An Adaptive Process-Wise Fitting Approach for Hydrological Modeling Based on Streamflow and Remote Sensing Evapotranspiration
(2024) In Water (Switzerland) 16(23).- Abstract
Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework... (More)
Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter’s kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology.
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- author
- Wang, Chen ; Mao, Huihui ; Nemoto, Tatsuya ; He, Yan ; Hu, Jinghao ; Li, Runkui ; Wu, Qian ; Wang, Mingyu ; Song, Xianfeng and Duan, Zheng LU
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- directed graph, learning rate, multivariate calibration, particle swarm optimization, triple collocation, uncertainties
- in
- Water (Switzerland)
- volume
- 16
- issue
- 23
- article number
- 3446
- publisher
- MDPI AG
- external identifiers
-
- scopus:85211920505
- ISSN
- 2073-4441
- DOI
- 10.3390/w16233446
- language
- English
- LU publication?
- yes
- id
- 06ca0d53-e77d-4832-aef6-3167bb644838
- date added to LUP
- 2025-01-22 10:48:43
- date last changed
- 2025-04-04 14:22:22
@article{06ca0d53-e77d-4832-aef6-3167bb644838,
abstract = {{<p>Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter’s kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology.</p>}},
author = {{Wang, Chen and Mao, Huihui and Nemoto, Tatsuya and He, Yan and Hu, Jinghao and Li, Runkui and Wu, Qian and Wang, Mingyu and Song, Xianfeng and Duan, Zheng}},
issn = {{2073-4441}},
keywords = {{directed graph; learning rate; multivariate calibration; particle swarm optimization; triple collocation; uncertainties}},
language = {{eng}},
number = {{23}},
publisher = {{MDPI AG}},
series = {{Water (Switzerland)}},
title = {{An Adaptive Process-Wise Fitting Approach for Hydrological Modeling Based on Streamflow and Remote Sensing Evapotranspiration}},
url = {{http://dx.doi.org/10.3390/w16233446}},
doi = {{10.3390/w16233446}},
volume = {{16}},
year = {{2024}},
}