检索结果-南通市图书馆

Journal of Mountain Science 2022年第9期19卷 2570-2586页

作者： CHENG Qi-shan CHEN Yu-min YANG Jia-xin CHEN Yue-jun XIONG Zhe-xin ZHOU An-nan School of Resource and Environment Science Wuhan UniversityWuhan 430079China Guangzhou Urban Planning&Design Survey Research Institute Guangzhou 510060China

snow water equivalent(SWE)is an important factor reflecting the variability of *** is important to estimate SWE based on remote sensing data while taking spatial autocorrelation into *** 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 *** sizes of 16×16,24×24 and 32×32 were selected to segment raster datasets into *** 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 *** show that precipitation has a great influence on SWE,while surface temperature and NDVI have little *** temperature,elevation and surface temperature have completely different effects in different *** 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 *** the segmentation size increases,the complexity of ESF model increases,but the accuracy is improved.

关键词： snow water equivalent Tibetan Plateau Raster segmentation Parallel eigenvector spatial filtering

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

在线全文

学校读者我要写书评

暂无评论

Journal of Mountain Science 2020年第7期17卷 1712-1723页

作者： Hojat GHANJKHANLO Mehdi VAFAKHAH Hossein ZEINIVAND Ali FATHZADEH Department of watershed Management Engineering Faculty of Natural ResourcesTarbiat Modares UniversityNoor64414-356Iran Department of watershed Management Engineering Lorestan UniversityKhoramabad68151-44316Iran Faculty of Agriculture and Natural Resources Ardakan UniversityArdakan89518-9549Iran

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 *** 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 ***,applicable methods that are able to predict nonlinear trends are *** this research,to predict SWE,the Sohrevard watershed located in northwest of Iran was selected as the case *** was collected,and the required maps were *** 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 *** was predicted using artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and regression *** results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression ***,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 ***,there were no significant differences between the two methods of ANN and ANFIS in SWE *** of both two sampling patterns had the highest sensitivity to the *** addition,the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature,respectively.

关键词： ANFIS ANN Latin hypercube sampling Systematic random sampling snow water equivalent snow depth

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

在线全文

学校读者我要写书评

暂无评论

Big Earth Data 2022年第4期6卷 420-434页

作者： Lingmei Jiang Jianwei Yang Cheng Zhang Shengli Wu Zhen Li Liyun Dai Xiaofeng Li Yubao Qiu State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of SciencesFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina National Satellite Meteorological Center China Meteorological AdministrationBeijingChina Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research StationNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchunChina

The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate ***,a long-time snow water equivalent(SWE)dataset is *** 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 *** 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 *** spatial patterns and trends of SWE over China present significant regional *** 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 *** increase of SWE occurred in the Northeast Plain,with an increase trend of 0.2 mm per *** in the Hengduan Mountains,it presented a downward trend of SWE,up to−0.3 mm per *** the North Xinjiang,SWE has an increasing trend in the Junggar Basin,while it shows a decreasing trend in the Tianshan and Altai Mountains.

关键词： snow water equivalent daily 1980-2020 passive microwave remote sensing China

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

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Research in Cold and Arid Regions 2012年第2期4卷 93-106页

作者： LiJuan Ma Yong Luo DaHe Qin National Climate Center Beijing 100081China Center for Earth System Science Tsinghua UniversityBeijing 100084China China Meteorological Administration Beijing 100081China State Key Laboratory of Cryospheric Sciences Cold and Arid Regions Environmental and Engineering Research InstituteChinese Academy of SciencesLanzhouGansu 730000China

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.

关键词： snow water equivalent projection CMIP3 Eurasia climate change simulation

Estimating snow depth or snow water equivalent from space

在线全文

学校读者我要写书评

暂无评论

Research in Cold and Arid Regions 2022年第2期14卷 79-90页

作者： LiYun Dai Tao Che Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research StationNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouGansu 730000China Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of SciencesBeijing 100101China

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 *** sensing techniques for snow depth mainly include passive microwave remote sensing,Synthetic Aperture Radar(SAR),Interferometric SAR(In SAR)and *** 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 *** 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 *** was found to have ability to detecting snow depth in 1980 s,but was not used for satellite active microwave remote sensing until *** Lidar was utilized to detect snow depth since the later period of 2000 *** estimation of snow depth from space has experienced significant progress during the last 40 ***,challenges or uncertainties still exist for snow depth estimation from *** this study,we review the main space remote sensing techniques of snow depth *** algorithms and their principles are described,and problems or disadvantages of these algorithms are *** was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated *** 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.

关键词： snow depth snow water equivalent remote sensing satellite

Discussion of Influences on snow water equivalent at Utah snow Courses

在线全文

学校读者我要写书评

暂无评论

Journal of Earth Science and Engineering 2015年第3期5卷 147-172页

作者： Randall P. Julander Jordan A. Clayton Utah snow Survey Natural Resources Conservation Service 245 Jimmy Doolittle Rd. Salt Lake City 84116 UT USA

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.

关键词： snow water equivalent Utah snow course vegetation changes over time.

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

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Technological Sciences) 2021年第7期64卷 1513-1527页

作者： HAN ZhongYing LONG Di HAN PengFei HUANG Qi DU MingDa HOU AiZhong State Key Laboratory of Hydroscience and Engineering Department of Hydraulic EngineeringTsinghua UniversityBeijing100084China Hydrological Forecast Center Ministry of Water Resources of the People’s Republic of ChinaBeijing100053China

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.

关键词： distributed hydrological model snowfall snow water equivalent snowmelt Lancang River Basin

Estimating Ground snow Load Based on Ground snow Depth and Climatological Elements for snow Hazard Assessment in Northeastern China

在线全文

学校读者我要写书评

暂无评论

International Journal of Disaster Risk Science 2022年第5期13卷 743-757页

作者： Huamei Mo Guolong Zhang Qingwen Zhang H.P.Hong Feng Fan Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbin 150090China Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbin 150090China Department of Civil and Environmental Engineering University of Western OntarioLondonOntario N6A 5B9Canada

Extreme snow loads can collapse *** load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air *** the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was *** developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of *** annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE *** return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design *** comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow *** use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.

关键词： Ground snow depth Ground snow load Northeastern China Precipitation data snow hazard mapping snow water equivalent

Patterns of trends in niveograph characteristics across the western United States from snow telemetry data

在线全文

学校读者我要写书评

暂无评论

Frontiers of Earth Science 2020年第2期14卷 315-325页

作者： S.R.FASSNACHT J.I.LÓPEZ-MORENO ESS-watershed Science Colorado State UniversityFort CollinsCO 80523-1472USA Cooperative Institute for Research in the Atmosphere Colorado State UniversityFort CollinsCO 80523-1375USA Natural Resources Ecology Laboratory Colorado State UniversityFort CollinsCO 80523-1499USA Instituto Pirenaico de Ecología CSICCampus de Aula Dei 50080Spain

The snowpack is changing across the globe,as the climate warms and *** used daily snow water equivalent(SWE)niveograph(time series of SWE)data from 458 snow telemetry(SNOTEL)stations for the period 1982 through *** indices based on amount,timing,time length,and rates were used to describe the annual temporal evolution in SWE accumulation and *** trends in these annual indices were computed over the time period for each station using the Theil-Sen *** trends were then clustered into four groups to determine the spatial pattern of SWE *** and precipitation data were extracted from the PRISM data set,due to the shorter time period of temperature measurement at the SNOTEL *** show that SNOTEL stations can be clustered in four clusters according to the observed trends in snow *** 1 stations are mostly located in the Eastern-and South-eastern most parts of the study area and they exhibit a generalized decrease in the indices related with peak SWE and snow *** stations recorded a negative trend in precipitation and an increase in *** 4 that is mostly restricted to the North and North-west of the study area shows an almost opposite pattern to cluster 1,due to months with positive trends and a more moderate increase of *** grouped in clusters 2 and 3 appear mixed with clusters 1 and 4,in general they show very little trends in the snow indices.

关键词： snow water equivalent cluster analysis SNOTEL

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

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Mountain Science 2021年第12期18卷 3147-3164页

作者： LI Qian YANG Tao LI Lan-hai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Ili Station for watershed Ecosystem Research Chinese Academy of SciencesXinyuan 835800China Key Laboratory for Mountain Hazards and Earth Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China CAS Research Centre for Ecology and Environment of Central Asia Urumqi 830011China Xinjiang Key Laboratory of water Cycle and Utilization in Arid Zone Urumqi 830011China

snow is a key variable that influences hydrological and climatic *** surface models employing snow physics-modules can simulate the snow accumulation and ablation ***,there are still uncertainties in modeling snow resources over complex terrain such as *** 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 *** tests were conducted based on different reanalysis forcing datasets and different land surface *** results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the *** model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing ***,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation ***,no significant improvement in accuracy was found in the date of snow depth maximum and melt *** 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.

关键词： WRF/Noah-MP model Initial and lateral boundary conditions Land surface properties snow depth snow water equivalent