Inter-Comparison of Different Bayesian Model Averaging Modifications in Streamflow Simulation

Darbandsari, Pedram; Coulibaly, Paulin

doi:10.3390/w11081707

Cited by 26 publications

(15 citation statements)

References 62 publications

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“…5]. For all model groups and both stations, this leads to large improvements in performance, highlighting the strength of using model ensembles rather than individual models, as reported by others (Duan et al 2007;Muhammad et al 2018;Darbandsari and Coulibaly 2019).…”

Section: Analysis Of Simulated Streamflow Ensemblessupporting

confidence: 54%

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

et al. 2021

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Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.

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Section: Analysis Of Simulated Streamflow Ensemblessupporting

confidence: 54%

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

et al. 2021

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“…Of the many possible ensemble methods, the method we apply here is Bayesian model averaging (BMA), which determines static weights for each model using the posterior probability [3,[7][8][9][10]. Other weighting methods such as reliability ensemble averaging [1] and combined performance-independence weighting [6,11] are similarly applicable to the proposed method in this study, though they were not actually considered here.…”

Section: Introductionmentioning

confidence: 99%

A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation

2020

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A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.

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“…The WMA is considered to be more informed by the calibration results of the fixed model structures than the naive average performed to obtain the SMA. These models represent typical multi‐model approaches in literature (e.g., Darbandsari & Coulibaly, 2019; Diks & Vrugt, 2010; Duan, Ajami, et al., 2007; Lane et al., 2019; Muhammad et al., 2018; Seiller et al., 2017; Velázquez et al., 2010).…”

Section: Methodsmentioning

confidence: 99%

Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model

Chlumsky

Mai

Craig

et al. 2021

Water Resources Research

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Hydrologic models have been used for numerous applications in the last number of decades. These applications include streamflow prediction, flood forecasting, or reservoir level forecasting, or in a scientific capacity to advance our understanding of hydrologic systems. Whether used in a predictive or scientific capacity, models are an abstraction of the complex natural system being simulated, and necessarily simplify the treatment of hydrological processes occurring in a watershed, either to facilitate computational expediency or in recognition of the large degree of uncertainty regarding how the watershed functions. In most practical cases, model calibration is required in order to reconcile model output with historical observation; conventionally, this calibration process focuses on tuning only model parameters. However, hydrologic models are typically considered to have four main components that contribute to model uncertainty (e.g., Beven, 2005;Butts et al., 2004;Gupta, Clark, et al., 2012): (1) input forcing data, such as precipitation and temperature, (2) model structure, which includes the algorithmic functions used to simulate the hydrologic cycle, (3) model parameters related to the selected hydrologic process algorithms (often the only degree of freedom used to tune model performance), and (4) an error term, which represents any uncertainty or deviation from reality not captured in the other three components of model uncertainty.This study focuses upon the simultaneous calibration of model structure and model parameters. We refer to model structure here as a combination of (a) the number and type of stores represented in the model and (b) the collection of algorithms used to describe the relationships between fluxes and storage in each hydrological response unit (HRU). Model structure can be further extended to other modeling decisions such as spatial discretization, but this extended definition is not evaluated here. Historically, most hydrologic models have been designed with a fixed model structure while the input data and model parameters may vary from watershed to watershed. These fixed model structures were typically chosen because they (1) adequately represented the hydrologic response of one or more watersheds, (2) generally respected the physics of water flow and storage, (3) were consistent with conceptual models of the water cycle garnered from field investigation, and/or (4) convention (Addor & Melsen, 2019). In many cases, trial and error was used to determine final model structure (e.g., Perrin et al., 2003). However, there are many such model structures that can generally meet these criteria, and as such, there exist dozens of fixed structure models which used different algorithms to represent various components of the water cycle. The fixed model structure concept

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Inter-Comparison of Different Bayesian Model Averaging Modifications in Streamflow Simulation

Cited by 26 publications

References 62 publications

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation

Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model

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