwater levels after installation of the deep drains. However, this study assessed the lateral extent of benefit for biological productivity and gave a measure of the area of benefit along the entire length of the drain. The method demonstrated the utility of spring NDVI images for rapid and relatively simple assessment of the change in site condition after implementation of drainage, but approaches for further improvement of the procedure were identified.

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A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin, Western Australia

Cited by 18 publications

References 32 publications

Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change

Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change

Cl/Br ratios and chlorine isotope evidences for groundwater salinization and its impact on groundwater arsenic, fluoride and iodine enrichment in the Datong basin, China

Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater

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