Snow Research Articles

Enabling Advanced Snow Physics Within Land Surface Models Through an Interoperable Model‐Physics Coupling Framework

AbstractAccurate estimation of snow accumulation and melt is a critical part of decision‐making in snow‐dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah‐MP and Noah. The original LSMs and the coupled models (Noah‐MP‐Crocus and Noah‐Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah‐Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah‐MP. The Coupled Noah‐MP‐Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Extended ozone depletion and reduced snow and ice cover-Consequences for Antarctic biota.

Stratospheric ozone, which has been depleted in recent decades by the release of anthropogenic gases, is critical for shielding the biosphere against ultraviolet-B (UV-B) radiation. Although the ozone layer is expected to recover before the end of the 21st century, a hole over Antarctica continues to appear each year. Ozone depletion usually peaks between September and October, when fortunately, most Antarctic terrestrial vegetation and soil biota is frozen, dormant and protected under snow cover. Similarly, much marine life is protected by sea ice cover. The ozone hole used to close before the onset of Antarctic summer, meaning that most biota were not exposed to severe springtime UV-B fluxes. However, in recent years, ozone depletion has persisted into December, which marks the beginning of austral summer. Early summertime ozone depletion is concerning: high incident UV-B radiation coincident with snowmelt and emergence of vegetation will mean biota is more exposed. The start of summer is also peak breeding season for many animals, thus extreme UV-B exposure (UV index up to 14) may come at a vulnerable time in their life cycle. Climate change, including changing wind patterns and strength, and particularly declining sea ice, are likely to compound UV-B exposure of Antarctic organisms, through earlier ice and snowmelt, heatwaves and droughts. Antarctic field research conducted decades ago tended to study UV impacts in isolation and more research that considers multiple climate impacts, and the true magnitude and timing of current UV increases is needed.

ОБРАБОТКА СПУТНИКОВОЙ ИНФОРМАЦИИ В ИНТЕРЕСАХ ГИДРОМЕТЕОРОЛОГИИ С ИСПОЛЬЗОВАНИЕМ НЕЙРОСЕТЕВЫХ ТЕХНОЛОГИЙ: ПОДХОДЫ НИЦ “ПЛАНЕТА”

The paper presents an experience of using artificial intelligence techniques, in particular, neural networks to solve relevant problems of hydrometeorology. The results of the investigations at the Planeta State Research Center on Space Hydrometeorology in detecting clouds and snow cover from the Himawari, Electro-L, and Meteor-M satellite data, as well as on classifying cloud types according to the AHI instrument data (Himawari-8) are reported. The findings of the work on retrieving values of total ozone and water vapor according to the infrared sensing devices are demonstrated. The work on detecting the boundaries of the ice cover and river floods from medium- and high-resolution satellite instruments, as well as the technologies for temperature and humidity sensing in the microwave spectrum are considered. The studies have shown that the use of neural network technologies provides the required accuracy of the received hydrometeorological information and high speed of processing incoming data.

Effect of Atmospheric Circulation on the Seasonal Dynamics of the Chemical Composition of the Snow Cover in the Pechora–Ilych Reserve

Effect of Atmospheric Circulation on the Seasonal Dynamics of the Chemical Composition of the Snow Cover in the Pechora–Ilych Reserve

Modification and Validation of the Soil–Snow Module in the INM RAS Climate Model

This paper describes the modification of a simple land snow cover module of the INM RAS climate model. The possible liquid water and refreezing of meltwater in the snow layer are taken into account by the proposed parameterization. This is particularly important for modelling the transition season, as this phenomenon is mainly observed during the formation and melting of the snow cover when the surface temperature fluctuates around 0 °C. The snow density evolution simulation is also added. This parameterization is implemented in the INM-CM snow module and verified on observation data using the ESM-SnowMIP-like protocol. As a result, the INM-CM mean climate snow melt periods are refined, particularly in middle and high latitudes. The snow-covered area according to the model is also improved. In the future, a modified version of the land snow module can be used, coupled with a snow albedo model that takes into account snow metamorphism. This module can also be applied to sea ice snow.

Evaluating the impact of climatic changes on streamflow in headwater mountain catchments with varying human pressure. An example from the Tatra Mountains (Western Carpathians)

Study regionTatra Mountains, Western Carpathians Study focusOur study aims to assess the impact of climatic changes on streamflow in headwater mountain catchments with varying human pressure. The research bases on hydrological and climatological data from a period 1971–2020 and includes trend analysis, wavelet analysis and paired catchment observations, comparing semi-natural and human-altered catchments. New hydrological insights for the regionAir temperature has increased (+0.4°C decade−1) in the region. Precipitation totals remained unchanged, however annual snowfall totals decreased (−69 mm decade−1, ∼6.3%) at the expense of rainfalls, especially in winter. Air temperature and precipitation exhibited altitude-dependent changes. Snow depths remained stable; however, snow cover duration has shortened (−2 to −4 days decade−1). The observed climatic changes have led to an increase in annual low flows (+7–11.5% decade−1), as well as winter low, average, and high flows, affecting the timing of snowmelt runoff. Streamflow changes were more pronounced in semi-natural catchments. Climatic-driven trends in streamflow were more evident in winter, whereas trends associated with human activities and land use were detected throughout the year. Discharge was more coherent with precipitation in human-altered catchments, and weaker in semi-natural catchments. The intensification of annual cycle in precipitation and semiannual cycle in snow cover at high altitudes, along with the intensification of semiannual cycle in streamflow, provide evidence of ongoing climatic changes.

Canopy cover and microtopography control precipitation-enhanced thaw of ecosystem-protected permafrost

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020–2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015–2020) to 56 ± 12 cm yr−1 (2020–2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Formation of subaerial taliks in Larsemann hills, princess Elizabeth land, East Antarctica

This study presents a mathematical model of heat and mass transfer for modelling the formation and evolution of taliks in the upper subsurface of the Larsemann Hills in East Antarctica. Four simulation scenarios were considered. The first one showed that thawed areas cannot form under snowfields. The second and third scenarios demonstrated the taliks did not form if the snow melted immediately after set the positive air temperatures and covered the surface 20 days before the set of negative air temperatures or immediately after that. According to the last scenario, talik forms when snow cover persists even with positive air temperature, and snow falls before the air temperature becomes negative. Modelling demonstrates that taliks in the Larsemann Hills exist, but they are mostly in an unstable state.

Application of surface energy balance Algorithm for land (SEBAL) for mapping spatiotemporal evapotranspiration over various climatic zones of Pakistan

To address the emerging issues of water scarcity in a hydrological realm, estimation of evapotranspiration (ET) at spatiotemporal scale is essential. It is crucial for understanding climate change, particularly for Pakistan's ongoing water resource management policy under the United Nations' Sustainable Development Goals (SDGs), where water is given top priority. In situ techniques can quantify ET, but with a small spatial footprint, it cannot accurately depict its regional distribution. Therefore, geospatial techniques and data, especially high resolution satellite imagery, estimate ET for large heterogeneous surfaces. The Surface Energy Balance Algorithm for Land (SEBAL) was employed in this study to estimate the spatiotemporal distribution of ET in various climatic zones of Pakistan using Landsat 8 images. A total of 48 satellite images were processed for six study sites (Skardu, Kalam, Peshawar, D.I. Khan, Quetta and Tando Jam) for the summer months from 2013 to 2020. Accuracy of SEBAL-based ET was evaluated with ground-based ET. The model performs well with daily rate of ET under southern desert hot climate of Tando Jam with highest pearson’s correlation coefficient (r) of 0.89, lowest root mean squared difference (RMSD) and mean bias error (MBE) of 0.42 mm/day and 0.26 mm/day, respectively. The steppe-hot and extremely humid climate of Peshawar follow Tando Jam, with r value of 0.82, RMSD value of 0.76 mm/day, and MBE value of 0.40 mm/day. Central desert hot climate of Dera Ismail Khan(D.I. Khan) has the lowest (though still better) r value at 0.74, while the other two indices (RMSD and MBE) do not differ significantly from other sites. A high RMSD (0.84 mm/day) for the daily ET rate is recorded in Skardu's northern, cold, snowy climate; the rest of the indices are comparable to D.I. Khan region. In Kalam, the ET rate for various land uses was calculated and was found highest for snow cover (4.66 to 6.45 mm/day), followed by forest cover (3.69 to 5.79 mm/day), and open water body (3.25 to 5.25 mm/day). The SEBAL model exhibitedtremendous potential to be applied under a range of climatic conditionsin this study. Evapotranspiration can provide vital information for monitoring hydrological extremes (droughts and floods), environmental protection, planning climate-resilient agriculture, combating climate change, and sustainable management of water resources.

Analysis of rapid snow and ice cover loss in mountain glaciers of arid and semi-arid regions using remote sensing data

This article investigates the effects of climate change on snow cover and glacier in mountainous glacial areas in northern region of Iran. Due to the lack of glacier measuring stations, accurately assessing changes in snow cover over the past two decades is a challenge for water experts. This research instead utilized remote sensing data from the MODIS sensor on the Terra satellite in Google Earth Engine (GEE) system covering the period from 2000 to 2020. The annual temperature changes near the Earth's surface, using ERA5 data, showed a significant increase of 1.5 °C in average temperature over the past thirty years. The Mann-Kendall test confirms the statistical significance of the temperature rise, with a significance value of 3.5 and a p-value of 0.002. For MODIS data validation, two methods of overall accuracy and the Kappa coefficient were used with 93% and 85% of accuracy. While the annual snowfall and volume have remained relatively stable over the past 20 years, the findings indicate a decreasing trend of snow cover during warmer months (summer) due to rising air temperatures. The findings show a strong negative correlation coefficient of −0.8 between snow cover and annual average temperature. The results indicate a 30% decrease in summer snow cover compared to the spatial extent of two decades ago. The rapid snowmelt poses a significant threat to permanent glaciers in the region as the protective snow cover diminishes, putting these glaciers at risk of completely melting in the future. The loss of these glaciers will have severe consequences for the environment, including impacts on local water supplies, river flows, and the ecosystem.

A 20-Year Ecotone Study of Pacific Northwest Mountain Forest Vulnerability to Changing Snow Conditions

(1) Background: Global climate change is expected to significantly alter growing conditions along mountain gradients. Landscape ecological patterns are likely to shift significantly as species attempt to adapt to these changes. We evaluated the extent to which spatial (elevation and canopy cover) and temporal (decadal trend and El Niño–Southern Oscillation/Pacific Decadal Oscillation) factors impact seasonal snowmelt and forest community dynamics in the Western Hemlock–True Fir ecotone region of the Oregon Western Cascades, USA. (2) Methods: Tsuga heterophylla and Abies amabilis seedling locations were mapped three times over 20 years (2002–2022) on five sample transects strategically placed to cross the ecotone. Additionally, daily ground temperature readings were collected over 10 years for the five transects using 123 data loggers to estimate below-canopy snow metrics. (3) Results: Based on validation using time-lapse cameras, the data loggers proved highly reliable for estimating snow cover. The method reported fewer days of snow cover as compared to meteorological station-based snow products for the region, emphasizing the importance of direct under-canopy field observations of snow. Snow season variability was most significantly impacted temporally by cyclical ENSO/PDO climate patterns and spatially by differences in canopy cover within the ecotone. The associated seedling analysis identified clear sorting of species by elevation within the ecotone but reflected a lack of a long-term trend, as species dominance in the seedling strata did not significantly shift along the elevation gradient over the 20-year study. (4) Conclusions: The data logger-based approach provided estimates of snow cover at ecologically significant locations and fine enough spatial resolutions to allow for the study of forest regeneration dynamics. The results highlight the importance of long-term, understory snow measurements and the influence of climatic oscillations in understanding the vulnerability of mountain areas to the changing climate.

Spatio-temporal dynamics in microalgal communities in Arctic land-fast sea ice

Sea ice microalgae are an important source of energy for the polar marine food web, representing the primary carbon source prior to pelagic phytoplankton blooms. Here we investigate community dynamics of sea ice microalgal communities in land-fast sea ice across six different fjords in high-Arctic Svalbard, Norway, during Spring (April – May). We found that light (0.1 – 23% incoming PAR / 0.1 – 193 μmol photons m-2s-1) played a central role in determining community composition, with more diverse assemblages observed in sites with more light transmitted to the bottom ice community. In April, microalgal assemblages were similar when under-ice light transmittance was similar, independent of geographical location, however this light-derived separation of community structure was not evident in May. At all sites, assemblages were dominated by pennate diatoms, with the most abundant taxon being Nitzschia frigida. However, with increasing under-ice light transmittance, we saw an increase in the relative abundance of Dinophyceae, Navicula spp. and Thalassiosira spp.. A positive relationship between light and δ13C enrichment and C:N ratios in the ice algal biomass demonstrated the effect of light on the biochemical composition of ice algae. Light did not correlate with cell abundance or chlorophyll a concentration. With anticipated changes to Arctic sea ice extent and snow cover as a result of climate change, we will see shifts in the light transmitted to the bottom ice community. These shifts, whether caused by reduced light transmittance from increased snow cover or increased light transmittance from thinning ice, snow depth or increased rainfall, will likely alter sea ice microalgal community composition, which in turn, may influence the success of secondary production and biogeochemical cycling in polar waters.

GNSS-R snow depth retrieval algorithm based on PSO-LSTM

The global navigation satellite system (GNSS)-interferometric reflectometry technique has been applied to retrieve snow depth, which has a high potential for application. The GNSS reflectometry classical algorithm retrieves the snow depth by extracting the frequency of the multipath signal and substituting it into an empirical formula. However, the retrieval errors of high and low snow depths are large due to the influence of factors such as surface vegetation and terrain environment. In this paper, we propose a snow depth retrieval algorithm based on a particle swarm optimized long short-term memory (PSO-LSTM) neural network. The algorithm extracted three characteristic parameters (frequency, amplitude, and phase) from the signal-to-noise ratio (SNR) data as inputs, and optimized the LSTM hyperparameters by the PSO algorithm to improve the retrieval accuracy for low snow depths and snow depths close to the antenna. The snow depth retrieval results of global positioning system L1 band SNR data collected from the P351 station in 2022 and AB33 station in 2017 were evaluated in this paper. The snow depth retrieval results of the PSO-LSTM algorithm for P351 station were in high agreement with the snow depth data provided by the snowpack telemetry network; the coefficient of determination ( R2 ) reached 0.986, and the root mean square error (RMSE) and mean absolute error (MAE) were 7.30 cm and 4.94 cm, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 53.0% and 30.4% for the retrieval results of snow depths below 15 cm at the P351 station, and by 76.8% and 84.4% for the retrieval results of snow depths above 117 cm from the 1st day to the 137th day, respectively. Similarly, the RMSE, MAE, and R2 for the 2017 retrieval results at AB33 station were 5.90 cm, 4.25 cm, and 0.965, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 47.9% and 33.0% for the retrieval results of snow depths below 15 cm at the AB33 station, and by 75.4% and 82.3% for the retrieval results of snow depths above 56 cm from the day 46 to day 120. In addition, the snow depth retrieval algorithm was proposed in this paper does not require antenna height and empirical formulas to realize snow depth retrieval, and at the same time, the algorithm effectively improved the retrieval accuracy for both high and low snow depths with strong robustness.

Impacts of snow cover seasonality on spring land surface phenology of forests in Changbai mountains of Northeast China

Snow cover phenology (SCP) strongly affects forest spring phenology (the start of growing season, SOS), but the underlying mechanism of the relationship varies. In this study, we aimed to analyze the relationship between forest SOS and SCP, and investigate the mechanisms about how changes of SCP affect forest SOS. To do so, we extracted forest SOS and SCP from multiple remote sensing datasets and analyzed the spatio-temporal patterns of both in Changbai Mountains (2001−2020). We assessed the relationships between SCP and forest SOS using partial least squares regression analysis and investigated the potential mechanism of SCP changes affecting on forest SOS using path analysis. We found earlier forest SOS (−0.5 days/year), prolonged snow cover duration (SCD, 0.43 day/year), and earlier snow cover end day (SCED, −0.1 days/year) in the region. The results indicated that SCD showed negative influence while SCED showed positive influence on forest SOS in most of the region. Results revealed that the influence of SCP on forest SOS was mainly through altering spring temperature and the dominant path of SCP influencing forest SOS followed hydrothermal gradients. Our study reveals new insights into the influence of changing SCP on forest SOS, which provides the theoretical basis for including SCP in the phenological models.

Snow mechanical property variability at the slope scale – implication for snow mechanical modelling

Abstract. Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and microtopographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covariates. The use of covariates in GAMs suggested that microtopographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.

Die another day: explanations based on qualitative comparative analysis (QCA) for the survival and non-survival of isolated ski lifts in Switzerland

Abstract. In the form of an explorative empirical study, this paper deals with the reasons for the survival and demise of isolated Swiss ski lifts. For the first time, all isolated lifts documented in Switzerland have been recorded and coded according to a total of six conditions. Using a set-theoretical research method in the form of qualitative comparative analysis (QCA), the study aims to identify the necessary conditions and configurations of sufficient conditions explaining (non-)survival. It transpires that closed isolated lifts tend to be outdated and have no technical snowmaking facilities. Moreover, it has become evident that the simultaneous occurrence of the lack of lift facility replacement, lack of technical snowmaking and high ski area competition has caused the closure of most isolated lifts. Low natural snow depth and low elevation difference, conversely, have not had a measurable impact. The causes for the survival of isolated lifts, by contrast, are extremely heterogeneous.

A newly developed model for estimating snow depth in ungauged areas

The presence of snow significantly affects the hydrological cycle and soil moisture globally. Nowadays, with the expansion of science, different satellites can measure snow depth all over the world. By establishing a conditional relationship between mean temperature and the Proportion of Snowfall to Total Precipitation (PSTP), this study proposes a method for snow depth estimation for ungauged stations. The case study of this research is Iran. Most of Iran's climate is arid due to its location in the Middle East, making it one of the countries with the lowest rainfall worldwide. On the other hand, this country enormously depends on water resources. Consequently, an accurate and valid estimate of snow amounts in Iran is essential. To reach this goal, we analyzed daily data from 12 synoptic and climatological stations between 1970 and 2020, including rainfall, snow depth, and mean temperature. For each station, the PSTP at 0.5 °C air temperature intervals was determined and fitted to a double sigmoid model that allows snow depth estimation. The evaluation of the snow depth approximated by the double sigmoid model was done using R2, RRMSE, EF, and Bias statistics against the observational data. R2 values in the Era-5 dataset in all stations are lower than 0.5. Additionally, it should be noted that in certain stations, the bias values surpass 20 mm. Furthermore, in 75 percent of the stations, the RRMSE values exceed 0.6. By utilizing the DSBE model, it is possible to achieve a reduction of 122.4 and 0.7 in Bias and RRMSE values, respectively. The R2 between observed and estimated snow depth was estimated between 0.81 and 1 in ten of the studied stations and 0.65 and 0.8 in the other two stations. Moreover, the findings imply that the proposed model is a suitable technique for estimating snow depth in remote areas lacking snowfall measurement.

Efficient Representation Learning of Satellite Image Time Series and Their Fusion for Spatiotemporal Applications

Satellite data bolstered by their increasing accessibility is leading to many endeavors of automated monitoring of the earth's surface for various applications. Such applications demand high spatial resolution images at a temporal resolution of a few days which entails the challenge of processing a huge volume of image time series data. To overcome this computing bottleneck, we present PatchNet, a bespoke adaptation of beam search and attention mechanism. PatchNet is an automated patch selection neural network that requires only a partial spatial traversal of an image time series and yet achieves impressive results. Satellite systems face a trade-off between spatial and temporal resolutions due to budget/technical constraints e.g., Landsat-8/9 or Sentinel-2 have high spatial resolution whereas, MODIS has high temporal resolution. To deal with the limitation of coarse temporal resolution, we propose FuSITSNet, a twofold feature-based generic fusion model with multimodal learning in a contrastive setting. It produces a learned representation after fusion of two satellite image time series leveraging finer spatial resolution of Landsat and finer temporal resolution of MODIS. The patch alignment module of FuSITSNet aligns the PatchNet processed patches of Landsat-8 with the corresponding MODIS regions to incorporate its finer resolution temporal features. The untraversed patches are handled by the cross-modality attention which highlights additional hot spot features from the two modalities. We conduct extensive experiments on more than 2000 counties of US for crop yield, snow cover, and solar energy prediction and show that even one-fourth spatial processing of image time series produces state-of-the-art results. FuSITSNet outperforms the predictions of single modality and data obtained using existing generative fusion models and allows for monitoring of dynamic phenomena using freely accessible images, thereby unlocking new opportunities.

Freezing and Mechanical Failure of a Habitat-Forming Kelp in the Rocky Intertidal Zone.

Kelp and other habitat-forming seaweeds in the intertidal zone are exposed to a suite of environmental factors, including temperature and hydrodynamic forces, that can influence their growth, survival, and ecological function. Relatively little is known about the interactive effect of temperature and hydrodynamic forces on kelp, especially the effect of cold stress on biomechanical resistance to hydrodynamic forces. We used the intertidal kelp Egregia menziesii to investigate how freezing in air during a low tide changes the kelp's resistance to breaking from hydrodynamic forces. We conducted a laboratory experiment to test how short-term freezing, mimicking a brief low-tide freezing event, affected the kelp's mechanical properties. We also characterized daily minimum winter temperatures in an intertidal E. menziesii population on San Juan Island, WA, near the center of the species' geographic range. In the laboratory, acute freezing events decreased the strength and toughness of kelp tissue by 8-20% (change in medians). During low tides in the field, we documented sub-zero temperatures, snow, and low canopy cover (compared to summer surveys). These results suggest that freezing can contribute to frond breakage and decreased canopy cover in intertidal kelp. Further work is needed to understand whether freezing and the biomechanical performance in cold temperatures influence the fitness and ecological function of kelp and whether this will change as winter conditions, such as freezing events and storms, change in frequency and intensity.

Perennial snow and ice cover change from 2001 to 2021 in the Hindu-Kush Himalayan region derived from the Landsat analysis-ready data

The changing climate directly affects spatial and temporal patterns of snow and ice cover globally and in the Hindu-Kush Himalayan (HKH) region. In the HKH, around 3.3 billion people across 11 countries depend on water originating from mountain glaciers and snowfields, and melting snow cover has a direct impact on their livelihood and well-being. Various studies have shown that the snow and ice cover in the HKH is declining at an alarming rate but have been limited in geographic, spatial and temporal scales. Here, we employed the Global Land Analysis and Discovery analysis ready Landsat time-series data (GLAD ARD) to map changes in perennial snow and ice between 2001 and 2021 at five-year epochs using decision tree ensemble models. These maps were used to create a stratified sampling design for reference data collection to estimate area and map accuracy. All five-year epoch maps have user's accuracies above 90% and producer's accuracies above 91%. Our sample data analysis showed that one-eighth of the extent of perennial snow and ice in the HKH, totalling 15,770 km2 (CI ± 3195 km2), disappeared over the last two decades with 105,935 km2 (CI ± 3396 km2) remaining in 2021. From map-based estimates, the largest decline of perennial snow and ice cover among the mountain ranges was found in the Himalayas with an estimated reduction of 5741 km2. Among HKH countries, China had the largest area of perennial snow and ice reduction of 10,654 km2, Nepal had the highest net perennial snow and ice reduction rate (31%). Among the river basins, the maximum net loss of perennial snow and ice cover between 2001 and 2021 was in the Indus (24.8%), followed by Brahmaputra (18.3%), and Tarim (15.7%). Our results confirm wide-spread loss of perennial snow and ice across the HKH region and provide both regionally consistent maps with derived area estimates at landform, national and basin-levels and methodology to continue monitoring snow and ice melt.