Pub Date : 2023-08-01DOI: 10.36334/modsim.2023.huang
Jiajia Huang, Wenyan Wu, Q. J. Wang, H. Maier, J. Hughes
: Reservoirs are essential infrastructure, supplying water for domestic, industrial and irrigation uses. Due to long-term climate and water demand changes, the performance of reservoirs may decrease throughout their lifespan, potentially requiring interventions such as reservoir expansion and/or water demand reduction. However, these interventions are often expensive and result in prolonged social and environmental disruptions. Consequently, there is a need to explore opportunities to enhance reservoir performance before these interventions are necessary. Adapting reservoir operation policies, which are functions to assist in making water release decisions, to cater for changed future water availability and demand conditions could best utilise the capacity of existing reservoir systems and potentially delay costly or disruptive interventions. In this study, the benefits of adapting reservoir policies as part of long-term reservoir management are demonstrated for a proposed water supply reservoir in the Northern Territory, Australia. This is done by directly optimising parameters (weights and biases) in an artificial neural network (ANN)-based reservoir operation policy model through a multi-objective robust optimisation framework with the aim to identify operation policies that can perform well under various plausible future conditions. Results show that the utilisation of adaptive operation policies can effectively manage future decadal changes in water availability and demand. Such policies generally show better performance, with lower water supply deficit and water storage violation values, compared to operation policies that remain stationary in the future, especially when the future is drier with increasing water demand (Figure 1). These changes in system performance can be explained by analysing the changes in the characteristics of ANN-based operation policy. For example, operation policies that adapt to conditions in the 2030s tend to release more water, leading to a significantly lower water supply deficit but a slightly higher water storage violation. Furthermore, the narrower performance range of adaptive operation policies compared to that of fixed operation policies indicates reduced performance uncertainty, which allows us to schedule additional interventions strategically, ensuring they are neither too late nor too soon. In summary, adaptive operation policies can provide various benefits for long-term reservoir management and ensure a reliable and secure source of water supply in a changing world.
{"title":"Adaptive operation policies for reservoir management in a changing world","authors":"Jiajia Huang, Wenyan Wu, Q. J. Wang, H. Maier, J. Hughes","doi":"10.36334/modsim.2023.huang","DOIUrl":"https://doi.org/10.36334/modsim.2023.huang","url":null,"abstract":": Reservoirs are essential infrastructure, supplying water for domestic, industrial and irrigation uses. Due to long-term climate and water demand changes, the performance of reservoirs may decrease throughout their lifespan, potentially requiring interventions such as reservoir expansion and/or water demand reduction. However, these interventions are often expensive and result in prolonged social and environmental disruptions. Consequently, there is a need to explore opportunities to enhance reservoir performance before these interventions are necessary. Adapting reservoir operation policies, which are functions to assist in making water release decisions, to cater for changed future water availability and demand conditions could best utilise the capacity of existing reservoir systems and potentially delay costly or disruptive interventions. In this study, the benefits of adapting reservoir policies as part of long-term reservoir management are demonstrated for a proposed water supply reservoir in the Northern Territory, Australia. This is done by directly optimising parameters (weights and biases) in an artificial neural network (ANN)-based reservoir operation policy model through a multi-objective robust optimisation framework with the aim to identify operation policies that can perform well under various plausible future conditions. Results show that the utilisation of adaptive operation policies can effectively manage future decadal changes in water availability and demand. Such policies generally show better performance, with lower water supply deficit and water storage violation values, compared to operation policies that remain stationary in the future, especially when the future is drier with increasing water demand (Figure 1). These changes in system performance can be explained by analysing the changes in the characteristics of ANN-based operation policy. For example, operation policies that adapt to conditions in the 2030s tend to release more water, leading to a significantly lower water supply deficit but a slightly higher water storage violation. Furthermore, the narrower performance range of adaptive operation policies compared to that of fixed operation policies indicates reduced performance uncertainty, which allows us to schedule additional interventions strategically, ensuring they are neither too late nor too soon. In summary, adaptive operation policies can provide various benefits for long-term reservoir management and ensure a reliable and secure source of water supply in a changing world.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"16 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":"115152291","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.hock
K. Hock, S. Staby, M. Gary, L. Kosowski, D. Blumson, H. T. Cao, S. Elsawah, N. Kempt, M. Richmond
: The changing nature of future land warfare has applied an increasing focus on an interplay between kinetic and non-kinetic effects, as well as soft and hard factors, on combat effectiveness. Potential impacts of new and emerging technologies on the soft factors is critical to simulate the complexity of future battlefields, yet also challenging to characterise and quantify. Models that are capable of jointly representing a broad spectrum of factors can help with insights into technological impacts that translate directly into land combat outcomes. To achieve this aim, the scope of such models needs to strike a balance between highly detailed technical simulations and highly abstracted attrition models in order to examine the interaction between crucial factors at the heart of the modern land warfare. Here we show the outputs of a model that represents the effect of soft factors like situational awareness, deception, and electromagnetic spectrum actions, as well as the decision-making that stems from these factors, on outputs such as kinetic engagement outcomes. We first provide an overview of implementation of these factors in a wider model architecture based on system dynamics that includes kinetic combat and attrition. We then focus on the model components that specifically deal with the force’s perception of battlefield. A key concept here is that of deception, represented in the model at various stages of decision-making. Force’s actions not only deny the opponent the ability to acquire information about the battlefield, but also degrade opposing force’s ability process information about dynamic battlefield situations. In addition to interfering with the sensing, force’s action also hinder the ability of the opponent to act advantageously on the information by increasing the proportion of incorrect information that is available to the opponent through deception. This then diminishes the opponent’s ability to implement decisions that would enhance its combat capabilities, notably degrading its ability to inflict casualties. The level of deception can be assessed from the discrepancy between the decisions that the opponent ends up making to guide the performance of its forces and the optimal level of decision-making that it would be making in the absence of force’s active effort to deceive it. Ultimately, high levels of negative perception stemming from detrimental decisions could also promote decision paralysis, further affecting the opponent’s ability to exert effective command and control over its forces. Such effects could be enhanced with the use of novel technologies that could exacerbate these negative feedbacks at various stages of the decision-making process. Overall, the model captures a range of soft effects and translates their impacts into operational success in the field, and as such provides an inclusive framework to explore the effects of future technologies on combat effectiveness. The presence of feedback loops in the model s
{"title":"Modelling the impacts of non-kinetic factors on combat effectiveness: The role of deception","authors":"K. Hock, S. Staby, M. Gary, L. Kosowski, D. Blumson, H. T. Cao, S. Elsawah, N. Kempt, M. Richmond","doi":"10.36334/modsim.2023.hock","DOIUrl":"https://doi.org/10.36334/modsim.2023.hock","url":null,"abstract":": The changing nature of future land warfare has applied an increasing focus on an interplay between kinetic and non-kinetic effects, as well as soft and hard factors, on combat effectiveness. Potential impacts of new and emerging technologies on the soft factors is critical to simulate the complexity of future battlefields, yet also challenging to characterise and quantify. Models that are capable of jointly representing a broad spectrum of factors can help with insights into technological impacts that translate directly into land combat outcomes. To achieve this aim, the scope of such models needs to strike a balance between highly detailed technical simulations and highly abstracted attrition models in order to examine the interaction between crucial factors at the heart of the modern land warfare. Here we show the outputs of a model that represents the effect of soft factors like situational awareness, deception, and electromagnetic spectrum actions, as well as the decision-making that stems from these factors, on outputs such as kinetic engagement outcomes. We first provide an overview of implementation of these factors in a wider model architecture based on system dynamics that includes kinetic combat and attrition. We then focus on the model components that specifically deal with the force’s perception of battlefield. A key concept here is that of deception, represented in the model at various stages of decision-making. Force’s actions not only deny the opponent the ability to acquire information about the battlefield, but also degrade opposing force’s ability process information about dynamic battlefield situations. In addition to interfering with the sensing, force’s action also hinder the ability of the opponent to act advantageously on the information by increasing the proportion of incorrect information that is available to the opponent through deception. This then diminishes the opponent’s ability to implement decisions that would enhance its combat capabilities, notably degrading its ability to inflict casualties. The level of deception can be assessed from the discrepancy between the decisions that the opponent ends up making to guide the performance of its forces and the optimal level of decision-making that it would be making in the absence of force’s active effort to deceive it. Ultimately, high levels of negative perception stemming from detrimental decisions could also promote decision paralysis, further affecting the opponent’s ability to exert effective command and control over its forces. Such effects could be enhanced with the use of novel technologies that could exacerbate these negative feedbacks at various stages of the decision-making process. Overall, the model captures a range of soft effects and translates their impacts into operational success in the field, and as such provides an inclusive framework to explore the effects of future technologies on combat effectiveness. The presence of feedback loops in the model s","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"24 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":"116656461","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.tang300
Y. Tang, P. C. X. Han, D. Dutta
: Rainfall runoff modelling is crucial for managing water supply, watershed management and flood forecasting, among other applications. This is particularly important for upstream headwater catchments because the resulting runoffs have a significant impact on storage levels and downstream water management. The amount of runoff generated by a catchment is determined by a multitude of factors, including the topography of the area, the soil characteristics, vegetation cover, land use, aquifer characteristics and many others. However, two factors that have a dominant influence on the amount of runoff generated are the quantity of rainfall precipitated over the catchment and the antecedent condition of the catchment. When a catchment is dry, most of the rainfall infiltrates into the soil, resulting in little to no runoff, even during relatively large rainfall events. On the contrary, when the catchment is wet, even a small rainfall event would potentially lead to runoff. Understanding how catchments response to different climate conditions is essential because it can lead to improved water resources management and better preparation for the impacts of changing climate. This study aims to investigate the capability of catchments to generate a certain amount of runoff after varying lengths of dry periods. Four study areas are selected from in northern inland NSW, including Namoi, Gwydir, Macquarie and Border Rivers, all representing different catchment characteristics (Figure 1). The catchment selection criteria for this study included unregulated headwater catchments with long flow records dating back to the 1940s or 1950s, sufficient periods with zero flow observations. Two catchments per valley were selected, each with different catchment sizes, to ensure a broad range of catchment conditions for analysis. To investigate the relationship between accumulated rainfall and flow events following periods of zero flow, we calculated the accumulated rainfall over different lengths of cease to flow periods, and then used histograms and boxplots to analyze the relationships. Results show that the amount of rainfall required for observed runoff generation in a catchment is influenced by the length of a drought and catchment size. Higher rainfall intensity and duration is essentially required for runoff generation after an extended cease to flow period. For larger catchments, the impact of catchment antecedent conditions is more pronounced, while such impact is less noticeable for comparatively smaller catchments. The study investigated the potential thresholds of rainfall that could trigger observed runoff after different lengths of cease to flow periods. The thresholds were then used to analyse the impacts of climate change on runoff generation using the new climate data of the regional water strategies in the study regions.
{"title":"Analysis of impact of catchment antecedent moisture conditions on runoff generations","authors":"Y. Tang, P. C. X. Han, D. Dutta","doi":"10.36334/modsim.2023.tang300","DOIUrl":"https://doi.org/10.36334/modsim.2023.tang300","url":null,"abstract":": Rainfall runoff modelling is crucial for managing water supply, watershed management and flood forecasting, among other applications. This is particularly important for upstream headwater catchments because the resulting runoffs have a significant impact on storage levels and downstream water management. The amount of runoff generated by a catchment is determined by a multitude of factors, including the topography of the area, the soil characteristics, vegetation cover, land use, aquifer characteristics and many others. However, two factors that have a dominant influence on the amount of runoff generated are the quantity of rainfall precipitated over the catchment and the antecedent condition of the catchment. When a catchment is dry, most of the rainfall infiltrates into the soil, resulting in little to no runoff, even during relatively large rainfall events. On the contrary, when the catchment is wet, even a small rainfall event would potentially lead to runoff. Understanding how catchments response to different climate conditions is essential because it can lead to improved water resources management and better preparation for the impacts of changing climate. This study aims to investigate the capability of catchments to generate a certain amount of runoff after varying lengths of dry periods. Four study areas are selected from in northern inland NSW, including Namoi, Gwydir, Macquarie and Border Rivers, all representing different catchment characteristics (Figure 1). The catchment selection criteria for this study included unregulated headwater catchments with long flow records dating back to the 1940s or 1950s, sufficient periods with zero flow observations. Two catchments per valley were selected, each with different catchment sizes, to ensure a broad range of catchment conditions for analysis. To investigate the relationship between accumulated rainfall and flow events following periods of zero flow, we calculated the accumulated rainfall over different lengths of cease to flow periods, and then used histograms and boxplots to analyze the relationships. Results show that the amount of rainfall required for observed runoff generation in a catchment is influenced by the length of a drought and catchment size. Higher rainfall intensity and duration is essentially required for runoff generation after an extended cease to flow period. For larger catchments, the impact of catchment antecedent conditions is more pronounced, while such impact is less noticeable for comparatively smaller catchments. The study investigated the potential thresholds of rainfall that could trigger observed runoff after different lengths of cease to flow periods. The thresholds were then used to analyse the impacts of climate change on runoff generation using the new climate data of the regional water strategies in the study regions.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"24 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":"121109751","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.jian531
J. Jian, D. Ryu, Q. J. Wang, H. Lee
: Recent studies demonstrated the efficacy of calibrating rainfall-runoff models using continuous measurements of water level in rivers. The water-level based calibration, that implements an inversed rating curve function in conventional rainfall-runoff models and incorporates a small number of regionalized discharge indices in the calibration (hereafter referred to as IRC_reg method), has important implications for extending rainfall-runoff modelling to basins with no discharge observations. However, the method is applicable only to basins equipped with water level sensors if we rely on ground-based observations. In this work, we demonstrate the efficacy of using remotely sensed water level data collected by an altimetry satellite, Jason 2, to calibrate a rainfall-runoff model. The altimeter-based calibration is applied to five study catchments in Australia, resulting in Nash Sutcliffe Efficiency (NSE) values of 0.31-0.66 (excluding one outlier), which are comparable with NSE values of 0.66-0.87 (daily observations) and 0.22-0.62 (10-day observations) for ground-based calibration. The altimetry-satellite-based calibration performance is highly correlated with river width. Previous studies recommended that the cross-sections of rivers along the satellite tracks should be wider than 350 meters to enable Jason 2 to estimate accurate water levels (Dumont et al., 2009; Markert et al., 2019). However, all rivers in this study are narrow rivers with widths ranging from 7 meters to 85 meters, which influence the accuracy of altimetry-based water level measurements and the subsequent calibration performances. Also, the 10-day temporal frequency of the Jason 2 is expected to affect the calibration performance.
最近的研究证明了使用连续测量河流水位来校准降雨径流模型的有效性。基于水位的定标方法(以下简称IRC_reg方法)实现了传统降雨径流模型的反向评级曲线函数，并在定标中纳入了少量区域化的流量指数，对于将降雨径流模型推广到无流量观测的流域具有重要意义。然而，如果我们依靠地面观测，该方法仅适用于配备了水位传感器的流域。在这项工作中，我们证明了使用由高空卫星Jason 2收集的遥感水位数据来校准降雨径流模型的有效性。基于高度计的校准应用于澳大利亚的五个研究集水区，得到的Nash Sutcliffe效率(NSE)值为0.31-0.66(不包括一个异常值)，与地面校准的NSE值0.66-0.87(每日观测)和0.22-0.62(10天观测)相当。基于卫星测高的定标性能与河流宽度高度相关。先前的研究建议，沿卫星轨道的河流断面宽度应大于350米，以使Jason 2能够准确估计水位(Dumont et al.， 2009;Markert et al.， 2019)。然而，本研究中所有河流都是狭窄的河流，宽度在7米到85米之间，这影响了基于高程的水位测量的精度和随后的校准性能。此外，Jason 2的10天时间频率预计会影响校准性能。
{"title":"A water-level based calibration of rainfall-runoff models using satellite altimetry data","authors":"J. Jian, D. Ryu, Q. J. Wang, H. Lee","doi":"10.36334/modsim.2023.jian531","DOIUrl":"https://doi.org/10.36334/modsim.2023.jian531","url":null,"abstract":": Recent studies demonstrated the efficacy of calibrating rainfall-runoff models using continuous measurements of water level in rivers. The water-level based calibration, that implements an inversed rating curve function in conventional rainfall-runoff models and incorporates a small number of regionalized discharge indices in the calibration (hereafter referred to as IRC_reg method), has important implications for extending rainfall-runoff modelling to basins with no discharge observations. However, the method is applicable only to basins equipped with water level sensors if we rely on ground-based observations. In this work, we demonstrate the efficacy of using remotely sensed water level data collected by an altimetry satellite, Jason 2, to calibrate a rainfall-runoff model. The altimeter-based calibration is applied to five study catchments in Australia, resulting in Nash Sutcliffe Efficiency (NSE) values of 0.31-0.66 (excluding one outlier), which are comparable with NSE values of 0.66-0.87 (daily observations) and 0.22-0.62 (10-day observations) for ground-based calibration. The altimetry-satellite-based calibration performance is highly correlated with river width. Previous studies recommended that the cross-sections of rivers along the satellite tracks should be wider than 350 meters to enable Jason 2 to estimate accurate water levels (Dumont et al., 2009; Markert et al., 2019). However, all rivers in this study are narrow rivers with widths ranging from 7 meters to 85 meters, which influence the accuracy of altimetry-based water level measurements and the subsequent calibration performances. Also, the 10-day temporal frequency of the Jason 2 is expected to affect the calibration performance.","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":"125000934","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.boland
J. Boland
: This paper describes the forecasting of 15 minute solar irradiation on a horizontal plane (GHI) for Seattle, USA, as well as 15 minute solar f arm output for Broken Hill, Australia. The goal is to set error bounds on the forecast, specifically estimating 15 quantiles, from essentially minimum to m aximum. In practice, the quantiles calculated are { 0 . 005 , 0 . 025 , 0 . 05 , 0 . 1 , 0 . 2 , . . . , 0 . 8 , 0 . 9 , 0 . 95 , 0 . 975 , 0 . 995 } . The forecast horizons for both variables are one step ahead (for time t + 1 time interval performed at time t ). The procedure entails first calculating point f orecasts, and then using quantile regression techniques to form the quantiles of the resulting noise terms. The modelling process is performed on a year’s data for 2017 for both locations, and then tested on data from 2018. In the standard modelling manner, the models developed for both the point forecasts and quantiles on the 2017 data are applied to the 2018 data, whereupon the quantiles are added to the point forecasts for initial verification of the efficacy of the procedure. The point forecast contains a model for the seasonality using Fourier series for the significant cycles. For GHI, they are once a year, once and twice a day, plus beat frequencies to modulate the daily cycle to suit the time of year. Since the solar farm has an oversized field, thus capping the output, the only necessary cycles are once and twice a day. Once the seasonality model is subtracted from the original series, the residuals are represented by an ARMA ( p, q ) forecast model. The combination of the models forms the point forecast. The noise terms from this process are modelled using quantile regression. For quantile level τ of the response, the goal is to
{"title":"Probabilistic forecasting for solar energy","authors":"J. Boland","doi":"10.36334/modsim.2023.boland","DOIUrl":"https://doi.org/10.36334/modsim.2023.boland","url":null,"abstract":": This paper describes the forecasting of 15 minute solar irradiation on a horizontal plane (GHI) for Seattle, USA, as well as 15 minute solar f arm output for Broken Hill, Australia. The goal is to set error bounds on the forecast, specifically estimating 15 quantiles, from essentially minimum to m aximum. In practice, the quantiles calculated are { 0 . 005 , 0 . 025 , 0 . 05 , 0 . 1 , 0 . 2 , . . . , 0 . 8 , 0 . 9 , 0 . 95 , 0 . 975 , 0 . 995 } . The forecast horizons for both variables are one step ahead (for time t + 1 time interval performed at time t ). The procedure entails first calculating point f orecasts, and then using quantile regression techniques to form the quantiles of the resulting noise terms. The modelling process is performed on a year’s data for 2017 for both locations, and then tested on data from 2018. In the standard modelling manner, the models developed for both the point forecasts and quantiles on the 2017 data are applied to the 2018 data, whereupon the quantiles are added to the point forecasts for initial verification of the efficacy of the procedure. The point forecast contains a model for the seasonality using Fourier series for the significant cycles. For GHI, they are once a year, once and twice a day, plus beat frequencies to modulate the daily cycle to suit the time of year. Since the solar farm has an oversized field, thus capping the output, the only necessary cycles are once and twice a day. Once the seasonality model is subtracted from the original series, the residuals are represented by an ARMA ( p, q ) forecast model. The combination of the models forms the point forecast. The noise terms from this process are modelled using quantile regression. For quantile level τ of the response, the goal is to","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":"125058773","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.hickson
R. Hickson, A. Rawlinson, M. E. Roberts, N. Faux
: Mental health is an important component of overall well-being, but over two in five Australians will experience a mental disorder in their lifetime. Anxiety and depression compose a large proportion of the mental disorders in Australia, and can substantially affect the lives of those affected. Stigma about these disorders is thought to adversely affect many aspects of treatment, including delaying treatment seeking behaviours, the duration required for treatment to take effect, and withdrawal from treatment. There have been findings showing strong social clustering of anxiety and/or depression. One such postulated reason for this is that contact with people suffering from anxiety and/or depression can increase the risk of otherwise unaffected people, which is a direct analogue to “transmission”. As such, we use a transmission model framework to investigate the changes in long-term prevalence of anxiety and/or depression as a result of stigma in a community affecting model pathways to and from treatment, using strata for those affected by stigma and those unaffected (neutral). The population is divided into states for those unaffected ( U ), affected by anxiety and/or depression ( A ), undergoing treatment ( T ), and with managed anxiety and/or depression ( M ). Those in the A and T states are considered to be experiencing acute affects of anxiety and/or depression and are able to affect others, whilst those in the M state are considered to still be receiving treatment but not longer able to affect others, and may be re-affected. We first calibrate our model, showing a strong linear relationship between our “ transmission” r ate ( β ) and the rate of spontaneously experiencing the disorders ( ν ) to capture the reported prevalence of anxiety and/or depression. We explore the effect of stigma on model pathways related to treatment parameters on this prevalence, using univariate and bivariate sweeps. Finally, we conduct a sensitivity analysis to gain insights on how parameter estimates and ranges will affect future prevalence estimates. We found that increasing levels of stigma in a community nonlinearly increased the burden of anxiety and/or depression. This result was consistent for all calibrated parameter combinations explored. We also showed that, as expected, modelled burden was most sensitive to the transmission rate ( β ), and next most sensitive to the average periods of time spent being actively treated ( ω , σ n ). We further explored the impact of the most sensitive combinations of the effects of stigma on the model parameters. Surprisingly, we found a strong relationship between the calibrated values of the spontaneous rate of experiencing the disorder ( ν ), and the transmission rate ( β ). This relationship suggested transmission was always larger, and is further evidence of a transmission framework being appropriate to explore anxiety and/or depression in this framework. It is important to emphasise that the progression of anxiety
{"title":"Modelling aspects of the effect of community stigma on the prevalence of anxiety and/or depression","authors":"R. Hickson, A. Rawlinson, M. E. Roberts, N. Faux","doi":"10.36334/modsim.2023.hickson","DOIUrl":"https://doi.org/10.36334/modsim.2023.hickson","url":null,"abstract":": Mental health is an important component of overall well-being, but over two in five Australians will experience a mental disorder in their lifetime. Anxiety and depression compose a large proportion of the mental disorders in Australia, and can substantially affect the lives of those affected. Stigma about these disorders is thought to adversely affect many aspects of treatment, including delaying treatment seeking behaviours, the duration required for treatment to take effect, and withdrawal from treatment. There have been findings showing strong social clustering of anxiety and/or depression. One such postulated reason for this is that contact with people suffering from anxiety and/or depression can increase the risk of otherwise unaffected people, which is a direct analogue to “transmission”. As such, we use a transmission model framework to investigate the changes in long-term prevalence of anxiety and/or depression as a result of stigma in a community affecting model pathways to and from treatment, using strata for those affected by stigma and those unaffected (neutral). The population is divided into states for those unaffected ( U ), affected by anxiety and/or depression ( A ), undergoing treatment ( T ), and with managed anxiety and/or depression ( M ). Those in the A and T states are considered to be experiencing acute affects of anxiety and/or depression and are able to affect others, whilst those in the M state are considered to still be receiving treatment but not longer able to affect others, and may be re-affected. We first calibrate our model, showing a strong linear relationship between our “ transmission” r ate ( β ) and the rate of spontaneously experiencing the disorders ( ν ) to capture the reported prevalence of anxiety and/or depression. We explore the effect of stigma on model pathways related to treatment parameters on this prevalence, using univariate and bivariate sweeps. Finally, we conduct a sensitivity analysis to gain insights on how parameter estimates and ranges will affect future prevalence estimates. We found that increasing levels of stigma in a community nonlinearly increased the burden of anxiety and/or depression. This result was consistent for all calibrated parameter combinations explored. We also showed that, as expected, modelled burden was most sensitive to the transmission rate ( β ), and next most sensitive to the average periods of time spent being actively treated ( ω , σ n ). We further explored the impact of the most sensitive combinations of the effects of stigma on the model parameters. Surprisingly, we found a strong relationship between the calibrated values of the spontaneous rate of experiencing the disorder ( ν ), and the transmission rate ( β ). This relationship suggested transmission was always larger, and is further evidence of a transmission framework being appropriate to explore anxiety and/or depression in this framework. It is important to emphasise that the progression of anxiety ","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"93 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":"123547722","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.pritchard
J. Pritchard, E. Csiro, Australia
: Vegetation uses a variety of water sources (local rainfall, floodwaters, unsaturated soil water, groundwater) throughout the different stages of the life cycles (seed, germination, flowering, growth, reproduction, pollination and seed dispersion) to sustain healthy functioning ecosystems. Groundwater is a critical source of water for terrestrial groundwater-dependent ecosystems (GDEs). Ecosystems that have access to groundwater can often sustain vibrant flora and fauna communities within otherwise dry landscapes. The frequency and timing of groundwater use by GDEs is highly dependent on local site hydrogeological characteristics and climatic conditions. On floodplains, the sources of groundwater used by terrestrial GDEs may be derived from localised alluvial aquifers recharged via bank recharge or by over-bank flooding or from regional aquifers that may recharge remotely but discharge to the floodplain where riparian vegetation occur. When assessing the impacts of water resource development on vegetation, it is essential to identify the sources of water used by vegetation, the timing of when the different sources of water are used (e.g. seasonally, during drought) and their connections to water resources with potential for future development (Doody et al. 2019). This study develops a conceptual model of the dynamics of water use by floodplain vegetation in the Victoria catchment, Northern Territory, and tests the utility of strontium isotopes for differentiating between the potential sources of water used by vegetation. Of particular interest in this study is differentiating between groundwater derived from localised versus regional aquifers. The potential sources of water used by vegetation can have distinct oxygen and hydrogen isotope compositions that are observable in vegetation if they are recharged in different environments or by different processes. Previous studies have used the stable isotopes of water to differentiate between sources of water used by vegetation (e.g. Canham et al. 2021). However, the oxygen and/or hydrogen isotope composition cannot always distinguish between all the potential sources of water available to vegetation and further lines of evidence are often required to irrefutably establish regional groundwater use. Strontium isotopes provide a complementary line of evidence to oxygen and hydrogen isotopes because the composition in groundwater is derived from meteoric input as well as the dissolution of Sr-bearing minerals within the aquifer system (e.g. Bullen and Kendall 1998). Strontium isotopes are not fractionated as they are taken up by plants (Graustein 1989) therefore the strontium isotope composition in plants should be consistent with the sources of water used. This study will analyse strontium isotopes in vegetation, soils, surface water and groundwater to test its applicability for differentiating between sources of water used by vegetation adjacent to a groundwater-fed creek in Victoria catchment, NT.
植被在生命周期的不同阶段(播种、发芽、开花、生长、繁殖、授粉和种子传播)使用各种水源(当地降雨、洪水、不饱和土壤水、地下水)来维持健康的功能生态系统。地下水是陆地地下水依赖生态系统的重要水源。拥有地下水的生态系统通常可以在干旱的土地上维持充满活力的动植物群落。gde使用地下水的频率和时间高度依赖于当地的水文地质特征和气候条件。在洪泛区，陆地gde使用的地下水来源可能来自局部的冲积含水层，这些含水层通过河岸补给或河岸上的洪水补给，或者来自区域含水层，这些含水层可能远程补给，但排放到河岸植被生长的洪泛区。在评估水资源开发对植被的影响时，必须确定植被使用的水源、不同水源的使用时间(例如季节性、干旱期间)以及它们与具有未来开发潜力的水资源的联系(Doody et al. 2019)。本研究开发了北领地维多利亚流域洪泛区植被用水动态的概念模型，并测试了锶同位素在区分植被使用的潜在水源方面的效用。本研究特别感兴趣的是区分来自局部和区域含水层的地下水。植被利用的潜在水源可能具有不同的氧和氢同位素组成，如果它们在不同的环境中或通过不同的过程进行补给，则可以在植被中观察到这些同位素组成。以前的研究使用水的稳定同位素来区分植被使用的水源(例如Canham et al. 2021)。然而，氧和/或氢的同位素组成并不总是能够区分植被可用的所有潜在水源，往往需要进一步的证据线来无可辩驳地确定区域地下水的使用情况。锶同位素为氧和氢同位素提供了补充证据，因为地下水中的成分来自大气输入以及含水层系统内含锶矿物的溶解(例如，Bullen和Kendall, 1998年)。锶同位素不会被分馏，因为它们被植物吸收(Graustein 1989)，因此植物中的锶同位素组成应与所使用的水源一致。这项研究将分析植被、土壤、地表水和地下水中的锶同位素，以测试其在区分新界维多利亚集水区地下水溪流附近植被使用的水源方面的适用性。
{"title":"Are terrestrial groundwater-dependent ecosystems dependent on groundwater from localised or regional aquifers?","authors":"J. Pritchard, E. Csiro, Australia","doi":"10.36334/modsim.2023.pritchard","DOIUrl":"https://doi.org/10.36334/modsim.2023.pritchard","url":null,"abstract":": Vegetation uses a variety of water sources (local rainfall, floodwaters, unsaturated soil water, groundwater) throughout the different stages of the life cycles (seed, germination, flowering, growth, reproduction, pollination and seed dispersion) to sustain healthy functioning ecosystems. Groundwater is a critical source of water for terrestrial groundwater-dependent ecosystems (GDEs). Ecosystems that have access to groundwater can often sustain vibrant flora and fauna communities within otherwise dry landscapes. The frequency and timing of groundwater use by GDEs is highly dependent on local site hydrogeological characteristics and climatic conditions. On floodplains, the sources of groundwater used by terrestrial GDEs may be derived from localised alluvial aquifers recharged via bank recharge or by over-bank flooding or from regional aquifers that may recharge remotely but discharge to the floodplain where riparian vegetation occur. When assessing the impacts of water resource development on vegetation, it is essential to identify the sources of water used by vegetation, the timing of when the different sources of water are used (e.g. seasonally, during drought) and their connections to water resources with potential for future development (Doody et al. 2019). This study develops a conceptual model of the dynamics of water use by floodplain vegetation in the Victoria catchment, Northern Territory, and tests the utility of strontium isotopes for differentiating between the potential sources of water used by vegetation. Of particular interest in this study is differentiating between groundwater derived from localised versus regional aquifers. The potential sources of water used by vegetation can have distinct oxygen and hydrogen isotope compositions that are observable in vegetation if they are recharged in different environments or by different processes. Previous studies have used the stable isotopes of water to differentiate between sources of water used by vegetation (e.g. Canham et al. 2021). However, the oxygen and/or hydrogen isotope composition cannot always distinguish between all the potential sources of water available to vegetation and further lines of evidence are often required to irrefutably establish regional groundwater use. Strontium isotopes provide a complementary line of evidence to oxygen and hydrogen isotopes because the composition in groundwater is derived from meteoric input as well as the dissolution of Sr-bearing minerals within the aquifer system (e.g. Bullen and Kendall 1998). Strontium isotopes are not fractionated as they are taken up by plants (Graustein 1989) therefore the strontium isotope composition in plants should be consistent with the sources of water used. This study will analyse strontium isotopes in vegetation, soils, surface water and groundwater to test its applicability for differentiating between sources of water used by vegetation adjacent to a groundwater-fed creek in Victoria catchment, NT.","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"16 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":"125361372","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.tan203
Daniel Tan, J. Teng, B. Croke, T. Iwanaga
: The Floodplain Ecological Response Model (FERM) is a conceptual model that takes a time series of spatial flood inundation data as input to model the condition of ecological targets across a floodplain, over time. FERM develops wetting and drying periods (referred to as “spells”) from the flood inundation data for a given grid cell and subsequently fits different preference curves depending on the type of spell. The parametrization of the preference curves is physically based allowing for calibration using expert knowledge or from data. Notably, the preference functions are infinitely differentiable, which reflects the smooth nature of the ecological response. Due to data constraints, daily Leaf Area Index (LAI) data for Eucalyptus lagiflorens (Black Box) was obtained from the WAVES (Zhang and Dawes, 1998) mass and energy balance model as a proxy for condition score from 1928 to 2017 for calibration. WAVES is parametrized using vegetation and soil parameters and requires meteorological data as input. The flood inundation data was taken from the Teng-Vaze-Dutta flood inundation model (TVD) (Teng et al., 2018) which uses gauge-flow timeseries data to model flooding. WAVES was run at three different proximities to the main river channel. Three parametrisations of FERM for Black Box were calibrated for each location. Condition scores calculated from remote sensing data using the method described in Cunningham et al. (2009) were used to validate FERM yearly, from 2009 to 2017 excluding 2011 (data for 2011 was unavailable). The Shuffled Complex Evolution algorithm (SCE-UA) (Duan et al., 1993) was used to calibrate FERM with the Nash Sutcliff Efficiency (NSE) metric. Prior to calibration, LAI values from WAVES (the calibration data) were smoothed with a yearly moving average to remove seasonality. The calibration ran with FERM’s seasonal oscillation amplitude parameter fixed to 0 (calibrated after) whilst all other parameters were free to be optimised. The seasonality removal allowed for faster convergence and better performing resultant parametrisations. Calibration ended with an NSE of approximately 0.55 and a Root Mean Squared Error of 0.14. Incorporating meteorological variables would improve performance but make forecasting significantly more difficult on large timescales. The parametrization for Black Box maintained a correlation coefficient of 0.8 on the validation data, demonstrating the model’s ability to capture spatial and temporal trends. FERM is currently implemented in Python and uses Cython to speed up computation. Consequently, FERM can compute yearly condition scores across the entire floodplain in under 10 minutes and can run 50,000 iterations of the Shuffled Complex Evolution Algorithm at a daily timestep in 45 minutes, both over a 100-year timespan. The speed of calibration presents an improvement on large regression models and executes significantly faster than complex process-based models. Future improvements to the model are pos
{"title":"Calibration of the Floodplain Ecological Response Model","authors":"Daniel Tan, J. Teng, B. Croke, T. Iwanaga","doi":"10.36334/modsim.2023.tan203","DOIUrl":"https://doi.org/10.36334/modsim.2023.tan203","url":null,"abstract":": The Floodplain Ecological Response Model (FERM) is a conceptual model that takes a time series of spatial flood inundation data as input to model the condition of ecological targets across a floodplain, over time. FERM develops wetting and drying periods (referred to as “spells”) from the flood inundation data for a given grid cell and subsequently fits different preference curves depending on the type of spell. The parametrization of the preference curves is physically based allowing for calibration using expert knowledge or from data. Notably, the preference functions are infinitely differentiable, which reflects the smooth nature of the ecological response. Due to data constraints, daily Leaf Area Index (LAI) data for Eucalyptus lagiflorens (Black Box) was obtained from the WAVES (Zhang and Dawes, 1998) mass and energy balance model as a proxy for condition score from 1928 to 2017 for calibration. WAVES is parametrized using vegetation and soil parameters and requires meteorological data as input. The flood inundation data was taken from the Teng-Vaze-Dutta flood inundation model (TVD) (Teng et al., 2018) which uses gauge-flow timeseries data to model flooding. WAVES was run at three different proximities to the main river channel. Three parametrisations of FERM for Black Box were calibrated for each location. Condition scores calculated from remote sensing data using the method described in Cunningham et al. (2009) were used to validate FERM yearly, from 2009 to 2017 excluding 2011 (data for 2011 was unavailable). The Shuffled Complex Evolution algorithm (SCE-UA) (Duan et al., 1993) was used to calibrate FERM with the Nash Sutcliff Efficiency (NSE) metric. Prior to calibration, LAI values from WAVES (the calibration data) were smoothed with a yearly moving average to remove seasonality. The calibration ran with FERM’s seasonal oscillation amplitude parameter fixed to 0 (calibrated after) whilst all other parameters were free to be optimised. The seasonality removal allowed for faster convergence and better performing resultant parametrisations. Calibration ended with an NSE of approximately 0.55 and a Root Mean Squared Error of 0.14. Incorporating meteorological variables would improve performance but make forecasting significantly more difficult on large timescales. The parametrization for Black Box maintained a correlation coefficient of 0.8 on the validation data, demonstrating the model’s ability to capture spatial and temporal trends. FERM is currently implemented in Python and uses Cython to speed up computation. Consequently, FERM can compute yearly condition scores across the entire floodplain in under 10 minutes and can run 50,000 iterations of the Shuffled Complex Evolution Algorithm at a daily timestep in 45 minutes, both over a 100-year timespan. The speed of calibration presents an improvement on large regression models and executes significantly faster than complex process-based models. Future improvements to the model are pos","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"53 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":"126632897","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.kandanaarachchi175
Sevvandi Kandanaarachchi, P. Kuhnert, A. Zammit‐Mangion, C. Wikle
: Spatio-temporal data underpin many critical processes such as weather, crop production, wildfire spread and epidemiological and disease function. Models of these processes can reveal changing characteristics in both space and time and can help inform decision-makers. A recent example is during the pandemic years, spatio-temporal models were used to inform public policy. While there are many spatio-temporal modelling methods and packages, tools specifically designed for exploratory data analysis are somewhat lacking. Exploratory data analysis is a vital step in the end-to-end process of statistical and machine learning modelling. A lack of tools for exploratory spatio-temporal data analysis may lead to researchers starting the modelling process prematurely and make suboptimal modelling choices. We aim to fill this gap by contributing stxplore – an R package equipped with useful functionality designed for spatio-temporal data exploration. All functions in stxplore are designed to provide visually useful outputs. Furthermore, all computations can be performed using either data frames or stars objects in the R framework. Data frames are traditional, general purpose data structures in R, used for tabular data, while s tars objects cater for geospatial data. These object classes are defined in the R package stars , which has gained popularity within the research community, and are a newer addition to the R geospatial package ecosystem. The package stxplore can work with either of these objects, i.e. the functions in stxplore can take either data frames or stars objects as input. The
{"title":"Sophisticated tools for spatio-temporal data exploration","authors":"Sevvandi Kandanaarachchi, P. Kuhnert, A. Zammit‐Mangion, C. Wikle","doi":"10.36334/modsim.2023.kandanaarachchi175","DOIUrl":"https://doi.org/10.36334/modsim.2023.kandanaarachchi175","url":null,"abstract":": Spatio-temporal data underpin many critical processes such as weather, crop production, wildfire spread and epidemiological and disease function. Models of these processes can reveal changing characteristics in both space and time and can help inform decision-makers. A recent example is during the pandemic years, spatio-temporal models were used to inform public policy. While there are many spatio-temporal modelling methods and packages, tools specifically designed for exploratory data analysis are somewhat lacking. Exploratory data analysis is a vital step in the end-to-end process of statistical and machine learning modelling. A lack of tools for exploratory spatio-temporal data analysis may lead to researchers starting the modelling process prematurely and make suboptimal modelling choices. We aim to fill this gap by contributing stxplore – an R package equipped with useful functionality designed for spatio-temporal data exploration. All functions in stxplore are designed to provide visually useful outputs. Furthermore, all computations can be performed using either data frames or stars objects in the R framework. Data frames are traditional, general purpose data structures in R, used for tabular data, while s tars objects cater for geospatial data. These object classes are defined in the R package stars , which has gained popularity within the research community, and are a newer addition to the R geospatial package ecosystem. The package stxplore can work with either of these objects, i.e. the functions in stxplore can take either data frames or stars objects as input. The","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"43 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":"126838636","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.lintern
A. Lintern, R. Sargent, Judy Hagan, P. Wilson, A. Western, Cami Plum, D. Guo
: Investigating trends in stream water quality is vital for protecting ecosystems and public health. Previous studies have identified that hydro-climatic drivers such as streamflow, temperature and seasonality can be crucial drivers of water quality changes over time. The importance of each of these drivers can vary spatially, with different streams having different key drivers that affect temporal trends in water quality. The aim of this study is to assess the key drivers of temporal variability in stream water quality, using a 27-year (1995–2022) water quality monitoring record from 136 stream monitoring sites across the state of Victoria (Australia). We investigate the key hydro-climatic drivers of temporal change in stream water quality. In this study, we address six key water quality parameters: dissolved oxygen (DO), electrical conductivity (EC), pH, turbidity, total phosphorus (TP) and total nitrogen (TN). We investigated the trends in water quality using a multiple linear regression model (Equation 1), fitted for each of the 136 sites and for each of the six constituents. This multiple linear regression model predicts concentration at site t (C t ) as a function of: streamflow (Q t ), seasonality ( seasonality ), and a long-term underlying trend ( t ). β t , β Q , β seasonality are regression coefficients for trend, streamflow and seasonality (respectively).
{"title":"Hydroclimatic drivers of stream water quality over 27 years: The role of streamflow, temperature and seasonality","authors":"A. Lintern, R. Sargent, Judy Hagan, P. Wilson, A. Western, Cami Plum, D. Guo","doi":"10.36334/modsim.2023.lintern","DOIUrl":"https://doi.org/10.36334/modsim.2023.lintern","url":null,"abstract":": Investigating trends in stream water quality is vital for protecting ecosystems and public health. Previous studies have identified that hydro-climatic drivers such as streamflow, temperature and seasonality can be crucial drivers of water quality changes over time. The importance of each of these drivers can vary spatially, with different streams having different key drivers that affect temporal trends in water quality. The aim of this study is to assess the key drivers of temporal variability in stream water quality, using a 27-year (1995–2022) water quality monitoring record from 136 stream monitoring sites across the state of Victoria (Australia). We investigate the key hydro-climatic drivers of temporal change in stream water quality. In this study, we address six key water quality parameters: dissolved oxygen (DO), electrical conductivity (EC), pH, turbidity, total phosphorus (TP) and total nitrogen (TN). We investigated the trends in water quality using a multiple linear regression model (Equation 1), fitted for each of the 136 sites and for each of the six constituents. This multiple linear regression model predicts concentration at site t (C t ) as a function of: streamflow (Q t ), seasonality ( seasonality ), and a long-term underlying trend ( t ). β t , β Q , β seasonality are regression coefficients for trend, streamflow and seasonality (respectively).","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"56 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":"115548172","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：