Integration of the MODIS Snow Cover Produced Into Snowmelt Runoff Modeling

Because of the difficulty of monitoring and measuring snow cover in mountainous watersheds, satellite images are used as an alternative to mapping snow cover to replace the ground operations in the watershed. Snow cover is one of the most important data in simulation snowmelt runoff. The daily snow cover maps are received from Moderate Resolution Imaging Spectroradiometer (MODIS), and are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). Simulating Snowmelt runoff is presented using SRM model as one of the major applications of satellite images processing and extracting snow cover in the Ghara - Chay watershed. The first results of modeling process show that MODIS snow covered area product can be used for simulation and forecast of snowmelt runoff in Ghara - Chay watershed. The studies found that the SCA results were more reliable in the study area.     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

高光谱遥感积雪制图算法及验证

雪盖面积是高山地区和季节雪盖区水文和气象模型的重要输入因子。机载和星载遥感数据提取的雪盖面积是融雪径流模型的重要组成部分。对应不同传感器件的光谱特征，多种分类方法被相继提出。但是，缺乏相对独立的验证手段来评价各种分类方法，其主要原因是缺乏地面真实状态。针对该现状，本研究利用高光谱图像的细分光谱特征，建立高光谱影像及其对应“地面真相”的像对数据库来发展和验证积雪制图算法，并展示MODIS积雪制图算法验证和ASTER混合像元分解雪盖制图算法研究的应用实例。     

Snow and glacier melt runoff model to estimate hydropower potential

Himalayan region has high concentrations of mountain glaciers. Large extent of this region is covered by seasonal snow during winter. Runoff generates from melting of these snow and glaciers is one of the important sources of water for the Himalayan Rivers. Glaciers and snowfields are distributed throughout the Himalayas and form a source of numerous streams. Due to steep slopes, all such streams have potential sites for hydropower generation. If this potential is fully utilized, it will help in generating power from environmentally friendly Run-of-River (RoR) hydropower stations. Considering these aspects, a stream flow simulation model was developed for small streams. This will help in estimation of average seasonal unrestricted hydropower potential of snow and glaciated streams for winter, summer, monsoon and autumn seasons. Information generated through remote sensing technique as glacier, permanent snow cover, seasonal snow cover, altitude of snow and glaciers were used in conjunction with daily maximum and minimum temperature, rainfall and discharge. The model was developed for Malana nala located in Parbati River basin near Kullu in Himachal Pradesh. It was validated at adjacent Tosh nala in the same basin. Seasonal runoff computed from the model is comparable with observed data for all seasons except Monsoon. Good results in autumn, winter and summer seasons demonstrates usefulness of runoff model to assess hydropower potential of snow and glaciated streams and therefore, the model was applied to ungauged Sorang Gad and Kirang Khad. In winter runoff was estimated as 1.8 and 1.69 cumecs for Kirang Khad and Sorang Gad, respectively. This is important, as viability of hydropower station depends upon winter stream runoff. These results suggest that the model is useful tool to assess initial estimate of hydropower potential for large number of snow and glaciated streams, for which no hydrological data is available.     

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.     

Accuracy assessment of MODIS/Terra snow cover product for parts of Indian Himalayas

Snow cover mapping is important for snow and glacier-related research. The spatial and temporal distribution of snow cover area is a fundamental input to the atmospheric models, snowmelt runoff models and climate models, as well as other applications. Daily snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite were retrieved for the period between 2004 and 2007, and pixels in these images were classified as cloud, snow or snow-free. These images have then been compared with ground snow depth (SD) measurements from the four observatories located at different parts of Himalayas. Comparison of snow maps with in situ data showed good agreement with overall accuracies in between 78.15 and 95.60%. When snow cover was less, MODIS data were found to be less accurate in mapping snow cover region. As the SD increases, the accuracy of MODIS snow cover maps also increases.     

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM + ) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS’ coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCA_MG), with reference SCA maps (SCA_REF) generated from binary classification of TM imagery. SCA_MG showed strong agreement with SCA_REF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCA_REF and SCA_MG ranged between −2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCA_REF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCA_MG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91.In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km²) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.     

Snow permittivity retrieval inversion algorithm for estimating snow wetness

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume.     

Water Balance Study for a Basin Integrating Remote Sensing Data and GIS

Water balance of a basin involves estimation of input precipitation, runoff, infiltration and evapotranspiration (ET). Although ET may have large variations over a big basin, it is commonly estimated using a few point measurements and this makes the estimation error prone. Satellite based remote sensing data provides few parameters for estimation of energy fluxes, at the land surface and atmosphere interaction in a distributed manner using the meteorological parameters. These parameters through surface energy balance equation have been used for the estimation of ET in this study. Various spatially distributed variables required for ET estimation; viz. NDVI, surface albedo, surface temperature etc. have been derived using remote sensing and ancillary data for Tapi basin located in western India. Beside this field data such as rainfall, air temperature, relative humidity, sunshine hours etc. have been used. For computation of runoff, Soil Conservation Services (SCS) approach has been considered. Tapi basin up to Ukai dam has been selected as the study area. Satellite data from National Oceanic and Atmospheric Administration (NOAA), Polar Orbiting Environmental Satellite, which carries the Advanced Very High Resolution Radiometer (AVHRR), have been used for preparation of various maps required for runoff and ET analysis. The results of runoff and ET have been compared with observed data for 2 years, 2002–2003 and the results have been found in good agreement with observed data.     

Estimation and validation of snow surface temperature using modis data for snow-avalanche studies in NW-Himalaya

Snow avalanche studies require different snow-meteorological parameters for avalanche forecasting. Snow surface temperature is one of the major parameters, which is responsible for the evolution of snow pack characteristics. In the present paper, the snow surface temperature was estimated using TERRA satellite based — Moderate resolution imaging spectroradiometer (MODIS) sensor for NW-Himalayas. Ground data observed by automatic weather stations (AWS) was used to calibrate the brightness temperature obtained by MODIS thermal bands data into the actual snow surface temperature data through regression analysis. A split window technique has been implemented for the estimation of snow surface temperature. The multi-date satellite derived snow surface temperature was validated with ground data of winter 2004–05 and 2005–06 collected at various observation stations located in different ranges of NW-Himalaya. Good correlations were observed for Upper Himalaya (0.98, 0.98), Middle Himalaya (0.92, 0.96) and Lower Himalaya (0.88, 0.82) for 2004–05 and 2005–06 winter respectively. Further, estimated snow surface temperature was also verified with snow-cover information collected by manned observatories and area delineated by thematic maps of snow surface temperature was validated with the different snow climatic zones of NW-Himalaya.     

基于微波辐射计的南极冰盖冻融时空变化分析

基于改进的小波冻融探测算法, 利用SMMR和SSM/I数据对南极冰盖1978年-2010年期间的冻融状况进行监测, 分析了南极冰盖近31年(1978-2010)和近10年(2000-2010)的整体和区域冻融时空变化特征, 结果表明:南极冰盖冻融变化受南极地区温度变化影响比较明显, 两者之间具有一定程度的正相关关系, 而且南极冰盖冻融具有南极东部和南极西部两种不同的冻融变化特征, 南极西部冰盖冻融变化剧烈, 南极东部冰盖冻融变化相对稳定, 甚至有减弱趋势。     

A GIS-based method for defining snow zones: application to the western United States

This study maps the geographic extent of intermittent and seasonal snow cover in the western United States using thresholds of 2000–2010 average snow persistence derived from moderate resolution imaging spectroradiometer snow cover area data from 1 January to 3 July. Results show seasonal snow covers 13% of the region, and intermittent snow covers 25%. The lower elevation boundaries of intermittent and seasonal snow zones increase from north-west to south-east. Intermittent snow is primarily found where average winter land surface temperatures are above freezing, whereas seasonal snow is primarily where winter temperatures are below freezing. However, temperatures at the boundary between intermittent and seasonal snow exhibit high regional variability, with average winter seasonal snow zone temperatures above freezing in west coast mountain ranges. Snow cover extent at peak accumulation is most variable at the upper elevations of the intermittent snow zone, highlighting the sensitivity of this snow zone boundary to climate conditions.     

An Assessment of Snow Avalanche Paths and Forest Dynamics Using Ikonos Satellite Data

Abstract

Ikonos panchromatic and multispectral satellite data were acquired in October 2000 and August 2002 for a test area along US Highway 2, the southern border of Glacier National Park (GNP), Montana, USA. The research goals were to map snow avalanche paths and to characterize vegetation patterns in selected paths for longitudinal (i.e., source, track, and runout) and transverse (i.e., inner, flanking, outer) zones as part of a study of forest dynamics and nutrient flux from paths into terrestrial and aquatic systems. In some valleys, as much as 50 percent of the area may be covered by snow avalanche paths, and as such, serve as an important carbon source servicing terrestrial and aquatic ecosystems. Snow avalanches move woody debris down‐slope by snapping, tipping, trimming, and excavating branches, limbs, and trees, and by injuring and scaring trees that remain in‐place. Further, snow avalanches alter the vegetation structure on paths through secondary plant succession of disturbed areas. Contrast and edge enhancements, Normalized Difference Vegetation Index (NDVI), and the Tasseled Cap greenness and wetness transformations were used to examine vegetation patterns in selected paths that were affected by high magnitude snow avalanches during the winter of 2001-2002. Using image transects organized in longitudinal patterns in paths and in forests, and transects arranged in transverse patterns across the sampled paths, the Tasseled Cap transforms (and NDVI values) were plotted and assessed. Preliminary results suggest that NDVI patterns are different for paths and forests, and Tasseled Cap greenness and wetness patterns are different for longitudinal and transverse zones that describe the morphology of snow avalanche paths. The differentiation of paths from the background forest and the characterization of paths by morphometric zones through remote sensing has implications for mapping forest disturbances and dynamics over time and for large geographic areas and for modeling nutrient flux in terrestrial and aquatic systems.     

Estimation of Sediment Yield and Selection of Suitable Sites for Soil Conservation Measures in Ahar River Basin of Udaipur,Rajasthan using RS and GIS Techniques

The present study aimed at quantification of sediment yield for Ahar River basin of Udaipur district in Rajasthan, India by a regional empirical model using RS and GIS techniques. The land use/land cover (LULC) map of the study area was prepared by supervised classification using satellite imagery of IRS-P6 LISS III. Overall accuracy of the prepared LULC map was 90.78%. The major portion of the study area (49%) is covered with rangeland. Slope map for the study area was developed using digital elevation model. The slope in most of the study area (40% of the total area) ranges from 1% to 4%. In addition, drainage density map of the study area was generated on micro-watershed basis. The study area is covered by a dendritic pattern of drainage which shows that rocks in the area are homogeneous and uniformly resistant to water flow. The drainage density in the study area is 1.11 km km⁻². Annual sediment yield of the study area was quantified by Garde model. The mean annual runoff and sediment yield for the area was 37.58 million m³ and 8,760 m³/year, respectively. Finally, appropriate sites for construction of soil conservation measures are suggested using Boolean logic analysis method on combined slope and drainage maps.     

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.     

Flood prediction and assessment of vulnerability risk in the southern coasts of the Caspian Sea

Abstract

The southern part of the Caspian Sea shoreline in Iran with a length of 813 km has different topographic conditions. Owing to sea fluctuation, these zones have various dimensions in different times. During the last years, the Caspian Sea experienced enormous destructive rises. The historical information and tidal gauge measurements showed different ranges of sea rise from ?30 m to ?22 m from the mean sea level. On the other hand, the probable flooding zone is related to slope gradient of coasts. To help the determination of the probable flooding area owing to sea level rises, the coastal zones can be modelled using geographic information system (GIS) environment as vulnerability risk rates. These rates would be useful for making decisions in coastal management programs. This study examined different scenarios of sea rise to determine hazard-flooding rates in the coastal cities of the Mazandaran province and classified them based on vulnerability risk rates. The 1:2000 scale topographic maps of the coastal zones were prepared to extract topographic information and construct the coastal digital elevation model. With the presumption of half-metre sea rise scenarios, the digital elevation models classified eight scenarios from ?26 to ?22 m. The flooding areas in each scenario computed for 11 cities respectively. The vulnerability risk rate in each rise scenario was computed by dividing the flooded area of each scenario to city area. The results showed that in the first four scenarios, from ?26 to ?24 m, the Behshahr, Joibar, Neka and Babolsar cites would be more vulnerable than other cites. Moreover, for the second four scenarios from ?24 to ?22 m sea level rise scenario, only the coastal area of Chalous city would be vulnerable. It was also observed that the coastal region of Behshahr would be critical in total scenarios. Further studies would be necessary to complete this assessment by considering social-economic and land use information to estimate the exact hazardous and vulnerable zones.     

Snow cover area variability assessment in the upper part of the Satluj river basin in India

In high-altitude areas, snow cover plays a significant role in mountainous hydrology. Satluj, which is a snow-fed river, is a part of the Indus River system in the western Himalayas. Snow cover area (SCA) variability in this river basin affects the spatio-temporal flow availability and avalanche events. Keeping this in mind, the present study focuses on SCA variability and its relationship with various topographical features such as elevation, slope and aspect. The study has been carried out in the upper part of the Satluj River Basin on the basis of MODIS Terra (MOD10A2) data from 2001 to 2014. It has been noticed that the average annual SCA in this part of the Satluj River Basin varies from 44 to 56% with an average of about 48% of the total basin area of 16, 650 km². Further, snow accumulation and depletion curves have been suggested for assessing the SCA in the study area.     

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.     

近10年新疆积雪面积时空变化研究

区域尺度积雪信息的时空监测对确定雪灾的影响范围及灾情等级划分具有重要意义。本文利用近10年的MODIS积雪产品,按月最大面积的规则合成;分析了新疆积雪覆盖面积的时空变化特征,结果表明:时间上,新疆积雪面积有减少的趋势。空间上,近10年新疆积雪季节内永久性积雪覆盖区域主要分布在阿勒泰山脉、天山北麓及沿昆仑山脉西南部。其中天山及阿尔泰山之间的河谷及盆地的草原积雪面积波动主导了新疆整体积雪总面积的波动。     

基于AVHRR/2 10 d合成数据的青藏高原亚像元雪填图

为了满足水文和气象模型对长时段积雪面积数据的需求,基于第二代甚高分辨率辐射计（second series of advanced very high resolution radiometer,AVHRR/2）的10 d合成数据提出了一种青藏高原地区AVHRR/2数据亚像元雪填图算法,将中分辨率遥感数据亚像元级积雪面积数据集延伸至30 a时间跨度。本文算法以多端元线性光谱混合分析模型为基础,以归一化植被指数、第一波段、第二波段等作为选取端元的指标,直接从AVHRR/2图像中自动选取所需雪端元与非雪端元。基于TM数据对该算法的AVHRR/2数据亚像元雪填图结果进行验证,其均方根误差接近0.1,在青藏高原山区具有较高的精度。