scholarly journals Application of a Snowmelt-Runoff Model Using Landsat Data

1979 ◽  
Vol 10 (4) ◽  
pp. 225-238 ◽  
Author(s):  
A. Rango ◽  
J. Martinec
Keyword(s):  
Input Parameter ◽  
Measured Temperature ◽  
Landsat Data ◽  
Snowmelt Runoff ◽  
Daily Streamflow ◽  
Temperature And Precipitation ◽  
Seasonal Runoff ◽  
High Degree ◽  
Runoff Model

The snowmelt-runoff model developed by Martinec (1975) has been used to simulate daily streamflow on the 228 km2 Din woody Creek basin in Wyoming, U.S.A. using snowcover extent from Landsat and conventionally measured temperature and precipitation. For the six-month snowmelt seasons of 1976 and 1974 the simulated seasonal runoff volumes were within 5 and 1%, respectively, of the measured runoff. Also the daily fluctuations of discharge were simulated to a high degree by the model. Thus far the limiting basin size for applying the model has not been reached, and improvements can be expected if the hydrometeorological data can be obtained from a station inside the basin. Landsat provides an efficient way to obtain the critical snowcover input parameter required by the model.

scholarly journals IMPACTS OF SNOWMELT AND CLIMATE CHANGE ON HIMALAYAN RIVERS: THE CASE OF DATA-SCARCE DUDHKOSHI RIVER IN EASTERN NEPAL

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Sudeep Pokhrel ◽  
Saraswati Thapa
Keyword(s):  
Maximum Temperature ◽  
Climate Projections ◽  
Snow Melt ◽  
Snow Hydrology ◽  
Statistical Downscaling Model ◽  
Snowmelt Runoff ◽  
Annual Average ◽  
Temperature And Precipitation ◽  
Increasing Trends ◽  
Runoff Model

Water from snow-melt is crucial to provide ecosystem services in downstream of the Himalayas. To study the fate of snow hydrology, an integrated modeling system has been developed coupling Statistical Downscaling Model (SDSM) outputs with Snowmelt Runoff Model (SRM) in the Dudhkoshi Basin, Nepal. The SRM model is well-calibrated in 2011 and validated in 2012 and 2014 using MODIS satellite data. The annual average observed and simulated discharges for the calibration year are 177.89 m3 /s and 181.47 m3 /s respectively. To assess future climate projections for the periods 2020s, 2050s, and 2080s, the SDSM model is used for downscaling precipitation, maximum temperature, and minimum temperature from the Canadian GCM model (CanESM2) under three different scenarios RCP2.6, RCP4.5 and RCP8.5. All considered scenarios are significant in predicting increasing trends of maximumminimum temperature and precipitation and the storehouse of freshwater in the mountains is expected to deplete rapidly if global warming continues.

scholarly journals Application of the Snowmelt Runoff Model in the Salang River Basin, Afghanistan Using MODIS Satellite Data

2016 ◽  
Vol 9 (1) ◽  
pp. 109-118
Author(s):  
Hedayatullah Arian ◽  
Rijan B. Kayastha ◽  
Bikas C. Bhattarai ◽  
Ahuti Shresta ◽  
Hafizullah Rasouli ◽  
...  
Keyword(s):  
River Basin ◽  
River Flow ◽  
Snowmelt Runoff ◽  
Annual Average ◽  
Temperature And Precipitation ◽  
Promising Tool ◽  
Daily Discharge ◽  
Input Variables ◽  
The Impact ◽  
Runoff Model

This study is carried out on the Salang River basin, which is located at the northern part of the Kabul River basin, and in the south facing slope of the Hindu Kush Mountains. The basin drains through the Salang River, which is one of the tributaries of the Panjshir River. The basin covers an area of 485.9km2 with a minimum elevation of 1653 m a.s.l. and a maximum elevation of 4770 m a.s.l. The Salang River sustains a substantial flow of water in summer months due to the melting of snow. In this study, we estimate daily discharge of Salang River from 2009 to 2011 using the Snowmelt Runoff Model (SRM, Version 1.12, 2009), originally developed by J. Martinec in 1975. The model uses daily observed precipitation, air temperature and snow cover data as input variables from which discharge is computed. The model is calibrated for the year 2009 and validated for 2010 and 2011. The observed and calculated annual average discharges for the calibration year 2009 are 11.57m3s-1 and 10.73m3s-1, respectively. Similarly, the observed and calculated annual average discharges for the validation year 2010 are 11.55m3s-1 and 10.07m3s-1, respectively and for 2011, the discharges are 9.05 m3s-1 and 9.6m3s-1, respectively. The model is also tested by changing temperature and precipitation for the year 2009. With an increase of 1°C in temperature and 10% in precipitation, the increases in discharge for winter, summer and annually are 21.8%, 13.5% and 14.8%, respectively. With an increase of 2°C in temperature and 20% in precipitation, the increases are 48.5%, 43.3% and 44.1%, respectively. The results obtained suggest that the SRM can be used as a promising tool to estimate the river discharge of the snow fed mountainous river basins of Afghanistan and to study the impact of climate change on river flow pattern of such basins.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.109-118

Modèles d'évolution du manteau nival et de la fonte des neiges

1989 ◽  
Vol 16 (3) ◽  
pp. 219-226
Author(s):  
Saâd Bennis ◽  
Paul-Édouard Brunelle
Keyword(s):  
Snow Cover ◽  
Key Words ◽  
Drainage Basin ◽  
Daily Temperature ◽  
Snow Pack ◽  
Snowmelt Runoff ◽  
Maximum Daily Temperature ◽  
Temperature And Precipitation ◽  
Simulation Results ◽  
Runoff Model

The predictive snowmelt runoff model (SRM), previously suggested by other authors, is reliable and easy to use. Furthermore, the only parameters required are temperature and precipitation, and density and thickness of the snow pack. The literature available indicates that simulation results with this model are generally satisfactory. However, data on the extent of the snow cover are not always available; this means that the snow pack must be calculated before the SRM can be used. Our purpose herein is to develop a model to evaluate the snowpack, which is to be used in conjunction with the SRM. The SRM was modified in that maximum daily temperature was used instead of the number of degrees-days. The snowmelt and snow cover models were calibrated and tested along the drainage basin of the Eaton River, a tributary of the Saint-François River in the province of Quebec. Key words: snowmelt, prediction, flooding. [Journal translation]

scholarly journals Seasonal forecast of Kharif flows from Upper Jhelum catchment

2016 ◽  
Vol 374 ◽  
pp. 137-142 ◽  
Author(s):  
Wolfgang Bogacki ◽  
M. Fraz Ismail
Keyword(s):  
Forecast Model ◽  
Flow Volume ◽  
Snowmelt Runoff ◽  
Temperature And Precipitation ◽  
Seasonal Flow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Depletion Curve ◽  
Set Up ◽  
Runoff Model

Abstract. An operational hydrological forecast model was set-up based on the Snowmelt-Runoff Model (SRM) in order to forecast Kharif flows from Upper Jhelum catchment. Zone-wise degree-day factor functions were derived by diagnostic calibration and are applied according to a defined temperature rule when melting starts. While predicting the depletion of snow-covered area by SRM's modified depletion curve approach, scenario runs with temperature and precipitation of past years are carried out which are evaluated statistically to forecast the seasonal flow volume.

scholarly journals Worldwide Testing of the Snowmelt Runoff Model with Applications for Predicting the Effects of Climate Change

1992 ◽  
Vol 23 (3) ◽  
pp. 155-172 ◽  
Author(s):  
A. Rango
Keyword(s):  
Climate Change ◽  
Water Supply ◽  
Snowmelt Runoff ◽  
Climatic Regions ◽  
Temperature And Precipitation ◽  
The World ◽  
Runoff Model ◽  
Hydrological Effects ◽  
Initial Results ◽  
Volume Difference

The Snowmelt-Runoff Model (SRM), a simple degree-day model, has been applied to over 50 basins in 15 countries around the world. Where results have been reported, the average R2 has been 0.84 and the average seasonal volume difference, Dv, has been 3.8 %. The testing of SRM has taken place on basins in different climatic regions, thus setting the stage for using SRM in evaluations of the hydrological effects of climate change. A method for using SRM in evaluations of climate change has been established and tested on several basins. Initial results show some potentially serious problems involving water supply, flooding, and drought. More testing in a variety of climatic regions is necessary along with improved specification of the changes in temperature and precipitation by region.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

scholarly journals Regionalizing rainfall–runoff model parameters to predict the daily streamflow of ungauged catchments in the dry tropics

2009 ◽  
Vol 40 (5) ◽  
pp. 433-444 ◽  
Author(s):  
David A. Post
Keyword(s):  
Input Data ◽  
Water Yield ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Wet Season ◽  
Daily Streamflow ◽  
Total Length ◽  
Dry Tropics ◽  
Rainfall Runoff Model ◽  
Runoff Model

A methodology has been derived which allows an estimate to be made of the daily streamflow at any point within the Burdekin catchment in the dry tropics of Australia. The input data requirements are daily rainfall (to drive the rainfall–runoff model) and mean average wet season rainfall, total length of streams, percent cropping and percent forest in the catchment (to regionalize the parameters of the rainfall–runoff model). The method is based on the use of a simple, lumped parameter rainfall–runoff model, IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data). Of the five parameters in the model, three have been set to constants to reflect regional conditions while the other two have been related to physio-climatic attributes of the catchment under consideration. The parameter defining total catchment water yield (c) has been estimated based on the mean average wet season rainfall, while the streamflow recession time constant (τ) has been estimated based on the total length of streams, percent cropping and percent forest in the catchment. These relationships have been shown to be applicable over a range of scales from 68–130,146 km2. However, three separate relationships were required to define c in the three major physiographic regions of the Burdekin: the upper Burdekin, Bowen and Suttor/lower Burdekin. The invariance of the relationships with scale indicates that the dominant processes may be similar across a range of scales. The fact that different relationships were required for each of the three major regions indicates the geographic limitations of this regionalization approach. For most of the 24 gauged catchments within the Burdekin the regionalized rainfall–runoff models were nearly as good as or better than the rainfall–runoff models calibrated to the observed streamflow. In addition, models often performed better over the simulation period than the calibration period. This indicates that future improvements in regionalization should focus on improving the quality of input data and rainfall–runoff model conceptualization rather than on the regionalization procedure per se.

scholarly journals Utilization of Satellite Snow-Cover Observations for Seasonal Streamflow Estimates in the Western Himalayas

1983 ◽  
Vol 14 (5) ◽  
pp. 257-266 ◽  
Author(s):  
B. Dey ◽  
D. C. Goswami ◽  
A. Rango
Keyword(s):  
Regression Model ◽  
Snow Cover ◽  
Multiple Regression ◽  
Snowmelt Runoff ◽  
Indus River ◽  
Snow Covered Area ◽  
Covered Area ◽  
Measured Flow ◽  
Seasonal Runoff ◽  
Kabul River

The results presented in this study indicate the possibility of seasonal runoff prediction when satellite-derived basin snow-cover data are related to point source river discharge data for a number of years. NOAA-VHRR satellite images have been used to delineate the areal extent of snow cover for early April over the Indus and Kabul River basins in Pakistan. Simple photo-interpretation techniques, using a zoom transfer scope, were employed in transferring satellite snow-cover boundaries onto base map overlays. A linear regression model with April 1 through July 31 seasonal runoff (1974-1979) as a function of early April snow cover explains 73% and 82% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. The correlation between seasonal runoff and snow cover is significant at the 97% level for the Indus River and at the 99% level for the Kabul River. Combining Rango et al.'s (1977) data for 1969-73 with the above period, the April snow cover explains 60% and 90% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. In an attempt to improve the Indus relationship, a multiple regression model, with April 1 through July 31, 1969-79, seasonal runoff in the Indus River as a function of early April snow-covered area of the basin and concurrent runoff in the adjoining Kabul River, explains 79% of the variability in flow. Moreover, a significant reduction (27%) in the standard error of estimate results from using the multi-variate model. For each year of the study period, 1969-79, a separate multiple regression equation is developed dropping the data for the year in question from the data-base and using those for the rest of the years. The snow cover area and concurrent runoff data are then used to estimate the snowmelt runoff for that particular year.The difference between the estimated and observed dircharge values averaged over the 11 year study period is 10%. Satellite derived snow-covered area is the best available input for snowmelt-runoff estimation in remote, data sparse basins like the Indus and Kabul Rivers. The study has operational relevance to water resource planning and management in the Himalayan region.

Effects of two crop rotations on seasonal runoff and soil loss in the Peace River region

1991 ◽  
Vol 71 (4) ◽  
pp. 533-543 ◽  
Author(s):  
L. J. P. Van Vliet ◽  
J. W. Hall
Keyword(s):  
Soil Loss ◽  
Crop Rotations ◽  
Snowmelt Runoff ◽  
Hordeum Vulgare L ◽  
Peace River ◽  
Residual Variation ◽  
River Region ◽  
Seasonal Runoff ◽  
Runoff And Soil Loss ◽  
Soil Losses

Four erosion plots were monitored from 1983 to 1989 (6 yr) to evaluate the effects of two crop rotations and their constituent crops on runoff and soil loss under natural precipitation near Fort St. John in the Peace River region of British Columbia. Rotation 1 consisted of two cycles of summerfallow — canola (Brassica rapa)-barley (Hordeum vulgare L.), and Rotation 2 included summerfallow — canola-barley-barley underseed to red fescue (Festuca rubra L.)-fescue-fescue. Rainfall and snowmelt runoff were collected and sampled throughout the year to determine seasonal runoff and soil losses. Over the 6 yr, the cumulative runoff and soil losses were consistently greater under Rotation 1 than under Rotation 2. There was a greater than fourfold difference in total soil loss, and 33–35% more total runoff. Rainfall-induced runoff and soil losses were significantly higher for Rotation 1 than for Rotation 2. Snowmelt runoff accounted for 90 and 96% of the total annual runoff and for 39 and 80% of the total annual soil loss from Rotations 1 and 2, respectively. Two large rainfall events during 1983 and 1987, each causing a soil loss in excess of 2000 kg ha−1, accounted for between 85 and 91% of the 6-yr total rainfall-induced erosion from Rotation 1. No differences in runoff or soil loss were detected among crops but the comparisons were insensitive because of high residual variation. Key words: Runoff, soil loss, erosion plots, crop rotations

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