Parameter values for snowmelt runoff modelling

Martinec, J.; Rango, A.

doi:10.1016/0022-1694(86)90123-x

Cited by 206 publications

(162 citation statements)

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“…A model run starts with a known or estimated discharge value and can proceed for an unlimited number of days, as long as the input variables -temperature, precipitation and snow covered area -are provided. As a test, a 10-year period was computed without reference to measured discharges (Martinec & Rango, 1986). …”

Section: Range Of Conditions For Model Applicationmentioning

confidence: 99%

“…Example of a possible distortion of a depletion curve due to a temporary increase in the snow coverage by a summer snowfall and to missing Landsat data from the preceding overflight (Hall & Martinec, 1985). (Martinec & Rango, 1986) in Equation (7) (Martinec & Rango, 1986 Figure 11. Range of recession coefficients, k, related to discharge Q resulting from various evaluations (Martinec & Rango, 1986 Figure 12.…”

mentioning

confidence: 99%

“…(Martinec & Rango, 1986) in Equation (7) (Martinec & Rango, 1986 Figure 11. Range of recession coefficients, k, related to discharge Q resulting from various evaluations (Martinec & Rango, 1986 Figure 12. Snowmelt hydrographs illustrating the conversion of computed runoff amounts for 24 hour perioids to calendar day periods (Martinec & Rango, 1986 (Martinec & Rango, 1989 Figure 14.…”

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confidence: 99%

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Implications of Shrinking Cryosphere Under Changing Climate on the Streamflows in the Lidder Catchment in the Upper Indus Basin, India

Romshoo

Dar

Rashid

et al. 2015

Arctic, Antarctic, and Alpine Research

120

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AcknowledgmentIt has taken the efforts of many people and the support of their organization during the last several years to allow us to reach this new milestone in snowmelt runoff modeling. The following organizations and people were particularly helpful and supportive: It is now possible to divide a basin into as many as 16 elevation or other zones in order to refine the modeling, while Version 4 only allowed 8. These improvements facilitate new developments in SRM applications which are already taking place: runoff modeling by using different land use zones, separating satellite mapping of snow and glaciers, runoff modeling in very large basins with an extreme elevation range, and others. The specific features of WinSRM Version 1.11 are explained in detail in this document in Sections 8.5, 8.6, 9, and 10.WinSRM Version 1.11 has been developed without sacrificing the advantages of the SRM Version 4, in particular the speed of getting results. Both versions are available on the Internet by accessing http://www.ars.usda.gov/Services/docs.htm?docid=8872. Should this link not be "current" for the reader, one can "search" on "SRM home" or "WinSRM" to locate a "current site".So far, four SRM workshops (in 1992, 1994, 1996, and 1998) have been organized at the University of Bern, Switzerland, with about 130 participants from 20 countries taking part. A fifth SRM workshop was organized in 2005 at New Mexico State University. In addition, the authors are available to assist users in overcoming special problems which may be encountered. INTRODUCTIONThe Snowmelt-Runoff Model (SRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. Most recently, it has also been applied to evaluate the effect of a changed climate on seasonal snow cover and runoff. SRM was developed by Martinec (1975) in small European basins. Thanks to the progress of satellite remote sensing of snow cover, SRM has been applied to larger and larger basins. Recently, the runoff was modelled in the basin of the Ganges River, which has an area of 917,444 km 2 and an elevation range from 0 to 8,840 m a.s.l. Contrary to the original assumptions, there appear to be no limits for application with regard to the basin size and the elevation range. Also, a dominant role of snowmelt does not seem to be a necessary condition. It is, however, advisable to carefully evaluate the formula for the recession coefficient.Runoff computations by SRM appear to be relatively easily understood. To date the model has been applied by various agencies, institutes and universities in over 100 basins, situated in 29 different countries as listed in Table 1. More than 80% of these applications have been performed by independent users, as is evident from 80 references to pertinent publications. Some of the localities are shown in Figure 1. SRM also successfully underwent tests by the World Meteorological Organization with regard to runoff simulations (WMO, 1986) and to partially simulated conditions of real ti...

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Section: Range Of Conditions For Model Applicationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Implications of Shrinking Cryosphere Under Changing Climate on the Streamflows in the Lidder Catchment in the Upper Indus Basin, India

Romshoo

Dar

Rashid

et al. 2015

Arctic, Antarctic, and Alpine Research

120

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“…The model of the snow cover evolution is based on the degree-day method (Martinec & Rango, 1986) which links the calculations of effective rainfall and snowmelt volumes to the daily mean temperature TJ°C). The increase in snow cover, i.e.…”

Section: The Arx Rainfall-runoff Modelmentioning

confidence: 99%

A comparison of parametric and non-parametric methods for runoff forecasting

Galeati

1990

Hydrological Sciences Journal

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The paper evaluates the performance of a non-parametric scheme, the nearest neighbour (NN) method, to predict the daily mean discharge in a mountain basin supplying a hydroelectric reservoir in northeastern Italy. The results are compared with those of an autoregressive model with exogenous input (ARX), coupled with a previously developed snow cover evolution model. Both methods give good performances, but the NN prediction requires a much simpler simulation structure. In the case investigated, for example, the snowpack accumulationmelting model can be completely eliminated.

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“…The value of K is obtained by solving the equation (6), where the constant x = 0.98 and y = 0.36 is obtained by a recession flow plot (Martinec and Rango, 1986): (5) Point evapotranspiration at the on-glacier AWS is estimated using the modified Hargreaves method (Sperna Weiland et al, 2012): (6) where, Ra is the extraterrestrial radiation (MJ m -2 hour -1 ), T is the hourly temperature and TR is the diurnal temperature range ( ᵒ C). The extraterrestrial radiation is estimated based on (Allen et al, 1998).…”

Section: Modelling Approachmentioning

confidence: 99%

Modified temperature index model for estimating the melt water discharge from debris-covered Lirung Glacier, Nepal

Parajuli¹,

Chand²,

Kayastha³

et al. 2015

Proc. IAHS

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In the Nepalese Himalayas, the complex topography, occurrence of debris covered glaciers, and limited data availability creates substantial difficulties for modelling glacier melt. The proper recognition of melt processes governs the accurate estimation of melt water from glacier dominated systems, even in the presence of debris-covered glaciers. This paper presents a glacier melt model developed for the Lirung subbasin of Langtang valley, which has both a clean glacier area, 5.86 km 2 , and a debris-covered glacier area, 1.13 km 2 . We use a temperature index approach to estimate sub-daily melt water discharge for a two week period at the end of monsoon, and the melt factor is varied according to the aspect and distributed to each grid processed from the digital elevation model. The model uses easily available data and simple extrapolation techniques capable of generating melt with limited data. The result obtained from this method provides accurate estimate with an R 2 value of 0.89, bias of 0.9% and Nash-Sutcliffe efficiency of 0.86, and suitable in Himalaya where data availability is major issue.

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Parameter values for snowmelt runoff modelling

Cited by 206 publications

References 7 publications

Implications of Shrinking Cryosphere Under Changing Climate on the Streamflows in the Lidder Catchment in the Upper Indus Basin, India

Implications of Shrinking Cryosphere Under Changing Climate on the Streamflows in the Lidder Catchment in the Upper Indus Basin, India

A comparison of parametric and non-parametric methods for runoff forecasting

Modified temperature index model for estimating the melt water discharge from debris-covered Lirung Glacier, Nepal

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