Geoscience is one of the least diverse STEM fields. While gender diversity has improved over the last few decades, ethnic and racial diversity in Geoscience doctoral programs show little to no progress since 1973 [1]. At various levels of academic hiring, recruitment and under-represented minority (URM) retention, the STEM community often refers to definitions of URM as laid out by the National Science Foundation. In all STEM disciplines, Hispanics, African American and American Indian or Alaskan natives are largely under-represented relative to national demographics. However, a more granular analysis of demographic data within the Geosciences shows that Asian Americans are also under-represented and face challenges similar to other minority groups [2]. To better understand current representation of different races and ethnicities within the multitude of STEM Disciplines, we have complied demographic data from the NSF survey of Earned Doctorates of 2019 to test whether there are differences in Asian American representation based on field of study [3]. According to the 2019 survey data, Asian Americans are significantly less represented in Geosciences compared to other STEM fields, particularly Biological and Biomedical sciences (BMS), Chemistry (CHEM), Mathematics and Computer Science (MCS), Physics (PHY) and Engineering (ENG). Asian American earned doctorates in BMS ~13%, CHEM ~11%, MCS ~14%, PHY ~12% and ENG ~17%, whereas for Geosciences that number is significantly low: ~4%. This current percentage of Asian Americans earned doctorates in the Geosciences is also lower relative to their national demographics. Importantly, Asian Americans include a broad spectrum of racial and ethnic groups which are not currently taken into consideration in these surveys. Our data analysis suggests that higher representation of Asian Americans in certain disciplines skews their overall representation within STEM, and particularly forms a misnomer in the Geosciences. Racial and ethnic representation, hence, should be re-considered based on demographic data of the related field of study, and not STEM as a whole. [1] Bernard & Cooperdock, 2018, Nature Geoscience [2] Ibarra et al., 2018, AGU Fall Meeting 2018 [3] https://ncses.nsf.gov/pubs/nsf21308/data-tables

Nuclear magnetic resonance technology can be used to simulate the T2 spectrum distribution characteristics of different fracture parameters of fractured shale. Based on scanning electron microscope data of lacustrine shale strata, a petrophysical model of fractured shale is established, and the shapes of pores and fractures are simplified into spheres and ellipsoids, respectively. According to the aspect ratio of the ellipsoid and the log Gaussian function to control the crack shape. The simulation results show that the NMR response of fractured shale is unique, which is related to fracture shape, fracture width and fracture density. The fracture width has a greater influence on the T2 spectrum distribution of fractured shale. With the increase of the crack width, the T2 spectrum distribution shows a “double peak”-“single peak”-“double peak” trend. As the crack density increases, the T2 spectrum distribution changes from “single peak” to “double peak”. The shale model with needle-shaped fractures contributes the most to the connectivity of the pore-fracture model, and the shale model with pie-shaped fractures contributes the least to the connectivity of the pore-fracture model.

Lakes are valuable and sensitive freshwater reservoirs with ecosystems that are highly vulnerable to many external threats. Specifically, changes in lake surface area and water level fluctuations are important parameters to detect changes in lake ecosystems. These fluctuations and shrinkage levels of the lake area occur due to both climatic and anthropogenic factors. Some examples of such include but are not limited to changes in meteorological factors, land use, potable water supply, water withdrawal for agricultural irrigation, and plant product pattern factors play an important role in the sustainability of the lake region and the water resources in the basin. In this paper, Seyfe Lake was selected as the primary research area. Seyfe Lake is located 30 km northeast of Kırsehir; the lake and its surroundings are a protected Ramsar site. The lake consists of a closed basin and the average area of the lake covers 35 km2, the calculation of which was obtained by interpretation of Landsat images and lake bathymetric studies. The average water depth of the lake is 1.65 m, with an elevation of 1110 m above sea level. The lake is recharged by rainfall, surface runoff, and underground flow. The discharge of the lake occurs via evaporation from the lake surface. The paper aims to evaluate the relationship between climatic and anthropogenic factors that caused the shrinkage of the Seyfe Lake surface area and water level changes in the lake to subsequently determine which factor predominantly posed a greater impact over three decades (1985-2020). Geological, hydrogeological, hydrological, and remote sensing surveys were carried out in the lake area and the basin. Furthermore, satellite images, meteorological data, lake bathymetric, and land cover maps were evaluated. It was determined that the lake surface area decreased from 66.87 km2 to 1.86 km2 and the lake area shrank by 93.78% between the years 1985 to 2020. In the period between 1990-2000, the total area of the lake was mainly controlled by climatic variations (precipitation and temperature). However, since the 2000s, there has been an increase in meteorological factors such as temperature and evaporation and contrastingly, a decrease in the amount of precipitation. When the land cover maps of the study area were evaluated, it was determined that rain-fed agriculture shifted to irrigated agriculture since 2006 within the lake basin. Additionally, it was concluded that the farmlands area increased and the transition to irrigated agriculture led to an increase in the amount of water used in agricultural irrigation with uncontrolled groundwater withdrawal from the wells in the lake basin. As a result of the research carried out in the study area, it was concluded that climatic and anthropogenic factors caused the decrease in the lake surface area and human activities impacted a greater share in the lake level change and shrinkage in the surface area in the last 15 years

Volatile organic compounds (VOCs) play a crucial role in influencing the air quality of the urban atmospheres, especially in nitrogen oxide (NOx) dominated regions like India. Additionally, the associated direct health risks necessitate the identification of VOC sources and their contribution to the VOC budget of a region. This study presents the seasonal variability of VOCs measured using a Proton Transfer Reaction Quadrupole Mass Spectrometer (PTR-QMS) during the year 2019 over the metropolitan Pune region. Also, the VOC sources have been identified using the US EPA PMF 5.0 model for different seasons. Toluene (summer: 2 μg/mΛ3, monsoon: 1.11 μg/mΛ3, winter: 7 μg/mΛ3), o-Xylene (summer: 1.41 μg/mΛ3, monsoon: 1.24 μg/mΛ3, winter: 5.67 μg/mΛ3), Acetaldehyde (summer: 2.80 μg/mΛ3, monsoon: 1.84 μg/mΛ3;, winter: 5.10 μg/mΛ3) and Acetone (summer: 5.95 μg/mΛ3, monsoon: 2.58 μg/mΛ3, winter: 6.21 μg/mΛ3) were found to be the prominent anthropogenic source-based emissions apart from Methanol (summer: 8.68 μg/mΛ3, monsoon: 5.24 μg/mΛ3, winter: 7.72 μg/mΛ3) which mostly has a biogenic source. While most of the identified sources (vehicular emissions, biomass burning, biogenic emissions, photochemical secondary products) are common throughout the year, their contribution to the total measured VOCs across seasons has varied considerably (9-17%, 8-12%, 5-14%, 19-27% respectively). Of the resolved factors, ozone formation potential (OFP) was found to be highest for photochemical secondary products (26%) during all the seasons, followed by vehicular emissions (23%) and background emissions (17%). The sources identified in this work are in agreement with a recent bottom to top emission inventory developed for the region which shows vehicular emissions as a dominant VOC source. This work highlights the role of meteorology in varying the VOCs concentration over the Pune region and eventually the local air quality.

The subduction of carbon and recycling to volcanoes affects planetary scale processes that set the composition of Earth’s surface and mantle environments. The largest flux of surficial carbon that subducts at trenches globally is sedimentary. This is paradoxical, as most carbonate dissolves and organic carbon oxidizes in the ocean before reaching the deep seafloor. Nonetheless, different events conspire to deliver variable fluxes of carbon, some of them large, to different trench sectors. Thus, sedimentary carbon subduction is not a global phenomenon – it is a regional one where heterogeneity rules. Here we have calculated the flux and isotopic composition of both incoming sediment and trench fill for organic and inorganic carbon at the world’s trenches. Our calculations are at the scale of kms along the trench, and so make predictions relevant to individual volcanoes as well as entire arc segments. A useful comparative metric is the carbon flux delivered by altered oceanic crust (AOC), which is a less variable input of largely inorganic carbon to all subduction zones. In some regions, subducting sedimentary carbon is much less than in the AOC unit subducts, for example, along the ~ 2000-km Tonga-Kermadec trench. Given the very high convergence rate, this region constitutes a large flux of carbonate with δ13C heavier than the mantle. At the other extreme, downgoing plates with km-thick turbidites deliver terrestrial organic carbon with δ13C lighter than the mantle. Trench segments that subduct >4X AOC carbon in sediments include the Nicobar Fan off Sumatra and the Aleutian and South Chile trenches. Regions with high biological productivity (Central America) and shallow seafloor (Hikurangi) supply large sedimentary carbonate fluxes with δ13C heavier than mantle. In addition to enormous isotopic heterogeneity (spanning from –25 to +1 per mil δ13C), bulk sediments also span a wide range in oxidation capacity, from carbonate to organic carbon. These highly heterogeneous point- and regional-sources of carbon fluxes, isotopes and oxidiation states provide natural laboratories to study recycling efficiencies at subduction zones and the creation of diamonds, carbonated melts and other carbon heterogeneities in the mantle.

Rice (Oryza sativa) is a major staple food crop in India occupying about 44 million ha (Mha) of cropped land in meeting food requirements for about 65% of the population. As water scarcity has become a major concern in changing climatic scenarios precise measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are needed to better manage the limited water resources and improve irrigation scheduling. The eddy covariance (EC) method was used to determine ETa and Kc of tropical lowland rice in eastern India over two years. Reference evapotranspiration (ETa) was estimated by four different approaches– the Food and Agriculture Organization-Penman-Monteith (FAO-PM) method, the Hargreaves, and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements of turbulent and available energy fluxes were taken using EC during two rice growing seasons: dry season (January-May) and wet season (July-November) and also in the fallow period where no crop was grown. Results demonstrated that the magnitude of average ETa during dry seasons (2.86 and 3.32 mm d-1 in 2015 and 2016, respectively) was higher than the wet seasons (2.3 and 2.2 mm d-1) in both the years of the experiment. The FAO-PM method best-represented ETa in this lowland rice region of India as compared to the other three methods. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet seasons (42–48%) and wet fallows (33-69%) period of both the years of study, suggesting that lateral heat transport was an important term in the energy balance calculation. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages; namely, initial, crop development, reproductive, and late-season were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76 and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in dry seasons, whereas Epan; performed better in wet seasons. The results further demonstrated that the Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation planning.

Drought is a recurring and extreme hydroclimatic hazard with serious impacts on agriculture and overall society. Delineation and forecasting of agricultural and meteorological drought are essential for water resource management and sustainable crop production. Agricultural drought assessment is defined as the deficit of root-zone soil moisture (RZSM) during active crop growing season, whereas meteorological drought is defined as subnormal precipitation over months to years. Several indices have been used to characterize droughts, however, there is a lack of study focusing on comprehensive comparison among different agricultural and meteorological drought indices for their ability to delineate and forecast drought across major climate regimes and land cover types. This study evaluates the role of RZSM from Soil Moisture Active Passive (SMAP) mission along with two other soil moisture (SM) based indices (e.g., Palmer Z and SWDI) for agricultural and meteorological drought monitoring in comparison with two popular meteorological drought indices (e.g., SPEI and SPI) and a hybrid (Comprehensive Drought Index, CDI) drought index. Results demonstrate that SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought events better than meteorological (e.g., SPI, SPEI) and hybrid (CDI) drought indices, whereas the latter three performed better in delineating meteorological drought across the contiguous USA during 2015–2019. SM-based indices showed skills for forecasting agricultural drought (represented by end-of-growing season gross primary productivity) in the early growing seasons. The results further confirm the key role of SM on ecosystem dryness and corroborate the SM-memory in land-atmosphere coupling.

Access to reliable and sustainable electricity is essential for meeting the needs of society, such as communications, clean water, healthcare, and heating and cooling, both today and into the future. Shifts in extreme weather and a changing climate are challenging traditional and renewable power grids, as evidenced by widespread outages from events like hurricanes and heat waves, which are increasing in intensity and frequency and have the propensity to harm infrastructure and diminish generation capacity. Understanding the changing climate allows utilities to be more resilient in proactively producing or distributing energy. Earth observations (EOs) provide actionable data for monitoring such change, but better collaboration between scientists and end users is needed to ensure data is accessible and relevant to decision-making. Utilizing a capacity building approach, this NASA-funded initiative aims to promote broader utilization of NASA EOs within the energy sector by transferring knowledge and bridging the gap between scientists and end users. To the untrained user, satellite data can be onerous to find and challenging to apply. To address these concerns, we engaged the U.S. Department of Energy and stakeholders across the sector to solicit input on the greatest challenges and opportunities utilities face relevant to resiliency and the usage of EOs. In response, and through an iterative process with end users, we compiled relevant NASA EOs into a user-friendly Esri StoryMap® and developed the first energy-focused NASA ARSET training, both publicly available, followed by broad outreach. The StoryMap® aims to reduce the burden of accessing and using EOs by including only the most applicable data with a focus on terrestrial variables, such as soil moisture and land surface temperature, along with tutorials and use cases. The ARSET training provides an in-depth look at using NASA products to support a more climate resilient energy sector and presents real-world, illustrative examples of the ways in which EOs can be used to better understand the impact of extreme events. This talk will report on the successes and challenges of this capacity building initiative, highlight components of the StoryMap® and ARSET training, and share lessons learned in facilitating increased uptake and use of EOs by the energy sector.

Martian global dust storms can raise large amounts of dust into the atmosphere. This dust has the ability to carry adsorbed water with it into the atmosphere. Once it arrives in the atmosphere, the dust can either adsorb additional water or release water vapor. What then happens when you introduce dust that may be carrying significant amounts of water with it that can turn to water vapor? We examined this process numerically by inserting dust into the atmosphere and re-equilibrating the dust with the vapor. We used the two main analogs for Martian regolith, volcanic tuff and palagonite. Palagonite could adsorb a maximum of about 11 wt % and volcanic tuff can adsorb up to 1 wt % of water. Water that is adsorbed on the dust grain while it is still on the surface will eventually be carried up and distributed in the atmosphere. Surface and atmospheric temperatures prior to and during the dust storm conditions affect two major variables: the amount of water carried up from the surface dust and the equilibrium between water vapor and adsorbed water. We found that water raised along with palagonite dust could increase the total water content of the atmosphere by up to about 20 % during the peak of a high opacity dust storm. Despite this increase, there was only a 1 % increase in water vapor. Thus, water in the adsorbed form represents a significant fraction of the water that can be transported during dust storms. Water introduced to the atmosphere via this method may be a significant contributor to the overall atmospheric water cycle.

Fluid-flux melting is commonly attributed as the primary driver of magmatism in arcs; however, decompression melting, mantle heterogeneity, and the amount and compositions of slab-derived materials (fluids vs. melts; crust vs. sediments), have all been suggested to play a role in producing the compositional diversity of primary arc basalts. In this contribution, we present the volatile, major, and trace element compositions of melt inclusions from basaltic magmas erupted at three volcanic centers in the Washington Cascades: Mount St. Helens (two basaltic tephras from the Castle Creek period, 2.0–1.7 ka), Indian Heaven Volcanic Field (two <600 ka basaltic hyaloclastitic tuffs), and Glacier Peak (basaltic tephra from Whitechuck and Indian Pass cinder cones). Melt inclusions have H2O and Cl contents that range from 0.3-2.2 wt% and 97-1011 ppm, respectively, and are hosted in olivine with compositions of Fo74-Fo85 (Mt St. Helens), Fo80-Fo87 (Indian Heaven) and Fo86-Fo89 (Glacier Peak). We find that trace element ratios such as Nb/Y demonstrate that the variability between samples is likely related to differences in mantle source compositions. Impressively, these ratios span nearly the entire range of arc magmas globally, from high Nb/Y compositions at Mount St. Helens that are similar to ocean island basalts, to low-K tholeiites from Indian Heaven and Glacier Peak that have Nb/Y ratios similar to N-MORB. Interestingly, a calc-alkaline basalt from Glacier Peak displays S/Y ratios that overlap with the highest values measured in arcs globally (Ruscitto et al., 2012). While all magma types, including calc-alkaline magmas from Indian Heaven and Glacier Peak, have H2O and Cl contributions from the downgoing plate (inferred from H2O/Y and Cl/Y ratios) that overlap with other Cascade Arc segments, the maxima measured in the Washington Cascades are markedly lower than those from other segments, including central Oregon, northern California, and the northern Garibaldi belt, consistent with Venugopal et al. (2020). This dataset adds to the growing inventory of primitive magma volatile concentrations along the Cascade Arc and provides insight into spatial distributions of mantle heterogeneity and the variable role of slab processes in the petrogenesis of arc magmas.

Abstract—Wildfires are a major global issue, costing the United States 71.1 to 347.8 billion dollars annually (Graham, 2020). Rising global temperatures have increased the frequency and intensity of wildfires (Cuevas-Gonzales, 2009). Climate change has thus created a need for new methods to examine the effects of wildfires. In this research, we evaluated how the 2018 Camp Fire environmentally impacted land cover in California, and the extent to which the area’s land cover suffered from long-term damage. Our hypothesis was that the affected land was damaged significantly, but recovered partially by the end of the investigated period. In order to assess the healing of a patch of land burnt in the Camp Fire, corrected reflectance images of the patch collected from NASA Worldview were analyzed using Python to return each image’s Pixel Greenness Value (PGI)–an original metric developed by our team that analyzes an image’s color content to return a numerical value corresponding to vegetation health. These values were then plotted. Over the course of 26 weeks after the Camp Fire, the patch of land partially regained its PGI. Major recovery occurred between weeks 7 and 15. We concluded that the area burnt in the Camp Fire only partially recovered, as the moving average of the PGI value only reached 81% of the baseline value by the end of the investigated period. Our findings demonstrate the environmental damage that wildfires can cause and the potential of PGI as a useful metric for assessing the impact of wildfires. Future studies could compare PGI results with those of the Normalized Difference Vegetation Index (NDVI), known for its usage in such vegetation recovery analyses. The case study could also repeat the experiment with Landsat data taken from the United States Geological Survey website, to account for the lack of atmospheric correction in NASA Worldview data. Key words: extreme event, wildfire, land cover, environmental science, programming

Current predictions for the changing Arctic focus on the evolution of sea ice from perennial to seasonal cover, with sea ice thinning as the Arctic warms. Significant efforts in developing climate forecasts for the Arctic on the decadal time-frame are underway. But, ships in transit inside the Arctic basin require high-resolution, near-term forecasts to provide guidance on scales of interest to their operations: 1-5 km. Climate predictions are insufficient for such short-term forecasts, as they are computationally intensive and at lower resolution than needed for single vessel operations. Deep learning techniques can offer the capability to enable rapid assimilation and analysis of multiple sources of information for these critical sea ice forecasts. In this study, data from NOAA’s Global Forecast System (GFS), Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature (SST), and Multisensor Analyzed Sea Ice Extent (MASIE) data from the National Snow and Ice Data Center (NSIDC) were used to develop the sea ice extent forecast model. All layers were sampled to the same 1 km Equal-Area Scalable Earth (EASE) grid. Sea ice prediction was achieved for subsequent weeks from seven days prior plus seven days of historical forecast data. The novelty in this approach was using forecast data as data to aid in the prediction of sea ice extent. The models were trained using data from the freeze-up periods of 2016, 2018, 2019, and 2020. A four-fold cross-validation was performed, with each year held out for validation. Our deep learning models are trained using overlapping 256x256 pixel tiles over the entire region of interest, the Beaufort Sea. Sigmoid outputs are averaged together in overlap regions, weighted by the distance from the edge to ensure smooth transitions from one subregion to the next. These are converted to a binary ice mask by thresholding at 0.5, then computing a per-pixel accuracy, which is averaged over the ocean/ice regions. Accuracy for a baseline persistence model was also computed over the seven-day forecast period. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days one to seven, the deep learning model accuracy dropped only to 93%. With these initial positive results, we plan to extend the model outputs from ice extent to ice concentration and thickness in future work.

Due to long term drought, engineered structures (e.g., dams and levees), and other stressors, river systems are at high risk of degradation. Riparian vegetation and river geomorphology are continuously changing. The change in river hydrology, geomorphology and riparian vegetation have cascading impacts on other ecological aspects of the river corridor system. In this study, spatiotemporal variations of the riparian vegetation and the river geomorphology have been characterized using machine learning techniques (in particular, random forest) over an evaluation period of three decades. The study area is the Middle Rio Grande, located in New Mexico, USA. For the study of vegetation, the normalized difference vegetation index (NDVI) was used. The land cover was classified, using Landsat images (1984 to 2020) collected from Landsat 5, 7 and 8, to determine the change in vegetation cover and river geomorphology. The trends of NDVI shows the increase in vegetation cover even during long term drought due to presence of groundwater dependent vegetation like cottonwoods. Similarly, the formation of new stable channel islands and narrowing of the channel are some major observations and changes in channel from this study. The availability of long-term datasets and machine learning algorithms in Google Earth Engine shows the potential in spatiotemporal analysis of riparian vegetation and river geomorphology. These long-term observations will help river managers to monitor the status of the riparian vegetation and the impacts on the river geomorphology.

Energy balance regimes (e.g., Radiative Convective Equilibrium or RCE) qualitatively characterize the low, mid, and high latitudes of Earth’s modern climate. Currently we do not have a complete quantitative understanding of the spatio-temporal structure of energy balance regimes. Here we use the vertically-integrated moist static energy budget to define a nondimensional number that quantifies when and where RCE is approximately satisfied in Earth’s modern climate. We find RCE exists yearround in the tropics and also in the Northern midlatitudes during summertime. Decomposing the seasonal RCE regime transition in the midlatitudes over land and ocean shows that RCE occurs predominantly over land. We use idealized models (energy balance and aquaplanet models) to test the hypothesis that the RCE regime occurs during midlatitude summer for land-like (small heat capacity) surface conditions. Consistent with the hypothesis, an aquaplanet model configured with a shallow mixed layer depth transitions to RCE in the midlatitudes during summertime whereas it does not for a deep mixed layer depth.

Mountain headwater streams provide unique ecological habitats that are influenced by underlying geologic structure, including bedrock depth, a characteristic that is often ill-defined. We evaluated the importance of low-permeability bedrock depth for summer stream temperature and channel dewatering patterns using horizontal-to-vertical spectral ratio (HVSR) seismic methods along 8 headwater streams in Shenandoah National Park (Virginia USA). HVSR data collected from 2015-2020 were complimented by spatially continuous surveys of stream channel dewatering during baseflow conditions (128.5 total km of stream length), multiyear stream temperature data from 64 locations, and repeated paired discharge observations using the salt dilution method. Median bedrock depth ranged 1.5 to 3.4 m, with half of the 8 stream corridors showing and average depth < 2 m. Measured bedrock depths were not well represented by existing large-scale geologic datasets or readily predicted based on topography. Two subwatersheds showed a general downstream deepening of the bedrock contact but others showed shallow bedrock throughout or had discrete, deeper bedrock zones (e.g. >20 m depth). The stream with the deepest average underlying bedrock contact supported the coldest summer temperatures, displayed a characteristic thermal signature of deeper groundwater influence, and did not dry during baseflow conditions. Patchy channel dewatering was observed at baseflow throughout the study area, as exemplified by the image below that shows a stream disconnected by a localized deposit of alluvium. Bedrock depth variability along the channel was associated with dewatering observed within some streams, though our results also indicate the importance of the shallow groundwater reservoir in maintaining streamflow and influencing paired streamflow gain and loss patterns. Our study demonstrates the importance of shallow, low-permeability bedrock contacts on stream-groundwater exchange, impacting channel habitat and connectivity within headwater stream networks.

In 2020, renewables became the second-largest source of electricity generation in the United States after natural gas (US EIA, 2021). In recent years, wind energy generation has overtaken hydropower as the dominant source of renewable generation in the United States, but hydropower continues to offer advantages, in particular large-scale storage, that makes it particularly valuable as a complement to other weather-driven renewables. This storage, in the form of reservoirs, is rarely managed exclusively to optimize hydropower generation. Instead, reservoirs are operated for flood control, ecosystem services, irrigation, water supply, navigation, and recreation as well as hydropower. Managing these competing demands in a changing climate with existing infrastructure creates difficult challenges, because all these demands are themselves subject to change as is the electricity demand itself. Yet many climate change impact studies continue to treat rivers as entirely natural systems and water resources infrastructure is ignored or treated as an afterthought. In this presentation, we will discuss recent climate change impact studies in both the northwestern and southeastern United States in which we quantified the effects of regulation on discharge and other variables. We will make the case that to develop new strategies for mitigating and adapting to climate change, it is paramount to account for humans as active agents in the hydrologic cycle. The first study focuses on the Columbia River Basin in the Pacific Northwest, the main hydropower producing region in the United States, and examines the effect of accounting for regulation on changes in high and low flow extremes. The second study focuses on the southeastern United States and evaluates the effects of regulation on estimated changes in flow, stream temperature, and habitat suitability. US EIA, 2021: Monthly Energy Review, July 2021. www.eia.gov/mer [Last accessed on 8/3/2021].

Mineral dust particles originate from a variety of arid regions around the world. Mineral dust directly modifies the Earth’s radiative balance through absorption and scattering. This radiative forcing varies strongly with mineral composition, yet there is still limited knowledge on the mineralogy of global dust source regions. Previously, 65 surface soil samples were collected worldwide, sieved to < 38 μm fraction and analyzed using XRD, SEM and re-suspended to determine scattering and absorption coefficients at three visible wavelengths (Engelbrecht et al. Atmos. Chem. Phys. 16, 2016). This dust collection represents global surface soils with comparable mineral compositions to windblown dust. For this research, we measured spectra of 26 of these samples selected from major dust source regions with compositional diversity. We measured these samples using laboratory reflectance spectroscopy in the visible and near-infrared (0.4 to 2.5 μm, VNIR) and long-wave infrared (2.5 to 25 μm, LWIR). These data are relevant to satellite imaging spectrometers, but will particularly inform measurements planned for EMIT and SBG. We compared the measured spectra to standard spectral libraries to identify dominant materials and to compare and contrast these with the major minerals identified via XRD. VNIR spectral analysis detected diagnostic absorptions for minerals such kaolinite, calcite, hematite, and goethite. Common silicates, quartz and feldspar, are abundant in the majority of these samples and are expected to have diagnostic features in the LWIR. LWIR reflectance is strongly dependent on particle size (e.g. Salisbury et al., 1991), though the 0-74 μm grain size fraction of pure silicate minerals still show characteristic signatures between 8 and 10 µm. Surprisingly, diagnostic silicate features were not observed in many of the samples. We identified quartz absorptions between 4.8 and 5.4 μm and at 14.3 μm. We are still trying to understand why the fundamental vibrational features of silicates are obscured, but it may be related to multiple overlapping features in mixtures, grain coatings, or anomalous dispersion. LWIR spectra also revealed numerous diagnostic carbonate features, particularly those near 4, 5.6, and 11.4 μm. We also identified carbonate in several samples where it had not been identified with XRD.

Mineral dust particles are ubiquitous in the atmosphere and can be transported vast distances affecting climate, air quality, and human health on a global scale. Mineralogical composition has a substantial impact on dust properties and their effects. Natural dust samples are both fine-grained and composed of many different minerals. Most commonly, X-ray diffraction (XRD) has been used to characterize dust mineralogy; however, this technique is less effective for identifying poorly crystalline or amorphous phases. We used Fourier Transform Infrared (FTIR) spectroscopy as a complementary method to identify minerals and their abundances. Long wave infrared (LWIR) spectra (2.5 to 25 μm) are sensitive to molecular bonds rather than crystallography providing additional details. We performed both XRD and reflectance spectroscopy to characterize 37 atmospheric dust samples collected in Ilam City, Iran. The dominant minerals in these samples are quartz, feldspar (albite), calcite and clays (illite, montmorillonite, kaolinite). LWIR reflectance is strongly dependent on particle size but published data of pure silicate minerals in the size range of the Ilam samples (0-63 μm) still show characteristic signatures between 8 and 10 µm (Salisbury et al. 1991; Wenrich and Christensen, 1996). Surprisingly, diagnostic silicate features were not observed in any of the samples although carbonate and OH bonds in the clay minerals were readily identified. Past studies have shown that porosity, grain size and packing can reduce the spectral contrast in the LWIR and additional effects include grain coatings or the interaction of multiple minerals. We also identify a peak at 7.8 µm which may be attributed to anomalous dispersion or the interaction of quartz and calcite in this spectral range. In order to understand the absence of Si-O features we made transmission measurements of representative samples in KBr pellets. Transmission is not influenced by multiple scattering and should clearly detect fundamental Si-O absorptions. Transmission spectra show broad features that include contributions from all silicate minerals (quartz, feldspar and clays) both near 10 μm and at longer wavelengths. We are using various spectral modeling techniques and will compare abundances derived from reflectance and transmission measurements.