Department of Earth System Science

Controlled Environment Agriculture (CEA) offers high-yield, climate-resilient food production, but high energy and resource demands challenge its sustainability. This paper synthesizes technologies that can improve outcomes across six categories—energy, CO2 utilization, building envelope, hardware, water, and process—plus colocation strategies. We evaluate 80 technologies and define ten implementation pathways bundling complementary technologies to reduce energy use, optimize water consumption, and minimize emissions. Regional application is demonstrated through five U.S. case studies spanning different climates. A logic framework guides pathway selection for case studies based on climate, infrastructure, and regulatory context, informing context-sensitive technology deployment. Results show energy intensity reductions of 3–55 %, ranging from energy management programs to comprehensive lighting retrofits; water savings of 20–40 % through closed-loop recirculation; and emissions reductions of 3–100 %, with strategic energy management achieving 3–5 % and renewable electricity paired with electrified heating achieving up to 100 %. Text mining revealed that energy, hardware, and process technologies account for 91 % of literature coverage. Water, building envelope, and CO2 utilization remain underexplored, indicating priorities for future research. This integrative approach to technology assessment supports growers, developers, and policymakers in aligning CEA system design with local conditions, improving resource efficiency and addressing gaps in cross-domain technology coverage.

Drought has consequences for microbial decomposition rates, including indirect effects through changes in plant litter chemistry. Here, we studied the impact of a decade-long drought on plant litter chemistry and microbial decomposition traits in a semi-arid ecosystem during an 18-month litter bag experiment. We investigated litter sourced from four conditions: grass and shrub vegetation under ambient and reduced precipitation. We hypothesized that litter chemistry drives microbial decomposition capabilities and enzyme activity due to vegetation differences and drought effects on litter chemistry. We found that carbohydrate-rich grass litter had a higher abundance of decomposition genes detected using metagenomics and enzyme activity than more recalcitrant shrub litter, which was richer in lignin and lipids; these patterns were related to substrate supply. Drought decreased some carbohydrate fractions in grass litter but did not change the lignin fraction in grass and shrub litter, suggesting that drought does not make litter more recalcitrant. Most decomposition genes and enzyme activities were not significantly affected by drought, thereby maintaining decomposition rates. Microbial community succession patterns-decreasing fungal abundance and increasing bacterial abundance with time-corresponded with decreasing chitin gene abundance and increasing peptidoglycan gene abundance over time, indicating microbial necromass recycling. We demonstrate minimal litter chemistry-mediated effects of drought but show significant changes in community composition and their decomposition capabilities over time, highlighting that complex microbial-chemical interactions under climate change can influence ecosystem-scale processes. IMPORTANCE: Climate change is causing more severe and frequent droughts in semi-arid ecosystems, affecting soil microbes breaking down plant litter. Our research focuses on understanding the less studied pathway of drought impact on microbes via changes in plant litter chemistry. Drought can alter the plant litter chemistry by changing the composition and physiology of plants, which can alter microbial decomposition and ecosystem-level carbon cycling. We investigated litter decomposition traits of microbial communities in grass and shrub litter under long-term drought. There were significant changes in litter chemistry under drought but no increase in lignin fraction. Despite this, microbial communities maintained their decomposition capabilities under drought, highlighting the ability of microbes to adapt and continue functioning. We also demonstrate unique microbial community succession patterns and dead biomass recycling, which can have implications for carbon cycling rates in the ecosystem. This study sheds light on the complex microbial interactions that affect ecosystem functioning under climate change.

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

Reliable predictions of dryland carbon fluxes require understanding the persistence and turnover of soil organic carbon (SOC). We measure radiocarbon to quantify the age of SOC and CO₂ released from soil respiration at 97 dryland sites across six continents. Here we show that bulk SOC contains little C fixed in the past 60 years, while respired CO₂ originates from both bomb-derived recent C and millennia-old C, challenging the idea that old C is chemically or physically protected. Radiocarbon suggests mean ages of ~2100 years for bulk SOC and ~520 years for respired CO₂, the latter far older than machine-learning (<50 years) or Earth system models predict. Aridity, net primary productivity, and SOC content are dominant predictors for radiocarbon signatures, with abrupt shifts to older C beyond an aridity threshold of ~0.87. Our findings underscore the need to incorporate the vulnerability of older carbon into models and land management strategies.

Natural disturbances are major drivers of tropical forest dynamics, yet their role in Central Africa’s old-growth rainforests, the world’s second largest tropical forest block, remains poorly quantified. Here we present the first regional assessment of windthrow, the uprooting or breakage of trees by wind. Using Landsat imagery from 2019 to 2020, we detected 74 windthrow events ≥30 ha, collectively affecting ~18,600 ha. These events were concentrated in eastern regions where mesoscale convective systems and extreme rainfall are most frequent. Sizes of windthrow events followed a Weibull distribution, with a single 3974 ha event accounting for one fifth of the total affected area. Event orientations aligned with prevailing storm outflows, and their timing coincided with peaks in extreme rainfall. For a subset of seven events with adequate temporal coverage before and after disturbance, near-infrared reflectance returned to pre-disturbance levels within months, indicating a rapid vegetation regrowth. Together, these findings show that windthrow is an important disturbance agent in Central Africa and must be considered in assessments of forest resilience under intensifying storm regimes.

Turmeric (Curcuma longa) is an economically important crop native to hot and humid regions of India. The current study aims to establish the basis for a multifaceted understanding of turmeric plants’ cellular and physiological responses to heat stress. We combined transcriptomic (RNA-seq) analysis, real-time photosynthesis data, and profiles of emissions of biogenic volatile organic compounds (BVOCs) to interpret the responses of turmeric plants to heat stress. RNAseq data indicated significant differential expression in 41.8% of genes, with photosynthesis-related genes overrepresented. Photosynthetic rate measurements demonstrated an almost complete failure of photosynthesis at 41 °C, followed by incomplete recovery at a normal temperature of 21 °C. Emissions of almost all detected BVOCs increased considerably during periods of heat treatment. The data establishes the ontological gene groups most affected, estimates the temperature tolerance of turmeric, and provides insights into plant responses to climate change.

Understanding how land-use change alters the flow of ecosystem services is critical for sustainability policy and planning. We conducted a systematic review of 459 peer-reviewed articles published between 2001 and March 2024 to (a) catalogue which ecosystem services have been studied and their geographic distribution, (b) trace methodological innovations in mapping, and (c) assess the integration of emerging data streams. Our analysis identified 23 ecosystem services spanning provisioning (food, water, raw materials), regulating (climate regulation, erosion control, flood protection), and cultural (recreation, aesthetics), with provisioning services most often mapped individually but regulating services dominating in category-level assessments. Regionally, Asia (especially China) contributed over half (51 %) of the case studies, while case studies in Europe, Africa, and the Americas are also increasing, aided by international collaborations in nearly 30 % of the studies. Biophysical modeling (98 % of papers) and economic valuation (55 %) remain foundational, supplemented by scenario analysis (11 %), participatory mapping (8 %), and a growing adoption of machine-learning techniques (27 %). Data diversity has expanded beyond Landsat (used in 90 % of studies) and land-cover products (84 %) to include Sentinel, Moderate Resolution Imaging Spectrometer (MODIS), elevation and topographic data, climate grids, soil moisture, and citizen-science platforms. Despite advances, persistent uncertainties arise from scale mismatches, data proxies, and model rigidity. We highlight emerging ensemble and hybrid approaches that blend advanced analytics with local knowledge, and call for capacity building in data-scarce regions. These directions promise more accurate, equitable, and actionable assessments to guide nature-based land-use decisions-makings.

Windthrow, defined as abrupt tree mortality caused by intense winds, is well documented in the Neotropics but remains largely unverified in Asian tropical forests. Using Landsat 8 imagery for the period 2020-2022 and established spectral (shortwave-infrared, near-infrared, and red band composites) and morphological criteria (fan-shaped geometry and sharp boundaries), this study verified the presence of windthrow across Sumatra, Borneo, and New Guinea and produced the first georeferenced inventory of 60 events in the region. Event sizes were right-skewed, with many small gaps and few large (>200 ha) disturbances. Orientations were anisotropic, with concentrations of windthrow events pointing west to northwest-to-north and eastward, both aligning with known pathways of organized convection in the Maritime Continent of Southeast Asia. Detection was conservatively biased low by cloud cover, long revisit intervals, and the exclusion of land-use areas. This study provides a verified baseline that enables reproducible mapping with higher resolution sensors and field observations to quantify windthrow frequency, severity, and ecological significance.

Characterizing the physical and dynamic meteorology of wildland fires has obvious socioeconomic importance and is necessary to develop not only firefighting but also mitigation strategies such as prescribed burns and effective fuel management practices such as forest thinning. However, despite significant progress over a century, there are shortcomings in our understanding of the physical processes governing wildland fire behavior. Although some research progress has been made in understanding how fires spread on grasslands, several aspects of fire behavior within the forest canopy environment are still not well-understood. This review is an attempt to organize the fluid mechanics of the mass, momentum, and energy transfer during wildland fire events through the lens of vegetation canopy turbulence. The structure, organization, and progress of the flame front and the buoyant plume through the canopy are shown to be intricately related to the coherent structures associated with fire–vegetation–atmosphere interaction, and potential future research directions are identified.