Fractional Snow Research Articles

IRainSnowHydro v1.0: A distributed integrated rainfall-runoff and snowmelt-runoff simulation model for alpine watersheds

Snowmelt runoff is an essential runoff component in alpine watersheds. On the Tibetan Plateau, the complex hydrometeorological and underlying surface conditions make a single runoff generation mode (either snowmelt-runoff or rainfall-runoff) cannot accurately simulate the runoff process. In this study, we developed a new method that combines the curve number, topographic index, and fractional snow cover to identify the sub-basin seasonal dominant runoff generation mode within the Jinsha River Basin. By constructing a surface ‘snow reservoir’ to depict snow melting impact on runoff generation, and quantitatively classifying the precipitation composition, an innovative integrated hydrological model named the distributed integrated Rainfall-runoff and Snowmelt-runoff simulation Hydrological model (iRainSnowHydro) is developed. With model, a method for identifying the seasonal varying dominant runoff generation mode is proposed. The results show that most sub-basins experience both snowmelt and rainfall driven runoff generation in spring, with snowmelt occurring earlier in regions of lower latitude and elevation. Besides, iRainSnowHydro performs well in daily runoff simulations at Zhimenda and Shigu stations with Nash coefficients of 0.81 and 0.85 in the calibration period, and 0.72 and 0.81 in the validation period. The correlation coefficient ranges from 0.92 to 0.96. Additionally, calculation through iRainSnowHydro indicates a noteworthy percentage of spring snowmelt water. Notably, the Zhimenda watershed, characterized by higher latitudes and elevations, displays an escalating trend from 56.6 % to 78.9 % of total precipitation for spring snowmelt water between 2014 and 2020, while the Shigu watershed maintains stable within 27 % ± 6 %. The methodologies outlined bear significance for simulating and predicting runoff in alpine watersheds and offers valuable insights into how snow cover responds to climate change on the Tibetan Plateau.

Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria

AbstractThe formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt‐induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km2) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017–09/2022. The model simulations are conducted using the intermediate complexity open‐source snow‐hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel‐1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite‐based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel‐2, which proved to provide valuable small‐scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non‐glaciated area being cloud‐free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow‐hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions.

Towards a gapless 1 km fractional snow cover via a data fusion framework

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales.

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.

Machine learning-based estimation of fractional snow cover in the Hindukush Mountains using MODIS and Landsat data

Accurate estimation of snow-covered area (SCA) is vital for effective water resource management, especially in snowmelt-dependent regions like the Kabul River Basin (KRB). It serves as a reference point for comparing expected variations in water availability driven by climate change, particularly in arid and semi-arid regions like the KRB. In this study, fractional snow cover (FSC) was estimated across the KRB using Landsat and moderate resolution imaging spectroradiometer (MODIS) datasets. For this purpose, 34 Landsat-8 and MODIS image-pairs were acquired covering the snowfall period from 01 October 2018 and 31 March 2021. The training dataset consisted of 31 image-pairs, while the remaining three were used as an independent test dataset. Sample sizes and training strategies (i.e., full, semi, and trimmed-models), three each, were evaluated to understand the relevance of the predictor variables. The full-model incorporated MODIS surface reflectance bands (SRB) 1–7 spectral indices, topography and landcover while the semi-model included MODIS SRB 1–7 and indices. The trimmed-model only utilized SRB 1–7. Random Forests (RF) facilitated FSC mapping with Landsat-8 data as a reference. The findings indicated comparable performance between full and semi models, whereas the trimmed-model exhibited weaker performance. The correlation coefficient (R) of the full, semi and trimmed models ranged from 0.83 to 0.92, 0.83–0.92 and 0.82–0.87 respectively. The models performed strongly in grassland regions (R = 0.89–0.90), but moderately in forested areas (R = 0.43–0.53). This approach results in improved MODIS-based SCA-mapping in the Hindukush Mountains, facilitating better water resource management in the region.

A New Retrieval Algorithm of Fractional Snow over the Tibetan Plateau Derived from AVH09C1

A New Retrieval Algorithm of Fractional Snow over the Tibetan Plateau Derived from AVH09C1

MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)

Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://doi.org/10.5281/zenodo.10005826 (Jiang et al., 2023a).

An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

Downscaling MODIS NDSI to Sentinel-2 fractional snow cover by random forest regression

ABSTRACT Imagery acquired by the Moderate-resolution Imaging Spectroradiometer (MODIS) provides a global archive of dailyNormalized Difference Snow Index (NDSI) at 500 m nominal resolution since the year 2000. While Sentinel-2 (S2) NDSI provides an increased spatial resolution of 20 m since the year 2015, the temporal resolution amounts to only 5 days and thus lacks the high temporal resolution of MODIS. Efforts to combine NDSI datasets for an increased temporal and spatial resolution have so far focused on the deriving binary snow cover maps or combining data from other sensors. In contrast, we produce fine scale (20 m) fractional snow cover (FSC) by downscaling MODIS NDSI to S2 resolution. Random forest regression predicts S2 NDSI based on dynamic features (MODIS NDSI, day-of-year) and static, topographic features for an alpine study site. Subsequently, FSC is derived from S2 NDSI. Cross-validation results in R 2 of 0.795 and RMSE of 0.155 for FSC and outperforms common resampling methods. Multi-annual S2 NDSI metrics are able to slightly improve model accuracy. Our results suggest that combining topographical data and low-resolution NDSI allows to produce daily, high-resolution S2 NDSI and FSC and improve fine scale characterization of snow cover dynamics in mountain landscapes.

Contrasting Responses of Land Surface Temperature and Soil Temperature to Forest Expansion During the Dormant Season on the Qinghai‐Tibet Plateau

AbstractAs one of the important ecological responses of ecosystem to global climate change, forest expansion can alter land surface energy budget and local microclimate. Land surface temperature (LST) and soil temperature (ST) indicate the above‐ and below‐ground thermal state, respectively. However, the lack of studies on the relationships between LST and ST changes after forest expansion hinders our understanding of the vegetation‐microclimate interactions, especially during the dormant season. We quantified the change of LST (∆LST) and ST (∆ST) after forest expansion in the dormant season on the Qinghai‐Tibet Plateau (QTP), and then explored their differences and linkages. The results showed that forest expansion significantly decreased LST by 0.75 ± 0.20°C, while significantly increased ST in three layers (0–7, 7–28, and 28–100 cm) by 0.31 ± 0.06, 0.29 ± 0.05, and 0.20 ± 0.04°C, respectively. The decreased LST was conducive to the preservation of snow, leading to larger snow depth, larger fractional snow cover and more snow cover days in forests than grasslands, which further promoted the increased ST through enhanced thermal insulation. Furthermore, it is suggested that the cooling impact of forest expansion on LST and warming impact on ST would both be stronger at humid sites than at dry sites. These findings will contribute to understand the vegetation‐microclimate‐snow interactions and the decoupling phenomenon between LST and ST in the dormant season influenced by vegetation change.

A 0.01° daily improved snow depth dataset for the Tibetan Plateau

Snowpack is highly sensitive to global warming, and an accurate understanding of the changes in snow depth (SD) is essential in analyzing the impacts of SD on both regional and global climate change. However, the application of SD datasets is limited owing to their coarse spatial resolution, especially at the basin scale. As a result, it is difficult to obtain high-quality, long-term gridded SD datasets at the kilometer scale, especially in cold and high-altitude regions. To address this issue, we combine an improved spatiotemporal downscaling algorithm and an efficient snow depletion curve method to develop a composite long-term daily 0.01° SD dataset over the Tibetan Plateau (TP) by integrating an enhanced 0.05° SD dataset and a cloud-gap-filled fractional snow cover (FSC) product. The new 0.01° SD product is evaluated against the ground-observed SD data from 90 meteorological stations during 2001–2010, indicating that the new 0.01° SD product (with a root mean square error of 1.27 cm d-1 and a mean absolute error of 0.31 cm d-1) performs better than its 0.05° old version, as well as five other widely used SD products that cover the TP region. Thus, the new SD product is used to analyze the trends in the spatial SD pattern during 2000–2018. The results indicate that the annual SD is experiencing a decreasing trend over the inner and edge regions of the TP but an increasing trend over the areas between the inner and edge regions of the TP, e.g., the northern Himalayas, and upstream of the Yellow River, Yangtze River, and Mekong River. A negative correlation between the SD changes and the air temperature changes and a positive correlation between the SD changes and the snowfall changes are found in snow-dominated regions. Notably, the correlation between the SD changes and the air temperature changes is stronger than that between the SD changes and the snowfall changes, indicating that the SD is more sensitive to changes in air temperature. The new high-quality product will provide a more accurate means for understanding the SD changes in this region and a more accurate source of SD data for use in scientific studies that relate to hydrology, meteorology, and disaster evaluation.

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.

FSC-USNet: Fractional Snow Cover Retrieval on the Tibetan Plateau by Integrating Improved Attention Mechanisms

FSC-USNet: Fractional Snow Cover Retrieval on the Tibetan Plateau by Integrating Improved Attention Mechanisms

Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (>1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm.

Synthesizing long-term satellite imagery consistent with climate data: Application to daily snow cover

High-resolution daily snow cover estimation is challenging due to irregular satellite data availability. In this study, we create synthetic 30 m snow cover images for dates with no satellite data. It is based on the relationship between meteorological predictors and available clear sky 30 m Landsat/Sentinel-2 snow cover images. Our approach relies on the fact that while snow cover can vary in a matter of days, its patterns may repeat from year to year if meteorological characteristics are similar. In this context, we apply a K-nearest neighbor algorithm with a similarity metric tailored to estimating snow cover.The approach is tested with different data availability scenarios to generate twenty years of daily synthetic snow cover images for two regions of interest in Switzerland. The results are assessed based on a leave-one-out analysis, comparisons with PlanetScope, MODIS, and Copernicus High Resolution Snow & Ice Fractional Snow Cover On Ground (HRSI-FSCOG) images, comparisons with data collected at ground stations, and a comparison with a simple snow accumulation and melt model.The performance of our mapping approach suggests that it is possible to use recurrent snow patterns and climate reanalysis to generate missing data that are virtually and statistically indistinguishable from real observations. The results also indicate that low-resolution climate data, such as in the ERA5-Land reanalysis dataset, provide sufficient performance in the generated synthetic snow maps, opening possibilities for applications in areas with limited in situ snow or climatic data. Our approach could be used to create historical snow cover images for Pre-Satellite periods, or even for future periods by using climate projections.

Snow Cover Reconstruction in the Brunswick Peninsula, Patagonia, Derived from a Combination of the Spectral Fusion, Mixture Analysis, and Temporal Interpolation of MODIS Data

Several methods based on satellite data products are available to estimate snow cover properties, each one with its pros and cons. This work proposes and implements a novel methodology that integrates three main processes applied to MODIS satellite data for snow cover property reconstruction: (1) the increase in the spatial resolution of MODIS (MOD09) data to 250 m using a spectral fusion technique; (2) a new proposal of snow-cloud discrimination; (3) the daily spatio-temporal reconstruction of snow extent and its albedo signature using the endmembers extraction and spectral mixture analyses. The snow cover reconstruction method was applied to the Brunswick Peninsula, Chilean Patagonia, a low-elevation (<1500 m a.s.l.) mid-latitude area. The results show a 98% agreement between MODIS snow detection and ground-based snow measurements at the automatic weather station, Tres Morros (53.3174°S, 71.2790°W), with fractional snow cover values between 20% and 50%, showing a close relationship between snow and vegetation type. The number of snow days compiled from the MODIS data indicates a good performance (Pearson’s correlation of 0.9) compared with the number of skiing days at the Cerro Mirador ski center, Punta Arenas. Although the number of seasonal snow days showed a significant increasing trend of 0.54 days/year in the Brunswick Peninsula during the 2000–2020 period, a significant decrease of −4.64 days/year was detected in 2010–2020.

Assessing robustness in global hydrological predictions by comparing modelling and Earth observations

ABSTRACTHydrological modelling to support hypotheses on Earth system boundaries or the accelerating water crisis is nowadays done at the global scale, with difficulties associated to model uncertainties. Here we bring a robustness analysis of internal model variables as an additional tool for model evaluation using data from six Earth observation products and the global catchment model World-Wide HYPE in a comparative study. The assessment shows that: (i) variables have high agreement in mid-latitude temperate regions; (ii) the variables with higher agreement, and associated with good model performance in streamflow, were actual evapotranspiration, fractional snow cover and snow water equivalent; and (iii) changes in total water storage showed very poor agreement, probably due to an insufficient number of aquifers in the model set-up. We propose this procedure as a standard complementary method in global hydrological modelling, highlighting the importance of justifying models before using them for scenario analysis or water accounting.

Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020

Snow cover in Northeast China (NC) is not only a vital variable in water availability but also a significant indicator of climate change. Limited by the short time span, incomplete spatial coverage, large uncertainty, and low spatial resolution of the current snow cover products, the long-term snow cover changes in NC remain unclear. To resolve this issue, a high-quality, long-term, and daily 500 m fractional snow cover (NCFSC) data were generated by integrating ERA5-land FSC data and several auxiliary data, with the help of a random forest (RF) regression model, during the period 1980–2020. The validation against in situ snow-depth observations from 2000 to 2018 indicated that the NCFSC effectively distinguished snow cover conditions (the overall accuracy (OA) was 0.88, producer’s accuracy (PA) was 0.78, user’s accuracy (UA) was 0.81, and Cohen’s kappa (CK) value was 0.71). The spatiotemporal comparison between the NCFSC and MODIS FSC data (MDFSC) during 2015–2018 showed that NCFSC was reliable and highly comparable, with bias, RMSE, and R values of −0.02, 0.14, and 0.73, respectively. Additionally, based on NCFSC, the spatiotemporal variations in snow cover in NC over the past 40 years were analyzed for the first time, and the results showed that the FSC had a significant decreasing trend (0.0017/year, p < 0.05). The FSC in farmland had the fastest decline rate, 0.0020/year (p < 0.05), and the snow cover areas decreased by approximately 20 % from 1.55×105 km2 in 1980 to 1.19×105 km2 in 2020. The snow onset date had a delayed trend (0.141 day/year, p > 0.05), the snow end date had a significant advanced trend (0.243 day/year, p < 0.05), and the snow duration days had a significant decreasing trend (0.384 day/year, p < 0.05) in NC. In summary, the NCFSC seamlessly depicts the spatiotemporal changes in snow cover in NC over the past 40 years, providing a scientific basis and decision-making support for hydrological studies and climate change.

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.

Development of the snow- and ice-accounting routine (SIAR)

This paper evaluates the degrees of freedom and complexity warranted in temperature-index models to jointly simulate local snow measurements, remotely-sensed snow cover and runoff in mountainous areas. To address this issue, the snow- and ice-accounting routine (SIAR) on top of the GR4J model was developed on a dataset covering 17 mountainous catchments (45 to 3580 km2) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow cover (FSC) computed from MODIS snow products and at least one chronicle of local measurements of snow water equivalent (SWE) acquired in each catchment for the period 2004–2016. SIAR was applied according to an adaptable number of elevation bands and different parametrizations ranging from 11 free parameters (precipitation orographic correction, temperature lapse rate, variation in the temperature lapse rate, snowfall adjustment, rainfall lapse rate, thermal inertia of the snow pack, constant and variable part of the degree-day snow melt factor, degree-day ice melt factor, 2-parameter hysteresis between SWE and FSC), to only fixed parameters. Results showed that the one-free-parameter SIAR is as efficient as more parametrized versions in simulating both local and distributed snow dynamics as well as runoff. Interestingly, using SIAR without any free parameters by fixing the snowfall adjustment to a median value of 60% only led to slight impairment of local SWE dynamics. Certain processes represented in SIAR (glacier-component, sublimation, simple energy balance, snowpack cold-content, variable melt factor) were then alternatively turned off to justify those retained in its final version. The modeling performances were also compared by applying SIAR with different distribution options ranging from full distribution according to 0.25 km2 cells to lumped mode. A number of equal-area elevation bands according to the catchment hypsometry proved to be a good compromise as it allowed snow and runoff simulations of similar accuracy to the full distribution mode, while limiting computational time. Finally, SIAR was compared with the Cemaneige snow routine, which showed its modeling performance was better. These findings suggest that it is possible, and even advisable, to limit the number of free parameters in temperature-index models in order to reduce problems of over-parameterization and equifinality.