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Evaluation of Precipitation and Temperature: An Analysis of In-Situ Observations Versus Gridded Data within the Great Salt Lake Basin
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| Type: | Resource | |
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| Created: | Apr 20, 2024 at 5:44 a.m. | |
| Last updated: | Nov 13, 2024 at 9:09 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 437 |
| Downloads: | 177 |
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Abstract
This study presents a comprehensive comparison of gridded datasets for the Great Salt Lake (GSL) basin, focusing on precipitation and temperature as the main inputs for hydrological balances. The evaluated gridded datasets include PRISM, DAYMET, GRIDMET, NLDAS-2, and CONUS404, with in-situ data used for assessing alignment and accuracy. Key metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) were employed to evaluate gridded dataset performance. Spatial and temporal accuracy analyses were conducted across different GSL basin regions to understand variations in accuracy. DAYMET emerged as the leading dataset for precipitation across most metrics, demonstrating consistent performance. For temperature, GRIDMET and PRISM ranked higher, indicating better representation of temperature patterns in the GSL basin. Spatial analysis revealed variability in accuracy for both temperature and precipitation data, emphasizing the importance of selecting suitable datasets for different regions to enhance overall accuracy. The insights from this study can inform environmental forecasting and water resource management in the GSL basin, assisting researchers and decision-makers in choosing reliable gridded datasets for hydrological studies.
Subject Keywords
Coverage
Spatial
Temporal
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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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