Regrowth after fire is critical to long-term persistence of chaparral shrub communities in southern California. This region is subject to frequent fire, habitat fragmentation, and protracted droughts linked to climatic change. Short-interval fire (SIF) is considered an inhibitor of recovery and cause of “type conversion” in chaparral, based on field studies of small extents and limited time periods. Sub-regional scale investigations based on remotely sensed data, however, suggest that SIF may explain little variance in postfire chaparral recovery. Drought may contribute to poor recovery or worsen the impact of repeated, short-interval fires. Previous studies have not shown whether drought reduces chaparral recovery significantly across the region, while variations in response among community types and climate zones are not well resolved. This research evaluates a regional pattern of chaparral recovery, based on series of Normalized Difference Vegetation Index (NDVI) from annual, June-solstice Landsat images (1984–2018). High resolution aerial images were used in validation and calibration. The main objectives were (1) to assess effects of fire-return interval and number of burns on chaparral recovery using 0.25 km2 sample plots (n = 528) which were paired and stratified for experimental control, and (2) to explain recovery variations across the region based on geospatial climate, vegetation, soil, terrain, and temporal drought metric data (seasonal precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index (PDSI)) from 982 locations. Results suggest that SIF is most impactful in sites that burned three times within 25 years. More substantial effects were observed due to drought. In particular, ecotonal chaparral bounding the Colorado Desert is most subject to drought impact. We also highlight utility in landscape-scale predictors of drought impact on recovering chaparral, including Very Atmospherically Resistant Index (VARI).

Since the inception and realization of the Global Positioning System (GPS) in the 1970-1980s, the Global Satellite Navigation System (GNSS) has become a ubiquous tool in civil, business, and scientific life. Major breakthroughs in our understanding of dynamic Earth processes were only achievable through this precise positioning technology. While positioning is the chief objective of the system, the nature of its design requires satellite signals to traverse the ionosphere and the troposphere, and results in signal reflections off the ground. In addition to crustal dynamics, this enables the study of the atmosphere and local environmental sensing, impacting fields far beyond solid earth research, including space physics, atmospheric science, glaciology, hydrology, and natural hazards. In this paper I review some of the history of this technology and its impact on the Earth sciences. Using the example of GPS, I introduce how satellite positioning systems work and how we can infer precise positions from the signals broadcast by the satellites. For this, I give an overview on reference systems, different observation models, the predominant precise positioning strategies and how the various error terms can be corrected. Once a solid understanding of precise positioning is developed , I present some of the complications that arise in high-rate (1 or more sample per second observations) sub-daily and real-time kinematic positioning, which is of great utility in the characterization and monitoring of many natural hazards. GNSS enables observations beyond precise positioning. I provide background and observation models for instantaneous velocity estimations, useful in real-time applications particularly where precise orbits and intial positions are not available, and GNSS reflectometry, which allows to perform local environmental sensing around GNSS monuments, including the inference of snow depth or tidal heights. Throughout the paper, each method is illustrated by a number of applications either from the literature or novel work. The focus is on some highlights from the last 1 2 Ronni Grapenthin decade of geodetic work, with a clear slant towards examples from solid Earth and hydrologic research.

Evidence from Arrokoth and comets strongly suggests a very low density for this and similar small Kuiper belt objects. Plausible compositions imply very high porosities, in excess of 70%, and low compaction crush strengths. If so, craters on Arrokoth (especially Sky, its largest) formed largely by compaction of pore space and material displacement. This is consistent with geological evidence from New Horizons imaging. High porosity reduces cratering efficiency in the gravity regime whereas compaction moves it towards a crush strength scaling. Compaction also guarantees that most impactor kinetic energy is taken up as waste heat near the impact point, with momentum transferred to the rest of the body by elastic waves only. Monte Carlo simulations of Sky-forming conditions indicate that the momentum imparted likely separated Arrokoth’s two lobes, but displacement was limited by dissipation at the neck. Unusual strength properties are not required to preserve Arrokoth’s bilobate configuration.

Evaluation of spatially distributed crop coefficient (Kc) for estimating evapotranspiration (ETc) based on remotely sensed imagery has become an essential topic in managing the demand for agricultural water. Currently, satellite (MODIS, Landsat, etc.) imageries are not insufficient to detect variability within the small agricultural field due to its lack of desired spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with various sensors like Multispectral (MS), Thermal, and Hyperspectral cameras is becoming an emerging technology to overcome these limitations over small agricultural fields. A field experiment is carried out in the Agricultural and Food Engineering (AGFE) Department, IIT Kharagpur, to estimate Kc over the small Agri. Field using UAV-based MS cameras during Kharif (monsoon) 2019-2020 season. Lysimeters are used for estimating daily ETc for conventionally irrigated paddy crops. Reference evapotranspiration (ET0) is also calculated using the weather data of the study area. High-resolution multispectral imageries are acquired using a quad-copter UAV. The imageries are pre-processed using Pix4Dmapper software, and various vegetation indices (such as NDVI, TNDVI, NDRE, RVI, GNDVI, and LCI) are evaluated. The vegetation indices (VIs) are correlated with ground truth Kc values and spatially distributed Kc maps for the whole study area are generated based upon the excellent correlation between the VIs and ground Kc. The spatial Kc maps clearly show the variation in Kc within the plots and will be helpful for the calculation of Kc for any field without a lysimeter experiment. Generated Kc maps describe the crop water demand by visual color variations within the field. This approach may be helpful in understanding the variability in crop water requirements within the field Keywords: UAV, Crop Coefficient (Kc), Crop Evapotranspiration (ETc), Vegetation Indices, Remote Sensing.

The common-pool nature of groundwater resources creates incentives to overpump that contribute to their rapid global depletion. In transboundary aquifers, users are separated by a territorial border and might face substantially different economic and hydrogeologic conditions that can alternatively dampen or amplify incentives to overpump. We develop a theoretical model that couples principles of game theory and groundwater flow to capture the combined effect of well locations and user asymmetries on pumping incentives. We find that user asymmetries in either energy cost, groundwater profitability or aquifer response tend to dampen incentives to overpump. However, combinations of two or more asymmetry types can substantially amplify common-pool overdraft, particularly when the same user simultaneously faces comparatively higher costs (or aquifer response) and profitability. We use this theoretical insight to interpret the emergence of the Disi agreement between Saudi Arabia and Jordan in association with the Disi-Amman water pipeline. By using bounded non-dimensional parameters to encode user asymmetries and groundwater connectivity, the theory provides a tractable generalized framework to understand the premature depletion of shared aquifers.

In this comment, we clarify that the specific contributing area is defined in the limit of an infinitesimal contour length. We also confirm that not all solutions obtained from the minimalist landscape evolution model of Bonetti et al. (2020) are rescaled copies of each other because of the crucial role of the boundary conditions. We use dimensional analysis and numerical simulations for a case where only one horizontal length scale enters the physical law to establish this point.

In the present work, we analysed different types of clouds observed at the limb as well as over the surface of the red planet, captured by MCC onboard the Indian Mars Orbiter Mission during orbit number 44,45,48,49, 50,51,179,187,190,191,466 and 474. The estimated height of the high-altitude cloud (HAC) varies from 40 to 76 km and horizontal spreading of 400 to 1100 km. We used ARC-GIS to detect the area over which the high-altitude clouds are observed. These detached layers of clouds are followed by a haze of dust and water-ice particles. MRO-MCS and MARCI confirm a few dust events during the appearance of the high-altitude cloud. The estimated wavelength of the Lee wave cloud varies from 38 to 44 km while wind speed varies from 54 to 64 m/sec. Aerosol Optical Depth (AOD) as a function of altitude varies from 0.6 to 2.4 for the red channel and 0.8 to 1.8 for the blue channel with an effective radius of 0.2 to 3.0 μm for the aerosol. We consulted the Global Circulation Model (GCM) as well as MOLA-DEM to frame the AOD output. Estimated wind speed profile from MCC and MARCI-MGDM suggested the circulation of strong wind across the globe and delivered input to the formation of clouds through different processes related to the atmosphere dynamics viz. Deep Convection, Orographic lift, Thermal updraft, etc. In our present study, we tried to estimate the parameter related to these processes and tried to correlate them with the observed events.

Atmospheric gravity waves (GWs) during the February 2018 sudden stratospheric warming (SSW) were simulated using the T639L340 whole neutral atmosphere general circulation model. Their characteristic morphology around the drastically evolving polar vortex was revealed by three-dimensional (3D) visualization and ray-tracing analyses. The 3D morphology of simulated GWs was described for the three key days that represent the pre-SSW, the mature stage for the vortex splitting, and the late SSW. The combination of strong winds along the polar vortex edge and underneath the tropospheric winds with similar wind directions consisted of the deep waveguide for the upward-propagating GWs, forming GW hot spots in the middle atmosphere. The GW hot spots associated with the development of the SSW were limited to North America and Greenland, and they included the typical upward-propagating orographic GWs with relatively long vertical wavelengths. Different types of characteristic GW signatures were also recognized around the Canadian sub-vortex (CV). The GWs having short vertical wavelengths formed near the surface and obliquely propagated over long distances along the CV winds. The non-orographic GWs with short vertical wavelengths formed in the middle stratosphere through the spontaneous adjustment of flow imbalance around the CV. Those GWs cyclonically ascended into the mesosphere along CV winds.

This paper presents a novel approach and algorithm to the problem of magnetic field interference cancellation of time-varying interference using distributed magnetometers and spacecraft telemetry with particular emphasis on the constrained computational and power requirements of CubeSats. The traditional approach to enable space-based low-amplitude and low-noise magnetometry is to develop a spacecraft magnetic cleanliness design and place the magnetometer sensor at the end of a boom far enough away from the bus to minimize remaining stray magnetic fields. In addition, secondary magnetometers are often placed partway along the boom to apply magnetic field gradiometry to clean the data further (e.g., NASA MMS has 8 meter booms with a sensor half-way down and another at the end). We employ a different approach taking advantage of low-cost chip-based magnetometers that can be placed throughout the satellite bus instead of utilizing a boom. The spacecraft magnetic field interference cancellation problem that we solve involves estimation of noise when the number of interfering sources far exceeds the number of sensors required to decouple the noise from the signal. The proposed approach models this as a contextual bandit learning problem and the proposed algorithm learns to identify the optimal low-noise combination of distributed magnetometers based on indirect information gained on spacecraft currents through telemetry. The algorithmic behaviors are tested with synthetically modeled spacecraft data and on real world data generated in a lab-based setting with telemetry and currents collected from the GRIFEX CubeSat and provides a way for accurate magnetic field measurements with CubeSats.

Demand for lithium for batteries is growing rapidly with the global push to decarbonize energy systems. The Salar de Atacama, Chile holds ~42% of the planet’s reserves in the form of brines hosted in massive evaporite aquifers. The mining of these brines and associated freshwater use has raised concerns over the sustainability of lithium extraction, yet large uncertainties remain regarding fundamental aspects of governing hydrological processes in these environments. This incomplete understanding has led to the perpetuation of misconceptions about what constitutes sustainable or renewable water use and therefore what justifies responsible allocation. We present an integrated hydrological assessment using tritium and stable oxygen & hydrogen isotopes paired with remotely sensed and terrestrial hydroclimate data to define unique sources of water distinguished by their residence time, physical characteristics, and connectivity to modern climate. Our results describe the impacts of major drought on surface and groundwaters and demonstrate that nearly all inflow to the basin is composed of water recharged >65 years ago. Still, modern precipitation is critical to sustaining important wetlands around the salar. Recent large rain events have increased surface water and vegetation extents and terrestrial water storage while mining-related water withdrawals have continued. As we show in this basin, poor conceptualizations of these complex hydrological systems have perpetuated the misallocation of water and the misattribution of impacts. These fundamental issues apply to many similar regions globally. Our new framework for hydrological assessment in these arid basins moves beyond calculating gross inputs-outputs at a steady-state to include all compartmentalized stores that constitute “modern” budgets.

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1 to 14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend which increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems, and adds to the growing body of work regarding the predictability of ecological systems broadly.

Water stress mapping in crops and its spatial disparity study at field scale is important for precise management of irrigation. Results obtained from conventional airborne practice (balloons, airplanes, and satellites) are less acceptable for timely irrigation management due to lack in spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with multispectral (MS) and thermal cameras with higher spectral and temporal resolutions can be used as a promising tool for preparing water stress maps under different water deficit conditions. In this study, Water Deficit Index (WDI) maps are generated at different days after sowing (DAS) in wheat crops under three different water conditions (WI (well water), WS1(irrigation at 5 days’ interval), and WS2 (irrigation at 11 days’ interval)) using the concept of Vegetation Index Trapezoid (VIT) using UAV based thermal and MS imageries. The UAV is flown at 60m altitude during the Rabi season 2018-19. After pre-processing of images in Pix4dMapper, nine vegetation indices are calculated from MS images and one of the indices, Normalized Green Red Difference Index (NGRDI) is selected based on the higher correlation with ground truth data (R2 greater than 0.5) and visual interpretation according to the real field condition to construct the VIT. Vegetation index and temperature values are calculated for four points of VIT by using four boundary conditions such as bare soil with (1) dry and (2) wet conditions, and full vegetation with (3) well-watered and (4) water stress conditions. By using the ArcGIS, geo-referencing of thermal images with respect to MS images is done to get the exact overlap of both images, and resampling of thermal and MS images are also carried out to get the same pixel size. WDI values are estimated using VIT of the surface-air temperature difference and NGRDI, and WDI maps are generated from the UAV-based thermal and MS imageries for potential detection of crop water stress. The conventional Crop Water Stress Index (CWSI) which is solely based on the crop canopy temperature is outperformed by the WDI, which is integration of composite land surface temperature (LST) and degree of greenness, and could be effective enough for irrigation water management. Keywords: UAV, Multispectral and Thermal imageries, NGRDI, WDI, and Wheat crop.

Our study uses field training data, airborne LiDAR (Light Detection and Ranging), imaging spectroscopy, and a Convolutional Neural Network (CNN) classifier to identify individual tree species in a mixed conifer forest in the Southern Sierra Nevada Mountains. The remote sensing data was collected on the National Ecological Observatory Network (NEON) Airborne Observation Platform in 2017. We trained the classifier using existing field plot data, and an independently collected validation dataset which identifies trees location of the 7 dominant species (Pine, Fir, Cedar and Oak), including condition and ‘live’ or ‘dead’ status. The LiDAR canopy height model was used to identify tree crowns and imaging spectroscopy data around these crowns were created as image ‘labels’. These species level ‘labels’ were used to train, test and validate a CNN tree species classifier. Our method achieved greater than 63-90% accuracy for all field validated stems and worked best for large diameter trees. On an independent tree stem dataset, we performed a species-level logistic regression to study which cases the classifier works most and least effectively. Spatially, in the southern areas scattered Black Oak were present and tree species often were confused with shrub species or covered by adjacent conifer species. In the north where the upper elevation forest is dominated by red and white fir the classifier achieved greater than 96% accuracy for larger canopy trees, with accuracy degrading to about 59-70% when smaller trees are included in the model. There is also genus level mis-classification, particularly between Red and White Fir species. There was high tree mortality in this forest and the classifier was effective in detecting large tree mortality, which also varies as a function of species and size of trees. This work leverages newly developed ‘deep learning’ tools which have yet to be extensively applied to the remote detection of large trees or in plant biogeography generally. This research is a proof-of-concept for forest community ecologists who want accurate ‘tree species maps’ to study how plants are distributed across space. Generally, this method will be of interest to biogeographers or remote sensing scientists looking to apply novel classification methods to problems beyond remote tree species identification.

This study develops a long short-term memory mode (LSTM) neural network algorithm for the prediction of tropical cyclone intensity (maximum wind speed). This is achieved by combing the statistical tools of machine learning and the dynamical earth system model of Typhoon Mangkhut (2018). The Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System is used to produce an ensemble of runs of Mangkhut to train the LSTM. Being able to predict the behavior of tropical cyclones allows for a way to analyze the complex dynamical inputs to investigate the driving forces behind periods of rapid intensification, stagnation, and weakening. The controlling parameters that produced the best prediction are the mean inner surface heat flux, amplitude of the first order asymmetry in the inner heat flux, the angle difference between the first order asymmetries of vorticity output at 850mb and 500mb, and the amplitude of first order asymmetry in the geopotential height at 850mb.

We perform physics-based simulations of earthquake rupture propagation on geometrically complex strike-slip faults. We consider many different realization of the fault roughness and obtain heterogeneous stress fields by performing dynamic rupture simulation of large earthquakes. We calculate the Coulomb failure function (CFF) for all these realizations so that we can quantify zones of stress increase/shadows surrounding the main fault and compare our results to seismic catalogs. To do this comparison, we use relocated earthquake catalogs from Northern and Southern California. We specify the range of fault roughness parameters based on past observational studies. The Hurst exponent (H) varies in range from 0.5 to 1 and RMS height to wavelength ratio ( RMS deviation of a fault profile from planarity) has values between 10-2 to 10-3. For any realization of fault roughness, the Probability density function (PDF) values relative to the mean CFF change show a wider spread near the fault and this spread squeezes into a narrow band as we move away from fault. For lower value of RMS ratio ( 10-3), we see bigger zones of stress change near the hypocenter and for higher value of RMS ratio ( 10-2), we see alternate zones of stress increase/decrease surrounding the fault to have comparable lengths. We also couple short-term dynamic rupture simulation with long-term tectonic modelling. We do this by giving the stress output from one of the dynamic rupture simulation (of a single realization of fault roughness) to long term tectonic model (LTM) as initial condition and then run LTM over duration of seismic cycle. This short term and long term coupling enables us to understand how heterogeneous stresses due to fault geometry influence the dynamics of strain accumulation in the post-seismic and inter-seismic phase of seismic cycle.

We employ a multi scale approach combining experiments and modelling to elucidate the impacts of small scale (sub-seismic resolution <10m) capillary pressure heterogeneities in field scale CO2 flow and trapping. We analyse 48 rock cores (~3cm length, 4cm diameter) covering the entire 100m interval of the Captain D sandstone in the Goldeneye field, UK North Sea. We experimentally measure porosity, capillary pressure, absolute permeability, relative permeability and trapping characteristics for the cores, which are used to create 3D numerical models with heterogeneities defined at the mm scale. These models are validated by predicting experimental X-Ray CT observations of saturation (mm scale) and pressure measurements (cm scale) at various flow rates and fractional flows of gas-water. The intrinsic, core scale properties are then used to populate 2D meso scale numerical simulations (50m x 10m size), with heterogeneities defined at cm scale using a geostatistical representation. We vary the correlation length and variance of the fields within the bounds of the experimental observations to investigate the impacts of small scale heterogeneity on vertical and lateral CO2 plume migration under different Capillary, Bond and Gravity numbers. At low flow potential, layered capillary pressure heterogeneities can speed up lateral plume migration by up to 20%, with gravitational segregation significantly enhancing the migration (See Fig. 1). In the vertical case, layered heterogeneities can significantly increase CO2 trapping, which is further enhanced with the inclusion of capillary pressure hysteresis. Finally, we derive capillary limit, upscaled equivalent properties from the meso scale simulations, which incorporate the impacts of small scale heterogeneity. These are used to represent the grid block properties in a full field 3D numerical model of the Goldeneye field (lateral ~5 km, depth 200m). We analyse the impact of varying relative permeabilities (with anisotropy) on the field scale plume migration and trapping. We see large differences compared to cases using intrinsic rock properties, indicating that proper inclusion of small scale heterogeneity is needed in upscaling workflows when predicting and assessing uncertainty in low flow potential CO2 plume migration at the field scale.

VirES is a Virtual workspace for Earth-observation Scientists, a service provided by the European Space Agency (ESA). VirES has firstly been established for ESA’s magnetic field mission Swarm as “VirES for Swarm’‘ and has been extended to ESA’s atmospheric dynamics mission Aeolus, which was launched in August 2018. The service is developed by the Austrian IT company EOX in strong collaboration with missions’ scientists. VirES is a web-based service (https://aeolus.services) that enables scientists to discover, visualize, select and download data of Earth-observation (EO) missions through an easy to operate graphical user interface. ”VirES for Aeolus” will provide access to Aeolus L1B, L2A, L2B, L2C products and auxiliary data. The first version 1.0 passed acceptance tests in April 2018 and developments towards Version 1.2 are in progress. The service is planned to be accessible for public use as soon as the mission’s phase E1 is completed and first data products are released by ESA.

Understanding and communicating the impact of uncertainty on scientific understanding is a critical unmet need in Earth Science. Challenging as uncertainty determination is for “low level” data, the impact of further processing must be understood, particularly when integrating diverse data types. For example, a fundamental source of diversity is data’s spatiotemporal distribution, for which Point, Grid, and Swath are the most important overarching geographical types. To bring different kinds of data together or bring observational data and model simulations together, observation and simulation values must be “regridded” onto the same grid, i.e. onto a comparable spatiotemporal representation. At the finest level, this requires a detailed understanding of instrumental fields of view and sensitivities. But regridding itself affects uncertainty, especially in situations where significant or irregular interpolation or even extrapolation are required. Furthermore, care must be taken combining diverse data lest the integrated product inherit the worst uncertainty characteristics of each. We have been developing capacities for indexing, regridding, and integrating observation and model simulation that scale to the size and diversity of Earth Science data. In this presentation, we review the fundamental problems associated with combining big, diverse Earth Science data for integrative analysis and how to quantitatively assess and propagate the uncertainties introduced. In particular, uncertainties associated with regridding diverse data for various popular grids and regridding schemes will be assessed.