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- W4322001612 abstract "Estimation of reference evapotranspiration (ETo) is necessary for hydrological modeling, water stress adaptation and agricultural water management. While numerous studies have addressed changes in temperature and precipitation patterns at different spatiotemporal scales for assessing hydroclimatic variability, similar analyses on regional evapotranspiration trends are limited. This can be attributed to lack of observed records of ETo at regional scales, and highlights the need for developing better models for estimating this variable. To address this research gap, we developed monthly 0.5° gridded ETo dataset for entire Germany using machine learning techniques and then investigated the temporal trends in ETo over the region. We utilized flux tower data (sensible heat flux, net radiation, soil heat flux, and latent heat flux) from multiple locations in the region to compute observed ETo using the surface energy balance method. Then, fed-forward back-propagation method (BPNN) is used for predicting monthly ETo with easily available input predictors such maximum temperature, minimum temperature, precipitation, soil moisture, short wave radiation, and wind speed. The BPNN is trained with various input combinations in order to estimate ETo with minimal input predictors, and their performance is assessed using metrics: coefficient of determination, mean absolute error, and root mean square error. The results showed that with all the input parameters, the coefficient of determination  for training and testing are 0.89 and 0.93 respectively, while the best parsimonious model (precipitation and downward shortwave radiation as predictors) gives 0.88 and 0.93 respectively. Gridded ETo estimated using the best parsimonious model is then used for assessing spatially varying trends in the variable at monthly and annual time scales over Germany using Mann-Kendall test and Sen’s slope. The long term analysis helps us to identify critical regions in the study area that needs attention for water resources management, drought mitigation and improved adaptation to changing climate.Keywords: Reference Evapotranspiration, Flux Tower, Surface Energy Balance, Feed Forward Back Propagation (BPNN), Trend Analysis, Mann-Kendall Test" @default.
- W4322001612 created "2023-02-26" @default.
- W4322001612 creator A5024879869 @default.
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- W4322001612 date "2023-05-15" @default.
- W4322001612 modified "2023-09-29" @default.
- W4322001612 title "Development of gridded monthly reference evapotranspiration dataset for Germany for long term trend analysis" @default.
- W4322001612 doi "https://doi.org/10.5194/egusphere-egu23-11681" @default.
- W4322001612 hasPublicationYear "2023" @default.
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