Interpretation and Utilization of Areal Snow-Cover Data from Satellites

Martinec, J.; Rango, A.

doi:10.1017/s0260305500000550

Cited by 22 publications

(11 citation statements)

References 3 publications

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“…This problem could be at least partially overcome by establishing a digital terrain model of the basin and by using Equation (8) The problem of different exposures of small grid units is greatly reduced if the water equivalent is evaluated as an areal average for an entire elevation zone of a basin. As explained elsewhere (Martinec and Rango, 1987), the so-called modified depletion curves of the snow coverage are used for this purpose.…”

Section: Cmd1mentioning

confidence: 99%

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Rango¹,

Martinec²

1995

J American Water Resour Assoc

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276

201

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The simple, empirical degree‐day approach for calculating snowmelt and runoff from mountain basins has been in use for more than 60 years. It is frequently suggested that the degree‐day method be replaced by the more physically‐based energy balance approach. The degree‐day approach, however, maintains its popularity, applicability, and effectiveness. It is shown that the degree‐day method is reliable for computing total snowmelt depths for periods of a week to the entire snowmelt season. It can also be used for daily snowmelt depths when utilized in connection with an adequate snowmelt runoff model for computing the basin runoff. The degree‐day ratio is shown to vary seasonally as opposed to being constant as is often assumed. Additionally, in order to evaluate the degree‐day ratio correctly, the changing snow cover extent in a basin during the snowmelt season must be taken into account. It is also possible to combine the degree‐day approach with a radiation component so that short time interval (<24 hours) computations of snowmelt depth can be made. When snowmelt input is transformed to basin output (runoff) by a snowmelt runoff model, there is little difference between the degree‐day approach and a radiation‐based approach. This is fortuitous because the physically‐based energy balance models will not soon displace the degree‐day methods because of their excessive data requirements.

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Section: Cmd1mentioning

confidence: 99%

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Rango¹,

Martinec²

1995

J American Water Resour Assoc

Self Cite

276

201

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show abstract

“… Good and Martinec [1987] indicate that as early as 1937 aerial photography was being used to obtain these sort of images. By the 1980s satellite products were being used to produce binary image time series [ Hall and Martinec , 1985; Martinec and Rango , 1987; Leavesley and Stannard , 1990]. Not surprisingly, the overwhelming use of binary images has been in modeling snowmelt runoff.…”

Section: Introductionmentioning

confidence: 99%

“…In one of the most comprehensive studies, Blöschl et al [1991a, 1991b] and Blöschl and Kirnbauer [1992] used weekly aerial photographs to derive SC and SF patterns, then compared these to the results of a distributed melt model. In most other instances [e.g., Martinec and Rango , 1987; Molotch and Margulis , 2008; DeBeer and Pomeroy , 2009] the images have been used primarily to determine the snow covered area (SCA), a statistical product that does not have the same data richness as the binary images themselves. The most recent trend seems to be the use of ground‐based automatic digital cameras to take oblique images from which maps of snow patterns and SCA can be derived [ Tappeiner et al , 2001; Schmidt et al , 2009; DeBeer and Pomeroy , 2009].…”

Section: Introductionmentioning

confidence: 99%

Using repeated patterns in snow distribution modeling: An Arctic example

Sturm

Wagner

2010

Water Resources Research

128

164

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[1] Snow distribution patterns are similar from one year to the next because they are largely controlled by the interaction of topography, vegetation, and consistent synoptic weather patterns. On a yearly basis none of these controls changes radically. As a consequence, deep and shallow areas of snow tend to be fixed in space, producing depth differences that may vary in absolute, but not relative, amounts from year to year. While this fact is widely known, the use of patterns in modeling snow cover distribution is limited. Here, on the basis of a training set of nine annual snow depth surveys from a small tundra basin in Alaska, we identify the climatological snow distribution pattern (CSDP). Using this and a few depth measurements, the snow distribution for years that were not included in the training set is predicted and mapped with a near-zero bias and RMSE that ranged from 4.4 to 10.4 cm. The accuracy of this strictly empirical approach to modeling the depth distribution is similar to, or better than, the output from a weather-driven physically based snow model. However, in our view a hybrid approach is best. Ingesting the CSDP into SnowModel, a widely used numerical code that simulates snow processes, the accuracy of the model output is improved by up to 60%. This hybrid approach retains the advantages of running a weather-driven numerical code but adds spatial accuracy currently only obtainable from observed snow patterns. The patterns can be captured in several ways, including aerial photography or satellite remote sensing during snowmelt.

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“…Snowmeh runoff Model (SRM) (MARTINEC and RANGO, 1987) based the degree day method and calculated the snowmelt runoff on a daily basic is effectively used with extensive testing. The results show the accuracy of SRM (BAUMGARTNER and RAN-GO, 1995).…”

Section: Snowmelt Runoff Calculation and Fore-castmentioning

confidence: 99%

Snow cover monitoring by remote sensing and snowmelt runoff calculation in the upper Huanghe River basin

et al. 2002

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The upper Huanghe(Yellow) River basin is situated in the northeast of the Qinghai-Xizang(Tibet)Plateau of China. The melt-water from the snow-cover is main water supply for the rivers in the region during springtime and other arid regions of the northwestern China, and the hydrological conditions of the rivers are directly controlled by the snowmeh water in spring. So snowmeh runoff forecast has importance for hydropower, flood prevention and water resources utilization. The application of remote sensing and Geographic Information System (GIS) techniques in snow cover monitoring and snowmeh runoff calculation in the upper Huanghe River basin are introduced amply in this paper. The key parame-ter~snow cover area can be computed by satellite images from multi-platform, multi-temporal and multi-spectral. A cluster of snow-cover data can be yielded by means of the classification filter method. Meanwhile GIS will provide relevant information for obtaining the parameters and also for zoning. According to the typical samples extracting snow covered mountainous region, the snowmelt runoff calculation models in the upper Huanghe River basin are presented and they are mentioned in detail also. The runoff snowmeh models based on the snow-cover data from NOAA images and observation data of runoff, precipitation and air temperature have been satisfactorily used for predicting the inflow to the Longyangxia Reservoir, which is located at lower end of snow cover region and is one of the largest reservoirs on the upper Huanghe River, during late March to early June. The result shows that remote sensing techniques combined with the ground meteorological and hydrological observation is of great potential in snowmelt runoff forecasting for a large river basin. With the development of remote sensing technique and the progress of the interpretation method, the forecast accuracy of snowmelt runoff will be improved in the near future. Large scale extent and few stations are two objective reality situations in China, so they should be considered in simulation and forecast. Apart from dividing, the derivation of snow cover area from satellite images would decide the results of calculating runoff. Field investigation for selection of the learning samples of different snow patterns is basis for the classification. KEY WORDS: upper Huanghe River; snowmelt runoff; remote sensing and GIS; snow cover area CLC number: P343 Document code: A

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Interpretation and Utilization of Areal Snow-Cover Data from Satellites

Cited by 22 publications

References 3 publications

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

Using repeated patterns in snow distribution modeling: An Arctic example

Snow cover monitoring by remote sensing and snowmelt runoff calculation in the upper Huanghe River basin

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Interpretation and Utilization of Areal Snow-Cover Data from Satellites

Cited by 22 publications

References 3 publications

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS1

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS1

Using repeated patterns in snow distribution modeling: An Arctic example

Snow cover monitoring by remote sensing and snowmelt runoff calculation in the upper Huanghe River basin

Contact Info

Product

Resources

About

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹

REVISITING THE DEGREE‐DAY METHOD FOR SNOWMELT COMPUTATIONS¹