Elke Eichelmann

The task of reliably partitioning evapotranspiration (ET) is imperative so that we can better understand how individual components of the terrestrial water flux are contributing to the global hydrological cycle and changing under a warming climate. By constraining how evaporation ( E ) and transpiration ( T ) separately adapt to increased global temperatures, we can make more accurate predictions in land surface models, further our understanding of plant water use, and better manage our limited water resources. Eddy covariance (EC) is a globally used technique that measures net biosphere-atmosphere fluxes, including ET, and if reliably partitioned, presents a promising way to constrain E and T values and trends across ecosystems. Several EC-based ET partitioning methods exist, and there is a need for an updated comprehensive guide to the available approaches. This systematic literature review was conducted with the objectives of (1) identifying EC-based ET partitioning methods and categorizing them based on underlying ecosystem assumptions, (2) determining the main advantages and disadvantages of each method dependent on their assumptions and data requirements, and (3) evaluating how broadly these methods have been applied based on geographic location and ecosystem type. The review identified 11 independent partitioning methods applied across 129 studies. Methods using assumptions of underlying water use efficiency (uWUE) and ecosystem conductance all use the relationship between ET and gross primary production with vapor pressure deficit (VPD) to estimate the transpiration ratio ( T / ET). Additionally, two machine learning based methods, one method assuming a linear relationship between ET and gross ecosystem photosynthesis, and four methods using high frequency EC data to estimate T / ET were identified. The uWUE methods, while the most frequently used partitioning approach, consistently predicted the lowest T / ET estimates when compared to both other EC and many non-EC based partitioning methods. The machine learning methods predicted the highest T / ET values compared to other EC-based methods which agreed well with values estimated with independent methods. Savannas and evergreen broadleaf forests had the highest T / ET of all ecosystem types while deserts and wetlands had the lowest. Leaf area index and soil water content were found to be the most important drivers of T / ET values and trends with VPD and air temperature also displaying significant effects. Of the global studies identified in this review, an average annual T / ET value of 0.573 ± 0.10 was found, a value that falls within the range of other studies using isotopic partitioning, remote sensing methods, and global estimates from various ecosystem models. More testing, specifically increased paired analyses of two or more EC-based ET partitioning methods on the same dataset and more validations against independent observations, are needed in order to fully understand the applicability of each method, their differences, and to better constrain global T / ET dynamics.

Despite the growing interest in carbon cycling in tidal‐wetland ecosystems we lack sufficient understanding of the degree to which saltmarsh ecosystems sequester carbon and the ecosystem dynamics of carbon dioxide (CO ₂ ) exchange, especially with tidal influence. The eddy covariance method was used to estimate a carbon budget at the ecosystem‐scale for an Irish Atlantic saltmarsh and investigate the effects of tidal inundation on net ecosystem exchange (NEE). NEE was partitioned into gross primary productivity (GPP) and ecosystem respiration (R _eco ). Biophysical drivers were determined on hourly, diurnal, and multiday timescales using wavelet analysis. Growing season R _eco and GPP during spring‐ and neap‐tide periods were also investigated to assess tidal impacts on CO ₂ flux magnitude. Derrymore saltmarsh acted as a CO ₂ sink for all but 4 months of the year, sequestering 307 g C m ⁻² yr ⁻¹ . Biophysical drivers of CO ₂ fluxes scaled temporally: hourly NEE and GPP were primarily regulated by latent energy ( E ), diurnal variations responded to both E and photosynthetically active radiation (PAR). On multiday scales, however, PAR emerged as the dominant driver. Similarly, while air temperature ( T _a ) governed R _eco at hourly and diurnal frequencies, multiday respiration was shaped by the combined influence of T _a , water temperature, and water level ( H _w ). Marsh full inundation ( H _w > 0 m) by tidal waters decreased daytime GPP up to 73%, and night‐time R _eco up to 45% during the growing season. This research shows that even in northern climates, saltmarshes can be productive and valuable carbon sinks, though their performance is strongly affected by tidal flooding.

The task of reliably partitioning evapotranspiration (ET) is imperative so that we can better understand how individual components of the terrestrial water flux are contributing to the global hydrological cycle and changing under a warming climate. By constraining how evaporation (E) and transpiration (T) separately adapt to increased global temperatures, we can make more accurate predictions in land surface models, further our understanding of plant water use, and better manage our limited water resources. Eddy covariance (EC) is a globally used technique that measures net biosphere-atmosphere fluxes, including ET, and if reliably partitioned, presents a promising way to constrain E and T values and trends across ecosystems. Several EC-based ET partitioning methods exist, and there is a need for an updated comprehensive guide to the available approaches. This systematic literature review was conducted with the objectives of 1) identifying EC-based ET partitioning methods and categorizing them based on underlying ecosystem assumptions, 2) determining the main advantages and disadvantages of each method dependent on their assumptions and data requirements, and 3) evaluating how broadly these methods have been tested based on geographic location and ecosystem type. The review identified 10 independent partitioning methods tested across 123 studies. Methods using assumptions of underlying water use efficiency (uWUE) and ecosystem conductance all use the relationship between ET and gross primary production with vapor pressure deficit (VPD) to estimate the transpiration ratio (T/ET). Additionally, two machine learning based methods, one method assuming a linear relationship between ET and gross ecosystem photosynthesis, and three methods using high frequency EC data to estimate T/ET were identified. The uWUE methods, while the most frequently used partitioning approach, consistently predicted the lowest T/ET estimates when compared to both other EC and non-EC based partitioning methods. The machine learning methods predicted the highest T/ET values compared to other EC-based methods which agreed well with values estimated with independent methods. Savannas and evergreen broadleaf forests had the highest T/ET of all ecosystem types while deserts and wetlands had the lowest. Leaf area index and soil water content were found to be the most important drivers of T/ET values and trends with VPD and air temperature also displaying significant effects. Of the global studies identified in this review, an average annual T/ET value of 0.58 was found, a value that falls within the range of other studies using isotopic partitioning, remote sensing methods, and global estimates from various ecosystem models. More testing, specifically increased paired analyses of two or more EC-based ET partitioning methods on the same dataset, is needed in order to fully understand the applicability of each method, their differences, and to better constrain global T/ET dynamics.

Due to the coupling of gas exchange in terrestrial ecosystems, transpiration (T) estimates are a key insight into global climate, water, and carbon patterns. While extensive datasets of evapotranspiration (ET) are abundant, largely due to global eddy covariance networks, independent measurements of T are relatively sparse. As such, there is a need to partition T out of eddy covariance-measured ET so that we can better understand how the individual components of the terrestrial water vapor flux are contributing to the global water cycle and changing under a warming climate. Physics-informed machine learning (PI-ML) presents a novel way to partition eddy covariance-measured ET even without extensive T datasets for validation. PI-ML works to constrain the model to obey underlying governing equations driving the system dynamics, giving unique insights into model estimates. Here, PI-ML is introduced to estimate the ecosystem transpiration ratio (T/ET) using eddy covariance data collected from a soybean field in Ontario, Canada. The model, founded on the principle that transpiration follows a sine curve over a 24hr period, constrains both the upper and lower bounds of T/ET by assuming transpiration is negligible overnight and that the agricultural site has periods with negligible soil evaporation. The PI-ML model was validated against leaf-level transpiration measurements collected over the 2025 growing season as well as compared to results from other eddy covariance-based ET partitioning methods. Preliminary results for the 2019 growing season showed the PI-ML estimated a daytime average T/ET of 0.584 in the soybean field, compared to 0.468 estimated from an underlying water use efficiency-based method and 0.653 estimated from a method using data-driven machine learning. The physically realistic T estimates produced by the PI-ML show the model’s ability to accurately represent the ecosystem dynamics of the soybean site where it was applied. Accurate T estimates give way for better management of our limited water resources, leading to increased water quality and food security when used in agricultural settings.

The task of reliably partitioning evapotranspiration (ET) is imperative so that we can better understand how individual components of the terrestrial water flux are contributing to the global hydrological cycle and changing under a warming climate. By constraining how evaporation (E) and transpiration (T) separately adapt to increased global temperatures, we can make more accurate predictions in land surface models, further our understanding of plant water use, and better manage our limited water resources. Eddy covariance (EC) is a globally used technique that measures net biosphere-atmosphere fluxes, including ET, and if reliably partitioned, presents a promising way to constrain E and T values and trends across ecosystems. Several EC-based ET partitioning methods exist, and there is a need for an updated comprehensive guide to the available approaches. This systematic literature review was conducted with the objectives of 1) identifying EC-based ET partitioning methods and categorizing them based on underlying ecosystem assumptions, 2) determining the main advantages and disadvantages of each method dependent on their assumptions and data requirements, and 3) evaluating how broadly these methods have been tested based on geographic location and ecosystem type. The review identified 10 independent partitioning methods tested across 123 studies. Methods using assumptions of underlying water use efficiency (uWUE) and ecosystem conductance all use the relationship between ET and gross primary production with vapor pressure deficit (VPD) to estimate the transpiration ratio (T/ET). Additionally, two machine learning based methods, one method assuming a linear relationship between ET and gross ecosystem photosynthesis, and three methods using high frequency EC data to estimate T/ET were identified. The uWUE methods, while the most frequently used partitioning approach, consistently predicted the lowest T/ET estimates when compared to both other EC and non-EC based partitioning methods. The machine learning methods predicted the highest T/ET values compared to other EC-based methods which agreed well with values estimated with independent methods. Savannas and evergreen broadleaf forests had the highest T/ET of all ecosystem types while deserts and wetlands had the lowest. Leaf area index and soil water content were found to be the most important drivers of T/ET values and trends with VPD and air temperature also displaying significant effects. Of the global studies identified in this review, an average annual T/ET value of 0.58 was found, a value that falls within the range of other studies using isotopic partitioning, remote sensing methods, and global estimates from various ecosystem models. More testing, specifically increased paired analyses of two or more EC-based ET partitioning methods on the same dataset, is needed in order to fully understand the applicability of each method, their differences, and to better constrain global T/ET dynamics.

Evapotranspiration (ET) is the dominant hydrologic flux in wetlands, and partitioning into transpiration (T) and evaporation (E) is essential for understanding water and carbon dynamics, guiding sustainable water management practices, and predicting responses to climate change in these systems. However, the presence of moss layers in many wetlands challenges the assumptions of commonly used partitioning methods. This study evaluates the performance of nine eddy covariance (EC)-based ET partitioning approaches across multiple moss-covered wetland sites located in boreal and the Canadian Rocky Mountains. The partitioning results from each approach were compared against independent measurement-based estimates, which were obtained using flux chamber, micro-lysimeters, sap flow sensors, and EC systems. Results showed that none of the evaluated methods provided both accurate and precise estimates of ET partitioning (T:ET), and no single method emerged as the most suitable for studied ecosystems. Despite this, the general agreement between modelled and measured T:ET values indicates that many of these approaches still provide valuable insights. Applying multiple methods concurrently is recommended, where possible, to enhance confidence in partitioning results. For researchers with access to high-frequency EC data, priority should be given to high-frequency EC-based methods due to their more consistent performance across sites. The findings also highlight the limitations of current partitioning approaches under evaporation-dominated conditions, and underscore the need to examine the mechanistic role of mosses, as well as to improve how optimal stomatal conductance theory is conceptualized and implemented in model formulations.

Extreme weather events (EWEs) including storms, heatwaves, floods, droughts, wildfires, and cold snaps present an increasing threat to protected habitats and species (PHAS) as well as ecosystem services they provide. Under ongoing climate change, the frequency, intensity, and spatial extent of such events are projected to increase, with disproportionately severe impacts on ecologically sensitive and conservation-priority areas. Despite this growing risk, existing vulnerability assessment frameworks for PHAS have primarily focused on long-term, gradual climatic shifts, largely overlooking the episodic and often catastrophic nature of EWEs. Here, we propose a structured methodological framework to assess the vulnerability of PHAS to extreme climatic events, based on the three IPCC vulnerability components including exposure, sensitivity, and adaptive capacity (ExP-SeN-AdC). The framework introduces the Extreme Event Vulnerability Index (EVI), an integrative metric designed to quantify vulnerability of PHAS to EWEs in a transparent, standardized, and scalable manner. The framework follows an eight-step process: (1) defining the assessment scope; (2) selecting ecologically meaningful indicators; (3) evaluating data availability and acquiring data; (4) constructing component indices for ExP-SeN-AdC; (5) computing the composite vulnerability metric; (6) evaluating uncertainties through validation and sensitivity analysis; (7) communicating and refining results through stakeholder engagement; and (8) formulating science-based conservation and adaptation recommendations. Finally, we demonstrate application of the proposed framework through a case study on Atlantic Salt Meadow habitats (Glauco-Puccinellietalia Maritimae) located within Dublin Bay’s Natura 2000 sites, encompassing a mosaic of Special Areas of Conservation (SACs) and Special Protected Areas (SPAs) under the EU Habitat Directive. The framework and its methodological approach lay the foundation for the vulnerability analysis and mapping, advancing adaptation planning, and guiding targeted interventions and conservation efforts, and the development of forward-looking conservation policies in the face of escalating extreme climatic threats.

The planetary boundary layer height (zi) is a key parameter in meteorology and climatology, influencing weather prediction, cloud formation, and the vertical transport of scalars and energy near Earth's surface. This study compares multiple machine learning (ML) models that predict zi from surface measurements at two sites in Central Amazonia—the Amazon Tall Tower Observatory (ATTO) and the Manacapuru site of the GoAmazon experiment (T3). Models were trained on ceilometer data with radiosonde measurements used for validation. We evaluated model performance by withholding approximately 10% of the data (as complete months) for testing, comparing predictions against ERA‐5 reanalysis data using RMSE, nRMSE, and R² metrics. Our results show that gradient boosted ensemble models using all available features perform best. A modified recursive feature elimination algorithm identified minimal sets of 5–7 surface measurements sufficient for accurate zi prediction, demonstrating potential for wider spatial monitoring using cost‐effective sensors. The study revealed previously unrecognized variables influential in determining zi, such as deep soil temperature measurements (40 cm), suggesting new avenues for investigating land‐atmosphere interactions. This study demonstrates the applicability of ML models to model zi.

Saltmarshes are important ecosystems for carbon capture and storage and play a vital role in carbon cycling processes. Despite their significance, the carbon fluxes within Irish saltmarshes remain poorly understood, with a notable absence of greenhouse gas flux data from coastal wetlands in the region. This study addresses this gap by employing the eddy covariance method to calculate carbon dioxide (CO2) fluxes from Derrymore saltmarsh, County Kerry, a natural estuarine saltmarsh on the west coast of Ireland, from May 2023 to the present. The tower is equipped with an open path CO2 and water vapour (H2O) infrared gas analyser (LI-7500, LI-COR biosciences) and a sonic anemometer (CSAT3, Campbell Scientific) set 3.2m above the marsh surface. This method allows us to get continuous high frequency CO2 and H2O data with measurements being taken ten times per second.Our findings reveal patterns in net ecosystem exchange (NEE), with higher values observed during autumn compared to summer months, attributed to reduced photosynthetic CO2 uptake. These findings are comparable to saltmarshes in other regions. With a project duration of 12 months, our hypothesis suggests Derrymore saltmarsh will act as a modest CO2 sink.The use of the eddy covariance method allows us to get an overall picture as to the extent to which this saltmarsh acts as a carbon sink, giving us a better understanding of the carbon dynamics from this Kerry saltmarsh. This ongoing project contributes vital data to a broader initiative in Irish saltmarsh research, aiming to establish a scientific foundation for a comprehensive management framework. The framework will guide efforts in saltmarsh protection, restoration, and optimisation of carbon sequestration. Our research underscores the importance of understanding local variations in carbon dynamics, paving the way for informed environmental strategies in the context of climate change.