Comparing single and multi-objective hydrologic model calibration considering reservoir inflow and streamflow observations

Bomhof, James; Tolson, Bryan A.; Kouwen, N.

doi:10.1080/07011784.2019.1623077

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…Process-driven models have also been adopted for hydrological purposes in the ARB, mainly for long-term river flow forecasting. Toth et al [29] investigated the annual variability of the Athabasca River using WATFLOOD, a widely used physical-based hydrological model [30][31][32][33][34]. Historical river flow records along with topography information, rainfall, and temperature were employed to forecast flow regimes at Fort McMurray.…”

Section: Graphical Presentation Of the Modelled Outputs Using Daily Average Flowmentioning

confidence: 99%

Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Belvederesi

Dominic

Hassan

et al. 2020

Water

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Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (ENS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

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Section: Graphical Presentation Of the Modelled Outputs Using Daily Average Flowmentioning

confidence: 99%

Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Belvederesi

Dominic

Hassan

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…Among the studies of hydrological uncertainty, the uncertainty of the model parameter is an important aspect [11][12][13][14]. In traditional hydrological forecasting studies, the model should be localized by parameter calibration [15][16][17][18]. However, many studies have found that it is difficult to find a certain set of parameters that can yield model performances much better than other parameter sets.…”

Section: Introductionmentioning

confidence: 99%

Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface

Yin

Liao

Lei

et al. 2020

Water

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Parameter uncertainty analysis is one of the hot issues in hydrology studies, and the Generalized Likelihood Uncertainty Estimation (GLUE) is one of the most widely used methods. However, the scale of the existing research is relatively small, which results from computational complexity and limited computing resources. In this study, a parallel GLUE method based on a Message-Passing Interface (MPI) was proposed and implemented on a supercomputer system. The research focused on the computational efficiency of the parallel algorithm and the parameter uncertainty of the Xinanjiang model affected by different threshold likelihood function values and sampling sizes. The results demonstrated that the parallel GLUE method showed high computational efficiency and scalability. Through the large-scale parameter uncertainty analysis, it was found that within an interval of less than 0.1%, the proportion of behavioral parameter sets and the threshold value had an exponential relationship. A large sampling scale is more likely than a small sampling scale to obtain behavioral parameter sets at high threshold values. High threshold values may derive more concentrated posterior distributions of the sensitivity parameters than low threshold values.

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A Survey of PSO Contributions to Water and Environmental Sciences

Ferdowsi

Mousavi

Hoseini

et al. 2022

Computational Intelligence for Water and Environmental Sciences

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Comparing single and multi-objective hydrologic model calibration considering reservoir inflow and streamflow observations

Cited by 10 publications

References 44 publications

Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface

A Survey of PSO Contributions to Water and Environmental Sciences

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