Seismic hazard due to fluid invasion in hydraulic fracturing, wastewater disposal, and enhanced geothermal systems have become a concern for industry and nearby residents. Some of the challenges associated with the fluid-induced seismic hazard are the estimation of the spatial effects of these industry operations as well as the presence or absence of aftershock triggering. In some cases (e.g. Geysers, California, Hoadley gas field of Alberta), aftershocks triggering do occur, while in other cases (e.g. Soultz‐sous‐Forêts, France), this is not the case. First, to address the spatial effects, using several previously published high-resolution well-log data, we first show that there is a tendency that porosity within the basement resembles fractional Gaussian noise (fGn), while above the basement it resembles fractional Brownian motion (fBm). Based on this observation, we introduce a novel conceptual model of the fluid-induced seismicity in disordered porous media by integrating the notion of fluid diffusion and invasion percolation with spatially correlated permeability and porosity. We find that our model does not only capture the observed variations in frequency-magnitude distribution of seismic events but it also exhibits a much slower decay in seismic activity at large distances for fBm compared to fGn. Second, to address the presence of aftershock triggering, we also introduce nonlinear viscoelastic effects in our model to augment the failure mechanics. This allows us to test whether the presence or absence of aftershocks is coupled to the validity of a time scale separation between fluid dynamics and nonlinear viscoelastic response, for example. Our findings can be directly incorporated in the seismic hazard assessment related to fluid injections.

Electric field measurements inside thunderstorms are essential to our understanding of thunderstorm charge structure, electrification, and lightning initiation. However, most existing measurements have been made by single instruments carried aloft by weather balloon, thus providing measurements made at a single point that moves through the storm on a timescale of tens of minutes. It is therefore difficult to interpret such data, since a change in observed field strength may be due to motion of the balloon into a region with different field or due to overall evolution of the storm's electrical structure with time. Separation of such spatial and temporal variability requires simultaneous measurements at multiple locations within the storm. This can be accomplished with a single weather balloon by carrying multiple independent electric field dropsondes aloft and releasing them one at a time, separated by short time intervals. The balloon payload design is optimized for low mass and use of off-the-shelf components whenever possible, releasing each dropsonde by a hot wire cut-down mechanism. Each dropsonde spins as it falls, measuring electric field as it rotates and sends data to a ground station in real-time. The dropsondes are designed to fall and rotate stably by use of aerodynamic simulations, with internal components robustly connected along the axis of the instrument to ensure the desired balance and alignment of the principal axes of the moment of inertia. The telemetry transmitters use simple low-cost low-power all-in-one transmitter chips. The telemetry ground station receives signals simultaneously from all dropsondes by a single software-defined radio receiver. Robust long-range communication is enabled by use of spread spectrum techniques and error correcting codes.

Forest biodiversity has been declining across the globe due to anthropogenic activities. Losses of biodiversity have led to reduced forest health and ecosystem services. Therefore, it has become necessary to monitor changes in biodiversity over wide geographic areas. Remote sensing has the potential to monitor biodiversity changes, but the accuracy in which it can be estimated is under debate. In this study, we tested 1) the relationship among distinct metrics of biodiversity, 2) the role topographic measures have on determining biodiversity, and 3) the ability of hyperspectral remote sensing to estimate biodiversity in temperate forests of the Northeastern United States. We characterized biodiversity according to four different metrics: species, functional, structural, and phylogenetic diversity. All four metrics were quantified using species inventory data as well as Light Detecting and Ranging (LiDAR) to calculate additional indices of structural diversity. A digital elevation model was used to obtain measures of slope, aspect, and other topographic indices such as topographic wetness. Hyperspectral imagery was used to obtain reflectance, entropy, and several vegetation indices. In our analyses, species, functional, and phylogenetic diversity were shown to be moderately correlated suggesting similarities between the metrics while correlations between structural diversity and the other metrics were weak. The calculated topographic indices also showed weak correlations with the biodiversity metrics suggesting that topography does not influence measures of biodiversity at the plot level. Depending on the biodiversity metric, relationships between the hyperspectral analyses and biodiversity were weak to moderately strong. These findings suggest that hyperspectral imagery holds some potential for estimating multiple metrics of biodiversity.

Measurements and models of global geophysical parameters such as potential fields, seismic velocity models and dynamic / residual topography are well represented as 2D coloured / contoured maps. However, as teaching aids and for outreach, they offer little impact. Modern 3D-printing techniques help to visualize these and other concepts that are difficult to grasp, such as the intangible structures in the deep Earth. We developed a simple method for portraying scalar fields by 3D printing modified globes of surface topography, representing the parameter of interest as additional, exaggerated topography. This is particularly effective for long-wavelength (>500 km) fields. The workflow uses only open source and free-to-use software, and the resulting models print easily and effectively on a cheap (<300 GBP, 400USD) desktop 3D-printer. We have printed 3D representations of different scalar fields, including models of the surface topography of rocky planets, which can be used in outreach events. These objects are powerful to explain the importance of plate tectonics in shaping a planet. The workflow was extended to 3D scalar fields by analogy to Russian nesting dolls, where the audience can remove shallower layers to see how structures change with depth. We applied this to global seismic tomography models resulting in prototypes of “seismic matryoshkas” (see Figure). The tactile nature of these objects ensures that anyone, including the visually impaired, can explore the structures present deep within our planet

Mapping Titan’s surface albedo is a necessary step to give reliable constraints on its composition. However, even after the end of the Cassini mission, surface albedo maps of Titan, especially over large regions, are still very rare, the surface windows being strongly affected by atmospheric contributions (absorption, scattering). A full radiative transfer model is an essential tool to remove these effects, but too time-consuming to treat systematically the ~50000 hyperspectral images VIMS acquired since the beginning of the mission. We developed a massive inversion of VIMS data based on lookup tables computed from a state-of-the-art radiative transfer model in pseudo-spherical geometry, updated with new aerosol properties coming from our analysis of observations acquired recently by VIMS (solar occultations and emission phase curves). Once the physical properties of gases, aerosols and surface are fixed, the lookup tables are built for the remaining free parameters: the incidence, emergence and azimuth angles, given by navigation; and two products (the aerosol opacity and the surface albedo at all wavelengths). The lookup table grid was carefully selected after thorough testing. The data inversion on these pre-computed spectra (opportunely interpolated) is more than 1000 times faster than recalling the full radiative transfer at each minimization step. We present here the results from selected flybys. We invert mosaics composed by couples of flybys observing the same area at two different times. The composite albedo maps do not show significant discontinuities in any of the surface windows, suggesting a robust correction of the effects of the geometry (and thus the aerosols) on the observations. Maps of aerosol and albedo uncertainties are also provided, along with absolute errors. We are thus able to provide reliable surface albedo maps at pixel scale for entire regions of Titan and for the whole VIMS spectral range.

The upcoming Psyche mission, part of the NASA Discovery program, will investigate the largest known M-type asteroid, 16 Psyche. Previous research has suggested that 16 Psyche may be an exposed planetary metallic core. With cratering being a fundamental process in modifying the surfaces of all solid planets and satellites, one of the main objectives of the mission is to characterize 16 Psyche’s impact crater morphology. Consequently, in order to understand the cratering histories on M-type asteroids, it is essential to investigate how impact variables affect crater formation and crater scaling. We conducted experiments at the NASA Ames Vertical Gun Range that formed hypervelocity impact craters in copper and aluminum blocks with different impact angles, impact speeds, projectile sizes, and projectile materials. In the experiments, impact angles ranged from 15° to 90°, impact speeds ranged from 1.91 km s-1 to 5.69 km s-1, projectiles were either 6.35 mm or 12.7 mm in diameter, and the projectile materials were aluminum, Pyrex, quartz, basalt, and serpentine. Afterwards, 3D scanning was applied to investigate the morphometry of the resultant experimental craters. We analyzed the scans to measure diameter, cratering efficiency, length, width, and depth variations within the craters. Additionally, the crater scans will be analyzed for their morphology, including their planform, slopes, and other asymmetries. From there, we will use pi scaling to develop crater scaling relationships that enable the results of this analysis to be used to interpret crater size and morphology on metallic asteroids. These results can help in interpreting craters on 16 Psyche and on other M-type asteroids. In turn, these observations will also help generate a richer understanding of the formation and evolution of our solar system.

This study investigates how course design for a first year introductory class on climate change impacts student understanding of this global issue. The course was developed to situate the scientific knowledge of climate change alongside the political, economic, and social dimensions of this human-caused crisis. This intentional course development was to give students a more holistic understanding of the causes, impacts, and solutions. The curriculum is widely interdisciplinary with scientific concepts, like the functioning of the climate system and tools of climate system research, taught alongside politics, economics, media analysis, and social justice. A teaching as research project was conducted to assess how adding the sociopolitical context impacted student's ability to situate their knowledge of the scientific basis of climate change using systems thinking. As climate change, and scientific research, operate within the social and political landscape students became more informed citizens through learning these connections. Student participants in the study have a range of backgrounds and were all majors within the college of natural sciences during their first semester of college, though not all majoring in earth sciences

Scientific meetings organized by professional organizations have been keystone activities of scientific culture and career advancement. They provide opportunities to share research results, promote discussion on current and emerging research and education needs, apprentice early career participants into the community, and foster professional partnerships. However, scientific meetings are not equally inviting or accessible for all scientists, particularly those from historically marginalized communities. Organizers of scientific meetings have historically not ensured diverse representation of speakers and those in leadership roles, or have not provided needed networking opportunities and professional learning to foster scientists from historically marginalized communities, who often do not have the visibility or networking opportunities needed for persistence and success in a scientific career. As a result, scientific meetings can be an isolating and stressful experience. People from historically excluded identities can encounter structural barriers, such as lack of childcare or safe bathroom spaces, and can experience harassment and bullying. Within professional societies, policies and procedures as well as unwritten norms, can perpetuate bias and exclusion. For instance, certain attire, hair styles, and speaking tone may be targeted as counter to historical norms of professionalism, which were established before BIPOC and women entered the STEM fields in larger numbers. But these challenges also present opportunities to change. Scientific meetings can instead serve as influential intervention points to advance an inclusive environment and climate for geoscientists from across institutions, career stages, and backgrounds. We present a few actionable strategies that professional societies and convening organizations can take before, during, and after scientific meetings to make them more equitable, accessible, and anti-racist. We offer guidance for scientific meeting policies, procedures, awards systems, and leadership opportunities to build structure for inclusion. We also share recommendations for how professional societies can support members to advocate for more equitable and anti-racist culture within scientific meetings and at their home institutions.

The explosion of the Deepwater Horizon (DWH) oil platform in April 2010 on the Gulf of Mexico (GOM) released more than 700 million liters of crude oil. This catastrophe is the largest oil spill in US history and one of the largest in the world that resulted in over 1000 km of oiled shoreline across four different states (FL, AL, MS, and LA). When the floating emulsified oil approached sandy beaches, a portion of the emulsified oil interacted with suspended sediments and sank, forming submerged oil mats (tarmats). Since their formation, hydrodynamic processes fragmented the tarmats to form tarballs. The tarballs are highly mobile in the nearshore environment and can be found on GOM beaches to date. The objective of this proposal is to complete a comprehensive long-term chemical characterization study to investigate the fate of petroleum biomarker compounds used for chemical-fingerprinting of the tarballs collected at different times over the past 10 years along the Alabama shoreline. Petroleum biomarkers are geochemical organic compounds present in crude oils that can be related to their unique biological precursors. Currently, the most common method used for identifying and fingerprinting the oil spill source is characterizing these biomarkers. Hence, the long-term fate of three groups of biomarker compounds (terpanes, steranes, and triaromatic steranes) present in the tarballs collected from the Alabama shoreline is investigated for using them for fingerprinting purposes. There have been contradicting literature data on the degradation levels of these biomarkers, and this field-scale characterization study demonstrates that most of the investigated biomarkers remain recalcitrant after 10 years of natural weathering. We also quantified the changes in the concentration levels of toxic polycyclic aromatic hydrocarbons (PAHs) present in these tarballs, and the results show that many of these PAHs are recalcitrant. These results are consistent with previous studies that indicated that certain heavy toxic and carcinogenic PAHs, such as chrysene, can be recalcitrant and remain in the tarballs for a long time. Therefore, it is important to track the levels of these recalcitrant PAHs in the tarballs to determine their potential toxicity effects.

Empirical Mode Decomposition (EMD) is used to examine the relationship between precipitation and surface temperature from six regions. Three regions are defined by physiography: world, ocean, and land. The other three regions are defined by averaged precipitation: dry, normal and wet. Monthly averaged daily precipitation rate from the Global Precipitation Climatology Project are compared with average monthly surface air temperature anomalies from the Goddard Institute for Space Studies using EMD. The EMD process produces component time series referred to as intrinsic mode functions (IMFs). Theses IMFs are ordered by frequency from high to low. Eight IMFs were produced for each the time series. The first three IMFs corresponded to seasonal, semi-annual and annual variations, respectively. IMF 4 to 6 corresponded to a biennial, pentennial and decadal climate signals, respectively. IMF 7 was related to the broad 20-30 year period, with the trend being revealed in IMF 8. The time series spanned the period from January 1980 to December 2015 at monthly intervals. Temperature and precipitation time series from six sampling regions were analyzed for evidence of correlation. Results from the analysis reveal the following: (1) The EMD process reveals both linear and non-linear trends. The trends are not entirely consistent between regions though they are highly correlated. (2) Apparent wave-to-wave interactions between high and low frequency components appear to be observed in the IMF 1 and 2. These distortions appear to correspond to the troughs and peaks in the decadal cycle captured in IMF 6 and may related to the solar cycle. (3) The correlation between precipitation and temperature increases with increasing IMF number.

The National Geoscience Faculty Survey (NGFS) was designed to probe how faculty teach in undergraduate geoscience courses, learn about pedagogy and instructional content, and participate in the geoscience education and research communities. The survey has been administered four times in 2004 (n = 2207), 2009 (n = 2874), 2012 (n = 2466), and 2016 (n = 2615). The original survey was developed as part of On the Cutting Edge, a National Science Foundation (NSF)-funded professional development program for geoscience faculty sponsored by the National Association of Geoscience Teachers (NAGT). The 2016 survey was developed by a research team involving leadership of On the Cutting Edge, InTeGrate, and SAGE 2YC, with support from their NSF grants. The NGFS dataset is a unique and valuable longitudinal resource. One of our strategies for providing access to NGFS data is to publish a free-to-download report with response frequencies for all questions in order to make aggregate, baseline data available to the community and to place the NGFS in the context of other national datasets. The report consists of five chapters: 1) An introduction to the survey, including how it was designed and administered; 2) A description of survey respondents, including demographic data, the type and amount of teaching they do, and how they engage with the geoscience education and research communities; 3) The characteristics and topics of courses taught; 4) The teaching strategies used in introductory geoscience courses and courses for geoscience majors; and 5) How and why instructors learn about and change their teaching practices. Our demographic data suggest that the 2016 survey respondents represent about 25% of the population of college-level geoscience instructors in the US. The 2012 and 2016 respondents are a representative sample of the range of disciplines, institution types, and geographic locations of the larger population, but slightly overrepresent more senior faculty (professors and associate professors). Our analyses highlight differences in the nature of teaching and classroom practices between introductory and majors-level geoscience courses, and differences in participation in the geoscience community between instructors at different institution types (two-year vs. four-year).

A drought is a slowly developing natural phenomenon that can occur in all climatic zones and can be defined as a temporary but significant decrease in water availability. Over the past three decades, the cost of droughts in Europe has amounted to over 100 billion euros and it is expected to considerably increase as future droughts are projected to be more severe and long-lasting. Although drought monitoring and management are largely studied in the literature, they often fail at yielding precise information on critical events occurrence and associated impacts such as reduction of electricity production or crop failures. This is due to the difficulty of capturing the complexity of drought dynamics, which evolve over diverse temporal (and spatial) scales, including short-term meteorological droughts, medium-term agricultural droughts, and long-term hydrological droughts, as well as the non-physical aspects related to droughts (water management, irrigation, etc.). In this work, we contribute a Machine Learning based framework named FRIDA (FRamework for Index-based Drought Analysis) for the identification of impact-based, site-specific drought indexes. FRIDA is a fully automated data-driven approach that relies on advanced feature extraction algorithms to identify relevant drought drivers from a pool of candidate hydro-meteorological variables. Selected predictors are combined into an index representing a surrogate of the drought conditions in the considered area, including either observed or simulated water deficits or remotely sensed information about the state of the crops. FRIDA is portable across different contexts for supporting the formulation of basin-specific indexes to better inform drought management strategies. Several real-world examples will be used to provide a synthesis of recent applications of FRIDA in case studies featuring diverse hydroclimatic conditions and variable levels of data availability.

Simultaneous pressures of climate change and increasing populations in urban areas have resulted in new stresses on stormwater infrastructure. Altered precipitation patterns require robust and versatile management strategies for stormwater, resulting in increased consideration of greenspace as infrastructure for communities with significant flood risk. There is particular interest in natural, minimally-engineered green infrastructure (GI). Such greenspaces can be heterogeneous and difficult to characterize but are often straightforwardly modelled as black-box systems within a landscape. Many natural sites cannot be approached so simply due to highly permeable interfaces with surrounding landscapes and it is often impossible to monitor the surrounds at anywhere near the same spatial or temporal resolution as within the boundaries of a study site, resulting in uncertainty about the actual benefit of natural greenspace for adjacent communities. We explored water storage in an urban green space, identifying spatio-temporal patterns of internal dynamics to holistically understand site behavior. A dense sensor network in a prairie wetland nature preserve within the Chicago metro area produced 4+ years of high-resolution surface and subsurface water level, soil moisture, precipitation, and air and water temperature data. Responses to weather events in the short term and to climate-driven seasonal effects in the longer term are then described via the combination of GIS methods and signal processing approaches. Power spectral and cross-correlation analyses contribute understanding of relevant timescales for further investigation. Applying hydrograph analysis methods to water level time series yields important statistics about the response of water table elevations throughout the prairie complex, including baseflow elevations and relaxation times. These statistics are used to develop spatial maps of event response as a function of site properties and to identify seasonal effects. Understanding the expected response of stormwater storage in a natural greenspace to a precipitation event has valuable utility for conservation groups and stormwater management utilities. The synthesis of these methods contribute to development of planning tools for siting, design, and management of GI.

The Arctic is one of the regions in our planet with strongest warming observed and it is also almost certain to continue to change in the near future. The continuous change in key indicators of Arctic climate change (e.g. increase of temperature, intensification of the hydrological cycle, and shortening of the spring snow cover) will have marked consequences on ecosystem carbon (C) sink-source functioning. Such consequences are, however, broadly uncertain. Comprehensively integrated ecosystem models with long-term in-situ data are essential to understand the Arctic C cycle sensitivity to climate change and explore robust future scenarios. Our aim is to quantify the relative sensitivity of Greenland’s C balance to climate change based on regional variation in C and N cycling in a tundra gradient. The key roadblocks to this understanding have been limited time series of C fluxes, and limited regional data. Now with observations from multiple data streams measured by the Greenland Ecosystem Monitoring (GEM) program over the last two decades in conjunction with proven ecosystem and climate models we 1) analyse the underlying processes and links between present climate and terrestrial C and N cycling and 2) forecast the variation of plant phenology, productivity, and respiration forward in time. We use an established but novel C cycle model, the Soil-Plant-Atmosphere model, applied to two GEM wetlands relying on previous substantiated efforts on source-code model implementation, model calibration, and validation based on quality-controlled long-term data. Additionally, our modelling framework is now forced with future projections from the regional climate model HIRHAM5 specifically designed to characterize the Greenland domain (typically left behind in global modelling analyses) following the IPCC greenhouse gas emission scenarios. We ask the ecological question: How sensitive is the C balance expected to be under warmer and wetter conditions forecasted for the 21st century? Although still preliminary, we found strong evidence that the net C exchange will be significantly exposed to higher temperatures and intensified precipitation levels increasing 10-80% the C sink strength by the end of the century, but lengthening of the growing season and nutrient availability will also play a significant role.

Many resource management studies focus on one resource. Humans, however, rely on multiple resources in a complicated way. A person may derive more well-being from one unit of a resource than from another; one resource may be substituted by another to some degree. How should one manage such coupled natural-human systems? In this work, we build on recent research that focuses on developing conceptual frameworks and mathematical models to understand such interactions. The multiple resource condition injects the concept of substitutability into models of coupled human-natural systems and affects how such systems should be governed. Substitutability has been mostly mentioned in the field of economics for a substitution of natural and human capitals. Similarly, one resource may substitute for other scarce resources in coupled human-natural systems since some of these resources are not completely independent. In this study, we revise and expand an existing conceptual framework to include two natural resources, resource users, governing agency and public infrastructure in a centralized governance structure, i.e., all the natural resources are managed by the same governing entity. We then devise a set of dynamical equations and relationships from different fields, such as a replicator equation, a population equation, and a CES production equation, to capture the dynamics of this coupled system. This analysis can provide a decision-support tool to design policies to sustainably govern the built environment where human, natural resources, and infrastructure are interconnected. Model analysis takes a multi-faceted perspective of both resource users and governing entities to assess policies against different levels of disturbance. The results reveal how substitutability and asymmetry in resource use affect the viable policies needed to maintain the system.

Spacecraft charge mitigation is critical for a host of space plasma measurement techniques. However, charge mitigation in tenuous space plasmas can be a difficult problem. It is especially difficult and essential during active experiments that feature ion or electron beams, as collection from the ambient plasma is often insufficient to balance the beam emission current. For electron emission experiments, the use of a plasma contactor that emits an ionized gas is the only practical option. A series of parametric chamber experiments were completed to address how spacecraft charge mitigation using a plasma contactor may scale in tenuous space plasmas. Experiments focus on how spacecraft potential scales with beam emission current, contactor current (the rate at which the contactor generates quasi-neutral plasma), and contactor expellant mass (ion mass). These experimental results are compared to scaling laws derived via Curvilinear Particle-In-Cell (CPIC) simulations for further validation and physical insights. Implications for improving space plasma measurements and enabling future active experiments such as the Connections Explorer (CONNEX) mission are discussed.

The Java margin is part of the 5600 km long Sunda Arc subduction zone that extends from Sumatra to the Lesser Sunda Islands and is dominated by earthquakes with magnitude less than 7. Although several 7.0 ≤ Mw < 8.0 earthquakes have occurred, there has been an absence of Mw>8. Previous earthquake relocation studies have mainly focused on the ml ≥ 4 earthquakes, while seismic tomography studies have mainly focused on the volcanic system in Central and East Java. In this study, we aim to image the entirety of Java margin to investigate segmentation along this margin. We use the arrival time dataset from 2009 – 2018 collected from 44 stations in the BMKG national seismic network to relocate earthquakes and invert for seismic velocity structure along the Java margin using a finite-difference tomography algorithm. A total of 6041 earthquakes, 68250 P- and 22795 S-phases, ml 1.9 – 7.5, were included in the inversion, resulting in 4883 high-quality relocations. The distribution of relocated events shows several isolated clusters of seismicity at the trench, which are distributed nearly vertical, from the near-surface to 80 km depth. Feature with Vp/Vs ~ 1.73, which higher compared to value along the trench, coincides to one of the isolated clusters. Gaps are observed between bands of seismicity at the trench and beneath the forearc region. The seismicity is distributed surrounding or between the high anomalies of residual bathymetry which represent the structure of the subducting slab. Beneath the forearc, bands of seismicity are observed between 30 – 80 km depth and below 100 km depth. Their distribution reveals a steeper slab geometry relative to previously published slab models (Slab 1.0 and Slab 2.0). Several features with Vp/Vs < 1.70 and higher Vp and Vs than the surrounding area coincides with the bands of seismicity observed between 30 – 80 km depth. Shallow structure is also well defined by the earthquake relocations outlining three major faults (Cimandiri, Kencana-Rakutai faults, and an unnamed fault located east of Opak fault). The relocation results and velocity structure show that the distribution of seismicity along the subduction zone is segmented. This segmentation is likely related to the structure of the subducting plate.

China is home to 12% of the world’s grasslands; second largest in the world. The productivity of Chinese grasslands rapidly declined from 10% degraded in 1970 to 90% degraded by the 2000s. Inner Mongolia, China, is composed of 67%, mainly temperate, grasslands. From 1947-2009 human and livestock populations in Inner Mongolia increased from 5.6-24.7 million and 8.4-96.0 million, respectively. These changes were not only due to rising population increasing food demand, but also policy and lifestyle changes. Though climate change and overgrazing are dominant drivers of degradation, research shows that policies increasing privatization of land rights have significant impacts, namely the Household Production Responsibility System (HPRS) of 1949. In China, grassland degradation is linked to environmental problems of sandstorms, losses of biodiversity and carbon sinks, and droughts. Grassland degradation led to widespread public health concerns including dust storms originating from the arid rangelands of Inner Mongolia that swept pollution across major population centers in China and other Asian countries. Inner Mongolia climate change is evident: from 1951-2009 mean temperature increased at 0.28°C per decade, doubling the global linear warming trend of 0.138°C. This temperature increase was accompanied by a 9.9 mm decrease in precipitation per decade, resulting in decreased gross primary productivity measured by Normalized Difference Vegetation Index (NDVI). This study aims to quantify the Balagaer River Watershed degradation in Inner Mongolia, China from 2000-2019 and explore correlations between climate change, NDVI values, and degradation status. Researchers used GIS analysis of MODIS NDVI data and regressions of NDVI on precipitation and potential evapotranspiration (PET). Differences in NDVI were calculated between years and classified degradation into three categories: slightly, moderately, and heavily degraded. These calculations quantified the percentage of the watershed occupied by each category. Correlations between NDVI and precipitation and NDVI and PET were conducted using Excel at three temporal scales: annual, pre-growing season (October to April), and growing season (May to September). It is expected that decreased precipitation contributed to decreased NDVI.