Gabriel George Katul

This study connects the dots between global extent of severe water scarcity (SWS) events and spikes in food price. The authors start with three staple crops (rice, wheat, and maize) using a empirical but crop specific characterization of severe water scarcity occurrence, which is defined based on the area of land cultivated with each studied crop that on a given year is under SWS conditions. Water scarcity is estimated based on 1, 3, and 12 months standardized precipitation evapotranspiration index during crop specific sensitive periods (SPs). SPs coincide with the 4 months prior to harvest. A SWS‐wheat price relationship model was developed for period 2001–2021 and tested for 1986–2000 and 2022–2024 with future projections calculated under climate change using climate model simulations from CMIP5 and CMIP6 up to 2100. The results show a marked increase in wheat prices driven by increasing SWS area, which is in turn a function of greenhouse gas emissions. The results indicate that the meanglobal mean temperature increase by 3°C (compared to 1951–1980) could lead to the tripling of the mean global wheat price compared to 2010 (after deflating).

The benefits of agrivoltaics (AV) are documented, with empirical evidence for their suitability for collocated energy generation, crop cultivation, and water savings, even for staple crops. What is lacking is a holistic modeling framework to design and optimize AV systems for specific crop types, micro‐environments, and panel configurations. To this end, a multiphysics model that simultaneously solves momentum, energy, and mass exchanges among interacting components, and determines the implications of AV on photosynthesis, water use, power generation, and worker thermal stress is introduced. The model is tested against field data from California and Minnesota. Its applications are illustrated for a representative AV scenario using weather data from central New Jersey, showcasing the multifaceted benefits of AV for a tomato farm under photovoltaic panels. In this scenario, the model predicts a 5.6 reduction in average daytime panel temperature (compared to a common photovoltaic installation), recovering 15% of the thermal energy losses. Despite a 47% reduction in light for the setup modeled here, net carbon assimilation declined by 31%. Daytime crop leaf temperature cooled by 1.84, and these reductions offset some of the decline in carbon assimilation. Another benefit is the reduction in transpiration losses by 22.4% compared to an open crop field. Finally, the average perceived temperature experienced by workers was reduced by approximately 4.46 during working hours. While these are illustrative results for a particular crop and climate, they demonstrate how the model enables integrated assessment and optimization of AV benefits across varying climates, crops, and photovoltaic technologies.

Fog prediction remains challenging because the physical processes governing its life cycle evolve across time scales and do not follow reversible or stationary dynamics. Using high‐frequency visibility observations from Sable Island, Canada, this study analyzes fog intensity and turbulent kinetic energy (TKE) for their time irreversibility and causal relations. Fog intensity exhibits temporal asymmetry in all stages, while TKE remains nearly reversible. The lead–lag structure between the two variables is stage dependent: TKE leads fog intensity during formation, the coupling becomes symmetric during the mature phase, and fog intensity leads TKE during dissipation. Notably, during fog formation, the strength of fog's intrinsic irreversibility increases linearly with the strength of its causal linkage to TKE, revealing that fog initiation is governed by a directional sequence of turbulence–moisture interactions. These findings demonstrate that fog is a non‐equilibrium, time‐asymmetric system, and that capturing its stage‐dependent directionality is required for enhanced fog prediction.

The longitudinal turbulent heat flux is central to the description of vertical momentum and energy transport in stratified atmospheric boundary layers, yet its scaling behavior with respect to thermal stratification remains uncertain in comparison to better‐studied quantities such as the vertical heat flux. Here, the scaling laws of the longitudinal heat flux and its co‐spectrum in the atmosphere close to the surface as a function of wall normal distance and thermal stratification are experimentally evaluated. Measurements were conducted under varying stability regimes ranging from unstable to slightly stable at two sites. The first experiment included five high‐temporal resolution (100 Hz) velocity and temperature sensors as well as a triaxial sonic anemometer all positioned within 2 m above a bare soil surface. The second experiment included a single triaxial sonic anemometer positioned at 5 m above the surface of a grass‐covered forest clearing. The analysis first examines the Reynolds‐averaged Navier‐Stokes equations for the longitudinal heat flux and applies similarity theory to identify the dominant terms. This analysis is then used to inform a co‐spectral budget, which is used to deduce the appropriate scaling laws at large and inertial subrange scales. The proposed theory aims to reconcile discrepancies reported across field, laboratory, and numerical studies, and highlights the importance of non‐conserved scale‐wise flux transfer mechanisms unique to longitudinal heat flux in turbulent flows even when the longitudinal heat flux transport term is small.

A mathematical framework is developed to quantify the stability of phloem transport and delineate “safe operating” regimes using bifurcation analysis. Sucrose production is linked to leaf photosynthesis using stomatal optimality, while sucrose translocation follows pressure-driven flow based on the Munch mechanism. The model couples xylem water potential, osmotic driving force, hydraulic resistance (using a sucrose-dependent viscosity), and distributed sink removal along the transport pathway. Systematic variation of key carbon transport controls, such as xylem water potential and sink strength, reveals multiple equilibria and stability boundaries beyond which phloem transport becomes unstable to small carbon loading fluctuations. Phloem failure emerges through a saddle-node bifurcation, yielding two stable sucrose loading states separated by an unstable branch where the lower equilibrium falls within reported sucrose loading ranges. The resulting stability maps provide a mechanistic basis for phloem vulnerability and suggest that vascular safety requires coordination between photosynthetic supply (e.g., $V_{c,max}$), pathway length, and transport capacity as water potential declines. This stability map provides a mechanistic constraint that can inform trait-based ecosystem modeling and provide hypotheses about acclimation and vulnerability across environments.

The dual challenge of meeting rising global food demand while accelerating the clean energy transition requires innovative land-use strategies. Agrivoltaics—the co-location of solar photovoltaics and agriculture—offers a transformative solution, yet global adoption has been hindered by a lack of standardized sunlight requirements for crops. Here, we introduce a framework to quantify crop-specific light requirements using the Daily Light Integral (DLI). We define two distinct thresholds: a “theoretical” DLI and a “real-world” DLI. Applying these thresholds to the world’s four staple crops (wheat, maize, rice, and soybean), we assess the global potential for co-generation without compromising food security. Our analysis reveals that even under conservative design scenarios SC40 that prioritize food security, agrivoltaic systems could generate over 100,000 TWh of electricity annually. Soybean and wheat demonstrate the highest compatibility, particularly in the Middle East, South Asia, and the Americas. These findings outline a quantitative pathway for sustainable land-use transitions, showing that multifunctional landscapes can simultaneously mitigate climate change, reduce water use, and strengthen rural resilience. Agrivoltaics exemplifies the water–energy–food–environment nexus at scale by improving land-use efficiency, creating favorable microclimates, and reducing evaporative demand.

The temporal variability of precipitation originates from the multiplicative effects of air uplift and vapor-to-liquid conversion. The latter is commonly represented by a product of conversion efficiency and water vapor fluctuations. How exogenous effects such as those related to uplift contribute to the precipitation variance and to the build-up of intermittency within the precipitation cascade is investigated. The analysis employs a large dataset of 5-minute precipitation time series collected in a domain characterized by a combination of orographic, frontal, and convective systems, each with at least 30 years in record length. Exogenous versus endogenous factors (i.e. those related to water vapor turbulent fluctuations) responsible for intermittency build-up across the precipitation cascade were explored using the so-called propagator kernel. This analysis shows that precipitation generation maintains its signature at scales one order of magnitude smaller than the integral time. This interaction between exogenous and endogenous factors appears more significant in high-elevation precipitation records. The overall effect of such interactions is to reduce the anomalous scaling at the sub-storm time scales.