Novel assessment and development of land surface modelling for irrigation schemes in Mediterranean apple orchards

Land-surface models (LSM) that simulate agricultural systems can provide key support for decision makers in precision irrigation and in the management of water resources under different climate scenarios. An accurate representation of irrigation in LSM is also crucial to understand how irrigation practices influence land-atmosphere processes from regional to global scale. Irrigation practices are increasingly integrated into LSM. However, challenges such as lack of data for model development and validation undermine the possibility to evolve current LSM into precision irrigation applications as well as into decision-making tools at the catchment scale and beyond.

In this study, we used the Community Land Model version 5 (CLM5) and assessed the representation of irrigation practices and consequent effect on crop yield in the model using a) the existing irrigation scheme of CLM5 and b) a novel irrigation data stream that allows to directly use observed irrigation data. Additionally, we used CLM5 to investigate irrigation requirements as well as the effect of deficit irrigation on crop yield and crop water use efficiency (CWUE) at the catchment scale (~45 km²). Model validation was supported by two highly instrumented apple orchards located in Agia (Greece) within the Pinios Hydrologic Observatory (PHO). From 2020, an ATMOS41 all-in-one climate station for monitoring meteorological data and a SoilNet sensor network for measuring soil moisture and matrix potential at various depths across 12 locations with SMT100 and TEROS21 sensors were used in both orchards. Additionally, a System SP cosmic-ray neutron sensor (CRNS) was installed in the centre of each field to monitor the field-averaged soil moisture, and several water meters were used to monitor irrigation rates in the orchards. Finally, one field was equipped with six SFM-1 sapflow sensors to estimate whole-tree transpiration and with six SnapShot Cloud 4G remote outdoor cameras.

We found that the novel irrigation data stream outperformed the existing scheme in terms of soil moisture simulation, even when the latter was manually adjusted to better mimic actual irrigation practices. However, both methods resulted in similar harvest predictions. Nonetheless, the fact that the existing scheme lacks the necessary flexibility to represent specific irrigation practices can have important implications for the simulation of infiltration, runoff, and sensible and latent heat fluxes. Furthermore, a 25 % irrigation reduction had negligible effect on simulated yield and CWUE at the catchment scale, while a 50 % reduction negatively affected both yield and CWUE depending on climatic conditions, soil properties, and irrigation timing (on average -30 % and -17 %, respectively). Although further process representations, such as the potential impact of deficit irrigation on crop quality, have yet to be implemented in CLM5, our results clearly show how CLM5 could be utilized for irrigation and water resources management at the field and catchment scales.