Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method

2010 ◽  
Vol 103 (5) ◽  
pp. 256-264 ◽  
Author(s):  
Jianqiang He ◽  
James W. Jones ◽  
Wendy D. Graham ◽  
Michael D. Dukes
Keyword(s):  
Likelihood Function ◽  
Estimation Method ◽  
Crop Model ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Generalized Likelihood Uncertainty Estimation

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 Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 317
Author(s):  
Fadhliani Umar ◽  
Zed Zulkafli ◽  
Badronnisa Yusuf ◽  
Siti Nurhidayu
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Peak Flow ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Model Framework ◽  
Behavioral Threshold ◽  
Continuous Simulation ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Streamflow Simulation

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1—considering the flexibility, modularity, and portability—and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e., the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. Thirty-two years’ continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatter plot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modeling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

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.

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

2018 ◽  
Author(s):  
Aynom T. Tweldebrahn ◽  
John F. Burkhart ◽  
Thomas V. Schuler
Keyword(s):  
Uncertainty Analysis ◽  
Parameter Uncertainty ◽  
Hydrological Model ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Observation Error ◽  
Conditioning Model ◽  
Model Predictions ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Parameter Uncertainty Analysis

Abstract. Parameter uncertainty estimation is one of the major challenges in hydrological modelling. 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 behavioural model capable of predicting streamflow within the limits in 100 % of the observations. As an alternative to relaxing the limits; the requirement for 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 behavioural models selected using the two GLUE methodologies.

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 Assessment of Streamflow Simulation for a Tropical Forested Catchment using Dynamic TOPMODEL – Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

2020 ◽  
Author(s):  
Fadhliani Umar ◽  
Zed Diyana ◽  
Badronnisa Yusuf ◽  
Siti Nurhidayu
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Peak Flow ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Model Framework ◽  
Behavioral Threshold ◽  
Continuous Simulation ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Streamflow Simulation

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1 - considering the flexibility, modularity and portability - and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e. the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. 32 years' continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

Model of the influence of Internet finance on monetary policy based on gibbs sampling and vector autoregression

2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Huang Shiwang
Keyword(s):  
Monetary Policy ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
Estimation Method ◽  
Financial Supervision ◽  
Var Model ◽  
Model Parameters ◽  
State Variables ◽  
Frequency Data ◽  
Internet Finance

The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.

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