Harmful algal blooms (HABs) caused by the dinoflagellate Karenia brevis on the West Florida Shelf have become a nearly annual occurrence causing widespread ecological and economic harm. Effects range from minor respiratory irritation and localized fish kills to large-scale and long-term events causing massive mortalities to marine organisms. Reports of hypoxia on the shelf have been infrequent; however, there have been some indications that some HABs have been associated with localized hypoxia. We examined oceanographic data from 2004 to 2019 across the West Florida Shelf to determine the frequency of hypoxia and to assess its association with known HABs. Hypoxia was present in 5 of the 16 years examined and was always found shoreward of the 50-meter bathymetry line. There were 2 clusters of recurrent hypoxia: midshelf off the Big Bend coast and near the southwest Florida coast. We identified 3 hypoxic events that were characterized by multiple conductivity, temperature, and depth (CTD) casts and occurred concurrently with extreme HABs in 2005, 2014, and 2018. These HAB-hypoxia events occurred when K. brevis blooms initiated in early summer months and persisted into the fall likely driven by increased biological oxygen demand from decaying algal biomass and reduced water column ventilation due to stratification. There were also four years, 2011, 2013, 2015, and 2017, with low dissolved oxygen located near the shelf break that were likely associated with upwelling of deeper Gulf of Mexico water onto the shelf. We had difficulty in assessing the spatiotemporal extent of these events due to limited data availability and potentially unobserved hypoxia due to the inconsistent difference between the bottom of the CTD cast and the seafloor. While we cannot unequivocally explain the association between extreme HABs and hypoxia on the West Florida Shelf, there is sufficient evidence to suggest a causal linkage between them.
We aim to identify the relative importance of vapour pressure deficit (VPD), soil water content (SWC) and photosynthetic photon flux density (PPFD) as drivers of tree canopy conductance, which is a key source of uncertainty for modelling vegetation responses under climate change. We use sap flow time series of 1858 trees in 122 sites from the SAPFLUXNET global database to obtain whole-tree canopy conductance (G). The coupling, defined as the percentage of variance (R2) of G explained by the three main hydrometeorological drivers (VPD, SWC and PPFD), was evaluated using linear mixed models. For each hydrometeorological driver we assess differences in coupling among biomes, and use multiple linear regression to explain R2 by climate, soil and vegetation structure. We found that in most areas tree canopy conductance is better explained by VPD than by SWC or PPFD. We also found that sites in drylands are less coupled to all three hydrometeorological drivers than those in other biomes. Climate, soil and vegetation structure were common controls of all three hydrometeorological couplings with G, with wetter climates, fine textured soils and tall vegetation being associated to tighter coupling. Differences across sites in the hydrometeorological coupling of tree canopy conductance may affect predictions of ecosystem dynamics under future climates, and should be accounted for explicitly in models.
The impacts of global change especially the recent climate-related extremes such as floods and droughts reveal significant vulnerability and exposure of freshwater ecosystems and related human systems to current climate variability. However, the effects of the extreme drought in the Okavango Delta system are not well understood and documented. Therefore, the objective of this use case was to apply the products from Digital Earth Africa namely: the Water Observation from Space (WOfS) derived from Landsat, vegetation cover baseline derived from Sentinel 2 data; and data from the meteorological agencies such as rainfall and measured river discharge data to evaluate the effects of drought in the Okavango Delta wetland system in relation to its upstream areas in Angola. In particular, we used the 2019 drought as a case study to assess inundation extent and vegetation cover dynamics with an emphasis on floodplain and dryland vegetation. Our preliminary results reveal that the Okavango Delta permanent marshes are resilient to drought, whereas seasonal floodplains are susceptible to drought. Further, we discovered that the geospatial location of floodplains has a direct effect on the timing of desiccation, with the western tributaries that flow into Lake Ngami and Thamalakane River being the last to dry out due to drought. In addition, we found that the drought phenomenon in the Cubango-Okavango River Basin region started earlier than 2019 spanning over a period of 5years; with 2018 as the year when the wetland system reached a minimum threshold for a tipping point triggered by the 2019 drought. In addition, the results contribute to the development of large-scale drought risk information and products for the Cubango- Okavango River Basin with a major focus in the Okavango Delta. Further, this use case provides recent baseline information on the effects of drought on vegetation cover and river flows in the Okavango Delta system at a landscape approach, which are essential elements for making informed science-based decisions on climate risks management and Sustainable Development Goals (SDGs) by relevant authorities in the Okavango Delta and the whole of Cubango-Okavango River Basin. In conclusion, this use case will be upscaled to other transboundary river basins in the Southern Africa Development Community.
Several bills moving through Congress are likely to provide significant funding for expanding research and results in climate change solutions (CCS). This is also a priority of the Biden-Harris Administration. The National Science Foundation (NSF) will be expected to distribute and manage much of this funding through its grant processes. Effective solutions require both a continuation and expansion of research on climate change–to understand and thus plan for potential impacts locally to globally and to continually assess solutions against a changing climate–and rapid adoption and implementation of this science with society at all levels. NSF asked AGU to convene its community to help provide guidance and recommendations for enabling significant and impactful CCS outcomes by 1 June. AGU was asked in particular to address the following: 1. Identify the biggest, more important interdisciplinary/convergent challenges in climate change that can be addressed in the next 2 to 3 years 2. Create 2-year and 3-year roadmaps to address the identified challenges. Indicate partnerships required to deliver on the promise. 3. Provide ideas on the creation of an aggressive outreach/communications plan to inform the public and decision makers on the critical importance of geoscience. 4. Identify information, training, and other resources needed to embed a culture of innovation, entrepreneurialism, and translational research in the geosciences. Given the short time frame for this report, AGU reached out to key leaders, including Council members, members of several committees, journal editors, early career scientists, and also included additional stakeholders from sectors relevant to CCS, including community leaders, planners and architects, business leaders, NGO representatives, and others. Participants were provided a form to submit ideas, and also invited to two workshops. The first was aimed at ideation around broad efforts and activities needed for impactful CCS; the second was aimed at in depth development of several broad efforts at scale. Overall, about 125 people participated; 78 responded to the survey, 82 attended the first workshop, and 28 attended the more-focused second workshop (see contributor list). This report provides a high-level summary of these inputs and recommendations, focusing on guiding principles and several ideas that received broader support at the workshops and post-workshop review. These guiding principles and ideas cover a range of activities and were viewed as having high importance for realizing impactful CCS at the scale of funding anticipated. These cover the major areas of the charge, including research and solutions, education, communication, and training. The participants and full list of ideas and suggestions are provided as an appendix. Many contributed directly to this report; the listed authors are the steering committee.
Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.
The North Atlantic phytoplankton bloom depends on a confluence of environmental factors that drive transient periods of exponential phytoplankton growth and interannual variability in bloom magnitude. I analyze interannual bloom variability in the North Atlantic via extreme value theory where the Generalized Extreme Value Distribution (GEVD) is fitted spatially to annual maxima of satellite-measured surface chlorophyll. I find excellent agreement between the observed distribution of interannual bloom maxima and those predicted from the GEVD. The spatial distribution of fitted GEVD parameters closely follows basin bathymetry where the largest extremes and heaviest distribution tails are found on the continental shelves and slopes. Trend analyses suggest weak evidence for changes in GEVD parameters, despite regional trends in mean chlorophyll levels and sea surface temperature. These results provide a framework to quantify interannual bloom variability and call for further work examining how extreme blooms propagate through food webs and contribute to carbon export.
The severity and frequency of wildfires have risen dramatically in recent years, drawing attention to the term ‘wildland-urban interface’ (WUI), the region where man-made constructions meet flammable vegetation. Herein, we mapped a finer-scale, novel linear WUI for California (CA) based on the intersection of boundaries of wildland vegetation and building footprint. The direct intersection is referred to as a direct WUI, whereas the intersection at 100-m is known as an indirect WUI. More fires were ignited closer to direct WUI than indirect WUI due to their proximity to communities. However, the overlap of past fire perimeters with indirect WUI is greater than that with direct WUI which shows that more areas were burned in the indirect WUI due to embers transported by strong wind gusts during large wildfires. The study’s findings will help land managers and policymakers in controlling fire dangers, land-use planning, and reducing threats to fire-prone communities.
Of immediate widespread concern is the accelerating transition from Holocene-like weather patterns to unknown, and likely unstable, Anthropocene patterns. A fell example is irreversible Arctic phase change. It is not clear if existing AOGCMs are adequate to model anticipated global impacts in detail; however, the GISS ModelE AOGCM can be used to locally compare and extend the PIOMAS Arctic ocean historical ice-volume dataset into the near future. Arctic Amplification (AA) mechanisms are poorly understood; to enable timely results, a simple linear, Arctic TOA grid-boundary energy-input is used to enforce AA, avoiding the perils of arbitrary modification of relatively well-studied parameterizations (e.g., restriction of cloud-top height to induce local warming). Only PIOMAS springtime/max and fall/min Arctic ice-volume decadal, linear trends were enforced. This temporally-broad grid-boundary modification produces a surprisingly detailed consonance with 10 out of 12 temporal profiles falling within 1-sigma of PIOMAS temporal data for the entire history modeled (2003 to 2021). The data are then integrated to 2050. The result is a zero-ice-volume, summer/fall half-year, beginning ca. 2035 (onset 1-sigma of ± ~5 years), with mean annual Arctic temperatures increasingly trending above freezing. Persistent, Arctic phase change follows this half-year transition about 20 years later. Also present in later stages, the 500 hPa height minimum is no longer nearly-coincident with the pole, suggesting jet stream disruption and its consequences. Hypothesized large clathrate-methane releases likely associated with Arctic temperature and phase change are also examined. A basic assumption is that the Arctic ice (i.e., temperature) must be preserved at all costs. This work establishes a reasonably detailed timeline for the Arctic phase change based on well-studied AOGCM physics, slightly tuned to decades of PIOMAS data. This result also points to the Arctic as a key, near-term site for localized, nondestructive intervention to mitigate Arctic phase change (e.g., Stjern ), thereby slowing the Holocene -> Anthropocene growing-season disruption. Although such an intervention cannot itself accomplish the requirements of the IPCC SP-15 , nor Planetary Boundaries theory, delaying the Arctic phase change will likely extend the time-window for accomplishing those critical tasks and ultimately to at least slow the rate of increase of climate emergencies.
Hyporheic zone reaction rates are highest just below the sediment-water interface, in a shallow region called the benthic biolayer. Vertical variability of hyporheic reaction rates leads to unexpected reaction kinetics for stream-borne solutes, compared to classical model predictions. We show that deeper, low-reactivity locations within the hyporheic zone retain solutes for extended periods, which delays reactions and causes solutes to persist at higher concentrations in the stream reach than would be predicted by classical approaches. These behaviors are captured by an upscaled model that reveals the fundamental physical and chemical processes in the hyporheic zone. We show how time scales of transport and reaction within the biolayer control solute retention and transformation at the stream scale, and we demonstrate that accurate assessment of stream-scale reactivity requires methods that integrate over all travel times.
Extreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster management and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short-wavelength radar data, as neither can “see” through dense forest canopies. The 2018 Hurricane Florence produced heavy rainfall and subsequent record-setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high-resolution full-polarized L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood-resilience goals.
Oxygen availability is decreasing in many lakes and reservoirs worldwide, raising the urgency for understanding how anoxia (low oxygen) affects coupled biogeochemical cycling, which has major implications for water quality, food webs, and ecosystem functioning. Although the increasing magnitude and prevalence of anoxia has been documented in freshwaters globally, the challenges of disentangling oxygen and temperature responses have hindered assessment of the effects of anoxia on carbon, nitrogen, and phosphorus concentrations, stoichiometry (chemical ratios), and retention in freshwaters. The consequences of anoxia are likely severe and may be irreversible, necessitating ecosystem-scale experimental investigation of decreasing freshwater oxygen availability. To address this gap, we devised and conducted REDOX (the Reservoir Ecosystem Dynamic Oxygenation eXperiment), an unprecedented, seven-year experiment in which we manipulated and modeled bottom-water (hypolimnetic) oxygen availability at the whole-ecosystem scale in a eutrophic reservoir. Seven years of data reveal that anoxia significantly increased hypolimnetic carbon, nitrogen, and phosphorus concentrations and altered elemental stoichiometry by factors of 2-5 relative to oxic periods. Importantly, prolonged summer anoxia increased nitrogen export from the reservoir by six-fold and changed the reservoir from a net sink to a net source of phosphorus and organic carbon downstream. While low oxygen in freshwaters is thought of as a response to land use and climate change, results from REDOX demonstrate that low oxygen can also be a driver of major changes to freshwater biogeochemical cycling, which may serve as an intensifying feedback that increases anoxia in downstream waterbodies. Consequently, as climate and land use change continue to increase the prevalence of anoxia in lakes and reservoirs globally, it is likely that anoxia will have major effects on freshwater carbon, nitrogen, and phosphorus budgets as well as water quality and ecosystem functioning.
Extreme weather conditions are associated with a variety of water quality issues that can pose harm to humans and aquatic ecosystems. Under dry extremes, contaminants become more concentrated in streams with a greater potential for harmful algal blooms, while wet extremes can cause flooding and broadcast pollution. Developing appropriate interventions to improve water quality in a changing climate requires a better understanding of how extremes affect watershed processes, and which places are most vulnerable. We developed a Soil and Water Assessment Tool model of the Cape Fear River Basin (CFRB) in North Carolina, USA, representing contemporary land use, point and non-point sources, and weather conditions from 1979 to 2019. The CFRB is a large and complex river basin undergoing urbanization and agricultural intensification, with a history of extreme droughts and floods, making it an excellent case study. To identify intervention priorities, we developed a Water Quality Risk Index (WQRI) using the load average and load variability across normal conditions, dry extremes, and wet extremes. We found that the landscape generated the majority of contaminants, including 90.1% of sediment, 85.4% of total nitrogen, and 52.6% of total phosphorus at the City of Wilmington’s drinking water intake. Approximately 16% of the watershed contributed most of the pollutants across conditions—these represent high priority locations for interventions. The WQRI approach considering risks to water quality across different weather conditions can help identify locations where interventions are more likely to improve water quality under climate change.
In closed-canopy forests, the availability of photosynthetically active light has been a focal point of research, emphasizing the role of light as a resource in limiting carbon assimilation and individual tree growth. However, light shapes the functioning of forest ecosystems through multiple mechanisms. Here, using a series of studies from a network of tree diversity experiments, we explore the multifaceted ways in which light---in terms of both quantity and quality---shapes productivity in mixed-species forests. Spectral reflectance from remote sensing of forest canopies is being increasingly used to detect how tree diversity influences productivity. We demonstrate that airborne imaging spectroscopy captures functionally important differences among canopies related to their structure, chemistry, and underlying biological interactions. Ground-based analyses can show in detail how photosynthetically active light is partitioned among species in mixed-species communities. We show that greater interception of light and greater efficiency of light use, generated by inter- and intra-specific differences, combine to enhance productivity in mixed-species forests. Light may shape forest function not only as a resource but also as a stressor and cue. Plants can perceive light at various wavelengths, use this information to assess their neighborhoods, and subsequently adjust their physiology and allocation. We characterize how light quality---from the ultraviolet to shortwave infrared---varies among and within canopies of differing diversity. We explore how these diversity-light quality relationships arise and connect across levels of biological organization from leaf-level trait expression to forest function. Together these studies lend insight into light-mediated mechanisms that drive relationships between biodiversity and productivity in forest ecosystems---insights that are crucial to predict how biodiversity change will affect future forest function.
Earthworms play a critical role in soil ecosystems. Analyzing the spatial structure of earthworm burrows is important to understand their impact on water flow and solute transport. Existing in-situ extraction methods for earthworm burrows are time-consuming, labor-intensive and inaccurate, while CT scanning imaging is complex and expensive. The aim of this study was to quantitatively characterize structural characteristics (cross-sectional area (A), circularity (C), diameter (D), actual length (Lt), tortuosity (τ)) of anecic earthworm burrows that were open and connected at the soil surface at two sites of different tillage treatments (no-till at Lu Yuan (LY) and rotary tillage at Shang Zhuang (SZ)) by combining a new in-situ tin casting method with three-dimensional (3D) laser scanning technology. The cross-sections of anecic earthworm burrows were almost circular, and the C values were significantly negatively correlated with D and A. Statistically, there were no significant differences in the τ values (1.143 ± 0.082 vs 1.133 ± 0.108) of anecic earthworm burrows at LY and SZ, but D (6.456 ± 1.585 mm) and A (36.929 ± 21.656 mm2) of anecic earthworm burrows at LY were significantly larger than D (3.449 ± 0.531 mm) and A (9.786 ± 2.885 mm2) at SZ. Our study showed that burrow structures at two different sites differed from each other. Soil tillage methods, soil texture and soil organic matter content at the two sites could have impacted earthworm species composition, variation of earthworm size and the morphology of burrows. The method used in this research enabled us to adequately assess the spatial structure of anecic earthworm burrows in the field with a limited budget.
Trees in seasonal climates may use water originating from both winter and summer precipitation. However, the seasonal origins of water used by trees have not been systematically studied. We used stable isotopes of water to compare the seasonal origins of water found in three common tree species across 24 Swiss forest sites sampled in two different years. Water from winter precipitation was observed in trees at most sites, even at the peak of summer, although the relative representation of seasonal sources differed by species. However, the representation of winter precipitation in trees decreased with site mean annual precipitation in both years; additionally, it was generally lower in the cooler and wetter year. Together, these relationships show that precipitation amount influenced the seasonal origin water taken up by trees across both time and space. These results suggest higher turnover of the plant-available soil-water pool in wetter sites and wetter years.
The process of evapotranspiration transfers water vapour from vegetation and soil surfaces to the atmosphere, the so-called latent heat flux (𝑄 LE), and thus crucially modulates Earth’s energy, water, and carbon cycles. Vegetation controls 𝑄 LE through regulating the leaf stomata (i.e., surface resistance 𝑟 s) and through altering surface roughness (aerodynamic resistance 𝑟 a). Estimating 𝑟 s and 𝑟 a across different vegetation types proves to be a key challenge in predicting 𝑄 LE. Here, we propose a hybrid modeling approach (i.e., combining mechanistic modeling and machine learning) for 𝑄 LE where neural networks independently learn the resistances from observations as intermediate variables. In our hybrid modeling setup, we make use of the Penman-Monteith equation based on the Big Leaf theory in conjunction with multi-year flux measurements across different forest and grassland sites from the FLUXNET database. We follow two conceptually different strategies to constrain the hybrid model to control for equifinality arising when estimating the two resistances simultaneously. One strategy is to impose an a priori constraint on 𝑟 a based on our mechanistic understanding (theory-driven strategy), while the other strategy makes use of more observational data and adds a constraint in predicting 𝑟 a through multi-task learning of the latent as well as the sensible heat flux (𝑄 H ; data-driven strategy). Our results show that all hybrid models exhibit a fairly high predictive skill for the target variables with 𝑅 2 = 0.82-0.89 for grasslands and 𝑅 2 = 0.70-0.80 for forests sites at the mean diurnal scale. The predictions of 𝑟 s and 𝑟 a show physical consistency across the two regularized hybrid models, but are physically implausible in the under-constrained hybrid model. The hybrid models are robust in reproducing consistent results for energy fluxes and resistances across different scales (diurnal, seasonal, interannual), reflecting their ability to learn the physical dependence of the target variables on the meteorological inputs. As a next step, we propose to test these heavily observation-informed parameterizations derived through hybrid modeling as a substitute for overly simple ad hoc formulations in Earth system models.
Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical, and biotic properties of soil. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. Here I will present root phenotyping approaches for assessing ecosystem service in a prospective cash cover crop; pennycress (Thlaspi arvense L.). In development is a large format mesocosm system that will allow 3D root system architecture analysis of multiple plants. Using this system, we will be assessing how variation in pennycress root system architecture can affect ecosystem service and abiotic stress tolerance with the plant to scale from single plant to canopy level traits.
Marine or coastal wetlands that host a diverse variety of flora and fauna are unique and fragile as they are subjected to changing coastlines and undergo dynamic spatial shifts with respect to tidal movements. In India, the Coastal Regulation Zone (CRZ) notification aims at the conservation of coastal regions, under the Environment (Protection) Act, 1986, and regulates developmental and construction activities within the CRZ regions of marine wetlands, in addition to the coastal belt. Remote sensing techniques can be of great use in understanding if the implementation of the CRZ has helped to regulate the proliferation of settlements in the wetland system. In this study, remote sensing techniques along with machine learning classifiers have been used for detecting and quantifying the recent settlements that have been built in the zones regulated by the CRZ of the Vembanad wetland of Kerala. Three standard change detection pre-processing techniques were used over Linear Imaging Self-Scanning Sensor (LISS) IV imagery which was followed by classification using machine learning algorithms: Support Vector Machine (SVM), random forest, and Artificial Neural Network (ANN) to identify the built-up erected in the CRZ region between 2012 and 2018. Comparing the performance of these classifiers, the random forest model was found to have the highest overall accuracy of 96%. It was found that the total area of new built-up that were constructed between 2012 and 2018 in the CRZ regions of 48 villages, that span across Ernakulam, Kottayam and Alappuzha districts of Kerala is 149 hectares. This usage of change detection techniques aided by machine learning algorithms over high-resolution LISS IV imagery would help to evaluate the effectiveness of the CRZ notification over other marine wetlands in India.