MODSIM2023, 25th International Congress on Modelling and Simulation.最新文献

{"title":"Two-monthly maximum water depth for the Murray�Darling Basin: Usage guidance","authors":"D. Penton, J. Teng, C. Ticehurst, S. Marvanek, A. Freebairn, J. Vaze, Fathaha Khanam, A. Sengupta","doi":"10.36334/modsim.2023.penton","DOIUrl":"https://doi.org/10.36334/modsim.2023.penton","url":null,"abstract":": The recently released two monthly maximum water depth maps by Teng et al., (2023) provide opportunities for scientists to examine the relationship between hydrological and ecological processes. The depth maps provide a consistent spatial estimate of flood water depth across the Murray–Darling Basin (MDB) over the past 35 years. The product is available from CSIRO’s Data Access Portal at (https://doi.org/10.25919/c5ab-h019) and through web portal (https://map.csiro.easi-eo.solutions/). The dataset including its validation against hydrodynamic models is described in Penton et al. (2023). This abstract provides guidance on how best to use the product to undertake further analysis. We recommend a four-step process to systematically account for the product’s accuracy. First, researchers should confirm with local sources (using web portal) that major floods in the region of interest are visible in the product (were cloud-free during acquisition). Second, most analyses will require around 20 two-monthly images so the model errors converge to a known statistical distribution (e.g. a Laplace or Cauchy distribution). Given enough images, it is then possible to remove the product bias by increasing the flood depth by 0.34 m (the median error estimated in the benchmark set). Third, when estimating the water depths for locations with permanent water storages (especially reservoirs) use a local data-source to infill. For example, infill with depths calculated from observed levels in large reservoirs using bathymetry, which are usually available from the reservoir operator (the bathymetry may need to be digitised). Finally, we recommend calculating the sensitivity of the results and conclusions to scaled depth inputs.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"13 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":"122103570","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}

{"title":"Barossa water security strategy: A demonstration of community leadership, strategic foresight, climate resilience and systems modelling","authors":"Ruby Leigh, Ashley B Kingsborough, S. Westra, Peta Brettig, A. Helfgott","doi":"10.36334/modsim.2023.leigh","DOIUrl":"https://doi.org/10.36334/modsim.2023.leigh","url":null,"abstract":": Securing water supply in the face of climate change requires an integrated response which incorporates perspectives and methods from a range of stakeholders and sectors. To illustrate the development of such an integrated response, a regional water security strategy was developed for the Barossa region for the period from the present to the year 2050. South Australia’s Barossa is known for its premium food, wine and agricultural sector, and has recently experienced water scarcity due to several consecutive dry years. Climate change is projected to result in a decline in natural water sources and growing irrigation demand in the future. Water is a key economic input to agricultural production, as well as being vital for the region’s environmental, cultural and amenity value. In order to achieve an integrated response to regional water security, it is necessary to consider diverse stakeholder interests and take into account water planning, policy, infrastructure, and demand considerations. to this end, the strategy was informed by a qualitative strategic foresight and resilience-based planning approach (Helfgott, 2018), as well as quantitative systems modelling, which included climate stress testing. The actions contained within the 2050 strategy were identified by community members and stakeholder organisations through a series of participatory workshops. Workshop participants identified actions to address water security and explicitly considered their effectiveness under diverse, yet plausible futures. These scenarios were developed by stakeholders to take into account key important and uncertain factors that may affect the future of the region (Lord et al., 2016). The strategy includes six strategic pillars, with each pillar setting out actions to achieve a shared vision for the future. In parallel with the participatory workshops, quantitative modelling was undertaken to ‘stress test’ some of the actions identified. This was achieved through the development of a system dynamics model that was used to evaluate the impact of climate change and a range of adaptive pathways on water security and environmental metrics. The system dynamics model was trained on more detailed component models (surface water, groundwater, and irrigation demand models), as well as various other sources of information (details in Westra et al., 2022). Under a mid-range estimate for the 2050s, the modelling showed that an additional 8 GL per annum of imported water is projected to be required to ensure there is no irrigation shortfall in the driest years (assuming the existing planted area is maintained). The strategy is designed to increase business and community confidence that long-term water security is being planned for. This analysis supports a systemic understanding of water security and the case for future investment, so that the region is empowered to achieve its vision, particularly in a changing climate. The opportunity exists for the Australian water industry","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"47 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":"117169596","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}

{"title":"Space-time velocimetry to estimate discharge during the 2022 River Murray flood","authors":"M. Gibbs, J. Hughes, C. Petheram","doi":"10.36334/modsim.2023.gibbs","DOIUrl":"https://doi.org/10.36334/modsim.2023.gibbs","url":null,"abstract":": Streamflow discharge measurement underpins a range of assessments, policy, and management related to resource management. The standard methods to measure discharge can be costly due to the time consuming and labour-intensive manual measurements required by highly specialized staff, particularly in remote and difficult to access sites. Surface velocity measurements achieved through video image analysis are becoming increasingly popular methods to estimate velocity and discharge, driven by remote pilot aircraft (RPA, or drone) and camera technology. These methods have the advantage of being non-intrusive and hence improved safety during high flow measurements, are suited to low flows and depths and inexpensive measuring equipment can be deployed remotely not requiring staff to be present. This paper demonstrates the application of video-based surface velocity methods during the peak of a high flow event in the River Murray in late 2022, peaking at approximately 200 GL/d (an annual exceedance probability of approximately 1 in 50). Six videos were recorded with an RPA and a mobile phone camera at five locations between the townships of Renmark and Berri. The Space Time Image Velocimetry (STIV) method was used to compute surface velocities and available survey information was used to derive coordinates to orthorectify the video as well as river cross section bathymetry. The STIV method uses changes in brightness of the river surface in the direction of flow (a distance in space in the image) over time (between video frames) to produce diagonal lines on a combined image, with the slope of the line representing the surface velocity. Three methods to estimate surface velocity were tested in combination with two methods to convert the surface velocity to the mean channel velocity. The deep learning method with a log-law relationship to derive mean channel velocity was found to perform the best for the videos recorded when compared to more traditional Acoustic Doppler Current Profiler discharge measurements recorded at the same time. The results demonstrate that relatively accurate discharge estimates can be achieved with minimal equipment, just a phone camera on the riverbank. The other data requirements, survey of points to orthorectify the video into real-world distances and survey of the river cross section to compute discharge, and the water level relative to these points, become the more significant data requirements to estimate discharge","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"112 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":"125915940","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}

{"title":"Microwave data and climate information�based streamflow prediction using the surrogate river discharge model in the Murray�Darling Basin","authors":"","doi":"10.36334/modsim.2023.yoon","DOIUrl":"https://doi.org/10.36334/modsim.2023.yoon","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"28 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":"124815034","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}

{"title":"Compound drought and extreme temperature impacts on Australian wheat yields under climate change","authors":"Siyi Li, Bin Wang, D. Liu, A. Huete, Q. Yu","doi":"10.36334/modsim.2023.li657","DOIUrl":"https://doi.org/10.36334/modsim.2023.li657","url":null,"abstract":": The frequency and intensity of extreme climate events have increased in many global agricultural regions since the twentieth century. However, the quantification of extreme events impact on crop yield was mainly focused on individual events like drought or heat stress. While there is evidence from numerous instances showcasing the destructive effects of compound extreme events on crop yield, surpassing those of individual events, the precise magnitude and long-term implications of these impacts remain unclear. Here we used Australia’s wheat growing belt including 12 subregions as the study area. The 32-year wheat yield data (1990-2021) for each region were obtained from Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). The APSIM model forced with historical climate data in 1990-2021 was used to simulate the wheat phenology and daily plant available water. We then determined the daily intensity of drought, heat, and frost events during the wheat reproductive period (WRP) based on the modelling outputs. Furthermore, the annual intensity of compound drought and extreme temperature (DET) was obtained by calculating the sum of the daily intensity during DET events in WRP. After removing the DET episodes, the daily intensities of the remaining stages for drought, heat, and frost were accumulatively summed, respectively, to represent the corresponding annual intensity of these three individual extreme events. Finally, we developed multiple linear regression models to determine the contribution of DET to wheat yield change. We aim to (1) study the characteristics of single and compound drought and extreme temperature events in 1990-2021; (2) quantify the impacts of DET on wheat yields in 12 subregions in the Australian wheat belt; (3) identify the relative importance of DET in low-yield years.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"13 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":"128663835","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}

{"title":"Developing satellite-derived nitrogen stable isotope ratio grids to globally monitor terrestrial plant nitrogen availability for 1984�2022","authors":"Jinyan Yang, Haiyang Zhang, Yiqing Guo, R. Donohue, T. McVicar, S. Ferrier, Warren Müller, Xiaotao Lü, Yunting Fang, Xiaoguang Wang, P. Reich, Xingguo Han, K. Mokany","doi":"10.36334/modsim.2023.yang582","DOIUrl":"https://doi.org/10.36334/modsim.2023.yang582","url":null,"abstract":": Nitrogen (N) availability regulates the productivity of terrestrial plants and the ecosystem services they provide. There is evidence for both increasing and decreasing plant N availability in different biomes","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"3 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":"127267010","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}

{"title":"Sustaining groundwater irrigation for food security in the northwest region of Bangladesh","authors":"","doi":"10.36334/modsim.2023.mainuddin","DOIUrl":"https://doi.org/10.36334/modsim.2023.mainuddin","url":null,"abstract":"The northwest region has the largest areas of cropping in Bangladesh and is crucial to Bangladesh’s recent attainment of rice grain food security. The northwest region alone supplies about 35% of the irrigated Boro rice and more than 60% of the wheat and maize of the whole country. The northwest region is also the region of greatest concern over falling groundwater levels, particularly in the Barind area, which have resulted in a lack of access to water for drinking and irrigation in some areas. However, it is not clear whether the declining groundwater levels result from an observed decline in rainfall, or from excessive use, or from some combination of these and possibly other factors. To address this challenge, the Australian Government, through its Department of Foreign Affairs and Trade (DFAT) Sustainable Development Investment Portfolio, is funding a project involving CSIRO and several Bangladeshi partners.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"1 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":"130599865","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}

{"title":"Digital twin�based river-system model","authors":"","doi":"10.36334/modsim.2023.seo610","DOIUrl":"https://doi.org/10.36334/modsim.2023.seo610","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"55 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":"132403439","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}

{"title":"Calibration and uncertainty analysis of groundwater models assisted by machine learning surrogates","authors":"J Sreekanth, D. Pagendam, Dan MacKinlay, T. Pickett, Petra Ku hn ert","doi":"10.36334/modsim.2023.janardhanan","DOIUrl":"https://doi.org/10.36334/modsim.2023.janardhanan","url":null,"abstract":": Numerical groundwater models are widely used for environmental decision support. They are often used for predictive analysis to evaluate the consequences of management decisions. Such modelling workflow involves history matching to calibrate the model parameters and predictive analysis including quantification of prediction uncertainties. Sometimes, groundwater models are also used in a simulation-optimization framework to identify optimal values of groundwater management decision variables that meet multiple constraints. Algorithms for model inversion (calibration), non-linear uncertainty and simulation-optimization analyses typically require hundreds to millions of forward runs of the numerical groundwater models. When complex groundwater models need to be used for such analyses, long run-times and numerical instabilities limit their applicability for such computationally demanding analys es. Many studies have demonstrated the applicability of machine learning (ML) surrogate models for approximating the responses of groundwater flow and transport models (Yu et al, 2020). More recent studies have also demonstrated the applicability of ML approaches for stochastic inversion of biophysical models (MacKinlay et al, 2023). In this study we explore potential applicability of surrogate models in assisting computationally demanding inversion, optimization and uncertainty analysis. In our first application, limited runs of a complex numerical groundwater model based on the MODFLOW code were used to train a surrogate model developed using Genetic Programming. The surrogate model was developed to approximate the functional relationship between the uncertain parameters of the model and its prediction of groundwater flow and head changes induced by extraction of groundwater for coal seam gas (CSG) development. The trained and validated surrogate model was used in a simulation-optimization framework to evaluate the trade-off between predicted maximum flow and groundwater head changes and CSG water extraction. The surrogate model trained and tested using 920 forward runs of the numerical groundwater model was used to evaluate 1.5 million combinations of model parameters to approximately evaluate the predictions using the simulation-optimization framework. The analysis showed that, within plausible range of model parameters and expected rates of CSG water extraction, CSG-induced maximum flow changes increase linearly with increases in water extraction volume and are directly proportional to the CSG-induced groundwater head drawdown.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"2 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":"130925561","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}

{"title":"Assessing climate change impacts on dual-priority water rights in carryover systems at basin scale","authors":"P. Ren, M. Stewardson, M. Peel, M. Turner, A. John","doi":"10.36334/modsim.2023.ren357","DOIUrl":"https://doi.org/10.36334/modsim.2023.ren357","url":null,"abstract":": Climate is an essential component of water management, but it projects an extra threat to freshwater systems. Changes in the mean and variability of climate variables (such as rainfall, and temperature) alter the hydrological mean and variability and impact water availability for humans and ecosystems. It is important to consider the effects of climate change as a core part of water planning to ensure a full accounting of risks. Here, we research the climate change impact on the yield of ‘dual-priority’ water rights systems across 12 Australian catchments based on four bias-corrected global climate models and a simple analytical technique (Ren et al. 2022). We first evaluated the feasibility of the analytical technique against a water resources simulation model in the Goulburn River basin, Australia. The results showed that this method performs well. Meanwhile, the results showed that under future climate conditions, the mean annual runoff of these catchments will decrease, but annual runoff variability will increase, except for some catchments in northern Australia. Similar to the trend of mean annual runoff, water availability of high-priority water rights (HPWR) and low-priority water rights (LPWR) decreased for most catchments except for some catchments in northern Australia. For example, under the Representative Concentration Pathway (RCP) 8.5 scenario, South Dandalup shows about -53.53% and -56.81% decrease in terms of HPWR and LPWR yield respectively in the 2070s. Overall, changes in mean annual runoff have a more significant influence on the water yield of high and low-priority water rights than annual flow variability.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"10 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":"131745439","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}