Lapse Rates and Spatial Interpolation of Air Temperature in Mountainous Terrain
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Snowfall over mountainous areas not only has important implications on the water cycle and the Earth’s radiation balance, but also causes potentially hazardous weather. However, snowfall detection remains one of the most difficult problems in modern hydrometeorology. We present a method for detecting snowfall events from optical satellite data for seasonal snow in mountainous areas. The proposed methodology is based on identifying expanded snow cover or suddenly declined snow grain size using time series images, from which it is possible to detect the location and time of snowfall events. The methodology was tested with Moderate Resolution Imaging Spectroradiometer (MODIS) daily radiance data for an entire hydrologic year from July 2014 to June 2015 in the mountainous area of the Manas River Basin, Northwest China. The study evaluated the recordings of precipitation events at eighteen meteorological stations in the study area prove the effectiveness of the proposed method, showing that there was more liquid precipitation in the second and third quarter, and more solid precipitation in the first and fourth quarter.
Hydrometeorology
Moderate-resolution imaging spectroradiometer
Spectroradiometer
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In the Hindukush-Himalaya (HKH) region, most of the population are largely dependent on snowmelt and glacier meltwaters. Snowmelt and glacier meltwaters are considered to be a key source of water resources. We evaluated the characteristics of snowfall and snowmelt in the HKH region, which elevation ranges from 1593 m to 5694 m. Moreover, we estimated the spatial and temporal Snow Water Equivalent (SWE) distribution combining remote sensing and snow model. For this purpose, we optimized the precipitation gradient (PG) for snowfall estimation and degree-day factor (DDF) for snowmelt distribution by the combination between MODIS and simulated snow cover area (SCA). Temperature lapse rate was calculated using the observation data, recorded at different altitudes and value of -9.0 ℃ Km-1 was obtained. As a result, we found the DDF value is 3 (mm ℃-1 day-1) for the low elevations (below 3000 m) and 3 to 5 (mm ℃-1 day-1) for the high elevations (above 3000 m). In addition, the precipitation distribution change with elevations the PG is 0 (m-1) for high altitudes (above 4500 m) and 0.001 (m-1) for low altitudes (below 4500 m). We estimated SCA and SWE through snow model using the two optimized parameters. MODIS and simulated SCA were examined and resulted in a determination coefficient of 0.98 and 0.96 for the years 2012-2013, 2013-2014 and 2014-2015, respectively. SWE accumulated from late October and continues until March. The SWE peaks in March and later on starts decreasing. The results justify that the snow model is applicable for water resources management under data-scarce and complex watersheds such as the HKH region.
Snowmelt
Elevation (ballistics)
Meltwater
Water equivalent
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Snowmelt
Snowpack
Meltwater
Degree day
Moderate-resolution imaging spectroradiometer
Water year
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Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However this provides no information on the actual amount of water stored in a snowpack i.e. the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ meteorological observations and a modified version of the seNorge snow model to estimate climate sensitivity of SWE and snowmelt runoff in the Langtang catchment in Nepal. Landsat 8 and MOD10A2 snow cover maps were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 % and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an Ensemble Kalman filter. The approach of modelling snow depth in a Kalman filter framework allows for data-constrained estimation of SWE rather than snow cover alone and this has great potential for future studies in the Himalayas. Climate sensitivity tests with the optimized snow model show a strong decrease in SWE in the valley with increasing temperature. However, at high elevation a decrease in SWE is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature. Finally the climate sensitivity study revealed that snowmelt runoff increases in winter and early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature.
Snowmelt
Snowpack
Meltwater
Elevation (ballistics)
Snow field
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Abstract Air temperature represents a key parameter for snow hydrology, as it controls the precipitation phase, as well as snow accumulation and snowmelt. Hydrological modelling in mountain regions like the Alps needs high‐resolution temperature fields as input, preferably at sub‐daily time steps. The estimation of such temperature fields is challenging due to the spatio‐temporal variability of environmental lapse rates (i.e. the decreasing of temperature with altitude) associated to complex topography. In this study, 10 years (2000–2009) of data from about 200 temperature stations were interpolated at 0, 6, 12 and 18 h Universal Time Coordinated (UTC) over a 1‐km resolution grid covering a window of 71 500 km 2 in the Northern French Alps. Three different kriging methods were tested. Kriging with elevation as external drift gave the best results in terms of mean absolute error, root mean square error and kriging standard deviation. Adding potential solar radiation as an additional external variable did not improve significantly the interpolation results. Prediction errors showed dependence on elevation and season, as well as on the time of interpolation, with globally better results in summer and daytime than in winter and night‐time. Despite some shortcomings that are discussed in the paper, the interpolated temperature fields look promising for further snowmelt and snow cover dynamics modelling studies. Copyright © 2012 John Wiley & Sons, Ltd.
Snowmelt
Elevation (ballistics)
Interpolation
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Snow cover mapping is often carried out using optical satellite imagery. In this paper the authors address the problem of snow mapping of small basins with areas only 10 times the spatial resolution of the sensor using subpixel analysis. This technique is currently being used in several basins in the Spanish Pyrenees and has been developed as part of a snow cover mapping and snowmelt runoff forecasting system which uses snow cover as an input far the Snowmelt Runoff Model (SRM). Spanish hydropower companies receive the snowmelt forecasts and use them as criteria in decision making for reservoir management. The accuracy of the forecast strongly depends an the snow cover accuracy.
Snowmelt
Subpixel rendering
Meltwater
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