Evaluation of Precipitation and Temperature: An Analysis of In-Situ Observations Versus Gridded Data within the Great Salt Lake Basin

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.

本研究针对大盐湖（Great Salt Lake, GSL）流域的格点数据集展开全面对比分析，以降水与气温作为水文平衡的核心输入变量。本次评估的格点数据集涵盖PRISM、DAYMET、GRIDMET、NLDAS-2及CONUS404，并采用原位观测数据对各数据集的一致性与精度进行校验。研究选取纳什-萨特克利夫效率系数（Nash-Sutcliffe Efficiency, NSE）、克林-古普塔效率系数（Kling-Gupta Efficiency, KGE）、均方根误差（Root Mean Square Error, RMSE）、平均绝对误差（Mean Absolute Error, MAE）与相关系数（Correlation Coefficient, CC）等关键指标，用以量化评估各格点数据集的性能表现。研究团队针对大盐湖流域不同区域开展时空精度分析，以探明数据集精度的时空变异特征。结果表明，在多数评估指标下，DAYMET在降水模拟领域表现最优，性能稳定一致；针对气温模拟，GRIDMET与PRISM的排名更为靠前，能够更精准地刻画大盐湖流域的气温分布格局。空间精度分析结果显示，气温与降水数据的精度均存在区域差异性，这凸显了针对不同流域区域遴选适配的格点数据集对提升整体水文模拟精度的重要性。本研究的分析结论可为大盐湖流域的环境预报与水资源管理提供科学参考，助力科研人员与决策者遴选适用于水文研究的可靠格点数据集。