Stepwise modeling and the importance of internal variables validation to test model realism in a data scarce glacier basin
(2020) In Journal of Hydrology 591.- Abstract
Model realism is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. Here, we employed a stepwise modeling approach that leverages flexible model structures and multi-source observations for robust streamflow simulation and internal variables validation with improved model realism. This framework is demonstrated in Yigong Zangbu River (YZR) basin, a data scarce glacier basin in the upper Brahmaputra River. We designed six experiments (Exp1–6) to use modeling as a tool to understand hydrological processes in this remote cold basin with extremely high altitude. In Exp1, we started with a distributed rainfall-runoff model (FLEXD) - representing... (More)
Model realism is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. Here, we employed a stepwise modeling approach that leverages flexible model structures and multi-source observations for robust streamflow simulation and internal variables validation with improved model realism. This framework is demonstrated in Yigong Zangbu River (YZR) basin, a data scarce glacier basin in the upper Brahmaputra River. We designed six experiments (Exp1–6) to use modeling as a tool to understand hydrological processes in this remote cold basin with extremely high altitude. In Exp1, we started with a distributed rainfall-runoff model (FLEXD) - representing the case that snow and glacier processes were ignored. Then, we stepwisely added snow and glacier processes into FLEXD, denoted as FLEXD-S (Exp2) and FLEXD-SG (Exp3), respectively, and such improvement of model structure led to significantly improved streamflow estimates. To explore the impact of different precipitation forcing on model performance, FLEXD-SG was driven by Theissen average (Exp3) and three individual stations’ precipitation (Exp4–6). The model realism was tested by observed hydrograph, snow cover area (SCA) and glacier mass balance (GMB). Results showed that a robust and realistic hydrological modeling system was achieved in Exp6. In this modeling study, we learned that: 1) stepwise modeling is effective in investigating catchment behavior, and snow and glacier melting are the dominant hydrological processes in the YZR basin; 2) internal variables validation is beneficial to test model realism in data scarce basin; 3) the FLEXD-SG model calibrated by only one year hydrograph is sufficient to reproduce snow and glacier variations; 4) precipitation of a single station as forcing data could outperform Theissen average; 5) based on the well tested model configuration in Exp6, we analyzed simulated results, and reconstructed the long term hydrography (1961–2013), to support the potential competence for decision making on water resources management in practice. The proposed framework may significantly improve our skills in hydrological modeling over data-poor regions.
(Less)
- author
- Gao, Hongkai ; Dong, Jianzhi ; Chen, Xi ; Cai, Huayang ; Liu, Zhiyong ; Jin, Zhihao ; Mao, Dehua ; Yang, Zongji and Duan, Zheng LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Data scarce basin, Glacier hydrology, Hydrological model, Model realism, Remote sensing, Upper Brahmaputra River
- in
- Journal of Hydrology
- volume
- 591
- article number
- 125457
- publisher
- Elsevier
- external identifiers
-
- scopus:85090286217
- ISSN
- 0022-1694
- DOI
- 10.1016/j.jhydrol.2020.125457
- language
- English
- LU publication?
- yes
- id
- 7b0144bc-956f-41d5-be92-8ddfd59bf801
- date added to LUP
- 2021-01-15 11:47:24
- date last changed
- 2022-04-26 23:40:19
@article{7b0144bc-956f-41d5-be92-8ddfd59bf801,
abstract = {{<p>Model realism is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. Here, we employed a stepwise modeling approach that leverages flexible model structures and multi-source observations for robust streamflow simulation and internal variables validation with improved model realism. This framework is demonstrated in Yigong Zangbu River (YZR) basin, a data scarce glacier basin in the upper Brahmaputra River. We designed six experiments (Exp1–6) to use modeling as a tool to understand hydrological processes in this remote cold basin with extremely high altitude. In Exp1, we started with a distributed rainfall-runoff model (FLEXD) - representing the case that snow and glacier processes were ignored. Then, we stepwisely added snow and glacier processes into FLEXD, denoted as FLEXD-S (Exp2) and FLEXD-SG (Exp3), respectively, and such improvement of model structure led to significantly improved streamflow estimates. To explore the impact of different precipitation forcing on model performance, FLEXD-SG was driven by Theissen average (Exp3) and three individual stations’ precipitation (Exp4–6). The model realism was tested by observed hydrograph, snow cover area (SCA) and glacier mass balance (GMB). Results showed that a robust and realistic hydrological modeling system was achieved in Exp6. In this modeling study, we learned that: 1) stepwise modeling is effective in investigating catchment behavior, and snow and glacier melting are the dominant hydrological processes in the YZR basin; 2) internal variables validation is beneficial to test model realism in data scarce basin; 3) the FLEXD-SG model calibrated by only one year hydrograph is sufficient to reproduce snow and glacier variations; 4) precipitation of a single station as forcing data could outperform Theissen average; 5) based on the well tested model configuration in Exp6, we analyzed simulated results, and reconstructed the long term hydrography (1961–2013), to support the potential competence for decision making on water resources management in practice. The proposed framework may significantly improve our skills in hydrological modeling over data-poor regions.</p>}},
author = {{Gao, Hongkai and Dong, Jianzhi and Chen, Xi and Cai, Huayang and Liu, Zhiyong and Jin, Zhihao and Mao, Dehua and Yang, Zongji and Duan, Zheng}},
issn = {{0022-1694}},
keywords = {{Data scarce basin; Glacier hydrology; Hydrological model; Model realism; Remote sensing; Upper Brahmaputra River}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Journal of Hydrology}},
title = {{Stepwise modeling and the importance of internal variables validation to test model realism in a data scarce glacier basin}},
url = {{http://dx.doi.org/10.1016/j.jhydrol.2020.125457}},
doi = {{10.1016/j.jhydrol.2020.125457}},
volume = {{591}},
year = {{2020}},
}