Impact of spruce forest on rainfall interception and seasonal snow cover evolution in the Western Tatra Mountains, Slovakia

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Snow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m 2 , mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at shorttime (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

Section: Materials and Methods Study Areamentioning

confidence: 99%

Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

Krajčí

Danko

Hlavčo

et al. 2016

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“…The snow interception affects snow accumulation under treetops (Garvelmann et al, 2013;Holko et al, 2009;Stähli et al, 2009;Strasser et al, 2011). Additionally, the forest influences turbulent fluxes (Pohl et al, 2006) and reduces the amount of short-wave solar radiation and thus snowmelt rates (Pomeroy et al, 2012;Schnorbus and Alila, 2013).…”

Section: Introductionmentioning

confidence: 99%

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

Jeníček

Pevná

Matějka

2017

Abstract:The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.

“…Throughfall was measured by the tipping bucket gauge in the nearby mature forest spruce (mean age 110 years). Our previous measurements on the site using a number of manually operated rain gauges (Holko et al, 2009) revealed high variability of interception even at the same typical locations (forest window, dripping zone, near stem zone). Therefore, the tipping bucket rain gauge used in this study was placed into a forest window which showed the smallest spatial variability of throughfall in the previous study.…”

Section: Description Of Study Area and Measured Datamentioning

confidence: 77%

“…The study strives to supplement the results of numerous studies conducted in the Western Carpathians by foresters and hydrologists (e.g. Zelený and Tichý, 1968;Majerčáková, 1983;Benetin et al, 1986;Valtýni, 1986;Tužinský, 1995;Lančarič et al, 2001;Minďáš, 2001;Pekárová et al, 2005, Halmová et al, 2006Miklánek et al, 2006;Holko et al, 2009) by focusing on the shorttime resolution (10-minutes) rainfall and throughfall data in a high-mountain forest.…”

Section: Introductionmentioning

confidence: 95%

Short-time measurements of interception in mountain spruce forest

Holko¹

2010

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Short-time measurements of interception in mountain spruce forestOpen area rainfall and throughfall measurements in the Western Tatra Mountains (altitude about 1500 m a.s.l.) made by tipping bucket gauges were used to estimate the usefulness of the short-time data in analysis of spruce interception. The 10-minute data from period 13 May-13 October 2009 did not reveal meaningful correlations between the open area rainfall and throughfall. Aggregated measurements representing individual rainfall events were more useful. They showed linear relationship between open area rainfall and throughfall for events with total rainfall depth in the open area exceeding 5 mm. Correlation between open area rainfall and throughfall for rainfall events with duration above 120 minutes was significantly better than for the shorter ones. Mean values of interception (percentage of open area rainfall which did not appear in throughfall) of individual rainfall events was high. When we excluded events for which throughfall was higher than the open area rainfall, mean interception for larger and longer rainfall events was 46% and 48%, respectively. For smaller (runoff depth below 5 mm) and shorter events (duration below 2 hours) the mean interception was 70% and 72%, respectively. However, the data revealed very high variability of interception.