**Author details**

dynamic boundary conditions were introduced throughout the simulation including soil tem-

After examining simulation outputs and comparing them to collocated micrometeorological data, it can be concluded that time series of fluxes during the dry period seemed to be reproduced fairly better than those obtained during the wet period. In general, *R*net has a good agreement between modeled and observed data for both periods with RMSE of 45 W m−2 in a dry period and 48 W m−2 during a wet period. The LE also was well predicted by the model with RMSE not exceeding 53 W m−2. This difference in turbulent fluxes agrees well with other studies in the same area over highly heterogeneous terrain with further canopy complexity [41, 42]. On the other hand, *G* was overestimated with the maximum difference of more than 100 W m−2 in a wet period and 44 W m−2 in a dry period. In addition, the measured *H* by EC and LAS instruments correlated well with the model in terms of the RMSE being in the range of 32–40 W m−2 which falls within the interval of fluxing difference across landscapes observed on the same area for heterogeneous surfaces [41, 42]. However, only the soil temperature correlated better during a wet period than a dry period, while the soil moisture and soil moisture potential was underestimated compared to the observed values. The low correlation in the wet period was due to significant influences of the synoptic variability introducing large changes in cloud coverage and precipitation that are difficult to reproduce by a single point one-dimensional model formulation. Nevertheless, dry conditions that are by far

the most stringent conditions for agriculture sustainability reproduces well.

implementing this model over unnatural setting systems.

considered in terms of atmospheric observations.

**Acknowledgements**

There are still several parameters such as the presence of vegetation above the soil, the swell, and shrink of soil that need to be investigated more in depth and the most important factor is the hydraulic properties of soil and its variability across landscape. This variable is more complicated, and there are many steps to reach an approximately correct value. In the current study, existing values were applied from previous work done around the same study site, while some other values were obtained from field and laboratory experiments. However, it is known that soils in agro-ecosystems tend to experience large changes in some of these properties and, therefore, are difficult to capture. This factor needs to be taken into account when

Finally, based on current numerical model outputs and field experimental observations allowed identification new challenges in northern agro-ecosystems. Improved representation of soil dynamics is necessary to improve fidelity in the simulations, and also there is a need to establish better strategies to compare single-point numerical modeling to scale-dependent micrometeorological observations. In addition, a large deviation in simulated soil profiles and heat exchanges reveals the highly heterogeneous nature of an aerodynamically simple terrain

The authors thank the support of the Agricultural and Forestry Experiment Station (AFES) School of Natural Resources and Extension at the University of Alaska Fairbanks and the

perature, soil moisture, and soil hydraulic properties.

80 Current Perspective to Predict Actual Evapotranspiration

Watcharee Ruairuen<sup>1</sup> \*, Gilberto J. Fochesatto<sup>2</sup> , Marco Bittelli3 , Elena B. Sparrow4 , Mingchu Zhang5 and William Schnabel6

\*Address all correspondence to: ruairuen@gmail.com

1 Department of Environmental Sciences, Faculty of Science and Technology, Surattani Rajabhat University, Surattani, Thailand

2 Department of Atmospheric Sciences, Geophysical Institute and College of Natural Science and Mathematics, University of Alaska Fairbanks, Fairbanks, Alaska, USA

3 Department of Agricultural Sciences, Università di Bologna, Bologna, Italy

4 International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, USA

5 School of Natural Resources and Extension, University of Alaska Fairbanks, Fairbanks, Alaska, USA

6 Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, Alaska, USA
