Runoff Response to Climate Warming and Forest Disturbance in a Mid-Mountain Basin

Langhammer, Jakub; Su, Ye; Bernsteinová, Jana

doi:10.3390/w7073320

Cited by 41 publications

(42 citation statements)

References 46 publications

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“…Berghuijs et al (2014) showed that a higher fraction of precipitation fallen as snow is associated with higher long-term mean streamflow in comparison to catchments with lower snowfall fraction. Higher air temperatures during spring affect the onset of snowmelt in streamflow shifting it towards earlier spring (Barnett et al, 2005;Godsey et al, 2014;Langhammer et al, 2015;Ledvinka, 2015). These changes lead to a higher fraction of annual flow occurring earlier in the water year as evident from many studies across the western United States (Cayan et al, 2001;Stewart et al, 2005;Day, 2009).…”

Section: Introductionmentioning

confidence: 98%

Importance of maximum snow accumulation for summer low flows in humid catchments

Jeníček

Seibert

Zappa

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Winter snow accumulation obviously has an effect on the following catchment runoff. The question is, however, how long this effect lasts and how important it is compared to rainfall inputs. Here we investigate the relative importance of snow accumulation on one critical aspect of runoff, namely the summer low flow. This is especially relevant as the expected increase of air temperature might result in decreased snow storage. A decrease of snow will affect soil and groundwater storages during spring and might cause low streamflow values in the subsequent warm season. To understand these potential climate change impacts, a better evaluation of the effects of inter-annual variations in snow accumulation on summer low flow under current conditions is central. The objective in this study was (1) to quantify how long snowmelt affects runoff after melt-out and (2) to estimate the sensitivity of catchments with different elevation ranges to changes in snowpack. To find suitable predictors of summer low flow we used long time series from 14 Alpine and pre-Alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. In general, the results indicated that maximum winter snow water equivalent (SWE) influenced summer low flow, but could expectedly only partly explain the observed inter-annual variations. On average, a decrease of maximum SWE by 10 % caused a decrease of minimum discharge in July by 6-9 % in catchments higher than 2000 m a.s.l. This effect was smaller in middle-and lower-elevation catchments with a decrease of minimum discharge by 2-5 % per 10 % decrease of maximum SWE. For higher-and middle-elevation catchments and years with below-average SWE maximum, the minimum discharge in July decreased to 70-90 % of its normal level. Additionally, a reduction in SWE resulted in earlier low-flow occurrence in some cases. One other important factor was the precipitation between maximum SWE and summer low flow. When only dry preceding conditions in this period were considered, the importance of maximum SWE as a predictor of low flows increased. We assessed the sensitivity of individual catchments to the change of maximum SWE using the non-parametric Theil-Sen approach as well as an elasticity index. Both sensitivity indicators increased with increasing mean catchment elevation, indicating a higher sensitivity of summer low flow to snow accumulation in Alpine catchments compared to lower-elevation pre-Alpine catchments.

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Section: Introductionmentioning

confidence: 98%

Importance of maximum snow accumulation for summer low flows in humid catchments

Jeníček

Seibert

Zappa

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Additionally, ongoing climate changes and consequent changes of winter and spring runoff represent positive feedback and might further underline the impact of land cover changes on runoff (Blahušiaková and Matoušková, 2015;Langhammer et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

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

Jeníček

Pevná

Matějka

2017

Journal of Hydrology and Hydromechanics

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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.

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“…The study area of the Roklanský Brook and the experimental subcatchments is located at the headwaters of the Sumava Mountains, Central Europe (Figure 1), and features an elevated montane plain with moderate hillslopes [13]. The area is homogeneous in terms of physiography.…”

Section: Study Areamentioning

confidence: 99%

Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series

Langhammer

Česák

2016

Water

Self Cite

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This paper analyzes the potential of a nu-support vector regression (nu-SVR) model for the reconstruction of missing data of hydrological time series from a sensor network. Sensor networks are currently experiencing rapid growth of applications in experimental research and monitoring and provide an opportunity to study the dynamics of hydrological processes in previously ungauged or remote areas. Due to physical vulnerability or limited maintenance, networks are prone to data outages, which can devaluate the unique data sources. This paper analyzes the potential of a nu-SVR model to simulate water levels in a network of sensors in four nested experimental catchments in a mid-latitude montane environment. The model was applied to a range of typical runoff situations, including a single event storm, multi-peak flood event, snowmelt, rain on snow and a low flow period. The simulations based on daily values proved the high efficiency of the nu-SVR modeling approach to simulate the hydrological processes in a network of monitoring stations. The model proved its ability to reliably reconstruct and simulate typical runoff situations, including complex events, such as rain on snow or flooding from recurrent regional rain. The worst model performance was observed at low flow periods and for single peak flows, especially in the high-altitude catchments.

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Runoff Response to Climate Warming and Forest Disturbance in a Mid-Mountain Basin

Cited by 41 publications

References 46 publications

Importance of maximum snow accumulation for summer low flows in humid catchments

Importance of maximum snow accumulation for summer low flows in humid catchments

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

Applicability of a Nu-Support Vector Regression Model for the Completion of Missing Data in Hydrological Time Series

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