The Southwest Watershed Research Center (SWRC) of the United States Department of Agriculture-Agricultural Research Service has been conducting arid and semiarid (dryland) watershed research since 1953. This included establishment and continuous operation of the Walnut Gulch Experimental Watershed (WGEW) in southeast Arizona. The 149 km2 ephemeral watershed is one of the most densely instrumented dryland research catchments in the world with a drainage area greater than 10 km2. This instrumentation captures many aspects of the hydrological cycle including how precipitation is partitioned into soil moisture, runoff and evapotranspiration and its subsequent effects on sediment transport, vegetation productivity, carbon sequestration, and groundwater recharge. The long-term, high-resolution record of observations on the WGEW enables understanding of the mean and variability of the hydrological processes, not well characterized with shorter term records, that fail to capture the large variability common to dryland regions. This presentation will highlight trends in temperature, precipitation, and runoff over the WGEW observation period. Additional research findings made by the SWRC and collaborators on erosion; plant productivity and carbon sequestration; the facilitation of soil water redistribution by plant roots; and groundwater recharge will also be presented.

Detailed analysis of volcanic thermal and gas emissions over time can constrain subsurface processes throughout the pre- and post-eruption phases. Time series analyses are commonly applied to high temporal datasets like the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is the first study using the entire Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) twenty-plus year archive. The ASTER archive presents a unique opportunity to quantify volcanic precursors and processes. The spatial, spectral, and radiometric resolution of its thermal infrared (TIR) subsystem allows detection of very low-magnitude surface temperature anomalies and passively emitted small gas plumes. We developed a new statistical algorithm to automatically detect these subtle anomalies and applied it to five recently active volcanoes with well-documented eruptions: Taal (Philippians), Popocatépetl (Mexico), Mt. Etna (Italy), Fuego (Guatemala), and Kluichevskoi (Russia). More than 3,300 ASTER level-1 terrain corrected (L1T), registered, radiance-at-sensor images were downloaded from the NASA EARTHDATA website. These were screened for significant summit cloud coverage, which removed approximately 25% of scenes. The remaining were converted to brightness temperature and a median background temperature per scene was determined from an annulus around the active crater to produce the temperature above background. The algorithm creates a rejection criterion value defined by the median absolute deviation to identify the thermal anomalies. The size and intensity of these anomalies as well as the detection, composition, emission rate of small plumes are retrieved one year prior to the known eruptions for each volcano to identify all precursory signals. The results of this study have the dual purpose of constraining volcanological processes that lead to eruptions as well as providing training data for machine learning modeling. Machine learning is an effective and well-established technique that provides rapid classification of volcanic activity such as thermal anomalies that exceed a certain size and/or intensity. The comparison of these two approaches is documented in a companion abstract in this session.

As it is known, Space Weather (SWx) phenomena can have dramatic impact on satellite navigation and HF radio communication systems, being also responsible for increases on radiation levels at flight altitudes. For this reason, in recent years the International Civil Aviation Organization (ICAO) has been showing great interest in operational SWx services for aviation purposes in these three domains. Four global SWx centers have been then appointed since November 2019 by ICAO to provide real-time SWx advisories for aviation users. In particular, HF COM conditions are assessed by monitoring the F2-layer critical frequency foF2 or the MUF(3000) ionospheric characteristic (MUF = Maximum Usable Frequency), the latter representing the highest HF radio frequency that can be used for communications over a standard distance of 3000 km via F2-layer ionospheric reflection. As one of the designed SWx centers, several key operational 24/7 products for HF COM conditions assessment have been developed within PECASUS (Partnership for Excellence in Civil Aviation Space weather User Services). Nowcasting and forecasting (1-24hr) maps over Europe of MUF(3000) and its ratio with respect to a background level are then developed by INGV, as a PECASUS partner. The MUF(3000) nowcasting uses all the available real-time ionosonde measurements in different locations in order to upgrade IRI-CCIR-based background maps, and Ordinary Kriging method for spatial interpolation. The MUF(3000) modeling performance was assessed comparing predicted values to measured ones over two test stations during strong geomagnetic storm periods, obtaining an overall RMSE < 2 MHz at both stations. The MUF(3000) predicted 1-24 hours ahead depends on foF2 and M(3000) ionospheric parameters: EUROMAP forecasting model and IRI model are used for the former and the latter, respectively. The method has been applied to Europe where there are ionospheric stations with long (for some solar cycles) historical data and current real-time foF2 observations. A mapping procedure applied to the European stations provides MUF(3000) short-term prediction over the whole area. The application of these methods to storm events occurred after November 2019 is here presented, in order to study the ionospheric conditions they provide when HF COM advisories are expected to be issued.

Satellite observations are widely used to investigate, monitor, and forecast volcanic activity. Spaceborne thermal infrared (TIR) measurements of high-temperature volcanic features improve our understanding of the underlying processes and our ability to identify reactivation of activity, forecast eruptions, and assess hazards. In particular, thermal changes, indicative of subtle pre-eruptive volcanic thermal activity, have been observed. Over the last several decades, different approaches have been explored to detect and estimate the temperature above background of these thermal anomalies. The most common approach relies on a spatial statistical analysis based on a scene by scene choice of the background temperature region. Satisfactory results have also been shown by using time series anomaly detection algorithms based on statistical profiling approach. Artificial intelligence (AI) is growing sharply in different remote sensing fields because of its capability to automatically learn patterns from the data. Here, we develop an AI approach to automatically detect volcanic thermal features by using spatiotemporal information from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), LANDSAT-8 Thermal Infrared Sensor (TIRS) and MODerate-resolution Imaging Spectroradiometer (MODIS) data acquired over several decades. Our goal is to exploit AI techniques using a combination of high temporal and high spatial resolution satellite data to improve thermal volcanic monitoring and detect very low-level anomalies caused by pre-eruptive activity. We use both low-spatial, high-temporal resolution MODIS data to detect hotter thermal features at short time scales; as well as high-spatial low-temporal resolution ASTER TIR and TIRS data to detect more subtle thermal changes otherwise missed by MODIS. Our analysis is conducted in Google Earth Engine (GEE), a cloud computing platform with fast access and processing of satellite data. The comparison with a new statistical algorithm is documented in a companion abstract in this session.

Antarctica has been proposed as the dominant source of the meltwater that entered the oceans during Meltwater Pulse 1b (MWP1b) that occurred approximately 11,500 years ago. The deglaciation of heavily glaciated fjords off the coast of Antarctica at approximately this time has provided support for this hypothesis. Further support for this scenario was provided by the fact that the highly non-monotonic relative sea level histories recorded at sites on the coast of Scotland, which had been heavily glaciated at last glacial maximum, could be explained by the inter-hemispheric sea level teleconnection associated with a significant deglaciation of Antarctic ice sheets at this time. That the magnitude of grounded ice loss from Antarctica at MWP1b time was adequate to provide the necessary RSL rise along the coast of Scotland has not been demonstrated. Furthermore, there exist implicit suggestions to the effect that a significant contribution to MWP1b must have also been delivered to the oceans by the abrupt northern hemisphere warming that occurred at the end of the Younger Dryas (YD) cold reversal, which also occurred approximately 11,500 years ago. This warming event occurred due to the rapid intensification of the Atlantic Meridional Overturning Circulation (AMOC) when it recovered after the YD. We present a fingerprinting analysis of the contribution of all major ice sheets to MWP1b using the ICE7G_NA (VM7) model of ice loading history and find that the best agreement between calculated sea level curves and observations is obtained with a minimal Antarctic contribution.

Inland waters are an important component of the global carbon budget, emitting CO2 to the atmosphere. However, our ability to predict carbon fluxes from stream systems remains uncertain as small scales of pCO2 variability within streams (100-102 m), which makes efforts relying on monitoring data uncertain. We incorporate CO2 input and output fluxes into a stream network advection-reaction model, representing the first process-based representation of stream CO2 dynamics at watershed scales. This model includes groundwater (GW) CO2 inputs, water column (WC), and benthic hyporheic zone (BHZ) respiration, downstream advection, and atmospheric exchange. We evaluate this model against existing statistical methods including upscaling and multiple linear regressions through comparisons to high-resolution stream pCO2 data collected across the East River Watershed in the Colorado Rocky Mountains (USA). The stream network model accurately captures topography-driven pCO2 variability and significantly outperforms multiple linear regressions for predicting pCO2. Further, the model provides estimates of CO2 contributions from internal versus external sources suggesting that streams transition from GW- to BHZ-dominated sources between 3rd and 4th Strahler orders, with GW, BHZ, and WC accounting for 49.3, 50.6, and 0.1% of CO2 fluxes from the watershed, respectively. Lastly, stream network model CO2 fluxes are 4-12x times smaller than upscaling technique predictions, largely due to inverse correlations between stream pCO2 and atmosphere exchange velocities. Taken together, this stream network model improves our ability to predict stream CO2 dynamics and efflux. Furthermore, future applications to regional and global scales may result in a significant downward revision of global flux estimates.

We perform Observation System Simulation Experiments (OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions over North America can be resolved using measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and similar high-resolution satellite sensors. We focus analysis on the impacts of i) spatial errors in the prior emissions, and ii) model transport errors. Along with a standard scale-factor (SF) optimization we conduct a set of inversions using alternative formalisms that aim to overcome limitations in the SF-based approach that arise for missing sources. We show that 4D-Var analysis of the TROPOMI data can improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors (42–93% domain-wide bias reduction; R increases from 0.51 up to 0.73). However, when both errors are present, no single inversion framework can successfully improve both the overall bias and spatial distribution of fluxes relative to the prior on the 25 km model grid. In that case, the ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg/d (comparable to that in the prior), with spurious source adjustments compensating for the transport errors. Increasing observational coverage through longer-timeframe inversions does not significantly change this picture. An inversion formalism that optimizes emission enhancements rather than scale factors exhibits the best performance for identifying missing sources, while an approach combining a uniform background emission with the prior inventory yields the best performance in terms of overall spatial fidelity—even in the presence of model transport errors. However, the standard SF optimization outperforms both of these for the magnitude of the domain-wide flux. For the common scenario in which prior errors are non-random, approximate posterior error reduction calculations for the inversions reflect the sensitivity to observations but have no spatial correlation with the actual emission improvements. This demonstrates that such information content analysis can be used for general observing system characterization but does not describe the spatial accuracy of the posterior emissions or of the actual emission improvements. Findings here highlight the need for careful evaluation of potential missing sources in prior emission datasets and for robust accounting of model transport errors in inverse analyses of the methane budget.

Southeast Asian biomass burning is a major pollutant source that contributes to poor air quality throughout the region. Thus, understanding these emissions is critical for predicting and mitigating their health impacts. While many studies have reported ground-based and satellite measurements, airborne measurements at a regional scale capable of tying the two together have not been common. The 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field project examined Southeast Asian regional sources and their effects on aerosol/cloud interactions using a combination of airborne, shipboard, and ground-based measurements. These flights sampled a variety of airmass sources over the Philippine, South China, and Sulu seas during both the southwest monsoon and monsoon transition periods. Measurements during CAMP2Ex provide a unique opportunity to investigate how these transported and local emissions affected air quality trends and airmass chemical composition. We present correlated airborne in situ enhancement ratios of CH4 to CO, using them to identify source regimes of either high urban or biomass burning influence as well as urban regimes with different emission factors. Combined with backtrajectory analysis using HYSPLIT, source regimes were examined for differences in ozone, reactive nitrogen, and aerosol chemical composition. While observed O3/CO enhancement ratios remain constant for differing urban source regimes, NOy/CO ratios varied across these regimes. For biomass burning sources, O3/CO enhancement ratios are observed to be lower than previously reported by measurements in the region.

High resolution in-situ measurements of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor (H2O) were made onboard the NASA HU-25 aircraft during the ACTIVATE (Aerosol Cloud meteorology Interactions oVer the western Atlantic Experiment) campaign during 2020 and 2021 in different seasons (winter through summer) over the mid-latitude western Atlantic Ocean. As most of the flights focused on the marine boundary layer (MBL) during the campaign, these trace gas observations are an excellent data set to examine seasonal variability of trace gas background values in the MBL without the influence of localized point sources. We will describe the variability of these trace gases in the MBL background by filtering out concentrated point sources using back trajectory analysis along with trace gas ratios. Additionally, the ocean is a significant sink of anthropogenic CO2 capturing about one quarter of total anthropogenic carbon. By looking at the MBL CO2 variation as a function of season, we discuss observed changes in CO2 uptake over the ocean. These high accuracy observations of trace gas backgrounds in the MBL along with characterizing seasonal effects on oceanic sequestering of anthropogenic CO2 will improve the understanding of seasonal variations and change in climate and inverse modelling over the ocean.

Connecting the sources of reactive nitrogen (Nr) emissions to downstream environmental impacts is challenging, since Nr “cascades” through sequential ecosystems. It is therefore important to examine a jurisdiction’s overall Nr emissions to gain perspective on whether total levels and specific forms of Nr emissions are being reduced. We study subnational (provincial) trends and variations in Nr emissions in Canada over 30 years (1990-2017) to examine the effects of key policy, socioeconomic, and technological changes on Nr emissions. We use data from national Nr emissions inventories, agricultural nutrient models, and wastewater treatment reports to estimate specific (N2O, NOx, NH3, and NO3), total (Gg N) and per capita (Kg N / capita) Nr emissions by province. We divide the initial sources of Nr emissions into 1) agricultural-related emissions, 2) fossil-fuel emissions, and 3) waste management emissions. Preliminary results show that annual total Nr emissions in Canada increased between 1990-2000 (1250 Gg to 1490 Gg), and have since decreased (to 1180 Gg). There was a redistribution of the main species of Nr, with decreases of NOx from fossil fuels (from 660 to 488 Gg Nr / year) and increases in agricultural Nr emissions (from 452 Gg to 537 Gg Nr / year). Provincial trends vary. Overall NOx emissions decreased across all provinces due to more stringent vehicle regulations, except in Alberta, where NOx emissions from export-oriented oil production increased. The increase in national agricultural emissions comes primarily from Saskatchewan whose combined agricultural emissions increased from 43 Gg to 97 Gg Nr year. Improving agricultural nitrogen-use efficiency, especially in these provinces, would be a key area for reducing Canada’s Nr emissions. However, given that diesel and gasoline vehicles still contribute 121 Gg Nr / year, electrifying vehicle fleets would also have considerable potential for reducing Nr emissions. Our study demonstrates the variation in and reconfiguration of drivers of Nr emissions at the sub-national scale in Canada, emphasizing the need to consider local contexts and relative contributions of different economic sectors when examining national Nr emissions.

Orca seamount is located in the Bransfield Strait, between the South Shetland Islands and the Antarctic Peninsula. The volcano developed on an extensional rift produced by a combination of slab rollback at the South Shetland trench and transtensional motions between the Scotia and Antarctic plates. From January 2019 to February 2020, the BRAVOSEIS project deployed a dense amphibious seismic network in the Bransfield region, comprising both land and ocean bottom seismometers (OBS), as well as moored hydrophones. We perform an analysis of the seismicity recorded in the area of Orca volcano using a subnetwork composed of 15 OBS around Orca seamount and its SW rift, covering a region of about 20 km x 10 km, with inter-station distances of ~4 km. OBS data are organized, visualized and analyzed using the SEISAN software package. Earthquake detection was achieved through an STA/LTA algorithm. We use a visual procedure including spectral analysis and filtering to discriminate local earthquakes from other types of signals. For earthquake location, we use P and S phase arrivals and a layered model derived from previous geophysical studies in the region. In this way, we identify and locate around 3000 earthquakes with magnitudes in the range from -1 to 3. There is a continuous background level of microearthquake activity, although a large part of the earthquakes occurred during a swarm in June-July 2019. Source depths are mostly concentrated in the first 10 km (within the crust). The epicentral distribution covers the whole area around the volcano, but it is clearly densest in the NE flank, where an intense seismic series started in September 2020.

Seismic source locations provide fundamental information on earthquakes and lay the foundation for seismic monitoring at all scales. Subsurface engineering operations, such as hydraulic fracturing, usually generate abundant microseismic events with low signal-to-noise ratios, thus raising a higher demand on the reliability and computational efficiency of seismic location methods. Stacking-based method involves reconstructing and focusing the radiated seismic source energy with a certain stacking operator, for example, the diffraction stacking operator or the cross-correlation stacking operator. They are noise-resistant, automatic, and data-driven. The source locations are resolved as images instead of discrete dots, offering more insights into source processes and surrounding structures. In this work, we conduct the performance evaluation and influential factor analysis with synthetic examples, to further improve the understanding of the method. Three categories of factors are investigated, including the velocity model, array geometry, and waveform complexity. Each of the three factors consists of several detailed factors, such as different array types and noise levels. Multiple parameters are considered for the performance evaluation, including location error/bias, imaging resolution, signal alignment with time shifts, and the computational cost. The proposed scheme is also applied to field microseismic datasets to demonstrate its feasibility. This study will be conducive to the design and evaluation of surface monitoring projects.

Stable isotope geochemistry of terrestrial carbonates provides important opportunities to answer questions about climates, environments, and ecosystems both in the present day and the geologic past. Here we present a case study from the Cretaceous Newark Canyon Formation (NCF) type section (~98–113 Ma), where we explore how climate and depositional settings influence the stable isotope record in highly variable lacustrine and palustrine carbonates. The NCF was deposited within the hinterland of the Sevier orogenic belt and allows us to examine how North American terrestrial climate changed during the mid-Cretaceous, a time of potentially significant regional surface uplift and increasing global temperatures related to the Cretaceous Thermal Maximum (Di Fiori et al., 2020; Huber et al., 2018). In this study, we find substantial inter- and intra-facies heterogeneity, despite having formed in the same overall climate setting, highlighting the differences between lacustrine and palustrine environments. Stable carbon, oxygen, and clumped isotopes (δ13C, δ18Ocarbonate, and Δ47) paired with optical and cathodoluminescence petrography from along-strike lateral and vertical stratigraphic sections show significant isotopic variability between and within seven carbonate facies (Fetrow et al., 2020). Palustrine deposition is interpreted to have occurred along a spectrum of shallow water depths preserved in two key palustrine sub-facies endmembers – shallower mottled micrite and deeper pebbly, peloid-rich micrite. These record mean Δ47 temperatures of 51ºC and 44°C, respectively. The mottled micrite has heavier calculated δ18O of formation water (δ18Owater) values indicating increased evaporative enrichment, which suggests more intense desiccation during deposition. Lacustrine sediments preserved in laminated biomicrite to massive micrite have mean Δ47 temperatures of 50ºC and 37°C, respectively. Elevated temperatures and δ13C, δ18Ocarb, and δ18Owater values more similar to values from NCF secondary spar veins indicate that the biomicrite sub-facies underwent diagenetic alteration. We will discuss the implications of these results for the NCF and the Cretaceous western USA paleoclimate record, as well as general lessons learned for interpreting mixed terrestrial carbonate facies records.

The astrogeodetic or Helmert deflection of the vertical (DoV) data, as opposed to data obtained through other geodetic instruments (e.g., GNSS (geodetic coordinates), levelling (normal or orthometric heights) and gravimeters (gravity data)), cannot be readily obtained by astrogeodetic instruments. In order to obtain these data, geodetic and astronomical latitude and longitude information is required. Currently, geodetic latitude and longitude (φ, λ) are measured with GNSS receivers, while astronomical latitude and longitude (Φ, Λ) are observed with Digital Zenith Camera Systems (DZCSs) or the QDaedalus system. The result of these observations are the astrogeodetic North-South (ξ=Φ-φ) and East-West (η=(Λ-λ)cosφ) DoV components. This study investigates the precision and accuracy of the newly installed tachymeter-based (multi station (MS) 60) QDaedalus astrogeodetic measurement system’s DoV data with the aid of promising DoV data from the Compact Digital Astronomical Camera (CODIAC) DZCS. For this study, we used the CODIAC (accuracy of ∼0.05″) and QDaedalus (accuracy of ~0.20″) systems to conduct parallel measurements over 2 nights (measurement duration of more than 20 hours) at the Zimmerwald Observatory in Switzerland. Both the CODIAC and QDaedalus systems were developed at ETH Zurich. CODIAC has the standard DZCS components seen in other DZCSs in Germany, Latvia, Turkey and China: telescope, charge-coupled device (CCD) camera, tiltmeters, focuser, single frequency GNSS receiver and antenna, laptop, and substructure. While each DZCS has a unique design, CODIAC has upgraded hardware components (such as the use of four tiltmeters-two Wyler and two Lipmann High-Resolution Tiltmeters) as well as advanced software and processing technology. The key components of the QDaedalus which differ from the DZCS are tachymeter, interface box, and mountable meniscus lens. Also, the measurement principle and data evaluation processes of these two systems differ; therefore, we also describe the data evaluation methods for both the CODIAC and QDaedalus systems. Finally, we discuss the results of the parallel measurements and their implications for further astrogeodetic work.

The flow and deformation of glacier ice in response to stress is often described using Glen’s Flow Law, a power-law relation that compactly represents ice rheology with a prefactor, A, and stress exponent, n. For natural ice, these parameters (and the parameters subsumed within them) come with large uncertainties that have not been robustly quantified with observations. Modern remote sensing technologies that collect data with finer resolutions and broader coverage provide us with an opportunity to robustly calibrate these rheological parameters for certain environments. Here, we utilize publicly available observations of ice sheet surface velocity and elevation acquired with remote sensing platforms to calibrate the flow law parameters over select Antarctic ice shelves. We build upon recent work that used remote sensing observations to quantify the relationship between ice stress and strain rate in extensional flow to infer an exponent of n = 4.1 +/- 0.4 for Antarctica. Here, we model two-dimensional flow and perform parameter calibration by constructing and training physics-informed neural networks (PINNs) to learn spatially-varying A and uniform n for each ice shelf. We cast the parameter estimation problem as a neural network optimization problem through minimization of a cost function that includes both data reconstruction errors and momentum balance residuals derived from the 2D shallow-shelf approximation. Additionally, we formulate the networks to predict spatially-varying uncertainties for A by using variational inference techniques, which approximate Bayesian inference (traditionally a computationally-intensive procedure) as an additional optimization objective. Finally, we demonstrate the use of time-dependent surface velocities, which are becoming increasingly more available over the ice sheets, to independently constrain the stress exponent n, confirming the appropriateness of n = 4 derived from previous work. Overall, calibration of these parameters with robust uncertainties are critical for placing observational constraints on prognostic ice flow model parameters and to improve our understanding of flow and fracture processes on ice shelves in Antarctica.

Nowadays, the time evolution of ITRF station positions is described by piece-wise linear models extended with exponential and logarithmic functions to account for post-seismic displacements. The ITRF2020 will also account for seasonal deformation by means of annual and semi-annual sine waves. However, part of the Earth’s surface deformation is not captured by those deterministic functions, such as inter-annual hydrological loading deformation, or high-frequency atmospheric loading deformation. To account for such aperiodic displacements, a reference frame in the form of a time series could be considered. This would require aperiodic motions of the different space geodetic stations to be tied in a common frame by means of co-motion constraints. The relevance of such constraints is however debatable. Indeed, common aperiodic movements between co-located space geodetic stations have thus far not been evidenced. This presentation describes the comparison of station position time series from the different space geodetic techniques in order to highlight whether or not common aperiodic movements can be detected at co-location sites. Those time series are extracted from the solutions provided by the techniques international services for the ITRF2014. They are first carefully aligned to a common reference frame in order to minimize differential network effect. Then, they are cleaned from linear, post-seismic and periodic signals (including seasonal deformation and technique systematic errors). Residual time series from co-located stations are finally confronted with each other.

To stabilize global warming within the 1.5-2.0°C Paris Agreement goal, greenhouse gas (GHG) emissions need to reach net-zero. Offsetting is required for net-zero emissions (emissions minus offsets), as achieving zero emissions is unrealistic. Offset schemes differ widely in their capacity to mitigate anthropogenic global warming (AGW). Rayer et al. (2021) provide offsetting guidelines to strengthen their long-term climate benefit, this chapter further develops this topic. We propose a framework for rating different offset types based on their climate risk, moving beyond the usual criteria of verification, audit, and additionality. We apply our offset climate ratings to some offsetting types and case studies including the sorts of schemes proposed by major oil companies, and the PAS 2060 carbon-neutrality standard. Some offsets provide little more than economic nudges towards emissions reduction. Climate offsetting requirements are more exacting. The proposed framework grades offsets from most to least benefit. Grades help classify offsets’ climate risks based on: helping terminate it, slowing warming, or only offering economic incentives with little climate benefit. Ultimately, to stabilize global warming, it will be necessary for policy to encourage the highest rated offsets under the proposed framework. Reference Rayer, Q. G., Jenkins, S., & Walton, P. (2021). Defining net-zero and climate recommendations for carbon offsetting. In Walker, T., Wendt, S., Goubran, S., & Schwartz, T. (Eds.), Beyond the 2ºC: Business and Policy Trajectories to Climate Change Adaptation (chapter 2). Palgrave Macmillan.

A new soil moisture index for monitoring drought occurrence and intensity is presented. The index is based on the integration of different remote sensing products and in situ observations. Due to a shortage of precipitation, droughts reduce vegetation productivity, and thus, aggravate the impact of moisture stress on pastureland. The spatial distribution of soil moisture index with high-resolution images in Mongolia is still being one of the essential goals in the remote sensing and rangeland community. Specifically, we examined a new composite Gobi soil moisture index (GI) based on the combination of Ocean Salinity (SMOS) Soil Moisture, several products from the MODIS satellite, and in situ Soil Moisture (SM) observations. A multiple linear regression method was used for the estimation of GI soil moisture index. The former includes the surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission, the Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST), normalized difference vegetation index (NDVI), potential evapotranspiration (PET). The latter includes a standardized precipitation index (SPI) from in-situ data. The validation of the approach is based on the relationship between SPI and in-situ soil moisture (SM) observations, and their comparison to remote sensing (RS) – derived indices. The results show that the correlation was statistically significant between GI and in-situ SM observations from the meteorological stations at 10 – 15 cm depths (p < 0.0001). The correlation between GI and SPI, as represented by the correlation coefficient (r) was 0.64. The GI empirical equations that utilize at least three key atmospheric variables are (a) NDVI, (b) land surface temperature, and (c) potential evapotranspiration. The established new GI soil moisture index was retrieved at the 1 km spatial resolution for Southwest Mongolia from 2000 to 2018, and their two summer months (July, August) were used for monitoring drought and vegetation response to the varying soil/climatic conditions. Now, based on the assessment of drought severity, the new soil moisture index allowed us to assess a large-scale spatial coherence of droughts across the Southwestern part of Mongolia.