Search

Abstract. Given the tradeoffs between spatial and temporal resolution, questions aboutresolution optimality are fundamental to the study of global snow. Answersto these questions will inform future scientific priorities and missionspecifications. Heterogeneity of mountain snowpacks drives a need for dailysnow cover mapping at the slope scale (≤30 m) that is unmet for avariety of scientific users, ranging from hydrologists to the military towildlife biologists. But finer spatial resolution usually requires coarsertemporal or spectral resolution. Thus, no single sensor can meet all theseneeds. Recently, constellations of satellites and fusion techniques havemade noteworthy progress. The efficacy of two such recent advances isexamined: (1) a fused MODIS–Landsat product with daily 30 m spatialresolution and (2) a harmonized Landsat 8 and Sentinel 2A and B (HLS) product with3–4 d temporal and 30 m spatial resolution. State-of-the-art spectral unmixingtechniques are applied to surface reflectance products from 1 and 2 tocreate snow cover and albedo maps. Then an energy balance model was run toreconstruct snow water equivalent (SWE). For validation, lidar-basedAirborne Snow Observatory SWE estimates were used. Results show thatreconstructed SWE forced with 30 m resolution snow cover has lower bias, ameasure of basin-wide accuracy, than the baseline case using MODIS (463 mcell size) but greater mean absolute error, a measure of per-pixelaccuracy. However, the differences in errors may be within uncertaintiesfrom scaling artifacts, e.g., basin boundary delineation. Other explanationsare (1) the importance of daily acquisitions and (2) the limitations ofdownscaled forcings for reconstruction. Conclusions are as follows: (1) spectrallyunmixed snow cover and snow albedo from MODIS continue to provide accurateforcings for snow models and (2) finer spatial and temporal resolutionthrough sensor design, fusion techniques, and satellite constellations arethe future for Earth observations, but existing moderate-resolution sensorsstill offer value.

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

Abstract. Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤ 30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: 1) a fused MODIS - Landsat product with daily 30 m spatial resolution; and 2) a harmonized Landsat 8 - Sentinel 2A/B (HLS) product with 2–3 day temporal and 30 m spatial resolution. State-of-art spectral unmixing techniques are applied to surface reflectance products from 1 & 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size), but higher mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts e.g., basin boundary delineation. Other explanations are 1) the importance of daily acquisitions and 2) the limitations of downscaled forcings for reconstruction. Conclusions are: 1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models; and 2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations.

Abstract: Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth's land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth's mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo‐photogrammetry at ∼30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo‐photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root‐mean‐square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine‐scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth's mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth's surface.

We present the first application of the Snow Covered Area and Grain size model (SCAG) to the Visible Infrared imaging Radiometer Suite (VIIRS) and assess these retrievals with finer‐resolution fractional snow cover maps from Landsat 8 Operational Land Imager (OLI). Because Landsat 8 OLI avoids saturation issues common to Landsat 1–7 in the visible wavelengths, we re-assess the accuracy of the SCAG fractional snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) that were previously evaluated using data from earlier Landsat sensors. Use of the fractional snow cover maps from Landsat 8 OLI shows a negative bias of −0.5% for MODSCAG and −1.3% for VIIRSCAG, whereas previous MODSCAG evaluations found a bias of −7.6% in the Himalaya. We find similar root mean squared error (RMSE) values of 0.133 and 0.125 for MODIS and VIIRS, respectively. The Recall statistic (probability of detection) for cells with more than 15% snow cover in this challenging steep topography was found to be 0.90 for both MODSCAG and VIIRSCAG, significantly higher than previous evaluations based on Landsat 5 Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+). In addition, daily retrievals from MODIS and VIIRS are consistent across gradients of elevation, slope, and aspect. Different native resolutions of the gridded products at 1 km and 500 m for VIIRS and MODIS, respectively, result in snow cover maps showing a slightly different distribution of values with VIIRS having more mixed pixels and MODIS having 7% more pure snow pixels. Despite the resolution differences, the snow maps from both sensors produce similar total snow-covered areas and snow-line elevations in this region, with R2 values of 0.98 and 0.88, respectively. We find that the SCAG algorithm performs consistently across various spatial resolutions and that fractional snow cover maps from the VIIRS instruments aboard Suomi NPP, JPPS–1, and JPPS–2 can be a suitable replacement as MODIS sensors reach their ends of life.

Abstract. Intrinsic albedo is the bihemispherical reflectance independent of effectsof topography or surface roughness. Conversely, the apparent albedo is thereflected radiation divided by the incident and may be affected bytopography or roughness. For snow, the surface is often rough, and these twooptical quantities have different uses: intrinsic albedo is used inscattering equations whereas apparent albedo should be used in energybalance models. Complementing numerous studies devoted to surface roughnessand its effect on snow reflectance, this work analyzes a time series ofintrinsic and apparent snow albedos over a season at a sub-alpine site usingan automated terrestrial laser scanner to map the snow surface topography.An updated albedo model accounts for shade, and in situ albedo measurementsfrom a field spectrometer are compared to those from a spacebornemultispectral sensor. A spectral unmixing approach using a shade endmember(to address the common problem of unknown surface topography) produces grainsize and impurity solutions; the modeled shade fraction is compared to theintrinsic and apparent albedo difference. As expected and consistent withother studies, the results show that intrinsic albedo is consistentlygreater than apparent albedo. Both albedos decrease rapidly as ablationhollows form during melt, combining effects of impurities on the surface andincreasing roughness. Intrinsic broadband albedos average 0.056 greater thanapparent albedos, with the difference being 0.052 in the near infrared or0.022 if the average (planar) topography is known and corrected. Fieldmeasurements of spectral surface reflectance confirm that multispectralsensors see the apparent albedo but lack the spectral resolution todistinguish between darkening from ablation hollows versus lowconcentrations of impurities. In contrast, measurements from the fieldspectrometer have sufficient resolution to discern darkening from the twosources. Based on these results, conclusions are as follows: (1) impurity estimatesfrom multispectral sensors are only reliable for relatively dirty snow withhigh snow fraction; (2) a shade endmember must be used in spectral mixturemodels, even for in situ spectroscopic measurements; and (3) snow albedomodels should produce apparent albedos by accounting for the shade fraction.The conclusion re-iterates that albedo is the most practical snowreflectance quantity for remote sensing.

Abstract: Over the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes. Furthermore, cloud computing and computational techniques are accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world's water and related challenges facing society? In this commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen by integrating multidisciplinary and multisensor data, leveraging commercial satellite measurements, and employing data assimilation, cloud computing, and machine learning. We provide several recommendations to these ends.

While climate change will challenge the future of California’s water resources, groundwater can buffer variability in precipitation and streamflow, if managed sustainably. Enhanced river recharge is an important tool to reach sustainable groundwater management in the California Central Valley (USA). Understanding and predicting recharge rates of river water, either natural river bank infiltration or managed aquifer recharge (MAR) during floods (Flood-MAR) or on agricultural land (Ag-MAR) is essential to evaluate the sustainability of groundwater management plans. Groundwater ages, combined with other isotopic and noble gas evidence, can elucidate surface water-groundwater interactions and support river recharge rates calculations over longer time periods.Our study is focused on the recharge from the Cosumnes River in the California Central Valley. The Cosumnes River forms the boundary between the Sacramento Valley groundwater basin to the north and the San Joaquin Valley groundwater basin to the south. For this study, 28 new samples were collected for the analysis of 3H/3He age, noble gases, and stable isotopes. 25 additional samples from the California Waterboards Groundwater Ambient Monitoring and Assessment (GAMA) Shallow Aquifer Assessment program were included, which were collected and analyzed by the USGS California Water Science Center in 2017.We find that 28% of groundwater in the San Joaquin – Cosumnes groundwater subbasin originated as river water recharge, based on the interpolated mean δ18O (7.7 ‰ ), compared with river water (-9 ‰) and local precipitation recharge (-7 ‰) end-members. River water is a source of modern recharge, resulting in high tritium concentrations close to the Cosumnes River. In contrast, ambient groundwater from local precipitation recharge is predominantly pre-modern or fossil, containing less than 1 pCi/L tritium. Combining groundwater ages with the distance to the river, aquifer thickness, and porosity, estimates of river water recharge rate vary between 0.02 km3/yr and 0.035 km3/yr. These quantitative estimates of river water recharge will constrain the numerical groundwater flow model for this basin and aid groundwater managers in developing sustainability plans to balance groundwater pumping with recharge rates.

Abstract. Sub-seasonal to seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging but has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called Impact of initialized Land Surface temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P), as the first international grass-root effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land/atmosphere interactions. LS4P focuses on process understanding and predictability, hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than forty groups worldwide have participated in this effort, including 21 Earth System Models, 9 regional climate models, and 7 data groups. This paper overviews the history and objectives of LS4P, provides the first phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST in the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation and its S2S prediction. LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations which both tend to limit the soil memory; and ii) reanalysis data, that are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Warming winters will reduce ice cover and change under-ice conditions in temperate mountain lakes, where snow contributes most of winter cover on lakes. Snow-dominated mountain lakes are abundant and highly susceptible to climate warming, yet we lack an understanding of how climate variation and local attributes influence winter processes. We investigated climatic and intrinsic controls on ice phenology, water temperature, and bottom-water dissolved oxygen (DO) in 15 morphologically diverse lakes in the Sierra Nevada and Klamath Mountains of California, USA, using high-frequency measurements from multiple (2–5) winters. We found that ice phenology was determined by winter climate variables (snowfall and air temperature) that influence ice-off timing, whereas ice-on timing was relatively invariant among years. Lake size and morphology mediated the effect of climate on lake temperature and DO dynamics in early and late winter. Rates of hypolimnetic DO decline were highest in small, shallow lakes, and were unrelated to water temperature. Temperature and oxygen dynamics were more variable in small lakes because heavy snowfall caused ice submergence, mixing, and DO replenishment that affected the entire water column. As the persistence of snow declines in temperate mountain regions, autumn, and spring climatic conditions are expected to gain importance in regulating lake ice phenology. Water temperature and DO will likely increase in most lakes during winter as snowpack declines, but morphological attributes such as lake size will determine the sensitivity of ice phenology and under-ice processes to climate change.

We present the first application of the Snow Covered Area and Grain size model (SCAG) to the Visible Infrared imaging Radiometer Suite (VIIRS) and assess these retrievals with finer‐resolution fractional snow cover maps from Landsat 8 Operational Land Imager (OLI). Because Landsat 8 OLI avoids saturation issues common to Landsat 1–7 in the visible wavelengths, we re-assess the accuracy of the SCAG fractional snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) that were previously evaluated using data from earlier Landsat sensors. Use of the fractional snow cover maps from Landsat 8 OLI shows a negative bias of −0.5% for MODSCAG and −1.3% for VIIRSCAG, whereas previous MODSCAG evaluations found a bias of −7.6% in the Himalaya. We find similar root mean squared error (RMSE) values of 0.133 and 0.125 for MODIS and VIIRS, respectively. The Recall statistic (probability of detection) for cells with more than 15% snow cover in this challenging steep topography was found to be 0.90 for both MODSCAG and VIIRSCAG, significantly higher than previous evaluations based on Landsat 5 Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+). In addition, daily retrievals from MODIS and VIIRS are consistent across gradients of elevation, slope, and aspect. Different native resolutions of the gridded products at 1 km and 500 m for VIIRS and MODIS, respectively, result in snow cover maps showing a slightly different distribution of values with VIIRS having more mixed pixels and MODIS having 7% more pure snow pixels. Despite the resolution differences, the snow maps from both sensors produce similar total snow-covered areas and snow-line elevations in this region, with R2 values of 0.98 and 0.88, respectively. We find that the SCAG algorithm performs consistently across various spatial resolutions and that fractional snow cover maps from the VIIRS instruments aboard Suomi NPP, JPPS–1, and JPPS–2 can be a suitable replacement as MODIS sensors reach their ends of life.

Abstract: Groundwater plays a critical role in sustaining agriculture in California's Central Valley (CV). However, groundwater storage in the CV has been declining by around 3 km3/year over the last several decades, with much larger declines during the 2007–2009 and 2012–2015 droughts. Managed Aquifer Recharge (MAR) can potentially mitigate existing overdraft by recharging excess streamflows (during flood periods) to the aquifers. However, the degree to which the existing CV groundwater overdraft might be mitigated by MAR is unknown. We applied a coupled surface water‐groundwater simulation model to quantify the potential for groundwater overdraft recovery via MAR. To quantify the potential benefit of MAR, we used the coupled surface water‐groundwater model to simulate water allocation scenarios where streamflow above the 90th or 80th percentiles was reallocated to aquifers, subject to constraints on the maximum depth of applied water (0.61 and 3.05 m). Our results show that MAR could recover 9–22% of the existing groundwater overdraft CV‐wide based on a 56‐year simulation (1960–2015). However, the impact of MAR varies strongly among regions. In the southern CV where groundwater depletion is most serious, the contribution of MAR to the overdraft recovery would be small, only 2.7–3.2% in the Tulare basin and 3.2–7.8% in the San Joaquin basin. However, transferring excess winter flow from the northern to the southern CV for MAR would increase the overdraft recovery to 30% in San Joaquin and 62% in Tulare. Our results also indicate that MAR has the potential to supplement summer low flows (52–73%, CV‐wide) and reduce flood peaks.

The land surface temperature (LST) changes in North America are very abnormal recently, but few studies have systematically researched these anomalies from several aspects, especially the influencing forces. After reconstructing higher quality MODIS monthly LST data (0.05° * 0.05°) in 2002-2018, we analyzed the LST changes especially anomalous changes and their driving forces in North America. Here we show that North America warmed at the rate of 0.02 °C/y. The LST changes in three regions, including frigid region in the northwestern (0.12 °C/y), the west coast from 20°N-40°N (0.07 °C/y), and the tropics south of 20°N (0.04 °C/y), were extremely abnormal. The El Nino and La Nina were the main drivers for the periodical highest and lowest LST, respectively. The North Atlantic Oscillation was closed related to the opposite change of LST in the northeastern North America and the southeastern United States, and the warming trend of the Florida peninsula in winter was closely related to enhancement of the North Atlantic Oscillation index. The Pacific Decadal Oscillation index showed a positive correlation with the LST in most Alaska. Vegetation and atmospheric water vapor also had a profound influence on the LST changes, but it had obvious difference in latitude.

Automated, reliable cloud masks over snow-covered terrain would improve the retrieval of snow properties from multispectral satellite sensors. The U.S. Geological Survey and NASA chose the currently operational cloud masks based on global performance across diverse land cover types. This study assesses errors in these cloud masks over snow-covered, midlatitude mountains. We use 26 Landsat 8 scenes with manually delineated cloud, snow, and land cover to assess the performance of two cloud masks: CFMask for the Landsat 8 OLI and the cloud mask that ships with Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance products MOD09GA and MYD09GA. The overall precision and recall of CFMask over snow-covered terrain are 0.70 and 0.86; the MOD09GA cloud mask precision and recall are 0.17 and 0.72. A plausible reason for poorer performance of cloud masks over snow lies in the potential similarity between multispectral signatures of snow and cloud pixels in three situations: (1) Snow at high elevation is bright enough in the "cirrus" bands (Landsat band 9 or MODIS band 26) to be classified as cirrus. (2) Reflectances of "dark" clouds in shortwave infrared (SWIR) bands are bracketed by snow spectra in those wavelengths. (3) Snow as part of a fractional mixture in a pixel with soils sometimes produces "bright SWIR" pixels that look like clouds. Improvement in snow-cloud discrimination in these cases will require more information than just the spectrum of the sensor's bands or will require deployment of a spaceborne imaging spectrometer, which could discriminate between snow and cloud under conditions where a multispectral sensor might not.

Bruce R. Barkstrom, principal investigator for NASA missions involved with understanding Earth’s radiation budget, committed his life to analyzing, interpreting, and stewarding Earth science data.

Abstract: The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP’s precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies’ preparation for future environmental challenges. Some key issues for future TP studies are also discussed.

Thermal infrared sensing from space is a well-developed field, but mixed pixels pose a problem for many applications. We present a field study in Dana Meadows, Yosemite National Park, California to scale from point (~2-m resolution) to aerial (~5-m resolution gridded, 1 km × 6 km extent) to satellite (MODIS, ~1000-m resolution, global extent) observations. We demonstrate how multiple thermal bands on MODIS can be used to separate snow and forest temperatures and determine the fractional snow-covered area (fSCA) over a 3 km × 3 km array of 9 MODIS grid cells. During the day, visible, near-infrared, and shortwave-infrared bands provide a first guess of fSCA and help to constrain the solution. This technique, which has estimated errors

This paper presents a Ku -band (14-16 GHz) CMOS frequency-modulated continuous-wave (FMCW) radar transceiver developed to measure dry-snow depth for water management purposes and to aid in retrieval of snow water equivalent. An on-chip direct digital frequency synthesizer and digital-to-analog converter digitally generates a chirping waveform which then drives a ring oscillator-based Ku -Band phase-locked loop to provide the final Ku -band FMCW signal. Employing a ring oscillator as opposed to a tuned inductor-based oscillator (LC-VCO) allows the radar to achieve wide chirp bandwidth resulting in a higher axial resolution (7.5 cm), which is needed to accurately quantify the snowpack profile. The demonstrated radar chip is fabricated in a 65-nm CMOS process. The chip consumes 252.4 mW of power under 1.1-V supply, making its payload requirements suitable for observations from a small unmanned aerial vehicle.

Common practice and conventional wisdom hold that fluctuations in air temperature control interannual variability in snowmelt and subsequent river runoff. However, recent observations in the Upper Colorado River Basin confirm that net solar radiation and by extension radiative forcing by dust deposited on snow cover exerts the primary forcing on snowmelt. We show that the variation in the shape of the rising limb of the annual hydrograph is controlled by variability in dust radiative forcing and surprisingly is independent of variations in winter and spring air temperatures. These observations suggest that hydroclimatic modeling must be improved to account for aerosol forcings of the water cycle. Anthropogenic climate change will likely reduce total snow accumulations and cause snowmelt runoff to occur earlier. However, dust radiative forcing of snowmelt is likely consuming important adaptive capacity that would allow human and natural systems to be more resilient to changing hydroclimatic conditions.

In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques - bagged regression trees and feed-forward neural networks - produced similar mean results, with 0-14% bias and 46-48mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

Abstract. The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain CA USA is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the Western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand weighed to measure its water content. For this dataset, we present a clean and continuous hourly record of selected measurements from both sites covering the 2011–2017 water years which can be used to run a variety of snow models. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with Digital Object Identifier: https://doi.org/10.21424/r4159q.

Abstract. In many mountains, snowmelt provides most of the runoff. In Afghanistan, few ground-based measurements of the snow resource exist. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance, but reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE early in the snowmelt season, we consider physiographic and remotely-sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the methods can accurately estimate SWE during the snow season in remote mountains.

Along many rivers, dams trap sediment and water released from the dams is clear. Downstream of the dam, temperature variability along the river is controlled by climate that warms or cools the water, the flow magnitude, and the spectral properties of water and the river's bed. Using field observations, a synoptic numerical model without calibration couples a full-spectrum radiation balance with turbulent heat fluxes, bed conduction, and a hydraulic model that estimates depth and velocity. We show that variations in the river's temperature are sensitive to the albedo of the sediment on the bed, especially at shallow depths and smaller discharges. However, about half the solar radiation lies in a spectral range where water is highly absorptive; in these wavelengths, absorption is independent of depth. In spring and summer with many hours of sunlight, releases of cold water will have limited influence on temperatures beyond tens of kilometers downstream of a dam.

Accurately estimating basin-wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50 m spatial resolution in several basins across the Western U.S. during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite-based snow estimates or models that can scale to whole mountain ranges, even those without ground-based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite-derived estimates of fractional snow-covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree-day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS.

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snowwater equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support. [ABSTRACT FROM AUTHOR]