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| Type: | Resource | |
| Storage: | The size of this resource is 47.6 GB | |
| Created: | Feb 29, 2024 at 11:21 p.m. | |
| Last updated: | Aug 05, 2024 at 3:03 p.m. (Metadata update) | |
| Published date: | Aug 05, 2024 at 3:02 p.m. | |
| DOI: | 10.4211/hs.a73bb03017fe4bff9e7b5f8a6a7daf55 | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Raster Content |
| Sharing Status: | Published |
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| Views: | 399 |
| Downloads: | 4 |
| +1 Votes: | 1 other +1 this |
| Comments: | No comments (yet) |
Abstract
This hydroshare provides the source code utilized for the model runs, calibration, input processing, data analysis and figure creation for the manuscript under review at JAWRA. The abstract of the manuscript is as follows: In this study, we evaluate the performance of the TETIS model structure of the Hillslope-Link Model (HLM), which is a distributed hydrologic model. We explore performance across the contiguous United States (CONUS) at 5046 United States Geological Survey (USGS) streamgages. Specifically, we compare observed daily discharge from 1981 through 2020 with long term continuous simulations from the HLM TETIS. To obtain model parameters across CONUS, we run calibration by partitioning the study area based on 234 HydroSHEDS level 5 basins and calibrating to a single representative location near the outlet of each basin. Next, we utilize the remaining USGS gages for validation. We assess the model performance with the Kling Gupta Efficiency (KGE) and bias. We find the median KGE across CONUS is 0.43, with 80% of the gages above 0 and 43% above 0.5. Furthermore, our results show there is a dependence of the model performance on climate regions, with arid basins performing worse than basins in cold and temperate regions. To improve the model performance, we recalibrate these arid basins and highlight an overall performance improvement. Next, we compare model performance between simulations with different precipitation inputs to examine the robustness of the selected model parameters. Overall, our study highlights the model’s flexibility in performing across regions with different runoff generation mechanisms and provides a basis for future.
Subject Keywords
Coverage
Spatial
Temporal
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Data Services
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Princeton University |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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