Creating long-term gridded fields of reference evapotranspiration in Alpine terrain based on a recalibrated Hargreaves method

Abstract

Abstract.A new approach for the construction of highresolution gridded fields of reference evapotranspiration for the Austrian domain on a daily time step is presented. Gridded data of minimum and maximum temperatures are used to estimate reference evapotranspiration based on the formulation of Hargreaves. The calibration constant in the Hargreaves equation is recalibrated to the Penman-Monteith equation in a monthly and station-wise assessment. This ensures, on one hand, eliminated biases of the Hargreaves approach compared to the formulation of Penman-Monteith and, on the other hand, also reduced root mean square errors and relative errors on a daily timescale. The resulting new calibration parameters are interpolated over time to a daily temporal resolution for a standard year of 365 days. The overall novelty of the approach is the use of surface elevation as the only predictor to estimate the recalibrated Hargreaves parameter in space. A third-order polynomial is fitted to the recalibrated parameters against elevation at every station which yields a statistical model for assessing these new parameters in space by using the underlying digital elevation model of the temperature fields. With these newly calibrated parameters for every day of year and every grid point, the Hargreaves method is applied to the temperature fields, yielding reference evapotranspiration for the entire grid and time period from 1961-2013. This approach is opening opportunities to create high-resolution reference evapotranspiration fields based only temperature observations, but being as close as possible to the estimates of the Penman-Monteith approach.

Reference crop evapotranspiration database in Spain (1961–2014)

“…For simplicity, the climatic variables were considered to be independent in the final step of the uncertainty estimation, which involved the propagation of the uncertainty of each climatic variable through the Penman-Monteith equation. Haylock et al (2008) pointed out that the variance of the kriging, which in this paper was used to estimate the uncertainty of each climatic grid, is not a true estimation of the uncertainty; however, an evaluation of the uncertainty estimations of each variable showed a good agreement between the MAE values and the estimated uncertainty values. Unfortunately, the uncertainty of ET o could not be verified because an independent subset of observatories that collected all variables was required to calculate ET o but was not available.…”

“…Climate grid generation involves many sources of data uncertainty, including uncertainty related to the observation, the interpolation process and other sources (Haylock et al, 2008). In this paper, we assumed that uncertainty was only related to the estimation processes, i.e., gap filling and interpolation.…”

“…During the last several years, a ET o climate grid at a 1 km spatial resolution over Great Britain was presented Robinson et al (2017). Haslinger and Bartsch (2016) presented a climate grid at a 1 km spatial resolution over Austria, but their grid was based on the Hargreaves equation.…”

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

“…Other regional studies have created gridded estimates of AED in Austria (Haslinger and Bartsch, 2016) and Australia (Donohue et al, 2010). Regional studies of trends in AED have seen varied results, with increasing AED seen in Romania (Paltineanu et al, 2012), Serbia (Gocic and Trajkovic, 2013), Spain (Vicente-Serrano et al, 2014), some regions of China (Li and Zhou, 2014) and Iran (Azizzadeh and Javan, 2015;Hosseinzadeh Talaee et al, 2013;Tabari et al, 2012), decreasing AED in Northeast India and regions in China (Yin et al, 2009;Song et al, 2010;Shan et al, 2016;Zhao et al, 2015;Zhang et al, 2015;Lu et al, 2016) and regional variability in Australia (Donohue et al, 2010) and China (Li et al, 2015).…”

Optimal Interpolation scheme to generate reference crop evapotranspiration

“…An alternative approach consists in using spatial interpolation to estimate the value of missing variables at the desired locations using data from nearby stations, an option that Tomas-Burguera et al (2017) showed to perform better than FAO-PMT or other simplied models such as Hargreaves and Samani, at least in the Iberian Peninsula (IP). This methodology have been tested in other regions, such as Greece (Mardikis et al, 2005), China (McVicar et al, 2007), Austria (Haslinger and Bartsch, 2016), or Great Britain (Robinson et al, 2017).…”