![]() Contribution analysis exhibited that the driving factors for the PET variation varied spatially and seasonally. There was a positive trend in PET for approximately 58%, 68%, 38%, 73% and 73% of all surveyed stations at annual, spring, summer, autumn and winter, respectively. Furthermore, this present study combining and quantitatively illustrated sensitivities and contributions of the meteorological factors to change in annual and seasonal PET. The Mann–Kendall test was used to determine the significance of long-term trends in PET and five meteorological factors (net radiation, wind speed, air temperature, vapor pressure deficit, relative humidity) at 56 meteorological stations in the Sichuan-Chongqing region from 1970 to 2020. Spatial distributions of PET trends and their main causes have not been fully investigated. Previous studies have focused on regionally average regional PET and its dominant factors. The excellent performance of gridMET temperature and RTMA indicates that the degree to which gridded data depend on station data is a primary factor determining correspondence.Īnalyzing the primary factors of potential evapotranspiration (PET) dynamic is fundamental to accurately estimating crop yield, evaluating environmental impacts, and understanding water and carbon cycles. The low-resolution products GLDAS and CFSv2 performed better than the finer resolution NLDAS product suggesting that spatial resolution is not a primary factor determining correspondence to station data. However, its performance was similar to its other parent product, NLDAS, for the remaining variables which reduced its ETref performance. gridMET temperature agreed relatively well with the station data due to its dependence on the PRISM station-interpolated data set. NDFD one-day forecasts outperformed most of the analysis products, likely due to its initialization with RTMA. RTMA was generally the best performing gridded data set for all variables and NLDAS was the worst for all variables except vapor pressure. Bias correction procedures may make these gridded data more suitable for generating ETref. These results indicate that gridded data should be carefully evaluated before being substituted for agricultural weather station data. ![]() The overestimation was mainly due to chronic overstatement of air temperature, shortwave radiation, and wind speed and understatement of humidity. The gridded weather data sets generally overestimated the standardized Penman-Monteith ETref produced from weather station data, with median biases ranging from 12 to 31 %. ETref along with the weather variables used to compute it – near-surface air temperature, vapor pressure, wind speed, and shortwave solar radiation – were compared. Six gridded weather data sets – GLDAS-1, NLDAS-2, the CFSv2 operational analysis, gridMET, RTMA, and NDFD – were compared to weather data collected from 103 weather stations located in well-watered settings across the conterminous United States. This study assessed the quality of gridded weather data for calculating reference evapotranspiration (ETref), which, by definition, represents a near maximum ET occurring in a well-watered agricultural environment.
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