The structural characterization of the sealed or shielded nuclear materials constitutes an indispensable aspect that necessitates a careful transportation, a limited interaction, and under certain circumstances an on-site investigation for the nuclear fields including but not limited to nuclear waste management, nuclear forensics, and nuclear proliferation. To attain this purpose, among the promising non-destructive/non-hazardous techniques that are performed for the interrogation of the nuclear materials is the muon tomography where the target materials are discriminated by the interplay between the atomic number, the material density, and the material thickness on the basis of the scattering angle and the absorption in the course of the muon propagation within the target volume. In this study, we employ the Monte Carlo simulations by using the GEANT4 code to demonstrate the capability of muon tomography based on the dual-parameter analysis in the examination of the nuclear waste barrels. Our current hodoscope setup consists of three top and three bottom plastic scintillators made of polyvinyl toluene with the thickness of 0.4 cm, and the composite target material is a cylindrical nuclear waste drum with the height of 96 cm and the radius of 29.6 cm where the outermost layer is stainless steel with the lateral thickness of 3.2 cm and the filling material is ordinary concrete that encapsulates the nuclear materials of dimensions 20×20×20 cm3. By simulating with a narrow planar muon beam of 1×1 cm 2 over the uniform energy interval between 0.1 and 8 GeV, we determine the variation of the average scattering angle together with the standard deviation by utilizing a 0.5-GeV bin length, the counts of the scattering angle by using a 1-mrad step, and the number of the absorption events for the five prevalent nuclear materials starting from cobalt and ending in plutonium. Via the duo-parametric analysis that is founded on the scattering angle as well as the absorption in the present study, we show that the presence of the nuclear materials in the waste barrels is numerically visible in comparison with the concrete-filled waste drum without any nuclear material, and the muon tomography is capable of distinguishing these nuclear materials by coupling the information about the scattering angle and the number of absorption in the cases where one of these two parameters yields strong similarity for certain nuclear materials.
There are gradients of conductivity and major ions in the coastal zone of the Eastern Georgian Bay of Lake Huron that appear to limit the spatial distribution of invasive dreissenid mussels. Rivers flowing into Georgian Bay from the Canadian Shield are relatively low in conductivity compared to the main body of Lake Huron, and so there is an observed gradient of solutes near the river mouths. The field observations show a strong positive correlation between conductivity and calcium concentration. Thus, we use conductivity to infer the solute concentrations required for the successful growth of dreissenid mussels. We observe most mussels in regions where specific conductivities were greater than 140 mS/cm. We use field observations to examine how the low calcium river water mixes within the coastal zone, which sets solute gradients that determine mussel distribution. When river flows are low, there is only a weak solute gradient across the coastal zone, implying an intrusion of open bay waters into the shallow embayments that is favourable for the growth of mussels. In contrast, when river flows are as much as 10 times higher, there is a strong solute gradient that extends further towards the lake, and the low calcium appears to inhibit and limit the growth of dreissenid mussels. Thus, the seasonal character of solute gradients helps describe the spatial distribution of dreissenid mussels and helps explain the localized absence of a species that is otherwise prevalent in much of the Laurentian Great Lakes.
In the last decades, the Eddy Covariance (EC) technique has become a standard method to measure net ecosystem CO2 exchange (NEE), but it doesn’t let to distinguish between Gross Primary Production (GPP) and Ecosystem Respiration (Reco). Olive (Olea europea L.) is one of the most important agrosystems on the Mediterranean basin, covering 9.5Mha and accounting for 98% of olive groves global surface. In this study we analyze the EC fluxes from an olive orchard of SE Spain with two soil treatments: 1) leaving spontaneous weed cover (WC) growing on the soil, and 2) inhibiting this growth with a glyphosate-based herbicide (WF). These two different treatments provide high differences in NEE, but the contribution of each component (trees, weed and soil) in the NEE require a better understanding. In this study, we analyze Eddy Covariance fluxes from an olive orchard in SE Spain at different altitudes (above and below the olive trees). To study carbon fluxes contribution of weed in the olive orchard 4 EC towers were installed, placing them on two different areas: one area in WC treatment and the other in WF treatment. On each area, a canopy tower and a subcanopy tower were installed. After a data-filtering during the growth season in which only wind directions coming from olive orchard alleys were accepted, preliminary results from the subcanopy towers show that there are prevailing CO2 emission values from the soil in the WF area and CO2 fixation from the weed in the WC area. On the other hand, during senescence period, CO2 emission fluxes were obtained from both subcanopy towers. These results layout the relevant place of subcanopy towers to understand the role in carbon cycle of the different components in an ecosystem.
High-resolution space-based spectral imaging of the Earth’s surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise. The different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA’s Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the intrinsic dimensionality decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. Intrinsic dimensionality is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.
Extensive industrial activities have led to an increase of the content of chromium in the environment, which causes serious pollution to the surrounding water, soil and atmosphere. The enrichment of chromium in the environment through the food chain ultimately affects human health. Therefore, the remediation of chromium pollution is crucial to development of human society. A lot of scholars have paid attention to bioremediation technology owing to its environmentally friendly and low-cost. Previous reviews mostly involved pure culture of microorganisms and rarely discussed the optimization of bioreduction conditions. To make up for these shortcomings, we not only introduced in detail the conditions that affect microbial reduction but also innovatively introduced consortium which may be the cornerstone for future treatment of complex field environments. The aim of this study is to summary chromium toxicity, factors affecting microbial remediation, and methods for enhancing bioremediation. However, the actual application of bioremediation technology is still facing a major challenge. The detoxification process inclusive of bioreduction and biosorption. When microorganisms are exposed to Cr(Ⅵ), related genes will be up-regulated (Chr promoter and copZ) to prevent intracellular molecules from being destroyed. Microorganisms provide electrons to reduce Cr(Ⅵ) through autogenous enzymes or externally addedreducing substances. The pH, temperature, concentration, and electron donor are the major factors affectting the bioreduction owing to their close correlation with enzyme activity. Biosorption is mainly based on electrostatic force, combined with chromium oxide anions through functional groups (hydroxyl, carboxyl, amino etc.） on the cell surface. Bioaccumulation is the active uptake of Cr(VI) by living cells, a process that usually depends on the forms and bioavailability of Cr(VI) and is also dependent on nutrients such as energy and carbon sources. The combination of these exogenous additives and mixed culture microorganisms greatly improves the tolerance and reduction efficiency of microorganisms. In previous reports on bioremediation technology, the main object of remediation was water bodies, and arable land is currently facing severe heavy metal pollution, so the future microbial remediation technology should also target the soil and atmosphere. In response to the research deficiencies has been proposed, the following suggestions are putforward: 1. Due to the complex actual environmental conditions, especially the soil, it is difficult to achieve the purpose of governance using purely cultured microorganisms. The use of mixed culture of microorganisms can improve the adaptability to the environment and the efficiency of treatment through the synergy between microorganisms. 2. For the complex medium of soil, the effect of using microorganisms alone may not be obvious. Therefore, combining plants and microorganisms can promote the application o
In light of the magnitude and pace of the environmental changes in the northern permafrost zone (NPZ) and their feedbacks to climate, contemporary, accurate and quantitative ecological forecasting has never been so paramount to the development of climate change adaptation and mitigation strategies. Yet, uncertainties associated with carbon (C) projections in the NPZ remain the largest to projections of global C budget and climate. While there are persisting lacks of data documenting important and emerging soil and vegetation dynamics in the NPZ, the volume, variety and accessibility of observational data in the NPZ has grown exponentially over the past decades and significantly improved our understanding of terrestrial C dynamic. Yet, a lag persists between large availability of historical, new and iterative data collections and the capacity of terrestrial biosphere models to fully incorporate this information, limiting advances in reducing the uncertainty of ecological forecasting in the NPZ. In this new project, we are developing the Arctic Carbon Monitoring and Prediction System (ACMPS), a data assimilation system that will use the information from field observations from ecological networks, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the NPZ. The ACMPS will be coupling model development and testing, data-assimilation techniques and near-term forecasting capacity to improve the accuracy of historical and future simulations of ecosystem permafrost and C dynamics across the NPZ. We will present the structure and workflow of the ACMPS, as well as preliminary assessment of model sensitivity and uncertainty analysis of soil and vegetation carbon fluxes, using a terrestrial biosphere model specifically developed to represent permafrost, vegetation and carbon dynamics in arctic and boreal ecosystems. Plain-language Summary We are presenting the Arctic Carbon Monitoring and Prediction System, a data assimilation system that uses field observation, remote sensing data and ecological modeling to reduce the uncertainty of the terrestrial carbon balance in the northern permafrost zone, and to better inform development of climate change adaptation and mitigation strategies.
An Urban Heat Island is a metropolitan area with higher air and surface temperatures than surrounding areas. The Urban Heat Island Effect (UHIE) is a relative measure of the heat in urban heat islands. This research study investigates how developed land cover and weather trends can be used to forecast the UHIE with two distinct modeling frameworks. Projections of future conditions can prepare scientists and communities to take greener initiatives and adapt their lifestyle to preserve the Earth. The study focuses on the Greater Austin Region (TX, USA) for initial feasibility, but aims to extend these methods to a national or global scale. The first technique uses machine learning (Keras sequential model) to identify correlations between factors closely linked to the UHIE. The tested factors were air and surface temperature, relative humidity, soil moisture, and population growth. Evident correlations were found and used to begin training a predictive model (artificial neural network). The second technique uses developed softwares in QGIS Modules for Land Use Change Evaluation (MOLUSCE), high resolution satellite imagery provided by Multi-Resolution Land Characteristics land cover/land use data, and distance from roadways and inland water bodies data in order to accurately predict the possible changes in 2022 to the Greater Austin Region. Major limitations throughout the research process include regional & temporal data inconsistencies, the narrow scope of factors and geographic region, and the time constraint of the NASA SEES internship. Given ample time and data, these analyses can be used in green efforts to moderate and reduce the causes of UHIE. They can also aid in further investigating water contamination, energy consumption, and human health, and make larger scale environmental simulations possible.
Cover crops may influence soil health and functioning. However, little is known about the role of belowground root architectural traits in linking cover crop diversity with rhizosphere soil ecosystem properties. We hypothesize that cover crop diversity may improve root traits, which in return, could influence its effects on essential indicators of soil physicochemical heterogeneity, such as the composition of soil aggregate-size classes and nutrients, the soil organic matter (SOM) and soil organic carbon (SOC) contents, and microbial communities. We studied the impact of cover plant diversity on root traits, soil properties and microbial communities. The four soil aggregate-size classes, such as large macro- (>2000μm), small macro- (<2000-500μm), meso- (<500-250 μm), and micro-aggregates (<250 μm) were separated by the dry sieving. Root traits such as surface area (cm2) and length (cm) were quantified by image analysis using Winrhizo. The soil nutrient, SOM, and SOC contents were determined by standard methods. Plant diversity improved productivity, root architectural traits, composition of soil aggregate-size classes and nutrients, SOM and SOC contents, composition and networking of microbial communities. Our results suggest that competition among plant roots in species-rich than poor communities may improve rhizosphere soil carbon storage, composition of soil aggregate-size classes and microbial communities.
Fusiform rust is a disease caused by the fungal pathogen Cronartium quercuum f. sp. fusiforme (Cqf). It is considered one of the most damaging and economically important diseases for loblolly pine (Pinus taeda L.), causing millions of dollars in damage and loss of products each year. Evaluating trees for disease resistance includes inoculation trials – these trials require artificial inoculation of the pathogen followed by visual inspection for disease incidence. Visual inspection can lead to incorrect classification due to human error or escaped susceptible (i.e. a susceptible individual with no symptoms). Here, we plan to use vibrational spectroscopy tools to improve the accuracy of phenotypic values. Vibrational spectroscopy tools allow for a user to obtain a single, comprehensive reading based on the chemical constituents of sample. Because pine trees mostly rely on chemical-based defenses, the relationship between chemical makeup and resistance is promising. We plan to collect spectra from 40 different loblolly pine families (20 with lower rust incidence and 20 higher rust incidence) over five different progeny test sites in the southeastern US, totaling 400 trees. We will use a handheld near-infrared (NIR) spectrometer for a real-time, in-field reading on phloem and needle tissue. In addition, phloem and needle tissue will be analyzed by a benchtop Fourier-transformed infrared (FT-IR) spectrometer. Using multivariate analyses and machine learning algorithms, spectral readings can be mined for patterns associated with fusiform rust disease resistance or susceptibility, which can be used to predict the phenotype of untested trees. The results of the two tools and two tissue types will be compared to evaluate the best method for identifying phenotype in the system. This chemical fingerprinting and classification approach to phenotyping loblolly pines will provide a more objective, efficient, and more accurate way to identify disease resistance in the field, thereby creating more robust forest stands against fusiform rust.
Arctic plants are small in stature and spectrally diverse, which presents challenges to current NASA missions to visualize effects of disturbance or directional vegetation change via mapping. Remotely sensed data having fine spatial (ca. 10 cm pixels) and spectral grain (eg. “hyperspectral”) will therefore help resolve patches of many arctic plant groups, such as dwarf shrubs, bryophytes and lichens and separate them from litter, wood or rock/soil. To address these challenges, in summer 2018 we sampled vegetation at 15 different sites around Fairbanks, Alaska using ground-based and airborne hyperspectral sensors under eight different AVIRIS ng flight lines next gen flight lines (circa 2017-2018). At each AVIRIS flight line, we estimated percent cover of plant functional types in eleven 1m2 quadrats every 10 m along a 100m transect. We then flew our UAV and imaging spectrometer (Headwall Micro A-series VNIR, 400-1000 nm, 330 bands, 10 cm pixels). Spectral signatures of any surfaces were sampled using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm, 1nm bands). We collected 600+ georeferenced scans from 70+ species/plant functional types at 25+ different sites around Alaska. Spectral profiles showed many different plant species have similar to indistinguishable signatures (eg. Paper birch and Alder) while many plant functional types that have been grouped together (eg. Moss) were very spectrally heterogenous. UAV-based hyperspectral imagery (ca. 4-10 cm pixels) resolved pure pixels of many artic plants. Our approach resolves fine grained ecological features, such as networks of circular patches (mostly lichens, brophytes and mineral soil) over very large areas (ca. 10,000 m2), created by small cryoturbation features (frost boils). We explore spectral unmixing and other statistical approaches to compare mapping results using our spectral library with AVIRIS ng (4 m pixels) and our UAV-based VNIR hyperspectral imagery.
Methane emissions from freshwater, mineral-soil wetlands represent an important portion of the global greenhouse gas budgets. We use long-term observations in Old Woman Creek (OWC), an estuarine wetland at the coast of Lake Erie. OWC is characterized by a fluctuating water level controlled by a natural sand barrier. OWC water levels are high when the barrier is closed. When it breaks, OWC is directly connected to the lake. Long term water level rise of Lake Erie provides a trend of water level at OWC. These changes to hydrology drive changes to the ecology. In OWC the dominant eco-hydrological patch types are mudflats, cattails (Typha), floating-leaf vegetation (Lotus, water lily), and open water. The seasonal and long term changes to water level lead to dynamic changes in the patch type composition, and as OWC gets deeper, mudflats and cattails give way to open water and floating-leaf vegetation. We developed an approach to classify the eco-hydrological patch type from remote sensing images. We used seasonal time series of NDVI from HLS (a composite dataset of Sentinel and Landsat). These time series were classified to patch types according to their similarity to the seasonal NDVI profiles of pixels identified and ground-truthed as pure pixels of a specific patch type. We then used the DG-SWEM high resolution hydrodynamic model to simulate the flow velocity throughout the wetland. Combining the eco-hydrological patch locations and the high-resolution flow simulation allowed for the calculation of an effective patch-type-level residence time. We found differences in residence times between the different patch types. We measured the relations between methane flux and CO2 uptake at the whole wetland scale, the vegetation patch scale, and directly from leaves. We found different methane-CO2 relations among the floating leaved and emergent species. Phenological transitions throughout the growing season continued to make an important effect only in Typha. Our observations represent a valuable foundation towards a more robust models of methane fluxes in wetlands at the resolution of within-wetland vegetation patch type, and resolving the effects of seasonal and within-season vegetation phenology in ecosystem-scale models.
Cyanobacterial Harmful Algal Blooms (CyanoHABs) are progressively becoming a major water quality and public health hazard worldwide. Untreated CyanoHABs can severely affect human health due to their toxin producing ability, causing physiological and neurological disorders such as non-alcoholic liver disease, dementia to name a few. Transfer of these cyanotoxins via food-chain only accelerates public health hazards. CyanoHABs can potentially also lead to a decline in aquatic and animal life, hampering recreational activities at waterbodies and ultimately affecting the country’s economy gravely. CyanoHABs require nutrient rich warm aquatic environments to bloom and their proliferation in increasingly warmer areas of the world can be an indirect indicator of global climate change. Many lakes in the United States have been experiencing such CyanoHABs in the summers, which only grow severe every coming year, and this is consistently leading to increased public health implications. A recent study (September, 2021) by the Centre for Disease Control quantified hospital visits with the trend of such CyanoHABs to indeed observe a strong correlation between the two. This necessitates a need for a user-friendly and accessible infrastructure to monitor inland and coastal waterbodies throughout the U.S for such blooms. We present a remote sensing-based approach wrapped in a lucid web-app, “CyanoTRACKER”, which can help detect CyanoHABs on a global level and act as an early warning system, potentially preventing/lessening public health implications. CyanoHABs are dominated by the Phycocyanin pigment, which absorbs sunlight strongly around 620 nm wavelength. Owing to this specific absorption characteristic and the availability of a satellite band at exactly 620 nm, we use the opensource Sentinel-3 OLCI satellite data to detect the presence of CyanoHABs. CyanoTracker is a user-friendly Google Earth Engine dashboard, which is easily accessible via only a browser and an internet connection and allows for a variety of near-daily analysis options such as: a) select any location throughout the world and view satellite image based on date-range of choice, b) click on any pixel in the satellite image and detect presence/absence of cyanobacteria, c) visualize the spatial spread as well as the temporal phenology of an ongoing bloom or a potential incoming bloom. This dashboard is easily accessible to water-managers and in fact, anyone who wishes to use it with minimal training and can effectively serve as an early warning system to CyanoHAB induced disease outbreaks.
It is typically assumed that dinitrogen (N2) fixation and denitrification are mutually exclusive processes in riverine ecosystems because N2 fixation is favored in high light, low nitrogen (N) environments but denitrification is favored under anoxic, high N conditions. Yet recent work in marine and lake ecosystems has demonstrated that N2 fixation can happen under high N conditions and in sediments, challenging this assumption. We conducted a cross-ecoregion study to test the hypothesis that N2 fixation and denitrification would co-occur in streams and rivers across a range of reactive N concentrations. Between 2017 and 2019, we sampled 30 streams in 13 ecoregions, using chambers to quantify N2 flux using membrane inlet mass spectrometry, N2 fixation using acetylene reduction, denitrification using acetylene block, and microbial diversity using 16S gene sequencing. 25 of the study streams were part of the National Ecological Observatory Network or the StreamPULSE network, which provided data on water temperature, light, nutrients, discharge and metabolism. We found that N2 fixation rates were detectable in half of the streams surveyed, and were most frequently detected on rock, wood, and/or macrophyte substrates. Denitrification potential was detected in all streams, with rates 1-2 orders of magnitude higher than N2 fixation rates and the highest rates measured in sediments. Substrate heterogeneity, and associated variation in environmental conditions, appeared to facilitate the coexistence of N2 fixation and denitrification in the study streams. Rates of denitrification were significantly positively related to streamwater nitrate concentrations (r2 = 0.35), but N2 fixation rates were not, despite the common simplifying assumption that denitrification dominates the N2 flux in streams under high N and N2 fixation only occurs under low N conditions. Additional analyses are exploring reach to watershed characteristics, and metabolic regimes as drivers of cross-ecoregion patterns in processes.
Temporal redox fluctuations alter the pools of reducible FeIII and greenhouse gas emissions in humid upland soils. However, it is less clear how the characteristics of these fluctuations (length, frequency, amplitude) impact biogeochemical rates. We hypothesized that anaerobic rates of FeIII reduction and CH4 emissions are sensitive to the length of soil oxygen deprivation. To test this hypothesis, we exposed a surface soil from the Luquillo Experimental Forest to three lengths of O2 perturbation during repeated redox oscillations: an anoxic interval of 6 d with oxic intervals of 8, 24, or 72 h. We found that shorter oxic intervals resulted in more anaerobic FeIII reduction, while longer oxic intervals stimulated higher anaerobic CH4 emissions (CO2 fluxes did not change). We propose that short O2 pulses stimulate Fe reduction by resupplying the FeIII electron acceptor, but do not last long enough to inhibit microbial Fe reducers; conversely long O2 pulses suppress microbial iron reducers to a greater extent than methanogens leading to enhanced CH4 emissions. Thus, the length of periodic oxidant exposure selectively enhances less thermodynamically favorable anaerobic processes by modulating the competitiveness of dominate anaerobic bacteria, which is important for regulating greenhouse gas emissions in redox dynamic soils.
We investigated the abundance of Pseudanabaena species and the concentration of the monoterpene 2-methylisoborneol (2-MIB) from July to October at three sampling sites in South Korea. To identify the main cause of 2-MIB occurrence in drinking water source, we characterized and performed a phylogenetic analysis of the 2-MIB synthase gene. Pseudanabaena was the dominant cyanobacterium (68–100%) among the samples. At all three sampling sites, a strong positive correlation was detected between 2-MIB concentrations and Pseudanabaena cell numbers. A phylogenetic analysis of 222 MIB sequences isolated from the water samples showed that all of the clones were affiliated with the Pseudanabaena MIB synthase gene, demonstrating that the 2-MIB in Han River drinking water source was produced by Pseudanabaena sp. Using a clone of the 2-MIB gene, network-based analysis and unweighted pair group method with arithmetic mean (UPGMA) analysis were used to examine temporal and spatial variation in the 2-MIB concentration and Pseudanabaena abundance. The network analysis showed greater temporal than spatial similarity among the 2-MIB gene clones. Together, our results demonstrate that Pseudanabaena was the main producer of 2-MIB. These findings provide important information for odor management in drinking water source.
Terrestrial vegetation is known to be an important sink for carbon dioxide (CO2). However, fluxes to and from vegetation are often not accounted for when studying anthropogenic CO2 emissions in urban areas. This project seeks to quantify urban biogenic fluxes in the Greater Toronto and Hamilton Area located in Southern Ontario, Canada. Toronto is Canada’s most populated city but also has a large amount of green-space, covering approximately 13 % of the city. In addition, vegetation is not evenly distributed throughout the region. We therefore expect biogenic fluxes to play an important role in the spatial patterns of CO2 concentrations and the overall local carbon budget. In order to fully understand biogenic fluxes they can be partitioned into the amount of CO2 sequestered via photosynthesis, gross primary productivity (GPP), and the amount respired by vegetation, ecosystem respiration (Reco). Solar induced chlorophyll fluorescence (SIF) measured from space has been shown to be a valuable proxy for photosynthesis and thus can be used to estimate GPP. Vegetation models, including the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) and the SIF for Modelling Urban biogenic Fluxes (SMUrF) model, have also been used to estimate both GPP and Reco In this study we compare modelled and SIF-derived biogenic CO2 fluxes at a 500 m by 500 m resolution, to ground-based flux tower measurements in Southern Ontario to determine how well these methods estimate biogenic CO2 fluxes. This study works towards determining the importance of biogenic fluxes in the Greater Toronto and Hamilton Area. Furthermore, the results of this work may inform policy makers and city planners on how urban vegetation affects CO2 concentrations and patterns within cities.
Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature. Lastly, modeled stable carbon isotope fractionation differed under canopy water stress which illustrates the value of carbon isotopes in helping to characterize ecosystem function and elucidate differences attributable to model structure. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.
Despite human-induced changes in floodplains over the past century, comprehensive data of long-term land use change within floodplains of large river basins are limited. Data of long-term and large-scale floodplain land use are required to effectively quantify floodplain functions and development trajectories. They also provide a holistic perspective on the future of floodplain management and restoration – and concomitantly flood-risk mitigation. Here, we present the first available dataset that provides spatially explicit estimates of land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at a 250-m resolution. We derived this MRB floodplain land use change dataset from two input data sources: (i) the high-resolution global floodplain extent dataset GFPLAIN250m, and (ii) the annual FOREcasting SCEnarios of Land-use Change (FORE-SCE) dataset for the continental United States. Our results suggest that MRB floodplains have transitioned irreversibly from natural ecosystems to predominantly agricultural land use (e.g., more than 10,000 km2 of wetlands have been lost due to agricultural expansion). Developed land use within the floodplain has also steadily increased. The dataset is publicly available through HydroShare: https://gishub.org/mrb-data as well as an interactive online map interface: https://gishub.org/mrb-floodplain. These products will support MRB resilience and sustainability goals by advancing data-driven decision making on floodplain restoration, buyout, and conservation scenarios.