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.

Sensitivity of the Snowmelt Runoff Model to snow covered area and temperature inputs

2014 ◽  
Vol 55 ◽  
pp. 30-38 ◽  
Author(s):  
Jonathan Kult ◽  
Woonsup Choi ◽  
Jinmu Choi
Keyword(s):  
Snowmelt Runoff ◽  
Snow Covered Area ◽  
Covered Area ◽  
Runoff 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 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.

scholarly journals Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru)

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3535
Author(s):  
Elmer Calizaya ◽  
Abel Mejía ◽  
Elgar Barboza ◽  
Fredy Calizaya ◽  
Fernando Corroto ◽  
...  
Keyword(s):  
Climate Change ◽  
Climatic Data ◽  
Climate Change Scenarios ◽  
Snowmelt Runoff ◽  
Snow Covered Area ◽  
Covered Area ◽  
Digital Elevation ◽  
Rcp 8.5 ◽  
Andean Glaciers ◽  
Elevation Model

Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers.

scholarly journals ESTIMATION OF SNOW DEPLETION CURVE FOR GANGOTRI BASIN USING MULTI-SOURCE REMOTE SENSING DATA

2021 ◽  
Vol V-3-2021 ◽  
pp. 197-202
Author(s):  
P. Verma ◽  
S. K. Ghosh ◽  
R. Ramsankaran
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Snowmelt Runoff ◽  
Multiple Sources ◽  
Snow Cover Area ◽  
Depletion Curve ◽  
Normalized Difference Snow Index ◽  
Runoff Model ◽  
Key Parameter

Abstract. Snow Depletion Curve derived from satellite images is a key parameter in Snowmelt Runoff Model. The fixed temporal resolution of a satellite and presence of cloud cover in Himalayas restricts accuracy of generated SDC. This study presents an effective approach of reducing temporal interval between two consecutive dates by integrating normalized Snow Cover Area estimated from multiple sources of satellite data. SCA is extracted by using Normalized Difference Snow Index for six snowmelt seasons from 2013 to 2018 for Gangotri basin situated in Indian Himalayas. This work also explores potential of recently launched Sentinel-3A for estimating SCA. Normalized SCA is utilized to eliminate the effect of difference in spatial resolution of various satellites. The result develops an important linear relation between SDC and time with a decrease in snow cover of 0.005/day that may be further refined by increasing the number of snowmelt seasons. This relationship may help scientific community in understanding hydrological response of glaciers to climate change.

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

scholarly journals Spatial Variability of Shortwave Irradiance for Snowmelt in Forests

2008 ◽  
Vol 9 (6) ◽  
pp. 1482-1490 ◽  
Author(s):  
John Pomeroy ◽  
Chad Ellis ◽  
Aled Rowlands ◽  
Richard Essery ◽  
Janet Hardy ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Variation ◽  
Snow Cover ◽  
Boreal Forests ◽  
Stand Structure ◽  
Snow Accumulation ◽  
Snow Covered Area ◽  
Covered Area ◽  
Depletion Curve ◽  
Natural Snow

Abstract The spatial variation of melt energy can influence snow cover depletion rates and in turn be influenced by the spatial variability of shortwave irradiance to snow. The spatial variability of shortwave irradiance during melt under uniform and discontinuous evergreen canopies at a U.S. Rocky Mountains site was measured, analyzed, and then compared to observations from mountain and boreal forests in Canada. All observations used arrays of pyranometers randomly spaced under evergreen canopies of varying structure and latitude. The spatial variability of irradiance for both overcast and clear conditions declined dramatically, as the sample averaging interval increased from minutes to 1 day. At daily averaging intervals, there was little influence of cloudiness on the variability of subcanopy irradiance; instead, it was dominated by stand structure. The spatial variability of irradiance on daily intervals was higher for the discontinuous canopies, but it did not scale reliably with canopy sky view. The spatial variation in irradiance resulted in a coefficient of variation of melt energy of 0.23 for the set of U.S. and Canadian stands. This variability in melt energy smoothed the snow-covered area depletion curve in a distributed melt simulation, thereby lengthening the duration of melt by 20%. This is consistent with observed natural snow cover depletion curves and shows that variations in melt energy and snow accumulation can influence snow-covered area depletion under forest canopies.

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.

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