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Abstract: In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m2 m−2), photosynthetic capacity (PC μmol m−2 s−1), or surface conductance in optimal conditions (Gs,opt, mmol m−2 s−1), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (FNPP) to leaf growth (af, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high af in summer, at the end of which LAI peaked, and PC and Gs,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (FGEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (FNEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of FGEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests. [ABSTRACT FROM AUTHOR]

Predicting the seasonal dynamics of ecosystem carbon fluxes is challenging in broadleaved evergreen forests because of their moderate climates and subtle changes in canopy phenology. We assessed the climatic and biotic drivers of the seasonality of net ecosystem-atmosphere CO2 exchange (NEE) of a eucalyptus-dominated forest near Sydney, Australia, using the eddy covariance method. The climate is characterised by a mean annual precipitation of 800mm and a mean annual temperature of 18°C, hot summers and mild winters, with highly variable precipitation. In the 4-year study, the ecosystem was a sink each year (-225gCm-2yr-1 on average, with a standard deviation of 108gCm-2yr-1); inter-annual variations were not related to meteorological conditions. Daily net C uptake was always detected during the cooler, drier winter months (June through August), while net C loss occurred during the warmer, wetter summer months (December through February). Gross primary productivity (GPP) seasonality was low, despite longer days with higher light intensity in summer, because vapour pressure deficit (D) and air temperature (Ta) restricted surface conductance during summer while winter temperatures were still high enough to support photosynthesis. Maximum GPP during ideal environmental conditions was significantly correlated with remotely sensed enhanced vegetation index (EVI; r2=0.46) and with canopy leaf area index (LAI; r2=0.29), which increased rapidly after mid-summer rainfall events. Ecosystem respiration (ER) was highest during summer in wet soils and lowest during winter months. ER had larger seasonal amplitude compared to GPP, and therefore drove the seasonal variation of NEE. Because summer carbon uptake may become increasingly limited by atmospheric demand and high temperature, and because ecosystem respiration could be enhanced by rising temperatures, our results suggest the potential for large-scale seasonal shifts in NEE in sclerophyll vegetation under climate change. [ABSTRACT FROM AUTHOR]

The critically endangered Cumberland Plain woodland within the greater Sydney metropolitan area hosts a dwindling refuge for melaleuca trees, an integral part of Australia's native vegetation. Despite their high carbon stocks, melaleucas have not explicitly been targeted for studies assessing their carbon sequestration potential, and especially little is known about their energy cycling or their response to increasing climate stress, precluding a holistic assessment of the resilience of Australia's forests to climate change. To improve our understanding of the role of melaleuca forest responses to climate stress, we combined forest inventory and airborne LiDAR data to identify species distribution and associated variations in forest structure, and deployed flux towers in a melaleuca-dominated (AU-Mel) and in a eucalypt-dominated (AU-Cum) stand to simultaneously monitor carbon and energy fluxes under typical growing conditions, as well as during periods with high atmospheric demand and low soil water content. We discovered that the species distribution at our study site affected the vertical vegetation structure, leading to differences in canopy coverage (75% at AU-Cum vs. 84% at AU-Mel) and plant area index (2.1 m2 m−2 at AU-Cum vs. 2.6 m2 m−2 at AU-Mel) that resulted in a heterogeneous forest landscape. Furthermore, we identified that both stands had comparable net daytime carbon exchange and sensible heat flux, whereas daytime latent heat flux (115.8 W m−2 at AU-Cum vs 119.4 W m−2 at AU-Mel, respectively) was higher at the melaleuca stand, contributing to a 0.3 °C decrease in air temperature and reduced vapor pressure deficit above the melaleuca canopy. However, increased canopy conductance and higher latent heat flux during moderate VPD or when soil moisture was low indicated a lack of water preservation at the melaleuca stand, highlighting the potential for increased vulnerability of melaleucas to projected hotter and drier future climates. Unlabelled Image • Integrating remote sensing with plot inventories and eddy covariance measurements • Monitoring ecosystem function in a fragmented, peri-urban environment • Ecosystem function linked to ecosystem structure • Species distribution affects stand structure, canopy coverage and leaf area index • Melaleuca stands more vulnerable to heat and moisture stress than eucalypt stands [ABSTRACT FROM AUTHOR]