Salt marshes are valuable ecosystems that provide numerous services and act as natural coastal defences by buffering storm waves and stabilising sediments. However, it is not clear whether they will be able to retain their resilience with accelerating rate in sea-level rise, possible increases in storm intensity and increasing land reclamation. The current paradigm is that a positive sediment budget supports the survival and accretion of salt marshes while a negative sediment budget causes marsh degradation. Here we present the results of a series of studies that used an integration of modelling and paleoenvironmental analysis and a sediment budget approach to investigate the resilience of estuaries and salt marshes to projected rise in sea-level, possible increases in storm activity and existing anthropogenic disturbance. The studies were conducted using the Ribble Estuary-NorthWest England-as a test case, the hydrodynamic model Delft3D to simulate the estuary morpho-dynamics under selected scenarios, and optically stimulated luminescence (OSL), geochemistry and particle size distribution analysis to reconstruct the past evolution and adaptation of the estuary morphology. Results showed that sea-level rise threatens estuary and marsh stability by promoting ebb dominance and triggering a net export of sediment. Conversely, storm surges aid the resilience of the system by promoting flood dominance and triggering a net import of sediment and have the potential to counteract the negative impact of sea-level rise by masking its effects on the sediment budget. The addition of embankments, on the other hand, can further promote ebb dominance in the system and intensify sediment export, further threatening marsh stability.References Leonardi, N., Ganju, N.K. and Fagherazzi, S. (2016). A linear relationship between wave power and erosion determines salt-marsh resilience to violent storms and hurricanes. Proceedings of the National Academy of  Sciences 113(1), 64-68. Ganju, N.K., Kirwan, M.L., Dickhudt, P.J., Guntenspergen, G.R., Cahoon, D.R. and Kroeger, K.D. (2015). Sediment transport-based metrics of wetland stability. Geophysical Research Letters, 42(19), 7992-8000.

A document by Shreyaa Sanjay. Click on the document to view its contents.

Flooding has been a significant problem over the past century in the United States (US), causing growing threats to human lives and socioeconomic damage. In the state of Kansas, since 1996, more than 1,500 flood events were recorded, resulting in an economic loss of between US$2b and US$5b. Many factors influence flood-susceptibility at a local scale. It may be helpful and timely to improve community resilience to flood disasters in Kansas. Our initial step was to assess factors that trigger flooding using Machine Learning (ML). Six ML algorithms: 1) Logistic Regression (LR); 2) Random Forest (RF); 3) Support Vector Machine (SVM); 4) K-nearest neighbor (KNN); 5) Adaptive Boosting (Ada Boost); 6) Extreme Gradient Boosting (XG boost) were used to evaluate their ability to classify locations in terms of floodsusceptibility. The learning data for these ML algorithms comprised a geo-spatial database of twelve floodsusceptibility factors from 1,528 flood inventories since 1996. The susceptibility factors comprised: rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, land surface temperature, and hydrographic soil type. The ML algorithms were compared, and the best algorithm was selected to estimate floodsusceptibility for each location in the geodatabase resulting in a flood-susceptibility map. A sensitivity analysis of floodsusceptibility factors indicated that the intensity or magnitude of the rainfall, land cover and soil type were the most significant factors for Kansas during this period.

To limit global warming below 2°C, a drastic overall reduction from current CO2 emissions is needed. We argue that scientists should also participate in this effort in their professional activity and especially Earth scientists, on the grounds of maintaining credibility and leading by example. The strategies and measures to reach a low-carbon scientific activity require detailed estimates of the current footprint of laboratories. Here, we present the footprint of six laboratories in Earth, environmental and space sciences, representative of the AGU community, with a comprehensive scope also including international research infrastructures. We propose a novel method to attribute the footprint of any research infrastructure to any given research laboratory. Our results highlight that most laboratories have annual footprints reaching 10-20 tonnes CO2 equivalent per person (tCO2e.p-1), dominated by infrastructures and specifically satellites in three cases (with footprints up to 11 tCO2e.p-1 or 60%), while air-travels and purchases remain within the top three sources in all cases (2-4 tCO2e p-1 or 10-30% each). Consequently, footprints related to commuting and laboratory functioning, about 2 tCO2e.p-1 (20%) or less, are relatively modest compared to infrastructures, purchases and air-travels. Thus, reduction measures ignoring infrastructures may not be able to achieve reductions larger than 20 to 35% even with flight quotas and a substantial reduction of purchases. Finally, we also discuss how a deeper transformation of scientific practices, away from a fast science ideal, could make Earth and environmental sciences more sustainable and at the forefront of a rapid and drastic social bifurcation.

Marine mesozooplankton play an important role for marine ecosystem functioning and global biogeochemical cycles. Their size structure, varying spatially and temporally, heavily impacts biogeochemical processes and ecosystem services. Mesozooplankton exhibit size changes throughout their life cycle, affecting metabolic rates and functional traits. Despite this variability, many models oversimplify mesozooplankton as a single, unchanging size class, potentially biasing carbon flux estimates. Here, we include mesozooplankton ontogenetic growth and reproduction into a 3-dimensional global ocean biogeochemical model, PISCES-MOG, and investigate the subsequent effects on simulated mesozooplankton phenology, plankton distribution, and organic carbon export. Utilizing an ensemble of statistical predictive models calibrated with a global set of observations, we generated monthly climatologies of mesozooplankton biomass to evaluate the simulations of PISCES-MOG. Our analyses reveal that the model and observation-based biomass distributions are comparable (r$_{pearson}$=0.40, total epipelagic biomass: 137TgC from observations vs. 232TgC in the model), with similar seasonality (r$_{pearson}$=0.25 for the months of maximal biomass). Including ontogenetic growth in the model induced cohort dynamics and variable seasonal dynamics across mesozooplankton size classes and altered the relative contribution of carbon cycling pathways. Younger and smaller mesozooplankton transitioned to microzooplankton in PISCES-MOG, resulting in a change in particle size distribution, characterized by a decrease in large particulate organic carbon (POC) and an increase in small POC generation. Consequently, carbon export from the surface was reduced by 10\%. This study underscores the importance of accounting for ontogenetic growth and reproduction in models, highlighting the interconnectedness between mesozooplankton size, phenology, and their effects on marine carbon cycling.

Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.

Existing research has employed various methods to quantify urban heat island (UHI) effects, but the ideal method for individual cities remains unclear. This study investigated how different methods influence UHI understanding in Addis Ababa, a tropical city facing UHI challenges. Three methods were compared: dynamic urbanization, natural and built-up fractions, and urban center vs. surrounding rural areas. Satellite data and spatial analyses revealed maximum daytime UHIs of 4°C and 3.1°C in summer and autumn, respectively. Examining the mean temperature differences between urban and rural areas across methods yielded diverse results. This suggests that while the ‘dynamic urbanization’ method is statistically favorable in this specific case, averaging results from multiple methods produced a more robust and generalizable approach to understanding UHIs in different urban contexts. Ultimately, this study highlights the importance of context-specific method selection for accurately understanding the complex interplay between urban and rural environments.

Significant interest and capital are currently being channeled into techniques for durable carbon dioxide removal (CDR) from Earth’s atmosphere. A particular class of these approaches — referred to as enhanced weathering (EW) — seeks to modify the surface alkalinity budget to durably store CO2 as dissolved inorganic carbon species. Here, we use SCEPTER — a reaction-transport code designed to simulate EW in managed lands — to evaluate the throughput and storage timescales of anthropogenic alkalinity in agricultural soils. Through a series of alkalinity flux simulations, we explore the main controls on cation storage and export from surface soils in key U.S. agricultural regions. We find that lag times between alkalinity modification and climate-relevant CDR can span anywhere from years to many decades, with background soil cation exchange capacity, agronomic target pH, and fluid infiltration all impacting the timescales of CDR relative to the timing of alkalinity input. There may be scope for optimization of weathering-driven alkalinity transport through variation in land management practice. However, there are tradeoffs with total CDR, optimal nutrient use efficiencies, and soil nitrous oxide (N2O) fluxes that complicate attempts to perform robust time-resolved analysis of the net radiative impacts of CDR through EW in agricultural systems. Although CDR lag times will be more of an issue in some regions than others, these results have significant implications for the technoeconomics of EW and the integration of EW into voluntary carbon markets, as there may often be a large temporal disconnect between deployment of EW and climate-relevant CDR.

The Indian summer monsoon precipitation trend from 1979 to 2022 shows a substantial 40% increase over Northwest India, which is in agreement with the future projections of the Coupled Model Intercomparison Project 6 (CMIP6). The observationally constrained reanalysis dataset reveals that a prominent sea surface warming in the western equatorial Indian Ocean and the Arabian Sea might be responsible for the rainfall enhancement through strengthening the cross-equatorial monsoonal flow and associated evaporation. We show that the cross-equatorial monsoon winds over the Indian Ocean are strengthening due to the merging of Pacific Ocean trade winds and rapid Indian Ocean warming. These winds also enhance the latent heat flux (evaporation), and in combination, this results in increased moisture transport from the ocean toward the land.

The High Seas, lying beyond the boundaries of nations’ Exclusive Economic Zones, cover the majority of the ocean surface and host roughly two thirds of marine primary production. Yet, only a small fraction of global wild fish catch comes from the High Seas, despite intensifying industrial fishing efforts. The surprisingly small fish catch could reflect economic features of the High Seas - such as the difficulty and cost of fishing in remote parts of the ocean surface - or ecological features resulting in a small biomass of fish relative to primary production. We use the coupled biological-economic model BOATS to estimate contributing factors, comparing observed catches with simulations where: (i) fishing cost depends on distance from shore and seafloor depth; (ii) catchability depends on seafloor depth or vertical habitat extent; (iii) regions with micronutrient limitation have reduced biomass production; (iv) the trophic transfer of energy from primary production to demersal food webs depends on depth; and (v) High Seas biomass migrates to coastal regions. Our results suggest that the most important features are ecological: demersal fish communities receive a large proportion of primary production in shallow waters, but very little in deep waters due to respiration by small organisms throughout the water column. Other factors play a secondary role, with migrations having a potentially large but uncertain role, and economic factors having the smallest effects. Our results stress the importance of properly representing the High Seas biomass in future fisheries projections, and clarify their limited role in global food provision.

A document by Alamgir Hossan. Click on the document to view its contents.

Using Unmanned Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS-borne Doppler radar is particularly attractive because it is suitable for real-time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross-sections (XSs) were surveyed within a 10 km stretch of Rönne Å in Sweden. Ground-truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at 1 m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peak model. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone-induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river surface velocity.

Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote- and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.  

The 2023 wildfire season in Canada was unprecedented in its scale and intensity. Spanning from late April to early November and extending across much of the forested regions of Canada, the season resulted in a record-breaking total area burned of approximately 15 million hectares, over seven times the historic national annual average. The impacts were profound with more than 200 communities evacuated (approximately 232,000 people), periods of dense smoke that caused significant public health concerns, and unprecedented demands on fire-fighting resources. The exceptional area burned can be attributed to several environmental factors that converged early in the season to enable extreme fire danger over much of the country. These factors included early snowmelt, interannual drought conditions in western Canada, and the rapid transition to drought in eastern Canada. Furthermore, the mean May-October temperature over Canada in 2023 was a staggering 2.2°C warmer than normal (1991-2020), enabling sustained extreme fire weather conditions throughout the fire season. These conditions led to a larger than normal proportion of very large fires (> 50,000 hectares), many having burned for months from the spring into the fall. Fires that started in May or June accounted for over two-thirds of the total area burned. Overall, the 2023 wildfire season in Canada was characterized by its exceptional scale and major societal impacts, setting new records and highlighting the increasing challenges posed by wildfires in the country.

Authors: Lisa Mandle1, Andrew Shea2,3, Emily Soth1, Jesse A. Goldstein1, Stacie Wolny1, Jeffrey R. Smith4,5, Rebecca Chaplin-Kramer6,7, Richard P. Sharp6,8, Mayur Patel1AffiliationsNatural Capital Project, Stanford University, Stanford, CA 94305 USAGlobal Sustainable Finance, Morgan Stanley, New York, NY 10036Current affiliation: Frontier & Stripe Climate, Stripe, South San Francisco, CA 94080Department of Ecology and Evolutionary Biology, Princeton University, Princeton New Jersey 08544High Meadows Environmental Institute, Princeton University, Princeton, New Jersey 08544Global Science, WWF. 131 Steuart St., San Francisco, CA 94105Institute on the Environment, University of Minnesota, 1954 Buford Ave., St. Paul, MN 55108SPRING, 5455 Shafter Ave., Oakland CA 94618Abstract: Aligning economic activities with the global sustainable development agenda requires understanding companies’ impacts on nature. Here, we present a new approach for quantifying the direct impacts of companies’ physical assets on nature based on global maps for eight ecosystem services and biodiversity metrics. We apply this approach to a set of over 2,000 global, publicly traded companies with 580,000 mapped physical assets. We find that companies in utility, real estate, materials, and financial sectors have the largest impacts on average, but there is substantial variation among companies within all sectors. In addition, we use high-resolution satellite imagery to assess the impact of active lithium mines based on their footprints. We show that the impact varies substantially among mines and can also be tracked across time for a mine. This approach enables differentiation among companies and assets based on their impacts to nature relative to their revenue or production.

A document by Freya Muir. Click on the document to view its contents.

Sea ice thickness (SIT) estimates derived from CryoSat-2 radar freeboard measurements are assimilated into the Met Office's global ocean–sea ice forecasting system, FOAM. We test the sensitivity of short-range forecasts to the snow depth, radar freeboard product and assumed radar penetration through the snowpack in the freeboard-to-thickness conversion. We find that modifying the snow depth has the biggest impact on the modelled SIT, changing it by up to 0.88 m (48%), compared to 0.65 m (33%) when modifying the assumed radar penetration through the snowpack and 0.55 m (30%) when modifying the freeboard product. We find a doubling in the thermodynamic volume change over the winter season when assimilating SIT data, with the largest changes seen in the congelation ice growth. Next, we determine that the method used to calculate the observation uncertainties of the assimilated data products can change the mean daily model SIT by up to 36%. Compared to measurements collected at upward-looking sonar moorings and during the Operation IceBridge campaign, we find an improvement in the SIT forecasts’ variability representation when assuming partial radar penetration through the snowpack and when improving the method used to calculate the CryoSat-2 observation uncertainties. This paper highlights a concern for future SIT data assimilation and forecasting, with the chosen parameterisation of the freeboard-to-thickness conversion having a substantial impact on model results.

Daniel Carrión1,2, Johnathan Rush3, Elena Colicino4, Allan C. Just5,61 Department of Environmental Health Sciences, Yale University School of Public Health, New Haven, Connecticut 06510 2 Yale Center on Climate Change and Health, Yale University School of Public Health, New Haven, Connecticut 065103 Element 84, Alexandria, Virginia, 223144 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York City, New York 100295 Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island 029036 Institute at Brown for Environment and Society, Brown University, Providence, Rhode Island 02912Corresponding author: Daniel CarriónEmail: [email protected] Contributions: DC and ACJ designed the research; DC and JR performed the research; DC analyzed the data with input from EC and ACJ; DC and ACJ drafted the manuscript; and all authors approved the final version of the manuscript.Competing Interest Statement: The authors have no competing interests to declare.Classification: Social sciences; sustainability scienceKeywords: environmental justice; extreme heat; energy insecurity; disparities

Using nine years of full-depth profiles from 55 Deep Argo floats in the Southwest Pacific Basin collected between 2014 and 2023, we find consistent warm anomalies compared to a long-term climatology below 2000 m ranging between 11\(\pm\)2 to 34\(\pm\)2m\(^o\)C, most pronounced between 3500 and 5000 m. Over this period, a cooling trend is found between 2000-4000 m and a significant warming trend below 4000 m with a maximum rate of 4.1\(\pm\)0.31 m\(^o\)C yr\(^{-1}\) near 5000 m, with a possible acceleration over the second half of the period. The integrated Steric Sea Level expansion below 2000 m was 7.9\(\pm\) 1 mm compared to the climatology with a trend of 1.3\(\pm\) 1.6 mm dec\(^{-1}\) over the Deep Argo era, contributing significantly to the local sea level budget. We assess the ability to close a full Sea Level Budget, further demonstrating the value of a full-depth Argo array.

Recent European heatwaves have significantly impacted forest ecosystems, leading to increased plant water stress. Advances in land surface models aim to improve the representation of vegetation drought responses by incorporating plant hydraulics into the plant functional type (PFT) classification system. However, reliance on PFTs may inadequately capture the diverse plant hydraulic traits (PHTs), potentially biasing transpiration and vegetation water stress representations. The detection of vegetation drought stress is further complicated by the mixing of different tree species and forest patches. This study uses the Community Land Model version 5.0 to simulate an experimental mixed-forest catchment with configurations representing standalone, patched mixed, and fully mixed forests. Biome-generic, PFT-specific, or species-specific PHTs are employed. Results emphasize the crucial role of accurately representing mixed forests in reproducing observed vegetation water stress and transpiration fluxes for both broadleaf and needleleaf tree species. The dominant vegetation fraction is a key determinant, influencing aggregated vegetation response patterns. Segregation level in PHT parameterizations shapes differences between observed and simulated transpiration fluxes. Simulated root water potential emerges as a potential metric for detecting vegetation stress periods. However, the model’s plant hydraulic system has limitations in reproducing the long-term effects of extreme weather events on needleleaf tree species. These findings highlight the complexity of modeling mixed forests and underscore the need for improved representation of plant diversity in land surface models to enhance the understanding of vegetation water stress under changing climate conditions.

Diagnosing land-atmosphere fluxes of carbon-dioxide (CO$_2$) and methane (CH$_4$), is essential for evaluating carbon-climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics, but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel 2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1\% to 1.0\%. By far the lowest yields occur in the wet season, consistent with Sentinel 2 cloud patterns. We find that increasing the spatial resolution of observations to $\sim$200\,m would increase yields by 2-3 orders of magnitude, and allow regular measurements in the wet season. Thus, the key effective tropical greenhouse gas observations lies in regularly acquiring high-spatial resolution data, rather than more frequent low-resolution measurements.

Diatom communities preserved in sediment samples are valuable indicators for understanding the past and present dynamics of phytoplankton communities, and their response to environmental changes. These studies are traditionally achieved by counting methods using optical microscopy, a time-consuming process that requires taxonomic expertise. With the advent of automated image acquisition workflows, large image datasets can now be acquired, but require efficient preprocessing methods. Detecting diatom frustules on microscope images is a challenge due to their low relief, diverse shapes, and tendency to aggregate, which prevent the use of traditional thresholding techniques. Deep learning algorithms have the potential to resolve these challenges, more particularly for the task of object detection. Here we explore the use of a Faster R-CNN (Region-based Convolutional Neural Network) model to detect siliceous biominerals, including diatoms, in microscope images of a sediment trap series from the Mediterranean Sea. Our workflow demonstrates promising results, achieving a precision score of 0.72 and a recall score of 0.74 when applied to a test set of Mediterranean diatom images. Our model performance decreases when used to detect fragments of these microfossils; it also decreases when particles are aggregated or when images are out of focus. Microfossil detection remains high when the model is used on a microscope image set of sediments from a different oceanic basin, demonstrating its potential for application in a wide range of contemporary and paleoenvironmental studies. This automated method provides a valuable tool for analysing complex samples, particularly for rare species under-represented in training datasets.

Land subsidence (LS) due to groundwater pumping has been the subject of many studies at various spatial scales, dimensions of impacts and degrees of economic characterization. Recent progress in remote sensing has led to assessments at the planetary level, with an emphasis on the city-impacts. In this paper we first conduct a review of recent economic assessments of LS impacts and evaluate their potential to inform decisions and to be applied in other urban environments, from which we derive desirable characteristics for future assessments. Then we propose a framework and methodology specific to the urban context, but applicable to any city, considering availability of appropriate data. The approach attempts to categorize levels of impacts on the built and natural infrastructure and propagates its consequences for public health, socioeconomic and the environment. From its application to the context of Jakarta, we conclude with recommendations regarding methodology, data, and governance level for the international community to support local actors. In addition to monitoring plans and regulatory interventions, we reaffirm the need of multidisciplinary teams to address the complex issue of the infrastructural risk of LS and its impacts. Acknowledgement A previous version of this working paper was prepared as a background paper to the World Bank study (World Bank, June 2023) "The Hidden Wealth of Nations: Groundwater in Times of Climate Change" 1 , and was partially funded by the World Bank.

A document by Orlando Viloria. Click on the document to view its contents.