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| Type: | Resource | |
| Storage: | The size of this resource is 157.7 KB | |
| Created: | Jan 21, 2020 at 10:35 p.m. | |
| Last updated: | Jan 21, 2020 at 11:31 p.m. (Metadata update) | |
| Published date: | Jan 21, 2020 at 11:31 p.m. | |
| DOI: | 10.4211/hs.cefee17cd5c94c2ebfb2681f5cdbdbdc | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1636 |
| Downloads: | 15 |
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Abstract
Understanding hydrological processes is essential for the management of water resources and for promoting catchment sustainability. In karst regions, high spatial heterogeneous landscapes, such as discontinuous soil distribution and complex network of matrices and conduits in hillslopes and depressions, result in different hydrological processes. However, most studies have mainly focused on the effects of the distribution of soil depth and the fast-slow flow in the matrices and conduits on hydrological processes, but they have ignored the different hydrological processes on hillslopes and depressions (HD). This study improved the VarKarst model by adding randomly distributed soil and epikarst depths (RSE), fast-slow flow (FS) and HD in six large catchments (1,213~5,454 km2) and one small catchment (1.25 km2). The combination of FS and HD (Scenario FS+HD) and the combination of RSE, FS, and HD (Scenario RSE+FS+HD) for the improved VarKarst model had the best performance (calibrated and validated KGE ranged from 0.54 to 0.89 and AIC ranged from -336.49 to 669.77) compared with other scenarios (original VarKarst, Scenario RSE, Scenario FS, Scenario HD, and Scenario RSE +FS). Particularly, these two scenarios performed better than the original VarKarst in reproducing the discharge of peaks and recessions. This study confirmed that the combination of HD, RSE, and FS improved VarKarst model for karst topography and the hillslopes. It also suggested that there is a need to separate the hillslopes and depressions for modeling karstic hydrological processes.
Subject Keywords
Coverage
Spatial
Temporal
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Content
readme.txt
Data description: 1. The model input data is a table data type with standard format of matlab (.mat) 2. Columns represent the observed precipitation (mm, *_*_P), potential evapotranspiration (mm, *_*_PET), discharge (mm, *_*_D) of seven catchments 3. The seven catchments are Chenqi (*_CQ_*), Changba (*_CB_*), Gaoche (*_GC_*), Leigongtan (*_LGT_*), Libo (*_LB_*), Shibantang (*_SBT_*), Yangchang (*_YC_*) in Xijiang (XJ_*_*) and Wujiang (WJ_*_*) catchment, southwest China. Notes: Time period of Chenqi is from 2016.07 to 2017.10, while other catchments is from 2009.01 to 2012.12
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http://creativecommons.org/licenses/by/4.0/
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