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.

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.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

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.

Hybrid Model Testing Technique for Deep-Sea Platforms Based on Equivalent Water Depth Truncation

In this paper, an inner turret moored FPSO which works in the water of 320 m depth, is selected to study the socalled ＂passively-truncated ＋ numerical-simulation＂ type of hybrid model testing technique while the tnmcated water depth is 160 m and the model scale ）, = 80. During the investigation, the optimization design of the equivalent-depth truncated system is performed by using the similarity of the static characteristics between the truncated system and the full depth one as the objective function. According to the truncated system, the corresponding physical test model is made. By adopting the coupling time domain simulation method, the tnmcated system model test is numerically reconstructed to carefully verify the computer simulation software and to adjust the corresponding hydrodynamic parameters. Based on the above work, the numerical extrapolation to the full depth system is performed by using the verified computer software and the adjusted hydrodyrmmic parameters. The full depth system model test is then performed in the basin and the results are compared with those from the numerical extrapolation. At last, the implementation procedure and the key technique of the hybrid model testing of the deep-sea platforms are summarized and printed. Through the above investigations, some beneficial conclusions are presented.

Optimal Design of Equivalent Water Depth Truncated Mooring System Based on Baton Pattern Simulated Annealing Algorithm

The highest similarity degree of static characteristics including both horizontal and vertical restoring force-displacement characteristics of total mooring system, as well as the tension-displacement characteristics of the representative single mooring line between the truncated and full depth system are obtained by annealing simulation algorithm for hybrid discrete variables （ASFHDV, in short）. A“baton” optimization approach is proposed by utilizing ASFHDV. After each baton of optimization, if a few dimensional variables reach the upper or lower limit, the boundary of certain dimensional variables shall be expanded. In consideration of the experimental requirements, the length of the upper mooring line should not be smaller than 8 m, and the diameter of the anchor chain on the bottom should be larger than 0.03 m. A 100000 t turret mooring FPSO in the water depth of 304 m, with the truncated water depth being 76 m, is taken as an example of equivalent water depth truncated mooring system optimal design and calculation, and is performed to obtain the conformation parameters of the truncated mooring system. The numerical results indicate that the present truncated mooring system design is successful and effective.

Ecological service assessment of human-dominated freshwater ecosystem with a case study in Yangzhou Prefecture, China

Freshwater ecosystems provide a host of services to humanity. These services are now rapidly being lost, not least because of the inability of making the impacts measurable. To overcome this obstacle, assessment frameworks for freshwater ecosystem services are needed. A simple water equivalent framework to assess the ecological services provided by freshwater ecosystems was developed in this study. It translated the occupation of freshwater ecosystem services into biologically freshwater volumes and then compares this consumption to the freshwater throughput, that is, the ecological capacity available in this region. In this way, we use the example of Yangzhou Prefecture, to account the main categories of human occupation of water ecosystem services. The result showed that there is a huge gap between the consumption and the supply of freshwater ecosystem services. This must encourage local government to make land-use and water management decisions both economically rational and environmentally sound.

Estimating water storage changes and sink terms in Volta Basin from satellite missions

The insufficiency of distributed in situ hydrological measurements is a major challenge for hydrological studies in many regions of the world. Satellite missions such as the Gravity Recovery and Climate Experiment （GRACE） and the Tropical Rainfall Measurement Mission （TRMM） can be used to improve our understanding of water resources beyond surface water in poorly gauged basins. In this study we combined GRACE and TRMM to investigate monthly estimates of evaporation plus runoff （sink terms） using the water balance equation for the period from January 2005 to December 2010 within the Volta Basin. These estimates have been validated by comparison with time series of sink terms （evaporation plus surface and subsurface runoff） from the Global Land Data Assimilation System （GLDAS）. The results, for the period under consideration, show strong agreement between both time series, with a root mean square error （RMSE） of 20.2 ram/month （0.67 mm/d） and a correlation coefficient of 0.85. This illustrates the ability of GRACE to predict hydrological quantities, e.g. evaporation, in the Volta Basin. The water storage change data from GRACE and precipitation data from TRMM all show qualitative agreement, with evidence of basin saturation at approximately 73 mm in the equivalent water column at the annual and semi-annual time scales.

The Numerical Scheme Development of a Simplified Frozen Soil Model

In almost all frozen soil models used currently, three variables of temperature, ice content and moisture content are used as prognostic variables and the rate term, accounting for the contribution of the phase change between water and ice, is shown explicitly in both the energy and mass balance equations. The models must be solved by a numerical method with an iterative process, and the rate term of the phase change needs to be pre-estimated at the beginning in each iteration step. Since the rate term of the phase change in the energy equation is closely related to the release or absorption of the great amount of fusion heat, a small error in the rate term estimation will introduce greater error in the energy balance, which will amplify the error in the temperature calculation and in turn, cause problems for the numerical solution convergence. In this work, in order to first reduce the trouble, the methodology of the variable transformation is applied to a simplified frozen soil model used currently, which leads to new frozen soil scheme used in this work. In the new scheme, the enthalpy and the total water equivalent are used as predictive variables in the governing equations to replace temperature, volumetric soil moisture and ice content used in many current models. By doing so, the rate terms of the phase change are not shown explicitly in both the mass and energy equations and its pre-estimation is avoided. Secondly, in order to solve this new scheme more functionally, the development of the numerical scheme to the new scheme is described and a numerical algorithm appropriate to the numerical scheme is developed. In order to evaluate the new scheme of the frozen soil model and its relevant algorithm, a series of model evaluations are conducted by comparing numerical results from the new model scheme with three observational data sets. The comparisons show that the results from the model are in good agreement with these data sets in both the change trend of variables and their magnitude values, and the new scheme, together with the algorithm, is more efficient and saves more computer time.

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.

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.

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.

Monthly gravity field recovery from GRACE orbits and K-band measurements using variational equations approach

The Gravity Recovery and Climate Experiment（GRACE） mission can significantly improve our knowledge of the temporal variability of the Earth＇s gravity field.We obtained monthly gravity field solutions based on variational equations approach from GPS-derived positions of GRACE satellites and K-band range-rate measurements.The impact of different fixed data weighting ratios in temporal gravity field recovery while combining the two types of data was investigated for the purpose of deriving the best combined solution.The monthly gravity field solution obtained through above procedures was named as the Institute of Geodesy and Geophysics（IGG） temporal gravity field models.IGG temporal gravity field models were compared with GRACE Release05（RL05） products in following aspects：（i） the trend of the mass anomaly in China and its nearby regions within 2005-2010; （ii） the root mean squares of the global mass anomaly during 2005-2010; （iii） time-series changes in the mean water storage in the region of the Amazon Basin and the Sahara Desert between 2005 and 2010.The results showed that IGG solutions were almost consistent with GRACE RL05 products in above aspects（i）-（iii）.Changes in the annual amplitude of mean water storage in the Amazon Basin were 14.7 ± 1.2 cm for IGG,17.1 ± 1.3 cm for the Centre for Space Research（CSR）,16.4 ± 0.9 cm for the GeoForschungsZentrum（GFZ） and 16.9 ± 1.2 cm for the Jet Propulsion Laboratory（JPL） in terms of equivalent water height（EWH）,respectively.The root mean squares of the mean mass anomaly in Sahara were 1.2 cm,0.9 cm,0.9 cm and 1.2 cm for temporal gravity field models of IGG,CSR,GFZ and JPL,respectively.Comparison suggested that IGG temporal gravity field solutions were at the same accuracy level with the latest temporal gravity field solutions published by CSR,GFZ and JPL.

WHU-Grace01s:A new temporal gravity field model recovered from GRACE KBRR data alone

A new temporal gravity field model called WHU-Grace01s solely recovered from Gravity Recovery and Climate Experiment （GRACE） K-Band Range Rate （KBRR） data based on dynamic integral approach is presented in this paper. After meticulously preprocessing of the GRACE KBRR data, the root mean square of its post residuals is about 0.2 micrometers per second, and seventy-two monthly temporal solutions truncated to degree and order 60 are computed for the period from January 2003 to December 2008. After applying the combi- nation filter in WHU-Grace01s, the global temporal signals show obvious periodical change rules in the large-scale fiver basins. In terms of the degree variance, our solution is smaller at high degrees, and shows a good consistency at the rest of degrees with the Release 05 models from Center for Space Research （CSR）, GeoForschungsZentrum Potsdam （GFZ） and Jet Pro- pulsion Laboratory 0PL）. Compared with other published models in terms of equivalent water height distribution, our solution is consistent with those published by CSR, GFZ, JPL, Delft institute of Earth Observation and Space system （DEOS）, Tongji University （Tongji）, Institute of Theoretical Geodesy （ITG）, Astronomical Institute in University of Bern （AIUB） and Groupe de Recherche de Geodesie Spatiale （GRGS}, which indicates that the accuracy of WHU-Grace01s has a good consistency with the previously published GRACE solutions.

Dosimetric Effects of Thermoplastic Immobilizing Devices on Surface Dose

Thermoplastic immobilizing masks have dosimetric effects on the patient’s skin dose. The thermoplastic percentage depth dose (PDD), equivalent thickness of water for the masks and surface doses were determined. The surface dose factors due to the thermoplastic mask was found to be 1.7949, 1.9456, 2.0563, 2.1967, 2.3827, 2.5459 and 2.6565 for field sizes of 5 × 5, 8 × 8, 10 × 10, 12 × 12, 15 × 15, 18 × 18 and 20 × 20 cm2 respectively which shifted the percentage depth dose curve to lower values. The physical thermoplastic thickness was measured to be between 2.30 and 1.80 mm, and the equivalent thicknesses of water, de, were determined to be between 1.2 and 1.00 mm. This meant that, as the mask thickness decreased, its water equivalent thickness also decreased. The presence of the mask material increased the skin dose to a factor of 1%. The thermoplastic mask factor was also found to be 0.99.

An improved modeling of precipitation phase and snow in the Lancang River Basin in Southwest China

Precipitation phase(e.g., rainfall and snowfall) and snow(e.g., snowpack and snowmelt runoff) in high-mountain regions may largely affect runoff generation, which is critical to water supply, hydropower generation, agricultural irrigation, and ecosystems downstream. Accurately modeling precipitation phase and snow is therefore fundamental to developing a better understanding of hydrological processes for high-mountain regions and their lower reaches. The Lancang River(LR, or the Upper Mekong River)in China, among the most important transboundary rivers originating from the Tibetan Plateau, features active dam construction and complex water resources allocation of various stakeholders in Southeast Asian countries under climate change. This study aims to improve precipitation phase and snow modeling for the LR basin with a hydrological model and multisource remotely sensed data. Results show that joint use of the Moderate Resolution Imaging Spectroradiometer(MODIS) land surface temperature product with high spatial resolution(1 km×1 km) and an air temperature product can more precisely distinguish precipitation phase than air and wet-bulb temperature products in the LR basin. Snowfall and snowmelt were found to be controlled primarily by rainfall and snowfall temperature thresholds in snow modeling. The rainfall and snowfall temperature thresholds derived from the hydrological model through calibration with remotely sensed snowpack at basin scales were considerably lower than those derived from in situ observations. Rainfall and snowfall temperature thresholds derived from in situ observations could lead to the overestimation of snowmelt runoff due mostly to the lack of representation of point-based measurements at basin scales. This study serves as a basis for better modeling and predicting snow for the LR basin and potentially other similar basins globally.

基于GIS模拟的Votkinsk水库流域积雪和融化过程（英文）

Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale at- mospheric Weather Research and Forecasting （WRF） model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin （184,319 km2）. The method is tested during three winter seasons （2012-2015）. The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent （SWE） and snow-covered areas （SCA）. The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data （ATBD-MOD 10） has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements （snow survey）.

Shortened duration and reduced area of frozen soil in the Northern Hemisphere

The changes in near-surface soil freeze-thaw cycles(FTCs)are crucial to understanding the related hydrological and biological processes in terrestrial ecosystems under a changing climate.However,long-term dynamics of soil FTCs at the hemisphere scale and the underlying mechanisms are not well understood.In this study,the spatiotemporal patterns and main driving factors of soil FTCs across the Northern Hemisphere(NH)during 1979–2017 were analyzed using multisource data fusion and attribution approaches.Our results showed that the duration and the annual mean area of frozen soil in the NH decreased significantly at rates of 0.13±0.04 days/year and 4.93104 km^(2)/year,respectively,over the past 40 years.These were mainly because the date of frozen soil onset was significantly delayed by 0.1±0.02 days/year,while the end of freezing and onset of thawing were substantially advanced by 0.21±0.02 and 0.15±0.03 days/year,respectively.Moreover,the interannual FTC changes were more drastic in Eurasia than in North America,especially at mid-latitudes(30
–45
N)and in Arctic regions(>75
N).More importantly,our results highlighted that near-surface air temperature(Ta)and snowpack are the main driving factors of the spatiotemporal variations in soil FTCs.Furthermore,our results suggested that the long-term dynamics of soil FTCs at the hemisphere scale should be considered in terrestrial biosphere models to reduce uncertainties in future simulations.