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European Geoscience Union General Assembly, EGU 2023, Vienna, Austria, 24 Apr 2023 - 28 Apr 2023; doi:10.5194/egusphere-egu23-3095, Soil moisture (SM) sensors are widely used to monitor soil water dynamics and support irrigation management with the aim of achieving better yields while reducing water consumption. Unfortunately, due to the small measuring volume of point-scale sensors, their soil moisture readings are often not representative for heterogeneous agricultural fields. Therefore, in such cases, sensors with larger sensing volume are needed to address spatially variable SM. A suitable technique is the cosmic ray neutron sensor (CRNS) as it integrates SM over a large volume with a radius of ~130-210 m and a penetration depth of ~15-85 cm. The CRNS method is based on the inverse relationship between measured environmental neutron density and the presence of hydrogen pools (e.g., SM) in the instrument surroundings. However, the ability of CRNS to accurately monitor areas with complex SM heterogeneities (e.g., small irrigated fields) and the influence of detector design were not yet investigated. In this study, we used the neutron transport model URANOS to simulate the effect of SM variations on a CRNS placed in the centre of squared irrigated fields (0.5 to 8 ha dimensions). For this, SM in the irrigated field and in the surrounding was altered between 0.05 and 0.50 cm3 cm-3 (500 simulations in total). In addition, we investigated the effect of employing high-density polyethylene (HDPE) moderators with different thickness (5 to 35 mm) as well as a 25 mm HDPE moderator with an additional gadolinium oxide thermal shielding. Results showed that, in heterogeneous SM scenarios, the 2 e-folding lengths footprint (R86) can become smaller or larger than what previous studies showed in homogeneous SM distributions. In addition, a thin HDPE moderator will result in relatively smaller R86 whereas thicker moderators and the addition of a thermal shielding will result in relatively larger R86. However, we found that a relatively small footprint is not directly related to a better monitoring of SM nearby the instrument. In fact, in all the investigated field dimensions, the 25mm HDPE moderator with gadolinium shielding showed the largest values of R86 but also the largest variations of detected neutrons with changing SM. In addition, such moderator showed the highest chances of detecting irrigation events that increase SM by 0.05 or 0.10 cm3 cm-3 in the irrigated area. Generally, detection was uncertain only for SM variations of 0.05 cm3 cm-3 in fields of 0.5 ha when initial SM was 0.02 cm3 cm-3 or higher. Although the results of this study suggest the feasibility of monitoring and informing irrigation with CRNS, we found that SM variations outside the irrigated field have a considerable influence on CRNS measurements. Especially in fields of 0.5 and 1 ha dimension, it can be impossible to distinguish whether a relative change in detected neutrons is due to irrigation or to SM variations in the surroundings. These results are relevant for irrigation monitoring and the combination of neutron transport simulations and real-world installations has the potential to establish CRNS as a decision support system for irrigation management.

Land surface models (LSMs) are increasingly being used to study how irrigated agriculture, the largest consumer of fresh water globally, affects crop growth, water resources status, and climate. This is especially of interest in dry and semi-dry agricultural regions such as the Mediterranean, where water scarcity, overexploitation and expected climate change impacts threaten local water resources. The simulation of these agricultural ecosystems necessitates comprehensive crop modules. Such modules must consider local irrigation patterns, crop types, and crop specific management practices to understand their influence on water and energy fluxes under present and future climates. This study explores the water footprint of fruit orchards in the Pinios Hydrological Observatory (PHO) in central Greece, using CLM5-FruitTree, a recent development of the Community Land Model version 5, to include deciduous fruit orchards and associated management practices. Initially, CLM5-FruitTree was setup and validated at field scale using data from two highly instrumented irrigated apple orchards within the PHO. The simulations used local climate, soil, crop management and phenology information. Model results were compared to observed apple yield, sap flow, irrigation amounts, and soil moisture. The latter was obtained from a distributed sensor network measuring soil moisture in three depths at 12 locations per field as well as two cosmic-ray neutron soil moisture sensors. The model was able to reproduce the soil moisture response to irrigation satisfactorily when the local irrigation schedule was considered. The simulated irrigation amount indicated that around 45% less water than the amount applied by the farmer could be used without reduction in yield. This suggests potential improvements in irrigation efficiency by reducing losses through evaporation or deep percolation. However, possible model weaknesses in the representation of soil properties and water fluxes should be further addressed. Successively, a modeling case for the PHO is set up to study the regional irrigation water consumption and the local groundwater aquifer recharge. Results from this study could help local authorities in the definition of water policies and serve as a basis for climate impact studies on regional irrigation management.

The agricultural sector is increasingly reliant on water availability, especially given expected increase of agricultural droughts related to climate change. Thus, improved soil moisture (SM) monitoring tools are needed to support more efficient water management strategies such as precision irrigation. A novel and non-invasive method is cosmic-ray neutron sensing (CRNS). It is characterized by a large footprint (~240m) and relies on the negative correlation between fast neutrons originating from cosmic radiation and SM. Despite promising results in the monitoring of SM dynamics and patterns, only a few studies explored the use of CRNS for irrigation management. In this study, two apple orchards of ~1.2 ha located in the Pinios Hydrological Observatory (Greece) were provided with CRNS probes. These were supported by extensive monitoring of SM and climate data in the context of the H2020 ATLAS project. In capturing irrigation events, the agreement between the CRNS and the validation measurements depended largely on a) the timing of irrigation, b) the CRNS calibration strategy, c) precipitation, and d) the management of the surrounding fields. In parallel, we performed neutron transport simulations of multiple scenarios with variable irrigated area and soil moisture by using the URANOS model. This allowed the study of how the surrounding environment influences the effectiveness of a CRNS sensor when its footprint is larger than the area of interest. This combination of simulations and experiments is providing key insights on how CRNS methods can move from a proof o concept to a relevant tool in actual precisionirrigation scenarios.

In the Mediterranean area, the agricultural sector often relies on irrigation. Additional pressure on these environments will come from the increasing occurrence of dry years and of heat waves that are connected to climate change. Thus, more efficient water management strategies and, when possible, precision irrigation practices should be implemented. So far, a wide range of soil moisture sensor-based systems have been successfully used to monitor soil water content (SWC) dynamics in agricultural contexts. The relatively new, and non-invasive, technique cosmic-ray neutron sensing (CRNS) relies on the negative correlation between fast neutrons originating from cosmic radiation and soil moisture content. It has the advantage of a large footprint, covering the field-scale. Despite this, only few studies explored the use of CRNS for irrigation management. In this study, we examine how CRNS can provide insights in the monitoring of irrigation and the associated fluctuations in SWC dynamics and water potential. In September 2020, two apple orchards of 1.2 and 1.3 ha in size and located in the Pinios Hydrological Observatory (Greece) were provided with an extensive instrumentation in the context of the Agricultural Interoperability and Analysis Systems (ATLAS) project. At each field, 12 SoilNet nodes measure volumetric soil moisture at three depths (5, 20, and 50 cm) and water potential at one depth (20 cm). Additionally, one Atmos41 compact climate station and a CRNS probe were placed in each of the two orchards. All these instruments are equipped with NB-IoT connections and transmit data in near-real time. The irrigation of both fields is monitored using water meter pumps equipped with a LoRaWan connection. Instrument installation was preceded by geophysical surveys and by extensive soil sampling and analysis. Moreover, soil samples from 18 locations were collected per CRNS probe on the day of installation to provide instrument calibration information. However, additional calibration was required due to temporary sensor failure and was performed using the surrounding SoilNet nodes. During the subsequent months, the CRNS probes generally showed good agreement with the SWC measured by the SoilNet stations as well as a strong response to precipitation events. Once the irrigation of the apple orchards started in April 2021, it was found that small and localized irrigation events were not featured in the CRNS signal. However, when prominent irrigation events started in May 2021, the CRNS probes showed good agreement with the changes in SWC and in water potential measured with the SoilNet nodes. Such agreement depended largely on the timing of the irrigation event as well as on the CRNP calibration strategy. These findings indicate the possibility for using the CRNS technique as a tool for precision irrigation.

Orchards and other perennial crops represent one percent of the total global agricultural area. A significant portion is found in Mediterranean Europe covering 16 % of the total agricultural land. Apples are one of the most popular and valuable fruits and make up one third of all European orchards, covering more than 463’000 ha. In contrast to annual crops, perennial tree crops can be productive for several decades before rotation is needed. Moreover, the standing biomass of such cultivation serves as carbon storage and thus influences the carbon cycle in a different way than annual crops. However, perennial tree crops are rarely considered in the land components of earth system models. This creates an important gap in the representation of biogeophysical and biogeochemical processes that are a consequence of such agro-ecosystems. In this work we implemented an apple tree crop sub-model named CLM-APPLE that allows the simulation of a new apple plant functional type (PFT) within the existing framework of the Community Land Model version 5 (CLM5). Similarly to the existing deciduous tree PFTs that are available in CLM5, the apple PFT is described by a perennial deciduous phenology with standing woody biomass and annual leaf shedding. During the active growth period, the phenology and the carbon and nitrogen allocation of vegetative and harvestable organs are described by distinct growth phases and are driven by a growing degree day summation. In this new PFT, both the storage pool and the photosynthetic pool contribute to tree growth since, in fruit trees, leaf and shoot development at the beginning of a growing season utilizes carbohydrate reserves and nitrogenous compounds that were accumulated during the previous season. Furthermore, agricultural practices such as transplanting, fertilization, and stem pruning are represented in the new sub-model. New parameters were introduced, calibrated, and then validated with extensive field measurements from an apple-growing region in the Adige River valley (Italy). Monthly biomass components and leaf area index (LAI), measured directly and independently over six representative plots, were used for model calibration. Additionally, multiple years of continuous soil respiration measurements, eddy covariance measurements, and yield data were used for model calibration and validation. Simulated patterns in seasonal biomass allocation and LAI showed good agreement with the observations. Similarly, the simulated average yield between 2010 and 2015 was within 5 % of the observed yield. However, the observed inter annual variability was not well represented in the simulations. Overall, the model validation using eddy covariance data demonstrated the ability of the model to represent the seasonal dynamics and magnitudes of ecosystem fluxes at the orchard level. Despite these encouraging results, an accurate representation of inter-annual yield dynamics and seasonal fluctuations of fluxes at the orchard-scale remains challenging, suggesting the existence of processes that are not yet implemented in the model. Nonetheless, the newly developed sub-model CLM-APPLE allows the exploration of the carbon allocation dynamics and ecosystem fluxes in orchards as well as their effects on the terrestrial biogeochemical cycle.