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At least 433 records · Page 24

Permafrost Landscape History Shapes Fluvial Chemistry, Ecosystem Carbon Balance, and Potential Trajectories of Future Change

Intensifying permafrost thaw alters carbon cycling by mobilizing large amounts of terrestrial substrate into aquatic ecosystems. Yet, few studies have measured aquatic carbon fluxes and constrained drivers of ecosystem carbon balance across heterogeneous Arctic landscapes. Here, we characterized hydrochemical and landscape controls on fluvial carbon cycling, quantified fluvial carbon fluxes, and estimated fluvial contributions to ecosystem carbon balance across 33 watersheds in four ecoregions in the continuous permafrost zone of the western Canadian Arctic: unglaciated uplands, ice-rich moraine, and organic-rich lowlands and till plains. Major ions, stable isotopes, and carbon speciation and fluxes revealed patterns in carbon cycling across ecoregions defined by terrain relief and accumulation of organics. In previously unglaciated mountainous watersheds, bicarbonate dominated carbon export (70% of total) due to chemical weathering of bedrock. In lowland watersheds, where soil organic carbon stores were largest, lateral transport of dissolved organic carbon (50%) and efflux of biotic CO 2 (25%) dominated. In watersheds affected by thaw-induced mass wasting, erosion of ice-rich tills enhanced chemical weathering and increased particulate carbon fluxes by two orders of magnitude. From an ecosystem carbon balance perspective, fluvial carbon export in watersheds not affected by thaw-induced wasting was, on average, equivalent to 6–16% of estimated net ecosystem exchange (NEE). Finally, in watersheds affected by thaw-induced wasting, fluvial carbon export approached 60% of NEE. Because future intensification of thermokarst activity will amplify fluvial carbon export, determining the fate of carbon across diverse northern landscapes is a priority for constraining trajectories of permafrost region ecosystem carbon balance.

54 ENVIRONMENTAL SCIENCES↗

Impacts of Large-Scale Urbanization and Irrigation on Summer Precipitation in the Mid-Atlantic Region of the United States

This study investigates how urbanization and irrigation in the United States east of the Rocky Mountains affect summer precipitation in the Mid-Atlantic region (MAR) using convection-permitting Weather Research and Forecasting model simulations with/without urbanization or irrigation. In this work, a feature tracking algorithm is used to identify precipitation from mesoscale convective systems (MCSs), isolated deep convection (IDC), and non-convective systems (NC). Overall, urbanization suppresses all three types of precipitation in the MAR by reducing water vapor content and convective available potential energy, while irrigation enhances IDC and NC precipitation but suppresses MCS precipitation. Examination of the MCS and IDC initiation locations indicates that irrigation suppresses MAR precipitation that is produced by MCSs initiated in the Great Plains and Midwest (GP) but enhances MAR precipitation from MCSs initiated locally within the region. Irrigation induces a mid-level cyclonic circulation anomaly centering in the Southeast, hindering the eastward propagation and development of MCSs from the GP.

54 ENVIRONMENTAL SCIENCES↗

Causes of Missing Snowmelt Following Drought

Abstract Water management in snowy mountainous regions hinges on forecasting snowmelt runoff. However, droughts are altering snowpack‐runoff relationships with ongoing debate about the driving mechanisms. For example, in 2021 in California, less than half of predicted streamflow arrived. Mechanisms proposed for this “missing” streamflow included changes in evapotranspiration ( ET ), rainfall, and subsurface moisture conditions. Here, we demonstrate that ET in drought years generates dry subsurface conditions that reduce runoff in subsequent years. A model including this legacy of depleted moisture storage reduced median error in 2021 forecasts from 60% to 20% at 15 minimally disturbed basins and from 18% to 2% at 6 water supply basins in the Sierra Nevada (basins range in area from 5 to 23,051 km 2 and mean annual precipitation from 814 to 1,549 mm). Our findings indicate that the relationship between snowpack and runoff will evolve as plant ecosystems respond to climate change and alter subsurface water storage dynamics.

Lapides, Dana A.↗

Midwinter Dry Spells Amplify Post‐Fire Snowpack Decline

Abstract Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite‐based fire detections with snow seasonality classifications to examine fire activity in California's seasonal and ephemeral snow zones. We find a nearly tenfold increase in fire activity during 2020–2021 versus 2001–2019. Accumulation season broadband snow albedo declined 25%–71% at two burned sites (2021 and 2022) according to in‐situ data relative to un‐burned conditions, with greater declines associated with increased burn severity. By enhancing snowpack susceptibility to melt, both decreased snow albedo and canopy drove midwinter melt during a multi‐week dry spell in 2022. Despite similar meteorological conditions in December–February 2013 and 2022–linked to persistent high pressure weather regimes–minimal melt occurred in 2013. Post‐fire snowpack differences are confirmed with satellite measurements. With growing geographical overlap between wildfire and snow, our findings suggest California's snowpack is increasingly vulnerable to the compounding effects of dry spells and wildfire.

54 ENVIRONMENTAL SCIENCES↗

The Diurnal Cycle of East Pacific Convection, Moisture, and CYGNSS Wind Speed and Fluxes

The far eastern tropical Pacific is one of the rainiest locations on Earth. This region is characterized by a robust diurnal cycle with precipitation initiating over the Andes mountains in Colombia in the late afternoon and moving offshore overnight while organizing into mesoscale convective systems (MCSs). As the MCSs move westward, they can seed tropical easterly waves that develop into tropical cyclones. Novel measurements of near-surface wind speeds and latent heat flux (LHFLX) from the Cyclone Global Navigation Satellite System (CYGNSS) mission and ERA5 reanalysis are used to evaluate changes to regional flow patterns, moisture, and LHFLX that favor a strong westward propagating diurnal cycle of precipitation in this region. Days with strongly versus weakly westward propagating diurnal precipitation are compared during the extended boreal summer of the CYGNSS era (May–October 2018–2021). Strongly propagating days have a more moist lower troposphere with stronger vertically integrated moisture flux convergence than days with weakly propagating precipitation. The enhanced low-level moisture is supported by increased wind-driven LHFLX from stronger regional flows including the Choco and Caribbean Low-Level Jets and Panama and Papagayo gap winds. Increased offshore convergence and vorticity also help sustain convection during the days with strongly propagating precipitation. Background variations in the physical environment, as opposed to diurnal variations, seem more important for supporting a robust diurnal cycle of precipitation in the region.

54 ENVIRONMENTAL SCIENCES↗

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗

Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions

Abstract The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low‐resolution (roughly 110 km) and high‐resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗

QRF4P-NRT: Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates Using Quantile Regression Forests

Accurate and reliable near-real-time satellite precipitation estimation is of great importance for operational large-scale flood forecasting and drought monitoring. The state-of-the-art precipitation post-processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P-NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near-real-time predictors to further improve the model performance. Using the Integrated Multi-satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG-E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG-E and is also superior to the bias-corrected product (IMERG Final Run, IMERG-F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG-E, raw IMERG-E, and IMERG-F using ground observation show that the corrected IMERG-E improves correlation coefficients (0.7), mean error (-0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG-E (0.31, -0.72 and 5.5 mm/day) and IMERG-F (0.34, -0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P-NRT improves on the various deficiencies of the raw IMERG-E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post-processing for near-real-time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.

54 ENVIRONMENTAL SCIENCES↗

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open-forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap-filled. These data are available to download from Bonner, Smyth, et al. at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process-based snow model. We demonstrate the utility of these data with a modeling experiment that explores open-forest differences and identifies opportunities for improvements in model representation.

54 ENVIRONMENTAL SCIENCES↗

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

Abstract Historical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.

54 ENVIRONMENTAL SCIENCES↗

Small-Magnitude Seismic Swarms in Central Utah (US): Interactions of Regional Tectonics, Local Structures and Hydrothermal Systems

Swarms in Central Utah are situated in the complex transition between the Basin and Range (BR) province and the Colorado Plateau. Transecting transverse structures, volcanic deposits, and hydrothermal systems complicate the extensional BR horst and graben structures and provide a multitude of plausible triggering mechanisms. Revisiting the catalog of the University of Utah Seismograph Stations (1981–2022), we analyze spatio-temporal patterns and characteristic features of seismic sequences. Swarms with alternating seismicity rates, bursts, and longer swarms with persistent moment release exhibit a remarkable diversity in temporal evolution. Swarm durations do not scale with cumulative seismic moment: swarms lasting less than 1 day can have similar cumulative seismic moments as month-long swarms. Here, we observe stationary swarms re-occurring for years (e.g., Mineral Mountains), as well as singular swarms in low-seismicity areas (e.g., activating a local structure). The swarms show a pronounced heterogeneity in triggering and driving mechanisms, observed in the detailed analysis of exemplary sequences (detections, relocations, moment tensors, waveform-based clustering, and repeater analysis). The 2022 Sevier Valley sequence activated a BR-related normal fault, the first resolved fault plane in the valley since 1983. The 2011 Circleville sequence is interpreted as a swarm triggered by mainshock-aftershock activity characterized by increasing magnitudes, changing rupture mechanisms, and a concentration of highly similar events in the second part of the sequence. By jointly discussing exemplary sequences and catalog statistics, we draw a comprehensive picture of swarm activity and its relation to geothermal and tectonic activity.

58 GEOSCIENCES↗

Atmospheric Rivers in the Eastern and Midwestern United States Associated With Baroclinic Waves

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long-wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

58 GEOSCIENCES↗

Weakened Orographic Influence on Cool-Season Precipitation in Simulations of Future Warming Over the Western US

High-resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain-shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high-resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming.

54 ENVIRONMENTAL SCIENCES↗

Spectral Hardness of X- and Gamma-Ray Emissions From Lightning Stepped and Dart Leaders

During the 2022 New Mexico monsoon season, we deployed two X-ray scintillation detectors, coupled with a 180 MHz data acquisition system to detect X-rays from natural lightning at the Langmuir Lab mountain-top facility, located at 3.3 km above mean sea level. Data acquisition was triggered by an electric field antenna calibrated to pick up lightning within a few km of the X-ray detectors. We report the energies of over 240 individual photons, ranging between 13 keV and 3.8 MeV, as registered by the LaBr3(Ce) scintillation detector. These detections were associated with four lightning flashes. Particularly, four-stepped leaders and seven dart leaders produced energetic radiation. Importantly, the reported photon energies allowed us to confirm that the X-ray energy distribution of natural stepped and dart leaders follows a power-law distribution with an exponent ranging between 1.09 and 1.96, with stepped leaders having a harder spectrum. Characterization of the associated leaders and return strokes was done with four different electric field sensing antennas, which can measure a wide range of time scales, from the static storm field to the fast change associated with dart leaders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Impacts of Topography-Based Subgrid Scheme and Downscaling of Atmospheric Forcing on Modeling Land Surface Processes in the Conterminous US

The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). In this study, a new topography-based subgrid structure referred to as topographic units (TGU) designed to better capture subgrid topographic effects, and methods to downscale atmospheric forcing to the land TGUs have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Effects of the subgrid scheme and downscaling methods on ELM simulated land surface processes are evaluated over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.

54 ENVIRONMENTAL SCIENCES↗

Impacts of Topography‐Driven Water Redistribution on Terrestrial Water Storage Change in California Through Ecosystem Responses

Abstract Lateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125° atmospheric forcing data during 1980–2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell‐averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long‐term influences of lateral water movement on modeled biogeochemical cycle.

54 ENVIRONMENTAL SCIENCES↗

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗