scholarly journals Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system

2021 ◽  
Vol 25 (10) ◽  
pp. 5603-5621
Author(s):  
Andrew J. Newman ◽  
Amanda G. Stone ◽  
Manabendra Saharia ◽  
Kathleen D. Holman ◽  
Nans Addor ◽  
...  
Keyword(s):  
Hydrologic Modeling ◽  
Initial Conditions ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Model Parameters ◽  
Model Structure ◽  
Structure Model ◽  
Return Periods ◽  
Modeling Framework ◽  
Flood Generation

Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

scholarly journals Identifying Sensitivities in Flood Frequency Analyses using a Stochastic Hydrologic Modeling System

2021 ◽  
Author(s):  
Andrew J. Newman ◽  
Amanda G. Stone ◽  
Manabendra Saharia ◽  
Kathleen D. Holman ◽  
Nans Addor ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Flood Frequency ◽  
Model Parameters ◽  
Model Structure ◽  
Structure Model ◽  
Return Periods ◽  
Hydrologic Modelling ◽  
Modelling Framework ◽  
Flood Generation ◽  
Model Structures

Abstract. This study assesses sources of variance in stochastic hydrologic modelling to support flood frequency analyses. The major components of the modelling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100,000 years at two watersheds representing different hydro-climates across the western United States. Ten hydrologic model structures were configured, calibrated and run within the Framework for Understanding Structural Errors (FUSE) modular modelling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100-member historical meteorology ensemble. A stochastic event-based hydrologic modelling workflow was developed using the calibrated models; millions of flood event simulations were performed at each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. The attribution of the variance of flood frequencies to each component of a stochastic hydrological modelling framework, including several hydrological model structures, is a novel contribution to the flood modelling literature. Results demonstrate that different components of the modelling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare events, while initial conditions are most influential for the more frequent events. However, the hydrological model structure and structure-parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

scholarly journals Simultaneously determining global sensitivities of model parameters and model structure

2020 ◽  
Vol 24 (12) ◽  
pp. 5835-5858
Author(s):  
Juliane Mai ◽  
James R. Craig ◽  
Bryan A. Tolson
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Structural Model ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Framework ◽  
Model Structure Uncertainty ◽  
Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

scholarly journals Simultaneously Determining Global Sensitivities of Model Parameters and Model Structure

2020 ◽  
Author(s):  
Juliane Mai ◽  
James R. Craig ◽  
Bryan A. Tolson
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Structural Model ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Framework ◽  
Model Structure Uncertainty ◽  
Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply an Extended Sobol' Sensitivity Analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that: (1) The xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration. (4) The analysis of time dependent process sensitivities regarding simulated streamflow is a helpful tool to understand model internal dynamics over the course of the year.

scholarly journals UAV-DEMs for Small-Scale Flood Hazard Mapping

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1717 ◽  
Author(s):  
Antonio Annis ◽  
Fernando Nardi ◽  
Andrea Petroselli ◽  
Ciro Apollonio ◽  
Ettore Arcangeletti ◽  
...  
Keyword(s):  
High Resolution ◽  
Hydrologic Modeling ◽  
Flood Hazard ◽  
Hydraulic Modeling ◽  
Flood Frequency ◽  
Hazard Mapping ◽  
Small Scale ◽  
Ungauged Basins ◽  
Modeling Framework ◽  
Flood Simulations

Devastating floods are observed every year globally from upstream mountainous to coastal regions. Increasing flood frequency and impacts affect both major rivers and their tributaries. Nonetheless, at the small-scale, the lack of distributed topographic and hydrologic data determines tributaries to be often missing in inundation modeling and mapping studies. Advances in Unmanned Aerial Vehicle (UAV) technologies and Digital Elevation Models (DEM)-based hydrologic modeling can address this crucial knowledge gap. UAVs provide very high resolution and accurate DEMs with low surveying cost and time, as compared to DEMs obtained by Light Detection and Ranging (LiDAR), satellite, or GPS field campaigns. In this work, we selected a LiDAR DEM as a benchmark for comparing the performances of a UAV and a nation-scale high-resolution DEM (TINITALY) in representing floodplain topography for flood simulations. The different DEMs were processed to provide inputs to a hydrologic-hydraulic modeling chain, including the DEM-based EBA4SUB (Event-Based Approach for Small and Ungauged Basins) hydrologic modeling framework for design hydrograph estimation in ungauged basins; the 2D hydraulic model FLO-2D for flood wave routing and hazard mapping. The results of this research provided quantitative analyses, demonstrating the consistent performances of the UAV-derived DEM in supporting affordable distributed flood extension and depth simulations.

scholarly journals The Bayesian Covariance Structure Model for Testlets

2020 ◽  
pp. 107699862094120
Author(s):  
Jean-Paul Fox ◽  
Jeremias Wenzel ◽  
Konrad Klotzke
Keyword(s):  
Random Effects ◽  
Random Effect ◽  
Covariance Structure ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Random Effects Model ◽  
Structure Model ◽  
Modeling Framework ◽  
Irt Models ◽  
Covariance Structure Model

Standard item response theory (IRT) models have been extended with testlet effects to account for the nesting of items; these are well known as (Bayesian) testlet models or random effect models for testlets. The testlet modeling framework has several disadvantages. A sufficient number of testlet items are needed to estimate testlet effects, and a sufficient number of individuals are needed to estimate testlet variance. The prior for the testlet variance parameter can only represent a positive association among testlet items. The inclusion of testlet parameters significantly increases the number of model parameters, which can lead to computational problems. To avoid these problems, a Bayesian covariance structure model (BCSM) for testlets is proposed, where standard IRT models are extended with a covariance structure model to account for dependences among testlet items. In the BCSM, the dependence among testlet items is modeled without using testlet effects. This approach does not imply any sample size restrictions and is very efficient in terms of the number of parameters needed to describe testlet dependences. The BCSM is compared to the well-known Bayesian random effects model for testlets using a simulation study. Specifically for testlets with a few items, a small number of test takers, or weak associations among testlet items, the BCSM shows more accurate estimation results than the random effects model.

Review of Electric Load Modeling Research

2013 ◽  
Vol 811 ◽  
pp. 627-630 ◽  
Author(s):  
Xue Song Zhou ◽  
Huan Liang ◽  
You Jie Ma
Keyword(s):  
Voltage Stability ◽  
Transient Stability ◽  
Load Flow ◽  
Model Parameters ◽  
Model Structure ◽  
Electric Load ◽  
Structure Model ◽  
Load Modeling ◽  
Load Model ◽  
Model Research

The effect of load model on the analyses of load flow, transient stability, small disturbance stability and voltage stability is analyzed. The importance of the load modeling research is emphasized. The development of component-based method and measurement-based method is reviewed. The advances on the load model research including the select ion of load model structure, model parameters identification, load model with the voltage stability analysis and the sensitivity of load model to the transient stability is summarized.

scholarly journals Macro-scale grid-based and subbasin-based hydrologic modeling: joint simulation and cross-calibration

2018 ◽  
Vol 21 (1) ◽  
pp. 77-91 ◽  
Author(s):  
Xuefeng Chu ◽  
Zhulu Lin ◽  
Mohsen Tahmasebi Nasab ◽  
Lan Zeng ◽  
Kendall Grimm ◽  
...  
Keyword(s):  
Hydrologic Modeling ◽  
Swat Model ◽  
Temporal Trends ◽  
Hydrologic Model ◽  
Modeling Framework ◽  
Joint Simulation ◽  
Macro Scale ◽  
Cross Calibration ◽  
Scale Grid ◽  
Grid Based

Abstract Watershed hydrologic models often possess different structures and distinct methods and require dissimilar types of inputs. As spatially-distributed data are becoming widely available, macro-scale modeling plays an increasingly important role in water resources management. However, calibration of a macro-scale grid-based model can be a challenge. The objective of this study is to improve macro-scale hydrologic modeling by joint simulation and cross-calibration of different models. A joint modeling framework was developed, which linked a grid-based hydrologic model (GHM) and the subbasin-based Soil and Water Assessment Tool (SWAT) model. Particularly, a two-step cross-calibration procedure was proposed and implemented: (1) direct calibration of the subbasin-based SWAT model using observed streamflow data; and (2) indirect calibration of the grid-based GHM through the transfer of the well-calibrated SWAT simulations to the GHM. The joint GHM-SWAT modeling framework was applied to the Red River of the North Basin (RRB). The model performance was assessed using the Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS). The results highlighted the feasibility of the proposed cross-calibration strategy in taking advantage of both model structures to analyze the spatial/temporal trends of hydrologic variables. The modeling approaches developed in this study can be applied to other basins for macro-scale climatic-hydrologic modeling.

Estimation of Gear Backlash: Theory and Simulation

1998 ◽  
Vol 120 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Jeffrey L. Stein ◽  
Churn-Hway Wang
Keyword(s):  
Computer Simulation ◽  
Quality Control ◽  
Open Loop ◽  
Measurement Noise ◽  
Model Parameters ◽  
Model Structure ◽  
Estimation Technique ◽  
Structure Model ◽  
Product Quality Control ◽  
Gear Backlash

Machine and product condition monitoring is important to product quality control, especially for unmanned manufacturing. This paper proposes a technique for the estimation of clearance in mechanical systems under dynamic conditions with specific application to the estimation of backlash in gear systems of servomechanisms. The technique is based on a momentum transfer analysis that shows that the change in the speed (defined as bounce) of the primary gear due to impact with the secondary gear is related to the magnitude of the backlash. An algorithm is presented to estimate the bounce in real-time. The algorithm estimates the bounce by computing the standard bounce which is defined as the standard deviation of the demodulated envelope of the primary gear speed. The standard bounce is shown to be a good measure of the bounce when the system is excited sinusoidally. The algorithm’s accuracy and sensitivity are verified through computer simulation of an open-loop DC servomechanism. An approximately linear relationship between the standard bounce and the backlash magnitude is observed. This holds for backlash values exceeding recommended tolerances by ±100 percent. The algorithm is also shown to be insensitive to changes in the simulation model structure, model parameters as well as system and measurement noise. The estimation technique is accurate, computationally simple, and requires no additional sensors if the servosystem to be monitored already has a conventional tachometer.

scholarly journals MOESHA: A Genetic Algorithm for Automatic Calibration and Estimation of Parameter Uncertainty and Sensitivity of Hydrologic Models

2017 ◽  
Vol 60 (4) ◽  
pp. 1259-1269 ◽  
Author(s):  
Bradley L. Barnhart ◽  
Keith A. Sawicz ◽  
Darren L. Ficklin ◽  
Gerald W. Whittaker
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Hydrologic Modeling ◽  
Hydrologic Model ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Output ◽  
Discharge Data

Abstract. Characterization of the uncertainty and sensitivity of model parameters is an essential facet of hydrologic modeling. This article introduces the multi-objective evolutionary sensitivity handling algorithm (MOESHA) that combines input parameter uncertainty and sensitivity analyses with a genetic algorithm calibration routine to dynamically sample the parameter space. This novel algorithm serves as an alternative to traditional static space-sampling methods, such as stratified sampling or Latin hypercube sampling. In addition to calibrating model parameters to a hydrologic model, MOESHA determines the optimal distribution of model parameters that maximizes model robustness and minimizes error, and the results provide an estimate for model uncertainty due to the uncertainty in model parameters. Subsequently, we compare the model parameter distributions to the distribution of a dummy variable (i.e., a variable that does not affect model output) to differentiate between impactful (i.e., sensitive) and non-impactful parameters. In this way, an optimally calibrated model is produced, and estimations of model uncertainty as well as the relative impact of model parameters on model output (i.e., sensitivity) are determined. A case study using a single-cell hydrologic model (EXP-HYDRO) is used to test the method using river discharge data from the Dee River catchment in Wales. We compare the results of MOESHA with Sobol’s global sensitivity analysis method and demonstrate that the algorithm is able to pinpoint non-impactful parameters, demonstrate the uncertainty of model results with respect to uncertainties in model parameters, and achieve excellent calibration results. A major drawback of the algorithm is that it is computationally expensive; therefore, parallelized methods should be used to reduce the computational burden. Keywords: Genetic algorithm, Hydrologic modeling, Model calibration, Sensitivity analysis, Uncertainty.

scholarly journals Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

2021 ◽  
Author(s):  
Daniel Bittner ◽  
Beatrice Richieri ◽  
Gabriele Chiogna
Keyword(s):  
Groundwater Recharge ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Time Dependent ◽  
Model Parameters ◽  
Lumped Parameter ◽  
Hydrodynamic Processes ◽  
Input Model ◽  
Karst Model ◽  
Input Uncertainties

AbstractUncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

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