Examination of snowmelt over Western Himalayas using remote sensing data

Tiwari, Sarita; Kar, S. C.; Bhatla, R.

doi:10.1007/s00704-015-1506-y

Cited by 14 publications

(23 citation statements)

References 16 publications

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“…However, there is no ideal method of automatically measuring SWE (Egli et al, 2009), and installation and maintenance of gauging stations at elevations where the majority of Himalayan snow cover resides and melts (4000-5000 m a.s.l.) (Immerzeel et al, 2009;Tiwari et al, 2016;Gurung et al, 2017) is logistically difficult, expensive, and often dangerous in poor weather conditions. Even where present, many of the meteorological stations in the Himalayas have large data gaps (e.g., Shea et al, 2015), and rain gauges are generally not adapted to measure solid precipitation, resulting in large instrumental uncertainties which negatively impact efforts to calibrate SWE products over larger spatial scales (Lang and Barros, 2004;Ma et al, 2009;Ménégoz et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Near Real-Time Measurement of Snow Water Equivalent in the Nepal Himalayas

et al. 2019

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Seasonal snow is an important component of the Himalayan hydrological system, but a lack of observations at high altitude hampers understanding and forecasting of water availability in this region. Here, we use a passive gamma ray sensor that measures snow water equivalent (SWE) and complementary meteorological instruments installed at 4962 m a.s.l. in the Nepal Himalayas to quantify the evolution of SWE and snow depth over a 2-year period. We assess the accuracy, spatial representativeness and the applicability of the SWE and snow depth measurements using time-lapse camera imagery and field observations. The instrument setup performs well for snowpacks >50 mm SWE, but caution must be applied when interpreting measurements from discontinuous, patchy snow cover or those that contain lenses of refrozen meltwater. Over their typical ∼6-month lifetime, snowpacks in this setting can attain up to 200 mm SWE, of which 10-15% consists of mixed precipitation and rain-on-snow events. Precipitation gauges significantly underrepresent the solid fraction of precipitation received at this elevation by almost 40% compared to the gamma ray sensor. The application of sub-daily time-lapse camera imagery can help to correctly interpret and increase the reliability and representativeness of snowfall measurements. Our monitoring approach provides high quality, continuous, near-real time information that is essential to develop snow models in this data scarce region. We recommend that a similar instrument setup be extended into remote Himalayan environments to facilitate widespread snowpack monitoring and further our understanding of the high-altitude water cycle.

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Section: Introductionmentioning

confidence: 99%

Near Real-Time Measurement of Snow Water Equivalent in the Nepal Himalayas

et al. 2019

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“…These values were obtained from extensive studies made by Tiwari et al . () on snowmelt processes in the Satluj basin. Final values of these parameters were obtained by carrying out further sensitivity studies as described below.…”

Section: Resultsmentioning

confidence: 99%

“…Tiwari et al . () have examined the variability of snowmelt factor for each month with respect to orography height. Mir et al .…”

Section: Introductionmentioning

confidence: 99%

“…Tiwari et al . () calculated the sensitivity of parameters (e.g. degree day factor and the relationship between snow cover area and snow water equivalent) in a simple snowmelt model for the Satluj River basin.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the rugged terrain and inaccessibility of higher reaches, a poor meteorological and hydrological observation network exists in the high altitude region of the Himalayas and the Satluj basin. For such a basin, it is a challenge to obtain representative meteorological data, to characterize the physical characteristics properly and to accurately represent the hydrological processes (Jain et al ., ; Tiwari et al ., , ).…”

Section: Introductionmentioning

confidence: 99%

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Temperature index based snowmelt runoff modelling for the Satluj River basin in the western Himalayas

Tiwari

Kar

Bhatla

et al. 2017

Meteorological Applications

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The Satluj River is one of the main rivers in the western Himalayas. Snowmelt runoff substantially contributes to the streamflow of the river in the spring and summer months. The unavailability of observed meteorological data (precipitation, surface temperature) spread across the Satluj basin is a major problem for modelling snowmelt runoff there. Researchers have used station observations available at certain points in the basin for modelling snowmelt and rainfall runoff in Satluj and have obtained mixed results. For this study, a new meteorological dataset was prepared by merging gridded precipitation from India Meteorological Department (IMD) and Aphrodite as well as station observations of Bhakra Beas Management Board (BBMB) in the basin. Maximum and minimum temperatures from the Climate Forecast System Reanalysis were also used for the entire basin. The Soil and Water Assessment Tool (SWAT) hydrology model was run using the new dataset to simulate runoff in the Satluj River and to evaluate the streamflow characteristics. Sensitivity experiments were carried out using temperature and precipitation lapse rates, routing methods (travel time vs Muskingum) and degree day factors to identify best suitable parameters for simulations. Three sets of calibration (1982-1992) and validation (1993-2003) simulations were carried out to estimate the performance of the SWAT model in simulating runoff in the Satluj basin. The experiment with precipitation data prepared using IMD, Aphrodite and BBMB stations provided the best performance with a Nash−Sutcliffe efficiency (NS) of 0.60 and R 2 = 0.70, which is a major improvement on the use of Aphrodite data only (NS = 0.13 and R 2 = 0.51). However, the model underestimates the streamflow in all seasons in all the simulations. It was found that the model is able to simulate the extreme streamflow cases in Satluj with reasonable skill.

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