The retroarc of the North American Cordilleran orogen in Nevada and Utah has been divided into the frontal Sevier fold-thrust belt in Utah, which accommodated shortening between ~145 and ~50 Ma, and a broad region of Nevada referred to as the ‘Sevier hinterland’. The hinterland is hypothesized to have developed into a high-elevation orogenic plateau (or ‘Nevadaplano’) at some point between the Late Jurassic and the Paleogene. Recent paleoaltimetry utilizing clumped isotope temperature estimates suggests that at least some basins on the Nevadaplano were at an elevation of 2.2-3.1 km by the latest Cretaceous. However, it remains uncertain precisely when the Nevadaplano attained these high elevations and if surface uplift developed steadily along with protracted shortening in the Sevier fold-thrust belt or occurred rapidly and was decoupled from the shortening record. In order to extend the surface elevation history of the Nevadaplano further back in time, we have investigated the type-exposure of the mid-Cretaceous (~113-98 Ma) Newark Canyon Formation (Knc) in central Nevada. The Knc records synorogenic sedimentation in the Sevier hinterland during the early to middle stages of shortening in the Sevier thrust belt. We will present terrestrial surface temperature estimates from clumped isotope analyses derived from palustrine, lacustrine, and pedogenic carbonate-bearing facies. Contextualized by structural evidence and corrected for secular climate change, these data suggest that the studied Knc basin had not developed substantial surface elevation by the mid-Cretaceous. However, there was likely some considerable surface relief in this region associated with active fold-thrust structures in the upper crust. Preliminary temperature estimates range between 22 and 70°C. These temperatures reflect a range of facies-specific differences in primary carbonate formation, as well as, diagenetic overprinting of some samples. Consistently warm temperatures throughout the stratigraphic section suggest that there was no significant cooling due to elevation gain between ~113 and ~98 Ma. We will discuss the implications of these results for the style and timing of deformation and surface uplift within the Nevadaplano.

Representation of canopy and tree function in earth system models (ESMs) is going through rapid advancements in the last decade. The simplistic big-leaf representation of canopy in model is being expanded and replaced by more complex canopy representations that include multiple functional types, species and age/size stages, multiple patches with different canopy height and leaf area characteristics, and vertically detailed representations with between 2 (light and shade) to n canopy leaf layers. Recently, the hydrodynamic approach to modeling stomata conductance has advanced, and many ESMs include a hydrodynamic version. Under the hydrodynamic approach, stomata respond to water availability in the xylem, and not directly in the soil. The result is a model that include the stem, and xylem dimensions as canopy characteristics for determining the flow and storage of water in the xylem. However, pipe diameter, area and volume do not scale linearly, and thus, the simulations cannot resolve canopy-scale hydrodynamics by pulling all the stem conductive area and volume to a single virtual patch scale, but instead, need to consider the hydrodynamics of a single virtual tree and scale its resulting fluxes to the entire canopy. While that is trivial in a homogeneous canopy, the difference between the tree-level description of the hydrodynamic canopy and the horizontally pulled description of the canopy in the surface-flux and radiation-exchange modules leads to a conundrum. Imagine a mixed canopy: 50% oaks and 50% maples. Assume oaks have 20% more LAI and are 1 m taller than the maples. As far as hydrodynamics are concerned, the evaporative demands of maple follow from light attenuation and leaf area profiles of a maple-like canopy. However the roughness length and aerodynamic resistance, and the resulting wind profile inside the canopy is not characteristic of maple or oak but is a result of the mixed canopy. I will present formulation for a consistent scaling of tree-level canopies to patch-level for mixed forests with multiple species of different functional types. The formulation scales canopy characteristics and the resulting canopy fluxes from tree to forest in an energy and mass-conservative way, and allow a smooth and consistent multi-species canopy description for hydrodynamic models in mixed-forests.

Submarine slope failures and the tsunamis they generate pose risks to coastal communities and infrastructure. While slope failures on passive margins represent some of the largest mass failures on Earth, little is known about their dynamics. The recurrence interval of submarine slope failures on passive margins is longer than on active margins, which facilitates thick sediment accumulation before failure, yields larger failures, and may be associated with higher potential for tsunami generation. While numerous studies model failure likelihood based on temporal distribution, overpressure, or earthquake proximity, there is limited insight linking initial conditions, preconditioning, slope failure initiation, and failure evolution. We observed dynamic submarine slope failure processes via physical experiments in a benchtop flume. Submarine slope failures were induced under controlled pore pressure with varied sand-clay mixtures (0%, 2%, 4%, and 5%, clay, by weight) constrained to a constant pre-failure slope geometry. Commercially obtained fine-grained sand (subangular quartz; 87% SiO2; D50 = 195 µm) and clay (dioctahedral smectite; 63% SiO2 and 21% Al2O3; D90 = 44 µm) were used. Pore pressure required to induce slope failure, slope-failure initiation and evolution, and post-failure morphology were recorded and analysed via photogrammetry. Numerical models were developed to quantify the physical processes observed in flume experiments. Increased clay content corresponded to increased cohesion and pore pressure required for failure. Subsurface fractures and tensile cracks were only generated in experiments containing clay. Falling head tests showed a log-linear relation between hydraulic conductivity and clay content which we used in our numerical models. Models of our experiments effectively simulate overpressure (pressure in excess of hydrostatic) and failure potential for (non)cohesive sediment mixtures. Overall our work shows the importance of clay in reducing permeability and increasing cohesion to create different failure modes due to overpressure. Ongoing work is investigating the effects of higher clay content and the role of seismic energy in slope failure morphology.

Years of the Maritime Continent or YMC was designed to improve our knowledge of the weather-climate systems over the MC and their numerical simulation and prediction skill. Since its first field campaign in November 2017, many intensive observations have been carried out under the coordination with the MC countries. Although currently some observations have been postponed due to COVID-19 pandemic, field campaigns are expected to continue beyond 2021. Up to now, some key information could be obtained, which suggest future approaches. In particular, diurnal cycle of rain near the coast is one of major targets, as they are dominant component of precipitation in this region. Some studies suggest the important role of temperature contrast between land and ocean, and inaccurate initial sea surface temperature conditions might cause a delay of offshore propagation of rainfall region. This fact provides a clue to improve simulation of precipitation behavior over the MC. In addition, during the campaign, several new observation tools have been introduced. For example, autonomous surface vehicle (ASV) had been deployed and measured surface meteorology as well as sea surface condition. Besides, GNSS-derived water vapor measurement was successfully carried out. Those results suggest that such ASVs equipped with new tools can be used to monitor accurate sea surface condition without significant cost and time. However, usually those instruments are not allowed to operate freely in the MC. Thus, based on collaboration with the MC countries, researchers in the MC are highly expected to take this role as regional representative. In this presentation, we will also show other results which provide tips for future direction.

GOSAT and GOSAT-2 have simultaneously observed both reflected SWIR solar light and TIR emissions with a single FTS mechanism with the Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) and TANSO-FTS-2 since 2009 and 2018, respectively. Their linear polarization bands provide information on light-path modification and make accurate remote sensing possible, even under aerosol and thin-cloud contaminated conditions. We can retrieve the difference between the partial column-averaged dry-air mole fractions of the two individual layers of lower and upper troposphere (LT and UT) by combining TIR and SWIR spectra data simultaneously, thereby constraining the accurate total column density of XCO2 and XCH4. TANSO-FTS has a two-axis agile pointing system, which allows cross-track and along-track motions It was originally designed for grid scan observations and viewing onboard calibration sources. After the pointing mechanism was switched from primary to secondary on 26 January 2015, We decided to make more frequent target observations, by uploading AT and CT pointing angles and observation timing as commands from the ground every day About 1000 locations are allocated to target observations such as calibration and validation site, megacities, or large emission sources. We define vertical layers of LT and UT not by temperature, but by the retrieved Psurf from individual O2 A band data. The pressure-height ranges of the LT and UT were taken as 0.6–1 Psurf and 0.2–0.6 Psurf, respectively. As the LT includes the entire boundary layer, analysis using XCO2 (LT) and XCH4 (LT) can double the signal of local emissions and remove the effects of CO2 and CH4 variability in the UT, which typically extends over a much wider area. We have targeted intense measurements over mega cities since 2016. We assume the average density of the upper troposphere is a background. We define XCO2 anomalies XCO2(LT)-XCO2(UT average), which show enhancement caused by local anthropogenic emissions. In 2020, we detected lower anomalies than previous years over mega cities such as Tokyo, Beijing, and New York.

The use of geophysical tools for subsurface characterization is a common practice in environmental studies and georesources engineering. The electrical conductivity of the subsurface is strongly influenced by the different properties of the subsurface such as pore fluid chemistry, and consequently, by subsurface processes that affect the spatial distribution of that chemistry, such as the mixing dynamics of pore fluids. In the context of freshwater-saline water interaction in coastal areas, changes in solute spatial distribution are coupled to density-driven flow, which can thus be monitored via geoelectrical measurements. Here, we study the Rayleigh Taylor instability and subsequent convection occurring due to the density difference between two miscible liquids when the lighter one is positioned on top of the denser one, a configuration that is relevant for saltwater-freshwater interactions in coastal aquifers. We simulate the convective process and monitor it numerically by computing the transverse apparent conductivity of the medium in time, as the convection develops. We then look for correlations between the geoelectrical signal and a global scalar measure of the convective process' advancement, namely the variance of the solute concentration field.

After three foreshocks the day before (M5.0, M4.7 and M4.1, respectively), a strong M6.4 Petrinja earthquake occurred on December 29, 2020, followed by thousands of aftershocks (the strongest was a January 6 M5.0 earthquake). This paper presents a unique multihazard sequence of complex events resulting in numerous cover-collapse sinkhole failures. Although the area heavily impacted by the earthquake was larger than 1,000 km2, all 91 sinkholes appeared within a 4 km2 area surrounding Mečenčani and Borojevići villages located 20–25 km SE of the epicentral area, during the three months following the main earthquake. That area was also previously prone to seldom sinkhole appearances, as evidenced by 45 documented fossil sinkholes. All 91 sinkholes opened as post-seismic events; the first one (the second biggest, 10.8x9.8 m in diameter and 3.6 m deep) started to open six hours after the strongest earthquake. The biggest sinkhole, 25x23 m in diameter and 11.7 m deep, opened seven days after the main earthquake and one day before the strongest aftershock; its total volume is larger than volume of all other 90 new sinkholes combined. The Mečenčani and Borojevići villages surroundings is the only area where a 4–15 m thick sequence of Holocene soil built of unsaturated low plasticity clays with gravel and sand interlayers and lenses covers the heavily karstified carbonate bedrock composed of alternating highly porous Miocene limestones and calcarenites. The unconfined aquifer within a soil is underlain by a well-permeable confined karst aquifer in which the water pressure during wet periods becomes subartesian to artesian, enabling significant erosion and formation of numerous caverns at the soil–limestone contact. Continuous removal of eroded sediment by groundwater flow through karstified systems in carbonates gradually expands cavernous space until a final cover-collapse. The 2020–2021 Petrinja earthquake sequence significantly accelerated these processes, resulting in 91 cover-collapse sinkholes opened during a three-months period, instead of usually one sinkhole opened every few years as reported by local people. It is interesting to note that during the strongest earthquake the water level in the unconfined aquifer was very close to the surface, and in the underlying karst aquifer artesian conditions prevailed.

Numeracy is the ability to understand and interpret numbers. The numeracy skills are required and relevant across all disciplines and especially in STEM majors. The diverse group of underrepresented students from two-year community college was assessed on the knowledge of basic units of measurement and unit conversions in this study. The student performance was assessed in a lesson plan administered during The Numeracy Infusion for College Educators (NICE) Program at Bronx Community College. The students were assessed to identify different units; to be able to perform basic unit conversions; to recognize the scale of bigger and smaller units; and associated vocabulary. The learning goals aimed at developing thinking skills in reading graphs, trends or patterns and develop a habit of comparing the values of measurement in one unit system to another system of units. The students were provided tools to remember the increasing and decreasing trends in the scale of units. The real-life examples were incorporated into the learning process. The assessment was administered at different stages of learning in this lesson plan. This study will present the assessment results of lesson plan administered to a diverse group of underrepresented students from various majors in community college education. The student performance assessment in this lesson plan highlights the urgency to incorporate tools and techniques for numeracy skills across the curriculum in various disciplines of higher education.

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

Social scientists have a long history of documenting disasters and natural extreme events’ behavioural response through the collection of perishable post-event data (Gruntfest 1977; Quarantelli and Dynes, 1977; Stalling, 1987; Quarantelli, 1997, 2003; Drabeck, 1999). Such empirical and theoretical foundations constitute a strong background to understand crisis responses and advance our knowledge of the drivers of human behavioural responses to fast evolving weather-related events. Outputs from this field of research show that public warning and behavioural response is a social process that takes several phases before a protective action is put in place (Mileti, 1995; Trainor et al., 2008, Parker et al., 2009, Lindell et al., 2004). These authors identified factors related to the characteristics of the hazard, the warning information characteristics, the situational and personal characteristics of the receiver and the socio-cultural context as strong determinants of the public behavioural response. In fast-moving events like flash-floods, the amount of time available to detect the threat and respond to it is so limited that protective actions often consist in dealing with contingent situations triggered by the irruption of dangerous circumstances in the middle of daily life activities and routines (Ruin et al., 2008, 2009; Terti et al., 2015). Understanding how people actually detect potentially dangerous circumstances and manage to timely adapt their routine to cope with the speed of the hazard evolution remains a challenge. Based on insights from post-event interviews, online surveys were used to quantitatively document behavioural responses associated with 3 catastrophic flash flood events that happened in southern France in 2014 and 2015. The coupled analysis of responses to these surveys with hydrometeorological parameters allows to better understand the link between the event magnitude and self-protective behaviours in the context of short-fuse weather events as flash floods. Knowledge gained from such an integrated approach is necessary for drawing lessons for the development of coupled human-natural system modeling and the prediction of the human vulnerability dynamics in short-fuse weather events.

The coupled Delft3D Flexible Mesh (D3D FM) - SWAN model with an unstructured grid has been developed recently to simulate storm surge and waves; and another open source model, Telemac-Tomawac, has been widely used for storm surge and wave propagation simulation in coastal areas for more than 20 years. However, the choice of a hydrodynamic model for a specific area in terms of cost, efficiency and accuracy is often a dilemma at the beginning of a modeling project. The objective of this research is to examine the effects of two software packages in terms of accuracy and performance with a case study in the Shanghai area of China. Model performance has been assessed based on model configuration, model calibration, grid generation and computational efficiency. Comparing measured water levels with model results, both approaches were able to accurately predict hydrodynamic conditions in a complex estuarine environment. Both models showed that it can efficiently simulate hydrodynamics in the coastal area under various scenarios for further climate adaptation research. Additionally, both models were used to simulate inundation propagation due to hypothetical failures of flood defenses in the coastal area. A comparison of inundation extent and maximum inundation depth showed that they were equally well-suited for overtopping and inundation simulation.

During the analysis - funded by The European Agricultural Fund for Rural Development (EAFRD) that has financed the EU’s contribution to rural development program in Umbria (Italy) – of meteorological data from automatic weather stations following the WMO requirements [1], it has become evident the need of using the newest global climatic data (ERA5, ECMWF [2]) to compute indices to perform an extended quality control over data and to give climate information to the end users. The peculiar regional environment with strong orographic modulation of the Umbria region and the consequent impact over the precipitation field together with the signal of a seasonal and intra-seasonal change of the temperature distribution, show a complex impact of the climate change over the Umbria region and broadly over Central Italy. This work is trying to show how to account for the impact of climate change over the phenology of vineyards using different indices as SPI , SPEI, and Winkler starting from the new reanalysis dataset with enhanced resolution in time and space. Moreover, within this project agronomists and plant pathologists are cooperating with meteorological scientists to analyze and relate weather epidemic development in order to reduce the economic impact and environmental effects of airborne plant disease epidemics and ultimately to make the end-users timely decisions about the effective and economical application of fungicides and about other tactics to manage plant diseases. [1] WMO. Guide to agricultural meteorological practices (WMO-no. 134). World Meteorological Organization: Geneva, Switzerland, 2010. [2] https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5

Deep learning techniques are used for capturing intricate structures of large-scale data by employing computational models of multiple processing layers that can learn and represent data with multiple levels of abstraction [1]. Such methods can include Convolutional Neural Networks, stacked auto-encoders and Long-Short Term Memory (LSTM) architectures. LSTM networks are suitable for dealing with time-dependent data through mapping input sequences to output sequences as it is done, for instance, in language modeling and speech recognition. One application that has recently attracted considerable attention within the geodetic community is the possibility of applying these techniques to account for the adverse effects of the ionospheric delays on the GNSS satellite signals. LSTM architectures model long-range dependencies in time series, making them appropriate for ionospheric modeling in GNSS positioning. This paper deals with a modeling approach suitable for predicting the ionospheric delay at different locations of the IGS network stations using the LSTM networks. We also incorporate a Bayesian optimization method for selecting the best configuration parameters of the LSTM network, thus improving network’s performance.

We used the process-based and tracer-aided ecohydrological model EcH2O-iso to assess the effects of vegetation cover on water balance partitioning and associated flux ages under temperate beech forest (F) and grassland (G) in Northern Germany. The model was tuned on the basis of a multi-criteria calibration against an unusually rich measured data set from a long-term monitoring site.. The calibration incorporates metrics of the energy balance, hydrological function and biomass accumulation. It resulted in good efficiency statistics for simulations of surface energy exchange, soil water content, transpiration and biomass production. The model simulations showed that the forest “used” more water than the grassland; from 620mm of average annual precipitation, losses were higher through interception (29% under F, 16% for G) and combined soil evaporation and transpiration (59% F, 47% G). As a result, groundwater recharge was greatly enhanced under grassland at 37% of precipitation compared with12% for forest. The model allowed us to track the ages of water in the different storage compartments and fluxes.In the shallow soil horizons, the average ages of soil water fluxes and evaporation were similar in both plots (∼1.5month), though transpiration and groundwater recharge were older under forest (∼6 months compared with∼3months for transpiration and∼12 months compared with∼10 months for groundwater). Flux tracking with Cl tracers provided independent support for the modelling results, though also highlighted effects of uncertainties in forest partitioning of evaporation and transpiration. This underlines the potential for tracer aided ecohydrological models in land use change studies. By tracking storage – flux – age interactions under different land covers, the effects on water partitioning and age distributions can be quantified and the implications for climate change assessed.Better conceptualisation of soil water mixing processes, and improved calibration data on leaf area index and root distribution appear obvious respective modelling and data needs for improved model results.

Changes in the quantity and quality of groundwater and water in the hydrological cycle in general have important implications for the evolution of water resources, the built environment, and terrestrial and aquatic ecosystems, globally. Exploitation of groundwater and other subsurface resources may lead to e.g. land subsidence, salt water intrusion, loss of important terrestrial and aquatic ecosystems and hence biodiversity. Together with biogeochemical flows of nitrogen and phosphorus and changes in the land-system and climate, these are currently considered the main environmental problems of the planet, which are breaching or close to breaching planetary boundaries. Changes in the hydrological cycle including groundwater is closely related to and affecting these changes. It is the ambition of the four GeoERA groundwater projects studying aspects of groundwater quantity and quality issues related to natural processes and human activities to further develop the European Geological Data Infrastructure as a leading information platform for groundwater data in Europe and one of the leading platforms, globally. Here we briefly present the contents and objectives of the four groundwater projects: HOVER - Hydrogeological processes and geological settings over Europe controlling dissolved geogenic and anthropogenic elements in groundwater of relevance to human health and the status of dependent ecosystems; RESOURCE - Resources of groundwater, harmonized at cross-border and Pan-European Scale; TACTIC – Tools for assessment of climate change impact on groundwater and adaptation strategies and VoGERA - Vulnerability of shallow groundwater resources to deep sub-surface energy-related activities. The four projects will deliver “FAIR” (Findable, Accesssible, Interoperable and Reusable) data and information via the European Geological Data Infrastructure easily accessible for all relevant endusers. This will improve our understanding of the subsurface and support common efforts for developing geoethical uses of the subsurface.

Apples and pears represent an important share of Belgium’s horticultural production but the sector has been subjected to a range of challenging meteorological conditions over the past decade. The most disastrous event was the extraordinarily severe frost night happening during peak flowering of commercially grown apple cultivars, in April 2017. This research aims at investigating how meteorological hazards in Belgium will evolve in space and time with changing climate and at challenging the claim of the insurance sector that the hazards are spatially invariant. It features the assessment of the probability and severity of frosts during the flowering period throughout the 21st century, using a set of regional climate model realizations from the CORDEX ensemble. To this end, the apple and pear tree phenology is modelled using the strong relation between air temperature during winter/early spring and dormancy break and consequently flowering. Local observations since 1950 of the phenology of major cultivars indeed confirm a shift in phenological stages in relation to warmer winters as an effect of climate change. Preliminary results from sequential phenological models, running on future daily mean temperatures, suggest that both the onset of flowering and the last frost event in spring are likely to occur earlier in the year, so that the challenge is to determine which trend is dominant and how the return periods of severe frost nights (< -2◦C) during sensitive stages evolve. Thereby, differences between cultivars and between orchard locations within Belgium are taken into consideration. Furthermore, the potential impact of shifting temperature regimes on the pollinator activity during the blossom period is taken into account, knowing that the most commonly commercially employed bee species are sensitive to colder and more variable temperatures. The distinct resulting hazard maps can be used to support decision making regarding adaptation and prevention measures at the field, farm and regional scales. Further downscaling will be possible by considering local pedologic and topographic conditions, while for validation, data about damage compensation claims will be used.

The most reliable way to develop poverty maps is through household surveys. However, in many developing countries, economic data from household surveys are infrequent. As a result, a data gap has emerged, making it impossible to identify and understand the locations of poor people. New approaches that integrate household survey data with non-traditional data sources (such as satellite imagery, call records, Wikipedia, google street view) using machine learning allows for improved resolution and scale in poverty mapping. Nevertheless, these studies developed poverty maps for study areas located in the same geographical region and did not map the location of poor people at the finest level (building level). Integrating income level from household survey data and building footprints from OpenStreetMap data with high-resolution satellite imagery, we extract the building rooftop images and classify them into two classes as poor(< = $5.50/day) and non-poor(> $5.50/day) based on the international poverty lines given by the world bank. We use these rooftop images as training data, develop a deep learning classification model, and estimate whether a building is poor or non-poor. We use physical factors like building area, elevation with rooftop images for transfer learning to contribute to the model’s accuracy. We attempt to build a versatile model that maps the locations of poor and non-poor people at the building level by developing, calibrating, and validating the model for Myanmar, Thailand, and Nicaragua study areas. Our findings show that for the model to travel well intra-regionally and inter-regionally, the study areas should be from the same geographical regions with similar roof types and percentages of people living in poverty. Combining the rooftop with income level is a fitting parameter to measure poverty, and roof color plays a crucial role along with the roof texture, shape to increase the model’s accuracy. The proposed methodology helps develop poverty maps for different income levels ($1.90, $3.20, $21.70, or any desired income level) from limited household survey data. This research study identifies the location and the total number of poor households living in a specific region which helps plan for effective poverty reduction, urban planning, and disaster prevention tailored to local conditions.

This project aims to quantify the resiliency of prairie ecosystems in the U.S. Pacific Northwest (PNW) to climate change. Prairies in this region sustain over one million beef cows, and cow­-calf production costs are expected to increase to offset warming-induced plant productivity loss. We investigated the above- and belowground effects of experimental warming in prairie ecosystems by assessing biogeochemical controls on and patterns of asymbiotic nitrogen fixation (ANF), plant species diversity, and legume cover to address a major challenge for sustainable agriculture in the region. We hypothesize that the effect of warming on prairie functional diversity increases soil asymbiotic nitrogen inputs by decreasing legume cover and soil nitrogen availability. We quantified the effects of decadal warming stress (+2.5ºC) on soil biogeochemical properties and plant species and functional diversity during fall and spring seasons in three sites along a 520­km latitudinal gradient—from central Washington to southern Oregon—representing a drought severity gradient. At each site, we collected composite soil samples from five co-­located prairie plots under control (ambient) and warming conditions. We incubated these soils using 15N-labeled dinitrogen (15N2), and quantified total soil carbon­, total and available nitrogen, and available phosphorus and iron pools to better understand the underlying mechanisms governing warming-­induced changes in ANF. We used a point intercept technique to survey plot-level plant community composition and calculate Shannon’s diversity index and percent cover of legumes (members of Fabaceae according to the Integrated Taxonomic Information System). Warming significantly decreased plant species diversity which also decreased along the drought severity gradient. Legume cover significantly increased from 3.1% in the north to 9.2% in the south. ANF response to warming varied by season and site, where rates increased with the drought severity gradient in the fall but decreased during the spring. Total soil inorganic nitrogen availability was the strongest predictor of ANF response to warming in the spring but not in the fall. Our study highlights the importance of using soil-plant-atmosphere interactions to assess prairie ecosystem resilience to climate change in the PNW.