The Permafrost Regionalization Map (PeRM): How well do observations, models and experiments represent the circumarctic-scale spatial variability in permafrost carbon vulnerability?

A large amount of organic carbon stored in permafrost soils across the high latitudes is vulnerable to thaw, decomposition and release to the atmosphere as a result of climate warming. Findings from observational, experimental and modeling studies all suggest that this process could lead to a significant positive feedback on future radiative forcing from terrestrial ecosystems to the Earth’s climate system. With respect to the magnitude and timing of this feedback, however, observational data show large variability across sites, experimental studies are few, and different models result in a wide range of responses. These issues represent fundamental limitations on improving our confidence in projecting future permafrost carbon release and associated climate feedbacks. Recent studies have brought new insight into – and even quantitative estimates for – these issues through broader data synthesis and model-data integration approaches. But, how representative of the circumarcticscale variability in permafrost carbon vulnerability are the data and models from these studies? To address this question, we developed a geospatial data synthesis and analysis framework designed to represent and characterize the variability in permafrost carbon vulnerability across the northern high latitudes. Here, we describe the rationale and methods used to develop the regionalization scheme, and then use the framework to assess the spatial representativeness of, and the variability described by, existing data sets defining the fundamental components and environmental drivers of permafrost carbon vulnerability. The Permafrost Regionalization Map (PeRM) considers the regional-scale environmental factors that generally determine the spatial variability in permafrost carbon vulnerability across the Arctic. The broadly-defined regional classification is based on a circumarctic spatial representation of the major environmental controls on a) the rate and extent of permafrost degradation and thaw, b) the quantity and quality of soil organic matter stocks, and c) the form of permafrost carbon emissions as CO2 and CH4. We chose a generalized, pragmatic approach that resulted in a feasible number of regional subdivisions (i.e.,‘reporting units’) based on an intersection of spatial data layers according to permafrost extent, permafrost distribution, climate regime, biome and terrain. The utility of the PeRM framework is demonstrated here through areal density analysis and spatial summaries of existing data collections describing the fundamental components of permafrost carbon vulnerability. We use this framework to describe the spatial representativeness and variability in measurements within and across PeRM regions using observational data sets describing active layer thickness, soil pedons and carbon storage, long-term incubations for carbon turnover rates, and site-level monitoring of CO2 and CH4 fluxes from arctic tundra and boreal forest ecosystems. We then use these regional summaries of the observational data to benchmark the results of a process-based biogeochemical model for its skill in representing the magnitudes and spatial variability in these key indicators. Finally, we discuss the on-going use of this framework as a basis for higher-resolution mapping of key regions of particular vulnerability to both press (active layer thickening) and pulse (thermokarst development) disturbances. This work is guiding on-going research toward characterizing permafrost degradation and associated vegetation changes through multi-scale remote sensing. Overall, this spatial data synthesis framework work provides a critical bridge between the abundant but disordered observational and experimental data collections and the development of higher-complexity process representation of the permafrost carbon feedback in geospatial modeling frameworks

Monitoring ecosystem dynamics in an Arctic tundra ecosystem using hyperspectral reflectance and a robotic tram system

Global change, which includes climate change and the impacts of human disturbance, is altering the provision and sustainability of ecosystem goods and services. These changes have the capacity to initiate cascading affects and complex feedbacks through physical, biological and human subsystems and interactions between them. Understanding the future state of the earth system requires improved knowledge of ecosystem dynamics and long term observations of how these are being impacted by global change. Improving remote sensing methods is essential for such advancement because satellite remote sensing is the only means by which landscape to continental-scale change can be observed. The Arctic appears to be impacted by climate change more than any other region on Earth. Arctic terrestrial ecosystems comprise only 6% of the land surface area on Earth yet contain an estimated 25% of global soil organic carbon, most of which is stored in permafrost. If projected increases in plant productivity do not offset forecast losses of soil carbon to the atmosphere as greenhouse gases, regional to global greenhouse warming could be enhanced. Soil moisture is an important control of land-atmosphere carbon exchange in arctic terrestrial ecosystems. However, few studies to date have examined using remote sensing, or developed remote sensing methods for observing the complex interplay between soil moisture and plant phenology and productivity in arctic landscapes. This study was motivated by this knowledge gap and addressed the following questions as a contribution to a large scale, multi investigator flooding and draining experiment funded by the National Science Foundation near Barrow, Alaska (71°17’01” N, 156°35’48” W): (1) How can optical remote sensing be used to monitor the surface hydrology of arctic landscapes? (2) What are the spatio-temporal dynamics of land-surface phenology (NDVI) in the study area and do hydrological treatment has any effect on inter-annual patterns? (3) Is NDVI a good predictor for aboveground biomass and leaf area index (LAI) for plant species that are common in an arctic landscape? (4) How can cyberinfrastructure tools be developed to optimize ground-based remote sensing data collection, management and processing associated with a large scale experimental infrastructure? The Biocomplexity project experimentally manipulated the water table (drained, flooded, and control treatments) of a vegetated thaw lake basin to investigate the effects of altered hydrology on land-atmosphere carbon balance. In each experimental treatment, hyperspectral reflectance data were collected in the visible and near IR range of the spectrum using a robotic tram system that operated along a 300m tramline during the snow free growing period between June and August 2005-09. Water table depths (WTD) and soil volumetric water content were also collected along these transects. During 2005-2007, measurements were made without experimental treatments. Experimental treatments were run in 2008 and 2009, which involved water table being raised (+10cm) and lowered (-10cm) in flooding and draining treatments respectively. A new spectral index, the normalized difference surface water index (NDSWI) was developed and tested at multiple spatial and temporal scales. NDSWI uses the 460nm (blue) and 1000nm (IR) bands and was to capture surface hydrological dynamics in the study area using the robotic tram system. When applied to high spatial resolution satellite imagery, NDSWI was also able to capture changes in surface hydrology at the landscape scale. Interannual patterns of land-surface phenology (measured with the normalized difference vegetation index - NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised when WTD was above ground level. NDVI and NDSWI were negatively correlated when WTD was above ground level, which held when scaled to MODIS imagery collected from satellite, suggesting that published findings showing a ‘greening of the Arctic’ may be related to a ‘drying of the Arctic’ in landscapes dominated by vegetated landscapes where WTD is close to ground level. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m 2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008. New cyberinfrastructure were developed to enhance management and quality control of large volumes of hyperspectral data collected during the study in collaboration with UTEP’s Cyber-ShARE Center of Excellence. Tools included Semantic Abstract Workflows and ontologies, software for data specification and verification, and an online vegetation spectral library. This study has shown that ground and satellite remote sensing studies that utilize experimental and observational (time series) data, in combination with interdisciplinary collaboration can improve capacities needed for monitoring arctic change

Investigating the cognitive, affective, and professional developments attained during the first year by the in-service science teachers enrolled in the MATs Program at UTEP

The aim of the study is to document the evolution and improvements, which the MAT participants accomplished in the first year of the MAT program. The design of the study combines and intertwines qualitative and quantitative methods to examine both cognitive and affective domains. The documentation takes place in a longitudinal approach, and is done via standardized instruments that have been checked for both validity and reliability and well known in the PER: Physics Education Research community, and via qualitative items such as personal journals, lesson plans, personal interactions, and class observations and surveys. Participants\u27 Science Teaching Efficacy (PSTE) increased by a small margin in the first semester and increased significantly towards the end of the second semester. Science Teaching Outcome Expectancy (STOE) decreased in the first semester but increased in the second semester. The qualitative data shows enough evidence for the participants\u27 efficacy belief changes as they progressed during the first year. One of the findings of the qualitative data showed that time management was the biggest hurdle the participants faced in attending this program

Finding the best Machine Learning algorithm to forecast sedimentation in a wetland ecosystem in India: A comparative analysis

Machine Learning (ML) methods are proposed within the academic literature as alternatives to traditional time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this study is to evaluate the performance of a range of ML and traditional statistical methods across multiple forecasting horizons. A subset of daily modeled sedimentation data in a coastal wetland in India for a 20 year period was used to carry out the study. After analyzing the data we created forecasting models that can forecast for daily, monthly, yearly and seasonal values of the parameter we are interested in. The classical time series models [1] considered for this analysis are Holt-Winters exponential smoothing, Moving Average(MA), AutoRegressive(AR), Autoregressive Moving Average(ARMA), AutoRegressive Integrated Moving Average(ARIMA), Seasonal AutoRegressive Integrated Moving Average(SARIMA), and Seasonal AutoRegressive Integrated Moving Average with Exogenous Variable(SARIMAX) models. RNN and Facebook Prophet models are used for deep learning model [2] analysis. After comparing the post-sample accuracy of popular ML methods thereupon of classical statistical ones, we found that the previous are dominated across both accuracy measures used and for all forecasting horizons examined and vice versa depending on the interval(i.e. daily, monthly or seasonal time interval) for which we're analyzing. Moreover, we observed that their computational requirements are considerably greater than those of classical statistical methods. The report explains how both the statistical and ML approaches work, discusses the results, and proposes some possible ways forward to decide which one to use. The empirical results found in our research stress the necessity for objective and unbiased ways to check the performance of forecasting methods which will be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions

Monitoring of Chilika Lake mouth dynamics and quantifying rate of shoreline change using 30 m multi-temporal Landsat data

Coastal erosion is one of the major and serious concerns for coastal communities residing in the low lying areas, especially near to estuary delta regions. These regions see lots of anthropogenic activities such as economic development, infrastructure and human settlement especially in rapidly developing countries such as India. Shoreline change is a natural process that occurs in coastal areas. But due to the stresses happening in the coast because of anthropogenic activities, understanding how shorelines change over time is important for sustainable management of coast. A crucial aspect of shoreline change monitoring is to identify the location and change over time which can be achieved by developing monitoring strategies using satellite remote sensing data. Performing shoreline change analysis using long term satellite records will help us to understand how shorelines respond to coastal development over time. In the present study we investigate shoreline erosion and accretion rate using three temporal Landsat scenes acquired over a thirty year period for the years 1988, 2000 and 2017. Digital Shoreline Change Analysis System (DSAS) an extension of ArcGIS software was used to compute rate of change statistics by calculating End Point Rate (EPR) values. We observed that Chilika coast is experiencing both erosion and accretion process with very high erosion rate of −13.6 m/yr and accretion of 13.5 m/yr, at Chilika Lake mouth. The average erosion and accretion rate of −1.13 m/yr and 1.41 m/yr were recorded for the study region

The Role of Remote Sensing in Modeling Landscape Change and Its Associated Carbon Cycle Impacts Across Terrestrial Arctic Ecosystems

Terrestrial ecosystems across the circumpolar Arctic region are undergoing unprecedented changes in structure and function as a result of rapid climate warming. Such changes have substantially altered energy, water and biogeochemical cycling in these regions, which has important global-scale consequences for climate and society. Recognizing the vulnerability of these ecosystems to change, scientists and decision-makers have identified a critical need for research that employs existing and new remote sensing technologies and methodologies to observe, monitor and understand changes in Arctic ecosystems. The unique capabilities provided by remote sensing imagery and data products have allowed us novel views of ecosystems and their dynamics over multiple scales in time and space across all regions of the globe. Here we offer a synthetic discussion of the recent and emerging science focused on understanding the dynamic landscape processes in Arctic terrestrial ecosystems using a variety of remotely-sensed information collected from passive and active sensors on ground-, aircraft- and satellite- based platforms. To consider the evolution of these technologies, methods and applications over recent decades, we look at key examples from the scientific literature that range from the use of radar sensors for local-scale characterization of active layer dynamics to the circumpolar-scale assessment of changes in vegetation productivity using long-term records of optical satellite imagery. This discussion has a particular focus on the use of remotely sensed data and products to parameterize, drive, evaluate and benchmark the modeling of Arctic ecosystem processes. We use these examples to demonstrate the opportunities for model-data integration, as well as to highlight the challenges of remote sensing studies in northern high latitude regions

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America

The outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (-0.11 °C to -2.6 °C), USA (-0.70 °C) and Canada (-0.27 °C) in March-May of the pandemic year 2020 compared to the mean of 2015-2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. -0.46 °C to -0.96 °C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1-2 °C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapor content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation

Assessing the regional-scale variability of permafrost carbon vulnerability based on observations, experiments and modeling

A large amount of organic carbon stored in permafrost soils across the high latitudes is vulnerable to thaw, decomposition and release to the atmosphere as a result of climate warming. This process is anticipated to be a significant positive feedback on future radiative forcing from terrestrial ecosystems to the Earth’s climate system. Here, we describe the development of a geospatial framework designed to characterize permafrost carbon vulnerability in the northern hemisphere. The broadly-defined regional classification is based on a Pan-Arctic spatial representation of the major environmental controls on a) the rate and extent of permafrost degradation and thaw, b) the quantity and quality of soil organic matter stocks, and c) the form of permafrost carbon emissions as CO2 and CH4. The framework was developed by integrating existing spatial data layers describing permafrost and ground ice conditions, bioclimatic zones, and topographic and geographic attributes. The resulting Permafrost Regionalization Map (PeRM) can be used for synthesis studies on permafrost carbon vulnerability, including data representativeness and gap analysis, model-data integration and model benchmarking. The utility of the PeRM framework is demonstrated here through areal density analysis and spatial summaries of existing data collections describing the fundamental components of permafrost carbon vulnerability. We use this framework to describe the spatial representativeness and variability in measurements within and across PeRM regions using observational data sets describing active layer thickness, soil pedons and carbon storage, longterm incubations for carbon turnover rates, and site-level monitoring of CO2 and CH4 fluxes from arctic tundra and boreal forest ecosystems. We then use these regional summaries of the observational data to benchmark the results of a process-based biogeochemical model for its skill in representing the magnitudes and spatial variability in these key indicators. Finally, we are using this framework as a basis for higher-resolution mapping of key regions of particular vulnerability to both press (active layer thickening) and pulse (thermokarst development) disturbances, which is guiding on-going research toward characterizing permafrost degradation and associated vegetation changes through multi-scale remote sensing. Overall, this work provides a critical bridge between the abundant but disordered observational and experimental data collections and the development of higher-complexity process representation of the permafrost carbon feedback in geospatial modeling frameworks