Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model

Srinivasan, Raghavan; Zhao, Kaiguang; Liew, Mike Van

doi:10.1002/hyp.7152

Cited by 143 publications

(101 citation statements)

References 54 publications

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“…GAs have been used to minimize errors in searching optimized model parameters based on inversion model [Reed et al, 2000;Mohanty, 2008, 2009;Zhang et al, 2009;Shin et al, 2012;. Zhang et al [2008] integrated several global optimization algorithms (i.e., GA, SCE-UA, PSO, etc.) with Soil and Water Assessment Tool and compared their performances in calibrating model input parameters.…”

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confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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Various hydrological models have been developed for estimating root zone soil moisture dynamics. These models, however, incorporated their own parameterization approaches indicating that the output from the different model inherent structures might include uncertainties because we do not know which model structure is correct for describing the real system. More recently, multimodel approaches using a Bayesian Model Averaging (BMA) scheme can improve the overall predictive skill while individual models retain their own uncertainties for simulated soil moisture based on a single set of weights in modeling under different land surface wetness conditions (e.g., wet, moderately wet, and dry conditions). In order to overcome their limitations, we developed a BMA-based multimodel simulation approach based on various soil wetness conditions for estimating effective surface soil moisture dynamics (0-5 cm) and quantifying uncertainties efficiently based on the land surface wetness conditions. The newly developed approach adapts three different hydrological models (i.e., Noah Land Surface Model, Noah LSM; Soil-Water-Atmosphere-Plant, SWAP; and Community Land Model, CLM) for simulating soil moisture. These models were integrated with a modified-microGA (advanced version of original Genetic Algorithm (GA)) to search for optimized soil parameters for each model. Soil moisture was simulated from the estimated soil parameters using the hydrological models in a forward mode. It was found that SWAP performed better than others during wet condition, while Noah LSM and CLM showed a good agreement with measurements during dry condition. Thus, we inferred that performance of individual models with different model structures can be different with land surface wetness. Taking into account the effects of soil wetness on different model performances, we categorized soil moisture measurements and estimated different weights for each category using the BMA scheme. Effective surface soil moisture dynamics were obtained by aggregating multiple weighted soil moisture. Our findings demonstrated that the effective soil moisture estimates derived by this approach showed a better match with the measurements compared to the original models and single-weighted outputs. Multimodel simulation approach based on land surface wetness enhances the ability to predict reliable soil moisture dynamics and reflects the strengths of different hydrological models under various soil wetness conditions.

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confidence: 99%

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

2015

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“…Zhang et al (2009) found GA to perform well compared to other optimization algorithms in the calibration of the SWAT model. Multi-objective GA have been used in the calibration of this model (Bekele and Nicklow, 2007;Whittaker et al, 2007) as well.…”

Section: Optimization Methodologymentioning

confidence: 96%

Calibration of a crop model to irrigated water use using a genetic algorithm

Bulatewicz

Jin

Staggenborg

et al. 2009

Hydrol. Earth Syst. Sci.

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Abstract. Near-term consumption of groundwater for irrigated agriculture in the High Plains Aquifer supports a dynamic bio-socio-economic system, all parts of which will be impacted by a future transition to sustainable usage that matches natural recharge rates. Plants are the foundation of this system and so generic plant models suitable for coupling to representations of other component processes (hydrologic, economic, etc.) are key elements of needed stakeholder decision support systems. This study explores utilization of the Environmental Policy Integrated Climate (EPIC) model to serve in this role. Calibration required many facilities of a fully deployed decision support system: geo-referenced databases of crop (corn, sorghum, alfalfa, and soybean), soil, weather, and water-use data (4931 well-years), interfacing heterogeneous software components, and massively parallel processing (3.8×10 9 model runs). Bootstrap probability distributions for ten model parameters were obtained for each crop by entropy maximization via the genetic algorithm. The relative errors in yield and water estimates based on the parameters are analyzed by crop, the level of aggregation (county-or well-level), and the degree of independence between the data set used for estimation and the data being predicted.

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“…The validation has both low P-factor and low R-factor, though its NSE and r 2 are high, which means the uncertainty exists in the result of the validation and the outcomes of the uncertainty and the accuracy are not consistent. The longer the period is, the more the inconsistency of the hydrological process increases, which makes calibration and validation more difficult [54].…”

Section: Source Of Errormentioning

confidence: 99%

Impact of Land Use on Frequency of Floods in Yongding River Basin, China

Zhang

Zhao

Wang

et al. 2016

Water

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Abstract:As the debates surrounding the negative influences of flood control using dams or reservoirs on the eco-environment become fierce, non-structural flood control measures like land use change gain more attention. This study researched the effect of integrated and single land use changes on three floods at small, medium and large scales, respectively, in Yongding River basin. A SWAT (Soil and Water Assessment Tool) model was used to simulate the effect of integrated and single land use changes on floods of different scales. The single land uses were set as S1, S2, S3 to represent the agricultural, grass and construction land changes. The results showed that: (1) the integrated land use changes reduced the small flood, the medium flood and the large flood by 14%, 13% and 5%; (2) the land use management functioned most effectively on medium-scale floods and least effectively on large-scale floods; (3) S1 decreased the medium floods optimally by 24% with a 7-day maximum runoff volume as the indicator and by 29% with a 1-day maximum flood discharge; (4) S2 reduced the medium floods optimally by 21% with runoff depth volume as the indicator; (5) S3 increased the medium floods optimally by 15% with a 1-day maximum flood discharge as the indicator.

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Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model

Cited by 143 publications

References 54 publications

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Calibration of a crop model to irrigated water use using a genetic algorithm

Impact of Land Use on Frequency of Floods in Yongding River Basin, China

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