Advanced sensitivity analysis of the impact of the temporal distribution and intensity in a rainfall event on hydrograph parameters in urban catchments: a case study

Abstract. Knowledge of the variability of the hydrograph of outflow from urban catchments is highly important for measurements and evaluation of the operation of sewer networks. Currently, hydrodynamic models are most frequently used for hydrograph modeling. Since a large number of their parameters have to be identified, there may be problems at the calibration stage. Hence, sensitivity analysis is used to limit the number of parameters. However, the current sensitivity analytical methods ignore the effect of the temporal distribution and intensity of precipitation in a rainfall event on the catchment outflow hydrograph. This article presents a methodology of constructing a simulator of catchment outflow hydrograph parameters (volume and maximum flow). For this purpose, uncertainty analytical results obtained with the use of the GLUE (generalized likelihood uncertainty estimation) method were used. A novel analysis of the sensitivity of the hydrodynamic catchment models was also developed, which can be used in the analysis of the operation of stormwater networks and underground infrastructure facilities. Using the logistic regression method, an innovative sensitivity coefficient was proposed to study the impact of the variability of the parameters of the hydrodynamic model depending on the distribution of rainfall, the origin of rainfall (on the Chomicz scale), and the uncertainty of the estimated simulator coefficients on the parameters of the outflow hydrograph. The developed model enables the analysis of the impact of the identified SWMM (Storm Water Management Model) parameters on the runoff hydrograph, taking into account local rainfall conditions, which have not been analyzed thus far. Compared with the currently developed methods, the analyses included the impact of the uncertainty of the identified coefficients in the logistic regression model on the results of the sensitivity coefficient calculation. This aspect has not been taken into account in the sensitivity analytical methods thus far, although this approach evaluates the reliability of the simulation results. The results indicated a considerable influence of rainfall distribution and intensity on the sensitivity factors. The greater the intensity and rainfall were, the lower the impact of the identified hydrodynamic model parameters on the hydrograph parameters. Additionally, the calculations confirmed the significant impact of the uncertainty of the estimated coefficient in the simulator on the sensitivity coefficients. In the context of the sensitivity analysis, the obtained results have a significant effect on the interpretation of the relationships obtained. The approach presented in this study can be widely applied at the model calibration stage and for appropriate selection of hydrographs for identification and validation of model parameters. The results of the calculations obtained in this study indicate the suitability of including the origin of rainfall in the sensitivity analysis and calibration of hydrodynamic models, which results from the different sensitivities of models for normal, heavy, and torrential rain types. In this context, it is necessary to first divide the rainfall data by origin, for which analyses will be performed, including sensitivity analysis and calibration. Considering the obtained results of the calculations, at the stage of identifying the parameters of hydrodynamic models and their validation, precipitation conditions should be included because, for the precipitation caused by heavy rainfall, the values of the sensitivity coefficients were much lower than for torrential ones. Taking into account the values of the sensitivity coefficients obtained, the calibration of the models should not only cover episodes with high rainfall intensity, since this may lead to calculation errors at the stage of applying the model in practice (assessment of the stormwater system operating conditions, design of reservoirs and flow control devices, green infrastructure, etc.).

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Parameter Dependencies of a Biomechanical Cervical Spine FSU - The Process of Finding Optimal Model Parameters by Sensitivity Analysis

10.5772/intechopen.98211 ◽

2021 ◽

Author(s):

Sabine Bauer ◽

Ivanna Kramer

Keyword(s):

Sensitivity Analysis ◽

Intervertebral Discs ◽

Model Parameters ◽

Biomechanical Behavior ◽

Functional Spinal Unit ◽

Spine Model ◽

Biomechanical Parameters ◽

Multi Body ◽

The Impact

The knowledge about the impact of structure-specific parameters on the biomechanical behavior of a computer model has an essential meaning for the realistic modeling and system improving. Especially the biomechanical parameters of the intervertebral discs, the ligamentous structures and the facet joints are seen in the literature as significant components of a spine model, which define the quality of the model. Therefore, it is important to understand how the variations of input parameters for these components affect the entire model and its individual structures. Sensitivity analysis can be used to gain the required knowledge about the correlation of the input and output variables in a complex spinal model. The present study analyses the influence of the biomechanical parameters of the intervertebral disc using different sensitivity analysis methods to optimize the spine model parameters. The analysis is performed with a multi-body simulation model of the cervical functional spinal unit C6-C7.

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Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4041622 ◽

2019 ◽

Vol 14 (2) ◽

Cited By ~ 1

Author(s):

Souransu Nandi ◽

Tarunraj Singh

Keyword(s):

Sensitivity Analysis ◽

Linear Systems ◽

Global Sensitivity Analysis ◽

Benchmark Problems ◽

Model Parameters ◽

Parameter Uncertainties ◽

Sobol Indices ◽

Global Sensitivity ◽

Time Invariant ◽

The Impact

The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.

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A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-2567-2011 ◽

2011 ◽

Vol 11 (9) ◽

pp. 2567-2582 ◽

Cited By ~ 41

Author(s):

H. Roux ◽

D. Labat ◽

P.-A. Garambois ◽

M.-M. Maubourguet ◽

J. Chorda ◽

...

Keyword(s):

Sensitivity Analysis ◽

Soil Properties ◽

Overland Flow ◽

Hydrological Model ◽

Extreme Event ◽

Model Parameters ◽

Peak Time ◽

Efficiency Coefficient ◽

Soil Saturation ◽

The Impact

Abstract. A spatially distributed hydrological model, dedicated to flood simulation, is developed on the basis of physical process representation (infiltration, overland flow, channel routing). Estimation of model parameters requires data concerning topography, soil properties, vegetation and land use. Four parameters are calibrated for the entire catchment using one flood event. Model sensitivity to individual parameters is assessed using Monte-Carlo simulations. Results of this sensitivity analysis with a criterion based on the Nash efficiency coefficient and the error of peak time and runoff are used to calibrate the model. This procedure is tested on the Gardon d'Anduze catchment, located in the Mediterranean zone of southern France. A first validation is conducted using three flood events with different hydrometeorological characteristics. This sensitivity analysis along with validation tests illustrates the predictive capability of the model and points out the possible improvements on the model's structure and parameterization for flash flood forecasting, especially in ungauged basins. Concerning the model structure, results show that water transfer through the subsurface zone also contributes to the hydrograph response to an extreme event, especially during the recession period. Maps of soil saturation emphasize the impact of rainfall and soil properties variability on these dynamics. Adding a subsurface flow component in the simulation also greatly impacts the spatial distribution of soil saturation and shows the importance of the drainage network. Measures of such distributed variables would help discriminating between different possible model structures.

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Sensitivity Analysis and Parameter Optimization of a Hydrodynamic Model for the Aeration Chamber in a Water-Lifting Aerator

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.663.888 ◽

2013 ◽

Vol 663 ◽

pp. 888-893

Author(s):

Xin Sun ◽

Fei Fei Duan ◽

Ting Lin Huang

Keyword(s):

Sensitivity Analysis ◽

Hydrodynamic Model ◽

Flow Resistance ◽

Water Velocity ◽

Force Balance ◽

Model Parameters ◽

Total Force ◽

Two Phase ◽

Optimized Model ◽

Resistance Coefficients

A one-dimensional hydrodynamic model for the aeration chamber in the water-lifting aerator was developed on the basis of the total force balance between the driving force and flow resistance acting on the gas-liquid two-phase flow. Water velocity in the aeration chamber was predicted under different combinations of flow resistance coefficients at the top (KT) and the entrance and exit (KE), the results of sensitivity analysis showed that both KT and KE have significant effect on the predicted water velocity and gas holdup in the riser of the aeration chamber. Taking the water velocity as the main objective, flow resistance coefficients of KT and KE were optimized as and , where Ulr is the superficial water velocity. Using optimized model parameters, the water velocities were well predicted within ±7% of the measured ones respectively.

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Sensitivity analysis of an integrated numerical flow model output to key parameters used in common qualitative vulnerability assessment methods

10.5194/egusphere-egu2020-10512 ◽

2020 ◽

Author(s):

Joanna Doummar ◽

Assaad H. Kassem

Keyword(s):

Sensitivity Analysis ◽

Vulnerability Assessment ◽

Groundwater Vulnerability ◽

Assessment Methods ◽

Qualitative Assessment ◽

Model Parameters ◽

Karst Aquifers ◽

Model Output ◽

Local Sensitivity ◽

The Impact

Qualitative vulnerability assessment methods applied in karst aquifers rely on key factors in the hydrological compartments usually assigned different weights according to their estimated impact on groundwater vulnerability. Based on an integrated numerical groundwater model on a snow-governed karst catchment area (Assal Spring- Lebanon), the aim of this work is to quantify the importance of the most influential parameters on recharge and spring discharge and outline potential parameters that are not accounted for in standard methods, when in fact they do play a role in the intrinsic vulnerability of a system. The assessment of the model sensitivity and the ranking of parameters are conducted using an automatic calibration tool for local sensitivity analysis in addition to a variance-based local sensitivity assessment of model output time series (recharge and discharge) &#160;for two consecutive years (2016-2017) to various model parameters. The impact of each parameter was normalized to estimate standardized weights for each of the process based key-controlling parameters. Parameters to which model was sensitive were factors related to soil, 2) fast infiltration (bypass function) typical of karst aquifers, 3) climatic parameters (melting temperature and degree day coefficient) and 4) aquifer hydraulic properties that play a major role in groundwater vulnerability inducing a temporal effect and varied recession. Other less important parameters play different roles according to different assigned weights proportional to their ranking. Additionally, the effect of slope/geomorphology (e.g., dolines) was further investigated. &#160;In general, this study shows that the weighting coefficients assigned to key vulnerability factors in the qualitative assessment methods can be reevaluated based on this process-based approach.&#160;&#160;&#160;

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Sensitivity Analysis of a Physiological Model for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD): Assessing the Impact of Specific Model Parameters on Sequestration in Liver and Fat in the Rat

Toxicological Sciences ◽

10.1093/toxsci/54.1.71 ◽

2000 ◽

Vol 54 (1) ◽

pp. 71-80 ◽

Cited By ~ 61

Author(s):

M. V. Evans

Keyword(s):

Sensitivity Analysis ◽

Physiological Model ◽

Specific Model ◽

Model Parameters ◽

Tetrachlorodibenzo P Dioxin ◽

The Impact

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Assessing water resources in China using PRECIS projections and a VIC model

Hydrology and Earth System Sciences ◽

10.5194/hess-16-231-2012 ◽

2012 ◽

Vol 16 (1) ◽

pp. 231-240 ◽

Cited By ~ 83

Author(s):

G. Q. Wang ◽

J. Y. Zhang ◽

J. L. Jin ◽

T. C. Pagano ◽

R. Calow ◽

...

Keyword(s):

Climate Change ◽

Water Resources ◽

Temporal Distribution ◽

Annual Runoff ◽

Model Parameters ◽

Infiltration Capacity ◽

Climate Conditions ◽

Vic Model ◽

Sustainable Water Resources Management ◽

The Impact

Abstract. Climate change is now a major environmental and developmental issue, and one that will increase the challenge of sustainable water resources management. In order to assess the implications of climate change for water resources in China, we calibrated a Variable Infiltration Capacity (VIC) model with a resolution of 50×50 km2 using data from 125 well-gauged catchments. Based on similarities in climate conditions, soil texture and other variables, model parameters were transferred to other areas not covered by the calibrated catchments. Taking runoff in the period 1961–1990 as a baseline, we studied the impact of climate change on runoff under three emissions scenarios, A2, B2 and A1B. Model findings indicate that annual runoff over China as a whole will probably increase by approximately 3–10% by 2050, but with quite uneven spatial and temporal distribution. The prevailing pattern of "north dry and south wet" in China is likely to be exacerbated under global warming.

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Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

10.32920/ryerson.14643702.v1 ◽

2021 ◽

Author(s):

Harry R. Manson

Keyword(s):

Sensitivity Analysis ◽

Curve Number ◽

Monte Carlo Analysis ◽

Model Parameters ◽

Rainfall Runoff ◽

Lumped Model ◽

Daily Average ◽

Rainfall Runoff Model ◽

The Impact ◽

Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

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Modelling gastrointestinal parasitism infection in a sheep flock over two reproductive seasons:in silicoexploration and sensitivity analysis

Parasitology ◽

10.1017/s0031182016000871 ◽

2016 ◽

Vol 143 (12) ◽

pp. 1509-1531 ◽

Cited By ~ 2

Author(s):

M. SACCAREAU ◽

C. R. MORENO ◽

I. KYRIAZAKIS ◽

R. FAIVRE ◽

S. C. BISHOP

Keyword(s):

Sensitivity Analysis ◽

Control Strategies ◽

Real Data ◽

Published Data ◽

Model Parameters ◽

Sheep Flock ◽

Source Of Infection ◽

Teladorsagia Circumcincta ◽

Host Nutrition ◽

The Impact

SUMMARYIn reproducing ewes, a periparturient breakdown of immunity is often observed to result in increased fecal egg excretion, making them the main source of infection for their immunologically naive lambs. In this study, we expanded a simulation model previously developed for growing lambs to explore the impact of the genotype (performance and resistance traits) and host nutrition on the performance and parasitism of both growing lambs and reproducing ewes naturally infected withTeladorsagia circumcincta. Our model accounted for nutrient-demanding phases, such as gestation and lactation, and included a supplementary module to manage the age structure of the ewe flock. The model was validated by comparison with published data. Because model parameters were unknown or poorly estimated, detailed sensitivity analysis of the model was performed for the sheep mortality and the level of infection, following a preliminary screening step. The parameters with the greatest effect on parasite-related outputs were those driving animal growth and milk yield. Our model enables different parasite-control strategies (host nutrition, breeding for resistance and anthelmintic treatments) to be assessed on the long term in a sheep flock. To optimizein silicoexploration, the parameters highlighted by the sensitivity analysis should be refined with real data.

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