Physical interactions of microplastics within vegetation and turbulent flows of freshwater systems are poorly understood. An experimental study was conducted to investigate the underlying physical transport mechanisms of microplastics over submerged canopies across a range of flow conditions common in the natural environment. The effects of changing canopy heights were investigated by testing two model canopies of varying stem heights, simulating seasonal variation. This study determined and compared the mixing and dispersion processes for microplastics and solutes and proposed a hydrodynamic model for quantifying microplastic mixing in submerged canopies. Longitudinal dispersion coefficients for neutrally buoyant microplastics (polyethylene) and solutes were significantly correlated within submerged model vegetation irrespective of the complexity of the flow regime. Hydrodynamic and solute transport models were shown to be capable of robust predictions of mixing for neutrally buoyant microplastics in environmental flows over a canopy, facilitating a new approach to quantify microplastic transport and fate.

Coastal saltwater intrusion (SWI) is one key factor affecting the hydrology, nutrient transport, and biogeochemistry of coastal marsh landscapes. Future climate change, especially intensified sea level rise (SLR), is expected to trigger SWI to encroach coastal freshwater aquifers more intensively. Numerous studies have investigated decadal/century scale SWI under SLR by assuming a static coastal landscape topography. However, coastal marshes are highly dynamic systems in response to SLR, and the impact of coastal marsh evolution on SWI has received very little attention. Thus, this study investigated how coastal marsh evolution affects future SWI with a physically-based coastal hydro-eco-geomorphologic model, ATS (Advanced Terrestrial Simulator). Our synthetic modeling experiments showed that it is very likely that the marsh elevation increases with future SLR, and a depression zone is formed due to the different marsh accretion rates between the ocean boundary and the inland. We found that, compared to the cases without marsh evolution, the marsh accretion may significantly reduce the surface saltwater inflow at the ocean boundary, and the evolved topographic depression zone may prolong the residence time of surface ponding saltwater, which causes distinct subsurface salinity distributions. We also found that the marshland may become more sensitive to the upland groundwater table that controls the freshwater flux to the marshes, compared with the cases without marsh evolution. This study demonstrates the importance of marsh evolution to the freshwater-saltwater interaction under sea level rise and can help improve our predictive understanding of the vulnerability of the coastal freshwater system to sea level rise.

The British Oceanographic Data Centre (BODC) celebrated its 50th anniversary in 2019. It holds data collected from 1773 to the present day. Holdings are multidisciplinary, heterogeneous data reflecting the full range of disciplines, platforms, temporal and spatial fieldwork scales typically encountered in oceanographic research and monitoring. These collections vary in granularity and contain data which are at different stages of curation ranging from raw data to standardised data products. BODC need to improve data services to meet the developing the expectations of the research community. These include the FAIR data principles, TRUSTed repository guidelines and CoreTrustSeal accreditation. This is a significant challenge within the constraints of resource available (both financial and human). The initial focus for BODC is making holdings citable with the following aspirations: Application of DOIs to data at the point of receipt by BODC. Publication of data papers and publication of DOIs for data products. Application of persistent identifiers to low level data granules where DOIs are not feasible. Application of persistent identifiers to datasets included in BODC API services and versioning of these data. Work with organisations or groups who include data curated by BODC in their products to enable the provenance of data to be unambiguous. Work with communities on joint data papers where BODC are a partner organisation. This will enable each type of data served by BODC to be unambiguously citable. The initial effort is being directed towards the application of DOIs to data submissions and publication of data papers for BODC curated data products.

A key to better constraining estimates of the ocean sink for fossil fuel emissions of carbon dioxide is reducing uncertainties in coastal carbon fluxes. A contributing factor in uncertainties in coastal carbon fluxes stems from the under sampling of seasonality and spatial heterogeneity. Our objectives were to i) assess satellite-based approaches that would expand the spatial and temporal coverage of the surface ocean pCO2 and sea-air CO2 flux for the northern Gulf of Mexico, and ii) investigate the seasonal and interannual variations in CO2 dynamics and possible environmental drivers. Regression tree analysis was effective in directly relating surface ocean pCO2 to satellite-retrieved (MODIS Aqua) products including chlorophyll, sea surface temperature, and dissolved/detrital absorption. Satellite-based assessments of sea surface pCO2 were made spanning the period from 2006-2010 and were used in conjunction with estimates of wind fields and atmospheric pCO2 to produce regional-scale estimates of air-sea fluxes. Seasonality was evident in air-sea fluxes of CO2, with an estimated annual average CO2 flux for the study region of -4.3 + 1.1 Tg C y-1, confirming prior findings that the Gulf of Mexico was a net CO2 sink. Interannual variability in fluxes was related to Mississippi River dissolved inorganic nitrogen inputs, an indication that human- and climate-related changes in river exports will impact coastal carbon budgets. This is the first multi-year assessment of pCO2 and air-sea flux of CO2 using satellite-derived environmental data for the northern Gulf of Mexico.

In this research, GRACE satellite data have been used in order to investigate the possibility of estimating the amount of groundwater used in agriculture. The level-2 data of the GRACE satellites have been used to estimate the monthly groundwater level changes in central plateau catchment in Iran during the period of 2003 to 2013. One degree grid is used along with corrections of soil moisture, canopy, rainfall and snowfall from GRACE satellite data with the CLM4 hydrology model. The results revealed the amount of current groundwater in Iran and agricultural usage from groundwater have been determined (the largest consumer of groundwaters). verification of the results has been done by comparing the GRACE satellite data and piezometric wells data. Furthermore, ArcMap (ArcGIS) software were used for data analysis.

Groundwater development will provide a more stable water source and enhance food security. Sustainable groundwater development requires collecting and analyzing data produced at global and national levels and disseminating that data and knowledge to end users such as States, NGOs, municipalities, businesses, and agropastoralists in a format that is useful for planning and decision-making. In developing countries, analyzing in situ measurements to de can be challenging due to sparsity of data and lack of tools and expertise. To address these problems we have developed a web-based geospatial tool that ingests in situ water level measurements and performs temporal and spatial interpolation to build interactive animated maps, time series plots, and long-term aquifer depletion curves. We use machine learning to find correlations among Earth observation data, such as precipitation or soil moisture, with water level data and perform more accurate interpolation. This approach ensures that scarce in situ data are used as effectively and accurately as possible. This tool helps water managers gain a better understanding of groundwater resources and determine how aquifers are responding to groundwater development, droughts, and climate change.

The bulk of the sediment found on the abyssal plain is transported from the shelf to the deep ocean by marine turbidity currents. They consecutively erode, transfer and deposit large amounts of sediment determining the characteristics of the marine environment. Further, their destructive nature is evidenced by several failures of subsea infrastructures that have been associated with their passage. Many numerical models have been developed to study field-scale turbidity currents. However, these models do not include sediment transport processes typical of coastal settings. As such they currently require the prescription of boundary conditions usually difficult to assess. Here we present a new modeling approach to hindcast marine turbidity currents that is based on a three-dimensional application of the process-based model Delft3D capable of predicting the initiation of sediment gravity flows driven by wind- and wave-induced processes. Detailed numerical simulations were carried out to investigate the nature of a turbidity current that impacted upon a submarine pipeline offshore Philippines after the nearby landfall of a tropical cyclone. Our simulations predict the triggering of a severe turbidity current only after the passage of the aforementioned tropical cyclone and the absence of any significant undercurrent associated with the other most relevant cyclones that passed near the study area during the lifespan of the pipeline, matching field observations in the form of pipeline shifting. Numerical results describe the development of rip currents that flush out water and sediment in cross-shore direction, ultimately triggering a turbidity current into the submarine canyon that cross the pipeline where its displacement was detected. Collapse of similarity profiles of the predicted undercurrent with experimental measurements and field observations demonstrates the reliability of the model in capturing the vertical structure of turbidity currents. As Delft3D has been demonstrated to accurately reproduce sediment transport processes associated with different environmental pressures in various geomorphological settings, the proposed modelling approach will allow for a deeper understanding of the mechanisms involved in the initiation of marine turbidity currents and their potential forecast.

Globally-significant quantities of carbon (C), nitrogen (N), and phosphorus (P) enter freshwater reservoirs each year. These inputs can be buried in sediments, respired, taken up by organisms, emitted to the atmosphere, or exported downstream. While much is known about reservoir-scale biogeochemical processing, less is known about spatial and temporal variability of biogeochemistry within a reservoir along the continuum from inflowing streams to the dam. To address this gap, we examined longitudinal variability in surface water biogeochemistry (C, N, and P) in two small reservoirs throughout a thermally-stratified season. We sampled total and dissolved fractions of C, N, and P, and chlorophyll-a from each reservoir’s major inflows to the dam. We found that time was generally a more important driver of heterogeneity in biogeochemical concentrations than space. However, dissolved nutrient and organic carbon concentrations had high site-to-site variability within both reservoirs, potentially as a result of shifting biological activity or environmental conditions. When considering spatially explicit processing, we found that certain locations within the reservoir, most often the stream-reservoir interface, acted as ‘hotspots’ of change in biogeochemical concentrations. Our study suggests that spatially explicit metrics of biogeochemical processing could help constrain the role of reservoirs in C, N, and P cycles in the landscape. Ultimately, our results highlight that biogeochemical heterogeneity in small reservoirs is driven more by seasonality than longitudinal spatial gradients, and that some sites within reservoirs play critically important roles in whole-ecosystem biogeochemical processing.

Cosmic ray neutron sensors (CRNS) allow to determine field-scale soil moisture content non-invasively due to the dependence of aboveground measured epithermal neutrons on the amount of hydrogen. Because other pools besides soil contain hydrogen (e.g. biomass), it is necessary to consider these for accurate soil moisture content measurements, especially when they are changing dynamically (e.g., arable crops, de- and reforestation). In this study, we compare four approaches for the correction of biomass effects on soil moisture content measurements with CRNS using experiments with three crops (sugar beet, winter wheat and maize) on similar soils: I) site-specific functions based on in-situ measured biomass, II) a generic approach, III) the thermal-to-epithermal neutron ratio (Nr) and IV) the thermal neutron intensity. Calibration of the CRNS during bare soil conditions resulted in root mean square errors (RMSE) of 0.097, 0.041 and 0.019 m3/m3 between estimated and reference soil moisture content of the cropped soils, respectively. Considering in-situ measured biomass for correction reduced the RMSE to 0.015, 0.018 and 0.009 m3/m3. When thermal neutron intensity was considered for correction, similarly accurate results were obtained. Corrections based on Nr and the generic approach were less accurate. We also explored the use of CRNS for biomass estimation. The use of Nr only provided accurate biomass estimates for sugar beet. However, significant site-specific relationships between biomass and thermal neutron intensity were obtained for all three crops. It was concluded that thermal neutron intensity can be used to correct soil moisture content estimates from CRNS and to estimate biomass.

A document by Sanjit Kumar Jena. Click on the document to view its contents.

Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks.  Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model.  However, because ML-UQ is a new tool, its limitations are not yet fully appreciated.  For example, some types of uncertainty are fundamentally unresolvable, including uncertainty that arises from data being out of sample, i.e., outside the distribution of the training data.  While it is generally recognized that ML-based point predictions (predictions without UQ) do not extrapolate well out of sample, this awareness does not exist for ML-based uncertainty.  When point predictions have a large error, instead of accounting for this error by producing a wider confidence interval, ML-UQ often fails just as spectacularly.  We demonstrate this problem by training ML with five different UQ methods to predict shortwave radiative transfer.  The ML-UQ models are trained with real data but then tasked with generalizing to perturbed data containing, e.g., fictitious cloud and ozone layers.  We show that ML-UQ completely fails on the perturbed data, which are far outside the training distribution.  We also show that when the training data are lightly perturbed -- so that each basis vector of perturbation has a little variation in the training data -- ML-UQ can extrapolate along the basis vectors with some success, leading to much better (but still somewhat concerning) performance on the validation and testing data.  Overall, we wish to discourage overreliance on ML-UQ, especially in operational environments.

The author provides stable and safe designs of coastal and river banks using a new structural concept which stably keeps frame structure inside of the embankments, and therefore protects the backside of lowland areas, where more than ten millions of people live and social investment facilities are distributed. The projects can proceed under the framework of Public Private Partnership (PPP). This new proposal simplifies the design of multi water hazards from the probabilistic collapse risks to deterministic planning for human lives and accumulated social public facilities around coastal mega cities such as Tokyo and Nagoya in Japan. Those Metropolitan citizens experienced historical Kanto earthquake in 1923 and largest Tone-River flooding in 1947. On the other hand, heaviest storm surge in Japan by Typhoon Isewan in 1959 around Nagoya City in the Ise Bay facing Pacific Ocean. These regions distribute in flat low elevation areas for conveniences of foreign trade with port facilities, industries, and traffic infrastructures including hub airports. Mean while, those coastal districts and citizens sometimes remains in legendary collapse risks, such as gigantic fires or pandemic infection diseases nowadays along with the catastrophic destructions by M8 to M9 earthquakes, inundation and river flooding against social frameworks to keep human cultural livelihoods in the hazardous era of this modern earth of Gaia.

The Red Sea is an extremely warm tropical sea that hosts diverse ecosystems; thus, it is important to understand its ecology in the context of global warming. Using a coupled physical–biogeochemical model validated against in situ data, we provide the first report on the diel cycle (i.e., diel variability) in the Red Sea chlorophyll (CHL) concentration, revealing near-sunset CHL maxima at 17h ± 1h local time over the entire basin. This CHL peak time is considerably later than those reported in most other oceans, suggesting low grazing rates in this high-irradiance tropical sea. Model-based analyses reveal that CHL diel cycle is predominantly controlled by irradiance, whereas longer-timescale (e.g., seasonal) CHL variability is regulated by nutrient availability, suggesting a light-limited biological production at diel timescale. The identified CHL diel cycle comprises a fundamental component of the Red Sea ecology and has implications for CHL remote sensing and in situ measurements.

Shifts in Southern Ocean (SO, 40-85S) shortwave (SW) cloud feedback (SWFB) towards more positive values are the dominant contributor to higher effective climate sensitivity (ECS) in Coupled Model Intercomparison Project phase 6 (CMIP6) models. The positive shift in SWFB in CMIP6 global climate model (GCMs) can be traced back to the greater reduction in low cloud cover and the weaker cloud liquid water response to warming in the SO. To evaluate how realistic the CMIP6 cloud response is, we connect the SO SWFB to changes in column-integrated liquid water mass (LWP) and the susceptibility of albedo to LWP in 50 CMIP5 and CMIP6 GCMs. In turn, we predict the responses of SO LWP to warming using a cloud-controlling factor (CCF) model. The combination of the CCF model and radiative susceptibility explains about 50% of the variance in the GCM-simulated SWFB in the SO. Observations of SW radiation fluxes, LWP, and reanalysis of CCFs are used to constrain the SO SWFB. This yields a constrained response of SO LWP to warming of 2.89-4.41 gm-2K-1, relative to the total GCM range of -0.48-9.33 gm-2K-1. The susceptibility of albedo to LWP is constrained to be 0.41-0.86 (kgm-2)-1, relative to the GCM range of 0.23-3.62 (kgm-2)-1, where albedo is unitless. The overall constraint on the contribution of SO SWFB to global cloud feedback is -0.19-0.05 Wm-2K-1, relative to GCM range of -0.28-0.27 Wm-2K-1. In summary, observations suggest a moderate negative to weak positive SO SWFB.

The question of how many satellites it would take to accurately map the spatial distribution of ionospheric outflow is addressed in this study. Given an outflow spatial map, this image is then reconstructed from a limited number virtual satellite pass extractions from the original values. An assessment is conducted of the goodness of fit as a function of number of satellites in the reconstruction, placement of the satellite trajectories relative to the polar cap and auroral oval, season and universal time (i.e., dipole tilt relative to the Sun), geomagnetic activity level, and interpolation technique. It is found that the accuracy of the reconstructions increases sharply from one to a few satellites, but then improves only marginally with additional spacecraft beyond ~4. Increased dwell time of the satellite trajectories in the auroral zone improves the reconstruction, therefore a high-but-not-exactly-polar orbit is most effective for this task. Local time coverage is also an important factor, shifting the auroral zone to different locations relative to the virtual satellite orbit paths. The expansion and contraction of the polar cap and auroral zone with geomagnetic activity influences the coverage of the key outflow regions, with different optimal orbit configurations for each level of activity. Finally, it is found that reconstructing each magnetic latitude band individually produces a better fit to the original image than 2-D image reconstruction method (e.g., triangulation). A high-latitude, high-altitude constellation mission concept is presented that achieves acceptably accurate outflow reconstructions.

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951-2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unraveled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, there is increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country were affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.

Apparently stable central Indian craton has been experiencing a number of micro-seismicity in and around the pristine Son-Narmada-South fault (SNSF), possibly because of creep along some reactivated or unidentified fracture planes. Drainage network being highly susceptible to even subtle active tectonics, a reconnaissance study of the area has been carried out using river morphometric analysis. Thereafter, a Principal Scattering Interferometry time series (PSInSAR-TS) analysis (Hooper et al 2004, 2007) was performed to precisely locate the surface-tip of the slip plane. And finally, the fracture geometry and orientation of the slip vector was enumerated by integrating PSINSAR with morphometric analysis. Generally, drainages respond to block uplift with abrupt incision and thereby making a very narrow channel width. However, delineating such incision from the standard Width-Depth ratio from DEM based studies becomes difficult, because of its coarser resolution. Therefore, considering rectangular channel flow for a stable river, the expected width was compared with the actual width of the river measured from high resolution Sentinel II images following the relation modified after Finnegan et al., 2005; which states that W =KA^(2/3)S^(-3/16), where the constant K depends on the rectangular channel geometry, manning constant and rainfall in the area, A is upper catchment area and S is slope of the river reaches. A NE-SW trending zone of narrower channel width was identified with this procedure which was interpreted as a result of block uplift, later confirmed from the rate of uplift preliminarily calculated using an expression derived from basic stream-power equation: U=K(((Z(a)-Z(o))/Χ)^n) (A^m), where the uplift (U) is a function of erodibility (K), river catchment area (A) and chi (χ) value as per Perron and Royden 2012. PSInSAR-TS analysis of the area delineated an average net-slip rate of 11mm/year along a number of NE-SW trending south dipping oblique slip reverse faults, with sinistral strike slip components, arranged in enechelon pattern and are softly linked to each other with jogs accommodating movements in normal sense. Lastly, the fault geometry was confirmed by comparing the deformation map generated from the InSAR technique with the field evidence and previous studies.

Tree damage can provide insights into internal dynamic pressure changes of pyroclastic density currents (PDC). On 18 May 1980, Mount St. Helens erupted a laterally directed PDC that decimated ~600km2 of forest, referred to as the blowdown zone. The head of the current contained the peak dynamic pressure, which uprooted or broke off most trees and stripped them of vegetation; however, some partially stripped tree trunks were left standing. Tree damage was assessed using aerial photography taken one month after the eruption. The flow direction of the PDC was mapped from shadows of root balls of toppled trees and directions of fallen trees. Along given flow paths, the density of standing trees was measured by the number of shadows within 200m2 areas. Towards the northwest, the average tree density increased from 0.01 to 0.58 (± 0.19) trees/m2 with distance. Additionally, analysis identified 95 clusters of trees still standing in the blowdown zone, situated on the lee sides of hills or plateaus. Blurry, cylindrical shadows versus well-defined, cylindrical shadows distinguished standing trees with foliage in clusters from those without. Five variables were used to determine the heights of trees: ground slope and aspect, bearing and length of shadows, and the sun angle above the horizon. Trees stripped of foliage in patches have average heights of 16 ± 7m and occur where the PDC reached 66 ± 24% of its runout. Foliage patches have average heights of 12 ± 7m and occur where the PDC reached 91 ± 9% of its runout. Tree heights in the patches indicate a localized height the peak dynamic pressure must jump as it travels over hills and away from its source. Patches with foliage imply that the peak dynamic pressure has risen above the tops of the trees, whereas patches without foliage suggest that the peak dynamic pressure was still low enough to damage trees even though the current had jumped over topography. Outside of the patches, increasing tree density suggests that dynamic pressure waned with distance.