scholarly journals Parameter estimation and uncertainty analysis in hydrological modeling

2021 ◽  
Author(s):  
Paulo A. Herrera ◽  
Miguel Angel Marazuela ◽  
Thilo Hofmann
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Hydrological Modeling

An integrated mechanistic modeling of a facultative pond: Parameter estimation and uncertainty analysis

2019 ◽  
Vol 151 ◽  
pp. 170-182 ◽  
Author(s):  
Long T. Ho ◽  
Andres Alvarado ◽  
Josue Larriva ◽  
Cassia Pompeu ◽  
Peter Goethals
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Mechanistic Modeling

scholarly journals Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 171 ◽  
Author(s):  
Hui Xie ◽  
Zhenyao Shen ◽  
Lei Chen ◽  
Xijun Lai ◽  
Jiali Qiu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Continuous Model ◽  
Base Flow ◽  
Moisture Distribution ◽  
Simulation Program ◽  
Runoff Generation ◽  
Hydrological Simulation ◽  
Hspf Model ◽  
Event Based

Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented based on the Hydrological Simulation Program–Fortran (HSPF) model. Calibrated parameters related to base flow and moisture distribution showed marked differences between the continuous and event-based modeling. Results of the regionalized sensitivity analysis identified event-dependent parameters and showed that gravity drainage and storage outflow were the primary runoff generation processes for both scenarios. The overall performance of the event-based simulation was better than that of the daily simulation for streamflow based on the generalized likelihood uncertainty estimation (GLUE). The GLUE analysis also indicated that the performance of the continuous model was limited by several extreme events and low flows. In the event-based scenario, the HSPF model performances decreased as the precipitation became intense in the event-based modeling. The structure error of the HSFP model was recognized at the initial phase of the rainfall-event period. This study presents a valuable opportunity to understand dominant controls in different hydrologic scenario and guide the application of the HSPF model.

scholarly journals Dynamic Modeling, Parameter Estimation, and Uncertainty Analysis in R

2019 ◽  
Vol 88 (10) ◽  
Author(s):  
Daniel Kaschek ◽  
Wolfgang Mader ◽  
Mirjam Fehling-Kaschek ◽  
Marcus Rosenblatt ◽  
Jens Timmer
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Dynamic Modeling

Comparative uncertainty analysis of copper loads in stormwater systems using GLUE and grey-box modeling

2007 ◽  
Vol 56 (6) ◽  
pp. 11-18 ◽  
Author(s):  
E. Lindblom ◽  
H. Madsen ◽  
P.S. Mikkelsen
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Conceptual Model ◽  
Input Data ◽  
Proposed Model ◽  
Model Predictions ◽  
Pros And Cons ◽  
Future Work ◽  
Stormwater Pollution ◽  
Made In

In this paper two attempts to assess the uncertainty involved with model predictions of copper loads from stormwater systems are made. In the first attempt, the GLUE methodology is applied to derive model parameter sets that result in model outputs encompassing a significant number of the measurements. In the second attempt the conceptual model is reformulated to a grey-box model followed by parameter estimation. Given data from an extensive measurement campaign, the two methods suggest that the output of the stormwater pollution model is associated with significant uncertainty. With the proposed model and input data, the GLUE analysis show that the total sampled copper mass can be predicted within a range of ±50% of the median value (385 g), whereas the grey-box analysis showed a prediction uncertainty of less than ±30%. Future work will clarify the pros and cons of the two methods and furthermore explore to what extent the estimation can be improved by modifying the underlying accumulation-washout model.

Parameter Estimation and Uncertainty Analysis of ORYZA_V3 Model Using the GLUE Method

2019 ◽  
Vol 62 (4) ◽  
pp. 941-949
Author(s):  
Junwei Tan ◽  
Qingyun Duan
Keyword(s):  
Parameter Estimation ◽  
Uncertainty Analysis ◽  
Field Experiments ◽  
Likelihood Function ◽  
Latin Hypercube Sampling ◽  
Model Errors ◽  
Uncertainty Estimates ◽  
Model Predictions ◽  
Glue Method ◽  
Likelihood Measures

Abstract. The Generalized Likelihood Uncertainty Estimation (GLUE) method is one of the popular methods for parameter estimation and uncertainty analysis, although it has been criticized for some drawbacks in numerous studies. In this study, we performed an uncertainty analysis for the ORYZA_V3 model using the GLUE method integrated with Latin hypercube sampling (LHS). Different likelihood measures were examined to understand the differences in derived posterior parameter distributions and uncertainty estimates of the model predictions based on a variety of observations from field experiments. The results indicated that the parameter posterior distributions and 95% confidence intervals (95CI) of model outputs were very sensitive to the choice of likelihood measure, as well as the weights assigned to observations at different dates and to different observation types within a likelihood measure. Performance of the likelihood measure with a proper likelihood function based on normal distribution of model errors and the combining method based on mathematical multiplication was the best, with respect to the effectiveness of reducing the uncertainties of parameter values and model predictions. Moreover, only the means and standard deviations of observation replicates were enough to construct an effective likelihood function in the GLUE method. This study highlighted the importance of using appropriate likelihood measures integrated with multiple observation types in the GLUE method. Keywords: GLUE, Likelihood measures, Model uncertainty, Crop model.

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