首页|Reference crop evapotranspiration for data-sparse regions using reanalysis products
Reference crop evapotranspiration for data-sparse regions using reanalysis products
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NSTL
Elsevier
? 2021 Elsevier B.V.Reasonable estimation of reference evapotranspiration (ETo) requires some climatic inputs which might be missing in areas with sparse data recording. This study aimed to assess performance of FAO56 Penman-Monteith (PM-ETo) fed by ERA5, MERRA2 and GLDAS2 outputs in estimating daily and monthly ETo under data limitation. The accuracy of PM-ETo calculated by interpolated factors and the temperature-based PM-ETo (PMT) was also studied. Additionally, performance of PM-ETo fed by the bias-corrected reanalysis products against the PMT with updated constant, i.e. recalibrated PMT, was investigated. Climatic data required to run PM-ETo were collected from 146 stations over Iran for 25 years. Results revealed that ERA5 provides more realistic daily and monthly ETo estimates relative to MERRA2 and GLDAS2 in 84% of cases. Furthermore, ERA5 surpassed the others in producing daily and monthly wind speed, vapor pressure deficit and mean temperature for the majority of locations. The average relative Mean Bias Error (rMBE) of ? 7.3% and 8.1% at monthly scale and of ? 11.1% and 9.8% at daily scale were found for MERRA2- and GLDAS2-estimated ETo, respectively, indicating ETo overestimation and underestimation by MERRA2 and GLDAS2, respectively. The ERA5 provided more satisfactory results, with normalized Root Mean Square Error of 15.2% and 22.7% for daily and monthly steps, respectively, relative to PMT for approximately 70% of sites. Moreover, ETo estimated by ERA5 had a smaller nRMSE than that simulated using the interpolated variables in around 60% of the sites. Therefore, under temperature data availability or existence of nearby sites, application of ERA5 is better suited to estimate ETo in our study area. The PM-ETo fed by bias-corrected ERA5 outputs also outperformed recalibrated PMT, illustrating that bias-correction seems to be a more accurate modification when complete datasets are available at least for a limited time. Overall, ERA5 products are robust surrogates for simulating ETo under data limitation on different temporal resolutions which is needed for decision making and planning processes.
Bias correctionData assimilationsData-poor areasERA5Temperature-based ETo
Nouri M.、Homaee M.
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Soil and Water Research Institute Agricultural Research Education and Extension Organization (AREEO)
Department of Mining Faculty of Engineering Tarbiat Modares University
NSTL
Elsevier
