Snow Accumulation Period Research Articles

АНАЛІЗ ВНУТРІШНЬОРІЧНОГО РОЗПОДІЛУ СТОКУ ВОДИ ЛІВОБЕРЕЖНИХ ПРИТОК ДНІСТРА В МЕЖАХ ТЕРНОПІЛЬСЬКОЇ ОБЛАСТІ

The Dniester basin is characterized by heterogeneity and diversity of water runoff formation conditions. Therefore, the issue of studying the basin of this river and its tributary basins has always received attention from scientists. Despite a fairly large number of publications, especially those analyzing the hydrological regime of the rivers of Ternopil, there are still no comprehensive studies of river-basin systems in the region. The Dniester River basin within Ternopil Oblast includes 1174 rivers and streams with a total length of 5195 km. The main feature of the Dniester basin’s hydrographic network is the absence of significant tributaries: there are only 6 medium rivers in Ukraine (two of which are within Ternopil Oblast – the Seret and the Zbruch rivers). Small rivers with a length of up to 10 km prevail, and their total length is 94% of the total length of all rivers in the Ukrainian part of the basin. This research focuses on small and medium rivers such as the Zolota Lypa, the Koropets, the Strypa, the Seret, the Nichlava, and the Zbruch. To assess the trends of long-term fluctuations in runoff characteristics and distribution of runoff within months and seasons, we used data from observations of the average annual water flow at 11 hydrological stations on the left bank of the Dniester. The period used for the research is from the beginning of the observation to 2020 inclusive. Using the difference integral curves, the long-term fluctuations of the average annual runoff of the left-bank tributaries of the Dniester for the selected observation period were estimated. It was found that the fluctuations in the runoff characteristics of the studied rivers are generally in-phase, and for some hydrological stations – synchronous. It has been established that the modern period is characterized by a downward trend in the values of runoff characteristics, which is associated with the impact of modern climate change and economic activity in river basins. Based on the analysis of the difference integral curves constructed for the studied rivers of the basin, a common water cycle was identified, which began in the mid-1960s and ended in the second half of the 1990s. Summarizing the above results of the study, we can conclude that, with the exception of some tributaries, fluctuations in the average annual runoff of the rivers on the left bank of the Dniester are natural. The current intra-annual distribution of runoff in the left-bank tributaries of the Dniester basin is characterized by a decrease in the volume of spring flood runoff, which is associated with an increase in air temperature during the period of snow accumulation and a decrease in precipitation in winter and an increase in the flow of the summer-autumn and winter low water marks as a percentage of the annual runoff between the studied periods..

Assimilation of airborne gamma observations provides utility for snow estimation in forested environments

Abstract. An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.

Detection of Winter Heat Wave Impact on Surface Runoff in a Periglacial Environment (Ny-Ålesund, Svalbard)

The occurrence of extreme warm events in the Arctic has been increasing in recent years in terms of their frequency and intensity. The assessment of the impact of these episodes on the snow season requires further observation capabilities, where spatial and temporal resolutions are key constraints. This study targeted the snow season of 2022 when a winter rain-on-snow event occurred at Ny-Ålesund in mid-March. The selected methodology was based on a multi-scale and multi-platform approach, combining ground-based observations with satellite remote sensing. The ground-based observation portfolio included meteorological measurements, nivological information, and the optical description of the surface in terms of spectral reflectance and snow-cover extent. The satellite data were obtained by the Sentinel-2 platforms, which provided ten multi-spectral acquisitions from March to July. The proposed strategy supported the impact assessment of heat waves in a periglacial environment, describing the relation and the timing between rain-on-snow events and the surface water drainage system. The integration between a wide range of spectral, time, and spatial resolutions enhanced the capacity to monitor the evolution of the surface water drainage system, detecting two water discharge pulsations, different in terms of duration and effects. This preliminary study aims to improve the description of the snow dynamics during those extreme events and to assess the impact of the produced break during the snow accumulation period.

The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Assessment of Snow Cover Pollution in the Location of Exposure to Thermal Power Facilities

Snow geochemical survey is successfully used by many researchers to assess the air pollution of territories. The study of the chemical composition of snow cover makes it possible to reveal the spatial distribution of pollution and provide a quantitative assessment of the actual pollution of urbanized territory during the period with a stable snow cover. Enterprises of the fuel and energy complex make the main contribution to air pollution in Ulan-Ude. The research considered the results of estimation of qualitative and quantitative composition of snow cover in the zone of potential negative impact of large quarter boiler houses. The analysis of NH4+, K+, Na+, Mg2+, Ba2+, Ca2+, Sr2+, Li+ cations and Cl–, NO2–, SO42–, NO3–, F–, PO43– anions distribution in snow water samples was carried out. Dust load on the examined territories has been calculated, which makes it possible to assess the volume of suspended particles deposited on the snow surface from atmospheric air during the whole period of snow accumulation.

The signal of snowmelt in streamflow and stable water isotopes in a high mountain catchment in Central Spain

Study regionPeñalara catchment, in the mountains of the Central System in Spain, Study focusFor the first time, we investigated the streamflow and streamwater isotopes during the snow accumulation and melting periods and over subsequent months in two snow seasons. The aim is to better understand the hydrological processes linked to snowmelt; to describe the temporal evolution and the interannual differences in isotopic streamwater; and to improve the understanding about the hydrological functioning of snowmelt water across the catchment. New hydrological insights for the regionThe isotopic signal of the streamwater progressively became isotopically depleted from the beginning of the melt period until the snow cover depletion of the catchment. Higher snowfall led to depleted isotopic values in the stream compared to a year with low snowfall. The interannual variability of the isotopic signature of streamwater during snowmelt may represent a difficulty to establishing reference values to be used in mixing models for hydrograph separation. The streamflow isotopic values had very limited sub-daily variation and showed slow temporal changes, suggesting a central role for alpine aquifers in explaining the hydrological functioning of the catchment, pointing to piston flow as a key process in streamflow generation.

Selection processes of Arctic seasonal glacier snowpack bacterial communities

BackgroundArctic snowpack microbial communities are continually subject to dynamic chemical and microbial input from the atmosphere. As such, the factors that contribute to structuring their microbial communities are complex and have yet to be completely resolved. These snowpack communities can be used to evaluate whether they fit niche-based or neutral assembly theories.MethodsWe sampled snow from 22 glacier sites on 7 glaciers across Svalbard in April during the maximum snow accumulation period and prior to the melt period to evaluate the factors that drive snowpack metataxonomy. These snowpacks were seasonal, accumulating in early winter on bare ice and firn and completely melting out in autumn. Using a Bayesian fitting strategy to evaluate Hubbell’s Unified Neutral Theory of Biodiversity at multiple sites, we tested for neutrality and defined immigration rates at different taxonomic levels. Bacterial abundance and diversity were measured and the amount of potential ice-nucleating bacteria was calculated. The chemical composition (anions, cations, organic acids) and particulate impurity load (elemental and organic carbon) of the winter and spring snowpack were also characterized. We used these data in addition to geographical information to assess possible niche-based effects on snow microbial communities using multivariate and variable partitioning analysis.ResultsWhile certain taxonomic signals were found to fit the neutral assembly model, clear evidence of niche-based selection was observed at most sites. Inorganic chemistry was not linked directly to diversity, but helped to identify predominant colonization sources and predict microbial abundance, which was tightly linked to sea spray. Organic acids were the most significant predictors of microbial diversity. At low organic acid concentrations, the snow microbial structure represented the seeding community closely, and evolved away from it at higher organic acid concentrations, with concomitant increases in bacterial numbers.ConclusionsThese results indicate that environmental selection plays a significant role in structuring snow microbial communities and that future studies should focus on activity and growth.A4vcHcKNh9Gahh9Z6pYiCtVideo

Statistical evaluation of snow accumulation and depletion from remotely sensed MODIS snow time series data using the SARIMA model

AbstractIn the remote and challenging terrain of the Himalayan region, accurate measurement of cyclic snow accumulation and depletion is a significant challenge. To overcome this, an attempt has been made in the present study by applying a statistical analysis of MODIS snow time series data with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model from 2003 to 2018 over the Beas river basin. The Box–Jenkins methodology of forecasting is based on the identification using seasonality, stationarity, ACF, and PACF plots; and estimation based on maximum likelihood techniques; and the last diagnostic checking based on the residual and error values have been used. Later, forecasting models have been proposed separately for the snow accumulation period (October–February) as (1,1,1) (0,1,3)19 and for the snow depletion period (March–September) as (1,1,1) (1,1,2)27 after calibration of the data (2003–2015) and the same were then validated using data (2016–2018). The accuracy assessment of the models has been checked using performance criteria like AIC, MSE, and RSS. The comparison of the forecasting models with the observed data showed a good agreement with R2 of 0.83 and 0.89 for snow accumulation and snow depletion, respectively. This research highlights the potential of utilizing satellite data and statistical modeling to address the challenges of monitoring snow cover in remote and inaccessible regions.

Особенности распределения снежного покрова в насаждениях Рудного Алтая

The article presents the results of examining the formation and peculiarities of snow cover in fir plantations compared to open shrubland in the conditions of Southwestern Altai obtained during 2020-2021 snow period. The forest stand affects snow distribution and characteristics in fir woods by capturing snow with tree crowns, snow accumulation around crown perimeter and, as a result, leads to the formation of near-trunk snowdrifts, in their turn facilitating higher snow accumulation among trees and at forest edges. The maximum snow cover thickness in fir woods ranged from 80 cm (under crowns) to 150 cm (among trees and along forest edges). In the open (control) zone, snow distribution appeared more homogenous – from 126 to 135 cm. During the period of maximum snow accumulation, snow density in fir plantations ranged from 0.19 to 0.26 g/cm3; and, from 0.24 to 0.29 g/cm3 in the control zone. Snow cover humidity likewise demonstrated significant differences between fir plantations and control site, and amounted to 258.3±13.47 mm and 279.7±12.80 mm, respectively. Whereas in the control (overgrown) zone the complete snow cover melting ended in the second 10-day period of April, in fir woods it finished in the second 10-day period of May. The research findings suggest that fir plantations in river catchment areas boost winter precipitation retention and slow down its loss resulting in more uniform water transition into subsurface runoff and longer period of river water replenishment.

Fractional Composition and Toxicity Coal–Rock of PM10-PM0.1 Dust near an Opencast Coal Mining Area and Coal-Fired Power Station

This study is aimed at elucidating the fractional composition, volume and toxicity of dust that is deposited in the snow cover for the period of snow accumulation at different distances from coal mines and a coal-fired power station in the Kemerovo region (Russia). During the filtration process, fractions of 10–0.1 µm and less than 0.1 µm were isolated and weighed. Light microscopy was used to estimate the size of dust particles in the 10–0.1 µm fraction. We found that the total volume and fractional composition of dust has no significant trend to change in the research space. The dust contamination is associated mainly with PM2 particles. Genotoxic tests on cell lines A549 and MRC-5 with different concentrations of dust showed high toxicity (including control points). Taking into account the fact that an increase in the concentration of PM leads to intensification in the toxicity of dust, we can determine that the territory within the studied boundaries is dangerous for the population. Our study is important for understanding the processes of formation, toxicity, transport and sedimentation in the snow cover from dust generated in the process of coal mining and the operation of a coal-fired power station.

A zero-inflated spatiotemporal analysis for snowpack variations and influence of environmental factors in the Northern Hemisphere

Snowpack will vary with climate change and is of vital importance for water supply, the energy balance, and heat exchange. Environmental factors such as thermal, atmospheric, terrestrial, and hydrological factors influence variations in snow mass. This study aimed to investigate associations with environmental factors and variations in snow mass changes based on satellite-retrieved products in the Northern Hemisphere (NH). A zero-inflated space–time autoregressive (ZI-STAR) model is proposed to identify the environmental factors associated with the SWE changes during the snow accumulation and ablation periods in North America and Eurasia because of the spatial patterns, temporal lag effect, and excessive zero observations captured with the satellite-based snow water equivalent (SWE) product. The proposed ZI-STAR model, which considers spatial, time lag, and zero-inflation effects together, offered the best overall performance for SWE simulations with high precision (average R2 of 0.90 while the others range from 0.69 to 0.84) and low spatially random distributed residuals (RMSE 23%–33% less than other model), compared to time-lag, and spatial autoregressive models. Based on the results, air temperature and snow precipitation dominate the variations in SWE across the period of interest. The influence of water vapor varies and the wind speed and topography have little effect due to the missing SWE observations in mountainous areas. The variations in the environmental factors for SWE furnish a foundation for further research about snow changes in the NH. The proposed ZI-STAR model also gives a feasible approach for spatio-temporal continuous variables modeling with excessive zero observations in other fields.

Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021

Accurate understanding the spatiotemporal variability of snow cover and snow phenology over High Mountain Asia (HMA) is of great interest because of their vital impacts on the hydrological processes, global climate system and regional water resource management. This study investigates the spatiotemporal variability of snow-covered extent (SCE), snow-covered days (SCD), snow onset date (SOD), snow end date (SED) and snow duration days (SDD) of HMA using the daily cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products from the hydrological year 2002 to 2020. The vertical difference of the effect of elevation on snow phenology and the response of snow phenology to climate change are explored. The results indicate that the monthly SCE over the whole HMA exhibited a slight decreasing in most months over the study period. The variability of snow cover and snow phenology over the whole HMA generally showed decreased SCD (significant decrease accounts for 15.73%), delayed SOD (significant delay accounts for 14.35%), advanced SED (significant advance accounts for 15.46%) and shortened SDD (significant shorten accounts for 16.01%). It is worth noting that the snow cover in the West Himalaya was enhancing with its increased SCE, increased SCD and lengthened SDD. There is an elevation dependence of snow phenology changes over HMA with the high-altitude mountain ranges generally having a higher SCD, earlier SOD, later SED and longer SDD than low altitude areas. The air temperature of snow accumulation period is the dominant factor affecting the interannual variation of SOD, and the air temperature of snow melt period is the dominant factor affecting the interannual variation of SED. The increase of air temperature leads to the delayed SOD, advanced SED and shortened SDD.

Development of a global operational snow analysis: The US Air Force Snow and Ice Analysis

This study describes the development of a new, near real-time (NRT) global operational snow analysis, called the US Air Force Snow and Ice Analysis (USAF-SI). USAF-SI is a timely replacement for USAF 557th Weather Wing (557 WW)’s Snow Depth Analysis Model (SNODEP), which provides a six-hourly global analysis of snow at 25-km resolution and is used by numerous other 557 WW programs and external customers. USAF-SI incorporates several major enhancements over SNODEP. USAF-SI employs additional ancillary datasets and updates the algorithms related to passive microwave-based snow depth (SD) retrieval, surface observation blending, and quality control. The spatial resolution of USAF-SI estimates is also enhanced from 25-km to 10-km. The quality of USAF-SI is evaluated by comparing it against independent in-situ measurements at ~2300 sites. USAF-SI demonstrates improved performance over SNODEP, particularly during the peak snow accumulation and spring snow melt periods. In addition, USAF-SI is compared with routinely available SD analysis such as Modern-Era Retrospective Analysis for Research and Applications, version 2, European Centre for Medium-Range Weather Forecasts ERA5-Land, and Canadian Meteorological Center. Overall, the NRT USAF-SI provides comparable skills to these high-latency products, while outperforming them in some of the regional evaluations.

STUDY ON SNOW COVER CHANGE BASED ON THE FUSION OF SENTINEL-2 AND MODIS IMAGES

Abstract. Snow cover is one of the features that change rapidly on the surface, and remote sensing data with high spatial and temporal resolution is an important means to monitor the dynamic changes of snow. However, due to the limitation of satellite conditions, it is difficult to acquire remote sensing images with both high temporal and spatial resolution at the same time. The classical Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) breaks through the limitations of a single sensor and effectively brings into play the complementary advantages of different observation platforms. In this paper, we take the Babao River Basin within the Qinghai-Tibet Plateau as the study area to investigate the effect of fusion of Sentinel-2 and MODIS (Moderate-resolution Imaging Spectroradiometer) based on the ESTARFM and analyze the fusion-supplemented snow accumulation period data. The results show that the fusion effect of Sentinel-2 and MODIS is good in terms of visual and multiple quantitative indicators. The snow accumulation area of the fused image is also close to the real image. By fusing to supplement the missing Sentinel-2 data, a variation map of snow NDSI (Normalized Difference Snow Index) in the study area was obtained. It was found that the snowfall process in the watershed appeared first in the surrounding uplands and then in the flat areas, and the maximum rate of snow cover area change was 116.17 km2. Therefore, the spatiotemporal data fusion of Sentinel-2 and MODIS based on ESTARFM algorithm can provide reliable satellite remote sensing data with a higher spatiotemporal resolution for snow accumulation change monitoring.

How do snow cover fraction change and respond to climate in Altai Mountains of China?

AbstractInvestigating the spatial and temporal changes in snow cover over mountain areas is significant for understanding the impact of regional climate variability. In this study, using cloud‐removed snow cover data, which are generated based on Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products, the spatiotemporal changes of snow cover fraction (SCF) and its relationship with temperature and precipitation changes from 2002 to 2020 were examined over Altai Mountains, China. The results demonstrate that the distribution and changes of SCF are highly spatiotemporally heterogeneous. Within a year, the maximum SCF occurs in January at 98.6%, and the minimum appears in July at 8.7%. The annual‐mean SCF shows an increasing trend at 0.09%·annum−1, owing to the significantly increasing SCF in the snow accumulation period at 0.5%·annum−1 and the decreasing SCF in the snow melting period at −0.2%·annum−1. The SCF distribution, as well as its interannual change, is greatly influenced by elevation. During the snow cover period, a positive linear correlation between SCF and elevation is found at 0.02%·m−1 (p < .01). The annual‐mean SCF decreases in the area below 1,200 m, whereas it increases in the area above 1,200 m. Accordingly, the elevation‐dependent SCF results in various SCF distributions on different slopes and watersheds. The SCF shows an apparent pattern in different aspects, with similar SCFs between the north and east aspects and between the west and south aspects but a difference between the northeast aspects and the westsouth aspects. The SCF is negatively correlated with air temperature (r = −0.74, p < 0.01) and positively correlated with precipitation (r = 0.74, p < 0.01). In addition, temperature shows a significant and larger correlation with SCF in both the snow accumulation and melting periods, indicating the major factor of temperature for the changes in SCF.

Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced Land Surface Model

This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> ] and without [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> ] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> . Comparison of SWE estimates from Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. River discharge using Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.

Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value &gt; 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM

This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as model input to estimate daily flow discharge. After model training and verification, two experimental simulations were conducted with hypothetical inputs instead of observed meteorological data to clarify the response of the trained model to the inputs. The first numerical experiment showed that even without input precipitation, the trained model generated flow discharge, particularly winter low flow and high flow during the snow melting period. The effects of warmer and colder conditions on the flow discharge were also replicated by the trained model without precipitation. Additionally, the model reflected only 17–39% of the total precipitation mass during the snow accumulation period in the total annual flow discharge, revealing a strong lack of water mass conservation. The results of this study indicated that a deep learning method may not properly learn the explicit physical relationships between input and target variables, although they are still capable of maintaining strong goodness-of-fit results.

Effects of climate change on depression‐focused groundwater recharge in the Canadian Prairies

AbstractSmall topographic depressions are ubiquitous in the glaciated terrain of the Northern Prairies characterized by a cold semiarid climate. Groundwater recharge in this region is focused in topographic depressions, which receive lateral inputs of snowmelt runoff in addition to vertical inputs of precipitation. The response of depression‐focused recharge to climate change is largely unknown due to the difficulty of representing the complex interaction between depressions and surrounding uplands in hydrological models. This study evaluates climate change effects on recharge using a process‐based hydrological model and the pseudo‐global warming (PGW) approach representing a climate of 2092–2100, which has a higher mean annual temperature (+4.9 °C) and precipitation (+135 mm or +27%) compared with the present climate. The recharge model is conditioned using field data from an instrumented grassland site in the Canadian Prairies. Under the present climate, snowmelt runoff occurred in March–April over frozen soil, which accounted for the majority of water transfer from the upland to the depression. Under the PGW scenario, the amount of snowmelt runoff in March–April decreased due to lower snow accumulation and shorter periods of frozen soil. Instead, runoff occurred in midwinter and also in summer months due to increased intensity and duration of summer storms. Despite the increased precipitation, mean annual recharge rates decreased from 10.2 to 3.2 mm yr–1, indicating the importance of considering the complex effects of warmer and wetter climate on hydrological processes in cold semiarid regions.

Central Asian rivers under climate change: Impacts assessment in eight representative catchments

Study regionEight river catchments within Central Asia. Study focusThe limited amount of water resources is already an issue in the Central Asian region, and climate change may be crucial for water availability and development of countries in the region. This study investigates potential climate change impacts on water resources in Central Asia to the end of the century by focusing on eight river catchments with diverse natural conditions located in different countries. The eco-hydrological model SWIM was setup, calibrated and validated for all selected catchments under study. Scenarios from five bias-corrected GCMs under Representative Concentration Pathways 4.5 and 8.5 were used to drive the hydrological model. New hydrological insights for the regionThe results show an increase of mean annual temperature in all catchments for both RCPs to the end of the century. The projected changes in annual precipitation indicate a clear trend to increase in the Zhabay and to decrease in the Murghab catchments, and for other catchments, they were smaller.The projected trends for river discharge are similar to those of precipitation, with an increase in the north and decrease in the south of the study region. Seasonal changes are characterized by a shift in the peak of river discharge up to one month, shortage of snow accumulation period, and reduction of discharge in summer months.