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| Type: | Resource | |
| Storage: | The size of this resource is 1.2 MB | |
| Created: | Jan 21, 2020 at 11:47 p.m. | |
| Last updated: | Jan 22, 2020 at 1:26 a.m. | |
| DOI: | 10.4211/hs.41b8e61a76bf4a2aa15abd1965babecc | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1264 |
| Downloads: | 47 |
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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
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Spatial
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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. 4. The simulated discharge and the discharge of peaks and recessions are in the file, named Simulation.xlsx. 5. The simulation of discharge is simulated by three scenarios (original VarKarst, scenario FS+HD, and scenario RSE+FS+HD) based on the VarKarst model. 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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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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