Changes in seasonal snowpack in mountain catchments in Czechia

2020 ◽  
Author(s):  
Ondrej Nedelcev ◽  
Michal Jenicek
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
Distributed Hydrological Model ◽  
Catchment Scale ◽  
Water Balance Components ◽  
Variable Model ◽  
Snow Cover Duration

<p>Seasonal snowpack is an important part of the water cycle and it has a large influence on runoff regime in mountain catchments of Central Europe. However, snow water equivalent (SWE) is decreasing in many mountain regions over the last decades and spring snowmelt occurs earlier in the year. This study aimed 1) to analyse long-term changes and trends in selected snowpack characteristics, such as SWE, snow cover duration, snowmelt onset and melt-out in 40 mountain catchments in Czechia in the period 1965–2014 and 2) to relate the detected changes to changes in air temperature and snowfall fraction at different elevations. Since the availability of time series of measured SWE at a catchment scale is limited, a conceptual semi-distributed hydrological model HBV-light was used to simulate daily SWE for defined elevation zones. Besides SWE, the model simulated other water balance components, such as runoff, soil moisture and groundwater recharge. The integrated multi-variable model calibration procedure was used to calibrate the model. Both observed runoff and SWE were used for evaluation of the model performance. Seasonal and monthly mean of SWE, as well as snow cover duration, snowmelt onset, snowmelt rates and melt-out were calculated for individual catchments and elevation zones. The non-parametric Mann-Kendall test was used to detect potential trends in simulated time series. The results showed significant decreasing trends in snowfall fraction for all catchments and elevations in the study period mostly due to an increase in air temperature. This resulted in a decrease in snow storages in most of catchments, especially in western parts of Czechia. However, a lot of regional differences exists and no trends in SWE were detected in some catchments. Decreasing trends in snow cover duration were detected as well, mostly because of earlier snowmelt onset and melt-out.</p>

scholarly journals Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

2017 ◽  
Vol 8 (4) ◽  
pp. 963-976 ◽  
Author(s):  
Jaak Jaagus ◽  
Mait Sepp ◽  
Toomas Tamm ◽  
Arvo Järvet ◽  
Kiira Mõisja
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Large Scale ◽  
Regime Shifts ◽  
The Arctic ◽  
Dry Period ◽  
Snow Cover Duration ◽  
Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting of trends and regime shifts during 1951–2015. Trend analysis is realised using the Mann–Kendall test and regime shifts are detected with the Rodionov test (sequential t-test analysis of regime shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at all 12 stations by 0.3–0.4 K decade−1. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days decade−1. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March and also in June. A time series of specific runoff measured at 21 stations had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 L s−1 km−2 decade−1, while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May have notably decreased indicating the shift of the runoff maximum to the earlier time, i.e. from April to March. Air temperature, precipitation, snow cover duration and specific runoff of rivers are highly correlated in winter determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter of 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in annual specific runoff correspond to the alternation of wet and dry periods. A dry period started in 1964 or 1963, a wet period in 1978 and the next dry period at the beginning of the 21st century.

Changes in snow storages and their impact on occurrence and extremity of runoff caused by rain-on-snow events

2020 ◽  
Author(s):  
Ondrej Hotovy ◽  
Michal Jenicek
Keyword(s):  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Model Performance ◽  
Catchment Scale ◽  
The Future ◽  
Snow Water ◽  
The Impact ◽  
Catchment Runoff

<p>Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently.</p><p>The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 59 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1980-2014 and to explain the role of different catchment attributes.</p><p>The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.</p>

scholarly journals Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

2017 ◽  
Author(s):  
Jaak Jaagus ◽  
Mait Sepp ◽  
Toomas Tamm ◽  
Arvo Järvet ◽  
Kiira Mõisja
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Large Scale ◽  
Regime Shifts ◽  
The Arctic ◽  
Dry Period ◽  
Snow Cover Duration ◽  
Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting trends and regime shifts during 1951–2015. Trend analysis is performed using the Mann-Kendall test and regime shifts are detected with the Rodionov test (Sequential T-test Analysis of Regime Shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at 12 observed stations by 0.3–0.4 K per decade. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days per decade. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March. Time series of specific runoff measured at 21 stations has had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 l/s per km2 per decade while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May has notably decreased indicating the shift of the runoff maximum to earlier time, i.e. from April to March. All meteorological and hydrological variables are highly correlated in winter, determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in the annual specific runoff correspond to the alternation of wet and dry periods. A dry period started since 1964 or 1963, a wet period since 1978 and the next dry period since the beginning of the 21st century.

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

scholarly journals The Changing Cryosphere: Pan-Arctic Snow Trends (1979–2009)

2011 ◽  
Vol 24 (21) ◽  
pp. 5691-5712 ◽  
Author(s):  
Glen E. Liston ◽  
Christopher A. Hiemstra
Keyword(s):  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Reanalysis Data ◽  
The Arctic ◽  
Regional Variability ◽  
Time Step ◽  
Snow Season ◽  
Regional Trends ◽  
Dominant Feature

Abstract Arctic snow presence, absence, properties, and water amount are key components of Earth’s changing climate system that incur far-reaching physical and biological ramifications. Recent dataset and modeling developments permit relatively high-resolution (10-km horizontal grid; 3-h time step) pan-Arctic snow estimates for 1979–2009. Using MicroMet and SnowModel in conjunction with land cover, topography, and 30 years of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, a distributed snow-related dataset was created including air temperature, snow precipitation, snow-season timing and length, maximum snow water equivalent (SWE) depth, average snow density, snow sublimation, and rain-on-snow events. Regional variability is a dominant feature of the modeled snow-property trends. Both positive and negative regional trends are distributed throughout the pan-Arctic domain, featuring, for example, spatially distinct areas of increasing and decreasing SWE or snow season length. In spite of strong regional variability, the data clearly show a general snow decrease throughout the Arctic: maximum winter SWE has decreased, snow-cover onset is later, the snow-free date in spring is earlier, and snow-cover duration has decreased. The domain-averaged air temperature trend when snow was on the ground was 0.17°C decade−1 with minimum and maximum regional trends of −0.55° and 0.78°C decade−1, respectively. The trends for total number of snow days in a year averaged −2.49 days decade−1 with minimum and maximum regional trends of −17.21 and 7.19 days decade−1, respectively. The average trend for peak SWE in a snow season was −0.17 cm decade−1 with minimum and maximum regional trends of −2.50 and 5.70 cm decade−1, respectively.

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2014 ◽  
Vol 11 (11) ◽  
pp. 12531-12571 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Forested Areas ◽  
Land Cover Map ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Snowpack energy balance and changes in the frequency and extremity of rain-on-snow events in the warming climate

2021 ◽  
Author(s):  
Ondrej Hotovy ◽  
Michal Jenicek
Keyword(s):  
Energy Balance ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Catchment Scale ◽  
The Future ◽  
Snow Water ◽  
The Impact ◽  
Catchment Runoff

<p>Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently. Heat from liquid precipitation constitutes one of the snowpack energy balance components. Consequently, snowmelt and runoff may be strongly affected by these temperature and precipitation changes.</p><p>The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements together with an analysis of changes in the snowpack energy balance, and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 40 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1965-2019 and to explain the role of different catchment attributes.</p><p>The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.</p>

Long-term (1900-2100) Hydrometeorological and Snow Water Equivalent reconstructions in the French Southern Alps (Mercantour Natural Parc).

2021 ◽  
Author(s):  
Thibault Mathevet ◽  
Cyril Thébault ◽  
Jérôme Mansons ◽  
Matthieu Le Lay ◽  
Audrey Valery ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Resources ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Regime ◽  
Southern Alps ◽  
Snow Line ◽  
Snow Water

<p>The aim of this communication is to present a study on climate variability and change on snow water equivalent (SWE) and streamflow over the 1900-2100 period in a mediteranean and moutainuous area.  It is based on SWE and streamflow observations, past reconstructions (1900-2018) and future GIEC scenarii (up to 2100) of some snow courses and hydrological stations situated within the French Southern Alps (Mercantour Natural Parc). This has been conducted by EDF (French hydropower company) and Mercantour Natural Parc.</p><p>This issue became particularly important since a decade, especially in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production or impacts on mountainous ecosystems (fauna and flora). As a water resources manager in French mountainuous regions, EDF developed and managed a large hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurements of a hundred of snow courses within the French Alps. EDF have been operating an automatic SWE sensors network, complementary to historical snow course network. Based on numerous SWE observations time-series and snow modelization (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2018 period. These reconstructions have been extented to 1900 using 20 CR (20<sup>th</sup> century reanalyses by NOAA) reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii (+4.5 W/m² and + 8.5 W/m² hypotheses). In the scope of this study, Mercantour Natural Parc is particularly interested by snow scenarii in the future and its impacts on their local flora and fauna.</p><p>Considering observations within Durance watershed and Mercantour region, this communication focuses on: (1) long term (1900-2018) analyses of variability and trend of hydrometeorological and snow variables (total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length, streamflow regimes) , (2) long term variability of snow and hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii.</p><p>Comparing old period (1950-1984) to recent period (1984-2018), quantitative results within these regions roughly shows an increase of air temperature by 1.2 °C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season duration by 15 days. Characterization of the increase of snow line height and SWE reduction are particularly important at a local and watershed scale. Then, this communication focuses on impacts on long-term time scales (2050, 2100). This long term change of snow dynamics within moutainuous regions both impacts (1) water resources management, (2) snow resorts and artificial snow production developments or (3) ecosystems dynamics.Connected to the evolution of snow seasonality, the impacts on hydrological regime and some streamflow signatures allow to characterize the possible evolution of water resources in this mediteranean and moutianuous region This study allowed to provide some local quantitative scenarii.</p>

scholarly journals Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

2017 ◽  
Vol 21 (7) ◽  
pp. 3325-3352 ◽  
Author(s):  
Christa Kelleher ◽  
Brian McGlynn ◽  
Thorsten Wagener
Keyword(s):  
Goodness Of Fit ◽  
Snow Water Equivalent ◽  
Expert Knowledge ◽  
Model Performance ◽  
Hierarchical Approach ◽  
Catchment Scale ◽  
Distributed Modeling ◽  
Distributed Models ◽  
Knowledge Based ◽  
Behavioral Parameter

Abstract. Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology–soil–vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale.

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

2016 ◽  
Vol 64 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Pavel Krajčí ◽  
Michal Danko ◽  
Jozef Hlavčo ◽  
Zdeněk Kostka ◽  
Ladislav Holko
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Flood Forecasting ◽  
Snow Accumulation ◽  
Small Scale ◽  
Climatic Data ◽  
Tatra Mountains ◽  
Snow Water

AbstractSnow 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 m2, 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 short-time (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.

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