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.

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

2019 ◽  
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, and our understanding of how changes in seasonal and perennial frozen ground disposition and linked storage dynamics affects 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 provide a potentially useful technique to quantify 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 (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 to 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 three years of data. The integration of isotope data in the spatially distributed model provided the basis to quantify 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 conceptualisation of spatially and temporally variant 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.

A Distributed Snow-Evolution Modeling System (SnowModel)

2006 ◽  
Vol 7 (6) ◽  
pp. 1259-1276 ◽  
Author(s):  
Glen E. Liston ◽  
Kelly Elder
Keyword(s):  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Vegetation Type ◽  
Atmospheric Model ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Canopy Interception ◽  
Meteorological Forcing ◽  
Spatially Distributed ◽  
Evolution Modeling

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

scholarly journals Climate change impacts on maritime mountain snowpack in the Oregon Cascades

2012 ◽  
Vol 9 (11) ◽  
pp. 13037-13081 ◽  
Author(s):  
E. Sproles ◽  
A. Nolin ◽  
K. Rittger ◽  
T. Painter
Keyword(s):  
Climate Change ◽  
Snow Water Equivalent ◽  
Regional Scale ◽  
Climate Change Impacts ◽  
Snow Accumulation ◽  
Distributed Model ◽  
Modeling Framework ◽  
Highly Sensitive ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. Globally maritime snow comprises 10% of seasonal snow and is considered highly sensitive to changes in temperature. This study investigates the effect of climate change on maritime mountain snowpack in the McKenzie River Basin (MRB) in the Cascades Mountains of Oregon, USA. Melt water from the MRB's snowpack provides critical water supply for agriculture, ecosystems, and municipalities throughout the region especially in summer when water demand is high. Because maritime snow commonly falls at temperatures close to 0 °C, accumulation of snow versus rainfall is highly sensitive to temperature increases. Analyses of current climate and projected climate change impacts show rising temperatures in the region. To better understand the sensitivity of snow accumulation to increased temperatures, we modeled the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with the SnowModel spatially distributed model. Simulations were evaluated using point-based measurements of SWE, precipitation, and temperature that showed Nash-Sutcliffe Efficiency coefficients of 0.83, 0.97, and 0.80, respectively. Spatial accuracy was shown to be 82% using snow cover extent from the Landsat Thematic Mapper. The validated model was used to evaluate the sensitivity of snowpack to projected temperature increases and variability in precipitation, and how changes were expressed in the spatial and temporal distribution of SWE. Results show that a 2 °C increase in temperature would shift peak snowpack 12 days earlier and decrease basin-wide volumetric snow water storage by 56%. Snowpack between the elevations of 1000 and 1800 m is the most sensitive to increases in temperature. Upper elevations were also affected, but to a lesser degree. Temperature increases are the primary driver of diminished snowpack accumulation, however variability in precipitation produce discernible changes in the timing and volumetric storage of snowpack. This regional scale study serves as a case study, providing a modeling framework to better understand the impacts of climate change in similar maritime regions of the world.

Spatial Simulation of Snow Processes

1980 ◽  
Vol 11 (5) ◽  
pp. 273-284
Author(s):  
Anton G. Thomsen
Keyword(s):  
Distributed Simulation ◽  
Landsat Imagery ◽  
Grid Cell ◽  
Simulation Models ◽  
Automatic Generation ◽  
Snow Accumulation ◽  
Distributed Model ◽  
Streamflow Generation ◽  
Spatially Distributed ◽  
Management Alternatives

A spatially distributed model for the simulation of snow accumulation and melt is presented. Watershed information on topography, vegetation and soils in digital terrain models (overlays) serve as the data base for watershed analysis, classification of snow in Landsat imagery and automatic generation of parameter decks for operating distributed simulation models of snowcover dynamics and streamflow generation. Snow processes are simulated within variable size grid-cell elements. The hydrograph resulting from spring snowmelt is simulated by a lateral flow model of streamflow generation driven by simulated snowmelt and rain inputs. Options are avilable for simulating the effects of forest management alternatives on selected areas. Snow course measurements and classified Landsat imagery are used for updating simulated parameters.

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.

Wind effects on the spatial distribution of snow and seasonal water balance in two Mediterranean basins

2020 ◽  
Author(s):  
Fabiola Pinto Escobar ◽  
Pablo A. Mendoza ◽  
Thomas E. Shaw ◽  
Jesús Revuelto ◽  
Keith Musselman ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Water Balance ◽  
Snow Water Equivalent ◽  
Climatic Conditions ◽  
Snow Accumulation ◽  
Distributed Model ◽  
Future Research ◽  
Blowing Snow ◽  
Wind Effects ◽  
Terrestrial Lidar

<p>Snow water equivalent is highly heterogeneous due to the spatial distribution of precipitation, local topographic characteristics, effects of vegetation, and wind. In particular, the latter has important effects on such distribution, controlling the preferential deposition of snowfall, transport (either by saltation or suspension) on the ground, and sublimation of blowing snow. In this work, we analyze the effects of incorporating redistribution by wind transport when modeling the seasonal water balance in two experimental catchments: (i) the Izas catchment (0.33 km²), located in the Spanish Pyrenees, with an elevation range of 2000-2300 m a.s.l., and (ii) Las Bayas catchment (2.45 km²), located in the extratropical Andes Cordillera (Chile) and elevation between 3400 and 3900 m a.s.l. After assessing model simulations using time series of snow depth and terrestrial lidar scans, we examine the water balance at the annual and seasonal scales, quantifying the different fluxes that govern snow accumulation and melting with a spatially distributed model that considers the physics of transport and the sublimation of blowing snow. Moreover, we characterize the sensitivity of dominant processes to changes in precipitation and temperature. The results of this investigation have important implications on current and future research, allowing to contrast wind effects in the spatio-temporal patterns of accumulation and melting in alpine and subalpine areas, identifying those processes that will be most affected under projected climatic conditions.</p>

scholarly journals Analysis of water balance and runoff generation in high latitude agricultural fields during mild and cold winters

2017 ◽  
Vol 48 (4) ◽  
pp. 957-968 ◽  
Author(s):  
H. Koivusalo ◽  
M. Turunen ◽  
H. Salo ◽  
K. Haahti ◽  
R. Nousiainen ◽  
...  
Keyword(s):  
Water Balance ◽  
High Latitude ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Northern Europe ◽  
Agricultural Fields ◽  
Runoff Generation ◽  
Growing Seasons ◽  
The North ◽  
The North Atlantic Oscillation

High-latitude conditions in northern Europe are characterised by short growing seasons (May–August) and long dormant seasons. Alternating mild and freezing conditions lead to variable snow accumulation–melt cycles affecting runoff generation, and consequently the loss of nutrients and sediments from agricultural fields. We assessed water balance in two subsurface drained clayey agricultural fields of different slopes (1% and 5%) in southern Finland to discern changes between mild and cold winters. The water balances of the two field sections were produced with a spatially distributed 3D hydrological model. Simulated snow water equivalent (SWE), drain discharge, tillage layer runoff and groundwater outflow from a 7-year period were examined during the dormant seasons (September–April) in relation to the North Atlantic Oscillation (NAO) index, which characterises phases related to mild and cold winters in northern Europe. Mild periods (positive NAO) were associated with more frequent runoff events, which were sustained throughout mild winters with lower SWE and shorter time of snow cover. Understanding and quantifying the water balance through periods of different weather patterns is essential as climate change is projected to increase the occurrence of positive NAO phases challenging the control of nutrient and sediment losses from agricultural fields.

scholarly journals Snowfall and snow accumulation processes during the MOSAiC winter and spring season

2021 ◽  
Author(s):  
David N. Wagner ◽  
Matthew D. Shupe ◽  
Ola G. Persson ◽  
Taneil Uttal ◽  
Markus M. Frey ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Drifting Snow ◽  
Snow Water ◽  
Abstract Data ◽  
Good Agreement

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation, and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.

scholarly journals The role and value of distributed precipitation data for hydrological models

2020 ◽  
Author(s):  
Ralf Loritz ◽  
Markus Hrachowitz ◽  
Malte Neuper ◽  
Erwin Zehe
Keyword(s):  
Predictive Performance ◽  
Distributed Model ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Convective Storms ◽  
Model Driven ◽  
Spatially Adaptive ◽  
Spatially Distributed ◽  
Mean Similarity ◽  
Precipitation Events

Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare the performance of three hydrological models at different periods and show that a distributed model driven by distributed rainfall yields only to improved performances during certain periods. These periods are dominated by convective storms that are typically characterized by higher spatial and temporal variabilities compared to stratiform precipitation events that dominate the rainfall generation in winter. Motivated by these findings we develop a spatially adaptive model that is capable to dynamically adjust its spatial structure during runtime to represent the varying importance of distributed rainfall within a hydrological model without losing predictive performance compared to a spatially distributed model. Our results highlight that adaptive modeling might be a promising way to better understand the varying relevance of distributed rainfall in hydrological models as well as reiterate that it might be one way to reduce computational times. They furthermore show that hydrological similarity concerning the runoff generation does not necessarily mean similarity for other dynamic variables such as the distribution of soil moisture.

scholarly journals A Tri-National program for estimating the link between snow resources and hydrological droughts

2015 ◽  
Vol 369 ◽  
pp. 25-30 ◽  
Author(s):  
M. Zappa ◽  
T. Vitvar ◽  
A. Rücker ◽  
G. Melikadze ◽  
L. Bernhard ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Environmental Isotopes ◽  
Snow Accumulation ◽  
Low Flow ◽  
Runoff Generation ◽  
Flow Paths ◽  
Central Switzerland ◽  
Snow Water ◽  
Analyse Data ◽  
Hydrological Droughts

Abstract. To evaluate how summer low flows and droughts are affected by the winter snowpack, a Tri-National effort will analyse data from three catchments: Alpbach (Prealps, central Switzerland), Gudjaretis-Tskali (Little Caucasus, central Georgia), and Kamenice (Jizera Mountains, northern Czech Republic). Two GIS-based rainfall-runoff models will simulate over 10 years of runoff in streams based on rain and snowfall measurements, and further meteorological variables. The models use information on the geographical settings of the catchments together with knowledge of the hydrological processes of runoff generation from rainfall, looking particularly at the relationship between spring snowmelt and summer droughts. These processes include snow accumulation and melt, evapotranspiration, groundwater recharge in spring that contributes to (the) summer runoff, and will be studied by means of the environmental isotopes 18O and 2H. Knowledge about the isotopic composition of the different water sources will allow to identify the flow paths and estimate the residence time of snow meltwater in the subsurface and its contribution to the stream. The application of the models in different nested or neighbouring catchments will explore their potential for further development and allow a better early prediction of low-flow periods in various mountainous zones across Europe. The paper presents the planned activities including a first analysis of already available dataset of environmental isotopes, discharge, snow water equivalent and modelling experiments of the (already) available datasets.

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