Benchmarking Active Subspace methods of global sensitivity analysis against variance-based Sobol' and Morris methods with established test functions

Sun, Xifu; Croke, Barry; Jakeman, Anthony J.; Roberts, Stephen

doi:10.1016/j.envsoft.2022.105310

Cited by 3 publications

(2 citation statements)

References 87 publications

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“…In this study, k was 31, p was 10, ∆ was 5/9, and r was 200. In the literature (Sun et al., 2022), p has been reported to range from 4 to 10 in literatures. Here, p was set to 10 to better reflect the nonlinear effect of model parameters on the simulation output.…”

Section: Methodsmentioning

confidence: 99%

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

Han

Chen

et al. 2023

Water Resources Research

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Traditional water quality observations are achieved via field sampling and laboratory chemical analysis, which involve high labor, financial and time costs (Das & Jain, 2017;Zulkifli et al., 2018). The data limitation is further exacerbated by policy, culture and technical barriers to free data sharing (Li et al., 2021).Recently, in situ water quality monitoring techniques based on various sensors have rapidly developed (Kruse, 2018;Singh et al., 2022). Sensor-based in situ monitoring avoids tedious sampling procedures and complex analytical processes and produces continuous and high-frequency observations. This approach has been increasingly applied worldwide and has produced a large amount of water quality data (Meyer et al., 2019;Park et al., 2020), providing an opportunity to adopt environmental big data to improve WWQ modeling. However, compared to traditional data, long-term sensing in complex natural environments is subject to more significant measurement errors stemming from biofouling, lack of equipment calibration, background ion interference and other factors (Mahmud et al., 2020;Makarov et al., 2021). Typical error types in sensor data include outliers, noise, constant values, missing data, bias due to incorrect calibration, sensor drift from the calibration level and other errors (Horsburgh et al., 2015;Teh et al., 2020). In addition, as most existing sensors are installed to

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Section: Methodsmentioning

confidence: 99%

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

Han

Chen

et al. 2023

Water Resources Research

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“…that can be fixed as constants) (Reusser et al, 2011;Wang and Solomatine, 2019). GSA, which ranks the relative influence of model parameters on model output (Sun et al, 2022), is generally recommended for hydrological models due to its advantages over local 285 sensitivity analysis methods, such as its ability to consider the influence of input parameters over their entire range of variation and its suitability for non-linear and non-monotonic models, thus providing results that are independent of modeller bias and a particular site (Song et al, 2015). Among the GSA methods widely applied to hydrological models, we chose a variance-based method as it can provide the most accurate and robust sensitivity indices for complex non-linear models (Reusser et al, 2011;Song et al, 2015;Wang and Solomatine, 2019).…”

Section: Sensitivity Analysis Of the Solute-transport Modelmentioning

confidence: 99%

Improving the internal hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations

Salmon-Monviola,

Fovet,

Hrachowitz

2024

Preprint

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Abstract. Improving the consistency of hydrological models, i.e. their ability to reproduce observed system dynamics, is required to increase their predictive power. As the use of streamflow data for calibration is necessary but not sufficient to constrain model and warrant model consistency, other strategies must be considered, in particular the use of additional data sources. The aim of this study is to test whether simultaneous calibration of dissolved organic carbon (DOC) and nitrate (NO3-) concentrations along with streamflow improves the hydrological consistency of a parsimonious solute-transport model. A multi-objective and multi-variable approach was used to evaluate the model in an intensive agricultural headwater catchment. Our results showed that using daily stream concentrations of DOC and NO3- together with streamflow data during calibration did not improve the model's ability to accurately predict streamflow for calibration or evaluation periods. However, the internal consistency of the model was improved for the simulation of low flows, groundwater storage and upstream soil storage, but not for the simulation of riparian soil storage. Parameter uncertainty decreased when the model was calibrated using solute concentrations, except for parameters related to fast and slow reservoir flow. This study shows the added value of using multiple data sources in addition to streamflow data for calibration, in particular DOC and NO3- concentrations, to constrain hydrological models for a better representation of internal hydrological states and flow. With the increasing availability of solute data from catchment monitoring, this approach provides an objective way to improve the internal consistency of hydrological models that can be used with confidence in scenario evaluation.

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Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

et al. 2024

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Benchmarking Active Subspace methods of global sensitivity analysis against variance-based Sobol' and Morris methods with established test functions

Cited by 3 publications

References 87 publications

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

Improving the internal hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations

Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

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