Areas of lakes that support emergent aquatic vegetation emit disproportionately more methane than open water but are under-represented in upscaled estimates of lake greenhouse gas emissions. These shallow areas are typically less than ~1.5 m deep and can be estimated through synthetic aperture radar (SAR) mapping. To assess the importance of lake emergent vegetation (LEV) zones to landscape-scale methane emissions, we combine airborne SAR mapping with field measurements of vegetated and open-water methane flux. First, we use Uninhabited Aerial Vehicle SAR (UAVSAR) data from the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) to map LEV in 4,572 lakes across four Arctic-boreal study areas and find it comprises ~16% of lake area, exceeding previous estimates, and exhibiting strong regional differences (averaging 59 [50–68]%, 22 [20-25]%, 1.0 [0.8-1.2]%, and 7.0 [5.0-12]% of lake areas in the Peace-Athabasca Delta, Yukon Flats, and northern and southern Canadian Shield, respectively). Next, we account for these vegetated areas through a simple upscaling exercise using paired methane fluxes from regions of open water and LEV. After excluding vegetated areas that could be accounted for as wetlands, we find that inclusion of LEV increases overall lake emissions by 21 [18-25]% relative to estimates that do not differentiate lake zones. While LEV zones are proportionately greater in small lakes, this relationship is weak and varies regionally, underscoring the need for methane-relevant remote sensing measurements of lake zones and a consistent criterion for distinguishing wetlands. Finally, Arctic-boreal lake methane upscaling estimates can be improved with more measurements from all lake zones.
The characterization of changes over the full distribution of precipitation intensities remains an overlooked and underexplored subject, despite their critical importance to hazard assessments and water resource management. Here, we aggregate daily in situ Global Historical Climatology Network precipitation observations within seventeen internally consistent domains in the United States for two time periods (1951-1980 and 1991-2020). We find statistically significant changes in wet day precipitation distributions in all domains – changes primarily driven by a shift from lower to higher wet day intensities. Patterns of robust change are geographically consistent, with increases in the mean (4.5-5.7%) and standard deviation (4.4-8.7%) of wet day intensity in the eastern U.S., but mixed signals in the western U.S. Beyond their critical importance to the aforementioned impact assessments, these observational results can also inform climate model performance evaluations.
Rockwall slope erosion is an important component of alpine landscape evolution, yet the role of climate and tectonics in driving this erosion remains unclear. We define the distribution and magnitude of periglacial rockwall slope erosion across 12 catchments in Himachal Pradesh and Jammu and Kashmir in the Himalaya of northern India using cosmogenic 10Be concentrations in sediment from medial moraines. Beryllium-10 concentrations range from 0.5±0.04x104 to 260.0±12.5x104 at/g SiO2, which yield erosion rates between 7.6±1.0 and 0.02±0.04 mm/a. Between ~0.02 and ~8 m of rockwall slope erosion would be possible in this setting across a single millennium, and >2 km when extrapolated for the Quaternary period. This erosion affects catchment sediment flux and glacier dynamics, and helps to establish the pace of topographic change at the headwaters of catchments. We combine rockwall erosion records from the Himalaya of Himachal Pradesh, Jammu and Kashmir and Uttarakhand in India and Baltistan in Pakistan to create a regional erosion dataset. Rockwall slope erosion rates progressively decrease with distance north from the Main Central Thrust and into the interior of the orogen. The distribution and magnitude of this erosion is most closely associated with records of Himalayan denudation and rock uplift, where the highest rates of change are recorded in the Greater Himalaya sequences. This suggests that tectonically driven uplift, rather than climate, is a first order control on patterns of rockwall slope erosion in the northwestern Himalaya. Precipitation and temperature would therefore come as secondary controls.
To better understand the role projected land-use changes (LUC) may play in future regional climate projections, we assess the combined effects of greenhouse-gas (GHG)-forced climate change and LUCs in regional climate model (RCM) simulations. To do so, we produced RCM simulations that are complementary to the North-American Coordinated Regional Downscaling Experiment (NA-CORDEX) simulations, but with future LUCs that are consistent with particular Shared Socioeconomic Pathways (SSPs) and related to a specific Representative Concentration Pathway (RCP). We examine the state of the climate at the end of the 21st Century with and without two urban and agricultural LUC scenarios that follow SSP3 and SSP5 using the Weather Research and Forecasting model (WRF) forced by one global climate model, the MPI-ESM, under the RCP8.5 scenario. We find that LUCs following different societal trends under the SSPs can significantly affect climate projections in different ways. In regions of significant cropland expansion over previously forested area, projected annual mean temperature increases are diminished by around 0.5-1.0℃. Across all seasons, where urbanization is high, projected temperature increases are magnified. In particular, summer mean temperature projections are up to 4-5℃ greater and minimum and maximum temperature projections are increased by 2.5-6℃, amounts that are on par with the warming due to GHG-forced climate change. Warming is also enhanced in the urban surroundings. Future urbanization also has a large influence on precipitation projections during summer, increasing storm intensity, event length, and the overall amount over urbanized areas, and decreasing precipitation in surrounding areas.
Waves and water level setup during storms can create overwashing flows across barrier islands. Overwashing flows can cause erosion, barrier breaching, and inlet formation, but their sediments can also be deposited and form washover fans. These widely different outcomes remain difficult to predict. Here we suggest that a breach develops when the sediment volume transported by overwashing flows exceeds the barrier subaerial volume. We form a simple analytical theory that estimates overwashing flows from storm characteristics, barrier morphology, and dune vegetation, and which can be used to assess washover deposition and breaching likelihood. Our theory suggests that barrier width and storm surge height are two important controls on barrier breaching. We test our theory with the hydrodynamic and morphodynamic model Delft3D as well as with field observations of 21 washover fans and 6 breaches that formed during hurricane Sandy. There is reasonable correspondence for natural but not for developed barrier coasts, where traditional sediment transport equations do not readily apply. Our analytical formulations for breach formation and overwash deposition can be used to improve long-term barrier island models.
The paleogeographic evolution of the western USA Great Basin from the Late Cretaceous to the Cenozoic is critical to understanding how the Cordillera at this latitude transitioned from Mesozoic shortening to Cenozoic extension. According to a widely applied model, Cenozoic extension was driven by collapse of elevated crust supported by crustal thicknesses that were potentially double the present ~30–35 km. This model is difficult to reconcile with more recent estimates of moderate regional extension (≤ 50%) and the discovery that most high-angle, basin–range faults slipped rapidly ca. 17 Ma, tens of millions of years after crustal thickening occurred. Here we integrate new and existing geochronology and geologic mapping in the Elko area of northeast Nevada, one of the few places in the Great Basin with substantial exposures of Paleogene strata. We improve age control for strata that have been targeted for studies of regional paleoelevation and paleoclimate across this critical time span. In addition, a regional compilation of the ages of material within a network of middle Cenozoic paleodrainages developed across the Great Basin shows that the age of basal paleovalley fill decreases southward roughly synchronous with voluminous ignimbrite flareup volcanism that swept south across the region ca. 45–20 Ma. Integrating these datasets with the regional record of faulting, sedimentation, erosion, and magmatism, we suggest that volcanism was accompanied by an elevation increase that disrupted drainage systems and shifted the continental divide east into central Nevada from its Late Cretaceous location along the Sierra Nevada arc. The north–south Eocene–Oligocene drainage divide defined by mapping of paleovalleys may thus have evolved as a dynamic feature that propagated southward with magmatism. Despite some local faulting, the northern Great Basin became a vast, elevated volcanic tableland that persisted until dissection by Basin and Range faulting that began ca. 21–17 Ma. Based on this more detailed geologic framework, it is unlikely that Basin and Range extension was driven by Cretaceous crustal overthickening; rather, pre-existing crustal structure was just one of several factors that that led to Basin and Range faulting after ca. 17 Ma—in addition to thermal weakening of the crust associated with Cenozoic magmatism, thermally supported elevation, and changing boundary conditions. Because these causal factors evolved long after crustal thickening ended, during final removal and fragmentation of the shallowly subducting Farallon slab, they are compatible with normal (~45–50 km) thickness crust beneath the Great Basin prior to extension and do not require development of a strongly elevated, Altiplano-like region during Mesozoic shortening.
Drought stress threatens vegetation dynamics across diverse ecosystems. Monitoring how vegetation responds to water stress is vital for ecological conservation. The response of vegetation photosynthesis to water availability variations in Southwest China from 2008 to 2018 is investigated in this study. The solar-induced fluorescence (SIF) derived from GOME-2 is used to characterize photosynthetic changes. We examined the sensitivity of SIF anomaly to standardized precipitation-evapotranspiration index (SPEI) at multiple time scales to evaluate the drought impacts on different ecosystems (i.e. forests, croplands, grasslands, and shrublands). We find that (1) SIF has significant yet weak correlations to SPEI across major ecosystems in Southwest China; (2) Forests are more sensitive to short-term droughts in comparison with other ecosystems. (3) Cropland, grassland, and shrubland are more subjected to long-term droughts compared to forests. Our findings indicate that, in Southwest China, satellite SIF may not be effective in monitoring the impact of drought on vegetation due to its weak response to SPEI. The robustness of using satellite-observed SIF to assess drought’s effects still needs to be further tested with high-resolution SIF data.
Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modelling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasise limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect”’ on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation–water interactions from regional to continental scales.
We investigate if the commonly neglected riverine detrital carbonate fluxes might balance several chemical mass balances of the global ocean. Particulate inorganic carbon (PIC) concentrations in riverine suspended sediments, i.e., carbon contained by these detrital carbonate minerals, was quantified at the basin and global scale. Our approach is based on globally representative datasets of riverine suspended sediment composition, catchment properties and a two-step regression procedure. The present day global riverine PIC flux is estimated at 3.1 ± 0.3 Tmol C/y (13% of total inorganic carbon export and 4 % of total carbon export), with a flux-weighted mean concentration of 0.26 ± 0.03 wt%. The flux prior to damming was 4.1 ± 0.5 Tmol C/y. PIC fluxes are concentrated in limestone-rich, rather dry and mountainous catchments of large rivers in Arabia, South East Asia and Europe with 2.2 Tmol C/y (67.6 %) discharged between 15 °N and 45 °N. Greenlandic and Antarctic meltwater discharge and ice-rafting additionally contribute 0.8 ± 0.3 Tmol C/y. This amount of detrital carbonate minerals annually discharged into the ocean implies a significant contribution of calcium (~ 4.75 Tmol Ca/y) and alkalinity fluxes (~ 10 Tmol(eq)/y) to marine mass balances and moderate inputs of strontium (~ 5 Gmol Sr/y), based on undisturbed riverine and cryospheric inputs and a dolomite/calcite ratio of 0.1. Magnesium fluxes (~ 0.25 Tmol Mg/y), mostly hosted by less-soluble dolomite, are rather negligible. These unaccounted fluxes help elucidating respective marine mass balances and potentially alter conclusions based on these budgets.
Agricultural emissions of ammonia (NH3) impact air quality, human health, and the vitality of aquatic and terrestrial ecosystems. In the UK, there are few direct policies regulating anthropogenic NH3 emissions and development of sustainable mitigation measures necessitates reliable emissions estimates. Here we use observations of column densities of NH3 from two space-based sensors (IASI and CrIS) with the GEOS-Chem model to derive top-down NH3 emissions for the UK at fine spatial (~10 km) and time (monthly) scales. We focus on March-September when there is adequate spectral signal to reliably retrieve NH3. We estimate total emissions of 272 Gg from IASI and 389 Gg from CrIS. Bottom-up emissions are 27% less than IASI and 49% less than CrIS. There are also differences in seasonality. Top-down and bottom-up emissions agree on a spring April peak due to fertilizer and manure application, but there is also a comparable summer July peak in the top-down emissions that is not in the bottom-up emissions and appears to be associated with dairy cattle farming. We estimate relative errors in the top-down emissions of 11-36% for IASI and 9-27% for CrIS, dominated by column density retrieval errors. The bottom-up versus top-down emissions discrepancies estimated in this work impact model predictions of the environmental damage caused by NH3 emissions and warrant further investigation.
Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.
Cosmic ray neutron sensors (CRNS) allow to determine field-scale soil moisture content non-invasively due to the dependence of aboveground measured epithermal neutrons on the amount of hydrogen. Because other pools besides soil contain hydrogen (e.g. biomass), it is necessary to consider these for accurate soil moisture content measurements, especially when they are changing dynamically (e.g., arable crops, de- and reforestation). In this study, we compare four approaches for the correction of biomass effects on soil moisture content measurements with CRNS using experiments with three crops (sugar beet, winter wheat and maize) on similar soils: I) site-specific functions based on in-situ measured biomass, II) a generic approach, III) the thermal-to-epithermal neutron ratio (Nr) and IV) the thermal neutron intensity. Calibration of the CRNS during bare soil conditions resulted in root mean square errors (RMSE) of 0.097, 0.041 and 0.019 m3/m3 between estimated and reference soil moisture content of the cropped soils, respectively. Considering in-situ measured biomass for correction reduced the RMSE to 0.015, 0.018 and 0.009 m3/m3. When thermal neutron intensity was considered for correction, similarly accurate results were obtained. Corrections based on Nr and the generic approach were less accurate. We also explored the use of CRNS for biomass estimation. The use of Nr only provided accurate biomass estimates for sugar beet. However, significant site-specific relationships between biomass and thermal neutron intensity were obtained for all three crops. It was concluded that thermal neutron intensity can be used to correct soil moisture content estimates from CRNS and to estimate biomass.
The future of Arctic social systems and natural environments is highly uncertain. Climate change will lead to unprecedented phenomena in the pan-Arctic region, such as regular shipping traffic through the Arctic Ocean, urban growth, military activity, expanding agricultural frontiers, and transformed Indigenous societies. While intergovernmental to local organizations have produced numerous synthesis-based visions of the future, a challenge in any scenario exercise is capturing the ‘possibility’ space of change. In this work, we employ a computational text analysis to generate unique thematic input for novel, story-based visions of the Arctic. Specifically, we develop a corpus of more than 2,000 articles in publicly accessible, English-language Arctic newspapers that discuss the future in the Arctic. We then perform a latent Dirichlet allocation, resulting in ten distinct topics and sets of associated keywords. From these topics and keywords, we design ten story-based scenarios employing the Mānoa mashup, science fiction prototyping, and other methods. Our results demonstrate that computational text analysis can feed directly into a creative futuring process, whereby the output stories can be traced clearly back to the original topics and keywords. We discuss our findings in the context of the broader field of Arctic scenarios and show that the results of this computational text analysis produce complementary stories to the existing scenario literature. We conclude that story-based scenarios can provide vital texture toward understanding the myriad possible Arctic futures.
Bed particles were tracked using passive integrated transponder (PIT) tags in a wandering reach of the San Juan River, British Columbia, Canada, to assess particle movement around three major bars in the river. In-channel topographic changes were monitored through repeat LiDAR surveys during this period and used in concert with the tracer dataset to assess the relationship between particle displacements and changes in channel morphology, specifically, the development and re-working of bars. This has direct implications for virtual velocity and morphologic based estimates of bedload flux, which rely on accurate estimates of the variability and magnitude of particle path lengths over time. Tracers were deployed in the river at three separate locations in the Fall of 2015, 2016, 2017 and 2018, with recovery surveys conducted during the summer low-flow season the year after tracer deployment and multiple mobilising events. Tracers exhibited path length distributions reflective of both morphologic controls and year to year differences related to the annual flow regime. Annual tracer transport was restricted primarily to less than one riffle-pool-bar unit, even during years with a greater number of peak floods and duration of competent flow . Tracer deposition and burial was focused along bar margins, particularly at or downstream of the bar apex, reflecting the downstream migration and lateral bar accretion observed on Digital Elevation Models (DEMs) of difference. This highlights the fundamental importance of bar development and re-working underpinning bedload transport processes in bar-dominated channels.
Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.
Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.
Terrestrial primary productivity plays a pivotal role as a forcing factor of atmospheric CO2 and drives biospheric carbon dynamics. India is one of the largest GHGs emitters, yet less is understood in carbon cycling in terrestrial ecosystems. Here we explored the trend and magnitude of gross and net productivities of India for the last two decades (2000 – 2019) by integrating satellite observation from MODIS, remote sensing-based CASA model and twenty DGVMs from the TRENDY ensemble. Preliminary results exhibited a unimodal response across the data products with an overall positive trend and a declining decadal trend for 2010 – 2019. Alongside, the SPEI drought severity index across various ecological zones indicated India was more positively sensitive to wet span than the dry. We found that the ecosystems were drastically shifting their nature to C source with a positive trend in the productivities and were mediated by the changing climate. The analysis also revealed the increasing decadal amplitude of GPP by 0.0884 Pg C/Year, NBP by 0.0096 Pg C/Year, NEP by 0.0195 Pg C/Year, NPP by 0.0448 Pg C/Year and NEE by 0.0161 Pg C/Year. CASA underestimated the magnitudes but with the temporal synchronisation of the ensemble. Seasonal variability across the agro-ecological zones was more sensitive and was an offset for the declining productivities in the primaeval forests of India. The monsoon season contributed to the interannual variability of India. Higher uncertainty in productivities was observed in the high greening areas, whereas it contradicted NBP by reflecting a stable trend. Our results underscore the nature of C variability in the terrestrial ecosystems of India; and, they indicate that C release has reacted stronger than the C uptake, which was substantially inferred from NEE across the ecological zones.
Yield forecasting can give early warning of food risks and provide theoretical support for food security planning. Climate change and land use change directly influence the regional yield and planting area of maize, but few existing studies have examined their synergistic impact on maize production. In this study, we combine system dynamic (SD), the future land use simulation (FLUS) and a statistical crop model to predict future maize yield variation in response to climate change and land use change. Specifically, SD predicts the future land use demand, FLUS simulates future spatial land use patterns, and a statistical maize yield model based on regression analysis is utilized to adjust the per hectare maize yield under four representative concentration pathways (RCPs). A phaeozem region in central Jilin Province of China is taken as a case study. The results show that the future land use pattern will significantly change from 2030 to 2050. Although the cultivated land is likely to reduce by 862.84 km2, the total maize yield in 2050 will increase under all four RCP scenarios due to the promotion of per hectare maize yield. RCP4.5 will be more beneficial to maize production than other scenarios, with a doubled total yield in 2050. Notably, the yield gap between different counties will be further widened, which necessitates the differentiated policies of agricultural production and farmland protection, e.g., strengthening cultivated land protection and crop management in low-yield areas, as well as taking adaptation and mitigation measures to coordinate climate change and crop production.