Comparing Approximated Heat Stress Measures Across the United States

Climate change is escalating the threat of heat stress to global public health, with the majority of humans today facing increasingly severe and prolonged heat waves. Accurate weather data reflecting the complexity of measuring heat stress is crucial for reducing the impact of extreme heat on health worldwide. Previous studies have employed Heat Index (HI) and Wet Bulb Globe Temperature (WBGT) metrics to understand extreme heat exposure, forming the basis for heat stress guidelines. However, systematic comparisons of meteorological and climate data sets used for these metrics and the related parameters, like air temperature, humidity, wind speed, and solar radiation crucial for human thermoregulation, are lacking. We compared three heat measures (HImax, WBGTBernard, and WBGTLiljegren) approximated from gridded weather data sets (ERA5-Land, PRISM, Daymet) with ground-based data, revealing strong agreement from HI and WBGTBernard (R 2 0.76-0.95, RMSE 1.69-6.64°C). Discrepancies varied by Köppen-Geiger climates (e.g., Adjusted R 2 HImax 0.88-0.95, WBGTBernard 0.79-0.97, and WBGTLiljegren 0.80-0.96), and metrological input variables (Adjusted R 2 T max 0.86-0.94, T min 0.91-0.94, Wind 0.33, Solarmax 0.38, Solaravg 0.38, relative humidity 0.51-0.74). Gridded data sets can offer reliable heat exposure assessment, but further research and local networks are vital to reduce measurement errors to fully enhance our understanding of how heat stress measures link to health outcomes.

Verifying Experimental Wet Bulb Globe Temperature Hindcasts Across the United States

Hot and humid heat exposures challenge the health of outdoor workers engaged in occupations such as construction, agriculture, first response, manufacturing, military, or resource extraction. Therefore, government institutes developed guidelines to prevent heat-related illnesses and death during high heat exposures. The guidelines use Wet Bulb Globe Temperature (WBGT), which integrates temperature, humidity, solar radiation, and wind speed. However, occupational heat exposure guidelines cannot be readily applied to outdoor work places due to limited WBGT validation studies. In recent years, institutions have started providing experimental WBGT forecasts. These experimental products are continually being refined and have been minimally validated with ground-based observations. This study evaluated a modified WBGT hindcast using the historical National Digital Forecast Database and the European Centre for Medium-Range Weather Forecasts Reanalysis v5. We verified the hindcasts with hourly WBGT estimated from ground-based weather observations. After controlling for geographic attributes and temporal trends, the average difference between the hindcast and in situ data varied from -0.64°C to 1.46°C for different Köppen-Geiger climate regions, and the average differences are reliable for decision making. However, the results showed statistically significant variances according to geographical features such as aspect, coastal proximity, land use, topographic position index, and Köppen-Geiger climate categories. The largest absolute difference was observed in the arid desert climates (1.46: 95% CI: 1.45, 1.47), including some parts of Nevada, Arizona, Colorado, and New Mexico. This research investigates geographic factors associated with systematic WBGT differences and points toward ways future forecasts may be statistically adjusted to improve accuracy.

Observations of Drifting Snow Using FlowCapt Sensors in the Southern Altai Mountains, Central Asia

Drifting snow is a significant factor in snow redistribution and cascading snow incidents. However, field observations of drifting snow are relatively difficult due to limitations in observation technology, and drifting snow observation data are scarce. The FlowCapt sensor is a relatively stable sensor that has been widely used in recent years to obtain drifting snow observations. This study presents the results from two FlowCapt sensors that were employed to obtain field observations of drifting snow during the 2017–2018 snow season in the southern Altai Mountains, Central Asia, where the snow cover is widely distributed. The results demonstrate that the FlowCapt sensor can successfully acquire stable field observations of drifting snow. Drifting snow occurs mainly within the height range of 80-cm zone above the snow surface, which accounts for 97.73% of the total snow mass transport. There were three typical snowdrift events during the 2017–2018 observation period, and the total snowdrift flux caused during these key events accounted for 87.5% of the total snow mass transport. Wind speed controls the occurrence of drifting snow, and the threshold wind speed (friction velocity) for drifting snow is approximately 3.0 m/s (0.15 m/s); the potential for drifting snow increases rapidly above 3.0 m/s, with drifting snow essentially being inevitable for wind speeds above 7.0 m/s. Similarly, the snowdrift flux is also controlled by wind speed. The observed maximum snowdrift flux reaches 192.00 g/(m2·s) and the total snow transport is 584.9 kg/m during the snow season. Although drifting snow will lead to a redistribution of the snow mass, any accumulation or loss of the snow mass is also affected synergistically by other factors, such as topography and snow properties. This study provides a paradigm for establishing a field observation network for drifting snow monitoring in the southern Altai Mountains and bridges the gaps toward elucidating the mechanisms of drifting snow in the Altai Mountains of Central Asia. A broader network of drifting snow observations will provide key data for the prevention and control of drifting snow incidents, such as the design height of windbreak fences installed on both sides of highways.

Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.

Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia

Surface processes on debris-covered glaciers are governed by a variety of controlling factors including climate, debris load, water bodies, and topography. Currently, we have not achieved a general consensus on the role of supraglacial processes in regulating climate–glacier sensitivity in High Mountain Asia, which is mainly due to a lack of an integrated understanding of glacier surface dynamics as a function of debris properties, mass movement, and ponding. Therefore, further investigations on supraglacial processes is needed in order to provide more accurate assessments of the hydrological cycle, water resources, and natural hazards in the region. Given the scarcity of long-term in situ data and the difficulty of conducting fieldwork on these glaciers, many numerical models have been developed by recent studies. This review summarizes our current knowledge of surface processes on debris-covered glaciers with an emphasis on the related modeling efforts. We present an integrated view on how numerical modeling provide insights into glacier surface ablation, supraglacial debris transport, morphological variation, pond dynamics, and ice-cliff evolution. We also highlight the remote sensing approaches that facilitate modeling, and discuss the limitations of existing models regarding their capabilities to address coupled processes on debris-covered glaciers and suggest research directions.

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

Extent Changes in the Perennial Snowfields of Gates of the Arctic National Park and Preserve, Alaska

Perennial snowfields in Gates of the Arctic National Park and Preserve (GAAR) in the central Brooks Range of Alaska are a critical component of the cryosphere. They serve as habitat for an array of wildlife, including caribou, a species that is crucial as a food and cultural resource for rural subsistence hunters and Native Alaskans. Snowfields also influence hydrology, vegetation, permafrost, and have the potential to preserve valuable archaeological artifacts. By deriving time series maps using cloud computing and supervised classification of Landsat satellite imagery, we calculated areas and evaluated extent changes. We also derived changes in elevations of the perennial snowfields that remained stable for at least four years. For the study period of 1985 to 2017, we found that total areas of perennial snowfields in GAAR are decreasing, with most of the notable changes in the latter half of the study period. Equilibrium areas, or bright areas, of the snowfields are shrinking, while ablation, or dark areas, are growing. We also found that the snowfields occur at higher elevations over time. Climate change may be altering the distribution, elevation, and extent of perennial snowfields in GAAR, which could affect caribou populations and subsistence lifestyles in rural Alaska.

Flow Analysis at the Snow Covered High Altitude Catchment via Distributed Energy Balance Modeling

Energy budget-based distributed modeling at high-altitude glacio-nival watersheds is essential to accurately describe hydrological processes and quantify the flow rates. In this study, SNOWPACK model and its distributed version Alpine3D are applied for the first time in Pakistan to simulate the runoff response of a high altitude glaciated catchment. The basic aim was to explore the feasibility of this modeling system and its future applications in the region. Final results demonstrated satisfactory performance of the model between measured and modeled discharges with Nash-Sutcliff Efficiency of 0.54. However, total simulated flow volume differs only 1.3 times as compared to measured discharge of the lake, located at the glacier snout. Flow composition analysis revealed that the runoff regime of the study site is strongly influenced by the snow and glacier melt runoff representing 53% snowmelt and 38% glacier melt contribution. Low model efficiency has been observed during glacier melting season due to inaccurate wind speed distribution and biased input met-data. It is concluded that high performance of this model can be achieved if the model is optimized over the catchment similar to the study site provided with long term data sets. This study leaves a firm foundation for the potential application of a highly accurate distributed energy balance model in the entire Karakoram and Himalaya region to understand the melt dynamics of such a rugged terrain glacier rich mountains.

Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps

[1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (the Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.