Maize (Zea mays L.) is one of the most consumed grains in the world and improving maize yield is of great importance for food security, especially under global climate change and more frequent severe droughts. However, traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of unmanned aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are essential for estimating plants’ physiological and biochemical traits. In this study, we developed machine learning models to estimate grain yield and flowering time of maize breeding lines using multi-temporal UAV-based hyperspectral imagery. The performance of multiple machine learning models and the efficacy of different hyperspectral features were evaluated on Genomes to Fields (G2F) experimental sites in Wisconsin. Results showed that ridge regression is the most robust model in estimating grain yield and flowering time, compared to random forest and support vector regression models. Furthermore, the ridge regression model achieved a correlation coefficient (r) of 0.551 for yield, 0.906 for days to silking, and 0.914 for days to anthesis when using the full-bands spectra features for estimation. In addition, we assessed the modeling performance using data acquired from different growing stages. The best time of applying the UAV survey was also identified in order to reduce the data collection efforts.

Our knowledge of glacial history of the western (Norwegian) part of the Barents Sea has greatly improved during the last decades, notably due to the high-resolution multibeam swath bathymetry data. In contrast, published seafloor data from the eastern part of the Barents Sea and the Kara Sea are much more sparse. This study presents new geophysical/geological evidence for reconstructing glacial dynamics of the eastern part of the Barents-Kara Ice Sheet during the Last Glacial Maximum and subsequent deglaciation. Archival data used in this study include more than 300,000 km of sparker and high-resolution Parasound profiles verified by boreholes drilled with continuous core recovery to 50-100 m below sea bed. This dataset was used to construct continuous geological cross-sections and a series of maps, including detailed bathymetry (in 10-m isobaths) and sediment thickness maps of major seismo-stratigraphic units. Based on the bathymetric and sediment thickness data we map megascale glacial lineations, drumlin-like ridges up to 50 m high and subglacial channels up to 100 m deep, as well as accumulations of glacial deposits (basal, lateral and end moraines) and ice-proximal acoustically transparent bodies (ATBs). Spatial and stratigraphic analysis of these bedforms enables us to put forward a new hypothesis that ice moved on the shelf from the Arctic Ocean along the Saint Anna Trough (SAT). Further south, near the northern tip of the Novaya Zemlya islands, the ice flow split into three major lobes moving to the southwest into the Barents Sea and to the south and southeast into the Kara Sea. Deglaciation in the study area progressed with several ice stillstands and subsequent readvances marked by end-moraines and accumulation of ice-proximal sediments. During deglaciation events, when the SAT became ice free due to iceberg calving, the ice flow reversed its direction toward the SAT, forming a fluting and a massive ATB on the western SAT slope. The exact timing and mechanisms of the ice transgression(s) from the Arctic Ocean are not well understood. Additional high-resolution data such as multibeam bathymetry surveys are needed to verify the spatiotemporal distribution of glaciogenic bedforms, and glaciological modeling is required to comprehend the ice dynamics and put it in the pan-Arctic context.

Sanbon Chaka Gosa1, Bogale Abebe Gebeyo12, Ravitejas Patil1, Ramón Mencia1, Dani Zamir1, Menachem Moshelion1# 1 The R.H. Smith Institute of Plant Sciences and Genetics in Agriculture, The R.H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, 76100 Israel 2 Current address: Department of Horticulture, College of Agriculture and Natural Resource, Dilla University, Dilla, Ethiopia #Corresponding Author Abstract Plant productivity in general and under stress, in particular, is a complex and comlitative trait largely influenced by environmental conditions. Among the most important traits are the whole-plant water-balance regulation mechanisms that dynamically change in order to maximize the metabolic activity of the plant. Due to the difficulty of high-throughput phenotyping of these physiological traits (e.g. transpiration, stomatal conductance, and photosynthesis), they are usually measured in static conditions or modeled based on only a few measuring points (low resolution). To overcome this challenge, we utilized a high-throughput gravimetric functional-phenotyping platform (PlantArray) along with a practical reverse phenotyping approach. We selected 30 tomato lines from multiple years of field yield data and functionally phenotyped them for their dynamic response curves using a variety of stress scenarios implemented using drought conditions (each plant received irrigation based on the amount of water it transpired). Our results show that resilient and tolerable traits, in the field, are associated with stomatal plastic conductance, i.e., maximum under well-irrigation, yet the rapid response to changes in environmental conditions (soil and atmospheric). The plastic traits of the idiotype lines were shown to increase water use efficiency (momentarily), thus maximizing yield in water deficit conditions. Based on manual characterizations of the idiotypes, it has been found that their abaxial surfaces have a greater density of stomata and a higher aperture during the early morning. Additionally, these lines showed rapid recovery after a drought. Our study concluded that reverse functional phenotyping can significantly reduce the pre-breading processes for yield-related traits. Keywords: Functional phenotyping, crops yield, —dynamic response, drought stress, stomatal conductance, reverse phenomics

Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist’s personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.

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

We have developed improved seismic hazard maps with the effect of local geology for the Washington, DC area. The input ground motion prediction equations, source model, and logic tree for the analysis is taken from the 2014 U.S. Geological Survey national seismic hazard model (Petersen et al., 2014). We have added an improved local geology model based on the overburden thickness map of Froelich (1976). As in our preliminary effort (Cramer et al., 2016) we use three shear-wave velocity profiles for Piedmont, Fall Line, and Coastal Plain regions (Olgun et al, 2015). We developed reference profiles from the three Olgun et al. profiles that extend to hard rock for site amplification relative to the rock conditions for the ground motion prediction equations. Our seismic hazard maps include both probabilistic (2% in 50 years) and scenario (M6.0 at Mineral, VA) maps. The local geology in the Washington DC area strongly amplifies higher frequency ground motions (peak ground acceleration, 0.2 s spectral acceleration) in keeping with the three site-specific profiles of Olgun et al. (2015) and the observations of Pratt et al. (2017). The soil response is driven by the 10 to 20 m thick low shear-wave velocity (200–300 m/s) top layers of the reference profiles. These low velocity layers are composed of residual soil and/or alluvium. The thicker Cretaceous Potomac Formation sediments, up to 600 m thick in the SE corner of the study area, have an effect on seismic hazard at 1.0 s and longer periods. The greatest effect on 1.0 s spectral acceleration seismic hazard is from the ~200 m thick sediments near the SE edge of Washington DC. Our maps have a resolution of 0.005 degree (500 m) and have some of the sub-km scale detailed geology variation in the Washington, DC area. These maps can serve as a guide to improving the understanding of seismic hazard and risk in the area and stimulate further work on a more detailed local geology model with higher resolution.

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.

The EarthCube Technology & Architecture Committee formed a Resource Registry Working Group (WG) to develop a framework for a registry of EarthCube (EC) resources, enabling users to discover scientific and technical resources (software, tools, vocabularies, etc.) that are relevant to their research. The registry will promote EC investments, reduce time to science, help enable interdisciplinary research, more clearly define what is EC, and provide a vehicle for tool and software producers to notify the community about new products, increase visibility, and gain recognition. A primary requirement is to enable systematic description of EarthCube computational resources in terms of their functionality and interfaces for utilization, to enable users to identify components that can work together in integrated workflows. This requires understanding the specifics of how a software component communicates—both the messaging protocol, and the syntax and semantics of information formats getting data into and out of a component. This registry would work in conjunction with schema.org dataset descriptions being developed by the community to streamline linkage of data and software components for research workflows. The WG created definitions for a set of resources to include in a first iteration of the registry, and a set of properties that should be specified for all resources, as well as properties specific to particular resource types. The suggested resource types are: Software, Interface/API, Interchange format, Dataset, Repository, Service, Platform, Vocabulary/ontology/Information model, Specification, Catalog/registry, and Use Case. Dataset and Use Case resources registration is out of scope for the WG project, to be handled separately. Elaboration of this registry is in the workplan for EarthCube, with the goal maximum reuse of existing vocabularies and technology and compatibility with related registry activities.

Changes in the geometry of ocean basins have been influential in driving climate change throughout Earth’s history. Here we focus on the appearance of the Greenland-Scotland Ridge (GSR) and try to understand its impact on the ocean state, including global circulation, heat transport, T and S properties and ventilation timescales, which will be useful for interpreting paleoproxies. To this end, we use a coupled atmosphere-ocean-sea ice model with idealized geometry and consider two geometrical configurations. The reference configuration (noridge) comprises two wide strips of land set 90° apart extending from the North Pole to 40°S, separating the Northern Hemisphere ocean into a small and a large basin. In the ridge configuration a zonally symmetric oceanic ridge, that extends across the Atlantic-like basin at 60°N, mimicking the GSR, is added. In addition, we consider two climatic limits of noridge: a warm case where the northern high latitudes are seasonally ice-free and a cold case where a perennial sea ice cover is present. In both cases of noridge deep-water formation occurs at the North Pole in the Atlantic-like basin. When the ridge is introduced, the flow of warm Atlantic water to the high latitudes is hampered and the ocean heat transport across 70°N decreases by ~60% which causes cooling and freshening north of the ridge. Downwelling shifts south of the ridge, thereby altering the structure of the upper overturning cell dramatically. Despite these changes, the Northern Hemisphere surface climate response is surprisingly small for the warm climate case. This is because the subpolar gyre circulation continues to transport warm water across the ridge, keeping the northern North Atlantic relatively warm and ice-free. In the colder climate case, however, the presence of sea ice provides a strong non-linear feedback, which amplifies the cooling induced by the ridge, and causes sea ice to expand. Our results highlight the possible disconnect between changes in the localization of deep-water formation, the structure of the AMOC and the properties of water masses and changes in Northern Hemisphere climate. Implications for the interpretation of paleoproxy records from the North Atlantic region will be discussed.

The Hawaiian Islands experienced record-high sea levels during 2017, which caused nuisance flooding in vulnerable coastal communities and exacerbate beach erosion, especially when positive sea level anomalies coincided with high tides. To build toward solutions for mitigating inundation risk, the predictability of daily-averaged sea level anomalies is investigated. Background sea level around the Hawaiian Islands was elevated during most of 2017 due to an oceanic Rossby-type planetary wave, which propagated slowly westward across the tropical North Pacific over the course of a year. The investigation focused on leveraging observed westward propagation that sea level anomalies exhibit over a range of timescales to make subseasonal predictions. Daily near-real-time gridded altimetry (CMEMS/AVISO) was used to specify upstream sea level at each site with propagation speeds based on mode-one baroclinic Rossby wave speeds. The skill of the predictions exceeds persistence at most locations around the archipelago out to a month or more lead time, but the skill is highly dependent on location even over the short distances spanned by the Hawaiian Ridge. Here, hindcast results are presented that establish where skillful subseasonal predictions can be made in Hawaii, as well as the barriers to predictability in locations where they cannot. These results inform the oceanographic and modelling communities about what processes need to be resolved in order to provide island communities with useful short-term sea level forecasts as the frequency of flooding events increases.

This poster presents field-scale numerical compositional simulations of CO2 storage mechanisms in the Morrow B sandstone of the Farnsworth Unit (FWU) located in Ochiltree County, Texas. The study examines structural-stratigraphic, residual, solubility and mineral trapping mechanisms. The reactive transport modeling incorporated evaluates the field’s potential for long-term CO2 sequestration and predicts the CO2 injection effects on the pore fluid composition, mineralogy, porosity and permeability. The dynamic CO2 sequestration simulation model was built from an upscaled geocellar model for the Morrow B. This model incorporated geological, geophysical, and engineering data including well logs, core, 3D surface seismic and fluid analysis. We calibrated the model with historical CO2-WAG miscible flood data and used it to evaluate the feasibility and mechanisms for CO2 sequestration. We used the maximum residual phase saturations to estimate the effect of gas trapped due to hysteresis. In addition, gas solubility in the aqueous phase was modelled as function of pressure, temperature and salinity. Lastly, the coupled geochemical reactions, i.e., the characteristic intra-aqueous and mineral dissolution/precipitation reactions were assimilated numerically as chemical equilibrium and rate-dependent reactions respectively. Additional scenarios that involve shut-in of wells were performed and the reservoir monitored for over 1000 years to understand possible mineralization. Changes in permeability as a function of changes in porosity caused by mineral precipitation/dissolution were calibrated to the laboratory chemo-mechanical responses. The study validates the effects of Morrow B petrophysical properties on CO2 storage potential within the FWU. Study results shows: EOR at the tertiary stage of field operations, total amount of CO2 stored in aqueous-gaseous-mineral phases, evolution and dissolution/precipitation of the principal accessory minerals and the time scale over which mineral sequestration took place in the FWU. This study relates the important physics and mechanisms for CO2 storage in the FWU and illustrates the use of the coupled reactive flow. The study serves as a is benchmark for future field-scale reactive transport CO2-EOR projects in similar fields throughout the world.

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.

Addressing research problems in Earth and environmental science usually requires combining data from multiple sources. This is facilitated by the use of common practices, vocabularies, interfaces and standards and recently it has been accelerated through connected communities of practice. This abstract will focus on the Earth Science Information Partners (ESIP) and the Australian Earth and Environment Science Information Partners (E2SIP) Over the last 20 years ESIP has built a community of practice in USA, supported by NASA, NOAA & USGS, through regular meetings and online forums to examine and develop emerging technologies. ESIP has become a braintrust and professional home for the Earth science data and informatics community where both peer-led education & training and the codevelopment of conventions, practices and guidelines have helped make Earth science data more interoperable. Through connections in the ESIP network and these boundary objects, ESIP has influenced the international community. The Australian Earth and Environment Science Information Partners (E2SIP) was recently established through liaison with ESIP to support similar functions in Australia. E2SIP is working with the National Earth and Environmental Sciences Facilities Forum which provides a common voice to government on behalf of long term science infrastructure. In addition, E2SIP, supported by programs from the National Research Infrastructure Strategy (NCRIS) such as the newly formed Australian Research Data Commons (ARDC), will convene workshops, courses, hackathons, and develop guidance and best practices tailored for the Australian community. This talk will explore how ESIP and E2SIP will work together, utilizing the collective impact framework orienting around a common shared agenda and leveraging a shared backbone structure in the U.S. and Australia. We will highlight our current understanding through a few case studies.

Interaction of magma with ground or surface water can lead to explosive phreatomagmatic eruptions. Poorly understood questions of this process center on effects of system geometry, length- and time scales, and these necessitate experiments at larger scale than previously conducted in order to investigate the thermohydraulic escalation behavior of rapid heat transfer. Previous experimental work either realized melt-water interaction at similar (meter-) scales, using a thermite-based magma analog in a confining vessel, or on smaller scale using about 0.4kg remelted volcanic rock in an open crucible, with controlled premix and a trigger event created by a low energy air gun pellet (about 5J kinetic energy). The new setup uses 55kg melt for interaction, and the timing and locaiton of the magma-water premix can be controlled on a scale up to 1m. A trigger mechanism is a falling hammer that drives a plunger into the melt (about 28K kinetic energy). Results show intense interaction at relatively low water to magma mass ratio. A video analysis quantifies the rate and amount of melt ejection and compares the results with those using the same melt in the smaller scale setup. Experiments show that on the meter scale intense interaction can start spontaneously without an external trigger if the melt column above the initial mixing location is larger than 0.3m. A dependency of system response on water mass flow rate was not observed.

Farmers in the indigenous Andean community of Cañar, Ecuador rely on an abundant wet season each growing year and are concerned that their wet seasons are becoming less rainy. The normal seasonal rainfall pattern in this region, the Andes of southern Ecuador, is driven primarily by the seasonal migration of the Intertropical Convergence Zone (ITCZ). Existing scientific literature suggests that the ITCZ’s seasonal migration pattern could be affected by anthropogenic climate change, thereby impacting the seasonal precipitation in the region. This study explores the possibility that the ITCZ’s migration pattern has already changed, which would validate the concerns of the Cañari people. This is accomplished by tracking the movement of the ITCZ over the past few decades using Outgoing Longwave Radiation (OLR) data. No obvious trend in the seasonal mean position of the ITCZ has been detected. However, OLR maps indicate an abnormal ITCZ signature during strong warm phases of the El Niño-Southern Oscillation (ENSO), consistent with previous studies. The interannual variability in the region’s seasonal precipitation is also associated with varying phases of ENSO. An additional component of this study is the development of a statistical model using ENSO indices as input, among other atmospheric indices, to provide seasonal precipitation forecasts for the tropical Andean wet season each year. Besides ENSO, another potentially useful index for seasonal prediction is the phase of the Quasi-Biennial Oscillation, which has been linked to precipitation variability in the tropics. The seasonal forecasting model is expected to help Ecuadorian highland farmers decide which of their crops to plant each year, based on the water needs of each crop.

There have always been some challenges within the remote sensing community related to the processing of contiguous spectral bands contained in hyperspectral datasets. Most approaches would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized MDI algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively using the optimized MDI algorithms. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of the optimized shape indices to discriminate between grass/pasture and grass/trees, tree and grass, and between types of tillage (corn-min and corn-notill) under object-based random forest approach. The results highlight the importance of MDI that completely utilizes the contiguous spectral bands to define the gradient of the curve and improve image classification accuracy.

Local ecological knowledge (LEK) is the experiential knowledge of local people gained through day-to-day interactions with the environment. LEK can provide detailed, real-time information about target species, ecological resources, and rapid state shifts in ecosystems. LEK is becoming more important as a source of data for conservation research and management. LEK can supplement conventional ecological surveys and data by providing rich context and detail on the state of local ecosystems by the people who work on these ecosystems every day as part of their livelihoods (Burbidge et al., 1988, Turvey et al., 2010b). Many communities in the Coastal Bend region of Texas have citizens whose livelihoods depend entirely on ecosystems as well as economies largely dependent on ecotourism. Ecotourism important to Coastal Bend economies includes recreational fishing, coastal parks, birding, and other forms of wildlife tourism. These same communities were majorly impacted by Hurricane Harvey and are continuing to slowly recover one year after the storm. Ecotourism stakeholders possess detailed knowledge on 1) changes to the ecosystem post Harvey across scales, 2) the needs for prioritized ecosystem restoration and conservation initiatives that may quicken ecotourism recovery post-Harvey, and 2) possible blind spots for conservation and resource management of which decision-makers may be unaware. Given the urgency and heavy financial burden of hurricane recovery, LEK can be pathway to resilience. Resilience in the Coastal Bend post-Harvey would see communities, ecosystems, and economies not only recovering quickly to their pre-storm states, but also harnessing the ability to absorb similar shocks in the future. LEK can act as a pathway to resilience during hurricane recovery as it is inexpensive, first-hand, detailed knowledge of changes to ecosystem functions linked to economic development. These changes may be addressed by decision-makers and resource managers looking to enable post-storm recovery. This presentation discusses how ecotourism-dependent communities in the Texas Coastal Bend use LEK to recover from Hurricane Harvey and build resilience to future extreme events as a model framework for how LEK can be used more widely to enhance resilience, respond to hazards, and facilitate adaptation.

River basin floods due to summer monsoon (June-September) rainfall are the major causes of infrastructure damage and loss of human lives in India. Thus, skillful forecasts of daily streamflows are crucial for flood mitigation. We develop an experimental forecasting system that combines a deterministic physical model forecast in a Bayesian Hierarchical Framework to generate an ensemble daily streamflow forecast. The physical hydrologic model based on the land surface model – Variable Infiltration Capacity (VIC) - developed in an experimental mode to model and forecast hydrologic systems over India is used. Rainfall forecast from the Indian Meteorological Department (IMD) at several lead times (1-day, 2-day, 3-day, 4-day, and 5-day) is used to drive the VIC model to provide a single deterministic forecast trace. A Bayesian Hierarchical Model (BHM) framework is developed to post-process the VIC model forecast and generate skillful daily ensemble streamflow forecast. We demonstrate the BHM framework to daily summer (July-August) streamflow forecast at five stations in the Narmada River Basin in Central India for the period 2003-2014 and, provide preliminary assessment for the period 2015-2018. In this framework, the daily streamflow at each station is modeled as Gamma distribution with time varying parameters, which are modeled as a linear function of potential covariates that include VIC model deterministic streamflow forecast and observed spatially-averaged precipitation from the previous days. With suitable priors on the parameters, posterior distributions of the parameters and predictive posterior distributions of the daily streamflows – and thus ensembles –are obtained. The skill of the probabilistic forecast is assessed a suite of metrics (correlation coefficient, and BIAS), rank histograms, and skill scores such as CRPSS. The model skills are also assessed for various flow thresholds. The BHM framework provides a novel, flexible and powerful approach to combine forecasts from multiple models (including qualitative) and provide a combined skill ensemble forecast. This will be of immense help to enable effective disaster management and mitigation strategies.