scholarly journals Generalized Likelihood Uncertainty Estimation Method in Uncertainty Analysis of Numerical Eutrophication Models: Take BLOOM as an Example

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Zhijie Li ◽  
Qiuwen Chen ◽  
Qiang Xu ◽  
Koen Blanckaert
Keyword(s):  
Uncertainty Analysis ◽  
Coastal Waters ◽  
Algal Bloom ◽  
Numerical Models ◽  
Estimation Method ◽  
Uncertainty Estimation ◽  
Production Model ◽  
3 Dimensional ◽  
Complex Processes ◽  
Generalized Likelihood Uncertainty Estimation

Uncertainty analysis is of great importance to assess and quantify a model's reliability, which can improve decision making based on model results. Eutrophication and algal bloom are nowadays serious problems occurring on a worldwide scale. Numerical models offer an effective way to algal bloom prediction and management. Due to the complex processes of aquatic ecosystem, such numerical models usually contain a large number of parameters, which may lead to important uncertainty in the model results. This research investigates the applicability of generalized likelihood uncertainty estimation (GLUE) to analyze the uncertainty of numerical eutrophication models that have a large number of intercorrelated parameters. The 3-dimensional primary production model BLOOM, which has been broadly used in algal bloom simulations for both fresh and coastal waters, is used.

scholarly journals Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 447 ◽  
Author(s):  
Huidae Cho ◽  
Jeongha Park ◽  
Dongkyun Kim
Keyword(s):  
Uncertainty Analysis ◽  
Estimation Method ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Calibration Data ◽  
Behavioral Models ◽  
Large Parameter ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Hybrid Uncertainty ◽  
Likelihood Measures

We tested four likelihood measures including two limits of acceptability and two absolute model residual methods within the generalized likelihood uncertainty estimation (GLUE) framework using the topography model (TOPMODEL). All these methods take the worst performance of all time steps as the likelihood of a model and none of these methods were successful in finding any behavioral models. We believe that reporting this failure is important because it shifted our attention from which likelihood measure to choose to why these four methods failed and how to improve these methods. We also observed how large parameter samples impact the performance of a hybrid uncertainty estimation method, isolated-speciation-based particle swarm optimization (ISPSO)-GLUE using the Nash–Sutcliffe (NS) coefficient. Unlike GLUE with random sampling, ISPSO-GLUE provides traditional calibrated parameters as well as uncertainty analysis, so over-conditioning the model parameters on the calibration data can affect its uncertainty analysis results. ISPSO-GLUE showed similar performance to GLUE with a lot less model runs, but its uncertainty bounds enclosed less observed flows. However, both methods failed in validation. These findings suggest that ISPSO-GLUE can be affected by over-calibration after a long evolution of samples and imply that there is a need for a likelihood measure that can better explain uncertainties from different sources without making statistical assumptions.

scholarly journals Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches

2018 ◽  
Vol 22 (9) ◽  
pp. 5021-5039 ◽  
Author(s):  
Aynom T. Teweldebrhan ◽  
John F. Burkhart ◽  
Thomas V. Schuler
Keyword(s):  
Uncertainty Analysis ◽  
Parameter Uncertainty ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Behavioral Model ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Observation Error ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Parameter Uncertainty Analysis

Abstract. Parameter uncertainty estimation is one of the major challenges in hydrological modeling. Here we present parameter uncertainty analysis of a recently released distributed conceptual hydrological model applied in the Nea catchment, Norway. Two variants of the generalized likelihood uncertainty estimation (GLUE) methodologies, one based on the residuals and the other on the limits of acceptability, were employed. Streamflow and remote sensing snow cover data were used in conditioning model parameters and in model validation. When using the GLUE limit of acceptability (GLUE LOA) approach, a streamflow observation error of 25 % was assumed. Neither the original limits nor relaxing the limits up to a physically meaningful value yielded a behavioral model capable of predicting streamflow within the limits in 100 % of the observations. As an alternative to relaxing the limits, the requirement for the percentage of model predictions falling within the original limits was relaxed. An empirical approach was introduced to define the degree of relaxation. The result shows that snow- and water-balance-related parameters induce relatively higher streamflow uncertainty than catchment response parameters. Comparable results were obtained from behavioral models selected using the two GLUE methodologies.

Sign in / Sign up

Export Citation Format

Share Document