Estimation of Snowmelt Runoff in Beas Basin,India

Abstract

Estimation of snowmelt runoff is very important in the Western Himalayan rivers in India where it is required to plan for hydropower generation and the water management during the non‐monsoon season. An attempt has been made to estimate snowmelt runoff on a 10 day average basis in Beas Basin up to Pandoh Dam during May, 1998 and November, 1999 using a Snowmelt Runoff Model (SRM), which is a degree day method. The input parameters for the model are derived from existing maps, satellite data, metrological and hydrological data. The relief of the basin is divided into 12 elevation zones of 500 m each. The temperature was extrapolated to these elevation zones using temperature lapse rate calculated using the observed temperature at seven stations within the basin. Snow covered area in the basin was determined using Indian Remote Sensing Satellites IRS ‐ 1C / 1D Wide Field Sensor (WIFS). The runoff from the snow covered area and snow free area was separately calculated in each elevation zone. The model parameter degree‐day factor is taken from literature and runoff coefficients for snow and rain are derived using the observed data. The total discharge at the dam site is computed by a weighted sum of runoff components from all the elevation zones. There is a good agreement between the observed and computed runoff with a coefficient of determination of 0.854 and the difference in volume is + 4.6 %.     

遥感和GIS支持下的分布式融雪径流过程模拟研究

基于遥感(RS)和地理信息系统(GIS)技术,设计和构建了一个分布式融雪径流模型,整个分布式融雪径流过程的模拟计算基于能量平衡和水量平衡,由分布式栅格融雪过程、分布式栅格产流过程以及分布式栅格汇流过程组成,融雪以及产汇流过程全部基于栅格尺度,全面实现了融雪过程的分布式模拟.分布式栅格融雪过程中对"度日法"加以改进,引入了"单元时段"的概念,从而得到了"度分融雪模型";针对融雪过程中颇为复杂的冻融反复性难题,提出了旨在解释积雪冻融反复性物理机制的"冻融系数"的重要概念,对于准确把握融雪过程的物理机制具有重要意义.同时基于GIS开发了分布式融雪径流模拟系统,为分布式融雪径流模型的运行提供了平台和技术支持,二者均为融雪洪水预警决策支持系统的核心模块部分.基于由MODlS等遥感数据得到的积雪信息、地表温度等下垫面信息,基础地理信息数据如DEM及其空间分析数据和大量野外同步观测数据(积雪信息、气象数据),对典型研究区新疆军塘湖流域2006年春季典型融雪期(2006-03-06,11:00-2006-03-10,11:00)内的洪水过程进行了模拟,模拟结果精度较高,平均精度0.82,达到了融雪洪水预警预报的业务需求标准.     

Snow and ice mask for the MODIS aerosol products

Atmospheric products have been derived operationally from multichannel imaging data collected with the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua spacecraft. Preliminary validations of the products were previously reported. Through analysis of more extensive time-series of MODIS aerosol products (Collection 4), we have found that the aerosol products over land areas are slightly contaminated by snow and ice during the springtime snow-melting season. We have developed an empirical technique using MODIS near-infrared channels centered near 0.86 and 1.24 /spl mu/m and a thermal emission channel near 11 /spl mu/m to mask out these snow-contaminated pixels over land. Improved aerosol retrievals over land have been obtained. Sample results from application of the technique to MODIS data acquired over North America, northern Europe, and northeastern Asia are presented. The technique has been implemented into the MODIS Collection 5 operational algorithm for retrieving aerosols over land from MODIS data.     

基于MODIS影像的内蒙古草原积雪监测

光学遥感源MODIS具有高光谱分辨率、高时间分辨率、高空间分辨率、全球范围内免费接收等优势,被广泛应用于洪涝、干旱、森林草原火灾、雪灾等自然灾害的动态监测领域。MODIS数据用于内蒙古草原积雪监测,提取积雪信息在国内尚属空白。本文利用MODIS L1B 500m分辨率数据,经过几何校正、去"双眼皮"预处理,根据归一化差分积雪指数(NDSI)算法和综合阈值判别法对内蒙古自治区2008年1月下旬大范围降雪进行积雪信息提取,制作积雪覆盖图。利用内蒙古生态与农业气象中心发布的雪情遥感监测信息验证积雪覆盖图的准确度。验证结果表明,MODIS数据用于大范围积雪监测非常有效。     

利用MODIS数据研究天山北麓Kriging雪深插值

以天山北麓为研究区,通过MOD10A2获得月最大雪盖,在无雪区构建虚拟气象站,提高了样本点数量和空间均匀性。运用普通Kriging和协Kriging法对12月至2月最大雪深进行了空间插值,利用交叉检验法对插值精度进行了评估。     

雪地上的森林冠层混合反照率模型

反照率通常定义为出射与入射的能量之比，是地表能量平衡中的关键因子。气候变化对反照率也很敏感。典型的气候变化预测是通过GCM与地表模型的结合进行的。目前在地表模型(如BATS模型)中，是通过空气动力学粗糙度Z0与降雪深度d两个参数来估计雪地上树冠的反照率的。在他们的工作基础上，重点考虑对于不同的太阳天顶角，直入一扇出反照率的方向性及其与叶面积指数(LAI)的关系。同时，为了保持BATS模型所需反照率模型的基本特征，主要在几何光学-辐射传输混合模型(GORT)的基础上做了一些近似来达到此目的。最后，以针叶林为例，用MODIS的反照率数据和BOREAS地面观测数据做了初步的模型验证。结果显示模型较符合实际情况。     

多时相MODIS影像土地覆盖分类比较研究

首先以黑龙江省多时相MODIS影像为试验数据,利用最大似然分类(MLC)、自组织神经网络(SONN)、支持向量机(SVM)以及决策树分类(DTC)等四种广泛使用的分类方法进行了土地覆盖遥感分类研究.并从分类精度、样本数量对分类器的影响、模型复杂度、参数的选择、分类速度等多个方面对4种分类方法进行了深入比较和分析.综合比较得出决策树分类法最优,而经典方法之一的最大似然分类法最稳定.进而将此二法推广到全国范围的土地覆盖分类试验中,并进行精度对比.本文所得出的结论将对于在类似的应用中如何选择合适的分类方法具有一定的参考价值.     

An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

基于IDL的MODIS 1B数据雪面温度反演

对MODIS数据的分裂窗算法进行了简要介绍,通过交互式数据语言(IDL)编程,实现了直接利用MODIS lB数据的雪面温度反演,并将反演结果存贮为标准的HDF文件格式,以供其他软件使用。以我国新疆北部(北疆)为例,将该算法的反演结果与气象站雪面温度观测资料进行对比,结果表明:系统反演得到的雪面温度分布规律与气象观测资料是一致的,反演的平均误差为1.73℃,基本反映了北疆地区的雪面温度分布情况。     

基于NDVI背景场的雪盖制图算法探索

NDSI算法提取MSS雪盖面积时,受到MSS影像缺少短波红外波段的局限。为充分精确提取MSS影像的雪盖面积,本文探索一种以NDVI为背景场的雪盖制图新思路。该方法首先在辐射校正时利用6S模型反演地表反射率,然后根据各地物的光谱特性差异和NDVI特性差异,在ENVI软件SPECTRAL模块中创建冰雪光谱阈值查找表。通过ETM＋和TM影像的三个例证,详细阐明该算法流程以及查找表的创建,并以NDSI对其雪盖制图进行精度验证。结果一致表明,与常规的分类方法（最大似然法）相比较,本文探索的NDVI背景场算法有更高的总体精度和Kappa系数。     

基于波段选择的MODIS全国土地覆盖分类

以MODIS多光谱和多时相数据为输入参数进行了全国土地覆盖分类研究。从试验区2007年MODIS 8 d数据的合成影像(MOD 09)中提取EVI、NDWI和NDSI 3个指数,并将其作为特征波段与原有的7波段(B1～B7)形成10波段影像。以统计分类J-M距离平均值和SVM分类总精度为标准评价不同波段对土地覆盖分类的贡献。在全国范围内,选择贡献最大的EVI、B7和B4这3个波段的月合成值,并分别对其作PCA变换,选取各PCA变换后的前3个波段进行分类运算。研究结果表明,在没有其他辅助信息的境况下,基于MODIS贡献最大的前3个波段结合多时相信息能够在中分辨率区域土地覆盖分类中取得较好的分类结果,其精度为78.04%。     

利用GPS-IR监测土壤含水量的反演模型

雪水当量的监测对于气候变化的预测、水资源管理、农业生产规划具有重要意义。GPS干涉反射(GPS interferometric reflectometry, GPS-IR)技术是一种十分有效的地表积雪监测技术,基于GPS-IR技术提出了一种雪水当量的快速估计方法。首先基于GPS-IR技术获取美国板块边界观测（plate boundary observatory,PBO）GPS站的雪深时间序列;然后利用美国积雪遥测（SNowTELemetry, SNOTEL）站观测数据构建雪水当量转换模型;最后以北美历史与预测气候数据项目（historical and projected climate data for North America,ClimateNA）的气候预测数据作为参数约束,将GPS日雪深快速转化为雪水当量,并对雪水当量估计与验证过程的影响因素进行评价。实验结果表明,基于GPS-IR技术得到的雪深序列具有良好可靠性,与观测值的相关系数（R²）达到0.98,均方根误差（root mean square error, RMSE）为11.1 cm,偏差（Bias）为-3.7 cm;快速转化模型对雪水当量估计具有较高精度（R²=0.98,RMSE=4.2 cm,Bias=-2.5 cm）与稳定性;转化模型时空稳定性较高,残差集中在5 cm内;气候预测数据的引入、积雪分布差异对雪水当量估计与验证影响较小。所提方法在积雪监测设备缺乏区域可实现雪水当量快速估计,同时也为现有积雪观测网络增强、积雪产品改善等研究提供参考。     

基于遥感卫星影像的积雪范围自动化提取

积雪是地球表面重要的覆盖物,也是全球大气与地表相互作用的重要影响因素.本文以资源三号和高景一号卫星遥感影像为主要数据源,将天山中段地区作为主要研究区,分别采用差值指数阈值法和影像亮度分割法,获取遥感影像积雪范围.对比分析可知,利用影像亮度分割法获取积雪范围的结果更准确.利用遥感手段识别积雪范围,对开展智能化地物识别具有...     

MODIS影像雪深遥感反演特征参数选择与模型研究

在综合分析已有研究成果的基础上,选择MODIS遥感影像,借助灰色系统理论,结合观测站实测雪深数据,选择雪深反演特征参数,构建反演模型,并定义多元回归模型的综合评价系数,进而从构建的多个回归模型中,选择出雪深反演最优模型。     

Monitoring of Snow Cover Variability in Chenab Basin Using IRS AWiFS Sensor

Monitoring of seasonal snow cover is important for many applications such as melt runoff estimation, climate change studies and strategic requirements. Contribution of seasonal snow melt runoff of Chenab River is significant and important to meet hydrological requirement at foothills. Seasonal snow cover of Chandra, Bhaga, Miyar, Bhut, Warwan and Ravi, six major tributaries of Chenab River, becomes crucial to assess the water availability. In addition, altitudinal distribution of snow cover significantly influences the melt runoff which is highly sensitive to minor variations in atmospheric temperature. In this investigation, remote sensing based Normalized Difference Snow Index technique has been used to generate 10 daily snow cover product. Snow cover monitoring of all the sub-basins were carried out for 10 years from 2004–2005 to 2013–2014 during hydrological year (October to June) using Advanced Wide Field Sensor (AWiFS) of Indian remote sensing satellite (IRS). Accumulation and ablation patterns of snow cover have also been analyzed for the six sub-basins. Accumulation and ablation pattern of snow cover, from 2004 to 2014 which shows slightly increasing trend for all the sub-basins. Meteorological data of Kelong at Bhaga sub-basin was also analysed. Average monthly snow line altitude was estimated for all the sub-basins using hypsographic curve. Chandra and Bhaga sub-basins are at higher altitude and Ravi sub-basin is at lower altitude. It was also observed that areal extent of snow reaches to lower altitude during last 5 years, particularly in Ravi sub-basin.     

利用CCD相机进行南极站积雪覆盖监测的方法

针对南极考察站区小尺度高时相的积雪覆盖信息目前还没有成熟的监测手段这一问题,详尽地介绍了利用CCD相机进行积雪覆盖监测的方法,包括对相片分割阈值的选取、感兴趣区(ROI)的划定以及像素点的坐标转换等技术流程;计算和分析了2014年8月的南极长城站区积雪覆盖变化情况;并在此基础上,对未来站区附近积雪动态变化监测研究做了展望。     

Estimation of Snow Cover Area,Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R² value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.     

基于混合像元分解的天山典型地区冰雪变化监测

针对中低分辨率遥感图像中存在大量混合像元,而传统的图像分类方法存在只能将某个像元归到某一类中,不能正确反映混合像元实际情况的问题.以新疆天山典型冰川覆盖区为例,根据TM/ETM+遥感图像的光谱特征,结合天山地区地表覆盖特点,在线性混合像元分解方法基础上,设计一种符合冰川地区特点的“冰雪-植被-裸露山体-阴影”端元组分模型.通过选择合适的端元并将其反射率值代入改进后的且满足约束条件的线性混合像元分解模型,得到各端元组分丰度图,进而精确提取出冰雪信息并计算其面积.1989年TM和2000年ETM+遥感图像冰雪信息提取结果表明,运用线性混合像元分解模型能很好地监测实验区的冰雪覆盖变化情况.     

An Assessment of the Effects of Cell Size on AGNPS Modeling of Watershed Runoff

This study investigates the changes in simulated watershed runoff from the Agricultural NonPoint Source (AGNPS) pollution model as a function of model input cell size resolution for eight different cell sizes (30 m, 60 m, 120 m, 210 m, 240 m, 480 m, 960 m, and 1920 m) for the Little River Watershed (Georgia, USA). Overland cell runoff (area-weighted cell runoff), total runoff volume, clustering statistics, and hot spot patterns were examined for the different cell sizes and trends identified. Total runoff volumes decreased with increasing cell size. Using data sets of 210-m cell size or smaller in conjunction with a representative watershed boundary allows one to model the runoff volumes within 0.2 percent accuracy. The runoff clustering statistics decrease with increasing cell size; a cell size of 960 m or smaller is necessary to indicate significant high-runoff clustering. Runoff hot spot areas have a decreasing trend with increasing cell size; a cell size of 240 m or smaller is required to detect important hot spots. Conclusions regarding cell size effects on runoff estimation cannot be applied to local watershed areas due to the inconsistent changes of runoff volume with cell size; but, optimal cells sizes for clustering and hot spot analyses are applicable to local watershed areas due to the consistent trends.