CAMELS benchmark models
| Authors: | |
|---|---|
| Owners: | Frederik Kratzert |
| Type: | Resource |
| Storage: | The size of this resource is 113.9 MB |
| Created: | Jul 09, 2019 at 11:30 a.m. |
| Last updated: | Dec 17, 2019 at 8:36 a.m. (Metadata update) |
| Published date: | Dec 17, 2019 at 8:36 a.m. |
| DOI: | 10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
|---|---|
| Views: | 6162 |
| Downloads: | 1104 |
| +1 Votes: | 1 other +1 this |
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Abstract
This data set contains the model outputs of different hydrology models calibrated using the same forcing data (Maurer) and the same calibration period for the CAMELS data set. The models are: SAC-SMA, VIC, HBV, FUSE and mHM. All of these models have been calibrated for each basin separately. Additionally, for VIC and mHM, also regionally calibrated model outputs exist. All models have been calibrated using the period 1 October 1999 until 30 September 2008 and were validated in the period 1 October 1989 until 30 September 1999.
Subject Keywords
Coverage
Spatial
Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
49.7927°
East Longitude
-65.5895°
South Latitude
24.4633°
West Longitude
-127.9918°
Leaflet Map data © OpenStreetMap contributors
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Related Resources
| This resource is referenced by | Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 2019. |
| The content of this resource is derived from | Newman, A. J., Mizukami, N., Clark, M. P., Wood, A. W., Nijssen, B., and Nearing, G.: Benchmarking of a physically based hydrologic model, Journal of Hydrometeorology, 18, 2215–2225, 2017. |
| The content of this resource is derived from | Seibert, J., Vis, M. J. P., Lewis, E., and van Meerveld, H. J.: Upper and lower benchmarks in hydrological modelling, Hydrological Processes, 32, 1120–1125, 2018. |
| The content of this resource is derived from | Mizukami, N., Rakovec, O., Newman, A. J., Clark, M. P., Wood, A. W., Gupta, H. V., and Kumar, R.: On the choice of calibration metrics for “high-flow” estimation using hydrologic models, Hydrology and Earth System Sciences, 23, 2601–2614, 2019 |
| The content of this resource is derived from | Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D., Nijssen, B., Rakovec, O., and Samaniego, L.: Towards seamless large-domain parameter estimation for hydrologic models, Water Resources Research, 53, 8020–8040, 2017. |
| The content of this resource is derived from | Rakovec, O., Mizukami, N., Kumar, R., Newman, A. J., Thober, S., Wood, A. W., Clark, M. P., and Samaniego, L.: Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States, J. Geophysical Research – Atmospheres., in review |
How to Cite
Kratzert, F. (2019). CAMELS benchmark models, HydroShare, https://doi.org/10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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