Reference:Reference-014667: Difference between revisions
From CSDMS
m Text replacement - "|MS_PublicationClusterID=3161929512" to "|MS_PublicationClusterID=3161929512 |Semantic_ID=0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe
" |
m Text replacement - " |Semantic_ID" to " |Semantic_ID" |
||
| Line 15: | Line 15: | ||
|FeatureRef=No | |FeatureRef=No | ||
|PublicationClusterID=0 | |PublicationClusterID=0 | ||
|MS_PublicationClusterID=3161929512 |Semantic_ID=0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe | |MS_PublicationClusterID=3161929512 | ||
|Semantic_ID=0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe | |||
|PublicationWhatKindOf=a module application description | |PublicationWhatKindOf=a module application description | ||
|PublicationNrofModels=a single module | |PublicationNrofModels=a single module | ||
Revision as of 19:47, 9 January 2022
| Author(s) | Kim, Taereem; Yang, Tiantian; Gao, Shang; Zhang, Lujun; Ding, Ziyu; Wen, Xin; Gourley, Jonathan J.; Hong, Yang; |
| BibType | journalArticle |
| Title | Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS |
| Editors | |
| Year | 2021-07 |
| Journal | Journal of Hydrology |
| Booktitle | |
| Volume | 598 |
| Pages | 126423 |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S0022169421004704 |
| DOI | 10.1016/j.jhydrol.2021.126423 |
| ISBN | |
| Note | Auto downloaded ref at: 2021-07-03
|
| |
|
| Feature reference | No |
| PublicationClusterID | 0 |
| MS_PublicationClusterID | 3161929512 |
| Semantic_ID | 0b1b1bfe7129ad9a906217ffe273ebc8cf2b7efe |
| Nr of citations | 70 |
| Sort of publication | a module application description |
| Sort of model publication | a single module |
| Is the CSDMS HPC used | No |
| If HPC is used, for what project was it?: | |
| Associated simulation movie if any: |
|
| Model(s) discussed: | CREST |
