Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.han529
Cunbo Han, Yaoming Ma
: The land surface is heterogeneous with respect to, for example, land use, plant cover, soil moisture, and topography over a wide range of spatial scales, which strongly influences the integrated energy and water budgets at the land surface and in the atmosphere. Soil moisture constitutes a key factor in the land surface and atmosphere coupled system regulating atmospheric states on time-scales ranging from the diurnal cycle to seasonal variations by affecting surface temperature, partitioning of surface net radiation, and inducing microscale to mesoscale circulations, which influence the development of the daytime convective planetary boundary layer (CBL), and thus, the initiation and intensity of convective clouds and precipitation. The impacts of soil moisture heterogeneity on the development of catchment-scale circulations (CCs) and the atmospheric boundary layer were studied applying large-eddy simulations (LESs). A continuous river-like soil moisture pattern, rather than discrete patch-like soil moisture pattern, was used to avoid unrealistic abrupt variation of the surface variables. A series of eight simulations with heterogeneous soil moisture were performed with horizontal valley widths ranging from 0.6 to 76.8 km, with the same 50 m horizontal grid spacing in each horizontal direction. In this way, the domain mean soil moisture values are maintained. The simulation results show that the intensity of organized circulations resulting from soil moisture heterogeneity is nonlinearly dependent upon soil moisture heterogeneity scale 𝜆𝜆 (SMHS) and horizontal gradient. Because of the large SMHS and strong soil moisture contrast, none of the simulations has reached a true steady state even after 24 h of simulation time. The intensity of organized circulations shows a sigmoidal dependence on SMHS. The optimal SMHS for horizontal transport is on the order of 19.2 km, while optimal SMHS for vertical motions occurs at 2.4 km. In these cases, the CCs also exert a strong influence on the boundary-layer structure and the entrainment layer. The potential temperature is not constant with height due to a weak mixing in the boundary layer for large SMHS cases. Differences in sensible heat flux profiles between the heterogeneous cases increase with increasing height and reach a maximum at the top of the CBL. Interestingly, boundary-layer height changes strongly with changing horizontal soil moisture gradient and SMHS while domain means, variances, and amplitudes of land surface energy fluxes are all almost identical. The entrainment flux and subsidence at the top of the CBL are jointly responsible for the CBL height variation. To mimic a more realistic soil moisture distribution, two-dimensional soil moisture heterogeneities were generated applying spatially correlated Gaussian fields based on a power law model. Three sets of LESs, with prescribed strong, weak, and unstructured soil moisture heterogeneity, were performed to investigate the influence of
{"title":"How land surface heterogeneity impacts on energy partitioning and atmospheric boundary development","authors":"Cunbo Han, Yaoming Ma","doi":"10.36334/modsim.2023.han529","DOIUrl":"https://doi.org/10.36334/modsim.2023.han529","url":null,"abstract":": The land surface is heterogeneous with respect to, for example, land use, plant cover, soil moisture, and topography over a wide range of spatial scales, which strongly influences the integrated energy and water budgets at the land surface and in the atmosphere. Soil moisture constitutes a key factor in the land surface and atmosphere coupled system regulating atmospheric states on time-scales ranging from the diurnal cycle to seasonal variations by affecting surface temperature, partitioning of surface net radiation, and inducing microscale to mesoscale circulations, which influence the development of the daytime convective planetary boundary layer (CBL), and thus, the initiation and intensity of convective clouds and precipitation. The impacts of soil moisture heterogeneity on the development of catchment-scale circulations (CCs) and the atmospheric boundary layer were studied applying large-eddy simulations (LESs). A continuous river-like soil moisture pattern, rather than discrete patch-like soil moisture pattern, was used to avoid unrealistic abrupt variation of the surface variables. A series of eight simulations with heterogeneous soil moisture were performed with horizontal valley widths ranging from 0.6 to 76.8 km, with the same 50 m horizontal grid spacing in each horizontal direction. In this way, the domain mean soil moisture values are maintained. The simulation results show that the intensity of organized circulations resulting from soil moisture heterogeneity is nonlinearly dependent upon soil moisture heterogeneity scale 𝜆𝜆 (SMHS) and horizontal gradient. Because of the large SMHS and strong soil moisture contrast, none of the simulations has reached a true steady state even after 24 h of simulation time. The intensity of organized circulations shows a sigmoidal dependence on SMHS. The optimal SMHS for horizontal transport is on the order of 19.2 km, while optimal SMHS for vertical motions occurs at 2.4 km. In these cases, the CCs also exert a strong influence on the boundary-layer structure and the entrainment layer. The potential temperature is not constant with height due to a weak mixing in the boundary layer for large SMHS cases. Differences in sensible heat flux profiles between the heterogeneous cases increase with increasing height and reach a maximum at the top of the CBL. Interestingly, boundary-layer height changes strongly with changing horizontal soil moisture gradient and SMHS while domain means, variances, and amplitudes of land surface energy fluxes are all almost identical. The entrainment flux and subsidence at the top of the CBL are jointly responsible for the CBL height variation. To mimic a more realistic soil moisture distribution, two-dimensional soil moisture heterogeneities were generated applying spatially correlated Gaussian fields based on a power law model. Three sets of LESs, with prescribed strong, weak, and unstructured soil moisture heterogeneity, were performed to investigate the influence of ","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"9 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":"114776334","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.tian126
Ran Tian, E. Stojanovski, Drew Miller
: Improving teaching quality has been an ongoing pursuit for policymakers, researchers and broader government agencies and organisations. Professional development (PD) initiatives directed at teachers are a recognised strategy for improving teaching quality, with the underlying intention to support improved student achievement. Yet there is limited evidence of the actual impact of PD directed at teachers on student learning outcomes. Quality Teaching Rounds (QTR), a well-recognised form of collaborative PD in Australia, is one of few approaches to provide evidence of impact on teaching quality and student outcomes. Through multiple Randomized Controlled Trials (RCTs), a statistically significant positive impact of QTR PD has been demonstrated on teaching practices and student outcomes. A further question that remains is whether the change in teaching quality through involvement in QTR PD improves student achievement. This underlying mechanism is yet to be explored in the QTR PD framework and has rarely been explored in PD settings internationally due to limited data that often prevents suitable statistical techniques to be employed. A clustered RCT investigating the impact of QTR PD on teaching quality and student achievement was conducted in 2019, involving 133 government primary schools in New South Wales (NSW), Australia. Data were collected from 222 teachers and 5146 students in Stage 2 (years 3-4) in Term 1 and these teachers and students were followed up in Term 4 of the same year. Teaching practices were observed and rated in the classroom using a pedagogical model, the Quality Teaching (QT) model, which contains 18 observable elements within three dimensions of teaching practice (Intellectual Quality, Quality Learning Environment and Significance), while students were assessed using the Progressive Achievement Test (PAT) in mathematics, which was measured using the scaled scores on 40 multiple choice questions. The data structure therefore comprised a combination of multilevel and longitudinal features along with latent constructs and multiple intervention groups (QTR vs wait-list control) that were being compared. This paper examines the underlying interconnected relationships between PD, teaching quality and student achievement by testing the hypothesis that the impacts of the QTR intervention on student achievement in mathematics was mediated by teaching quality. Multilevel structural equation modelling (MSEM) with 2-2-1 design is investigated for these data. Student PAT scores in mathematics were significantly higher, on average (0.11SD [95% CI = 0.01,0.20]) in the intervention group (QTR) compared to those in the control group for the Intellectual Quality (IQ) dimension of the QT model. This demonstrates the statistically significant mediation effect of Intellectual Quality (IQ) on student learning outcomes.
提高教学质量一直是政策制定者、研究人员和更广泛的政府机构和组织的持续追求。针对教师的专业发展计划是一项公认的提高教学质量的策略，其根本目的是支持提高学生的成绩。然而，针对教师的PD对学生学习成果的实际影响的证据有限。质量教学轮次(QTR)是澳大利亚公认的合作PD形式，是提供对教学质量和学生成果影响证据的少数方法之一。通过多个随机对照试验(RCTs)， QTR PD对教学实践和学生成绩有统计学上显著的积极影响。另一个问题是，通过参与QTR PD来改变教学质量是否能提高学生的成绩。这种潜在机制尚未在QTR PD框架中进行探索，并且由于数据有限，通常无法采用适当的统计技术，因此很少在国际PD设置中进行探索。2019年，在澳大利亚新南威尔士州(NSW)的133所公立小学开展了一项调查QTR PD对教学质量和学生成绩影响的聚类随机对照试验。本研究收集了第一学期第二阶段(3-4年级)222名教师和5146名学生的数据，并在同年第四学期对这些教师和学生进行了随访。使用教学模型，即质量教学(QT)模型，在课堂上观察和评价教学实践，该模型包含教学实践三个维度(智力质量，优质学习环境和重要性)中的18个可观察元素，而使用数学进步成就测试(PAT)对学生进行评估，该测试使用40个选择题的缩放分数来衡量。因此，数据结构包括多层和纵向特征的组合，以及正在比较的潜在构念和多个干预组(QTR与等候名单对照)。本文通过检验QTR干预对学生数学成绩的影响是由教学质量中介的假设，来检验PD、教学质量和学生成绩之间潜在的相互关系。研究了基于2-2-1设计的多层结构方程模型(MSEM)。在QT模型的智力质量(IQ)维度上，干预组(QTR)学生的数学PAT分数显著高于对照组(平均0.11SD [95% CI = 0.01,0.20])。这表明智力质量(IQ)对学生学习成果的中介作用具有显著的统计学意义。
{"title":"A novel application of multilevel SEM: Teaching quality as mediator between intervention and student achievement","authors":"Ran Tian, E. Stojanovski, Drew Miller","doi":"10.36334/modsim.2023.tian126","DOIUrl":"https://doi.org/10.36334/modsim.2023.tian126","url":null,"abstract":": Improving teaching quality has been an ongoing pursuit for policymakers, researchers and broader government agencies and organisations. Professional development (PD) initiatives directed at teachers are a recognised strategy for improving teaching quality, with the underlying intention to support improved student achievement. Yet there is limited evidence of the actual impact of PD directed at teachers on student learning outcomes. Quality Teaching Rounds (QTR), a well-recognised form of collaborative PD in Australia, is one of few approaches to provide evidence of impact on teaching quality and student outcomes. Through multiple Randomized Controlled Trials (RCTs), a statistically significant positive impact of QTR PD has been demonstrated on teaching practices and student outcomes. A further question that remains is whether the change in teaching quality through involvement in QTR PD improves student achievement. This underlying mechanism is yet to be explored in the QTR PD framework and has rarely been explored in PD settings internationally due to limited data that often prevents suitable statistical techniques to be employed. A clustered RCT investigating the impact of QTR PD on teaching quality and student achievement was conducted in 2019, involving 133 government primary schools in New South Wales (NSW), Australia. Data were collected from 222 teachers and 5146 students in Stage 2 (years 3-4) in Term 1 and these teachers and students were followed up in Term 4 of the same year. Teaching practices were observed and rated in the classroom using a pedagogical model, the Quality Teaching (QT) model, which contains 18 observable elements within three dimensions of teaching practice (Intellectual Quality, Quality Learning Environment and Significance), while students were assessed using the Progressive Achievement Test (PAT) in mathematics, which was measured using the scaled scores on 40 multiple choice questions. The data structure therefore comprised a combination of multilevel and longitudinal features along with latent constructs and multiple intervention groups (QTR vs wait-list control) that were being compared. This paper examines the underlying interconnected relationships between PD, teaching quality and student achievement by testing the hypothesis that the impacts of the QTR intervention on student achievement in mathematics was mediated by teaching quality. Multilevel structural equation modelling (MSEM) with 2-2-1 design is investigated for these data. Student PAT scores in mathematics were significantly higher, on average (0.11SD [95% CI = 0.01,0.20]) in the intervention group (QTR) compared to those in the control group for the Intellectual Quality (IQ) dimension of the QT model. This demonstrates the statistically significant mediation effect of Intellectual Quality (IQ) on student learning outcomes.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"15 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":"114480225","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.liu327
{"title":"Using remotely sensed data to understand global lake colour variability","authors":"","doi":"10.36334/modsim.2023.liu327","DOIUrl":"https://doi.org/10.36334/modsim.2023.liu327","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"77 2-3 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":"116582525","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.owens
{"title":"From modelling to measurements: Bridging gaps in modelling with measured vegetation, evapotranspiration and soil moisture data","authors":"","doi":"10.36334/modsim.2023.owens","DOIUrl":"https://doi.org/10.36334/modsim.2023.owens","url":null,"abstract":"","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"42 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":"117169650","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.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.oshea
D. O’Shea, R. Nathan, Ashish Sharma, C. Wasko
: Accurate estimation of Annual Exceedance Probabilities (AEPs) of extreme rainfalls through rainfall frequency analysis (RFA) is a critical step in the production of intensity-frequency duration relationships, which are used to inform engineering design for flood mitigation and disaster response. The most common approach to rainfall frequency analysis used in both academic literature and industry practice is to fit the three parameter Generalised Extreme Value (GEV) distribution to a series of annual maximum (AMS) rainfalls. Motivated by empirical evidence that rainfall AMS in the United States (Karlovits & Schaefer, 2020) and Australia (Nathan et al., 2016) are not well represented by the GEV distribution we explore fitting the more flexible four-parameter Kappa distribution. Use of the Kappa distribution in hydrology has been largely limited to regional studies that pool data from many sites owing to the data requirements of fitting the Kappa’s two shape parameters. As an alternative we present a two-step approach for fitting the Kappa distribution to peaks-over-threshold (POT) series based on maximum likelihood estimation. The approach separately models storm intensity and the arrival frequency. First, a Generalized Pareto distribution describing storm intensity is fitted, followed by a Binomial distribution for storm arrivals. We compare the performance of this two-step Kappa approach to an analogous two-step GEV approach, and to Kappa and GEV distributions fitted to AMS, using both synthetic and real-world data representative of Australian climatic conditions. Our results show that the two-step Kappa approach performs better than the GEV distribution at estimating extreme rainfall quantiles over a wide range of parent distributions (O’Shea et al., 2023).
通过降雨频率分析(RFA)准确估计极端降雨的年超过概率(AEPs)是产生强度-频率持续时间关系的关键步骤，该关系用于通知工程设计以减轻洪水和灾害响应。在学术文献和工业实践中，最常用的降雨频率分析方法是将三参数广义极值(GEV)分布拟合到一系列年最大降雨量(AMS)。经验证据表明，美国(Karlovits & Schaefer, 2020)和澳大利亚(Nathan et al.， 2016)的降雨量AMS不能很好地代表GEV分布，因此我们探索拟合更灵活的四参数Kappa分布。由于拟合Kappa的两个形状参数的数据要求，Kappa分布在水文学中的使用在很大程度上局限于从许多地点汇集数据的区域研究。作为一种替代方法，我们提出了一种基于极大似然估计的Kappa分布拟合到超过阈值的峰值(POT)序列的两步方法。该方法分别模拟了风暴强度和到达频率。首先拟合描述风暴强度的广义帕累托分布，然后拟合风暴到达的二项分布。我们将这种两步Kappa方法的性能与类似的两步GEV方法进行比较，并将Kappa和GEV分布拟合到AMS中，使用代表澳大利亚气候条件的合成和真实数据。我们的研究结果表明，在广泛的母分布范围内估计极端降雨分位数时，两步Kappa方法比GEV分布表现更好(O’shea et al.， 2023)。
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Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.melton
Dirk Melton
: A longitudinal case study on a pro-sport team within a national competition conceptualized the athletic department (referred to by practitioners as the high-performance system: HPS), as a complex social-ecological system. It was evident the HPS is multi-variate, heterogeneous, non-linear, cross-scale, and dynamic system, where knowledge is a primary critical organisation resource. It is proposed that the creation, accumulation, validation, storage, absorption, and transfer of knowledge required for org capability development plays an integral role enhancing HPS resilience, i.e., the ability of the HPS to withstand and adapt to disturbances without major functional and structural change. HPS governance inadequacies and associated systems thinking capacity constraints impact system understanding, thus inhibiting strategy development, resource allocation, and system management, culminating in poor team performance. With an aim to enhance knowledge flow, capability development, and system resilience; system dynamics as a research method was employed, with a view to build a simulation model to improve understanding of the HPS. To assist creation of the model, a theoretical frame was developed integrating the ecological science construct, adaptive governance, into the HPS. This construct was operationalized by the sequential and cumulative application of three concepts from the ‘Resource-based View’ (RBV) school of strategy, ‘the knowledge creation spiral’, ‘absorptive capacity’, and ‘the learning organisation’. During the case study research, three (3) years of unfettered access to the HPS was provided by the ‘Head Coach’ and ‘General Manager’. A pragmatic constructivist philosophy drove the creation of system dynamics-action research mixed methods approach, considered for this project to be the most suitable to build, test, and validate a simulation model that would assist both strategy theory development and practitioner learning. The model attempts to establish the flow of knowledge through the HPS as it impacts the development of seventy-seven (77) capabilities. Determination of the specific capabilities, hierarchical capability structure, and cross-scale capability connectivity displayed in the model, evolved throughout the duration of the project, using considerable practitioner input. The foundations of the final structure consisted of the four main HPS dimensions, strategy, operations, coaching, and team development, aligned and integrated with the respective adaptive governance dimensions, science, policy, adaptive management, and system performance. The operationalisation of each integrated dimension, using the three (3) RBV concepts, established four (4) structures, each representing an adaptive life cycle (ALC). Each ALC consisted of nineteen (19) capabilities, structured across three (3) capability levels or orders, the final structure mimicking the ‘limits-to-growth’ system dynamics archetype. Capability connectivity within the ALC was
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Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.jian467
J. Jian, F. M. Johnson, L. Marshall
: Anthropogenic climate change is affecting all parts of the hydrological cycle and will increasingly lead to changes in water availability. Assessments of climate change on hydrological systems generally use conceptual rainfall runoff models. However most conceptual rainfall runoff models use Potential Evapotranspiration (PET) as a forcing and as a result calculate Actual Evapotranspiration (AET) internally as a model state. This is a problem in the context of climate change impact assessments because it is likely that vegetation processes will change in the future with both rainfall changes and changes in CO 2. However, the effects of those changes in vegetation processes on ET are not explicitly modelled in conceptual rainfall runoff models and therefore cannot currently be quantified. Whilst process-based ecohydrological models can be used to assess such questions (Stephens et al., 2020), their high computational cost and extensive data requirements, mean that it is not practical to use them operationally for future water resources assessments. Thus, a major gap in current hydrological practice is how PET and AET future changes can be included in catchment models. In this presentation, we present results of a sensitivity analyses of future streamflow projections to a range of ET formulations in conceptual rainfall runoff models. The analyses are carried out for the Kedumba Catchment in NSW. We modified a conceptual rainfall model to include the PML_V2 model (Gan et al., 2018) which incorporates LAI data to generate more accurate canopy conductance in ET. The modified rainfall runoff model was then forced with future LAI and climate simulations and the streamflow projections compared to results from the conceptual models calibrated over the historical period with different PET formulations. Using this approach, we assess the uncertainty in streamflow estimates from both model structural choices and future climate scenarios. Future work will compare these simpler model structures to a physically-based ecohydrological model which has dynamic vegetation
{"title":"Modelling the impacts of future evapotranspiration changes on streamflow","authors":"J. Jian, F. M. Johnson, L. Marshall","doi":"10.36334/modsim.2023.jian467","DOIUrl":"https://doi.org/10.36334/modsim.2023.jian467","url":null,"abstract":": Anthropogenic climate change is affecting all parts of the hydrological cycle and will increasingly lead to changes in water availability. Assessments of climate change on hydrological systems generally use conceptual rainfall runoff models. However most conceptual rainfall runoff models use Potential Evapotranspiration (PET) as a forcing and as a result calculate Actual Evapotranspiration (AET) internally as a model state. This is a problem in the context of climate change impact assessments because it is likely that vegetation processes will change in the future with both rainfall changes and changes in CO 2. However, the effects of those changes in vegetation processes on ET are not explicitly modelled in conceptual rainfall runoff models and therefore cannot currently be quantified. Whilst process-based ecohydrological models can be used to assess such questions (Stephens et al., 2020), their high computational cost and extensive data requirements, mean that it is not practical to use them operationally for future water resources assessments. Thus, a major gap in current hydrological practice is how PET and AET future changes can be included in catchment models. In this presentation, we present results of a sensitivity analyses of future streamflow projections to a range of ET formulations in conceptual rainfall runoff models. The analyses are carried out for the Kedumba Catchment in NSW. We modified a conceptual rainfall model to include the PML_V2 model (Gan et al., 2018) which incorporates LAI data to generate more accurate canopy conductance in ET. The modified rainfall runoff model was then forced with future LAI and climate simulations and the streamflow projections compared to results from the conceptual models calibrated over the historical period with different PET formulations. Using this approach, we assess the uncertainty in streamflow estimates from both model structural choices and future climate scenarios. Future work will compare these simpler model structures to a physically-based ecohydrological model which has dynamic vegetation","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":"115424093","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.crosbie259
R. Crosbie, S. Charles, G. Fu, G. Hodgson, D. Dutta, A. McCallum
: The new climate data and modelled streamflow produced by DPE-Water for the development of NSW regional water strategies have been used to investigate how a drying climate could impact groundwater resources across NSW. The new climate data consists of two 10,000-year sequences of stochastically generated daily rainfall and potential evapotranspiration representing historical and future (dry scenario) climate conditions, respectively. Future rainfall projections have a wide range of uncertainty; hence a dry scenario was chosen to allow the assessment of an extreme climate risk. The modelled streamflow from the historical and future stochastic climate sequences were used to estimate changes to localised recharge through losing streams and overbank flooding. Changes in modelled streamflow were used to estimate how stage height and thus in-stream recharge from losing streams may change. The changes in recharge from in-stream losses were estimated to vary from -55.4% to -3.4% across NSW. Overbank flooding recharge changes estimated from the streamflow changes were more extreme than the other estimated recharge changes, with a projected range from -90.5% to +56.1% (with only a single gauge, out of 42 investigated, producing an increase).
{"title":"Simulating the impacts of reduced streamflow on localised groundwater recharge in NSW","authors":"R. Crosbie, S. Charles, G. Fu, G. Hodgson, D. Dutta, A. McCallum","doi":"10.36334/modsim.2023.crosbie259","DOIUrl":"https://doi.org/10.36334/modsim.2023.crosbie259","url":null,"abstract":": The new climate data and modelled streamflow produced by DPE-Water for the development of NSW regional water strategies have been used to investigate how a drying climate could impact groundwater resources across NSW. The new climate data consists of two 10,000-year sequences of stochastically generated daily rainfall and potential evapotranspiration representing historical and future (dry scenario) climate conditions, respectively. Future rainfall projections have a wide range of uncertainty; hence a dry scenario was chosen to allow the assessment of an extreme climate risk. The modelled streamflow from the historical and future stochastic climate sequences were used to estimate changes to localised recharge through losing streams and overbank flooding. Changes in modelled streamflow were used to estimate how stage height and thus in-stream recharge from losing streams may change. The changes in recharge from in-stream losses were estimated to vary from -55.4% to -3.4% across NSW. Overbank flooding recharge changes estimated from the streamflow changes were more extreme than the other estimated recharge changes, with a projected range from -90.5% to +56.1% (with only a single gauge, out of 42 investigated, producing an increase).","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"30 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":"123419357","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}
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