
Zheng Duan
Senior lecturer

An Adaptive Process-Wise Fitting Approach for Hydrological Modeling Based on Streamflow and Remote Sensing Evapotranspiration
Author
Summary, in English
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.
Department/s
- Dept of Physical Geography and Ecosystem Science
- BECC: Biodiversity and Ecosystem services in a Changing Climate
- MERGE: ModElling the Regional and Global Earth system
Publishing year
2024
Language
English
Publication/Series
Water (Switzerland)
Volume
16
Issue
23
Document type
Journal article
Publisher
MDPI AG
Topic
- Oceanography, Hydrology, Water Resources
Keywords
- directed graph
- learning rate
- multivariate calibration
- particle swarm optimization
- triple collocation
- uncertainties
Status
Published
ISBN/ISSN/Other
- ISSN: 2073-4441