The Sustainable Development Goals (SDGs), aiming for global targets by 2030, are tracked by a monitoring framework comprising 231 environmental, social, and economic indicators. The framework provides data to assess whether, across countries, environmental policies are: 1. Addressing environmental pressures, 2. Linked to environmental improvements, and 3. Linked with social benefits delivered by healthy environments. While several studies have analysed the implementation and impacts of the SDGs, there remains a critical research gap in assessing the linkage between environmental policies and their potential to deliver multiple ecological and social benefits. This study examines the efficacy of environmental policies and their implications for global environmental health and social wellbeing. We use a generalised linear modeling approach to test for correlations between SDG indicators. We show that some environmental policies, particularly protected areas and sustainable forest certification, are linked with environmental improvements, mainly in forest and water ecosystems. However, we find no evidence that environmental improvements are linked with positive social impacts. Finally, environmental pressures, including freshwater withdrawal, domestic material consumption, and tourism, are linked with environmental degradation. Environmental policy responses are generally increasing across countries. Despite this, the state of the environment globally continues to decline. Governments must focus on understanding why environmental policies have not been sufficient to reverse environmental decline, particularly concerning the pressures that continue to degrade the environment. To better track progress towards sustainable development, we recommend that the SDG monitoring framework is supplemented with additional indicators on the state of the environment.

Arctic permafrost thaw holds the potential to drastically alter the Earth’s surface in Northern high latitudes. We utilize high-resolution Large Eddy Simulations to investigate the impact of the changing surfaces onto the neutrally stratified Atmospheric Boundary Layer (ABL). A stochastic surface model based on Gaussian Random Fields modeling typical permafrost landscapes is established in terms of two land cover classes: grass land and open water bodies, which exhibit different surface roughness length and surface sensible heat flux. A set of experiments is conducted where two parameters, the lake areal fraction and the surface correlation length, are varied to study the sensitivity of the boundary layer with respect to surface heterogeneity. Our key findings from the simulations are the following: The lake areal fraction has a substantial impact on the aggregated sensible heat flux at the blending height. The larger the lake areal fraction, the smaller the sensible heat flux. This result gives rise to a potential feedback mechanism. When the Arctic dries due to climate heating, the interaction with the ABL may accelerate permafrost thaw. Furthermore, the blending height shows significant dependency on the correlation length of the surface features. A longer surface correlation length causes an increased blending height. This finding is of relevance for land surface models concerned with Arctic permafrost as they usually do not consider a heterogeneity metric comparable to the surface correlation length.

Enhanced weathering (EW) is a promising strategy to remove atmospheric CO2 by amending agricultural and forestry soils with ground silicate materials. However, the current model-based assessments of EW potential face uncertainties stemming from the intricate interplay among soil physical, chemical, and biotic processes, compounded by the absence of a detailed model-data comparison, mostly due to the limited availability of comprehensive data. Here, we address this critical gap by advancing and validating an ecohydrological and biogeochemical model for EW dynamics in soils. We conduct a hierarchical validation in which model results are critically compared to four experimental datasets of increasing complexity, from simple closed incubation systems to open mesocosm experiments. The comparison demonstrates the model ability to capture the dynamics of primary variables, including rock alkalinity release and CO2 sequestration. The comparison also reveals that weathering rates are consistently lower than traditionally assumed by up to two orders of magnitude. We finally discuss avenues for further theoretical and experimental explorations.

As oceanic moisture evaporates, it leaves a signature on sea surface salinity. Roughly 10% of the moisture that evaporates over the ocean is transported over land, allowing the salinity fields to be a predictor of terrestrial precipitation. This research is among the first in published literature to assess the role of sea surface salinity for improved predictions on low-skill summertime subseasonal timescales for terrestrial precipitation predictions. Neural networks are trained with the CESM2 Large Ensemble using North Atlantic salinity anomalies to quantify predictability of U.S. Midwest summertime heavy rainfall events at 0 to 56-day leads. Using explainable artificial intelligence, salinity anomalies in the Caribbean Sea and Gulf of Mexico are found to provide skill for subseasonal forecasts of opportunity, e.g. confident and correct predictions. Further, a moisture-tracking algorithm applied to reanalysis data demonstrates that the regions of evaporation identified by neural networks directly provide moisture that precipitates in the Midwest.

Mesoscale-to-microscale coupling is an important tool to conduct turbulence-resolving multiscale simulations of realistic atmospheric flows, which are crucial for applications ranging from wind energy to wildfire spread studies. Different techniques are used to facilitate the development of realistic turbulence in the large-eddy simulation (LES) domain while minimizing computational cost. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method using momentum perturbation (SCPM-M) to accelerate turbulence generation in boundary-coupled LES simulations using the Weather Research and Forecasting (WRF) model. We simulate a convective boundary layer (CBL) to characterize the production and dissipation of turbulent kinetic energy (TKE) and the variation of TKE budget terms. Furthermore, we evaluate the impact of applying momentum perturbations of three magnitudes below, up to, and above the CBL on the TKE budget terms. Momentum perturbations greatly reduce the fetch associated with turbulence generation. When applied to half the vertical extent of the boundary layer, momentum 1 perturbations produce an adequate amount of turbulence. However, when applied above the CBL, additional structures are generated at the top of the CBL, near the inversion layer. The magnitudes of the TKE budgets produced by SCPM-M when applied at varying heights and with different perturbation amplitudes are always higher near the surface and inversion layer than those produced by No-SCPM, as are their contributions to the TKE. This study provides a better understanding of how SCPM-M reduces computational costs and how different budget terms contribute to TKE in a boundary-coupled LES simulation.

ABSTRACT Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to measure the extent of management-driven bias in fecal coliform observations collected in a particular area. 1. INTRODUCTIONHealthy estuarine environments are critical for maintaining ecological stability, coastal economies, and human health standards. In order to maintain and even improve these habitats, metrics of current and past conditions must be evaluated to inform proper management. Water quality measurements can be used to indicate overall estuarine health and can aid in understanding increasing coastal threats such as rising sea levels, increased salinities, and urbanization. Long-term water quality analysis is key for developing target thresholds for future management action as well as assessing the efficacy of past management measures (Cloern et al., 2016). The value of historical observations in advancing understanding of estuarine water quality has been demonstrated by multi-decadal studies of several systems, including the San Francisco Bay area (Beck et al., 2018; Cloern et al., 2016), May River, South Carolina (Souedan et al., 2021), Texas’s coastline (Bugica et al., 2020), and the Chesapeake Bay area (Zhang et al., 2018; Harding et al., 2019). Most notably, long-term water quality monitoring in the Chesapeake Bay has led to the identification of climatic and anthropogenic drivers for certain water quality parameters and subsequent evaluation of the effectiveness of past management and restoration efforts (Kemp et al., 2005; Leight et al., 2011; Zhang et al., 2018; Harding et al., 2019).Datasets used for prior longitudinal water quality studies are commonly a product of governmental agencies developing localized programs, like the Chesapeake Bay Program (Chesapeake Bay Monitoring Program, 2022), in response to increasing population and significant degradation of vital estuarine ecosystems. While national and regional efforts have attempted to provide unbiased, sustained monitoring, these programs currently lack the spatial extent needed to capture coastwide water quality trends. The National Estuarine Research Reserve System (NERRS) is one of the few organizations with dedicated coastal water quality monitoring stations, which are included as part of the NERRS System Wide Monitoring Program (SWMP) that maintains 355 coastal water quality monitoring stations across 29 designated coastal reserves along the USA coastline (National Estuarine Research Reserve System, 2022). Compared to the over 13,500 freshwater monitoring stations maintained by the United States Geological Survey (USGS, 2022), the relatively small number of water quality monitoring stations across coastal and estuarine waters (NOAA Tides & Currents, 2022; US EPA, 2022) are likely not representative of the variations in environmental conditions that we observe across the tens of thousands of miles of shoreline along the United States.Because of the limited number of unbiased monitoring programs, the ability to use water quality data from regulatory operations presents a potentially valuable resource for assessing long-term estuarine conditions. Regulatory programs differ from monitoring programs by collecting water quality samples to meet regulatory requirements and inform short-term decision-making. For example, in North Carolina (NC), there are four NERRS SWMP monitoring stations and eight coastal stations with water quality data available through the USGS (South Atlantic Water Science Center, North Carolina Office, 2022) and fifty stations from the NC Ambient Monitoring System (Water Quality Portal, 2021), but the NC Division of Marine Fisheries (NCDMF) shellfish sanitation program maintains 1,924 water quality monitoring stations. In fact, state shellfish sanitation programs across the USA collect an abundance of water quality observations, and often have for decades. Shellfish mariculture is highly dependent on water quality monitoring due to the direct influence that ambient conditions have on the safety of shellfish meat consumption. The U.S. Food and Drug Administration’s National Shellfish Sanitation Program (NSSP) was developed in 1925 to maintain public safety and human health standards in relation to the consumption of shellfish grown in potentially polluted waters (NSSP, 2019). The implementation of the NSSP has resulted in systematic sampling of water quality for day-to-day fisheries regulation, specifically for Fecal Indicator Bacteria (FIB), a group of bacteria that are commonly used as a proxy measure for harmful pathogen loads in the waterway that could potentially be incorporated into shellfish meat through filter feeding. Thus, fecal coliforms (FC), a type of FIB, and other environmental factors that contribute to FC load and water quality, are regularly measured in shellfish growing waters due to the food safety implications. As a product of this regular testing, fisheries operations have accumulated decades of data with the potential to provide insights on historical trends with wide spatial extents, potentially filling gaps in long-term water quality monitoring capacity.However, because of the limited resources and industry specific priorities, regulatory data can maintain underlying biases as a result of the sampling methodology used to collect the water quality sample. Often, the collection of a sample can be motivated by day-to-day operational decisions, such as weather, the availability of field technicians, and ease of collection. These operational decisions lead to non-random sampling that provides observations that are not always representative of the system’s true dynamics. Engaging regulatory personnel to understand their fisheries management and sampling decisions is necessary to properly analyze the observations collected by shellfish sanitation programs.For example, the NSSP permits states to employ one of two sampling strategies when collecting regulatory water quality data in shellfish growing waters: adverse pollution condition sampling and systematic random sampling. The adverse pollution condition sampling strategy describes sampling in periods when known contamination events (commonly due to point-source pollution events or rainfall events) have degraded the water quality, and data collected under these conditions capture peak contamination. States must collect “a minimum of five samples… annually under adverse pollution conditions from each sample station in the growing area” (NSSP, 2019) to meet NSSP sampling requirements. In contrast, the systematic random sampling strategy describes the collection of data across “a statistically representative cross section of all meteorological, hydrographic, and/or other pollution events” (NSSP, 2019), resulting in the data collection under varied environment and climactic conditions. For state programs that use systematic random sampling, the NSSP requires samples be collected at least 6 times throughout the year (NSSP, 2019). As a result of the requirements for the conditions under which the two systems of sampling can take place, the resulting data may be biased and impact their utility for use in long-term water quality assessments. With our growing reliance on aquaculture and the expanding value of shellfish production driving the development of fisheries management infrastructure (Azra et al., 2021), long-term datasets available through shellfish sanitation programs will become increasingly valuable. Realizing the potential of regulatory datasets to inform long-term water quality trends is a vital next step for assessing the health of our coastal ecosystems, but research is needed to determine the utility of these data for water quality analyses.The goal of this study was to utilize shellfish management data to infer long-term spatiotemporal trends in water quality parameters, including FC and salinity, while accounting for variation in routine sampling conditions and environmental landscapes. Study objectives included (1) analyzing spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, (2) identifying possible management and environmental drivers of fecal coliform trends, and (3) assessing the feasibility of using these monitoring data to infer long-term water quality dynamics. We focused on North Carolina’s shellfish waters as a representative study system due to the availability of public, digitized multidecadal data, and the region’s rapidly growing population, wide variety of land use characteristics along the coast, presence of the second largest estuarine system in the contiguous USA, and growing shellfish industry. Ultimately, this study demonstrates the application of shellfish management data for long-term water quality trend analysis in estuaries, informs future resource management strategies, and reveals new insights into the functioning of coastal systems.

Geometric characteristics of subaqueous bedforms, such as height, length and leeside angle, are crucial for determining hydraulic form roughness and interpreting sedimentary records. Traditionally, bedform existence and geometry predictors are primarily based on uniform, cohesionless sediments. However, mixtures of sand, silt and clay are common in deltaic, estuarine, and lowland river environments, where bedforms are ubiquitous. Therefore, we investigate the impact of fine sand and silt in sand-silt mixtures on bedform geometry, based on laboratory experiments conducted in a recirculating flume. We systematically varied the content of sand and silt for different discharges, and utilized a UB-Lab 2C (a type of acoustic Doppler velocimeter) to measure flow velocity profiles. The final bed geometry was captured using a line laser scanner. Our findings reveal that the response of bedforms to an altered fine sediment percentage is ambiguous, and depends on, among others, bimodality-driven bed mobility and sediment cohesiveness. When fine, non-cohesive material (fine sand or coarse silt) is mixed with the base material (medium sand), the hiding-exposure effect comes into play, resulting in enhanced mobility of the coarser material and leading to an increase in dune height and length. However, the addition of weakly-cohesive fine silt reduces the mobility, suppressing dune height and length. Finally, in the transition from dunes to upper stage plane bed, the bed becomes unstable and bedform heights vary over time. The composition of the bed material does not significantly impact the hydraulic roughness, but mainly affects roughness via the bed morphology, especially the leeside angle.

The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single-satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N to 37.4°S). The unique reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS’s indication of water existence. Berkeley-RWAWC provides monthly, near-real-time inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water datasets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal Berkeley-RWAWC’s enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley-RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.

The Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) is proposed as a novel Urgent Computing (UC) seismic workflow that focuses on short-time reports of synthetic estimates of the consequences of moderate to large earthquakes. UC combines High-Performance Computing (HPC), High-Performance Data Analytics (HPDA), and optimized solvers to perform numerical simulations during or immediately after emergency situations, typically within a few minutes to a few hours. Complex edge-to-end UC workflows coordinate the execution of multiple model realizations to account for input and model uncertainties and can provide decision-makers with numerical estimates of the outcomes of emergency scenarios, such as earthquakes addressed by UCIS4EQ. UCIS4EQ is being driven toward operational maturity thanks to the technological and scientific developments within the eFlows4HPC project. Based on containerised micorservices, this workflow is fully orchestrated by the PyCOMPSs workflow manager to automatically prepare and manage physics-based deterministic simulation suites for rapid synthetic results. Through pre-computed and on-the-fly simulations, UCIS4EQ delivers estimates of relevant ground motion parameters, such as peak ground velocity, peak ground acceleration, or shaking duration, with very high spatial resolution. The physics-based engine includes pre-trained Machine Learning (ML) models fed with pre-computed simulation databases, as well as deterministic 3D simulations on demand, providing results in minutes and hours, respectively. The combined results, when well-calibrated, could lead to a new generation of ground shaking maps that complement GMPEs for rapid hazard assessment.To demonstrate the potential use of UC in seismology,  in this work we show the UCIS4EQ simulation of the M7.1 Puebla earthquake that occurred in central Mexico on the 19th of September 2017. With a hypocentre at 18.40ºN, 98.72ºW and 57 km depth, the Puebla earthquake was located about 150 km southeast from Mexico City. Identified as a severe event (VIII) in the Modified Mercalli Intensity scale, it resulted in a total of 370 killed and around 6000 injured, as well as structural damages, downed telephone lines, and ruptured gas mains.

The carbon (C) dynamics of boreal coniferous swamps are a largely understudied component of wetland carbon cycling. We investigated the above- and below-ground carbon stocks and growing season carbon dioxide (CO2) and methane (CH4) fluxes from a representative wooded coniferous swamp in northern Alberta, Canada in 2022. Tree inventories, understory vegetation biomass and peat cores were collected across three sub-sites within the broader swamp, with gas flux collars placed in the dominant plant communities present. Alongside the C flux measurements, environmental variables such as water table depth, soil temperature and growing season understory green leaf phenology were measured. Our results show that these wooded coniferous swamps store large volumes of organic C in their biomass and soil (134 kg C m-2), comparable with other wetland and forest types, although 95% of the total C stock at our site was within the soil organic carbon. We also found that understory CO2 and CH4 fluxes indicated that the ground layer of the site is a source of greenhouse gases (GHG) to the atmosphere across the growing season. However, we did not measure litterfall input, tree GHG fluxes or net primary productivity of the overstory, therefore we are not able to say whether the site is an overall source of C to the atmosphere. This study provides a much-needed insight into the C dynamics of these under-valued wetland ecosystems and we highlight the need for a coordinated effort across boreal regions to try to improve inventories of C stocks and fluxes.

GSD (grain size distribution), constitutes a paramount parameter for comprehending the behavior and dynamic mechanics of mass movements, such as debris flows, rock avalanches, sediment transport etc. Alongside traditional sieving methodologies, the past few decades have witnessed a growing interest in photo-sieving, the technique of deducing GSD directly from photographic data. Photo-sieving holds promise for augmenting the spatial and temporal resolution of superficial GSD analysis by virtue of its accessibility, reduced labor intensity, and non-invasive nature. Moreover, the integration of aerial photography within the discipline enables to include the coarse-grained fraction, expanding the scope of particle size analysis beyond the capabilities of traditional sieving. This study introduces a novel algorithm for extracting the coarse-grained fraction using UAV (unmanned aerial vehicle) photography. This novel approach enables us to analyze hectare-scale extents, probing tens of thousands of clasts - surpassing previous similar techniques by two orders of magnitude - and generates a detailed map of the position and dimensions of each particle within the sedimentary system. Furthermore, the algorithm exhibits remarkable resilience in navigating real-world complexities, effectively discerning clasts from vegetation, anthropogenic artifacts, and handling exceptionally large boulders, rendering it suitable for application in diverse field settings. We anticipate that this technique could become a valuable tool for advancing our understanding of debris flow and rock avalanche dynamics, sediment transport processes, and the stability of landslide dams.

A document by Brady Jones. Click on the document to view its contents.

Satellite-derived velocity data products and mass loss estimates can be problematic when applied to slow-moving systems with smaller magnitude changes such as the accumulation zone of the Llewellyn Glacier of the Juneau Icefield. As a result, inter-annual variability may be systematically over or under predicted. Ground survey data has the potential to provide a long term, higher resolution record of slow but significant changes in the accumulation zones of glaciers. Here we compare satellitebased velocity data with GPS data collected in a 2010 ground survey across the Llewellyn Glacier.

Pre-print: Ground Motion Shaking Predictions Based on Machine Learning and Physics-based SimulationsEarthquakes constitute a major threat to human lives and infrastructure, hence it is crucial to quickly assess the intensity of ground motions after a major seismic event. Rapid estimation of the intensity of ground vibrations is essential to assess the impact after a major earthquake occurs. The Machine Learning Estimator for Ground Shaking Maps (MLESmap) introduces an innovative approach that harnesses the predictive capabilities of Machine Learning (ML) algorithms, utilizing high-quality physics-based seismic scenarios. MLESmap aims to provide ground intensity measures within seconds following an earthquake. The inferred information can produce shaking maps of the ground providing quasi-real-time affectation information to help us explore uncertainties quickly and reliably. To develop the MLESmap technology, we used ground-motion simulations generated by the CyberShake platform. Originally designed for Southern California, this physics-based Probabilistic Seismic Hazard Methodology was migrated to the South Iceland Seismic Zone recently. Our methodology follows a three-step process: simulation, training, and deployment. By employing this approach, we can generate the next generation of ground shake maps, incorporating essential physical information derived from wave propagation, such as directivity, topography, and site effects. Remarkably, the evaluation times for MLESmap are comparable to empirical Ground Motion Models, whereas the predictive capacity of the former is superior for the Mw > 5 earthquakes.In this work, we present the application of the MLESmap methodology in South West Iceland.

Unprecedented warming of Arctic–boreal regions (ABR) has poorly understood consequences on carbon cycle processes. Uncertainties in annual methane (CH4) budgets partly arise because of limited data availability during winter. In this study, winter CH4 flux measurements were conducted using the snowpack diffusion gradient method over five ABR ecosystem types in Canada and Finland: closed–crown and open–crown coniferous boreal forest, boreal wetland and erect–shrub and prostrate–shrub tundra. Boreal forest uplands acted as net CH4 sinks, while the boreal wetland acted as net CH4 source during winter. We identified several wetland tundra CH4 emission hotspots and large spatial variability in boreal wetland CH4 emissions. In the boreal forest uplands, soil liquid water content was identified as an important environmental control of winter CH4 fluxes. Our results indicate non–negligible winter CH4 flux, which must be accounted for in annual carbon balance and terrestrial biosphere models over ABR.

Methane (CH4) is the second most important atmospheric greenhouse gas (GHG) and forest soils are a significant sink for atmospheric CH4. Uptake of CH4 by global forest soils is affected by nitrogen (N) deposition; clarifying the effect of N deposition helps to reduce uncertainties of the global CH4 budget. However, it remains an unsolved puzzle why N input stimulates soil CH4 flux (RCH4) in some forests while suppressing it in others. Combining previous findings and data from N addition experiments conducted in global forests, we proposed and tested a “stimulating-suppressing-weakening effect” (“three stages”) hypothesis on the changing responses of RCH4 to N input. Specifically, we calculated the response factors (f) of RCH4 to N input for N-limited and N-saturated forests across biomes; the significant changes in f values supported our hypothesis. We also estimated the global forest soil CH4 uptake budget to be approximately 11.2 Tg yr–1. CH4 uptake hotspots were located predominantly in temperate forests. Furthermore, we quantified that current level of N deposition reduced global forest soil CH4 uptake by ~3%. This suppression effect was more pronounced in temperate forests than in tropical or boreal forests, likely due to differences in N status. The proposed “three stages” hypothesis in this study generalizes the diverse effects of N input on RCH4, which could help improve experimental design. Additionally, our findings imply that by regulating N pollution and reducing N deposition, soil CH4 uptake can be significantly increased in the N-saturated forests in tropical and temperate biomes.

Authors: Jennifer D. Watts1; Stefano Potter1; Brendan M. Rogers1, Anna-Maria Virkkala1, Greg Fiske1, Kyle A. Arndt1, Arden Burrell1, Kevin Butler2, Bob Gerlt2, John Grayson2, Tatiana A. Shestakova3,4, Jinyang Du5, Youngwook Kim6, Frans-Jan Parmentier7, Susan M. Natali11 Woodwell Climate Research Center, 149 Woods Hole Road, Falmouth, MA, 02540.2 Environmental Systems Research Institute (Esri). 380 New York Street, Redlands, CA, 92373.3 Department of Agricultural and Forest Science and Engineering, University of Lleida, Av. Alcalde Rovira Roure, 191, Lleida, Spain, 25198.4 Joint Research Unit CTFC–AGROTECNIO–CERCA, Av. Alcalde Rovira Roure, 191, Lleida, Spain, 25198.5 Numerical Terradynamic Simulation Group (NTSG), ISB 415, 32 Campus Drive, Missoula, MT, 59812.6 Department of Biology, United Arab Emirates University, Al Ain, Abu Dhabi, UAE, 15551.7 Center for Biogeochemistry of the Anthropocene, Department of Geosciences, University of Oslo, N-0315, Oslo, Norway.

A comprehensive survey and subject-expert interviews conducted among agricultural researchers investigated perceived value and barriers to the adoption of unoccupied aerial systems (UAS) in agricultural research.  The study involved 154 respondents from 21 countries representing various agricultural sectors. The survey identified three key applications considered most promising for UAS in agriculture: precision agriculture, crop phenotyping/plant breeding, and crop modeling. Over 80% of respondents rated UAS for phenotyping as valuable, with 47.6% considering them very valuable. Among the participants, 41% were already using UAS technology in their research, while 49% expressed interest in future adoption. Current users highly valued UAS for phenotyping, with 63.9% considering them very valuable, compared to 39.4% of potential future users. The study also explored barriers to UAS adoption. The most commonly reported barriers were the "High cost of instruments/devices or software" (46.0%) and the "Lack of knowledge or trained personnel to analyze data" (40.9%). These barriers persisted as top concerns for both current and potential future users. Respondents expressed a desire for detailed step-by-step protocols for drone data processing pipelines (34.7%) and in-person training for personnel (16.5%) as valuable resources for UAS adoption. The research sheds light on the prevailing perceptions and challenges associated with UAS usage in agricultural research, emphasizing the potential of UAS in specific applications and identifying crucial barriers to address for wider adoption in the agricultural sector.

Monitoring greenhouse gas (GHG) emissions is crucial for developing effective mitigation strategies. Recent advances in satellite remote-sensing measurements allow us to ack greenhouse gas emissions globally. This study assesses CO2 emissions from various point/local sources, particularly power plants in India, using eight years of concurrent high-resolution OCO-2 satellite measurements. Gaussian plume (GP) model was used to evaluate the power plant emissions reported in the Carbon Brief (CB) database. In total (39 cases), 42 different power plant CO2 emissions were assessed, with 26 of them being assessed more than once. The estimated power plant CO2 emissions were within ± 25% of the emissions reported in the CB database in 11 out of 39 cases and within ± 50% in 18 cases. To evaluate the EDGAR and ODIAC CO2 emission inventories in terms of missing or highly underestimated sources, we estimated the cross-sectional (CS) CO2 emission flux for 47 cases. We identified the possible omission of power plant emissions in three cases for both inventories. Furthermore, we also showed 21 cases in which CO2 emissions from unknown (non-power plant) sources were highly underestimated in the EDGAR and ODIAC CO2 emission inventories. Due to the simplicity of the employed approaches and their lower computational requirements compared to other methods, they can be applied to large datasets over extended time periods. This enables the acquisition of initial emission estimates for various sources, including those that are unknown or underestimated.

Because of human population growth, global livestock, and associated ammonia, emisions are projected to increase through the end of the century, with possible impacts on atmospheric chemistry and climate. In this study, we propose a methodology to project global gridded livestock densities and NH3 emissions from agriculture until 2100. Based on future regional livestock production and constrained by grassland distribution evolution, future livestock distribution has been projected for three Shared Socio-economic Pathways (SSP2-4.5, SSP4-3.4, and SSP5-8.5) and used in the CAMEO process-based model to estimate the resulting NH3 emissions until 2100. Our global future emissions compare well with the range estimated in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), but some significant differences arise within the SSPs. Our global future ammonia emissions in 2100 range from 50 to 70 TgN.yr−1 depending on the SSPs, representing an increase of 30 to 50 % compared to present day. Africa is identified as the region with the most significant regional emission budget worldwide, ranging from 10 to 16 TgN.yr−1 in 2100. Through a set of simulations, we quantified the impact of climate change on future NH3 emissions. Climate change is estimated to contribute to the emission increase of up to 20%. The produced datasets of future NH3 emissions is an alternative option to IAM-based emissions for studies aiming at projecting the evolution of atmospheric chemistry and its impact on climate.

A document by Andy Baker. Click on the document to view its contents.

Atmospheric aerosols play an important role in the Earth's climate system. We present the analysis of atmospheric molecules/particles collected with a sampling system that can fly under regular weather balloons. The flights took place on 10 October 2022 from Reims and on 13 December 2022 from Orléans (France). The samples collected on activated carbon filters were analyzed by high-resolution mass spectrometry (Orbitrap Q-Exactive). Using Desorption electrospray ionization (DESI), we could derive hundreds of chemical formulas for organic species present in different layers from the troposphere to the stratosphere (up to 20 km). Measurements of O3, CO, and aerosol concentrations a few hours before these flights took place to contextualize the sampling.

Accurate wildfire forecasting can inform regional management and mitigation strategies in advance of fire occurrence. Existing systems typically use fire danger indices to predict landscape flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data-driven machine learning approach to construct a global daily ~9 km resolution Probability of Fire (PoF) model operating at multiple lead times. The PoF model outperforms existing indices, providing accurate forecasts of fire activity up to 10 days in advance, and in some cases up to 30 days. The model can also be used to investigate historical shifts in regional fire patterns. Furthermore, the underlying data driven approach allows PoF to be used for fire attribution, isolating key variables for specific fire events or for looking at the relationships between variables and fire occurrence.

The Regional Climate Modeling system (RegCM) has undergone a significant evolution over the years, leading for example to the widely used versions RegCM4 and RegCM4-NH. In response to the demand for higher resolution, a new version of the system has been developed, RegCM5, incorporating the non-hydrostatic dynamical core of the MOLOCH weather prediction model. In this paper we assess the RegCM5’s performance for 5 CORDEX-CORE domains, including a pan-European domain at convection-permitting resolution. We find temperature biases generally in the range of -2 to 2 degrees Celsius, higher in the northernmost regions of North America and Asia during winter, linked to cloud water overestimation. Central Asia and the Tibetan Plateau show cold biases, possibly due to sparse station coverage. The model exhibits a prevailing cold bias in maximum temperature and warm bias in minimum temperature, associated with a systematic overestimation of lower-level cloud fraction, especially in winter. Taylor diagrams indicate a high spatial temperature pattern correlation with ERA5 and CRU data, except in South America and the Caribbean region. The precipitation evaluation shows an overestimation in South America, East Asia, and Africa. RegCM5 improves the daily precipitation distribution compared to RegCM4, particularly at high intensities. The analysis of wind fields confirms the model’s ability to simulate monsoon circulations. The assessment of tropical cyclone tracks highlights a strong sensitivity to the tracking algorithms, thus necessitating a careful model interpretation. Over the European region, the convection permitting simulations especially improve the diurnal cycle of precipitation and the hourly precipitation intensities.