Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.trim
A. Trim, G. Podger, D. Dutta, J. Puertas, Qld Vic, Sa
: The Southern Connected System (SCS) is located within the Murray–Darling Basin, Australia. It comprises the Snowy Mountains Hydro-electric Scheme, the Murrumbidgee river system, and the Murray– Darling Basin river system. These systems are described by four river system models: the Snowy Hydro Scheme, Upper Murrumbidgee, regulated Murrumbidgee and the Lower-Darling and Murray. However, more broadly many other upstream river system models contribute flow and allocation information. These include the NSW Barwon-Darling model, and Victoria Kiewa, Ovens, and Goulburn-Broken-Campaspe-Coliban-Loddon models. Noting that the Barwon-Darling model receives contributions from 10 upstream river system models, including contributions from 5 Queensland river system models (Figure 1). Previously the SCS models were largely run independently with inputs from upstream models as fixed inputs for a range of development scenarios. However, there are a range of feedbacks between these models that are sufficiently large enough that they need to be considered. The SCS modelling suite considers the connections and feedbacks via an iterative approach. This is the first time that these feedbacks have been considered. This paper describes the physical and management connections between the models that describe the SCS. It details the feedbacks between the models and how this was managed within the modelling framework. It provides insights into the relative importance of the different variables and the importance of considering these within the broader modelling process of downstream systems. The results demonstrate the significance of modelling feedbacks through iterations and the need to be considered in future modelling of the SCS.
{"title":"Modelling the Murray-Darling Basin Southern Connected System","authors":"A. Trim, G. Podger, D. Dutta, J. Puertas, Qld Vic, Sa","doi":"10.36334/modsim.2023.trim","DOIUrl":"https://doi.org/10.36334/modsim.2023.trim","url":null,"abstract":": The Southern Connected System (SCS) is located within the Murray–Darling Basin, Australia. It comprises the Snowy Mountains Hydro-electric Scheme, the Murrumbidgee river system, and the Murray– Darling Basin river system. These systems are described by four river system models: the Snowy Hydro Scheme, Upper Murrumbidgee, regulated Murrumbidgee and the Lower-Darling and Murray. However, more broadly many other upstream river system models contribute flow and allocation information. These include the NSW Barwon-Darling model, and Victoria Kiewa, Ovens, and Goulburn-Broken-Campaspe-Coliban-Loddon models. Noting that the Barwon-Darling model receives contributions from 10 upstream river system models, including contributions from 5 Queensland river system models (Figure 1). Previously the SCS models were largely run independently with inputs from upstream models as fixed inputs for a range of development scenarios. However, there are a range of feedbacks between these models that are sufficiently large enough that they need to be considered. The SCS modelling suite considers the connections and feedbacks via an iterative approach. This is the first time that these feedbacks have been considered. This paper describes the physical and management connections between the models that describe the SCS. It details the feedbacks between the models and how this was managed within the modelling framework. It provides insights into the relative importance of the different variables and the importance of considering these within the broader modelling process of downstream systems. The results demonstrate the significance of modelling feedbacks through iterations and the need to be considered in future modelling of the SCS.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115781566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.wasko
C. Wasko, D. Guo, M. Ho, R. Nathan, E. Vogel
: Engineering design, floodplain management, and water resources planning all require estimates of extreme rainfall and flooding. However, as we plan and design for the future, the historical records we have used in the past are no longer representative of the future due to climate change. Our climate system is experiencing many changes: rising temperatures are increasing the saturation vapor pressure increasing extreme rainfalls; changes in circulation patterns are shifting the frequency of rainfall events; and changes in the mean annual rainfall and time between rainfall events are impacting on the soil moisture conditions before a rainfall event. Hence, if we are to correctly specify the level of risk in future design and planning and decisions, all these changes need to be accounted for in our estimates of extreme rainfall and flooding. Here, we project extreme rainfall and flooding (in the form of frequency curves) across Australia’s diverse climate and, in doing so, develop a simple, robust methodology that can be readily used for flood projections. We first calibrate the rainfall-runoff model GR4J across 467 Hydrologic Reference Stations using observed rainfall, potential evapotranspiration (PET), and streamflow. The calibration uses a novel objective function which aims to match flood quantiles. The hydrological models across all catchments are then evaluated in terms of flood frequency, Nash-Sutcliffe Efficiency (NSE), and the trend in annual maxima, to ensure that the processes causing changes in flood frequency are captured. For use in future projections, rainfall and PET climate model data from four GCMs and four different bias-correction methods are obtained from the Australian Bureau of Meteorology (
{"title":"Australia-wide projections of extreme rainfall and flooding","authors":"C. Wasko, D. Guo, M. Ho, R. Nathan, E. Vogel","doi":"10.36334/modsim.2023.wasko","DOIUrl":"https://doi.org/10.36334/modsim.2023.wasko","url":null,"abstract":": Engineering design, floodplain management, and water resources planning all require estimates of extreme rainfall and flooding. However, as we plan and design for the future, the historical records we have used in the past are no longer representative of the future due to climate change. Our climate system is experiencing many changes: rising temperatures are increasing the saturation vapor pressure increasing extreme rainfalls; changes in circulation patterns are shifting the frequency of rainfall events; and changes in the mean annual rainfall and time between rainfall events are impacting on the soil moisture conditions before a rainfall event. Hence, if we are to correctly specify the level of risk in future design and planning and decisions, all these changes need to be accounted for in our estimates of extreme rainfall and flooding. Here, we project extreme rainfall and flooding (in the form of frequency curves) across Australia’s diverse climate and, in doing so, develop a simple, robust methodology that can be readily used for flood projections. We first calibrate the rainfall-runoff model GR4J across 467 Hydrologic Reference Stations using observed rainfall, potential evapotranspiration (PET), and streamflow. The calibration uses a novel objective function which aims to match flood quantiles. The hydrological models across all catchments are then evaluated in terms of flood frequency, Nash-Sutcliffe Efficiency (NSE), and the trend in annual maxima, to ensure that the processes causing changes in flood frequency are captured. For use in future projections, rainfall and PET climate model data from four GCMs and four different bias-correction methods are obtained from the Australian Bureau of Meteorology (","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"404 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115865644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.dunstall
S. Dunstall, D. Gunasegaram, Canchen Jiang, Hao Wang
: Systems can be subject to exogenous uncertainty, that is, uncertainty about the future stimuli on the system (e.g., the time and location of bushfires occurring in a region) and/or the parameters of important influences that lie at the boundary of the system (e.g., the time-varying price of grid-sourced electricity). All real systems have exogenous uncertainty but for some systems a deterministic model can be sufficient for good decision-making about system design and/or operation. The Operations Research (OR) literature has tended to favour deterministic models but there is much literature associated with optimisation under uncertainty. For the most part this uncertainty is wholly exogenous in the literature. In physical science and engineering the endogenous uncertainty and unpredictability of systems is ever-present. Knowledge of the physics which underpins the system’s behaviour is almost never complete enough to enable high-accuracy prediction. The predictions which are achievable are often semi-empirical in nature ─ being partly physics informed (model driven) and using observed experimental data to fill knowledge gaps (data driven). This means that the response of a system to a control action might only be quite imprecisely known even if exogenous influences are kept entirely at bay. Endogenous uncertainty is less commonly tackled in the OR literature. This is partly for practicality because combinatorial optimisation problems are quite difficult enough. This is also partly a result of context. With regards to the latter, modelling for planning and scheduling problems, for example, does not benefit much from questioning the accuracy of the underlying physics. In these cases it is considered enough to restrict and/or refer the uncertainty and unpredictability to the exogenous influences (such as traffic congestion in transportation, or the arrival of new tasks at a production system). There are favourable circumstances where methods for optimisation under uncertainty do not involve generating and fitting functions to somewhat large amounts of data. For example, if uncertainties are about discrete event realizations and are few in number, then a scenario tree can be enumerated and a problem can be solved using multi-stage stochastic programming. As problems get more complex, methods for optimisation under uncertainty can be said to become data-driven approaches, as exemplified when estimating the cost-to-go function in approximate dynamic programming using a Least Squares Monte Carlo method. Our motivation here is to explore the notion that data-driven physics representations and data-driven stochastic optimisation might not need to be treated as two compartmentalized tasks. Might we be able to undertake approximate dynamic programming for process control and physics-based model fitting for process prediction simultaneously? How might this work? What then might the relationship become between physical experimentation, “digital twins”, and
{"title":"Optimal decision making and control with uncertain events, uncertain physics, or both","authors":"S. Dunstall, D. Gunasegaram, Canchen Jiang, Hao Wang","doi":"10.36334/modsim.2023.dunstall","DOIUrl":"https://doi.org/10.36334/modsim.2023.dunstall","url":null,"abstract":": Systems can be subject to exogenous uncertainty, that is, uncertainty about the future stimuli on the system (e.g., the time and location of bushfires occurring in a region) and/or the parameters of important influences that lie at the boundary of the system (e.g., the time-varying price of grid-sourced electricity). All real systems have exogenous uncertainty but for some systems a deterministic model can be sufficient for good decision-making about system design and/or operation. The Operations Research (OR) literature has tended to favour deterministic models but there is much literature associated with optimisation under uncertainty. For the most part this uncertainty is wholly exogenous in the literature. In physical science and engineering the endogenous uncertainty and unpredictability of systems is ever-present. Knowledge of the physics which underpins the system’s behaviour is almost never complete enough to enable high-accuracy prediction. The predictions which are achievable are often semi-empirical in nature ─ being partly physics informed (model driven) and using observed experimental data to fill knowledge gaps (data driven). This means that the response of a system to a control action might only be quite imprecisely known even if exogenous influences are kept entirely at bay. Endogenous uncertainty is less commonly tackled in the OR literature. This is partly for practicality because combinatorial optimisation problems are quite difficult enough. This is also partly a result of context. With regards to the latter, modelling for planning and scheduling problems, for example, does not benefit much from questioning the accuracy of the underlying physics. In these cases it is considered enough to restrict and/or refer the uncertainty and unpredictability to the exogenous influences (such as traffic congestion in transportation, or the arrival of new tasks at a production system). There are favourable circumstances where methods for optimisation under uncertainty do not involve generating and fitting functions to somewhat large amounts of data. For example, if uncertainties are about discrete event realizations and are few in number, then a scenario tree can be enumerated and a problem can be solved using multi-stage stochastic programming. As problems get more complex, methods for optimisation under uncertainty can be said to become data-driven approaches, as exemplified when estimating the cost-to-go function in approximate dynamic programming using a Least Squares Monte Carlo method. Our motivation here is to explore the notion that data-driven physics representations and data-driven stochastic optimisation might not need to be treated as two compartmentalized tasks. Might we be able to undertake approximate dynamic programming for process control and physics-based model fitting for process prediction simultaneously? How might this work? What then might the relationship become between physical experimentation, “digital twins”, and ","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124486838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.kapsis84
M. Kapsis, P. Pudney, W. Miller, G. Freebairn
: Can a team of low-cost defending missiles provide a more cost-effective approach to intercepting a high-performance attacking missile than a single high-cost defending missile? The trajectory of the high-performance attacking missile is unknown and predicting the possible paths brings uncertainties to intercept calculations. Although low-cost defending missiles may have limited abilities to detect and pursue the attack-ing missile, they will have the ability to cooperate with each other to see and intercept the attacking missile. The objective is to find a cost-effective solution that will maximise the probability of hitting (and stopping) the attacking missile. Two key questions need to be addressed: what is the best strategy for a team of cooperating defending missiles, and what are the cost-effective missile characteristics for a team of cooperating defending missiles? This paper develops a method for determining which characteristics of defending missiles give the best per-formance. A set of 1728 different defending missile teams were generated by varying the number of missiles, missile speed, seeker performance and missile manoeuvrability. The results show that seeker range has the greatest influence on performance. Whether a team of low-cost defending missiles is more cost-effective than a single high-performance defending missile depends on the relative costs of the two missile types. Although increasing the number of defending missiles can ensure good performance of a missile defence system, in cases where the unit cost of a low-cost missile remains high, teams of low-cost cooperating defending missiles might not be a cost-effective solution.
{"title":"Determining the best type of defending missile for cooperative missile defence","authors":"M. Kapsis, P. Pudney, W. Miller, G. Freebairn","doi":"10.36334/modsim.2023.kapsis84","DOIUrl":"https://doi.org/10.36334/modsim.2023.kapsis84","url":null,"abstract":": Can a team of low-cost defending missiles provide a more cost-effective approach to intercepting a high-performance attacking missile than a single high-cost defending missile? The trajectory of the high-performance attacking missile is unknown and predicting the possible paths brings uncertainties to intercept calculations. Although low-cost defending missiles may have limited abilities to detect and pursue the attack-ing missile, they will have the ability to cooperate with each other to see and intercept the attacking missile. The objective is to find a cost-effective solution that will maximise the probability of hitting (and stopping) the attacking missile. Two key questions need to be addressed: what is the best strategy for a team of cooperating defending missiles, and what are the cost-effective missile characteristics for a team of cooperating defending missiles? This paper develops a method for determining which characteristics of defending missiles give the best per-formance. A set of 1728 different defending missile teams were generated by varying the number of missiles, missile speed, seeker performance and missile manoeuvrability. The results show that seeker range has the greatest influence on performance. Whether a team of low-cost defending missiles is more cost-effective than a single high-performance defending missile depends on the relative costs of the two missile types. Although increasing the number of defending missiles can ensure good performance of a missile defence system, in cases where the unit cost of a low-cost missile remains high, teams of low-cost cooperating defending missiles might not be a cost-effective solution.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124859514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.mcphee
M. McPhee, B. Walmsley, B. Littler, J. Siddell, E. Toohey, V. Oddy, R. Falque, A. Virgona, Teresa Vidal-Calleja, A. Alempijevic
: CattleAssess3D (https://www.youtube.com/watch?v=Dnv9Tiswg2U) integrates a 3-dimensional (3D) real-time assessment of Bos tarus and European breeds of cattle using off-the-shelf Red Green Blue-Depth (RGB-D) structured light cameras with the BeefSpecs drafting tool (Walmsley et al. 2014; http://beefspecs.agriculture.nsw.gov.au/drafting). CattleAssess3D is designed to assist producers manage risks associated with meeting carcass market specifications [P8 fat depth (P8 fat, mm) and hot standard carcass weight (HSCW, kg)]. Failure to meet carcass market specifications costs over AU$51 million/year to the southern Australian beef industry and even more when feeding costs to produce a non-compliant product are taken into consideration.
{"title":"CattleAssess3D: 3D camera technology integrated with BeefSpecs drafting tool to assist �meeting market specifications�","authors":"M. McPhee, B. Walmsley, B. Littler, J. Siddell, E. Toohey, V. Oddy, R. Falque, A. Virgona, Teresa Vidal-Calleja, A. Alempijevic","doi":"10.36334/modsim.2023.mcphee","DOIUrl":"https://doi.org/10.36334/modsim.2023.mcphee","url":null,"abstract":": CattleAssess3D (https://www.youtube.com/watch?v=Dnv9Tiswg2U) integrates a 3-dimensional (3D) real-time assessment of Bos tarus and European breeds of cattle using off-the-shelf Red Green Blue-Depth (RGB-D) structured light cameras with the BeefSpecs drafting tool (Walmsley et al. 2014; http://beefspecs.agriculture.nsw.gov.au/drafting). CattleAssess3D is designed to assist producers manage risks associated with meeting carcass market specifications [P8 fat depth (P8 fat, mm) and hot standard carcass weight (HSCW, kg)]. Failure to meet carcass market specifications costs over AU$51 million/year to the southern Australian beef industry and even more when feeding costs to produce a non-compliant product are taken into consideration.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131704075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.athukorala550
Rajitha Athukorala, Joshua A. Simmons, Sally Cripps, R. Vervoort
: Streamflow timeseries in different parts of the world have their own unique features, posing challenges for hydrological modelling. In Australia, more than 70% of the rivers are non-perennial (i.e. rivers which have no flow for at least part of the year) (Shanafield et al., 2020) and the transition from zero or low flows to high flows is often rapid, resulting in flash floods. Therefore, any forecasting model for the mean and variability of Australian streamflow needs to account for these unique features. To account for rivers which have no streamflow for part of the year we propose a Bayesian Hierarchical Mixture of Experts (BHME) model where the streamflow distribution has two components. The first component of the mixture is a point mass at zero for zero flows, and the second is a Gamma distribution for non-zero flows. As in all hydrological modelling, streamflow data is derived from river height observations via a rating curve which maps river heights to discharge rates. In this paper we take a Bayesian approach and use the posterior mean of stream discharge given river height data for this mapping. To identify zero streamflow, we take the lowest recorded river height, compute the expected value of stream flow given this height, and its corresponding 95% credible interval. If a streamflow observation is lower than the lower limit of this credible interval, then we categorise it as zero. The probability of streamflow at any given day, belonging to either the zero or non-zero flow component is modelled using a logistic regression. The logistic regression model as well as the parameters of the Gamma distribution are parameterized to depend on upstream streamflow and rainfall from the previous day. The second approach is another BHME model with two components to model sudden changes in non-zero flow regimes common to Australian rivers. The two components in this approach are both Gamma densities which are parameterized to depend on upstream streamflow and rainfall from the previous day. The mixture weights in this approach depends on the same set of covariates through a logit link function as in the first approach. The models are estimated in a Bayesian framework using Hamiltonian Monte Carlo with the No-U-Turn Sampler (NUTS) (Homan and Gelman, 2014), to perform the required multidimensional integration and generate samples from the posterior distribution of the quantities of interest. These approaches provide a statistically robust method to model zero observations as well as sudden changes in streamflow. The logistic regression in the first approach provides useful information regarding the transition from flow to no flow (and vice versa) which a single component model cannot provide. The two-component model in the second approach provides better fit to the data and better predictive densities compared to a single component model. The transitions from one component to another in the second approach provides useful information in sudden change
{"title":"Disentangling hydrological mixtures","authors":"Rajitha Athukorala, Joshua A. Simmons, Sally Cripps, R. Vervoort","doi":"10.36334/modsim.2023.athukorala550","DOIUrl":"https://doi.org/10.36334/modsim.2023.athukorala550","url":null,"abstract":": Streamflow timeseries in different parts of the world have their own unique features, posing challenges for hydrological modelling. In Australia, more than 70% of the rivers are non-perennial (i.e. rivers which have no flow for at least part of the year) (Shanafield et al., 2020) and the transition from zero or low flows to high flows is often rapid, resulting in flash floods. Therefore, any forecasting model for the mean and variability of Australian streamflow needs to account for these unique features. To account for rivers which have no streamflow for part of the year we propose a Bayesian Hierarchical Mixture of Experts (BHME) model where the streamflow distribution has two components. The first component of the mixture is a point mass at zero for zero flows, and the second is a Gamma distribution for non-zero flows. As in all hydrological modelling, streamflow data is derived from river height observations via a rating curve which maps river heights to discharge rates. In this paper we take a Bayesian approach and use the posterior mean of stream discharge given river height data for this mapping. To identify zero streamflow, we take the lowest recorded river height, compute the expected value of stream flow given this height, and its corresponding 95% credible interval. If a streamflow observation is lower than the lower limit of this credible interval, then we categorise it as zero. The probability of streamflow at any given day, belonging to either the zero or non-zero flow component is modelled using a logistic regression. The logistic regression model as well as the parameters of the Gamma distribution are parameterized to depend on upstream streamflow and rainfall from the previous day. The second approach is another BHME model with two components to model sudden changes in non-zero flow regimes common to Australian rivers. The two components in this approach are both Gamma densities which are parameterized to depend on upstream streamflow and rainfall from the previous day. The mixture weights in this approach depends on the same set of covariates through a logit link function as in the first approach. The models are estimated in a Bayesian framework using Hamiltonian Monte Carlo with the No-U-Turn Sampler (NUTS) (Homan and Gelman, 2014), to perform the required multidimensional integration and generate samples from the posterior distribution of the quantities of interest. These approaches provide a statistically robust method to model zero observations as well as sudden changes in streamflow. The logistic regression in the first approach provides useful information regarding the transition from flow to no flow (and vice versa) which a single component model cannot provide. The two-component model in the second approach provides better fit to the data and better predictive densities compared to a single component model. The transitions from one component to another in the second approach provides useful information in sudden change","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132034876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.collins
B. Collins, S. Attard, Z. Banhalmi-Zakar, Y. Everingham
: Water pollution and climate change are among the greatest threats to the iconic Great Barrier Reef (GBR). Improving the GBR resilience requires a multi-dimensional approach considering the complex interactions between farm management, farm economics, GHG emission, and green finance systems. There is a need for a tool that conveniently measures improvements in sustainability at the paddock scale and can link improved management to sustainable finance systems. An interactive rapid assessment and visualisation tool named Irrigation Rapid Assessment Tool (i-RAT; https://i-rat.net) was developed to evaluate the impacts of different irrigation practices on sugarcane farmers and extension staff. The tool combines the power of computer modelling with the knowledge and experience of local cane growers and advisors to enable quick and easy comparison of thousands of combinations of farm management scenarios, which is impossible to do in the field. i-RAT was developed via a participatory process based on the conceptual framework by Jakku and Thorburn (2010). Four focus groups were consulted through the design and implementation of i-RAT to ensure a wide range of end-users were considered in the design process. i-RAT allows farmers to compare expected yield and water/energy costs under the ‘current’ irrigation scenario (i.e., irrigation system (furrow, sprinkler, or subsurface drip), frequency, and rate) with those under a ‘new’ scenario. Farmers can select (through drop-down menus) the appropriate options for numerous variables, including soil type, climate, tillage, and N fertilisation. Outputs consist of text and graphics summarising the impact of the change in irrigation management on individual paddocks’ finance, water and energy consumption, water quality, productivity, and greenhouse gas emission. i-RAT also informs sugarcane farmers about new opportunities provided by, for example, engaging in carbon markets.
{"title":"i-RAT: An interactive rapid assessment tool to assess economic and environmental impacts of different sugarcane irrigation practices","authors":"B. Collins, S. Attard, Z. Banhalmi-Zakar, Y. Everingham","doi":"10.36334/modsim.2023.collins","DOIUrl":"https://doi.org/10.36334/modsim.2023.collins","url":null,"abstract":": Water pollution and climate change are among the greatest threats to the iconic Great Barrier Reef (GBR). Improving the GBR resilience requires a multi-dimensional approach considering the complex interactions between farm management, farm economics, GHG emission, and green finance systems. There is a need for a tool that conveniently measures improvements in sustainability at the paddock scale and can link improved management to sustainable finance systems. An interactive rapid assessment and visualisation tool named Irrigation Rapid Assessment Tool (i-RAT; https://i-rat.net) was developed to evaluate the impacts of different irrigation practices on sugarcane farmers and extension staff. The tool combines the power of computer modelling with the knowledge and experience of local cane growers and advisors to enable quick and easy comparison of thousands of combinations of farm management scenarios, which is impossible to do in the field. i-RAT was developed via a participatory process based on the conceptual framework by Jakku and Thorburn (2010). Four focus groups were consulted through the design and implementation of i-RAT to ensure a wide range of end-users were considered in the design process. i-RAT allows farmers to compare expected yield and water/energy costs under the ‘current’ irrigation scenario (i.e., irrigation system (furrow, sprinkler, or subsurface drip), frequency, and rate) with those under a ‘new’ scenario. Farmers can select (through drop-down menus) the appropriate options for numerous variables, including soil type, climate, tillage, and N fertilisation. Outputs consist of text and graphics summarising the impact of the change in irrigation management on individual paddocks’ finance, water and energy consumption, water quality, productivity, and greenhouse gas emission. i-RAT also informs sugarcane farmers about new opportunities provided by, for example, engaging in carbon markets.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134440258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.wahid
{"title":"Pathway to impact: Sustainable Development Investment Portfolio","authors":"","doi":"10.36334/modsim.2023.wahid","DOIUrl":"https://doi.org/10.36334/modsim.2023.wahid","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133918076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.liu287
{"title":"Spatial modelling of understorey evapotranspiration based on the maximum entropy production method","authors":"","doi":"10.36334/modsim.2023.liu287","DOIUrl":"https://doi.org/10.36334/modsim.2023.liu287","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133941145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.eccles
R. Eccles
: Climate change is expected to significantly alter hydrological regimes throughout the world, affecting water resources and the frequency of floods and droughts. While these factors have been widely studied throughout the literature there have been relatively few studies that have evaluated the impacts of climate change on saline intrusion. This study aimed to assess how climate change coupled with sea level rise would impact on saline intrusion along the Logan-Albert estuary in Southeast Queensland. The one-dimensional MIKE HYDRO model was applied with the advection dispersion model for this purpose. Observed streamflow and modelled tides were applied as boundary conditions to drive the model, which was calibrated against monthly observed salinity concentrations obtained from Healthy Land and Water along the length of the Logan and Albert estuaries. Tidal boundaries were assumed to have a salinity concentration of 35 PSU, while the upstream inflows were assumed to have a salinity concentration of 0.1 PSU. The impacts of climate change and sea level rise on saline intrusion were investigated. An ensemble of 11 high-resolution climate models forced under high (Representative Concentration Pathway 8.5 - RCP8.5) were obtained from the Queensland Department of Environment and Science (Syktus et al., 2020). These models were applied to simulate the catchments hydrological response to climate change. Three future periods were evaluated (2020s, 2050s, and 2080s), which were assessed against a model baseline (1980-2010). The simulated hydrologic response from the ensemble of climate models were applied as upstream boundary conditions to the hydrodynamic salinity model. The impacts of sea level rise were considered under the RCP8.5 scenario by altering the modelled tides, which were adopted as downstream boundaries. Hydrological modelling results showed that high and mean flows were projected to decrease significantly in the future under RCP8.5 and that these decreases would become largest by the end of the century. By the 2050s and 2080s a majority of the climate models indicated decreased streamflow, whereas for the 2020s there was no clear indication on the sign of change. This decrease in freshwater inflows when combined with elevated sea levels due to sea level rise led to significant increases in salinity concentrations along the estuary, particularly along the mid estuaries. These increases were most substantial by the end of the century, when streamflow inputs were lowest and sea level rise (and therefore tidal intrusion) was highest. These changes could have a range of implications for agriculture and the environment. For instance, sugarcane is the primary industry located along the lower Logan-Albert floodplain and may be impacted by this increased saline intrusion, through changes in groundwater levels and salinity concentrations. This study provides a useful framework for assess saline intrusions changes as a result of climate change, which m
:气候变化预计将显著改变全世界的水文制度，影响水资源以及洪涝和干旱的频率。虽然这些因素在整个文献中得到了广泛的研究，但评估气候变化对盐水入侵影响的研究相对较少。本研究旨在评估气候变化与海平面上升如何影响昆士兰州东南部洛根-阿尔伯特河口沿岸的盐水入侵。本文采用了一维MIKE HYDRO模型和平流弥散模型。观测到的河流流量和模拟潮汐被用作驱动模型的边界条件，该模型是根据洛根和阿尔伯特河口沿岸健康土地和水每月观测到的盐度浓度进行校准的。潮汐边界假定盐度浓度为35 PSU，而上游流入假定盐度浓度为0.1 PSU。研究了气候变化和海平面上升对盐碱化入侵的影响。从昆士兰州环境与科学部(Syktus et al.， 2020)获得了11个高分辨率强迫气候模式的集合(代表性浓度路径8.5 - RCP8.5)。应用这些模型模拟了流域对气候变化的水文响应。根据模型基线(1980-2010)评估了三个未来时期(2020年、2050年和2080年)。将气候模式集合模拟的水文响应作为水动力盐度模型的上游边界条件。在RCP8.5情景下，通过改变模拟潮汐作为下游边界来考虑海平面上升的影响。水文模拟结果显示，在RCP8.5下，预计未来高流量和平均流量将显著减少，到本世纪末，这些减少将达到最大。到2050年代和2080年代，大多数气候模式表明流量减少，而到2020年代，没有明确的迹象表明变化的迹象。淡水流入的减少，加上海平面上升导致的海平面升高，导致河口沿岸，特别是河口中部的盐度浓度显著增加。这些增加在本世纪末最为显著，当时河流输入量最低，海平面上升(因此潮汐入侵)最高。这些变化可能对农业和环境产生一系列影响。例如，甘蔗是位于洛根-阿尔伯特下游洪泛平原沿线的主要产业，可能会受到盐水入侵增加的影响，通过地下水水位和盐度浓度的变化。这项研究为评估气候变化导致的盐水入侵变化提供了一个有用的框架，这可能很容易应用于澳大利亚其他地方的河口。
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