A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategy Article Swipe
Ruochen Sun
,
Felipe Hernández
,
Xu Liang
,
Huiling Yuan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.02390
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.02390
Increasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with highest resolution in parameter search space, while the least sensitive ones are explored with the coarsest resolution at beginning. This is followed by an optimization based iterative calibration procedure consisting of a series of sub-tasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each sub-task, the parameter ranges within each group are systematically refined from their previously estimated ranges which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization-based approach was performed using a quasi-synthetic double-model setup experiment to calibrate 134 parameters and two well-known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient.
Related Topics
Concepts
Equifinality
Calibration
Computer science
Divide and conquer algorithms
A priori and a posteriori
Algorithm
Sensitivity (control systems)
Parameter space
Residual
Resolution (logic)
Estimation theory
Mathematical optimization
Data mining
Mathematics
Artificial intelligence
Statistics
Engineering
Electronic engineering
Philosophy
Epistemology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.02390
- https://arxiv.org/pdf/2004.02390
- OA Status
- green
- Cited By
- 6
- References
- 94
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3014254547
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3014254547Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2004.02390Digital Object Identifier
- Title
-
A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-04-06Full publication date if available
- Authors
-
Ruochen Sun, Felipe Hernández, Xu Liang, Huiling YuanList of authors in order
- Landing page
-
https://arxiv.org/abs/2004.02390Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2004.02390Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2004.02390Direct OA link when available
- Concepts
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Equifinality, Calibration, Computer science, Divide and conquer algorithms, A priori and a posteriori, Algorithm, Sensitivity (control systems), Parameter space, Residual, Resolution (logic), Estimation theory, Mathematical optimization, Data mining, Mathematics, Artificial intelligence, Statistics, Engineering, Electronic engineering, Philosophy, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2022: 1, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
94Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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