Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Direct measurement of snow water equivalent(SWE)in snow-dominated mountainous areas is difficult,thus its prediction is essential for water resources management in such areas.In addition,because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution,statistical models are not usually able to present acceptable results.Therefore,applicable methods that are able to predict nonlinear trends are necessary.In this research,to predict SWE,the Sohrevard Watershed located in northwest of Iran was selected as the case study.Database was collected,and the required maps were derived.Snow depth(SD)at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling(LHS),and snow density at 18 points were randomly measured,and then SWE was calculated.SWE was predicted using artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and regression methods.The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method.Moreover,based on most of the efficiency criteria,the efficiency of ANN,ANFIS and regression methods under LHS pattern were observed higher than the systematic random sampling pattern.However,there were no significant differences between the two methods of ANN and ANFIS in SWE prediction.Data of both two sampling patterns had the highest sensitivity to the elevation.In addition,the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature,respectively.

An enhanced method for estimating snow water equivalent in the central part of the Tibetan Plateau using raster segmentation and eigenvector spatial filtering regression model

Snow water equivalent(SWE)is an important factor reflecting the variability of snow.It is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into account.Based on the segmentation method,the relationship between SWE and environmental factors in the central part of the Tibetan Plateau was explored using the eigenvector spatial filtering(ESF)regression model,and the influence of different factors on the SWE was explored.Three sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into blocks.The eigenvectors of the spatial adjacency matrix of the segmented size were selected to be added into the model as spatial factors,and the ESF regression model was constructed for each block in parallel.Results show that precipitation has a great influence on SWE,while surface temperature and NDVI have little influence.Air temperature,elevation and surface temperature have completely different effects in different areas.Compared with the ordinary least square(OLS)linear regression model,geographically weighted regression(GWR)model,spatial lag model(SLM)and spatial error model(SEM),ESF model can eliminate spatial autocorrelation with the highest accuracy.As the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.

Estimating snow depth or snow water equivalent from space

Satellite remote sensing is widely used to estimate snow depth and snow water equivalent(SWE)which are two key parameters in global and regional climatic and hydrological systems.Remote sensing techniques for snow depth mainly include passive microwave remote sensing,Synthetic Aperture Radar(SAR),Interferometric SAR(In SAR)and Lidar.Among them,passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency.Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer(SMMR)freely provided multi-frequency passive microwave data.SAR was found to have ability to detecting snow depth in 1980 s,but was not used for satellite active microwave remote sensing until 2000.Satellite Lidar was utilized to detect snow depth since the later period of 2000 s.The estimation of snow depth from space has experienced significant progress during the last 40 years.However,challenges or uncertainties still exist for snow depth estimation from space.In this study,we review the main space remote sensing techniques of snow depth retrieval.Typical algorithms and their principles are described,and problems or disadvantages of these algorithms are discussed.It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography.With increasing number of freely available SAR data,future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.

Discussion of Influences on Snow Water Equivalent at Utah Snow Courses

Snow data collection systems in the western United States were originally designed to forecast water supply and may be subject to several sources of bias. In addition to climate change and weather modification effects, site-specific effects may be introduced from vegetation changes, site physical changes, measurement technique, and sensor changes. This paper examines changes in Utah＇s snowpack conditions over the past decade compared with all previous measurement years, focusing on the 15 snow courses with the longest observational record within the state of Utah. Although patterns in snowpack data consistent with those that would be expected due to temperature h as greater declines at lower elevations and latitudes--were not identified, snow water equivalent decreased at sites with significant increases in vegetation coverage. Additionally, we provide a list of 22 snow courses in Utah that are best-suited for long-term climate analysis.

Snow water equivalent over Eurasia in the next 50 years projected by aggregated CMIP3 models

Based on remote sensing snow water equivalent （SWE） data, the simulated SWE in 20C3M experiments from 14 models attend- hag the third phase of the Coupled Models for Inter-comparison Project （CMIP3） was first evaluated by computing the different percentage, spatial correlation coefficient, and standard deviation of biases during 1979-2000. Then, the diagnosed ten models that performed better simulation in Eurasian SWE were aggregated by arithmetic mean to project the changes of Eurasian SWE in 2002-2060. Results show that SWE will decrease significantly for Eurasia as a whole in the next 50 years. Spatially, significant decreasing trends dominate Eurasia except for significant increase in the northeastern part. Seasonally, decreasing proportion will be greatest in summer indicating that snow cover in wanner seasons is more sensitive to climate warming. However, absolute decreasing trends are not the greatest in winter, but in spring. This is caused by the greater magnitude of negative trends, but smaller positive trends in spring than in winter. The changing characteristics of increasing in eastern Eurasia and decreasing in western Eurasia and over the Qinghai-Tibetan Plateau favor the viewpoint that there will be more rainfall in North China and less in the middle and lower reaches of the Yangtze River in summer. Additionally, the decreasing rate and extent with significant decreasing trends under SRES A2 are greater than those under SRES B1, indicating that the emission of greenhouse gases （GHG） will speed up the decreasing rate of snow cover both temporally and spatially. It is crucial to control the discharge of GHG emissions for mitigating the disappearance of snow cover over Eurasia.

Multi-Satellite and Sensor Derived Trends and Variation of Snow Water Equivalent on the High-Latitudes of the Northern Hemisphere

Utilizing more than 30 years of satellite-microwave sensor derived snow water equivalent data on the high-latitudes of the northern hemisphere we investigate regional trends and variations relative to elevation. On the low-elevation tundra regions encircling the Arctic we find high statistically significant trends of snow water equivalent. Across the high Arctic Siberia and Far East Russia through North America and northern Greenland we find increasing trends of snow water equivalent with local region variations in strength. Yet across the high Arctic of western Russia through Norway we find decreasing trends of snow water equivalent of varying strength. Power density spectra identify significant power at quasi-biennial and associated lunar nodal cycles. These cycles of the upper atmosphere circulation, ENSO and ocean circulation perturbations from tides forms the causative linkage between increasing snow water equivalent on low-elevation tundra landscapes and decreasing coastal sea ice cover as part of the Arctic system energy and mass cycles.

Snow water content estimation from measured snow temperature

The vertical temperature profiles of snow and sea ice have been measured in the Arctic during the 2nd Chinese National Arctic Research Expedition in 2003(CHINARE2003).The high-resolution temperature profile in snow is solved by one-dimensional heat transfer equation.The effective heat diffusivity,internal heat sources are identified.The internal heat source refers to the penetrated solar radiation which usually warms the lower part of the snow layer in summer.By temperature gradient analysis,the zero level can be clarified quantitatively as the boundary of the dry and wet snow.According to the in situ time series of vertical temperature profile,the time series of water content in snow is obtained based on an evaluation method of snow water content associated with the snow and ice physical parameters.The relationship of snow water content and snow temperature and temporal-spatial distribution of snow water content are presented

Remote Sensing, Model-Derived and Ground Measurements of Snow Water Equivalent and Snow Density in Alaska

Snow water equivalent (SWE) is important for investigations of annual to decadal-scale changes in Arctic environment and energy-water cycles. Passive microwave satellite-based retrieval algorithm estimates of SWE now span more than three decades. SWE retrievals by the Advanced Microwave Scanning Radiometer for the Earth Observation System (AMSR-E) onboard the NASA-Aqua satellite ended at October 2011. A critical parameter in the AMSR-E retrieval algorithm is snow density assumed from surveys in Canada and Russia from 1940s-1990s. We compare ground SWE measurements in Alaska to those of AMSR-E, European Space Agency GlobSnow, and GIPL model. AMSR-E SWE underperforms (is less than on average) ground SWE measurements in Alaska through 2011. Snow density measurements along the Alaska permafrost transect in April 2009 and 2010 show a significant latitude-gradient in snow density increasing to the Arctic coast at Prudhoe Bay. Large differences are apparent in comparisons of our measured mean snow densities on a same snow cover class basis March-April 2009-2011 Alaska to those measured in Alaska winter 1989-1992 and Canadian March-April 1961-1990. Snow density like other properties of snow is an indicator of climate and a non-stationary variable of SWE.

Snow Water Equivalent Estimation for a Snow-Covered Prairie Grass Field by GPS Interferometric Reflectometry

The amount of water stored in snowpack is the single most important measurement for the management of water supply and flood control systems. The available water content in snow is called the snow water equivalent (SWE). The product of snow density and depth provides an estimate of SWE. In this paper, snow depth and density are estimated by a nonlinear least squares fitting algorithm. The inputs to this algorithm are global positioning system (GPS) signals and a simple GPS interferometric reflectometry (GPS-IR) model. The elevation angles of interest at the GPS receiving antenna are between 50 and 300. A snow-covered prairie grass field experiment shows potential for inferring snow water equivalent using GPS-IR. For this case study, the average inferred snow depth (17.9 cm) is within the in situ measurement range (17.6 cm ± 1.5 cm). However, the average inferred snow density (0.13 g.cm-3) overestimates the in situ measurements (0.08 g.cm-3 ± 0.02 g.cm-3). Consequently, the average inferred SWE (2.33 g.cm-2) also overestimates the in situ calculations (1.38 g.cm-2 ± 0.36 g.cm-2).

Using ground penetrating radar to assess the variability of snow water equivalent and melt in a mixed canopy forest, Northern Colorado

Snow is an important environmental variable in headwater systems that controls hydrological processes such as streamflow, groundwater recharge, and evapotran- spiration. These processes will be affected by both the amount of snow available for melt and the rate at which it melts. Snow water equivalent （SWE） and snowmelt are known to vary within complex subalpine terrain due to terrain and canopy influences. This study assesses this variability during the melt season using ground penetrating radar to survey multiple plots in northwestern Colorado near a snow telemetry （SNOTEL） station. The plots include south aspect and flat aspect slopes with open, coniferous （subalpine fir, Abies lasiocarpa and engelman spruce, Picea engelmanii）, and deciduous （aspen, popu- lous tremuooides） canopy cover. Results show the high variability for both SWE and loss of SWE during spring snowmelt in 2014. The coefficient of variation for SWE tended to increase with time during snowmelt whereas loss of SWE remained similar. Correlation lengths for SWE were between two and five meters with melt having correlation lengths between two and four meters. The SNOTEL station regularly measured higher SWE values relative to the survey plots but was able to reasonably capture the overall mean loss of SWE during melt. Ground Penetrating Radar methods can improve future investiga- tions with the advantage of non-destructive sampling and the ability to estimate depth, density, and SWE.

Daily snow water equivalent product with SMMR,SSM/I and SSMIS from 1980 to 2020 over China

The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system.Thus,a long-time snow water equivalent(SWE)dataset is necessary.This work presents a daily SWE product of 1980-2020 with a linear unmixing method through passive microwave data including SMMR,SSM/I and SSMIS over China after cross-calibration and bias-correction.The unbiased root-mean-square error of snow depth is about 5-7 cm,corresponding to 10-15 mm for SWE,when compared with stations measurements and field snow course data.The spatial patterns and trends of SWE over China present significant regional differences.The overall slope trend presented an insignificant decreasing pattern during 1980-2020 over China;however,there is an obvious fluctuation,i.e.a significant decrease trend during the period 1980-1990,an upward trend from 2005 to 2009,a significant downward trend from 2009 to 2018.The increase of SWE occurred in the Northeast Plain,with an increase trend of 0.2 mm per year.Whereas in the Hengduan Mountains,it presented a downward trend of SWE,up to−0.3 mm per year.In the North Xinjiang,SWE has an increasing trend in the Junggar Basin,while it shows a decreasing trend in the Tianshan and Altai Mountains.

Variation of snow water resources in northwestern China,1951—1997

An observation study is carried out on snow mass amount estimate in northwestern China by using microwave derived snow depth charts employing data from SMMR in conjunction with daily snow depth, density and snow cover duration records for 46 ground climate stations. Spatial patterns, seasonal cycle, and interannual variation of snow cover are discussed. Results show that snow cover is the second largest water supply over the arid northwestern China,and unlike most other areas in the world, northwestern China did not experience any decrease in snow cover since 1987.Secular trends reveal systematic increase in snow mass and durations. Analysis of snow cover-climate relationship indicates that gradual increase in snow cover is primarily in response to increase in snow season precipitation.

Trace-element ions in snow,ice and water from Antarctic and Arctic with reference of their water vapor chemistry

The some trace elements in the Antarctic and Arctic snow, ice, water were studied using the methodology and theory of water vapor chemistry. The concentrations of ions Zn 2+ , Cd 2+ , Pb 2+ , Cu 2+ , Sn 4+ , Bi 3+ in Antarctic and Arctic snow a significant spatial similarity; they are also close to those defined elsewhere on the basic of studies of water vapor chemistry: on average Zn 2+ 5.0 μg/L, Cd 2+ 0.080 μg/L, Pb 2+ 0.030 μg/L, Cu 2+ 0.70 μg/L, Sn 4+ 0.99 μg/L, Bi 3+ 0.18 μg/L. Apparently, the ion concentration in the Antarctic and Arctic region represent natural baseline values and are controlled by natural water cycles.

Temporal-spatial characteristics of observed key parameters of snow cover in China during 1957-2009

Using observed snow cover dam from Chinese meteorological stations, this study indicated that annual mean snow depth, Snow Water Equivalent （SWE）, and snow density during 1957-2009 were 0.49 cm, 0.7 ram, and 0.14 g/cm3 over China as a whole, re- spectively. On average, they were all the smallest in the Qinghai-Tibetan Plateau （QTP）, and were greater in northwestern China （NW）. Spatially, the regions with greater annual mean snow depth and SWE were located in northeastern China including eastern Inner Mongolia （NE）, northern Xinjiang municipality, and a small fraction of southwestern QTP. Annual mean snow density was below 0.14 g/cm3 in most of China, and was higher in the QTP, NE, and NW. The trend analyses revealed that both annual mean snow depth and SWE presented increasing trends in NE, NW, the QTP, and China as a whole during 1957-2009. Although the trend in China as a whole was not significant, the amplitude of variation became increasingly greater in the second half of the 20th century. Spatially, the statistically significant （95%-level） positive trends for annual mean snow depth were located in western and northem NE, northwestem Xinjiang municipality, and northeastem QTP. The distribution of positive and negative trends for annu- al mean SWE were similar to that of snow depth in position, but not in range. The range with positive trends of SWE was not as large as that of snow depth, but the range with negative trends was larger.

Measurements of physical characteristics of summer snow cover on sea ice during the Third Chinese Arctic Expedition

The spatial distribution of snow cover on the central Arctic sea ice is investigated here based on the observations made during the Third Chinese Arctic Expedition. Six types of snow were observed during the expedition： new/recent snow, melt-fi＇eeze crust, icy layer, depth hoar, coarse-grained, and chains of depth hoar. Across most measurement areas, the snow surface was covered by a melt-freeze crust 2-3 cm thick, which was produced by alternate strong solar radiation and the sharp temperature decrease over the summer Arctic Ocean. There was an intermittent layer of snow and ice at the base of the snow pack. The mean bulk density of the snow was 304.01~29.00 kg/m3 along the expedition line, and the surface values were generally smaller than those of the sub- surface, confirming the principle of snow densification. In addition, the thicknesses and water equivalents of the new/recent and total-layer snow showed a decreasing trend with latitude, suggesting that the amount of snow cover and its spatial variations were mainly determined by precipitation. Snow temperature also presented significant variations in the vertical profile, and ablation and evaporation were not the primary factors in the snow assessment in late summer. The mean temperature of the surface snow was -2.01±0.96℃, which was much higher than that observed in the interface of snow and sea ice.

Impact of forcing data and land surface properties on snow simulation in a regional climate model:a case study over the Tianshan Mountains,Central Asia

Snow is a key variable that influences hydrological and climatic cycles.Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes.However,there are still uncertainties in modeling snow resources over complex terrain such as mountains.This study employed the National Center for Atmospheric Research’s Weather Research and Forecasting(WRF)model coupled with the Noah-Multiparameterization(Noah-MP)land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains.Six tests were conducted based on different reanalysis forcing datasets and different land surface properties.The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests.The model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing datasets.Additionally,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters.However,no significant improvement in accuracy was found in the date of snow depth maximum and melt rate.The best performance was achieved using ERA5 with modified land cover and vegetation parameters(mean bias=−4.03 mm and−1.441 mm for snow depth and SWE,respectively).This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains.

Combined effects of snow depth and nitrogen addition on ephemeral growth at the southern edge of the Gurbantunggut Desert,China

Water and nitrogen （N） inputs are considered as the two main limiting factors affecting plant growth.Changes in these inputs are expected to alter the structure and composition of the plant community,thereby influencing biodiversity and ecosystem function.Snowfall is a form of precipitation in winter,and snow melting can recharge soil water and result in a flourish of ephemerals during springtime in the Gurbantunggut Desert,China.A bi-factor experiment was designed and deployed during the snow-covering season from 2009 to 2010.The experiment aimed to explore the effects of different snow-covering depths and N addition levels on ephemerals.Findings indicated that deeper snow cover led to the increases in water content in topsoil as well as density and coverage of ephemeral plants in the same N treatment; by contrast,N addition sharply decreased the density of ephemerals in the same snow treatment.Meanwhile,N addition exhibited a different effect on the growth of ephemeral plants：in the 50％ snow treatment,N addition limited the growth of ephemeral plants,showing that the height and the aboveground biomass of the ephemeral plants were lower than in those without N addition; while with the increases in snow depth （100％ and 150％ snow treatments）,N addition benefited the growth of the dominant individual plants.Species richness was not significantly affected by snow in the same N treatment.However,N addition significantly decreased the species richness in the same snow-covering depth.The primary productivity of ephemerals in the N addition increased with the increase of snow depth.These variations indicated that the effect of N on the growth of ephemerals was restricted by water supply.With plenty of water （100％ and 150％ snow treatments）,N addition contributed to the growth of ephemeral plants; while with less water （50％ snow treatment）,N addition restricted the growth of ephemeral plants.

The responses of hydro-environment system in the Second Songhua River Basin to melt water

Based on the continuous monitoring data of hydrology and water quality in the period from 1972 to 1997, the responses of hydro-environment system to melt water in the Second Songhua River basin were derived. Because of melt water, the water quality in the Second Songhua River is good and changes very except that the contents of Hg and Mn in the water are higher. The contribution of melt water to the water fluxes in the Second Songhua River basin is distinct: the water flow in April increases remarkably, reaches the peak in the upper reaches. The pollutant contributions and water pollution indices (WPIs) of the Second Songhua River in April are high in the upper reaches while that in the lower reaches are low. The responses of hydro-environment system to melt water of that basin are affected by content of packed snow and the underlining surface systems.

Study on Diagnosis Weather Process and Flight Impact of Heavy Snowfall in Northeast China “11/2021”

Based on ERA5 reanalysis data, the characteristics of weather situation, water vapor condition, dynamic uplift condition, energy condition, ice accumulation environment and flight effect of aircraft in the heavy snowfall process in northeast China from November 5 to 12, 2021 are analyzed. The results show that the heavy snowfall process in Northeast China is caused by the combination of Northeast China Cold Vortex, trough, low level frontal cyclone and cold front. Through the analysis of the physical field, it is found that the sufficient water vapor transport is from the south and the southeast, the convergence rising in the lower layer, divergence “pumping” in the upper layer, air flow rising in the vertical plane and a large amount of convection effective potential energy are all contributing to the heavy snowfall. The impact of heavy snowfall on flight mainly includes low visibility and ice accumulation. Water vapor flux, water vapor flux divergence, vertical velocity, potential temperature and convective effective potential energy can all be used as the judging indexes of heavy snowfall forecast.

Chemical characteristics and influencing factors of snow in eastern Qilian Mountains, China

As an important indicator of environmental and climate changes, snow chemical properties can be used to reflect microcosmic changes, large-scale environmental and climate changes. 174 groups of snow samples were collected from four different rivers, Jinta river, Sishui river, Binggou river, and Nancha river, in the eastern Qilian Mountains in west China from May 2014 to October 2017. The characteristics of inorganic ions, Ca2+, Mg2+, Na+, K+, Cl–, NO3–, HCO3–, and SO2–, in the samples were analyzed by Dionex-600 and Dionex-3000 ion chromatograph. The results show that Ca2+ is the main cation, while HCO3– is the main anion;the ion concentration of snow is higher than that of rain. After careful analysis, we draw the conclusion that due to the controlling of the westerly wind, the atmosphere of the Qilian Mountains is dry with high dust content in winter and spring, which makes the ions in the snow mainly derive from the weathering of carbonate rock and sulfate rock. The ions in snow cover mainly come from land-sourced dust, while less contribution is from marine sources and human activities.