Chlorophyll autofluorescence from photosynthetic organisms arises from multiple populations of antennae and reaction centers that are regulated by both photochemical and non-photochemical quenching mechanisms within the complexes of the electron transport chain (PETC). This study develops a flash sequence to separate these two contributions, revealing five flux bottlenecks in the reactions of the PETC and the CBB cycle that limit carboxylation and growth. The data show that the PETC and CBB cycle operate synchronously, governed by the law of mass action and microscopic reversibility, with CO₂ fixation in the CBB accelerating upstream reactions in both linear and cyclic electron pathways.

In this study, we present an extension to the Monin–Obukov similarity theory (MOST) for the roughness sublayer (RSL) over short vegetation. We test our theory using temperature measurements from fiber optic cables in an array-shaped set-up. This provides a high vertical measurement resolution that enables us to measure the sharp temperature gradients near the surface. It is well-known that MOST is invalid in the RSL as the flow is distorted by roughness elements. However, to derive the surface temperature, it is common practice to extrapolate the logarithmic profiles down to the surface through the RSL. Instead of logarithmic behaviour defined by MOST near the surface, our observations show near-linear temperature profiles. This log-to-linear transition is described over an aerodynamically smooth surface by the Van Driest equation in classical turbulence literature. Here we propose that the Van Driest equation can also be used to describe this transition over a rough surface, by replacing the viscous length scale with a surface length scale L_s that represents the size of the smallest eddies near the grass structures. We show that L_s scales with the geometry of the vegetation and that the model shows the potential to be scaled up to tall canopies. The adapted Van Driest model outperforms the roughness length concept in describing the temperature profiles near the surface and predicting the surface temperature.

Wind machines are increasingly used to mitigate spring frost damage in agricultural sectors. Complementing quasi-3D temperature measurements to quantify the warming effects of wind machines (Dai et al., 2023), this study develops a numerical model to quantify warming effects on air and plant tissues and resolve the dynamic interplay between turbulent rotating plumes and canopy structure. We implement an integrated model in a large-eddy simulation and validate the model against field observations. Simulation results show remarkable agreement with the air mixing and warming effects observed during wind machine operation in Dai et al. (2023). Simulation results reveal significant air and leaf warming near the wind machine due to direct jet-mixing. Beyond 20 m from the machine (3–4 rotor diameters), while wind velocities drop rapidly, the warming is sustained and gradually decreases over distance. This sustained warming, without direct jet mixing, likely results from the advection of jet-entrained warm air. The warming extends 150 m upstream and 550 m downstream, influenced by the background wind. This difference is attributed to the interaction between the machine-induced jet and the background wind, forming convergence patterns when jets oppose the wind and extended warming plumes in wave-like patterns when jets align with the wind. Cross-stream warming symmetrically extends about 250 m. Within these warming regions, leaf temperatures closely follow air temperatures due to strong turbulent heat exchanges. Outside the warming zone, radiative cooling prevails, bringing the leaf–air temperature difference back to approximately 1 degree. These findings collectively give new insights into interactions between the induced warming plumes and air flows within the canopy and provide a useful tool to optimize operational wind machine deployment. This integrated model uniquely provides a full, multi-process representation of outdoor reality with respect to wind machine operation in orchards.

Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.

Wind machines have been increasingly used for frost damage mitigation in the agricultural community. During radiative frost nights, wind machines are used to erode near-surface thermal inversion by air mixing. The underlying mixing processes remain poorly understood. A full picture of warming effects caused by air mixing requires measurements with wide coverage and high resolution. Our study aimed to quantify the magnitude and area of warming by air mixing and identify the characteristic mixing processes downwind and upwind. We installed 9 km of fiber optic cables in a 6.75 ha orchard block, creating two horizontal planes and three vertical profiles. Quasi-3D temperature responses with spatial sampling and temporal resolution of 25 cm and 10 s, respectively, were obtained before and during machine operation. We found a 50% reduction of the local inversion strength (8 K) over 0.42 ha at 1 m and 0.46 ha at 2 m height. The warming area for a 30% reduction extends to 2.81 and 2.52 ha, respectively. As the propeller rotates 360°, the weak background wind substantially impacts the air mixing processes downwind and upwind. When jets blow along with background wind, the warming plumes arrive earlier than the jet due to horizontal advection from earlier warmed sections. The warming plumes consequently accumulate downwind and penetrate deep into the canopy. In contrast, in upwind direction, wind drag resistance causes warming plumes arrive later than the jet. Quadrant analysis reveals that flux transport during the machine operation is dominated by sweeping and ejection motions. Intermittent downdrafts of warm air and updrafts of cool air result in efficient vertical heat exchange. This feature makes wind machines highly effective in raising canopy airspace temperature to mitigate frost damage.

Plant transpiration accounts for about half of all terrestrial evaporation. Plants need water for many vital functions including nutrient uptake, growth and leaf cooling. The regulation of plant water transport by stomata in the leaves leads to the loss of 97% of the water that is taken up via their roots, to the atmosphere. Measuring plant-water dynamics is essential to gain better insight into its roles in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for plant-water dynamics measurement and highlight several promising innovations for future monitoring. Some of the techniques we will cover include: gas exchange for stomatal conductance and transpiration monitoring, lysimetry, thermometry, heat-based sap flow monitoring, reflectance monitoring including satellite remote sensing, ultrasound spectroscopy, dendrometry, accelometry, scintillometry, stable water isotope analysis and eddy covariance. To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques are required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly account for spatial heterogeneity, a dense network sampling is needed.

Thornthwaite's formula is globally an optimum candidate for large-scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (formerly FAO-56), which is the most data-demanding method and is commonly used as the benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity, and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature, rainfall, and Er data of the period 1950-2000 at 30arcsec resolution (1km at Equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er/Ep ratios by setting the ratios' weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep <45mm per month because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe; California, USA; and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula Eps using local coefficients led to a reduction of RMSE from 37.2 to 30.0mmm-1 for monthly step estimations and from 388.8 to 174.8mmyr-1 for annual step estimations compared to Ep using as a benchmark the values of the Er method. The corrected Eps and the original Ep Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as a benchmark the respective indices estimated by Er. The analysis was made using the validation data of the stations, and the results showed that the correction of the Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with Er from 63% to 76% for the case of Thornthwaite classification and from 76% to 93% for the case of UNEP classification. The performance of both aridity indices using the corrected formula was extremely improved in the case of non-humid classes. The global database of local correction factors can support applications of reference evapotranspiration and aridity index assessment with the minimum data requirements (i.e., temperature) for locations where climatic data are limited.

Norovirus infections are among the major causes of acute gastroenteritis worldwide. In Germany, norovirus infections are the most frequently reported cause of gastroenteritis, although only laboratory confirmed cases are officially counted. The high infectivity and environmental persistence of norovirus, makes the virus a relevant pathogen for water related infections. In the 2017 guidelines for potable water reuse, the World Health Organization proposes Norovirus as a reference pathogen for viral pathogens for quantitative microbial risk assessment (QMRA). A challenge for QMRA is, that norovirus data are rarely available over long monitoring periods to assess inter-annual variability of the associated health risk, raising the question about the relevance of this source of variability regarding potential risk management alternatives. Moreover, norovirus infections show high prevalence during winter and early spring and lower incidence during summer. Therefore, our objective is to derive risk scenarios for assessing the potential relevance of the within and between year variability of norovirus concentrations in municipal wastewater for the assessment of health risks of fieldworkers, if treated wastewater is used for irrigation in agriculture. To this end, we use the correlation between norovirus influent concentration and reported epidemiological incidence (R²=0.93), found at a large city in Germany. Risk scenarios are subsequently derived from long-term reported epidemiological data, by applying a Bayesian regression approach. For assessing the practical relevance for wastewater reuse we apply the risk scenarios to different irrigation patterns under various treatment options, namely “status-quo” and “irrigation on demand”. While status-quo refers to an almost all-year irrigation, the latter assumes that irrigation only takes place during the vegetation period from May - September. Our results indicate that the log-difference of infection risks between scenarios may vary between 0.8 and 1.7 log given the same level of pre-treatment. They also indicate that under the same exposure scenario the between-year variability of norovirus infection risk may be > 1log, which makes it a relevant factor to consider in future QMRA studies and studies which aim at evaluating safe water reuse applications. The predictive power and wider use of epidemiological data as a suitable predictor variable should be further validated with paired multi-year data.