Given the key role, wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA-ISRO SAR (NISAR) mission. In this study, the capability of L-band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana is investigated using two object-based machine learning approaches: Support Vector Machine -(SVM) and Random Forest (RF). L-band Unmanned Aerial Vehicle SAR (UAVSAR) data is exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean-shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L-band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman-Durden, and Aghababaee features have the highest contribution to the overall accuracy.

The China-France Oceanography Satellite (CFOSAT), launched on 29 October 2018, is the world’s first satellite that carries both a real aperture radar spectrometer and a fan-shaped beam rotary scanning scatterometer. This study examined the retrieval results for the scatterometer onboard the CFOSAT with respect to global buoys from NDBC, TAO/TRITON, PIRATA, RAMA, PMEL, and MNR in 2019. For the scatterometer products in the range of 4-24 ms-1, the root-mean-square error (RMSE) of the wind speed was 1.1 ms-1, and the wind direction RMSE was 20.4{degree sign} at the global scale. In the tropics, the wind speed and wind direction RMSEs were 1.1 ms-1 and 21.5{degree sign}, respectively, while the corresponding RMSEs decreased to 1.1 ms-1 and 18.8{degree sign} in the subtropics. The error statistics were larger in the range of 0-4 ms-1, which is beyond the designed measurement accuracy, and the wind speed was overestimated. Overall, the CFOSAT measurements are reliable in the range of 4-24 ms-1, thus meeting the accuracy requirements of the technical design. Finally, the influence of surface currents on the CFOSAT measurements was analyzed. The results indicate that the differences between the CFOSAT measurements and buoy observations in the tropics were reduced by excluding the effect of the surface currents.

In the Subantarctic sector of the Southern Ocean, vertical entrainment of dissolved iron (DFe) triggers the seasonal productivity cycle. However, diminishing physical supply of new Fe during the spring to summer transition rapidly drives epipelagic microbial communities to rely upon recycled DFe for growth. Hence, subpolar waters evolve seasonally from a high fe ratio system (i.e., [uptake of new Fe]/[uptake of new+recycled Fe]) to a low fe ratio system. Here, we tested how resident microbes within a cyclonic eddy respond to different Fe/ligand inputs which mimic entrained new DFe (Fe-NEW), diffusively-supplied regenerated DFe (Fe-REG), and a control with no addition of DFe (Fe-NO). After 6 days, 3.5 (Fe-NO, Fe-NEW) to 5-fold (Fe-REG) increases in Chl a were observed despite ~2.5-fold range between treatments of initial DFe. Marked differences were also evident in the proportion of in vitro DFe derived from recycling to sustain phytoplankton growth (Fe-REG, 30% recycled c.f. 70% Fe-NEW, 50% Fe-NO). This trend supports the concept that DFe/ligands released from subsurface particles are more bioavailable than new DFe collected at the same depth. This additional recycling may be mediated by bacteria. Indeed, by day 6 bacterial production (BP) was comparable between Fe-NO and Fe-NEW but~2 fold higher in Fe-REG. Interestingly, a preferential response of phytoplankton (haptophyte-dominated) relative to bacteria was also found in Fe-REG. In contrast, in Fe-NEW and Fe-NO the proportion of diatoms increased. Hence, different modes of Fe/ligand supply modify BP and Fe bioavailability to phytoplankton that may drive distinctive floristic shifts and biogeochemical signatures.

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. SC metrics included in the structure-function relationship as well as the strength of drivers was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how SC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.

Water scarcity becomes more severe and there will need to be a concerted effort to ensure that water supply remains secure. To mitigate the danger of water scarcity using an energy efficient method for water desalination is crucial. This review objectively compares the most practical thermal and reverse osmosis desalination technologies and the latest energy recovery opportunities for each process. This analysis reveals that thermal and reverse osmosis desalination should not be considered as a competitor since (mostly) the feed water quality and the final permeate water standards are the main parameters that impose choosing either of the most appropriate desalination method or hybridization of both approaches. Desalination of seawater using improved thermal desalination in which pre-heated helium gas instead of air is used to increase evaporation efficiency up to 3 times, is a promising technique that could make this sub-boiling thermal desalination approach the future of thermal desalination. The effect of Renewable energy resources, BCE, multiple stage units and optimizing liquid height in the dehumidifier were particularly evaluated in the HDH process which could perfectly handle the sub-boiling thermal desalination approach. Results of this comprehensive review can aid decision-makers by manifesting the main developments in desalination techniques to a reasonable degree of accuracy.

The Glacier Energy and Mass Balance (GEMB) module of NASA’s Ice-sheet and Sea-level System Model (ISSM) can simulate the evolution of firn density profiles in the ice sheet snowpack. It is a column model that can give detailed subsurface parameters such as layer depth, snow grain growth, depth dependent albedo, layer density etc. These parameters are model simulation outputs, and lack of observations makes them difficult to evaluate on a broader scale. In this work, we take advantage of remotely-sensed snow conditions in the top ~15m of the firn; more specifically, the layer depth vs density profiles, measured through the Snow Radar sensor from NASA’s Operation IceBridge mission. We use these observations to calibrate the GEMB module over the Greenland Ice Sheet. The module optimized with the help of actual observations will become more reliable for predicting glacial mass loss and associated global sea level rise over time.

Actions addressing anthropogenic climate change are paramount to survival; however, there are limitations to the current binary approach which considers adaptation and mitigation as separate actions. Insights from Indigenous pluralistic ontology reveals anticipatory capacity to include components of adaptation as well as mitigation. Drawing from our research in the Pamir Mountains of Tajikistan, ecological calendars build anticipatory capacity for climate change. Anticipatory capacity, having the ability to envision possible and sustainable futures, occurs in response to the changes in the environment. It includes elements of foresight as these actions are simultaneously in preparation for upcoming uncertainty. These two aspects are elements of the adaptation-mitigation binary respectively. As illustrated by the ecological calendars in the Bartang Valley of Tajikistan, this approach has been carried out for many generations and is founded upon context specificity, intellectual pluralism, and relations between the agropastoralists and transformations in their habitat. Reconceptualizing the adaptation-mitigation binary is not bound to the boarders of the Pamir Mountains, rather it is a practice that is relevant globally.

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. SC metrics included in the structure-function relationship as well as the strength of drivers was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how SC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.

Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60-300 km scales. A geographic and scale dependent seasonal cycle appears throughout much of the mid-latitudes, with eddy kinetic energy at scales less than 60 km peaking 1-4 months before that at 60-300 km scales. Spatial patterns in this lag align with geographic regions where the conversion of potential to kinetic energy are seasonally varying. In mid-latitudes, the conversion rate peaks 0-2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade, and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed.

Check out our video abstract: https://youtu.be/CGzFNU70yUg To date, the perspective of forest ecohydrologists has heavily focused on leaf-water interactions – leaving the ecohydrological roles of bark under-studied, oversimplified, or omitted from the forest water cycle. Of course, the lack of study, oversimplification, or omission of processes is not inherently problematic to advancing ecohydrological theory or operational practice. Thus, this perspective outlines the relevance of bark-water interactions to advancing ecohydrological theory and practice: (i) across scales (by briefly examining the geography of bark); (ii) across ecosystem compartments (i.e., living and dead bark on canopies, stems, and in litter layers); and, thereby, (iii) across all major hydrologic states and fluxes in forests (providing estimates and contexts where available in the scant literature). The relevance of bark-water interactions to biogeochemical aspects of forest ecosystems is also highlighted, like canopy-soil nutrient exchanges and soil properties. We conclude that a broad ecohydrological perspective of bark-water interactions is currently merited.

Solar Energy Particles (SEPs) can be associated with solar flares and coronal mass ejections (CMEs) and offer energy spectra ranging from few KeVs to many GeVs. These events can occur without any notable indication and alter the radiation environment of the inner solar systems, which can potentially lead to precarious conditions for humans in space, affect the interior of spacecraft’s sensitive electronics, and trigger radio blackouts. Identifying the most critical physical parameters of the Solar Dynamic Observatory (SDO) to detect SEPs can allow for a swift response against its adverse effects. With the profusion of high-quality time series data from the SDO, which accounts for the modulating background of magnetic activity and the inherently dynamic phenomenon of pre-flares and post-flare phases; antithetical to non-representative data with the point-in-time measurements employed earlier, selection of vital parameters for solar flare classification using machine learning algorithms appears to be a well-fitted problem in this realm. The primary issue of dealing with multivariate time series data (mvts) is the large number of physical parameters operating at a rapid frequency, making the data dimensionality very high and thus causing the learning process to curb. Moreover, manually selecting vital parameters is a tedious and costly task on which experts may not always agree on the results. In response, we examined feature subset selection using multiple algorithms on both mvts data and the statistical features derived from mvts segments (vectorized data). We used the SWAN-SF (Space Weather Analytics for Solar Flares) benchmark dataset collected from May 2010 - September 2018 to conduct our experiments. The comprehensive study gives a stable scheme to recognize the critical physical parameters, which boosts the learning process and can be used as a blueprint to foretell future solar flare episodes.

A halo Coronal Mass Ejection can have a devastating impact on Earth by causing damage to satellites and electrical transmission line facilities and disrupting radio transmissions. To predict the orientation of the magnetic field (and therefore the occurrence of a geomagnetic storm) associated with an occurring CME, filaments’ sign of magnetic helicity can be used. This would allow us to predict a geomagnetic storm. With the deluge of image data produced by ground-based and space-borne observatories and the unprecedented success of computer vision algorithms in detecting and classifying objects (events) on images, identification of filaments’ chirality appears to be a well-fitted problem in this domain. To be more specific, Deep Learning algorithms with a Convolutional Neural Network (CNN) backbone are made to attack this very type of problem. The only challenge is that these supervised algorithms are data-hungry; their large number of model parameters demand millions of labeled instances to learn. Datasets of filaments with manually identified chirality, however, are costly to be built. This scarcity exists primarily because of the tedious task of data annotation, especially that identification of filaments’ chirality requires domain expertise. In response, we created a pipeline for the augmentation of filaments based on the existing and labeled instances. This Python toolkit provides a resource of unlimited augmented (new) filaments with labeled magnetic helicity signs. Using an existing dataset of H-alpha based manually-labeled filaments as input seeds, collected from August 2000 to 2016 from the big bear solar observatory (BBSO) full-disk solar images, we augment new filament instances by passing labeled filaments through a pipeline of chirality-preserving transformation functions. This augmentation engine is fully compatible with PyTorch, a popular library for deep learning and generates the data based on users requirement.

Modeling the terrestrial impacts of the sun’s solar wind is critical to understanding geomagnetic storms. We use a database of 144 storms from 2010-2019 and showed how these storms affect magnetometers on the ground. We also extracted profiles of the magnetic field along the magnetotail. Skill scores are assigned to the individual stations on the ground based on how well they can forecast magnetic indeces like SYM-H and AL. We us our Space Weather Modeling Framework’s geospace configuration. Our model includes coupling of 3D MHD solver (BATSRUS), the Rice Convection Model, and the Ridley Ionospheric Model. We have found that all stations have a positive Heidke Skill Score which is encouraging in terms of space weather forecasting.

Enhanced precipitation of magnetospheric energetic particles during substorms increases ionospheric electron density and conductance. Such enhancements, which have timescales of a few hours, are not reproduced by the current ionospheric models. Using EISCAT (Tromso) measurements we reconstruct the substorm related response of electron densities and conductances in the ionosphere with respect to the intensity of substorm injections. We also investigate how the intensity of the response is influenced by the variations of the plasma sheet high energy (tens keV) fluxes and solar wind state. To characterise the intensity of substorm injection at a 5min time step we use the midlatitude positive bay (MPB) index which basically responds to the substorm current wedge variations. We build response functions (LPF filters) between T0-1h and T0+4hrs (T0 is a substorm onset time) in different MLT sectors to estimate the magnitude and delays of the ionospheric density response at different altitudes. The systematic and largest relative substorm related changes are mostly observed in the lowest part of E and in D regions. It starts and reaches maximum magnitude near midnight, from which it mainly propagates toward east, where it decays when passing into the noon-evening sector. Such MLT structure corresponds to the drift motion of the injected high energy electron cloud in the magnetosphere. Besides the injection intensity, we look at how the magnitude of the response depends on the energetic (tens keV) fluxes level in the plasma sheet before the substorm onset. We use a previously developed empirical model of the plasma sheet fluxes with solar wind parameters as inputs to count the plasma sheet fluxes with energy 10, 31 and 93 keV in the reference point of transition region (6 Re, 270o SM Long). We found that during enhanced high energy fluxes in the plasma sheet before the substorm (fast solar wind, high solar wind reconnection electric field) the background ionisation in the ionosphere, as well as the peak ionisation value during the substorm, are higher. This implies that together with substorm intensity the prehistory of the plasma sheet/solar wind state forms the magnitude development of substorm related ionospheric response. Research was supported by Russian Ministry of Science and Higher Education grant № 075-15-2021-583

Low-lying coastal zones are prone to flooding from multiple drivers such as storm surge (oceanographic), excessive river discharge (fluvial), and/or surface runoff (pluvial). The flooding impacts can be exacerbated, depending on local characteristics, when flooding is intensified by concurrent (or successive) occurrence of multiple drivers known as ‘compound flooding’. Recently, compound flooding drivers are becoming more frequent and intense leading to more adverse impacts. In this study, we carry out a continental scale analysis for the CONUS coastline at locations with sufficiently long overlapping records to characterize the changes in dependence and co-occurrence between the compound flooding drivers over time. We also investigate the changes in dependence over time during tropical and extratropical seasons. Lastly, we assess how the dependence structure varies with time. We use observations (gauge records) for the analysis. Dependence between different pairs is assessed using co-occurrence counts and statistical measures for dependence (Kendall’s rank correlation coefficient, τ). The dependence structures (particularly the tails of bivariate distributions) are compared using Kullback–Leibler (KL) Divergence to assess if there are significant changes in tails of bivariate distributions over time. This analysis provides a comprehensive characterization of changes in compound flooding potential around the CONUS coastline. This will provide insights on where and how compound flooding potential has changed over time to be incorporated in flood risk assessments and planning.

Currently, a future satellite mission of precipitation observations is discussed in Japan. From a low-orbit satellite, it is difficult to directly observe temporal evolution of precipitating clouds. The dynamical structure of precipitation helps better understandings of the lifecycle of precipitating clouds. Thus, the Doppler capability of a spaceborne precipitation radar is expected to provide global information of the motion for various precipitating clouds. However, the Doppler measurements of precipitation from space is challenging because of a fast-moving platform and a radar’s finite field of view (FOV). Since the radar onboard the spacecraft quickly passes above precipitating clouds, the decorrelation of precipitation signals due to the beam broadening effect degrades the Doppler measurement accuracy. Moreover, a spatial variability of precipitation within the FOV causes mixing of the motion between precipitating particles and spacecraft, which is called as an effect of the non-uniform beam filling (NUBF). This study investigates the Doppler capability of the spaceborne precipitation radar based on simulation experiments by using the high-spatial resolution ground radar and numerical model data. Here, we discuss two Ku-band Doppler radar systems: A) a large one antenna system and B) a two-antenna system. Since the contamination of the platform motion is proportional to the platform velocity and the radar’s beamwidth, the large antenna system mitigates the contamination due to the platform motion. On the other hand, the two-antenna system adopts the displaced phase center antenna (DPCA) technique. A signal processing with two antennas cancels out the platform motion so that mitigation of the beam broadening and NUBF effects is expected even if the FOV is coarse than the large antenna system. A quantitative evaluation between the two systems is conducted. For the large antenna system (FOV of 2.5 km), the mean Doppler velocity error of precipitation (> 15 dBZ) is evaluated in the range from 2.3 to 5 .0 m/s. Although the large error is originated from a residual error of the imperfect NUBF correction, the error is mitigated from 0.7 to 1.5 m/s when a 5-km average in the along-track direction is applied. For the two-antenna system (FOV of 5 km), the error is evaluated in the range from 0.6 to 1.1 m/s.

Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording total solar irradiance observations. These measurements provide estimates of the Earth’s energy imbalance, i.e. the difference of energy absorbed and emitted by our planet. With this amount of TSI data, solar irradiance reconstruction models can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). Various algorithms have been proposed to merge the various TSI measurements recorded over the last 4 decades. We develop a 3-step algorithm based on data fusion, including a stochastic noise model to take into account the short and long-term correlations. We develop a wavelet filter in order to eliminate specific correlations introduced by the data fusion. Comparing with previous products,the mean value difference is below 0.1 W/m2and the discrepancy with the solar minima is mostly below 0.05 W/m2. Next, we model the frequency spectrum of this 40-year TSI composite time series with a Generalized Gauss-Markov model(with white noise) due to an observe flattening at high frequencies. It allows us to fit a linear trend in these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is ∼ -0.009+/-0.01 W/(m2.yr). We conclude that the trend in these composite time series is mostly an artifact due to the solar noise.

Plate boundary deformation zones represent a challenge in terms of understanding their underlying geodynamic drivers. Active deformation is well constrained by GNSS observations in the SW Balkans, Greece and W Turkey, and is characterized by variable extension and strike slip in an overall context of slow convergence of the Nubia plate relative to stable Eurasia. Diverse, and all potentially viable, forces have been proposed as the cause of the observed surface deformation, e.g., asthenospheric flow, horizontal gravitational stresses (HGSs) from lateral variations in gravitational potential energy, and rollback of the Hellenic slab. We use Bayesian inference to constrain the relative contribution of the proposed driving and resistive regional forces. Our models are spherical 2D finite element models representing vertical lithospheric averages. In addition to regional plate boundaries, the models include well-constrained fault zones like north and south branches of the North Anatolian Fault, Gulf of Corinth and faults bounding the Menderes Massif. Boundary conditions represent geodynamic processes: (1) far-field relative plate motions (2) resistive fault tractions (3) HGSs mainly from lateral variations in topography and Moho topology (4) slab pull and trench suction at subduction zones. The magnitude of each of these is a parameter in a Bayesian analysis of the models in the context of horizontal GNSS velocities. The search yields a probability distribution over all parameters, allowing us to determine mean/median parameter values, robustly estimate parameter uncertainties, and identify tradeoffs. Significant trench suction forces from the Hellenic slab act on the overriding Aegean Sea, including along the Pliny-Strabo STEP Fault. Resistive tractions on most plate boundaries and faults are low. The best-fitting models compare well with paleomagnetic rotations and fault slip rates from previous studies.