scholarly journals Influence of snow ablation and frozen ground on spring runoff generation in the Mogot Experimental Watershed, southern mountainous taiga of eastern Siberia

2006 ◽  
Vol 37 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Kazuyoshi Suzuki ◽  
Jumpei Kubota ◽  
Tetsuo Ohata ◽  
Valery Vuglinsky
Keyword(s):  
Soil Moisture ◽  
Snow Water Equivalent ◽  
Soil Surface ◽  
Eastern Siberia ◽  
Snowmelt Runoff ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Spring Runoff ◽  
Snow Water ◽  
The Difference

Snowmelt runoff is one of the most important discharge events in the southern mountainous taiga of eastern Siberia. The present study was conducted in order to understand the interannual variations in snowmelt infiltration into the frozen ground and in snowmelt runoff generation during the snowmelt period in the southern mountainous taiga in eastern Siberia. Analysis of the obtained data revealed the following: (1) snowmelt infiltration into the top 20 cm of frozen ground is important for evaluating snowmelt runoff generation because frozen ground absorbed from 22.9% (WY1983) to 61.5% (WY1981) of the maximum snow water equivalent. The difference in snowmelt infiltration for the two years appears to have been caused by the difference in snowmelt runoff generation; (2) the snowmelt runoff ratio increased with (i) increase in the fall soil moisture just before the soil surface froze and (ii) increase in the maximum snow water equivalent. The above results imply that the parameters governing snowmelt infiltration in the boreal taiga region in eastern Siberia are fall soil moisture and the maximum snow water equivalent, as is the case in the simple model presented by Gray et al.

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 Effects of preferential flow on snowmelt partitioning and groundwater recharge in frozen soils

2019 ◽  
Vol 23 (12) ◽  
pp. 5017-5031 ◽  
Author(s):  
Aaron A. Mohammed ◽  
Igor Pavlovskii ◽  
Edwin E. Cey ◽  
Masaki Hayashi
Keyword(s):  
Soil Moisture ◽  
Groundwater Recharge ◽  
Preferential Flow ◽  
Frozen Soil ◽  
Time Lags ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Soil Matrix ◽  
Frozen Soils ◽  
Flow Through

Abstract. Snowmelt is a major source of groundwater recharge in cold regions. Throughout many landscapes snowmelt occurs when the ground is still frozen; thus frozen soil processes play an important role in snowmelt routing, and, by extension, the timing and magnitude of recharge. This study investigated the vadose zone dynamics governing snowmelt infiltration and groundwater recharge at three grassland sites in the Canadian Prairies over the winter and spring of 2017. The region is characterized by numerous topographic depressions where the ponding of snowmelt runoff results in focused infiltration and recharge. Water balance estimates showed infiltration was the dominant sink (35 %–85 %) of snowmelt under uplands (i.e. areas outside of depressions), even when the ground was frozen, with soil moisture responses indicating flow through the frozen layer. The refreezing of infiltrated meltwater during winter melt events enhanced runoff generation in subsequent melt events. At one site, time lags of up to 3 d between snow cover depletion on uplands and ponding in depressions demonstrated the role of a shallow subsurface transmission pathway or interflow through frozen soil in routing snowmelt from uplands to depressions. At all sites, depression-focused infiltration and recharge began before complete ground thaw and a significant portion (45 %–100 %) occurred while the ground was partially frozen. Relatively rapid infiltration rates and non-sequential soil moisture and groundwater responses, observed prior to ground thaw, indicated preferential flow through frozen soils. The preferential flow dynamics are attributed to macropore networks within the grassland soils, which allow infiltrated meltwater to bypass portions of the frozen soil matrix and facilitate both the lateral transport of meltwater between topographic positions and groundwater recharge through frozen ground. Both of these flow paths may facilitate preferential mass transport to groundwater.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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.

ASSESSMENT OF CHANGES IN THE CHARACTERISTICS OF THE WATER, SNOW AND ICE REGIMES IN THE BASINS OF THE SMALL NORTHERN DVINA RIVER UNDER MODERN CONDITIONS AND THEIR POSSIBLE CHANGES IN THE FUTURE

2021 ◽  
pp. 117-127
Author(s):  
M. V. GEORGIEVSKY ◽  
◽  
N. I. GOROSHKOVA ◽  
V. A. KHOMYAKOVA ◽  
A. V. STRIZHENOK
Keyword(s):  
Snow Cover ◽  
Climate Warming ◽  
Snow Water Equivalent ◽  
Winter Precipitation ◽  
Spring Runoff ◽  
The Future ◽  
Snow Water ◽  
Project Data ◽  
The Impact ◽  
Slight Downward Trend

The article presents an analysis of the impact of climate change on the main characteristics of ice phenomena, snow cover and the water regime in the Small Northern Dvina River basin occurring in recent decades. Recently, a significant climate warming has been observed in the basin. As a result, winters are getting warmer and shorter. There is also an increase in winter precipitation and the number of thaws. Climate warming directly affects the duration of snow cover, which decreases both due to the later formation and to the earlier destruction of snow. There is also a slight downward trend in the annual values of the maximum snow water equivalent, which may be the result of an increase in the number of thaws in winter, when a part of the snow cover melts contributing to the winter river runoff. The analysis of the main characteristics of the ice cover on the rivers of the studied basin shows that their changes are similarly to changes in the snow cover: there is a reduction in the freeze-up period due to its later formation and earlier complete destruction. The maximum ice thickness on the rivers of the basin also tends to decrease. There is an increase in winter and a decrease in spring runoff. Predictive estimates of changes in the observed trends in the future are presented in the fi nal part of the article based on the CMIP5 project data.

scholarly journals Spatially distributed tracer-aided runoff modelling and dynamics of storage and water ages in a permafrost-influenced catchment

2019 ◽  
Vol 23 (6) ◽  
pp. 2507-2523 ◽  
Author(s):  
Thea I. Piovano ◽  
Doerthe Tetzlaff ◽  
Sean K. Carey ◽  
Nadine J. Shatilla ◽  
Aaron Smith ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Snow Accumulation ◽  
Yukon Territory ◽  
Distributed Model ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Isotope Data ◽  
Spatially Distributed ◽  
Thaw Layer

Abstract. Permafrost strongly controls hydrological processes in cold regions. Our understanding of how changes in seasonal and perennial frozen ground disposition and linked storage dynamics affect runoff generation processes remains limited. Storage dynamics and water redistribution are influenced by the seasonal variability and spatial heterogeneity of frozen ground, snow accumulation and melt. Stable isotopes are potentially useful for quantifying the dynamics of water sources, flow paths and ages, yet few studies have employed isotope data in permafrost-influenced catchments. Here, we applied the conceptual model STARR (the Spatially distributed Tracer-Aided Rainfall–Runoff model), which facilitates fully distributed simulations of hydrological storage dynamics and runoff processes, isotopic composition and water ages. We adapted this model for a subarctic catchment in Yukon Territory, Canada, with a time-variable implementation of field capacity to include the influence of thaw dynamics. A multi-criteria calibration based on stream flow, snow water equivalent and isotopes was applied to 3 years of data. The integration of isotope data in the spatially distributed model provided the basis for quantifying spatio-temporal dynamics of water storage and ages, emphasizing the importance of thaw layer dynamics in mixing and damping the melt signal. By using the model conceptualization of spatially and temporally variable storage, this study demonstrates the ability of tracer-aided modelling to capture thaw layer dynamics that cause mixing and damping of the isotopic melt signal.

Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture

2020 ◽  
Vol 240 ◽  
pp. 111668 ◽  
Author(s):  
Eunsang Cho ◽  
Jennifer M. Jacobs ◽  
Ronny Schroeder ◽  
Samuel E. Tuttle ◽  
Carrie Olheiser
Keyword(s):  
Soil Moisture ◽  
Gamma Radiation ◽  
Snow Water Equivalent ◽  
Snow Water

scholarly journals Geocryological conditions triggering thermokarst processes in Central Yakutia

2020 ◽  
Vol 163 ◽  
pp. 02007
Author(s):  
Nataliia Nesterova ◽  
Olga Makarieva ◽  
Alexander Fedorov ◽  
Andrey Shikhov
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
River Basins ◽  
Soil Freezing ◽  
Abrupt Increase ◽  
Landsat Images ◽  
Thermokarst Lakes ◽  
Central Yakutia ◽  
Snow Water

The use of the Central Yakutia Landsat images revealed an increase in the area of thermokarst lakes by two times for the Suola and Taatta River basins and a quarter times in the Tanda River basin during the period 2000-2019. The abrupt increase in the lakes area is due to shortterm periods of abnormal rising in the active layer temperature, which are caused by high values of snow water equivalent and total annual precipitation. Increased soil moisture and the warming effect of snow cover led to the decrease of the intensity of soil freezing and increase of the temperature of the ground top layer. The combination of these factors triggered the activation of thermokarst processes, which led to a sharp, more than 1.5 times, increase of the thermokarst lakes area in 2007-2008.

Estimation of snowmelt runoff in the Peace River region using a soil moisture budget

1993 ◽  
Vol 73 (4) ◽  
pp. 489-501 ◽  
Author(s):  
H. N. Hayhoe ◽  
R. G. Pelletier ◽  
L. J. P. van Vliet
Keyword(s):  
Soil Moisture ◽  
Snow Depth ◽  
Frozen Soil ◽  
Moisture Budget ◽  
Snowmelt Runoff ◽  
Peace River ◽  
Erosion Prediction ◽  
Spring Runoff ◽  
River Region ◽  
Soil Moisture Budget

Rainfall and snowmelt runoff on soil frozen below the surface are recognized as important factors contributing to soil loss in Canada. The risk of rain on frozen soil has been quantified, and the amount of snowmelt on frozen soil has been estimated. This study extends such research to derive a climate-based model to estimate winter and spring runoff. This could result in a more accurate erosion prediction for areas where snowmelt is a major source for runoff. Selected components of the Water Erosion Prediction Project (WEPP) model and the versatile soil moisture budget (VB) were tested on observed data for two study sites in the Peace River region. The version of the WEPP model available to us estimated snow depth, soil frost depth and frequency of freeze–thaw cycles. However, the results did not adequately match observed data. The VB was modified in this study to improve the estimate of potential winter and spring runoff, and it was shown that incorporating observations of snow depth improved the estimate of the time and amount of snowmelt runoff. The modified runoff model was validated with data collected in the Peace River area of northern Alberta and British Columbia and with published data from the Prairies. Key words: Snowmelt, runoff, soil moisture budget

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