nasa-gesdisc/es-publications-researchareas huggingface dataset:
Does the recent revival of Western Disturbances govern the Karakoram Anomaly? (2022)

Abstract The global retreat in glaciers is considered to be one of the critical indicators of climate change. However, the glaciers of the Karakoram (KR) region of the KarakoramHimalayas (KH) stand out because of their divergent response, displaying a surge as opposed to glaciers in other regions. This phenomenon is known as the Karakoram anomaly. Although many factors control the establishment and sustenance of the anomaly, the present study establishes winter precipitation associated with western disturbances (WDs) over the KH as one of the key drivers behind its emergence. To examine the role of WDs, a tracking algorithm is applied to 39 seasons (NovemberMarch) for three separate (ERA5, MERRA-2, and NCEP-CFSR/CFSv2) reanalysis datasets. The associated reanalysis ensemble statistics of WD properties produced in terms of their intensity, precipitation/snowfall volumes, and wind speed suggest a revival in recent years over the core-anomaly regions. However, the frequency has remained steady. The Karakoram has witnessed a rise of 10% in precipitation intensity associated with WDs in recent decades. The high percentage of snowfall received by the Karakoram (65%) from WDs relative to the total seasonal snowfall suggests a crucial role in modulating the regional mass-balance anomaly. Simultaneously, the amount of snowfall from non-WD sources in the Karakoram has had a statistically significant decline of 17% in recent decades, coinciding with the anomaly period. The enhanced intensity of WDs is found to be associated with changes in increased baroclinic instability and a shift of the subtropical westerly jet mean latitudinal position.

Research areas: Cryospheric Indicators
Calcium carbonate corrosivity in an Alaskan inland sea (2014)

Abstract. Ocean acidification is the hydrogen ion increase caused by the oceanic uptake of anthropogenic CO2, and is a focal point in marine biogeochemistry, in part, because this chemical reaction reduces calcium carbonate (CaCO3) saturation states () to levels that are corrosive (i.e.,   1) to shell-forming marine organisms. However, other processes can drive CaCO3 corrosivity; specifically, the addition of tidewater glacial melt. Carbonate system data collected in May and September from 2009 through 2012 in Prince William Sound (PWS), a semienclosed inland sea located on the south-central coast of Alaska and ringed with fjords containing tidewater glaciers, reveal the unique impact of glacial melt on CaCO3 corrosivity. Initial limited sampling was expanded in September 2011 to span large portions of the western and central sound, and included two fjords proximal to tidewater glaciers: Icy Bay and Columbia Bay. The observed conditions in these fjords affected CaCO3 corrosivity in the upper water column (< 50 m) in PWS in two ways: (1) as spring-time formation sites of mode water with near-corrosive  levels seen below the mixed layer over a portion of the sound, and (2) as point sources for surface plumes of glacial melt with corrosive  levels ( for aragonite and calcite down to 0.60 and 1.02, respectively) and carbon dioxide partial pressures (pCO2) well below atmospheric levels. CaCO3 corrosivity in glacial melt plumes is poorly reflected by pCO2 or pHT, indicating that either one of these carbonate parameters alone would fail to track  in PWS. The unique  and pCO2 conditions in the glacial melt plumes enhances atmospheric CO2 uptake, which, if not offset by mixing or primary productivity, would rapidly exacerbate CaCO3 corrosivity in a positive feedback. The cumulative effects of glacial melt and airsea gas exchange are likely responsible for the seasonal reduction of  in PWS, making PWS highly sensitive to increasing atmospheric CO2 and amplified CaCO3 corrosivity.

Research areas: Cryospheric Indicators, Atmospheric/Ocean Indicators
Photogrammetric retrieval and analysis of small scale sea ice topography during summer melt (2016)

The paper presents a setup for photogrammetric retrievals of small scale sea ice surface topography using low-altitude aerial imagery. The setup features two digital cameras, a combined GPS receiver/inertial navigation system (INS) unit, and a laser range finder. The components are fit in a single aerodynamic enclosure mounted outside a helicopter cabin. Results from its first deployment during the field campaign on Arctic sea ice north of Svalbard during summer 2012 are shown. Comparison of photogrammetrically derived digital elevation models (DEMs) with in situ measurements of sea ice topography made on melting first year sea ice demonstrated the ability of the method to accurately recover the topography of sea ice including melt ponds with depths down to at least 0.3m. The inter-comparison of the photogrammetrically derived DEM and in situ measured elevations yielded estimates of a root mean square error (RMS) of about 0.04m and bias of 0.03m, both for sea ice freeboard and melt pond depths. The bimodality of the probability density function of measured melt pond depths was also accurately reproduced in the reconstructed DEM. Discrepancies between the measured and DEM distributions were within the range of the inferred uncertainty of the photogrammetric and in situ techniques, with some of the bias likely associated with sea ice melt during the time elapsed between in situ and aerial measurements.

Research areas: Cryospheric Indicators
Multiscale Changes in Snow Over the Tibetan Plateau During 1980-2018 (2020)

The Tibetan Plateau has experienced substantial warming during the last few decades. As a result, the cryosphere of the Tibetan Plateau, including the snow cover, has shown significant changes. We characterized the changes in snow over the Tibetan Plateau using several snow-related indices, including snow depth and snow-covered days based on the MERRA-2 and JRA-55 reanalysis data set and passive microwave (MW) satellite observations. The analyses were performed over different time periods—including the last 38, 30, 20, and 10 snow seasons—from 1980 to 2018. The results show that the significant trends in snow depth and snow-covered days are dependent on both the season and region and vary with the data set and snow-related index used. There was a clear decrease in the annual maximum consecutive snow-covered days (CSCDMax), and this was characterized by a later begin time and an earlier end time for both MERRA-2 and JRA-55, but only a later begin time for MW during the time period 1980-2018. The changes in CSCDMax were generally dominated by changes in the begin time, which were mainly caused by the increase in the average 2-m air temperature for November-December. The relatively weak trends in the end time were a result of the combined impact of warmer temperatures and more precipitation in March-April. The CSCDMax averaged over the Tibetan Plateau showed decreasing trends in all studied time periods for JRA-55 and MW and in the last 38 and 10 snow seasons for MERRA-2 during the time period 1980-2018.

Research areas: Cryospheric Indicators
Carbon Dioxide Production in Bedrock Beneath Soils Substantially Contributes to Forest Carbon Cycling (2020)

Soils are widely considered the primary terrestrial organic matter pool mediating carbon transactions with the atmosphere and groundwater. Because soils are both a host and a product of rhizosphere activity, they are thought to mark the location where photosynthetic fixation of carbon dioxide (CO<SUB>2</SUB>) is balanced by the oxidation of organic matter. However, in many terrestrial environments, the rhizosphere extends below soils and into fractured bedrock, and it is unknown if the resulting biological and hydrologic dynamics in bedrock have a significant impact on carbon cycling. Here we show substantial production of CO<SUB>2</SUB> in weathered bedrock at 4-8 m below the thin soils (&lt;0.5 m thick) of a Northern California forest using innovative monitoring technology for sampling gases and water in fractured rock. The deep CO<SUB>2</SUB> production supports a persistent upward flux of CO<SUB>2(g)</SUB> year-round from bedrock to soil, constituting between 2% and 29% of the average daily CO<SUB>2</SUB> efflux from the land surface. When water is rapidly transported across the fractured bedrock vadose zone, nearly 50% of the CO<SUB>2</SUB> produced in the bedrock dissolves into water, promoting water-rock interaction and export of dissolved inorganic carbon (DIC) from the unsaturated zone to groundwater, constituting as much as 80% of the DIC exiting the hillslope. Such CO<SUB>2</SUB> production in weathered bedrock is subject to unique moisture, temperature, biological, and mineralogical conditions which are currently missing from terrestrial carbon models.

Research areas: Land Surface/Agriculture Indicators
PMIP4/CMIP6 last interglacial simulations using three different versions of MIROC: importance of vegetation (2021)

Abstract. We carry out three sets of last interglacial (LIG) experiments, named lig127k, and of pre-industrial experiments, named piControl, both as part of PMIP4/CMIP6 using three versions of the MIROC model: MIROC4m, MIROC4m-LPJ, and MIROC-ES2L. The results are compared with reconstructions from climate proxy data. All models show summer warming over northern high-latitude land, reflecting the differences between the distributions of the LIG and present-day solar irradiance. Globally averaged temperature changes are 0.94 K (MIROC4m), 0.39 K (MIROC4m-LPJ), and 0.43 K (MIROC-ES2L). Only MIROC4m-LPJ, which includes dynamical vegetation feedback from the change in vegetation distribution, shows annual mean warming signals at northern high latitudes, as indicated by proxy data. In contrast, the latest Earth system model (ESM) of MIROC, MIROC-ES2L, which considers only a partial vegetation effect through the leaf area index, shows no change or even annual cooling over large parts of the Northern Hemisphere. Results from the series of experiments show that the inclusion of full vegetation feedback is necessary for the reproduction of the strong annual warming over land at northern high latitudes. The LIG experimental results show that the warming predicted by models is still underestimated, even with dynamical vegetation, compared to reconstructions from proxy data, suggesting that further investigation and improvement to the climate feedback mechanism are needed.

Research areas: Atmospheric/Ocean Indicators, Land Surface/Agriculture Indicators
Vegetation dynamics and ecosystem service values changes at national and (2019)

Identification of vegetation changes and economic valuation of natural resources are important to strengthen national economy and sustainable environment development. This study identified the changes in vegetation and ecosystem service values at the national and provincial scales in Nepal from 2000 to 2017. Mann Kendall test statistics and Sen's slope were computed for temporal and spatial normalized difference vegetation index (NDVI) values in each pixel having a spatial resolution of 250 m and at 16-days interval. Land cover types were defined based on the NDVI values and applied ecosystem service values (ESV) coefficient. Results show that NDVI has significantly increased in Nepal with an average trend of 0.0018 yr<SUP>-1</SUP> during 2000-2017. Except for Province 6, the NDVI has increased significantly in all the provinces. Results additionally suggested a 27.88% greening in Nepal. At the provincial scale, the highest (56.41%) and lowest (12.52%) greening were observed in Provinces 2 and 6, respectively. In 2017, the total ESV in Nepal was 21.88 billion USD which showed a 1.15 billion USD higher than in year 2000. The ESV has increased in forests but decreased in the croplands, grasslands and barren lands between 2000 and 2017. The ESV of national forest are estimated at 19.17 billion USD in 2017. The highest value of 4.17 billion USD and the lowest of 1.09 billion USD were found in Provinces 1 and 2, respectively. Meanwhile, the available ESV per capita was relatively higher in Province 6 and lower in the Province 2. As the ESV is important lifeline for the society, this study provides crucial information about how this important environmental parameter has changed over time in Nepal.

Research areas: Land Surface/Agriculture Indicators
Global change in terrestrial ecosystem detected by fusion of microwave and optical satellite observations (2021)

The detection of global land change via satellite observation is a major challenge in improving the understanding of global environmental change. In this study, we develop a new vegetation index which can be used as a proxy for the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects global land change which has been reported by previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is an important signal for detecting global change in the terrestrial ecosystem.

Research areas: Land Surface/Agriculture Indicators
On the sensitivity of the Amazon surface climate to two land‐surface hydrology schemes using a high‐resolution regional climate model (RegCM4) (2022)

Two 11-year simulations were conducted to investigate the influence of two runoff schemes in the community land model version 4.5 (CLM45) on the Amazon surface energy balance and surface climate using a high-resolution regional climate model (RegCM4-CLM45). The default scheme is TOPMODEL (TOP), while the alternative is Variable Infiltration Capacity (VIC). In the two simulations, the vegetation status is prescribed (satellite phenology; SP). The first simulation was designated as SP-TOP, while the second simulation was referred to as SP-VIC, and both of them were evaluated using reanalysis products (e.g., ERA5) and micrometeorology data measurements. Results show that the SP-VIC severely underestimates latent heat and overestimates sensible heat fluxes, more than SP-TOP in comparison with the ERA5. This explains the large warm bias observed in the winter season. On the other hand, the SP-VIC shows a slightly smaller dry bias than SP-TOP against the Climate Research Unit (CRU) data. Our results show that SP-VIC does not improve the quality of the simulation compared to SP-TOP, which suggests the necessity of additional calibration of the VIC surface parameters using in situ observations of the Amazon and revising the VIC runoff scheme to perform new sensitivity experiments. The same needs to be done with SP-TOP.

Research areas: Land Surface/Agriculture Indicators
Mapping soil microbial residence time at the global scale (2021)

AbstractSoil microbes are the fundamental engine for carbon (C) cycling. Microbial residence time (MRT) therefore determines the mineralization of soil organic C, releasing C as heterotrophic respiration and contributing substantially to the C efflux in terrestrial ecosystems. We took use of a comprehensive dataset (2627 data points) and calculated the MRT based on the basal respiration and microbial biomass C. Large variations in MRT were found among biomes, with the largest MRT in boreal forests and grasslands and smallest in natural wetlands. Biogeographic patterns of MRT were found along climate variables (temperature and precipitation), vegetation variables (root C density and net primary productivity), and edaphic factors (soil texture, pH, topsoil porosity, soil C, and total nitrogen). Among environmental factors, edaphic properties dominate the MRT variations. We further mapped the MRT at the global scale with an empirical model. The simulated and observed MRT were highly consistent at plot (R2= .86), site (R2 = .88), and biome (R2 = .99) levels. The global average of MRT was estimated to be 38 (5) days. A clear latitudinal biogeographic pattern was found for MRT with lower values in tropical regions and higher values in the Arctic. The biome and globallevel estimates of MRT serve as valuable data for parameterizing and benchmarking microbial models.

Research areas: Land Surface/Agriculture Indicators
Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas (2022)

Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.04770.1176 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas.

Research areas: Land Surface/Agriculture Indicators
An Improved Optimization Scheme for Representing Hillslopes and (2020)

Understanding hydrological processes is essential for management of water resources and for promoting catchment sustainability. In karst regions, landscapes are spatially heterogeneous and include discontinuous soil distribution, and complex networks of matrices and conduits in hillslopes and depressions, which result in variation in hydrological processes. However, most previous studies have mainly focused on the effects of the distribution of soil depth and the fast-slow flow in the matrices and conduits on hydrological processes and ignored the different hydrological processes related to hillslopes and depressions. This study improved the VarKarst model by adding randomly distributed soil and epikarst depths (RSE), fast-slow flow (FS), and hillslopes and depressions (HD). The improved model was calibrated and validated in six large catchments (1,213-5,454 km<SUP>2</SUP>) and one small catchment (1.25 km<SUP>2</SUP>). Results showed that the schemes by combining FS and HD (Scenario FS + HD) and combining RSE, FS, and HD (Scenario RSE + FS + HD) improved model performance (calibrated and validated KGE ranged from 0.54 to 0.89 and AIC ranged from -336.49 to 669.77) compared models that included other schemes (original VarKarst, Scenario RSE, Scenario FS, Scenario HD, and Scenario RSE + FS), especially when reproducing discharge of peaks and recessions. These results suggest that there is a need to separate the hillslopes and depressions when modeling karstic hydrological processes.

Research areas: Land Surface/Agriculture Indicators
Progress in Time-Series Soil Moisture Retrieval Using L-and S-Band Radar Backscatter (2022)

L- and S-band observations from NASA's Passive/Active L/S band (PALS) sensor from the SMEX02 campaign were used to estimate soil moisture. The retrieval process is based on the alpha approximation method. This method utilizes a time-series of normalized radar backscatter measurements as well as ancillary information to estimate soil moisture over the Walnut Creek watershed. The resulting retrieved soil moistures are compared to in-situ soil moisture measurements at multiple test sites within the watershed. The calculations show reasonable results for both L- and S-band and provide further insight into the use of L- and S-bands for the upcoming NASA/ISRO mission.

Research areas: Validation
Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China (2022)

Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.018110a1, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province.

Research areas: Land Surface/Agriculture Indicators