Explore the vast range of titles available.

Global land cover data are widely used in weather, climate, and hydrometeorological models. The Collection 5.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) product is found to have a substantial amount of interannual variability, with 40% of land pixels showing land cover change one or more times during 2001–10. This affects the global distribution of vegetation if any one year or many years of data are used, for example, to parameterize land processes in regional and global models. In this paper, a value-added global 0.5-km land cover climatology (a single representative map for 2001–10) is developed by weighting each land cover type by its corresponding confidence score for each year and using the highest-weighted land cover type in each pixel in the 2001–10 MODIS data. The climatology is validated by comparing it with the System for Terrestrial Ecosystem Parameterization database as well as additional pixels that are identified from the Google Earth proprietary software database. When compared with the data of any individual year, this climatology does not substantially alter the overall global frequencies of most land cover classes but does affect the global distribution of many land cover classes. In addition, it is validated as well as or better than the MODIS data for individual years. Also, it is based on higher-quality data and is validated better than the Global Land Cover Characteristics database, which is based on 1 year of Advanced Very High Resolution Radiometer data and represents a widely used first-generation global product.

Earth's terrestrial near‐subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high‐resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (∼1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models. Key Points: We have quantified the thicknesses of permeable layers above bedrock for Earth System Models We distinguish among uplands and lowlands, using optimal models for each to predict depth to bedrock The data set honors the geologic, topographic, and climatic controls on permeable layer thicknesses

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

Global land-cover data are widely used in regional and global models because land cover influences land–atmosphere exchanges of water, energy, momentum, and carbon. Many models use data of maximum green vegetation fraction (MGVF) to describe vegetation abundance. MGVF products have been created in the past using different methods, but their validation with ground sites is difficult. Furthermore, uncertainty is introduced because many products use a single year of satellite data. In this study, a global 1-km MGVF product is developed on the basis of a “climatology” of data of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index and land-cover type, which removes biases associated with unusual greenness and inaccurate land-cover classification for individual years. MGVF shows maximum annual variability from 2001 to 2012 for intermediate values of average MGVF, and the standard deviation of MGVF normalized by its mean value decreases nearly monotonically as MGVF increases. In addition, there are substantial differences between this climatology and MGVF data from the MODIS Continuous Fields (CF) Collection 3, which is currently used in the Community Land Model. Although the CF data only use 2001 MODIS data, many of these differences cannot be explained by usage of different years of data. In particular, MGVF as based on CF data is usually higher than that based on the MODIS climatology from this paper. It is difficult to judge which product is more realistic because of a lack of ground truth, but this new MGVF product is more consistent than the CF data with the MODIS leaf area index product (which is also used to describe vegetation abundance in models).

Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

There is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.

Reductions in snow accumulation and melt in headwater basins are increasing the water stress on forest ecosystems across the western US. Forest thinning has the potential to reduce water stress by decreasing sublimation losses from canopy interception; however, it can also increase snowpack exposure to sun and wind. We used the high-resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model to investigate the effect of two virtual forest thinning scenarios on the snowpack of two adjacent watersheds (54 km2 total) in the Lake Tahoe Basin, California, where forest thinning is being planned. SnowPALM realistically represents small-scale snow-forest interactions to simulate the impact of virtual thinning experiments in which trees <10 and <20 m are removed. In general, thinning results in an overall increase in peak snow water equivalent and snowmelt. Areas around sheltered tree clusters have the largest increases of snowmelt due to decreases of canopy sublimation, while more open and exposed areas show a small decrease due to increases in snowpack sublimation. At the 30-m forest stand scale, existing forest structure controls the efficacy of thinning, where forest stands with mean leaf area index (LAI) >3 m2/m2 and 5–15-m tall show the largest increases in snow accumulation (up to 450 mm) and melt volume (up to 650 mm). Despite the role of tree- and stand-scale thinning on snowmelt, macroscale effects were limited to slightly larger increases in melt volumes at mid to low elevation slopes (<2,300 masl) and south facing areas per unit of LAI removed. A decision support tool using machine learning (random forest) was developed to synthesize SnowPALM results, and was applied to neighboring watersheds. These results will inform ongoing forest management practices in California, and improve our understanding of the effects of snow-forest interactions at scales relevant to water management.

California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

It is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors. Key Points Accumulated snowfall is a strong predictor of peak SWE and snow depth at SNOTEL and COOP sites For this data, spatial interpolation of SWE is improved by first normalizing by accumulated snowfall This interpolation produces maps of SWE comparable to existing maps that are based on much more data

Across much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April–June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April–June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April–June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biases when a more realistic amount of snow is on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April–June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring–summer transition over snowy regions.