Parameter sensitivity of automated baseflow separation for snowmelt‐dominated watersheds and new filtering procedure for determining end of snowmelt period

2018 ◽  
Author(s):  
Denitza D. Voutchkova ◽  
Scott N. Miller ◽  
Kenneth G. Gerow
Keyword(s):  
Parameter Sensitivity ◽  
Baseflow Separation ◽  
Snowmelt Period ◽  
Filtering Procedure

Evaluating a Sewershed Urban Storm Water Model for Variability in Parameter Sensitivity and Resolution Effects

2020 ◽  
Author(s):  
Zhaokai Dong ◽  
Daniel Bain ◽  
Murat Akcakaya ◽  
Carla Ng
Keyword(s):  
Pipe Flow ◽  
Peak Flow ◽  
Model Performance ◽  
Parameter Sensitivity ◽  
Geometric Parameters ◽  
Storm Water ◽  
Parameter Estimates ◽  
Kappa Statistics ◽  
Thiessen Polygon ◽  
Urban Storm

A high-quality parameter set is essential for reliable stormwater models. Model performance can be improved by optimizing initial parameter estimates. Parameter sensitivity analysis is a robust way to distinguish the influence of parameters on model output and efficiently target the most important parameters to modify. This study evaluates efficient construction of a sewershed model using relatively low-resolution (e.g., 30 meter DEM) data and explores model sensitivity to parameters and regional characteristics using the EPA’s Storm Water Management Model (SWMM). A SWMM model was developed for a sewershed in the City of Pittsburgh, where stormwater management is a critical concern. We assumed uniform or log-normal distributions for parameters and used Monte Carlo simulations to explore and rank the influence of parameters on predicted surface runoff, peak flow, maximum pipe flow and model performance, as measured using the Nash–Sutcliffe efficiency metric. By using the Thiessen polygon approach for sub-catchment delineations, we substantially simplified the parameterization of the areas and hydraulic parameters. Despite this simplification, our approach provided good agreement with monitored pipe flow (Nash–Sutcliffe efficiency: 0.41 – 0.85). Total runoff and peak flow were very sensitive to the model discretization. The size of the polygons (modeled subcatchment areas) and imperviousness had the most influence on both outputs. The imperviousness, infiltration and Manning’s roughness (in the pervious area) contributed strongly to the Nash-Sutcliffe efficiency (70%), as did pipe geometric parameters (92%). Parameter rank sets were compared by using kappa statistics between any two model elements to identify generalities. Within our relatively large (9.7 km^2) sewershed, optimizing parameters for the highly impervious (>50%) areas and larger pipes lower in the network contributed most to improving Nash–Sutcliffe efficiency. The geometric parameters influence the water quantity distribution and flow conveyance, while imperviousness determines the subcatchment subdivision and influences surface water generation. Application of the Thiessen polygon approach can simplify the construction of large-scale urban storm water models, but the model is sensitive to the sewer network configuration and care must be taken in parameterizing areas (polygons) with heterogenous land uses.

Field Testing Parameter Sensitivity of the Two-Term Infiltration Equation Using Differentiated Linearization

2003 ◽  
Vol 2 (3) ◽  
pp. 358-367 ◽  
Author(s):  
Vincenzo Bagarello ◽  
Massimo Iovino
Keyword(s):  
Field Testing ◽  
Parameter Sensitivity ◽  
Testing Parameter

Local and global parameter sensitivity within an ecophysiologically based forest landscape model

2019 ◽  
Vol 117 ◽  
pp. 1-13 ◽  
Author(s):  
Patrick F. McKenzie ◽  
Matthew J. Duveneck ◽  
Luca L. Morreale ◽  
Jonathan R. Thompson
Keyword(s):  
Parameter Sensitivity ◽  
Forest Landscape ◽  
Landscape Model ◽  
Global Parameter ◽  
Forest Landscape Model

Investigation into steering wheel nibble

2002 ◽  
Vol 216 (4) ◽  
pp. 267-277 ◽  
Author(s):  
U Neureder
Keyword(s):  
Design Of Experiments ◽  
Natural Frequency ◽  
Dry Friction ◽  
Parameter Sensitivity ◽  
Steering Wheel ◽  
New Model ◽  
The Past ◽  
Force Variation

Many studies of mechanisms contributing to steering wheel nibble have been carried out in the past. This paper deals with some aspects that have not yet been studied, or those that have been presented by several authors but are deemed to be controversial. Firstly, an overview of stimulation sources (disturbance factors), and the significance these have with respect to steering nibble, is given. As an example of the controversial aspects of the problem, this paper deals with the assumption of dry friction in steering gear models and its conflict with the observed transfer of vibration caused by small (realistic) amounts of imbalance or tyre force variation. After modelling the steering gear resistance correctly, it is possible to identify, in the steering gear, a natural frequency that contributes reasonably to the nibble phenomenon. Based on this new model, a CAE study on parameter sensitivity, using the ‘design of experiments’ approach, is presented.

scholarly journals Calculation of stability reliability of soft base controlled unstable rock and parameter sensitivity analysis

2021 ◽  
Vol 768 (1) ◽  
pp. 012096
Author(s):  
Lei Wang ◽  
Wei Wang ◽  
Yu-na Qi ◽  
Zhan-dong Su ◽  
Chao Shen
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Unstable Rock

Flux Observer-Based MTPF/MTPV-Operation with Low Parameter Sensitivity Applying Deadbeat-Direct Torque and Flux Control

2021 ◽  
pp. 1-1
Author(s):  
Hadi El Khatib ◽  
Mario Pena ◽  
Benjamin Grothmann ◽  
Emebet Gebeyehu Gedlu ◽  
Michael Saur
Keyword(s):  
Parameter Sensitivity ◽  
Flux Control ◽  
Flux Observer
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