Snow Variables Research Articles

Evaluation of future hydropower potential for mountainous headwaters in response to climate change

Snowmelt runoff and hydropower generation performance together with reservoir storages were evaluated for climate projections in two mountainous headwaters in Türkiye’s north (Black Sea Region) and south (Mediterranean Region). The EU-CORDEX database was used to obtain an ensemble of different Global Climate Models under two scenarios. Moreover, evaluations were performed regarding the Global Warming Levels. The results indicate that a dramatic increase in temperatures and alterations in precipitation patterns will bring significant decreases in snow variables and inflows, which will be particularly pronounced in the southern basin. HBV-Light and WEAP model were used to estimate future reservoir inflows and hydropower generation projections through reservoir operations, respectively. The hydropower generation is anticipated to decline up to 35% and 9% in the southern and northern basins, respectively, for far future (2075-2099). Four management scenarios (including increased irrigation and operational hydropower capacity) were assessed for reconsideration of operation strategies.

Encounters with ice and snow in preschool outdoor practices – and the becoming of (un)sustainability values

ABSTRACT Wintry conditions, including ice and snow, last for six months of the year in northern Scandinavia, and thus strongly influence many preschool outdoor educational practices, but there has been little research on their impact and implications. To address this gap, we have explored preschool teachers’ experiences of everyday outdoor practices during the winter in the region. Through an analysis rooted in post-human and new materialist perspectives, we have identified human agents as well as non-human agents and their complex intra-actions between variations in ice and snow (e.g. ice-crust and sticky snow). In addition to illustrating how non-human, temporal agents may directly affect outdoor preschool practices, our study shows that diverse intra-actions in human-nature encounters play important roles in the becoming of ice, snow and associated (un)sustainable values. The results indicate a need for further critical examination of how preschool children’s outdoor encounters are framed by seasonal and climatic changes.

Spatiotemporal Variability in Snow and Land Cover in Sefid-Rud Basin, Iran

Snow cover has a key role in balancing the Earth’s surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen’s slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen’s slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of −0.51 and −0.68 (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change.

A Simple and Robust CryoSat‐2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval

AbstractCryoSat‐2 has been successful in observing sea ice thickness from space by providing ice freeboard information. The initial estimate of the ice freeboard, called radar freeboard, is obtained by analyzing the observed waveform using a retracker. A series of corrections are needed to convert the radar freeboard to the ice freeboard. Those are the physical effects (e.g., changes in wave propagation speed and the distribution of scattering at snow and ice surfaces, etc.) and the bias of the retracker; however, traditionally, only the wave speed correction has been applied due to lack of enough information to perform the complete correction. Here, an alternative correction method for the CryoSat‐2 radar freeboard derived using the Threshold First‐Maximum Retracker Algorithm with a 50% threshold (TFMRA50) is proposed. Snow depth was used as a predictor for the correction, similar to the traditional wave speed correction, but the coefficients were empirically determined by performing a direct comparison of the radar freeboard from CryoSat‐2 and the ice freeboard from airborne observations. Consequently, this new empirical correction treats the physical effects and the retracker bias as a whole, which have been difficult to separate in the retrieval process. In this paper, we demonstrate that the retrieval accuracy of snow and ice variables and the consistency of the two independent retrieval methods are improved when the new correction is applied. The result of this study emphasizes the importance of compatibility between the retracker and the freeboard correction method.

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014)

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions.

Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022

Abstract. A detailed understanding of snow cover and its possible feedback on climate change on the Tibetan Plateau (TP) is of great importance. However, spatiotemporal variability in snow phenology (SP) and its influencing factors on the TP remain unclear. Based on the daily gap-free snow cover product (HMRFS-TP) with 500 m resolution, this study investigated the spatiotemporal variability in snow cover days (SCDs), snow onset date (SOD), and snow end date (SED) on the TP from 2002 to 2022. A structural equation model was used to quantify the direct and indirect effects of meteorological factors, geographical location, topography, and vegetation greenness on SP. The results indicate that the spatial distribution of SP on the TP was extremely uneven and exhibited temporal heterogeneity. SP showed vertical zonality influenced by elevation (longer SCD, earlier SOD, and later SED at higher elevations). A total of 4.62 % of the TP area had a significant decrease in SCDs, at a rate of −1.74 d yr−1. The SOD of 2.34 % of the TP area showed a significant delayed trend, at a rate of 2.90 d yr−1, while the SED of 1.52 % of the TP area had a significant advanced trend, at a rate of at −2.49 d yr−1. We also found a strong elevation dependence for the trend in SCDs (R=-0.73). Air temperature, precipitation, wind speed, and shortwave radiation can directly affect SP as well as indirectly affect it by influencing the growth of vegetation, whereas the direct effect was much greater than the indirect effect. Geographical location (latitude and longitude) and topographic conditions (elevation and slope) indirectly affected SP by modulating meteorological conditions and the growth of vegetation. Vegetation primarily influences SP by intercepting the snow and regulating the balance of the solar radiation budget. Regarding the total effect, air temperature was found to be the dominant factor. This study contributes to the understanding of snow variation in response to global warming over the past 2 decades by providing a basis for predicting future environmental and climate changes and their impacts on the TP.

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

Changing snow water storage in natural snow reservoirs

Abstract. This work introduces a novel snow metric, snow water storage (SwS), defined as the integrated area under the snow water equivalent (SWE) curve (units: length-time, e.g., m d). Unlike other widely used snow metrics that capture snow variables at a single point in time (e.g., maximum SWE) or describe temporal snow characteristics (e.g., length of snow season), SwS is applicable at numerous spatial and temporal scales. This flexibility in the SwS metric enables us to characterize the inherent reservoir function of snowpacks and quantify how this function has changed in recent decades. In this research, changes in the SwS metric are evaluated at point, gridded and aggregated scales across the conterminous United States (hereafter US), with a particular focus on 16 mountainous Environmental Protection Agency (EPA) Level III Ecoregions (ER3s). These ER3s account for 72 % of the annual SwS (SwSA) in the US, despite these ER3s only covering 16 % of the US land area. Since 1982, spatially variable changes in SwSA have been observed across the US with notable decreasing SwSA trends in the western US and in the 16 mountainous ER3s. All mountainous ER3 (except for the Northeastern Highlands in New England) exhibit decreasing trends in SwSA resulting in a 22 % overall decline in SwSA across mountainous ER3s. The peak monthly SwS (SwSM) occurs in March at all spatial scales, while the greatest percentage loss of SwSM occurs early in the snow season, particularly in November. Unsurprisingly, the highest elevations contribute most to SwSA in all mountain ranges, but the specific elevations that have experienced loss or gain in SwSA over the 39-year study period vary between mountain ranges. Comparisons of SwS with other snow metrics underscore the utility of SwS, providing insights into the natural reservoir function of snowpacks, irrespective of SWE curve variability or type (e.g., ephemeral, mountain, permanent). As we anticipate a future marked by increased climate variability and greater variability in mountain snowpacks, the spatial and temporal flexibility of snow metrics such as SwS may become increasingly valuable for monitoring and predicting snow water resources.

Variability in wet and dry snow radar zones in the North of the Antarctic Peninsula using a cloud computing environment.

This work investigated the annual variations in dry snow (DSRZ) and wet snow radar zones (WSRZ) in the north of the Antarctic Peninsula between 2015-2023. A specific code for snow zone detection on Sentinel-1 images was created on Google Earth Engine by combining the CryoSat-2 digital elevation model and air temperature data from ERA5. Regions with backscatter coefficients (σ⁰) values exceeding -6.5 dB were considered the extent of surface melt occurrence, and the dry snow line was considered to coincide with the -11 °C isotherm of the average annual air temperature. The annual variation in WSRZ exhibited moderate correlations with annual average air temperature, total precipitation, and the sum of annual degree-days. However, statistical tests indicated low determination coefficients and no significant trend values in DSRZ behavior with atmospheric variables. The results of reducing DSRZ area for 2019/2020 and 2020/2021 compared to 2018/2018 indicated the upward in dry zone line in this AP region. The methodology demonstrated its efficacy for both quantitative and qualitative analyses of data obtained in digital processing environments, allowing for the large-scale spatial and temporal variations monitoring and for the understanding changes in glacier mass loss.

Rendering the Bluish Appearance of Snow: When Light Transmission Matters.

Material appearance is largely determined by complex light attenuation processes. The distinct bluish colorations that can be observed when light is transmitted through snow are among the most striking outcomes of these processes. In this article, we present a method for the predictive rendering of this phenomenon taking into account the variability of snow's physical and morphological characteristics. To achieve that, we employ an approach centered on the effective use of spectral transmittance data obtained using a first-principles light transport model for snow. The suitability of the proposed method to rendering applications is illustrated through the synthesis of images depicting the bluish appearance of snow under different illumination conditions.

A Study on Temporal Variation of Snow and Glacier in Langtang Basin

Snow cover and glacier in the Himalaya play a major role for generating stream flow in South Asia. It is already observed that the snowline is retreating in the Himalaya region. While there are relatively more studies on glaciers fluctuation in this region, studies on snow cover are relatively rare. Present study is aimed to analyze the temporal variation of snow and glacier cover (both debris and clean ice covered) of Langtang area within the catchment 432.32 sq.km at outlet 28°12'10.78"N and 85°27'44.58"E. Currently, we used intermittent medium resolution satellite imageries (Landsat) to study the fluctuation in snow cover and glacier area in the Langtang catchment. Our analysis revealed that the glaciers in the study area are tentatively shrinking in average. More specifically, the Landsat Satellite images of the concerned area processed through ERDAS Imagine and their detailed analysis conducted in ArcGIS show that the minimum snow coverage area is declining and maximum snow coverage is in the average range and in the case of debris coverage glacier and clean ice glacier, the trend is declining. The novelty of this study was to develop a robust process having potentiality to predict snow coverage that would be ultimately applicable to agricultural sectors.

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.

Numerical method used for the simulation of fluid–solid transitions based on the VOF and SAM models and prediction of snow distributions on large-span roofs

During a snowfall, the surfaces of the snow beds on roofs change dynamically with time and wind conditions. The generation of body-fitted computational fluid dynamics grids for simulating dynamic snow surfaces using the moving-grid method remains challenging. Therefore, a numerical method was developed to simulate the fluid–solid transition based on the volume of the fluid model and solidification and melting models. The snowfall process was divided into n stable steps, and a stable state calculation method was adopted to calculate the air and snow phases. The dynamic snow boundary was simulated at each stage by converting the fluid or solid characteristics of the domain in which the grids resided. Based on the results of the (n−1)th stage, the fluid domain grids buried by the snow were filled with liquid and frozen into the solid domain. Instead, the fluid in the frozen grids, where snow was about to be eroded, was evacuated and converted into fluid-domain grids. Thus, a complex and changeable snow boundary could be adapted without re-meshing. A modified wall friction velocity equation was derived to correct the wall friction velocity at the snow boundary owing to the serrated boundary of the solidified grids, thereby improving calculation accuracy. The numerical simulation results were verified via field observations of snow distribution on the stepped flat and gable roof models. The snow variation on a full-scale, three-span, suspended cable roof under snow-fall conditions was predicted using the proposed method.

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km2 at low elevations in spring and 86.5 km2 at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year−1 in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research.

A dynamic link between spring Arctic sea ice and the Tibetan Plateau snow increment indicator

Tibetan Plateau snow has long been recognized as a sensitive indicator of climate variability. However, the commonly used snow indicator cannot fully represent the interannual variability in late autumn Tibetan Plateau snow. Here, we establish a snow indicator for Tibetan Plateau snow variability: the snow cover extent increment within late autumn. This snow increment indicator improves the standard deviation by 72.6% and diabatic cooling by 89.7% over the entire Tibetan Plateau, which shows a robust link to the subsequent spring Arctic sea-ice concentration. Faster Tibetan Plateau snow cover extent increment leads to stronger Tibetan Plateau cooling and disturbance along the subtropical westerly jet, thereby inducing a Tibetan Plateau-Arctic wave train and a spring Arctic cyclonic anomaly. This cyclonic system favors the reduction in spring Arctic sea-ice by equatorward sea ice drift, implying a link between spring Arctic sea-ice and the Tibetan Plateau snow increment indicator through the wave train and sea-ice dynamic processes.

Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

Patterns and drivers for benthic algal biomass in sub-Arctic mountain ponds

This study investigated the spatial variation in total benthic algal biomass and within cyanobacteria, green algae, and diatoms in sub-Arctic ponds. Additionally to more widely used explanatory variables, snowmelt and ice duration were considered as their importance on algal communities is poorly understood. The data comprised algal biomasses from 45 sub-Arctic ponds in the Finnish Lapland. A generalized linear model and hierarchical partitioning were used to identify the significantly influential variables. Cyanobacteria were the most abundant algal group. Trace elements (e.g. Fe, Al, and Mn) were the most significant explanatory variable group in explaining algal biomasses. Macronutrients apart from K were found insignificant in all models. There were positive relationships between some algal biomasses indicating no strong competition between them. Snow and ice variables were found insignificant for all models, but they could have an important secondary role on algal communities. The results highlight the importance of trace elements in shaping algal biomasses in sub-Arctic ponds and thus their wider use in research can be advocated to better understand the productivity of nutrient poor and acidic waters in sub-Arctic regions. Focussing on benthic algal biomasses and the chemical composition of sub-Arctic freshwaters provides important information on the aquatic primary production.

Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest

AbstractSnow interacts with its environment in many ways and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially in forested environments. To increase the understanding of the spatio‐temporal variability of snow and to validate snow models, reliable observation data at similar spatial and temporal scales is needed. For these purposes, airborne LiDAR surveys or time series derived from ground‐based snow sensors are commonly used. However, these are limited either to one point in space or in time. A new, extensive data set of daily snow variability in a sub‐alpine forest in the Alptal valley, Switzerland is presented. The core data set consists of a dense sensor network, repeated high‐resolution LiDAR data acquired using a fixed‐wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, four distinct spatial clusters of similar snow depth dynamics are determined. By combining these spatial clusters with the observed snow depth time series, daily high‐resolution maps of snow depth and snow water equivalent (SWE) are derived. These products are the first to our knowledge that provide spatio‐temporally continuous snow depth and SWE based almost exclusively on field data. The presented workflow is transferrable to different regions, climates and scales. Moreover, the results allow future field campaigns to find representative sensor locations and target their LiDAR surveys to derive similar continuous products with less involved effort.

A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates

Abstract. Accurate snowpack simulations are critical for regional hydrological predictions, snow avalanche prevention, water resource management, and agricultural production, particularly during the snow ablation period. Data assimilation methodologies are increasingly being applied for operational purposes to reduce the uncertainty in snowpack simulations and to enhance their predictive capabilities. This study aims to investigate the feasibility of using a genetic particle filter (GPF) as a snow data assimilation scheme designed to assimilate ground-based snow depth (SD) measurements across different snow climates. We employed the default parameterization scheme combination within the Noah-MP (with multi-parameterization) model as the model operator in the snow data assimilation system to evolve snow variables and evaluated the assimilation performance of the GPF using observational data from sites with different snow climates. We also explored the impact of measurement frequency and particle number on the filter updating of the snowpack state at different sites and the results of generic resampling methods compared to the genetic algorithm used in the resampling process. Our results demonstrate that a GPF can be used as a snow data assimilation scheme to assimilate ground-based measurements and obtain satisfactory assimilation performance across different snow climates. We found that particle number is not crucial for the filter's performance, and 100 particles are sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the filter-updating performance, and dense ground-based snow observational data always dominate the accuracy of assimilation results. Compared to generic resampling methods, the genetic algorithm used to resample particles can significantly enhance the diversity of particles and prevent particle degeneration and impoverishment. Finally, we concluded that the GPF is a suitable candidate approach for snow data assimilation and is appropriate for different snow climates.