三江源积雪覆盖时空变化研究外文翻译资料

 2023-01-08 12:23:40

本科毕业设计（论文）

外文翻译

European Snow Cover Characteristics between 2000 and 2011Derived from Improved MODIS Daily Snow Cover Products

作者：Andreas J. Dietz *, Christoph Wohner and Claudia Kuenzer

国籍：Germany

出处：Remote Sens. 2012, 4, 2432-2454.

原文正文：

1. Introduction

Snow cover is an important variable for water availability [1], it influences the radiation budget of the Earth surface [2,3], and may cause natural disasters such as avalanches or floods after the melting process [4,5]. Within Europe especially in Norway, Sweden, and Switzerland, snow is an important source of freshwater for reservoirs and the subsequent production of electricity [6–8]. Climate change affects parameters such as snow cover duration (SCD), generally leading to its decrease, a delayed snow cover onset and earlier snow melt [9–12]. Between 1970 and 2004, SCD decreased for most of Northern and Eastern Europe [13,14] and is projected to decrease in the future [15]. Lower elevations are more vulnerable to a reduced SCD than mountainous terrain [16]. Possible changes also include fewer frost days, reduced snow depth, reduced snow water equivalent, changes in soil frost and snow-fed river systems [15,17–19]. The analysis of remotely sensed data allows for high temporal and high areal coverage of the land surface, therefore forming an ideal tool to observe status and dynamics of snow coverage [20].

The aim of this study is to generate a medium resolution snow cover inventory of Europe, including SCD as well as information about snow cover start (SCS) and snow cover melt (SCM). These parameters can provide information about the current status of snow cover characteristics on a continental scale and are a useful source for future climate change studies. Additionally, the results from single snow cover seasons can be used to identify areas with exceptionally long or short SCD and early or late SCS and SCM. Such abnormal events can have severe impacts on economy or the environment.

This study will fill a gap in currently available snow cover products, since they are available either in very coarse resolution (~25km or less) on a hemispherical scale [14,21] or in medium resolution (~500 m) only for single countries such as, e.g., Switzerland [22], Poland [23], or Norway [24]. The continental snow cover inventory presented here is the first medium resolution product for Europe that spans the full operational phase of MODIS (since 2000). Our datasets contribute to the satellite-based climate products as stated by GCOS concerning horizontal (1 km) and temporal (daily) resolution [25]. The extent of the included landmass covers nearly 6.3 million kmsup2;.

2. Base Data and Study Region

2.1. Satellite Data

We used the daily snow cover products MOD10A1 (from Terra MODIS, available since March 2000) and MYD10A1 (from Aqua MODIS, available since July 2002) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to calculate snow cover parameters including SCD, SCS and SCM for Europe. The MODIS product is provided by the National Snow and Ice Data Center (NSIDC) and available for the whole globe. The spatial resolution is 500 m per pixel. Based on the Snowmap algorithm developed especially for MODIS [26], the accuracy of the daily snow cover data reaches 93% under clear sky conditions [27] as it was confirmed by several independent studies [28–33]. The Snowmap algorithm relies on MODIS bands 4 (0.545–0.565 micro;m) and 6 (1.628–1.652 micro;m) to calculate the Normalized Difference Snow Index (NDSI), which is most useful for detecting snow covered areas [34]. Since band 6 is non-functional on Aqua MODIS, band 7 (2.015–2.155 micro;m) is utilized to derive the snow cover product MYD10A1 [35]. We acquired and processed more than 102,000 single tiles of MOD10A1 [36] and MYD10A1 [37] for the years from 2000 to 2011 from the metadata and service discovery tool Reverb [38]. Though the data is available on a daily basis some days are missing from the time series (Table 1). The methods section will outline how we dealt with these missing observations.

Shuttle Radar Topography Mission (SRTM) data was also used for this study. The elevation information contained in the SRTM data was consulted to compare SCD with topography. Results are provided in Section 4. SRTM data was acquired from the CGIAR-CSI SRTM 90m Database [39].

2.2. Snow Depth Station Data

Snow depth information from the European Climate Assessment amp; Dataset Project (ECAamp;D) was utilized for accuracy assessment of our results [40]. This data originates from meteorological stations and includes mean daily snow depth measurements for the full time series from 2000 to 2011 [41]. 896 snow depth stations from this archive fall within our study area, mostly concentrated on the Netherlands and Norway (Figure 1). Several stations also exist for Germany, Sweden, Switzerland, and Slovenia but there is no snow depth data available, e.g., for France, England, Poland, or Finland. We used the blended version of the daily snow depth data: Gaps in this time series are filled based on nearby stations if they lie within 12.5 km horizontal and 25 m vertical distance [41].

2.3. Study Region

Figure 1 presents an overview of the study region, the position of processed MODIS tiles, as well as the extent and the intensity of polar darkness. The climate in Europe is diverse. Norway, Iceland, Finland and large parts of Sweden (north of ~60°N) are characterized by a humid snow climate. In this region, the longest SCD, early SCS and late SCM are expected. In Central Europe, Englan

剩余内容已隐藏，支付完成后下载完整资料

英语原文共 23 页，剩余内容已隐藏，支付完成后下载完整资料

资料编号：[272194]，资料为PDF文档或Word文档，PDF文档可免费转换为Word

原文和译文剩余内容已隐藏，您需要先支付 30元 才能查看原文和译文全部内容！立即支付

以上是毕业论文外文翻译，课题毕业论文、任务书、文献综述、开题报告、程序设计、图纸设计等资料可联系客服协助查找。

您可能感兴趣的文章