Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes.
Base flow, as an important component of runoff, is the main recharge source of runoff during the dry period, especially in the Yellow River Basin located in a semiarid area. However, the process of obtaining base flow has great uncertainty when considering hydrological simulations. Thus, in this stu...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0261859&type=printable |
| _version_ | 1827176311556145152 |
|---|---|
| author | Bing Yan Yi Xu |
| author_facet | Bing Yan Yi Xu |
| author_sort | Bing Yan |
| collection | DOAJ |
| description | Base flow, as an important component of runoff, is the main recharge source of runoff during the dry period, especially in the Yellow River Basin located in a semiarid area. However, the process of obtaining base flow has great uncertainty when considering hydrological simulations. Thus, in this study, a three-step framework is proposed, i.e., the particle swarm optimization (PSO) algorithm is used to calibrate model parameters under different subbasin partitioning schemes; then, the hydrograph separation (HYSEP), Improved United Kingdom Institute of Hydrology (IUKIH) and Lyne and Hollick filter (Lyne-Hollick) methods are used to separate the baseflow from the total runoff process, thereby exploring the uncertainty impacts of baseflow segmentation methods on the hydrological simulation process. The subsample-variance-decomposition method is used to quantify the independent and interactive uncertainty in the hydrological simulation process. The results show that the Topmodel model can be better applied to the source area of the Yellow River (the KGE values in the Sub5, Sub13, Sub21, Sub29, Sub37 and Sub13 scenarios were 0.91 and 0.65, 0.94 and 0.86, 0.94 and 0.88, 0.92 and 0.82, 0.95 and 0.89, and 0.92 and 0.83, respectively). The subbasin division uncertainty had less impact on simulated streamflow during the dry season and had a significant impact in the wet season, such as, the subbasin division uncertainty caused the difference between the median of the simulated streamflow to be as high as 213.09 m3/s in August but only 107.19 m3/s in January; Meanwhile, the baseflow segmentation method uncertainty has a significant impact on the annual mean streamflow values under different subbasin segmentation schemes. In addition, the baseflow values estimated by the Lyne-Hollick and HYSEP methods were obviously higher than those estimated by the IUKIH method during the wet season. The uncertainty influence of subbasin partitioning schemes and baseflow segmentation methods had significant differences on hydrological processes in different periods. The uncertainty influence of subbasin partitioning schemes was dominant in the dry season, accounting for 86%, and the baseflow segmentation methods took second place, accounting for approximately 12%. In the wet season, the uncertainty influence of the baseflow segmentation methods was gradually weakened, which may have been due to the uncertainty influence of the hydrological model. These results provide a reference for the calibration and validation of hydrological model parameters using baseflow components. |
| first_indexed | 2024-03-11T12:49:57Z |
| format | Article |
| id | doaj.art-b3a421d6633846489d18ed94b27b5813 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-21T04:25:19Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b3a421d6633846489d18ed94b27b58132024-07-28T05:31:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01173e026185910.1371/journal.pone.0261859Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes.Bing YanYi XuBase flow, as an important component of runoff, is the main recharge source of runoff during the dry period, especially in the Yellow River Basin located in a semiarid area. However, the process of obtaining base flow has great uncertainty when considering hydrological simulations. Thus, in this study, a three-step framework is proposed, i.e., the particle swarm optimization (PSO) algorithm is used to calibrate model parameters under different subbasin partitioning schemes; then, the hydrograph separation (HYSEP), Improved United Kingdom Institute of Hydrology (IUKIH) and Lyne and Hollick filter (Lyne-Hollick) methods are used to separate the baseflow from the total runoff process, thereby exploring the uncertainty impacts of baseflow segmentation methods on the hydrological simulation process. The subsample-variance-decomposition method is used to quantify the independent and interactive uncertainty in the hydrological simulation process. The results show that the Topmodel model can be better applied to the source area of the Yellow River (the KGE values in the Sub5, Sub13, Sub21, Sub29, Sub37 and Sub13 scenarios were 0.91 and 0.65, 0.94 and 0.86, 0.94 and 0.88, 0.92 and 0.82, 0.95 and 0.89, and 0.92 and 0.83, respectively). The subbasin division uncertainty had less impact on simulated streamflow during the dry season and had a significant impact in the wet season, such as, the subbasin division uncertainty caused the difference between the median of the simulated streamflow to be as high as 213.09 m3/s in August but only 107.19 m3/s in January; Meanwhile, the baseflow segmentation method uncertainty has a significant impact on the annual mean streamflow values under different subbasin segmentation schemes. In addition, the baseflow values estimated by the Lyne-Hollick and HYSEP methods were obviously higher than those estimated by the IUKIH method during the wet season. The uncertainty influence of subbasin partitioning schemes and baseflow segmentation methods had significant differences on hydrological processes in different periods. The uncertainty influence of subbasin partitioning schemes was dominant in the dry season, accounting for 86%, and the baseflow segmentation methods took second place, accounting for approximately 12%. In the wet season, the uncertainty influence of the baseflow segmentation methods was gradually weakened, which may have been due to the uncertainty influence of the hydrological model. These results provide a reference for the calibration and validation of hydrological model parameters using baseflow components.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0261859&type=printable |
| spellingShingle | Bing Yan Yi Xu Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes. PLoS ONE |
| title | Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes. |
| title_full | Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes. |
| title_fullStr | Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes. |
| title_full_unstemmed | Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes. |
| title_short | Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes. |
| title_sort | quantifying interaction uncertainty between subwatersheds and base flow partitions on hydrological processes |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0261859&type=printable |
| work_keys_str_mv | AT bingyan quantifyinginteractionuncertaintybetweensubwatershedsandbaseflowpartitionsonhydrologicalprocesses AT yixu quantifyinginteractionuncertaintybetweensubwatershedsandbaseflowpartitionsonhydrologicalprocesses |