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 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 Toward a Novel Laser-Based Approach for Estimating Snow Interception

2020 ◽  
Vol 12 (7) ◽  
pp. 1146 ◽  
Author(s):  
Micah Russell ◽  
Jan U. H. Eitel ◽  
Andrew J. Maguire ◽  
Timothy E. Link
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Primary Objective ◽  
Snow Accumulation ◽  
Future Research ◽  
Snow Density ◽  
Canopy Interception ◽  
Terrestrial Lidar ◽  
Large Trees

Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium-sized trees, and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception volume with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled the comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ −1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data in difficult to access terrain, and calibrating snow interception models to new forest types around the globe.

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.

scholarly journals Snow-Distribution Modelling in the Ammassalik Region, South East Greenland

2003 ◽  
Vol 34 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
B. Hasholt ◽  
G.E. Liston ◽  
N.T. Knudsen
Keyword(s):  
Snow Water Equivalent ◽  
Transport Model ◽  
Digital Terrain Model ◽  
Climatic Conditions ◽  
Blowing Snow ◽  
Orographic Effects ◽  
Terrain Model ◽  
Snow Distribution ◽  
Western Side ◽  
Snow Transport

The Ammassalik region is characterized by a strong alpine relief, with altitudes up to 1,000 m. Glaciers are located mainly on the western side of ridges. The climate is low arctic, with annual precipitation amounts of more than 1,000 mm, which falls mainly as snow. Furthermore very strong storms occur frequently throughout the region. All together these factors support strong snow redistribution by wind, which likely explains the glacier locations, and also explains the observed regional runoff differences. The aim of this study is to apply the Liston & Sturm snow-transport model (SnowTran-3D) to elucidate the snow distribution according to the actual climatic conditions. A digital terrain model was used to determine the terrain forcing of the wind field. Precipitation data from the Ammassalik meteorological station were corrected for aerodynamic errors and orographic effects. Wind, temperature and humidity were obtained from a station located on a nunatak 515 m.a.s.l. at the equilibrium line on the Mittivakkat Glacier. The recorded winter accumulation (balance) of snow on the glacier was used for model calibration and testing. Significant snow transport from east-facing slopes to west-facing slopes was confirmed by the model. The drift accumulations were greatest at the head of the glacier, just on the lee side of the ridge east of the glacier. In some areas, as much as 10% of the precipitation was returned to the atmosphere by blowing-snow sublimation. An average snow water equivalent of 113 cm was obtained (not including some minor areas having snow depths as great as 4 m). These results compare well with glacier observations of 114 cm collected in May 1998 (during the field survey the 4 m areas are omitted because of crevasse hazards). Future work will use the model to test scenarios that include changes in wind regime. 1

scholarly journals Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

2017 ◽  
Author(s):  
Kristian Förster ◽  
Florian Hanzer ◽  
Elena Stoll ◽  
Adam A. Scaife ◽  
Craig MacLachlan ◽  
...  
Keyword(s):  
Water Balance ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Winter Season ◽  
General Circulation Models ◽  
Snow Accumulation ◽  
European Alps ◽  
Seasonal Forecasts ◽  
Estimation Model

Abstract. This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months lead time from two coupled atmosphere–ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As the snowpack is a hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. This predictive skill is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal predictions may be capable of predicting tendencies of hydrological model storages in parts of Europe.

scholarly journals Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

2018 ◽  
Vol 22 (2) ◽  
pp. 1157-1173 ◽  
Author(s):  
Kristian Förster ◽  
Florian Hanzer ◽  
Elena Stoll ◽  
Adam A. Scaife ◽  
Craig MacLachlan ◽  
...  
Keyword(s):  
Water Balance ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Winter Season ◽  
General Circulation Models ◽  
Snow Accumulation ◽  
European Alps ◽  
Seasonal Forecasts ◽  
Estimation Model

Abstract. This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere–ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.

scholarly journals Field-scale water balance closure in seasonally frozen conditions

2017 ◽  
Vol 21 (11) ◽  
pp. 5401-5413 ◽  
Author(s):  
Xicai Pan ◽  
Warren Helgason ◽  
Andrew Ireson ◽  
Howard Wheater
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Accumulation ◽  
Frozen Soil ◽  
Blowing Snow ◽  
Soil Drainage ◽  
Field Scale ◽  
Significant Term ◽  
The Common ◽  
Precipitation Gauge

Abstract. Hydrological water balance closure is a simple concept, yet in practice it is uncommon to measure every significant term independently in the field. Here we demonstrate the degree to which the field-scale water balance can be closed using only routine field observations in a seasonally frozen prairie pasture field site in Saskatchewan, Canada. Arrays of snow and soil moisture measurements were combined with a precipitation gauge and flux tower evapotranspiration estimates. We consider three hydrologically distinct periods: the snow accumulation period over the winter, the snowmelt period in spring, and the summer growing season. In each period, we attempt to quantify the residual between net precipitation (precipitation minus evaporation) and the change in field-scale storage (snow and soil moisture), while accounting for measurement uncertainties. When the residual is negligible, a simple 1-D water balance with no net drainage is adequate. When the residual is non-negligible, we must find additional processes to explain the result. We identify the hydrological fluxes which confound the 1-D water balance assumptions during different periods of the year, notably blowing snow and frozen soil moisture redistribution during the snow accumulation period, and snowmelt runoff and soil drainage during the melt period. Challenges associated with quantifying these processes, as well as uncertainties in the measurable quantities, caution against the common use of water balance residuals to estimate fluxes and constrain models in such a complex environment.

scholarly journals Comparison of selected methods used for the calculation of the snowpack spatial distribution, Bystřice River basin, Czechia

Geografie ◽  
2014 ◽  
Vol 119 (3) ◽  
pp. 199-217 ◽  
Author(s):  
Dana Kučerová ◽  
Michal Jeníček
Keyword(s):  
Spatial Distribution ◽  
Cross Validation ◽  
Snow Water Equivalent ◽  
Field Measurements ◽  
Snow Accumulation ◽  
Water Volume ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Residual Kriging ◽  
Linear Regressions

The knowledge of the water volume stored in the snowpack, including its spatial distribution, is vital for many hydrological applications. Such information is useful for hydrological forecasts and it is often used for the calibration of snowmelt runoff models. Data from four field measurements of the snow water equivalent (SWE) carried out in two winter seasons were assessed by ten interpolation methods. Measurements from both snow accumulation and snowmelt periods were evaluated. The ability of methods to predict SWE at unmeasured locations was assessed by the means of cross validation. The best prediction accuracy of SWE was achieved by means of multiple a simple linear regressions, residual kriging and cokriging methods. The accuracy was enhanced by the use of elevation, aspect, slope and vegetation as variables in the calculation of the SWE. Elevation and vegetation show a significant correlation with the SWE in the study area. The multiple regression gave best results for snow accumulation period. However, the spatial variability of SWE was not successfully explained for snowmelt periods.

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.

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