Statistical self-similarity of spatial variations of snow cover: verification of the hypothesis and application in the snowmelt runoff generation models

2001 ◽  
Vol 15 (18) ◽  
pp. 3343-3355 ◽  
Author(s):  
L. S. Kuchment ◽  
A. N. Gelfan
Keyword(s):  
Snow Cover ◽  
Spatial Variations ◽  
Snowmelt Runoff ◽  
Runoff Generation ◽  
Self Similarity

scholarly journals Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation

2010 ◽  
Vol 14 (2) ◽  
pp. 339-350 ◽  
Author(s):  
L. S. Kuchment ◽  
P. Romanov ◽  
A. N. Gelfan ◽  
V. N. Demidov
Keyword(s):  
Snow Cover ◽  
Initial Conditions ◽  
Tree Cover ◽  
Special Focus ◽  
Snowmelt Runoff ◽  
Runoff Generation ◽  
Runoff Simulation ◽  
Physically Based Model ◽  
Physically Based ◽  
Derived Data

Abstract. A technique of using satellite-derived data for constructing continuous snow characteristics fields for distributed snowmelt runoff simulation is presented. The satellite-derived data and the available ground-based meteorological measurements are incorporated in a physically based snowpack model. The snowpack model describes temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The remote sensing data used in the model consist of products include the daily maps of snow covered area (SCA) and SWE derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites as well as available maps of land surface temperature, surface albedo, land cover classes and tree cover fraction. The model was first calibrated against available ground-based snow measurements and then applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The satellite-derived SWE data were used for assigning initial conditions and the SCA data were used for control of snow cover simulation. The simulated spatial distributions of snow characteristics were incorporated in a distributed physically based model of runoff generation to calculate snowmelt runoff hydrographs. The presented technique was applied to a study area of approximately 200 000 km2 including the Vyatka River basin with catchment area of 124 000 km2. The correspondence of simulated and observed hydrographs in the Vyatka River are considered as an indicator of the accuracy of constructed fields of snow characteristics and as a measure of effectiveness of utilizing satellite-derived SWE data for runoff simulation.

scholarly journals The hierarchy of controls on snowmelt-runoff generation over seasonally-frozen hillslopes

2016 ◽  
Author(s):  
Anna E. Coles ◽  
Willemijn M. Appels ◽  
Brian G. McConkey ◽  
Jeffrey J. McDonnell
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Water Content ◽  
Soil Surface ◽  
Snowmelt Runoff ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Spring Runoff ◽  
Moisture Conditions ◽  
Seasonally Frozen Regions

Abstract. Understanding and modeling snowmelt-runoff generation in seasonally-frozen regions is a major challenge in hydrology. Partly, this is because the controls on hillslope-scale snowmelt-runoff generation are potentially extensive and their hierarchy is poorly understood. Understanding the relative importance of controls (e.g. topography, vegetation, land use, soil characteristics, and precipitation dynamics) on runoff response is necessary for model development, spatial extrapolation, and runoff classification schemes. Multiple interacting process controls, the nonlinearities between them, and the resultant threshold-like activation of runoff, typically are not observable in short-term experiments or single-season field studies. Therefore, long-term datasets and analyses are needed. Here, we use a 52-year dataset of runoff, precipitation, soil water content, snow cover, and meteorological data from three monitored c.5 ha hillslopes on the Canadian Prairies to determine the controls on snowmelt-runoff, their time-varying hierarchy, and the interactions between the controls. We use decision tree learning to extract information from the dataset on the controls on runoff ratio. Our analysis shows that there was a variable relationship between total spring runoff amount and either winter snowfall amount or snow cover water equivalent. Other factors came into play to control the fraction of precipitated water that infiltrated into the frozen ground. In descending order of importance, these were: total snowfall, snow cover, fall soil surface water content, melt rate, melt season length, and fall soil profile water content. While mid-winter warm periods in some years likely increased soil water content and/or led to development of impermeable ice lenses that affected the runoff response, hillslope memory of fall soil moisture conditions played a strong role in the spring runoff response. The hierarchy of these controls was condition-dependent, with the biggest differences between high and low snow cover seasons, and wet and dry fall soil moisture conditions. For example, when snow cover was high, the top three controls on runoff ratio matched the overall hierarchy of controls, with fall soil surface water content being the most important of these. By comparison, when snow cover was low, fall soil surface content was relatively unimportant and superseded by four other controls. Existing empirical methods for predicting infiltration into frozen ground failed to adequately predict runoff response at our site. Our analysis of the hierarchy of controls on meltwater runoff will aid in focusing new model approaches and understanding what to focus future measurement campaigns on in snowmelt-dominated, seasonally-frozen regions.

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 Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

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, snow cover data provide no direct 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 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. Snow cover data from Landsat 8 and the MOD10A2 snow cover product 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 (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the 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. At high elevation a decrease in SWE due to higher air temperature 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.

scholarly journals Snow cover as a morphogenic agent determining ground climate, landforms and runoff in the Valdecebollas massif, Cantabrian Mountains

2020 ◽  
Vol 46 (1) ◽  
pp. 81-102 ◽  
Author(s):  
A. Pisabarro
Keyword(s):  
Snow Cover ◽  
Physical Environment ◽  
Satellite Images ◽  
Runoff Generation ◽  
Cantabrian Mountains ◽  
Data Loggers ◽  
Geomorphological Map ◽  
Snow Distribution ◽  
Geomorphological Processes ◽  
Early Snowmelt

Snowfalls are important meteorological events affecting the physical environment of the Cantabrian Mountains. This work analyzes the effects of snow on several elements such as relief, landforms, ground climate and snowmelt waters. The ground thermal regime and associated parameters were studied using temperature data loggers and satellite images and were described in combination with observed geomorphological processes and landforms. A geomorphological map was drawn up and trends in climate patterns and runoff were calculated. Ground temperature monitoring in warm years is not optimal, though allow to know the limit conditions for developing cold processes. Results show that geomorphological processes are not significant and that solifluction deriving from snowmelt, is the only active process in years without freeze or with thick snow cover. Snowfall evolution in recent decades in correlation with flow water and climate features provide the certainty that snow distribution also affects efficacy in runoff generation and moves the flow peak in rivers due to early snowmelt.

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 Hydrological effects of Norway spruce and European beech on snow cover in a mid-mountain region of the Polana mts., Slovakia / Hydrologický vplyv smreka obyčajného a buka lesného na snehovú pokrývku v stredohorských polohách pohoria poľana na slovensku

2012 ◽  
Vol 60 (4) ◽  
pp. 319-332 ◽  
Author(s):  
Matus Hribik ◽  
Tomas Vida ◽  
Jaroslav Skvarenina ◽  
Jana Skvareninova ◽  
Lubomir Ivan
Keyword(s):  
Snow Cover ◽  
Norway Spruce ◽  
Forest Canopy ◽  
Biosphere Reserve ◽  
European Beech ◽  
Mountain Region ◽  
Runoff Generation ◽  
Forest Stands ◽  
Snow Melting ◽  
Beech Stand

The paper evaluates the results of a 6-year-monitoring of the eco-hydrological influence of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus silvatica L.) forest stands on the hydro-physical properties of snow cover. The experiment was carried out in the artificially regenerated 20-25-year-old forest stands approaching the pole timber stage in the middle mountain region of the Polana Mts. - Biosphere reserve situated at about 600 m a.s.l. during the period of maximum snow supply in winters of years 2004 - -2009. Forest canopy plays a decisive role at both the snow cover duration and spring snow melting and runoff generation. A spruce stand is the poorest of snow at the beginning of winter. High interception of spruce canopy hampers the throughfall of snow to soil. During the same period, the soil surface of a beech stand accumulates greater amount of snow. However, a spruce stand accumulates snow by creating snow heaps during the periods of maximum snow cumulation and stand´s microclimate slows down snow melting. These processes are in detail discussed in the paper. The forest stands of the whole biosphere reserve slow down to a significant extent both the snow cover melting and the spring runoff of the whole watershed.

scholarly journals Rapid changes in snow cover at low elevations in the Sierra Nevada, California, U.S.A.

1997 ◽  
Vol 25 ◽  
pp. 367-370 ◽  
Author(s):  
Richard Kattelmann
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Mountain Range ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Distributed Models ◽  
Rapid Changes ◽  
Spatially Distributed ◽  
Precipitation Type ◽  
River Forecasting

Snow cover in the intermittent snow zone of the Sierra Nevada can occupy more than 10 000 km2 of the mountain range, but it has received relatively little attention in river forecasting. Snow is deposited at lower elevations only during the cold storms of winter, and remains there only for a few days or weeks. When cold storms have created a thin snow cover at low elevations, a subsequent warm storm can melt this snow in just a few hours and increase the runoff response dramatically. Operational hydrological models and river-forecasting procedures have tended to overlook contributions from the intermittent-snow zone, focusing instead on rainfall-runoff or melt from the snowpack zone at higher elevations. Data-collection efforts are minimal in this zone, too. Ideally, spatially distributed models of snowmelt and runoff generation are needed to account for the typically large differences in snow cover on different aspects in the intermittent snow zone. Although aircraft and satellite imagery would be most desirable to monitor the distribution of snow cover in the intermittent-snow zone, even a few climate stations that report precipitation type and snow presence would be a major improvement over the present situation in the Sierra Nevada.

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