Fractional Snow-covered Area Research Articles

HIGH-RESOLUTION SATELLITE ESTIMATION OF SNOW COVER FOR FLOOD ANALYSIS IN EAST KAZAKHSTAN REGION

The increasing frequency of extreme weather events linked to climate change has made flood forecasting an important issue, particularly in mountainous regions where snowmelt is a major driver of seasonal flooding. This study explores the application of snow cover estimation techniques to assess snowmelt dynamics and their potential impact on flood risks in the Ulba and Uba basins in East Kazakhstan. To achieve this, high-resolution multispectral satellite imagery from the Sentinel-2 Surface Reflectance dataset is used, focusing on images collected between March and October for the years 2021 to 2024. The images are processed in Google Earth engine platform with strict filtering based on spatial intersection with the basins and cloud cover pixels percentage, ensuring high-quality data for snow cover analysis. The study utilizes multiple remote sensing indices for snow cover estimation. The normalized difference snow index is calculated using the green and shortwave infrared bands to detect snow-covered pixels. Fractional snow-covered area is derived from the NDSI using the 'FRA6T' relationship, offering a more nuanced estimate of snow distribution across the basins. Additionally, a near-infrared to shortwave infrared ratio threshold is employed to minimize confusion between snow and water, improving the detection of snow cover, particularly in regions near water bodies or during melt periods. The resulting snow cover maps and fSCA estimates provide a detailed picture of snow distribution and melt dynamics, contributing to the assessment of snowmelt’s role in flood risk development. The obtained insights can assist in refining flood forecasting models, improving early warning systems, and supporting informed water resource management in vulnerable regions.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

Connecting Global Modes of Variability to Climate in High Mountain Asia

Oscillations in global modes of variability (MoVs) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic oscillation (AO), Eurasian teleconnection, Indian Ocean dipole (IOD), North Atlantic oscillation (NAO), and El Niño southern oscillation (Nino34), with winter and summer climates in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2 m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and a lagged correlation analysis is used to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and central China in both winter and summer and a positive correlation in western China in summer. PRECTOT has a positive correlation with all MoVs in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time windows and specific regions within HMA that exhibit high correlations between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe.

Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.

Towards a conceptualization of the hydrological processes behind changes of young water fraction with elevation: a focus on mountainous alpine catchments

Abstract. The young water fraction (Fyw*), defined as the fraction of catchment outflow with transit times of less than 2–3 months, is increasingly used in hydrological studies that exploit the potential of isotope tracers. The use of this new metric in catchment intercomparison studies is helpful to understand and conceptualize the relevant processes controlling catchment functioning. Previous studies have shown surprising evidence that mountainous catchments worldwide yield low Fyw*. These low values have been partially explained by isolated hydrological processes, including deep vertical infiltration and long groundwater flow paths. However, a thorough framework illustrating the relevant mechanisms leading to a low Fyw* in mountainous catchments is missing. The main aim of this paper is to give an overview of what drives Fyw* variations according to elevation, thus clarifying why it generally decreases at high elevation. For this purpose, we assembled a data set of 27 study catchments, located in both Switzerland and Italy, for which we calculateFyw*. We assume that this decrease can be explained by the groundwater storage potential, quantified by the areal extent of Quaternary deposits over a catchment (Fqd), and the low-flow duration (LFD) throughout the period of isotope sampling (PoS). In snow-dominated systems, LFD is strictly related to the snowpack persistence, quantified through the mean fractional snow cover area (FSCA). The drivers are related to the catchment storage contribution to the stream that we quantify by applying a cutting-edge baseflow separation method to the discharge time series of the study sites and by estimating the mean baseflow fraction (Fbf) over the PoS. Our results suggest that Quaternary deposits could play a role in modulating Fyw* elevation gradients via their capacity to store groundwater, but subsequent confirmation with further, more detailed geological information is necessary. LFD indicates the proportion of PoS in which the stream is sustained and dominated by stored water coming from the catchment storage. Accordingly, our results reveal that the increase of LFD at high elevations, to a large extent driven by the persistence of winter snowpacks and the simultaneous lack of a liquid water input to the catchments, results in lower Fyw*. In our data set, Fbf reveals a strong complementarity with Fyw*, suggesting that the latter could be estimated as Fyw*≃1-Fbf for catchments without stable water isotope measurements. As a conclusion, we develop a perceptual model that integrates all the results of our analysis into a framework for how hydrological processes control Fyw* according to elevation. This lays the foundations for an improvement of the theory-driven models.

Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)

Abstract. Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

Automated observation of physical snowpack properties in Ny-Ålesund

The snow season in the Svalbard archipelago generally lasts 6–10 months a year and significantly impacts the regional climate, glaciers mass balance, permafrost thermal regime and ecology. Due to the lack of long-term continuous snowpack physical data, it is still challenging for the numerical snow physics models to simulate multi-layer snowpack evolution, especially for remote Arctic areas. To fill this gap, in November 2020, an automated nivometric station (ANS) was installed ∼1 km Southwest from the settlement of Ny-Ålesund (Spitzbergen, Svalbard), in a flat area over the lowland tundra. It automatically provides continuous snow data, including NIR images of the fractional snow-cover area (fSCA), snow depth (SD), internal snow temperature and liquid water content (LWC) profiles at different depths with a 10 min time resolution. Here we present the first-year record of automatic snow preliminary measurements collected between November 2020 and July 2021 together with weekly manual observations for comparison. The snow season at the ANS site lasted for 225 days with an annual net accumulation of 117 cm (392 mm of water equivalent). The LWC in the snowpack was generally low (<4%) during wintertime, nevertheless, we observed three snow-melting events between November and February 2021 and one in June 2021, connected with positive temperature and rain on snow events (ROS). In view of the foreseen future developments, the ANS is the first automated, comprehensive snowpack monitoring system in Ny-Ålesund measuring key essential climate variables needed to understand the seasonal evolution of the snow cover on land.

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Abstract. Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot characterize snow cover mapping performance under forest canopies or at the meter scale. Here, we use 3 m spatial resolution snow depth maps collected on 116 d by an aerial laser scanner to validate band ratio and spectral-mixture snow cover mapping algorithms. Such a comprehensive evaluation of sub-canopy snow mapping performance has not been undertaken previously. The following standard (produced operationally by an agency) products are evaluated: NASA gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A1F, NASA gap-filled Visible Infrared Imaging Radiometer Suite (VIIRS) VNP10A1F, and United States Geological Survey (USGS) Landsat 8 Level-3 Fractional Snow Covered Area. Two spectral-unmixing approaches are also evaluated: Snow-Covered Area and Grain Size (SCAG) and Snow Property Inversion from Remote Sensing (SPIReS), both of which are gap-filled MODIS products and are also run on Landsat 8. We assess subpixel snow mapping performance while considering the fractional snow-covered area (fSCA), canopy cover, sensor zenith angle, and other variables within six global seasonal snow classes. Metrics are calculated at the pixel and basin scales, including the root-mean-square error (RMSE), bias, and F statistic (a detection measure). The newer MOD10A1F Version 61 and VNP10A1F Version 1 product biases (− 7.1 %, −9.5 %) improve significantly when linear equations developed for older products are applied (2.8 %, −2.7 %) to convert band ratios to fSCA. The F statistics are unchanged (94.4 %, 93.1 %) and the VNP10A1F RMSE improves (18.6 % to 15.7 %), while the MOD10A1F RMSE worsens (12.7 % to 13.7 %). Consistent with previous studies, spectral-unmixing approaches (SCAG, SPIReS) show lower biases (−0.1 %, −0.1 %) and RMSE (12.1 %, 12.0 %), with higher F statistics (95.6 %, 96.1 %) relative to the band ratio approaches for MODIS. Landsat 8 products are all spectral-mixture methods with low biases (−0.4 % to 0.3 %), low RMSE (11.4 % to 15.8 %), and high F statistics (97.3 % to 99.1 %). Spectral-unmixing methods can improve snow cover mapping at the global scale.

The Multiple Snow Data Assimilation System (MuSA v1.0)

Abstract. Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open-source, community-driven project dedicated to snow data assimilation, which makes it difficult to compare existing algorithms and fragments development efforts. Here we introduce a new data assimilation toolbox, the Multiple Snow Data Assimilation System (MuSA), to help fill this gap. MuSA was developed to fuse remotely sensed information that is available at different timescales with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e., the number of particles and their prior covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (central Pyrenees). Furthermore, we conducted a joint-assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single-cell experiment. In light of these experiments, we discuss the pros and cons of the assimilation algorithms, including their computational cost.

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

The extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.

A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021

Water stored in mountain snowpacks (i.e., snow water equivalent, SWE) represents an important but poorly characterized component of the terrestrial water cycle. The Western United States snow reanalysis (WUS–SR) dataset is novel in its combination of spatial resolution (~500 m), spatial extent (31°–49° N; 102°–125° W), and temporal continuity (daily over 1985–2021). WUS–SR is generated using a Bayesian framework with model-based snow estimates updated through the assimilation of cloud-free Landsat fractional snow-covered area observations. Over the WUS, the peak SWE verification with independent in situ measurements show correlation coefficient, mean difference (MD), and root mean squared difference (RMSD) of 0.77, −0.15 m, and 0.28 m, respectively. The effects of forest cover and Landsat image availability on peak SWE are assessed. WUS–SR peak SWE is well correlated (ranging from 0.75 to 0.91) against independent lidar-derived SWE taken near April 1st, with MD <0.15 m and RMSD <0.38 m. The dataset is useful for characterizing WUS mountain snow storage, and ultimately for improving snow-derived water resources management.

Improving Mountain Snow and Land Cover Mapping Using Very-High-Resolution (VHR) Optical Satellite Images and Random Forest Machine Learning Models

Very-high-resolution (VHR) optical imaging satellites can offer precise, accurate, and direct measurements of snow-covered areas (SCA) with sub-meter to meter-scale resolution in regions of complex land cover and terrain. We explore the potential of Maxar WorldView-2 and WorldView-3 in-track stereo images (WV) for land and snow cover mapping at two sites in the Western U.S. with different snow regimes, topographies, vegetation, and underlying geology. We trained random forest models using combinations of multispectral bands and normalized difference indices (i.e., NDVI) to produce land cover maps for priority feature classes (snow, shaded snow, vegetation, water, and exposed ground). We then created snow-covered area products from these maps and compared them with coarser resolution satellite fractional snow-covered area (fSCA) products from Landsat (~30 m) and MODIS (~500 m). Our models generated accurate classifications, even with limited combinations of available multispectral bands. Models trained on a single image demonstrated limited model transfer, with best results found for in-region transfers. Coarser-resolution Landsat and MODSCAG fSCA products identified many more pixels as completely snow-covered (100% fSCA) than WV fSCA. However, while MODSCAG fSCA products also identified many more completely snow-free pixels (0% fSCA) than WV fSCA, Landsat fSCA products only slightly underestimated the number of completely snow-free pixels. Overall, our results demonstrate that strategic image observations with VHR satellites such as WorldView-2 and WorldView-3 can complement the existing operational snow data products to map the evolution of seasonal snow cover.

Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.

A machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area product

Snow is a major element of the cryosphere with significant impact on the Earth's water cycle and global energy budget. Acquiring consistent and long time series data on the spatial extent of snow cover doubtlessly plays a key role in our understanding and modeling of the current and future environmental dynamics. Remote sensing offers a powerful tool for continuous retrieval of snow cover information by utilizing snow's contrasting reflectance characteristics at optical wavelengths. The pre-operational H35 covers the Northern Hemisphere, and it is the successor of the operational Pan-European H12 daily fractional snow-covered area (fSCA) product at ~1 km. Both products are developed through the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of EUMETSAT by exploiting AVHRR channels. This study is focused on developing an alternative fully data-driven H35 product with improved accuracy using a machine learning (ML)-based approach. Multivariate adaptive regression splines (MARS) algorithm is trained by using AVHRR reflectance data as well as the well-known snow and vegetation indices (i.e., NDSI and NDVI) to generate the new version of H35 fSCA product. The reference fSCA maps required for the training of MARS models are obtained from the higher resolution Sentinel 2 multispectral imagery. The MARS-based fSCA models are validated against an initial test dataset composed of 15 Sentinel 2 scenes over European Alps, Tatra Mountain Range, and Turkey. The final MARS-H35 product is then rigorously assessed over the whole Northern Hemisphere within a temporal domain spanning from Nov 2018 to Nov 2019. The quantitative testing process involves the use of reference data in both continuous and dichotomous scales: i) Sentinel 2 derived reference fSCA maps, ii) ERA5-Land snow depth data, iii) MODIS MOD10A1 NDSI snow cover data, and finally iv) in-situ snow depth data. Additionally, qualitative assessment is also performed by visually comparing MARS-H35/MODIS false-color and MARS-H35/Sentinel 2-derived reference fSCA image pairs over various geographic regions. The overall results indicate that: i) the proposed MARS-H35 fSCA product overperforms the original H35, and ii) it has higher capability in detecting the fine variations in the extent of snow cover, especially across the fringes of the slopes in complex mountainous terrains.

Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Modelling runoff generation in high-elevation Alpine catchments requires detailed knowledge on the spatio-temporal distribution of snow storage. With Sentinel-2 MultiSpectral Instrument (MSI), it is possible to map snow cover with a high temporal and spatial resolution. In contrast to the coarse MODIS data, Sentinel-2 MSI enables the investigation of small-scale differences in snow cover duration in complex terrains due to gravitational redistribution (slope), energy balance and wind-driven redistribution (aspect). In this study, we describe the generation of high-resolution spatial and temporal snow cover data sets from Sentinel-2 images for a high-elevation Alpine catchment and discuss how the data contribute to our understanding of the spatio-temporal snow cover distribution. The quality of snow and cloud detection is evaluated against in-situ snow observations and against other snow and cloud products. The main problem was in the false detection of snow in the presence of clouds and in topographically shaded areas. We then seek to explore the potential of the generated high-resolution snow cover maps in calibrating the gravitational snow redistribution module of a physically based snow model, especially for an area with a very data-scarce point snow observation network. Generally, the calibrated snow model is able to simulate both the mean snow cover duration with a high F1 accuracy score of > 0.9 and the fractional snow-covered area with a correlation coefficient of 0.98. The snow model is also able to reproduce spatio-temporal variability in snow cover duration due to surface energy balance dynamics, wind and gravitational redistribution.

Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products

Study RegionAlaska and hydrologically connected portions of Canada Study FocusRugged terrain and harsh conditions make observational hydroclimatic data, especially runoff data, extremely limited across most of Alaska. Consequently, many previous hydrologic studies in Alaska have focused on smaller-domain trends, where pockets of hydroclimatic observations are clustered. Using a combination of reanalysis and remotely sensed gridded products, we develop a calibration framework for a monthly resolution water budget model, over the entire Alaskan domain, that does not rely on runoff. The domain is comprised of distributed model response units (MRUs), each representing a Hydrologic Unit Code 12 basin. We use a Fourier Amplitude Sensitivity Test (FAST) to inform a two-step calibration process; first calibrating to actual evapotranspiration and then to fractional snow-covered area (fSCA), resulting in a range of parameter solutions for each MRU. New Hydrological Insights for the RegionUsing this framework, the model performs best in the moderate to dry Alaskan interior; in the absence of glaciation and where permafrost is generally not continuous. Modelled runoff for recent decades shows increased runoff trends at lower latitudes and slightly decreased runoff trends at higher latitudes within the Alaskan domain. These findings reveal that reliance upon non-runoff fluxes for model calibration is viable in this cold region and that better characterization of hydroclimatic data is necessary to improve confidence in cold region modeling studies.

Unraveling the Controls on Snow Disappearance in Montane Conifer Forests Using Multi‐Site Lidar

AbstractSnow disappearance date (SDD) affects the ecohydrological dynamics of montane forests, by altering water availability, forest fire regime, and the land surface energy budget. The forest canopy modulates SDD through competing processes; dense canopy intercepts snowfall and enhances longwave radiation while shading snowpack from shortwave radiation and sheltering it from the wind. Limited ground‐based observations of snow presence and absence have restricted our ability to unravel the dominant processes affecting SDD in montane forests. We apply a lidar‐derived method to estimate fractional snow cover area (fSCA) at two relatively warm sites in the Sierra Nevada and two colder sites in the Rocky Mountains, which we link to SDD. With the exception of late season snowpack and low fSCA, snow retention is longer under low vegetation density than under high vegetation density in both warm and cold sites. Warm forests consistently have longer snow retention in open areas compared to dense under canopy areas, particularly on south‐facing slopes. Cold forests tend to have longer snow retention under lower density canopy compared to open areas, particularly on north‐facing slopes. We use this empirical analysis to make process inferences and develop an initial framework to predict SDD that incorporates the role of topography and vegetation structure. Building on our framework will be necessary to provide better forest management recommendations for snowpack retention across complex terrain and heterogenous canopy structure.

Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around 163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region.

Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent

Effective water resources management in California relies substantially on real-time information of snow water equivalent (SWE) at basin scale and mountain ranges given that mountain snowpacks provide the primary water supply for the State. However, SWE estimation based solely on remote sensing, modeling, or ground observations does not meet contemporary operational requirements. In this context, this study develops a data-fusion framework that combines multi-source datasets including satellite-observed daily mean fractional snow-covered area (DMFSCA), snow pillow SWE measurements, physiographic data, and historical SWE patterns into a linear regression model (LRM) to improve SWE estimates in real-time. We test two LRMs: a baseline regression model (LRM-baseline) that uses physiographic data and historical SWE patterns as independent variables, and an FSCA-informed regression model (LRM-FSCA) that includes the DMFSCA from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery as an additional independent variable. By incorporating the satellite-observed DMFSCA, LRM-FSCA outperforms LRM-baseline with increased median R2 from 0.54 to 0.60, and reduced median PBIAS of basin average SWE from 2.6% to 2.2% in the snow pillow SWE cross-validation. LRM-FSCA explains 87% of the variance in the snow course SWE measurements with 0.1% PBIAS, while LRM-baseline explains a lower 81% variance with 1.4% PBIAS, both of which show higher accuracy than SWE estimates from the two operational SWE datasets: the Snow Data Assimilation System (SNODAS, 73% and -2.4%, respectively) and Nationtional Water Model (NWM, 75% and -15.9%, respectively). Additionally, LRM-FSCA explains 85% of the median variance in the Airborne Snow Observatory SWE with -9.2% PBIAS, which is comparable to the LRM-baseline (86% and -11.3%, respectively) and considerably better than SNODAS (64% and 28.2%, respectively) and NWM (33% and -30.1%, respectively). This study shows a substantial model improvement by constraining the geographical and seasonal variation on snow-cover via satellite observation and highlights the values of using multi-source observations in real-time SWE estimation. The developed SWE estimation framework has crucial implications for effective water supply forecasting and management in California, where climate extremes (e.g., droughts and floods) require particularly skillful monitoring practices.

A seasonal algorithm of the snow-covered area fraction for mountainous terrain

Abstract. The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.