A document by Fabiha Rahman. Click on the document to view its contents.

When the COVID-19 pandemic forced institutions to close their physical spaces, many researchers and professors were required to work from home. Media outlets and individuals wondered if women specifically would be shouldering the childcare and/or homeschooling responsibilities, decreasing the time they had for professional activities. Preliminary analyses show mixed results: some have seen decreased submissions from women (Vincent-Lamarre et al., 2020; Del Boca et al., 2020), with at least one other showing an increase in submissions from women (Dolan & Lawless, 2020); and another suggests the rate of male-athored submissions to preprint archives are growing faster than those from women (Frederickson 2020). AGU has been monitoring our monthly submission data to understand how the pandemic and stay-at home orders are affecting our submissions from specific demographics—based on author gender, age, and country. Our analysis finds that the pandemic so far has not resulted in a decrease in submissions to AGU journals; nor has there so far been a decrease in the participation of women as submitting authors. Instead, we have seen an increase in submissions from most author groups compared to year-to-date numbers in previous years.

Rapid population growth in Bangladesh has led to an increase in the rate of urbanization across the country. Runoff is increased due to an increase in impervious surfaces as a result of urbanization. Sylhet City Corporation (SCC) is a metropolitan city in northeastern Bangladesh that is frequently afflicted by urban flooding and waterlogging as a result of storm runoff due to heavy rainfall. A substantial portion of the city suffers from severe drainage congestion, which is caused by the inadequate drainage system, lack of maintenance of drainage system, garbage dumping in the drains and canals, and illegal encroachment of drainage canals. The review of the existing drainage system and the causes of the drainage congestion in the city area have been assessed in this study. This study also focuses on the stormwater management of Goali chhara sub-system of eastern part of Sylhet City Corporation which is responsible for waterlogging in the study area. The Goali chhara sub-system has been modeled using GeoSWMM to analyze the stormwater runoff and to prepare a masterplan of drainage network considering the climate change issues. The design water level of the Surma river has been estimated from historical river water level data using the Gumbel distribution method. Moreover, analyzing the historical rainfall data of Sylhet Station, the IDF curve for short-duration rainfall has been developed and the design rainfall intensity of 2-hour and 5-year return period has been estimated. These hydrological parameters have been used in the model and prepare the drainage network masterplan. The model has been calibrated using the estimated runoff coefficients from the land use map for the sub-catchments. The stream cross-sections for the 10-year return period were found to be larger than that obtained for the 5-year return period. This research will help the local authorities of Sylhet City Corporation by allowing them to make drainage capacity expansion decisions that will tackle this critical challenge for the next 30-40 years.

Evidence based on sparse tree-ring data suggests a severe sustained drought occurred in the 2nd century CE that could have rivaled medieval period droughts in the Colorado River basin (Gangopadhyay et al. 2022). Most of these tree-ring data have been used in gridded drought reconstructions (Cook et al., 2010) which extend back to 1 CE over an area that includes the intermountain western US. However, the 2nd century drought has not been highlighted in prior studies given the sparseness of the data available for this time period. A new reconstruction of Colorado River flow based on these data documents a notably severe and sustained drought over much of the 2nd century (Gangopadhyay et al. 2022). While this reconstruction suggests that the drought exceeds the severity and duration of any drought in the past 2000 years, a complete assessment of the 2nd century drought is challenging due to the sparseness of data. In this poster presentation, we describe the tree-ring data available, along with other proxy data that provide evidence for the 2nd century drought and support its severity. In our conclusions, we discuss outstanding questions and thoughts for further work.

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. The development of analytical methods for improved estimation and reduced manual inspection are needed to better characterize the effects of genetics and the environment in key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times in the season, together with ground-truth data over thousands miscanthus accessions to determine flowering time, culm height, and biomass yield traits. We compared the performance of Convolutional Neural Network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates (3D-spatio-temporal) architectures to evaluate the value of persistent monitoring and the type of features to predict the traits. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height and biomass production was improved through use of 3D-spatio-temporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatio-temporal analyses compared to the best 2D-spatial architectures manifested in up to: 23 % improvements in R 2 and 20 % reduction in mean absolute error (MAE). The integration of

Object detection algorithms have heavily relied on deep learning techniques to estimate the count of grapes as the resulting quality of grapes is directly correlated to its yield. With temporal analytics, early actions and logistical organizing can be performed to maintain the quality of grapes. However, the issue with using these object detection algorithms is that they are limited to counting only the visible grapes, thus omitting hidden grapes, and in turn affecting the true estimate of grape yield. Many grapes are occluded because of either the compactness of the bunch cluster, or due to canopy interference. Therefore, models need to be able to estimate the unseen berries in order to give a near true yield estimate. An end-to-end framework is proposed in which the grape clusters are first segmented using a deep learning model, after which the extracted candidate regions of grape clusters are fed to a CNN regression model that can estimate the count of berries by incorporating a correction factor. A new dataset is also proposed which consists imagery of grape clusters, along with their ground truth values of grape count and weight. The proposed framework will also be tested using three open-source datasets and will encourage future research in determining which features of grapes can be leveraged to correct grape counting algorithms and produce higher accurate results.

Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). Although solar flare-driven HF absorption is a well-known impact of SWF, the occurrence of a frequency shift on radio wave signal traversing the lower ionosphere in the early stages of SWF, also known as “Doppler Flash”, is newly reported and not well understood. Some prior investigations have suggested two possible sources that might contribute to the manifestation of Doppler Flash: first, enhancements of plasma density in the D and lower E regions; second, the lowering of the reflection point in the F region. Observations and modeling evidence regarding the manifestation and evolution of Doppler Flash in the ionosphere are limited. This study seeks to advance our understanding of the initial impacts of solar flare-driven SWF. We use WACCM-X to estimate flare-driven enhanced ionization in D, E, and F-regions and a ray-tracing code (Pharlap) to simulate a 1-hop HF communication through the modified ionosphere. Once the ray traveling path has been identified, the model estimates the Doppler frequency shift along the ray path. Finally, the outputs are validated against observations of SWF made with SuperDARN HF radars. We find that changes in refractive index in the D and lower E regions due to plasma density enhancement are the primary cause of Doppler Flash.

Wireless sensor networks are increasingly important in monitoring water quality changes. High frequency monitoring can be used to gather water quality information, identify existing problems and improve water quality management activities. However, missing data are unavoidable because of network communication issues, sensor maintenance or failure. Data interpolation is a process for constructing missing values based on known data points. Though traditional methods like polynomial or linear interpolation are widely used in sensor data pre-processing, there are still many limitations. Firstly, current interpolation methods give poor estimations when a continuous number of data within a period of time are missing. Secondly, many interpolation methods reconstruct missing data based on other parameters available at the same time step. When all the data are missing, these methods cannot be used. In our work, we are developing a sequence-to-sequence interpolation model (SIM) for recovering missing data sequences in wireless sensor networks. SIM uses the state-of-the-art sequence-to-sequence architecture. It consists of two parts: an encoder that reads from the source water quality time series data and a decoder that generates the missing data sequences. In our design, Bi-directional Long Short Term Memory Network is used as the encoder and decoder due to its capability in using both past and future information for a given time. The attention mechanism is applied to make the SIM focus on different parts of the input time series when interpolating missing values at different time steps. We evaluated the SIM by using time series data from Queensland government’s water quality monitoring network. Compared to Seasonal-ARIMA, the SIM reduced 23.2% MAE and 40.3% RMSE when recovering missing data in 2 adjacent time steps. The reason for the superior performance is that SIM interpolates missing data based on both the inner relationships between water quality parameters and the accumulated information through time.

A document by Hesam Saeidi. Click on the document to view its contents.

We propose a solution with edge image processing and long-range connectivity named AICropCAM that can be used in drones, ground platforms, or as distributed sensor networks for plant phenotyping. We have successfully run multiple image classification, segmentation, and object detection models on this platform. Classification models help classify images based on image quality, crop type, and phenological stage. Object detection models could detect and count the number of plants, weeds, and insects and expand to count the flowers, fruits, and leaves. Segmentation models can separate the canopy from the background and potentially segment traits that indicate the nutrient deficit or disease. Canopy segmentation results help estimate leaf area index and chlorophyll content. Because the models run sequentially, like a decision tree, there is flexibility to select the most accurate model considering the crop type and the crop’s phenological stage that helps scan fields with multiple crops. The generated information is geo-tagged and transmitted through low throughput long-range communication protocol (e.g., LoRa) to cloud data storage. AICropCAM reduces 2-megabyte image files to around 100-byte actionable data, resulting in massive savings in data storage and transmission costs. This edge image capturing and processing system is open to improvement with new neural network predictive models and faster edge computers. This system provides plant scientists and crop breeders a low-cost, flexible phenotyping tool to extract multiple crop traits related to abiotic and biotic stress responses.

Fire emissions are an important component of global models, which help to understand the influence of sources, transport and chemistry on atmospheric composition. Global fire emission inventories can vary substantially due to the assumptions made in the emission creation process, including the defined vegetation type, fire detection, fuel loading, fraction of vegetation burned and emissions factors. Here, we focus on the uncertainty in emission factors and the resulting impact on modeled composition. Our study uses the Community Atmosphere Model with chemistry (CAM-chem) to model atmospheric composition for 2014, a year chosen for the relatively quiet El Niño Southern Oscillation activity. We focus on carbon monoxide (CO), a trace gas emitted from incomplete combustion and also produced from secondary oxidation of volatile organic compounds (VOCs). Fire is a major source of atmospheric CO and VOCs. Modeled CO from four fire emission inventories (CMIP6/GFED4s, QFED2.5, GFAS1.2 and FINN1.5) are compared after being implemented in CAM-chem. Multiple sensitivity tests are performed based on CO and VOC emission factor uncertainties. We compare model output in the 14 basis regions defined by the Global Fire Emissions Database (GFED) team and evaluate against CO observations from the Measurements of Pollution in the Troposphere (MOPITT) satellite-based instrument. For some regions, emission factor uncertainty spans the results found by using different inventories. Finally, we use modeled ozone (O3) to briefly investigate how emission factor uncertainty influences the atmospheric oxidative environment. Overall, accounting for emission factor uncertainty when modeling atmospheric chemistry can lend a range of uncertainty to simulated results.

We present our first laboratory calibration and field results of a newly developed commercial ice nucleation chamber, the so-called PINE. The PINE instrument is developed based on the design of the AIDA cloud chamber (Möhler et al., 2003) to advance online atmospheric ice nucleation research. A unique aspect of the PINE chamber includes its plug-and-play feature (so it runs on a standard power outlet), autonomous cryo-cooler-based temperature-ramping operation, capability of quantifying INPs in different IN modes (e.g., immersion freezing and deposition mode at >-60 °C), small particle loss through the system (~5% for <5 m diameter particles), and sensitive optical particle detection of INP concentration (≤0.1 L-1 at T > -15 °C), promising stand-alone operation at remote locations. To date, the PINE chamber has been calibrated using test aerosol particles with known properties (e.g., illite NX). Briefly, test particles were exposed to ice supersaturation conditions, where a mixture of droplets and ice crystals were formed during the ‘expansion’ experiment. A comparison of our calibration test results to other techniques will be presented. Further, the PINE instrument has been tested in field campaigns in the Southern Great Plains. With a turnover time of ~6 minutes, PINE ran continuously and scanned at different temperature intervals to assess different INP episodes. We made sure to assess at least a few degrees of common temperature interval in a series of scan. Our first field results will be shown. Our results suggest that using this autonomous instrument may be critical to minimize error sources in high-temperature and supermicron INP research. Acknowledgement: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-SC0018979) – work packages 1-2 of Implications of Aerosol Physicochemical Properties Including Ice Nucleation at ARM Mega Sites for Improved Understanding of Microphysical Atmospheric Cloud Processes. References: • DeMott, P. J. et al. Resurgence in ice nuclei measurement research. Bull. Amer. Meteorol. Soc. 92, 1623, doi:10.1175/bams-d-10-3119.1 (2011). • Möhler, O. et al. Experimental investigation of homogeneous freezing of sulphuric acid particles in the aerosol chamber AIDA. Atmos. Chem. Phys. 3, 211-223 (2003).

Maize (Zea mays ssp. Mays) is one of the most essential cereal crops in the world. As climate changes, breeding for high nitrogen-use efficiency maize genetic materials without sacrificing the yield becomes more urgent than anytime before. Image-based high-throughput phenotyping, functioning as a key element in plant breeding efforts, is critically important and has the potential to relieve difficulties in phenotypic scoring on breeding pipelines. Numberless studies using RGB images related to biomass and agronomically important traits have mostly focused on above-ground traits. The belowground root-related traits, however, have not been intensively studied. The objective of this study is to investigate the root architecture and phenotypic properties (number of aerial roots, stem diameter, internode length, and fresh root weight) of roots of the hybrid and inbred maize lines grown under low and high nitrogen conditions. In this study, a collection of BGEM lines (n = 304 inbred and n = 197 hybrids) were planted on the field in low and high nitrogen conditions. The root samples of n = 2,100 plants were collected, and the soil around the roots was washed out for automated image-based phenotyping. The roots were imaged with the completely automated conveyor belt LemnaTec system. A high variation in root structure, stem diameter, number of aerial roots, and internode length was observed among the genotypes. Our root phenotyping pipeline and traits extracted from these images will enhance root biology and facilitate breeding for below-ground root traits.

Melt ponds play an important role in the seasonal evolution of Arctic sea ice. During the melt season, snow atop the sea ice begins to metamorphose and melt, forming ponds on the ice. These ponds reduce the albedo of the surface, allowing for increased solar energy absorption and thus further melting of snow and ice. Analyzing the spatial distribution and temporal evolution of melt ponds helps us understand the sea ice processes that occur during the summer melt season. It has been shown that the inclusion of melt pond parameters in sea ice models increases the skill of predicting the summer sea ice minimum extent. Previous studies have used remote sensing imagery to characterize surface features and calculate melt pond statistics. Here we use new observations of melt ponds obtained by the Digital Mapping System (DMS) flown onboard NASA Operation IceBridge (OIB) during two Arctic summer melt campaigns which surveyed thousands of kilometers of sea ice and resulted in more than 45,000 images. One campaign was conducted in the Beaufort Sea (July 2016), and one in the Lincoln Sea and the Arctic Ocean north of Greenland (July 2017). Using these data we expect to advance our understanding of the differences and similarities between melt pond features on young, thin sea ice seen in the Beaufort Sea versus those on multi-year ice. We have developed a pixel-based classification scheme by considering the different RGB spectral values associated with each surface type. We identify four sea ice surface types (level ice, rubbled ice, open water, and melt ponds). The classification scheme enables the calculation of parameters including melt pond fraction, ice concentration, melt pond area, and melt pond dimensions. We compare results with data from the Airborne Topographic Mapper (ATM), a laser altimeter also operated during these OIB missions. Given the extent over which the OIB data are available, regional information may be derived. Leveraging existing satellite data products, we examine whether the high-resolution airborne statistics are representative of the region and can be scaled up for comparison against satellite-derived parameters such as ice concentration and extent.

Geoscientists often spend significant research time identifying, downloading, and refining geospatial data before they can use it for analysis. Exploring interdisciplinary data is even more challenging because it may be difficult to evaluate data quality outside of one’s expertise. QGreenland, a newly funded EarthCube project, is designed to remove these barriers for interdisciplinary Greenland-focused research and analysis via an open data, open platform Greenland GIS tool. QGreenland will combine interdisciplinary data (e.g., glaciology, human health, geopolitics, hydrology, biology, etc.) curated by an international Editorial Board into a unified, all-in-one GIS environment for offline and online use. The package is designed for the open source GIS platform QGIS. QGreenland will include multiple levels of data use: 1) a fully downloadable base package ready for offline use, 2) additional disciplinary and/or high-resolution data extension packages for select download, and 3) online-access-only data to facilitate especially large datasets or updating time series. Software development has begun and we look forward to discussing techniques to create the best open access, reproducible methods for package creation and future sustainability. We also now have a beta version available for experimentation and feedback from interested users and the Editorial Board. The version 1 public release is slated for fall 2020, with two subsequent annual updates. As an interdisciplinary data package, QGreenland is designed to aid collaboration and discovery across fields. Along with discussing QGreenland development, we will also provide an example use case to demonstrate the potential utility of QGreenland for researchers, educators, planners, and communities.

The Laccadive-Chagos Ridge (LCR) is a prominent aseismic ridge in the Indian Ocean. The origin and nature of the crust beneath the LCR have been debated. Based on Ar-Ar geochronology of the volcanic basement rocks from ODP wells 713 and 715 from Chagos and Maldives ridges, a hotspot trail model was proposed for the genesis of the LCR. On the other hand, based on geophysical studies, the LCR has been inferred as a continental sliver/hyperextended continental crust which has undergone heavy underplating and Reunion hotspot volcanism. Even though these two different hypotheses differ on the genesis and nature of the LCR, both agree that the ridge experienced extensive Reunion hotspot trail volcanism, making the crust's nature challenging to decipher. We report Ar-Ar ages of two rhyolite rock samples from a well drilled on the Padua bank located on the northern extent of the Laccadive ridge. These acidic rocks are at a depth of ~1700m and ~2300m and underlie the Tertiary sediments forming the basement in this well. The previously determined K-Ar geochronology ages are 60.2 Ma and 102 Ma. The precise Ar-Ar dates of these rocks determined in the present study are 76.2 ± 0.4 Ma and 77.5 ± 0.5 Ma (2σ) respectively. These acidic rocks (78-76 Ma) coupled with onshore acidic rocks of the St-Mary's island (87-84 Ma) and Ezhimala (95-93 Ma) suggest continuous extension between the India and Laccadive ridge after the Indo-Madagascar continental breakup (88 ma). Contrary to the earlier hypothesis, the acidic rocks of Laccadive Ridge are older than the Reunion Plume trail volcanism (62-60 Ma). The geophysical and geochronological evidence suggests that the Laccadive ridge is continental in nature, and the volcanism predates the Reunion plume volcanism. These evidences suggest that the Laccadive Ridge is a continental thinned crust that has undergone rift-related volcanism rather than a hotspot trail.

The Brazilian National Institute for Space Research (INPE) produces research that helps to understand climate and weather dynamics in Brazil and in the world, with significant impacts on national public and private strategic planning. Among the essential information for the studies are the rainfall data. In this context, ensuring the quality of this data has a direct impact on the reliability of the forecasts and analysis generated from them. Thus, this study, which is a partnership between INPE, the Laboratory of Atmospheric Physics and Polytechnic School of USP and the ARM-DoE (Atmospheric Radiation Measurement Climate Research Facility), aimed to establish computational tools that could deal with the quality of data from rain in accordance with the main international directives. Thus, it was proposed for this study the development of a specific toolkit for data from the Micro Rain Radar (MRR), disdrometers PARSIVEL2 and RD80, and rain gauge that would help researchers from INPE, USP and partners to: standardize the preparation of raw data for internationally accepted formats; processing figures to support quick analyses; analyze and process data quality and, finally, record metadata and quality analysis for publication in international data repositories.

The increased occurrence of catastrophic events caused by climate change-induced mass movements affects human welfare and results in huge economic losses, making the development of early warning systems (EWSs) crucial to everyday decisions of local governments and communities. The state-of-the-art (SOTA) EWSs can only provide information on the level of direct danger of mass movement that each village is exposed to. This is the result of highly sensitive and coarse classifications and aggregated predictions at regional level, potentially leading to a poor perception of risk and inadequate local management plans. Also, this does not account for the indirect effects of mass movements on the local communities at social and economic levels, e.g., a town that can be cut out of the transportation and communication systems because of landslides blocking the roads around it is currently not considered by SOTA EWSs.To overcome this issue, we developed a novel machine learning scheme that investigates the environmental (hydrological and geological) characteristics of mass movements and the connectivity information of formal settlements and road network data in terms of graph structures. In particular, we study the interaction of the probabilistic mass movement susceptibility (derived from the environmental properties by means of a supervised ensemble graph neural network) on the graph representing the road network connecting the formal settlements. As a result, we derive for each formal settlement a probability of being indirectly affected by mass movements (e.g., the probability to be isolated as a result of mass movements affecting their surroundings) by graph spectral clustering.We tested this architecture (named Intergraph) on the Norwegian territory, taking advantage of over 68,000 incidents of reported mass movements since 1957. Our approach achieved an overall performance of 86.25% with the 2020 Gjerdrum quick clay incident as a demonstrated case study. With the intensifying effects of climate change, our study has opened an opportunity to develop solutions for adaptation and mitigation through a new holistic graphical perspective to assess various large-scale geospatial datasets of risk elements such as exposure, vulnerability, and hazard.