Reference crop evapotranspiration for data-sparse regions using reanalysis products

Milad Nouri and Mehdi Homaee

Agricultural Water Management, 2022, vol. 262, issue C

Abstract: 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.

Keywords: Bias correction; Data assimilations; Data-poor areas; ERA5; Temperature-based ETo (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:262:y:2022:i:c:s0378377421005965

DOI: 10.1016/j.agwat.2021.107319

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