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Hydrological models are becoming progressively complex, leading to unclear internal model behavior, increasing uncertainty, and the risk of equifinality. Accordingly, our study provided a research framework based on global sensitivity analysis, aiming at unraveling the process-level behavior of high-complexity models, teasing out the main information, and ultimately exploiting its usage for model parameterization. The Distributed Hydrology-Soil-Vegetation Model implemented in a mountainous watershed was used. Results indicated that 5 soil parameters and 5 vegetation parameters were most important to control the streamflow responses, while their importance varied greatly throughout the simulation period. Four typical patterns of parameter importance corresponding to different watershed conditions (i.e., flood, short dry-to-wet, fast recession, and continuous dry periods) were successfully distinguished. Using this clustered information, parameters with short dominance times were more identifiable over the clusters (time periods) in which they were most important. The reduced posterior parameter space also slightly improved the model performance. • Clustering teases out the main information of time-varying parameter importance. • Current and antecedent watershed conditions affect parameter importance patterns. • Parameters dominating in short time periods have improved identifiability. • Narrowed parameter uncertainty ranges slightly improve model performance. [ABSTRACT FROM AUTHOR]

This paper presents two data-driven virtual sensors to estimate the time-series of the probability of high-concentration occurrence of naturally occurring radioactive materials (NORMs; 238U and 222Rn) in groundwater based on the in-situ groundwater quality monitoring data and geological information. The random forest was applied to estimate the NORM concentration based on the actual in-situ groundwater quality data, rock type, and the aquifer depth. Additionally, this study proposes three data sampling techniques (i.e., under-sampling, synthetic minority over-sampling, and a complex sampling) to improve the model applicability and accuracy. The developed models were validated using the actual data acquired from 201 locations in South Korea. The models for 238U and 222Rn showed estimation accuracies of 85% and 80%, respectively; the models with over-sampling showed better performance. All the results verified the usefulness of the developed models as virtual sensors for providing immediate information on the in-situ presence of NORMs in groundwater. [Display omitted] • Naturally occurring radioactive material (NORMs) in groundwater can be problematic. • Virtual sensor for in-situ estimating the probability of high-concentration of NORMs. • Major geochemical factors for NORMs were evaluated using statistical analyses. • Methods for solving data imbalance problem in training virtual sensor were proposed. • Major factors for the estimation were consistent with the statistical analysis result. [ABSTRACT FROM AUTHOR]

The last half a century has seen spectacular progresses in computing and modelling in a variety of fields, applications, and methodologies. Over the same period, a cross-disciplinary field known as sensitivity analysis has been making its first steps, evolving from the design of experiments for laboratory or field studies, also called 'in-vivo', to the so-called experiments 'in-silico'. Some disciplines were quick to realize the importance of sensitivity analysis, whereas others are still lagging behind. Major tensions within the evolution of this discipline arise from the interplay between local vs global perspectives in the analysis as well as the juxtaposition of the mathematical complexification and the desire for practical applicability. In this work, we retrace these main steps with some attention to the methods and through a bibliometric survey to assess the accomplishments of sensitivity analysis and to identify the potential for its future advancement with a focus on relevant disciplines, such as the environmental field. • The manuscript offers a detailed timeline of sensitivity analysis, tracing its evolution from local to global methods. • The manuscript includes a bibliometric survey assessing sensitivity analysis achievements and identifying future directions and areas of growth in the field. • We believe our research significantly contributes to the academic discourse on sensitivity analysis, aligning well with the focus of your journal. [ABSTRACT FROM AUTHOR]

The capability of cropping system models of depicting the crop and soil-related processes implies a high number of parameters. The aim of this work was to detect the key parameters, and the associated processes, of the ARMOSA cropping system model, considering two target outputs, crop yield and nitrogen leaching. A global sensitivity analysis (SA) was carried out in two steps: (1) the Morris method considering the whole set of parameters; (2) Sobol analysis was applied to the Morris outcome. The simulation was run on winter wheat in four soil types in Marchfeld (Austria, 2010–2018). Parameters affecting crop yield was the critical nitrogen concentration, the potential CO 2 assimilation rate, and the drought sensitivity parameter. Nitrogen leaching was mainly affected by the decomposition of litter and the early aboveground biomass growth. The parameters ranking did not appreciably change across soil types. This study offers a quick and replicable methodology for model calibration. [Display omitted] • ARMOSA is a process-based cropping system model. • A sensitivity analysis was performed for yield and N leaching. • Critical N concentration, potential C assimilation, drought sensitivity affect yield. • Litter decomposition and early growth affect N leaching. • Parameter ranking does not largely vary in different soils. [ABSTRACT FROM AUTHOR]

Accurate land cover information is essential for hydrometeorological modeling, as it defines parameters governing land-atmosphere-vegetation interactions, water partitioning, and routing. This study presents a controlled sensitivity analysis that evaluated the impact of land cover resampling methods on hydrologic simulations within the WRF-Hydro/NWM framework. The choice of resampling algorithms affected simulations but was generally limited to arid environments, catchments featuring minor land cover classes with high hydrologic impact, or low-flow predictions. We tested two distinct spatial aspects: areal proportions of land cover classes and spatial patterns in land cover. Areal proportions influenced vertical hydrologic fluxes at the catchment scale and subsequently affected streamflow characteristics. In contrast, spatial arrangement alone had a marginal impact on vertical fluxes but could still induce limited alterations in streamflow characteristics through routing processes. These results suggest that spatially distributed land cover, as used in physics-based model structures, has a limited impact on watershed-scale hydrologic simulations. • Resampling methods for land cover affect catchment-scale WRF-Hydro simulations. • Areal proportion of land cover outweighed spatial pattern in hydrologic prediction. • Large changes in spatial patterns of land cover had little impact on runoff response. • Models using soil columns as a basic unit may be insensitive to land cover patterns. [ABSTRACT FROM AUTHOR]

Computational modeling is frequently incorporated into environmental decision-making in order to capture inherently complex relationships and system dynamics. The complexity of such models often lies in various heterogeneous effects that arise due to the interaction of different input factors or due to designed structural variation in the model. In the past, various sensitivity analysis approaches have been implemented in attempts to identify essential decision factors. However, existing sensitivity analysis methods fail to capture critical information in the presence of heterogeneous effects. In this paper, the recently introduced simulation decomposition (SimDec) visualization method is extended to include quantitative sensitivity analysis. The framework is tested on several decision-making problems and is shown to capture heterogeneous behavior. A formal definition and classification of heterogeneous effects for computational models is introduced. The framework is open-sourced in a variety of scientific programming languages. • Sensitivity analysis methods fail to capture heterogeneous effects. • A novel quantitative SimDec framework is able to address heterogeneous model behavior. • Three case studies exhibit various types of heterogeneous effects. • The definition of heterogeneous effects in computational models is introduced. • SimDec open-source packages in Python, Julia, R, and Matlab are created. [ABSTRACT FROM AUTHOR]

PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models such as those used in the earth, environmental and engineering sciences. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent a wide range of methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of new or existing algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in groundwater. • Introduces a comprehensive easy-to-use toolbox for probabilistic analysis of models. • Automated methods for surrogate modeling, sensitivity analysis, uncertainty quantification. • Inference and experimental design methods reduce uncertainty in model inputs and predictions. • Multi-fidelity methods reduce computational cost when multiple models are available. • Provides extensive benchmark problems for validation and tutorials for learning. [ABSTRACT FROM AUTHOR]

Hydrologic models such as the Storm Water Management Model (SWMM) and the Hydrologic Simulation Program-Fortran (HSPF) are widely used to evaluate the impacts of urban development on watersheds and receiving waters. We compare the ability of these two models at simulating streamflow, peak flow, and baseflow from an urban watershed. The most sensitive hydrologic parameters for HSPF were related to groundwater; for SWMM, it was imperviousness. Both models simulated streamflow adequately; however, HSPF simulated baseflow better than SWMM, while, SWMM simulated peak flow better than HSPF. Global Sensitivity Analysis showed that variability of streamflow for SWMM was higher than that of HSPF, while variability of baseflow for HSPF was greater than that of SWMM. Further, analysis of extreme storm events indicated that the runoff coefficient for SWMM was slightly greater than HSPF for recurrence intervals of 1, 2, 5, and 10-yr.; the opposite was the case for recurrence intervals greater than 10 yrs. Image 107955 • We modeled streamflow using HSPF and SWMM for an urban watershed. • SWMM and HSPF simulated streamflow adequately. • SWMM was better at predicting peak flow, HSPF was better at predicting baseflow. • HSPF was sensitive to groundwater parameters; SWMM to imperviousness. • During extreme events, the runoff coefficient for SWMM was slightly greater. [ABSTRACT FROM AUTHOR]

Two dimensional hydraulic models are useful to reconstruct maximum discharges and uncertainties of historic flood events. Since many model runs are needed to include the effects of uncertain input parameters, a sophisticated 2D model is not applicable due to computational time. Therefore, this papers studies whether a lower-fidelity model can be used instead. The presented methodological framework shows that a 1D-2D coupled model is capable of simulating maximum discharges with high accuracy in only a fraction of the calculation time needed for the high-fidelity model. Therefore, the lower-fidelity model is used to perform the sensitivity analysis. Multiple Linear Regression analysis and the computation of the Sobol' indices are used to apportion the model output variance to the most influential input parameters. We used the 1926 flood of the Rhine river as a case study and found that the roughness of grassland areas was by far the most influential parameter. • A novel framework is set up to enable historic flood reconstruction. • A lower-fidelity physically based surrogate hydraulic model is developed which is capable of simulating maximum discharges with high accuracy compared to a sophisticated 2D model. • Sensitivity analysis can be applied for factor prioritization to determine on which input parameter to focus during historical geometry reconstruction to reduce model output variance. • The uncertainty of the maximum discharge is mostly influenced by the roughness of grasslands which is in this study the roughness class with the largest surface area. [ABSTRACT FROM AUTHOR]

Abstract Many applications of global sensitivity analysis (GSA) do not adequately account for the dynamical nature of earth and environmental systems models. Gupta and Razavi (2018) highlight this fact and develop a sensitivity analysis framework from first principles, based on the sensitivity information contained in trajectories of partial derivatives of the dynamical model responses with respect to controlling factors. Here, we extend and generalize that framework to accommodate any GSA philosophy, including derivative-based approaches (such as Morris and DELSA), direct-response-based approaches (such as the variance-based Sobol' , distribution-based PAWN , and higher-moment-based methods), and unifying variogram-based approaches (such as VARS). The framework is implemented within the VARS-TOOL software toolbox and demonstrated using the HBV-SASK model applied to the Oldman Watershed, Canada. This enables a comprehensive multi-variate investigation of the influence of parameters and forcings on different modeled state variables and responses, without the need for observational data regarding those responses. Highlights • Introduces a "unifying" multi-method approach to global sensitivity analysis. • Properly accounts for the dynamical nature of Earth and environmental system models. • Discusses how the full spectrum of sensitivity information may be compressed. • Provides software to implement the new approach in the VARS-TOOL toolbox. [ABSTRACT FROM AUTHOR]

Abstract Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question 'How uncertain is the prediction?' Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their range of existence, and this is equally true of a sensitivity analysis. Despite this, many uncertainty and sensitivity analyses still explore the input space moving along one-dimensional corridors leaving space of the input factors mostly unexplored. Our extensive systematic literature review shows that many highly cited papers (42% in the present analysis) fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and recognized good practices. We end by exploring possible reasons for this problem, and suggest some guidelines for proper use of the methods. Highlights • A systematic review of 280 scientific papers mentioning sensitivity analysis has been performed. • The analysis addresses the use of SA in the context of mathematical modelling, focusing on highly cited works. • Many highly-cited papers (42% in the present analysis) present a SA of poor quality. • The results, while discipline-dependent, point to a worrying lack of standards and good practices. • Some guidelines for proper use of the methods are suggested. [ABSTRACT FROM AUTHOR]

Abstract VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the " Variogram Analysis of Response Surfaces " framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model. Highlights • Introduces a next-generation toolbox for sensitivity and uncertainty analysis. • Provides a multi-method approach that unifies different theories and strategies. • Accounts for dynamical properties of Earth and environmental systems models. • Provides various sampling strategies including progressive Latin hypercube sampling. • Facilitates handling of high-dimensional models with hundreds of uncertain factors. [ABSTRACT FROM AUTHOR]

Abstract The emplacement of lava flows is mainly controlled by the topography, which is an essential input parameter to all numerical simulation models. The sensitivity of these models to the uncertainties in topographic data, and the consequent error propagation, may have non-negligible effects on the accuracy and reliability of the modelling of the lava flow hazard. Quantified analysis of the model sensitivity is thus of primary importance in constraining the uncertainty in the prospected lava inundation hazard, with implications for civil protection purposes. We assess the impact of errors, uncertainties and level of detail in topographic data on the lava flow emplacement modelled by the MAGFLOW cellular automaton. We show that the most influential external factors are rheology and vent geolocation, and that increasing the automaton resolution improves the accuracy of simulations, while making them more sensitive to perturbations in topographic data. Highlights • We present a method for model sensitivity analysis to topographic data uncertainties. • Rheological parameters influence the model sensitivity. • Model resolution affects sensitivity more than data resolution. • Georeferencing errors have the highest impact on lava flow emplacement. • Up-to-date topographic data is recommended, even if at low spatial resolution. [ABSTRACT FROM AUTHOR]

Abstract Strategic Environmental Assessment (SEA) requires taking into account the sensitivity of the area likely to be affected when identifying and characterising potential impacts resulting from plans and programmes. This entails consideration of the intrinsic characteristics that make the area susceptible to change, and incorporation of stakeholder values and concerns. A geospatial approach to these considerations facilitates identification of potential land-use conflicts and better informs planning decisions. Building on online geoprocessing advancements, we have developed an Environmental Sensitivity Mapping web tool containing novel functionality to support SEA. This paper presents the methodological framework for its participative multi-criteria approach and its architecture, and describes its functionality simulating a practical application. The web tool has the potential to break down data access and specialised skills barriers while providing a means for enhancing SEA consistency and transparency. However, data limitations remain affecting its applicability, and further technological advancements may help enhance its functionality. Highlights • Sensitivity mapping can enhance baseline information for environmental assessments. • A novel online geoprocessing tool supports systematic sensitivity assessment. • It enables combining context-specific environmental criteria and value judgments. • Pilot testing it through stakeholder engagement has validated its applicability. • The tool promotes assessment consistency and transparency but data issues remain. [ABSTRACT FROM AUTHOR]

Abstract Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs. [ABSTRACT FROM AUTHOR]

Abstract Dynamic global vegetation models (DGVMs) are of crucial importance for understanding and predicting vegetation, carbon, nitrogen and water dynamics of ecosystems in response to climate change. Their complexity, however, creates challenges for model analysis and data integration. A solution is to interface DGVMs with established statistical computing environments. Here we introduce rLPJGUESS, an R-package that couples the widely used DGVM LPJ-GUESS with the R environment for statistical computing, making existing R-packages and functions readily available to perform complex analyses with this model. We demonstrate the advantages of this framework by using rLPJGUESS to perform several otherwise laborious tasks: first, a set of single simulations, followed by global and local sensitivity analyses, a Bayesian calibration with a Markov-Chain Monte Carlo (MCMC) algorithm, and a predictive simulation with multiple climate scenarios. Our example highlights the opportunities of interfacing existing models in earth and environmental sciences with state-of-the-art computing environments such as R. Highlights • Dynamic vegetation models must be coupled to statistical computing environments. • rLPJGUESS is an R package designed to couple LPJ-GUESS with the R software. • We demonstrate the utility of rLPJGUESS using a complete modelling case study. • rLPJGUESS makes complex statistical methods readily available to LPJ-GUESS. • rLPJGUESS provides an approach easily applicable to other DGVMs. [ABSTRACT FROM AUTHOR]

Abstract Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Comprehensive spatial and temporal sensitivity analyses were applied to the NitroScape model, a deterministic spatially distributed model describing nitrogen transfers and transformations in rural landscapes. Simulations were led on a theoretical landscape that represented five years of intensive farm management and covering an area of 3 km 2. Cluster analyses were applied to summarize the results of the sensitivity analysis on the ensemble of model outputs. The methodology we applied is useful to synthesize sensitivity analyses of models with multiple space-time input and output variables and could be ported to other models than NitroScape. Highlights • Novel analytical methods for dynamic and spatially distributed models were proposed. • Spatial resolution at which the model is run was found to impact most of outputs. • Visualization methods summarized spatially distributed output and temporal outputs. • Cluster analysis grouped together outputs that are sensitive to same sets of inputs. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the applicability of model emulation to speed up simulation time of CPU intensive environmental models. Polynomial chaos expansion (PCE) emulators are constructed for three case studies of increasing complexity. The level of emulator training and the order of polynomial necessary to sufficiently build accurate emulators for each model are investigated. Although the PCE emulators shown here do not approximate well the outputs of parameter rich models (80 + parameters), results demonstrate that the emulators mimic closely outputs of relatively simple, low dimensional, simulation models (15 parameters or less). Furthermore, the PCE emulators are tested with applications such as Global Sensitivity Analysis (GSA). Results illustrate the advantages and drawbacks of using classical PCE emulators for treating computational limitation of complex environmental models. Highlights • PCE emulators are constructed with sparse grid sampling for 3 environmental models. • Accuracy, efficiency, and fidelity of emulators were tested. • For models that have low to medium complexity, the emulators worked well. • For a hyper-complex model with 87 parameters, the emulators were relatively weak. • Dimensionality reduction of the hyper-complex model allowed for better emulation. [ABSTRACT FROM AUTHOR]

Abstract Drainage-system management relies on results from urban stormwater models; errors in these models may have serious implications. Inaccurate field data or overly simplified models may cause the complex response of an urban basin to a rainfall event to be inadequately represented. Before undertaking expensive studies to gather and analyze additional data, it is reasonable to understand what enhancement in model performance would result if individual uncertainties could be decreased. This paper uses data collected during field monitoring campaigns to calibrate and validate a hydrologic and sediment-transport model within the Storm Water Management Model (SWMM). Solid accumulation and disappearance rates were identified as factors generating the highest model sensitivity. A generalized evaluation matrix is presented that considers both the uncertainty in input variables and the associated sensitivity in model response to inform model performance expectations and guide investments in model improvement toward actions with maximum benefit. Highlights • The impact that the uncertainty associated with sediment build-up and wash-off processes has on water quality model predictions was analyzed. • A method based on model uncertainty and sensitivity was developed to assess and predict the accuracy of water quality models. • This approach will assist users in making appropriate model performance conclusions. [ABSTRACT FROM AUTHOR]

A groundwater seepage face exists where the water table intersects the land surface, partitioning groundwater discharge to a waterbody between subaerial groundwater discharge (SGWD) and submerged subsurface discharge. We conducted numerical experiments to assess the effects of surface water level and other parameters on seepage face formation and groundwater discharge partitioning for two-dimensional catchment cross-section domain with two slopes - a bank slope near the waterbody and an upland slope. We performed local and global sensitivity tests for the onset of SGWD, the seepage length fraction and the seepage flow fraction for diverse topographic and hydrogeologic conditions. The results show that the ratio of recharge to hydraulic conductivity required for the onset of SGWD for both slopes varies up to three orders of magnitude with the changing water level. We also find that changing water levels has opposing effects on the metrics of the seepage faces of the two slopes. • Ratio of recharge to hydraulic conductivity (R/K) controls the seepage faces. • Seepage faces onset with lower R/K on uplands than on banks in flat catchments. • Length of seepage face on banks decreases with increasing surface water level. • Seepage faces on banks are more sensitive to surface water level in flat catchments. • Subaerial groundwater discharge increases with lower surface water levels. [ABSTRACT FROM AUTHOR]

For watersheds with complex stream network or geographical heterogeneity, multisite calibration in the distributed mode is preferred for the SWAT model because parameters for sub-watersheds are calibrated independently. However, existing tools either can only perform calibration in the lump mode by calibrating parameters in identical ways across the whole watershed or perform sequential and cascading calibration. In this study, an open source toolbox named Distributed Model Parameter Optimization Toolbox for the SWAT model (DMPOTSWAT) is developed to perform multisite calibration in both the distributed and lump modes. Other functions including model evaluation with default parameter values, sensitivity analysis, selecting best parameter sets, uncertainty analysis, and visualization of calibration output are also provided. These functions are illustrated with a case study. The development of this new toolbox will facilitate calibrating the SWAT and potentially other hydrologic models in the distributed mode. [Display omitted] • An open source toolbox conducting multisite calibration of the SWAT model was developed. • The toolbox is capable of calibrating models in both the lump and distributed modes. • Automatic procedure is designed to divide sub-watersheds, whose parameters are updated independently in the distributed mode. • Weights for statistics and outlets are included to adjust their importance in the evolution of objective functions. [ABSTRACT FROM AUTHOR]

Topography is a fundamental factor influencing the emplacement of lava flows. We assess the impact of topographic resolution on the thermorheological PyFLOWGO model, specifically with digital elevation model (DEM) resolutions commonly available for Earth and other planetary bodies where flow modeling is relevant. We examined PyFLOWGO output parameters (e.g., channel length, core temperature, and flow velocity) to assess model sensitivity to topography in order to document model uncertainties and optimize future application. This study uses rheologic and topographic data from the Tolbachik, Russia 2012–2013 eruption as model constraints. Using moderate to lower resolution topographic data overestimates the channelized flow length by up to 35% due to differences in the distribution of slopes topographic variability at different resolutions and resulting effects on the modeled lava velocity down channel. Determining the impact of topography on thermorheological lava flow models such as PyFLOWGO is important to correctly interpret the results for channelized flows. • We present a topographic sensitivity analysis for the PyFLOWGO lava flow model. • Examined DEM resolutions were based on common Earth and planetary datasets. • A model step size equal to or smaller than the DEM resolution should be used. • DEM resolution has a quantifiable impact on the modeled flow length (up to 35%). [ABSTRACT FROM AUTHOR]

Abstract This paper explores spatial sensitivities of 1D and 2D shallow water (SW) models of branched urban flood flows, based on an experimental data-set and a variance decomposition method for various combination of uncertainty sources. General sensitivity patterns of SW model for subcritical flows show that: simulated water height variance is explained upstream by inflow discharge and roughness whereas downstream it is fully explained by downstream water height, influence of lateral inflows propagates in both directions. High sensitivities to roughness can be local in space thus identifying the strongest hydraulic controls for orienting calibration efforts is needed. 2D sensitivities show: the filtering effect of branched network topography on inflow, the zone of influence of boundary conditions, the role of large streets as global flow pattern separators, the difference in roughness sensitivity patterns with 1D. This sensitivity analysis of SW models could be a base for studying flow path and uncertainty propagation. Highlights • GSA framework for branched urban flood flows modelling. • Sobol spatial sensitivities of 1D and 2D shallow water models. • Identification of hydraulic controls for various uncertainty combinations. • Filtering effect of branched network topography quantified. [ABSTRACT FROM AUTHOR]

In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) approaches in wildfire modelling are computationally more efficient than vector-based methods. Despite this advantage, these methods suffer from the lack of physical basis and distortion in simulated fire shapes. To overcome these drawbacks and to perform a real-time interactive crown fire simulation for training use, a new CA model is proposed in which a new spread mechanism is introduced based on the physical equations of reaction and radiation. Then, through a series of crown fire simulations in uniform and heterogeneous synthetic fuel beds, the functional validation of the model and the sensitivity analyses on vegetation parameters are performed. Finally, a case study is discussed to illustrate the application of the model with real-world data. Without any mathematical correction, this method is intrinsically free from the distortion problems found in several CA approaches. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study comparing different uncertainty analysis (UA) and sensitivity analysis (SA) methods, focussing their usefulness for the output analysis of land use/land cover change (LUCC) agent-based models (ABMs). As a result, a workflow to integrate UA and SA is presented to evaluate ABMs outputs. We developed a baseline scenario and performed a comprehensive investigation of the impacts that differences in sample sizes, sample techniques, and SA methods may have on the model output. The analysis is done in the context of a particular agent-based simulator with a LUCC model in a Brazilian Cerrado case study. The experiments indicate that there are known challenges to be overcome by the use of statistical methods. Even though the presented analysis was done over a particular simulator, we intend to contribute to the community that understands the importance of statistical validation techniques to improve the level of confidence in agent-based simulation outputs. [ABSTRACT FROM AUTHOR]

Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types. [ABSTRACT FROM AUTHOR]

Integrated modelling is a promising approach to simulate processes operating within complex environmental systems. It is possible, however, that this integration may lead to computationally expensive compositions. In order to retain the process fidelity without loss of accuracy, the use of Kriging metamodels is proposed to perform Monte Carlo simulation and sensitivity analysis, in lieu of compositions developed using the model linking standard OpenMI. Results from the Monte Carlo simulation showed that the metamodels were in a good agreement with the original responses. However, metamodels provided a less accurate approximation of the original output distribution for the composition which involved a stronger non-linear behaviour. The fast runtimes of the metamodels allowed for increased computational budgets leading to an accurate screening of the important parameters for an Elementary Effects Test. Overall, Kriging metamodels provided significant computational savings without compromising the quality of the outcomes, even using small training data sets. [ABSTRACT FROM AUTHOR]

Risk assessment and decision making in ecology, hydrology and biology often employ dynamic models with multiple calibrations. The global sensitivity analysis of models is usually completed at each time step of a single output. However, due to the enormous volume of data and model complexity, a single index cannot give a full-scale analysis of such models. The purposes of this paper are: (1) to apply T-pooling for analysing multiple outputs at a lower computational cost; (2) to consider the influence of the correlations among the outputs and the output dimensions on sensitivity analysis; and (3) to propose a procedure that combines the Sobol' index for a single output and the generalised sensitivity method and T-pooling index for multiple outputs to analyse dynamic models comprehensively. The proposed procedure and index are applied to a Hydrologiska Byråns Vattenbalansavdelning (HBV) model with three calibrations to provide an uncertainty analysis across time periods ranging from a single time step to the entire time period. [ABSTRACT FROM AUTHOR]

This paper describes a novel sensitivity analysis method, able to handle dependency relationships between model parameters. The starting point is the popular Morris (1991) algorithm, which was initially devised under the assumption of parameter independence. This important limitation is tackled by allowing the user to incorporate dependency information through a copula. The set of model runs obtained using latin hypercube sampling, are then used for deriving appropriate sensitivity measures. Delft3D-WAQ (Deltares, 2010) is a sediment transport model with strong correlations between input parameters. Despite this, the parameter ranking obtained with the newly proposed method is in accordance with the knowledge obtained from expert judgment. However, under the same conditions, the classic Morris method elicits its results from model runs which break the assumptions of the underlying physical processes. This leads to the conclusion that the proposed extension is superior to the classic Morris algorithm and can accommodate a wide range of use cases. [ABSTRACT FROM AUTHOR]

Despite existence of many developed approaches for identifying optimal time of Water Meters (WMs) replacement, less attention has been paid to the impact of WM failure risk on utilities revenues and the proper time of replacement. Here, the Asset Management (AM) approach is introduced as a holistic managing framework optimizing Life Cycle Costs (LCC), especially incorporating WM failure risk in asset replacement decisions. The proposed AM framework is based on four core steps. The application of the proposed framework for a real case study has shown confidence in finding the economical time of WM replacement. It was further shown that the risk costs associated with WM failure have higher impacts on optimal time of WM replacement than the revenue lost due to its inaccuracy. The sensitivity analyses of input data values show that water price, initial investment and accuracy degradation rate have the highest impacts on LCCs amongst other parameters. [ABSTRACT FROM AUTHOR]

Quality and efficiency of computational fluid dynamics (CFD) simulation of pedestrian level wind environment in a complex urban area are often compromised by many influencing factors, particularly mesh quality. This paper first proposes a systematic and efficient mesh generation method and then performs detailed sensitivity analysis of some important computational parameters. The geometrically complex Hong Kong Polytechnic University (HKPolyU) campus is taken as a case study. Based on the high-quality mesh system, the influences of three important computational parameters, namely, turbulence model, near-wall mesh density and computational domain size, on the CFD predicted results of pedestrian level wind environment are quantitatively evaluated. Validation of CFD models is conducted against wind tunnel experimental data, where a good agreement is achieved. It is found that the proposed mesh generation method can effectively provide a high-quality and high-resolution structural grid for CFD simulation of wind environment in a complex urban area. [ABSTRACT FROM AUTHOR]

Despite various criticisms of GLUE (Generalized Likelihood Uncertainty Estimation), it is still a widely-used uncertainty analysis technique in hydrologic modelling that can give an appreciation of the level and sources of uncertainty. We introduce an augmented GLUE approach based on a Gaussian Process (GP) emulator, involving GP to conduct a Bayesian sensitivity analysis to narrow down the influential factor space, and then performing a standard GLUE uncertainty analysis. This approach is demonstrated for a SWAT (Soil and Water Assessment Tool) application in a watershed in China using a calibration and two validation periods. Results show: 1) the augmented approach led to the screening out of 14–18 unimportant factors, effectively narrowing factor space; 2) compared to the more standard GLUE, it substantially improved the sampling efficiency, and located the optimal factor region at lower computational cost. This approach can be used for other uncertainty analysis techniques in hydrologic and non-hydrologic models. [ABSTRACT FROM AUTHOR]

The Variable Infiltration Capacity (VIC) hydrologic and river routing model simulates the water and energy fluxes that occur near the land surface and provides useful information regarding the quantity and timing of available water within a watershed system. However, despite its popularity, wider adoption is hampered by the considerable effort required to prepare model inputs and calibrate the model parameters. This study presents a user-friendly software package, named VIC-Automated Setup Toolkit (VIC-ASSIST), accessible through an intuitive MATLAB graphical user interface. VIC-ASSIST enables users to navigate the model building process through prompts and automation, with the intention to promote the use of the model for practical, educational, and research purposes. The automated processes include watershed delineation, climate and geographical input set-up, model parameter calibration, sensitivity analysis, and graphical output generation. We demonstrate the package's utilities in various case studies. [ABSTRACT FROM AUTHOR]

Many crop models use the NRCS Curve Number method to estimate runoff, but the simplified assumptions of this method are rarely considered in model uncertainty assessments. The associated uncertainty may be high for cropping systems with a significant part of bare soil like vineyards, specifically under a Mediterranean climate. In this work, we evaluate for a vineyard crop model the structure uncertainty coming from its uncertain runoff module. We introduce a new method based on additional knowledge about the runoff process and on a mathematical property of the model structure. Situations characterized in terms of soil water content and mean runoff conditions are studied for two applications of the vineyard model and guidelines for model users are derived. This work shows that uncertainty quantification can benefit from the knowledge of mathematical properties of a model and provide clear guidelines to model users. [ABSTRACT FROM AUTHOR]

Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection. [ABSTRACT FROM AUTHOR]

Impervious areas change hydrological processes, reducing infiltration and evapotranspiration, and increasing direct runoff. Stormwater practices using green infrastructure are implemented locally to control runoff and preserve the hydrological cycle. Applying these techniques in semiarid and Mediterranean regions requires accounting for aspects related to the maintenance of green areas. This study develops the Integrated Hydrological Model at Residential Scale, a continuous model for representing the performance and irrigation of green stormwater facilities at residential scales. Among other relevant process, the model simulates evaporation from bare soil and redistribution between soil layers. Different components of the model were tested using laboratory and numerical experiments, and then an application to a case study and a sensitivity analysis were carried out. The model identifies significant differences in the performance of a rain garden with different vegetation, climate and irrigation practices and provides good insight for the maintenance needs of green infrastructure for runoff control. [ABSTRACT FROM AUTHOR]

This paper investigates the commonly overlooked “sensitivity” of sensitivity analysis (SA) to what we refer to as parameter “perturbation scale”, which can be defined as a prescribed size of the sensitivity-related neighbourhood around any point in the parameter space (analogous to step size Δ x for numerical estimation of derivatives). We discuss that perturbation scale is inherent to any (local and global) SA approach, and explain how derivative-based SA approaches (e.g., method of Morris) focus on small-scale perturbations, while variance-based approaches (e.g., method of Sobol) focus on large-scale perturbations. We employ a novel variogram-based approach, called Variogram Analysis of Response Surfaces (VARS), which bridges derivative- and variance-based approaches. Our analyses with different real-world environmental models demonstrate significant implications of subjectivity in the perturbation-scale choice and the need for strategies to address these implications. It is further shown how VARS can uniquely characterize the perturbation-scale dependency and generate sensitivity measures that encompass all sensitivity-related information across the full spectrum of perturbation scales. [ABSTRACT FROM AUTHOR]

The biogeochemical model, PnET-BGC, has been used to evaluate the long-term acid-base response of surface waters to changes in atmospheric acid deposition. We propose a methodology to identify the input factors of greatest model sensitivity and propagate uncertainty of input factors to model outputs. The quantified model uncertainty enabled application of an “exceedance probability” approach to determine allowable atmospheric deposition in the form of Total Maximum Daily Loads (TMDLs) for twelve acid-impaired streams in Great Smoky Mountains National Park. Results indicate that acidification of surface water resulting from acidic deposition has been substantial. Even if current atmospheric deposition is reduced to pre-industrial levels, only one of the twelve impaired streams might be recovered to its site-specific standard by 2050. Our sensitivity analysis indicates that the model is most sensitive to precipitation quantity, air temperature and calcium weathering rate, and suggests further research to improve characterization of these inputs. [ABSTRACT FROM AUTHOR]

We report on measuring uncertainty in the computational results of the wind wave model SWAN (Simulating WAves Nearshore) on an unstructured mesh and exploring the sources of this uncertainty. We considered an area in the vicinity of the Saint Petersburg Flood Prevention Facility Complex and treat the results as risks on using the model results for the Facility gate operation. We found that model response uncertainty is significant for small waves ( H s < 0.3 m ), and the results variability is moderate ( ≈ 10 % ) for the extreme values of the model prediction. Thus the risk on the use of model results to predict flood threats is low together with visible uncertainty about the prediction of flood start time. Uncertainty in wind velocity has a substantial influence on the model response. In addition, uncertainty in the bathymetry, water level, and breaker index affects model output. Therefore, result uncertainty can be decreased obtaining more certain values of these model inputs. [ABSTRACT FROM AUTHOR]

Manual calibration of distributed models with many unknown parameters can result in problems of equifinality and high uncertainty. In this study, the Generalized Likelihood Uncertainty Estimation (GLUE) technique was used to address these issues through uncertainty and sensitivity analysis of a distributed watershed scale model (SAHYSMOD) for predicting changes in the groundwater levels of the Rechna Doab basin, Pakistan. The study proposes and then describes a stepwise methodology for SAHYSMOD uncertainty analysis that has not been explored in any study before. One thousand input data files created through Monte Carlo simulations were classified as behavior and non-behavior sets using threshold likelihood values. The model was calibrated (1983–1988) and validated (1998–2003) through satisfactory agreement between simulated and observed data. Acceptable values were observed in the statistical performance indices. Approximately 70% of the observed groundwater level values fell within uncertainty bounds. Groundwater pumping (Gw) and hydraulic conductivity (Kaq) were found to be highly sensitive parameters affecting groundwater recharge. [ABSTRACT FROM AUTHOR]

Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses. [ABSTRACT FROM AUTHOR]

Process-based models of fluid flow and heat transport in fluvial systems can be used to quantify unknown spatial and temporal patterns of hydrologic fluxes and to predict system response to change. In this study, a deterministic stream heat budget model, the HFLUX Stream Temperature Solver (HFLUX), is developed and evaluated using field studies. Field studies are conducted across two sites with different streamflow rates (0.07 vs 1.4 m 3 /s), and point sources versus diffuse sources of groundwater discharge, to demonstrate model transferability. A winter versus summer comparison at one site suggests latent heat flux should be derived using energy-based methods in summer and mass transfer approaches during winter. For each field study, HFLUX successfully modeled stream temperatures through space and time with normalized root mean square errors of 3.0–6.2%. Model calibration to observed temperature data in order to quantify groundwater contributions and a sensitivity analysis are demonstrated using HFLUX. [ABSTRACT FROM AUTHOR]

Models of biophysical processes are often time-consuming and their inputs are frequently correlated. This situation of non-independence between the inputs is always a challenge in view of simultaneously achieving a global sensitivity analysis of the model output and a metamodeling of this output. In this paper, a novel practical method is proposed for reaching this two-fold goal. It is based on a truncated Polynomial Chaos Expansion of the output whose coefficients are estimated by Partial Least Squares Regression. The method is applied to a computer model for heterogeneous canopies in arable crops, aimed to predict crop:weed competition for light. We now have fast-running metamodels that simultaneously provide good approximations of the outputs of this computer model and a clear overview of its input influences thanks to new sensitivity indices. [ABSTRACT FROM AUTHOR]

A novel approach is presented for model calibration and global sensitivity analyses in which stage frequency distributions (SFD) are used as objective functions, not single values or time series, for example water levels or flows, which are traditionally used in hydraulic and hydrological modelling. This allows the stochastic nature of the input parameter to be characterised which is an important feature for some hydraulic processes of northern rivers such as ice jam formation. The observation SFD can also be used as part of an objective function for global sensitivity analysis, also a novelty presented in this paper, to determine the sensitivity of input parameter and boundary condition frequency distributions on different flooding regimes. [ABSTRACT FROM AUTHOR]

Global Sensitivity Analysis (GSA) is an essential technique to support the calibration of environmental models by identifying the influential parameters (screening) and ranking them. In this paper, the widely-used variance-based method (Sobol') and the recently proposed moment-independent PAWN method for GSA are applied to the Soil and Water Assessment Tool (SWAT), and compared in terms of ranking and screening results of 26 SWAT parameters. In order to set a threshold for parameter screening, we propose the use of a “dummy parameter”, which has no influence on the model output. The sensitivity index of the dummy parameter is calculated from sampled data, without changing the model equations. We find that Sobol' and PAWN identify the same 12 influential parameters but rank them differently, and discuss how this result may be related to the limitations of the Sobol' method when the output distribution is asymmetric. [ABSTRACT FROM AUTHOR]

This study advances model diagnostics for snowmelt-based hydrological systems using Sobol’ sensitivity analysis, illuminating parameter sensitivities and contrasting model structural differences. We consider several distinct snow-dominated locations in the western United States, running both SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, a physically-based model. Model performance is rigorously evaluated through global sensitivity analysis and a temperature warming analysis is conducted to explore how model parameterizations affect portrayals of climate change. Both VIC and SNOW-17 produce comparable results with SNOW-17 performing slightly better for shallower snowpacks and VIC performing better for deeper snowpacks. However, the lack of sensitivity of SNOW-17 to climate warming suggests that it may not be as reliable as a more sensitive model like VIC. Inter-model differences presented here offer insights into physical features with greatest uncertainty and may inform future model development and planning activities. [ABSTRACT FROM AUTHOR]

This research provides a new approach to assess groundwater vulnerability to contamination from anthropogenic activities and sea water intrusion. The DRASTIC and GALDIT parametric methods were then linked to a novel land use index to create a more robust “global risk index”, useful for assessing aquifer vulnerability to pollution and seawater intrusion risk. In addition, sensitivity analysis was used to evaluate the effect of each individual parameter on the final models. The vulnerability to pollution and the seawater intrusion contamination maps show three classes of water resources degradation: low, moderate and high, relating to the intrinsic properties. In addition, the global risk map shows three risk classes': low (25%), moderate (64%) and high (11%) depending on the hydrogeological characteristics, land use, distance from the coast and human impacts in most of the study area. The modified models were statistically compared with the nitrate concentration and the water resistivity values for validation. These maps are considered indispensable for sustainable land use planning and groundwater management of the shallow aquifer. [ABSTRACT FROM AUTHOR]

We submitted the semi-empirical, process-based wind-risk model ForestGALES to a variance-based sensitivity analysis using the method of Soboĺ for correlated variables proposed by Kucherenko et al. (2012). Our results show that ForestGALES is able to simulate very effectively the dynamics of wind damage to forest stands, as the model architecture reflects the significant influence of tree height, stocking density, dbh, and size of an upwind gap, on the calculations of the critical wind speeds of damage. These results highlight the importance of accurate knowledge of the values of these variables when calculating the risk of wind damage with ForestGALES. Conversely, rooting depth and soil type, i.e. the model input variables on which the empirical component of ForestGALES that describes the resistance to overturning is based, contribute only marginally to the variation in the outputs. We show that these two variables can confidently be fixed at a nominal value without significantly affecting the model's predictions. The variance-based method used in this study is equally sensitive to the accurate description of the probability distribution functions of the scrutinised variables, as it is to their correlation structure. [ABSTRACT FROM AUTHOR]

This paper deals with global sensitivity analysis of computer model output. Given an independent input sample and associated model output vector with possibly the vector of output derivatives with respect to the input variables, we show that it is possible to evaluate the following global sensitivity measures: (i) the Sobol’ indices, (ii) the Borgonovo's density-based sensitivity measure, and (iii) the derivative-based global sensitivity measure of Sobol’ and Kucherenko. We compare the efficiency of the different methods to address factors fixing setting , an important issue in global sensitivity analysis. First, global sensitivity analysis of the Ishigami function is performed with the different methods. Then, they are applied to two different responses of a soil drainage model. The results show that the polynomial chaos expansion for estimating Sobol’ indices is the most efficient approach. [ABSTRACT FROM AUTHOR]