scholarly journals Combining geophysical and geomorphological data to reconstruct the development of relief of a medieval castle site in the Spessart low mountain range, Germany

2021 ◽  
Author(s):  
Julian Trappe ◽  
Christian Büdel ◽  
Julia Meister ◽  
Roland Baumhauer
Keyword(s):  
Mountain Range ◽  
Low Mountain Range

SWAT-G, a version of SWAT99.2 modified for application to low mountain range catchments

2002 ◽  
Vol 27 (9-10) ◽  
pp. 641-644 ◽  
Author(s):  
K Eckhardt ◽  
S Haverkamp ◽  
N Fohrer ◽  
H.-G Frede
Keyword(s):  
Mountain Range ◽  
Low Mountain Range

Quantification of Hortonian overland flow generation and soil erosion in a Central European low mountain range using rainfall experiments

CATENA ◽  
2014 ◽  
Vol 113 ◽  
pp. 202-212 ◽  
Author(s):  
V. Butzen ◽  
M. Seeger ◽  
S. Wirtz ◽  
M. Huemann ◽  
C. Mueller ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Overland Flow ◽  
Mountain Range ◽  
Central European ◽  
Flow Generation ◽  
Low Mountain Range

scholarly journals Genetic diversity and dispersal potential of the stonefly Dinocras cephalotes in a central European low mountain range

2014 ◽  
Vol 33 (1) ◽  
pp. 181-192 ◽  
Author(s):  
Vasco Elbrecht ◽  
Christian K. Feld ◽  
Maria Gies ◽  
Daniel Hering ◽  
Martin Sondermann ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Mountain Range ◽  
Central European ◽  
Dispersal Potential ◽  
Low Mountain Range

Fire–vegetation relationships during the last glacial cycle in a low mountain range (Eifel, Germany)

2021 ◽  
Vol 562 ◽  
pp. 110140
Author(s):  
Arne Kappenberg ◽  
Wulf Amelung ◽  
Nadine Conze ◽  
Frank Sirocko ◽  
Eva Lehndorff
Keyword(s):  
Mountain Range ◽  
Glacial Cycle ◽  
Last Glacial ◽  
Low Mountain Range ◽  
The Last Glacial

Determining dew and hoar frost formation for a low mountain range and alpine grassland site by weighable lysimeter

2018 ◽  
Vol 563 ◽  
pp. 372-381 ◽  
Author(s):  
Jannis Groh ◽  
Veronika Slawitsch ◽  
Markus Herndl ◽  
Alexander Graf ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Alpine Grassland ◽  
Mountain Range ◽  
Frost Formation ◽  
Low Mountain Range ◽  
Grassland Site

scholarly journals Storm-runoff processes in a mainly waterlogged low mountain range catchment in the national park Hunsrück-Hochwald, SW-Germany: Assessing the impact of wetland restoration using multiple linear regression analysis and ANCOVA.

2020 ◽  
Author(s):  
Julian Zemke
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Wetland Restoration ◽  
Linear Regression Analysis ◽  
Storm Runoff ◽  
Mountain Range ◽  
Infiltration Capacity ◽  
Flood Prevention ◽  
Low Mountain Range ◽  
The Impact

This study investigates potential effects of wetland restoration on storm flow dynamics in a mainly waterlogged low mountain range catchment located in SW-Germany. Here, wetland drainage networks are being sealed, aiming to achieve rising soil water tables and reestablished peat vegetation. With the help of hydrograph separation, multiple linear regression (MLR) and covariance analysis (ANCOVA), runoff-governing storm properties and sealing influences were analyzed. Results show, that not only natural storm parameters (precipitation sum, rainfall intensity, antecedent precipitation and temperature) exert influence on storm-runoff, but sealings also led to significantly altered processes: On the one hand, storm-runoff coefficients increased in sealed catchments, resulting most likely from more saturated soils, providing a smaller infiltration capacity. This is a desired effect of rewetting but coincidently a downside regarding storm flood prevention. On the other hand, lag times, meaning the timespan between rainfall occurrence and the hydrograph starting to rise, were noticeably prolonged. This effect potentially can be beneficial when it comes to storm flood prevention. Overall, statistical models including sealings showed more satisfactory results describing stormflow variance compared to models without sealings. Therefore, sealings do exert – statistically proven – an effect on storm runoff. The heterogeneity of the results, representing a dense gauge network spread over an investigation area of roughly 7.5 km² shows, that a high-resolution sampling, both spatially and temporally, is vital. That is since runoff processes in waterlogged low mountain range catchments are still poorly understood.

scholarly journals Comparing model sensitivities of different landscapes using the ecohydrological SWAT model

2009 ◽  
Vol 21 ◽  
pp. 91-98 ◽  
Author(s):  
B. Schmalz ◽  
N. Fohrer
Keyword(s):  
Water Discharge ◽  
Swat Model ◽  
Influential Factors ◽  
Shallow Aquifer ◽  
Sensitivity Analyses ◽  
Soil Parameters ◽  
Mountain Range ◽  
Landscape Features ◽  
Low Mountain Range ◽  
Sensitive Parameters

Abstract. Lowland areas are characterised by specific properties, such as flat topography, low hydraulic gradients, shallow groundwater, and high potential for water retention in peatland and lakes. The investigated mesoscale catchments Stör, Treene and Kielstau are located in Northern Germany within lowland areas. Covering areas from 50 to 517 km2, these rural catchments have sandy, loamy and peaty soils and are drained in high fraction by open ditches and tile drainage. Using the river basin model SWAT, sensitivity analyses were carried out through an automatic routine that is based on the Latin-Hypercube (LH) and a One-factor-At-a-Time (OAT) sampling. The objective of this study is to investigate how specific landscape features influence the model behaviour. There are two research questions: a) What are the most sensitive parameters in the studied lowland catchments? b) What differences occur between these landscape features in comparison to mountainous or low mountain range catchments? The results show that groundwater and soil parameters were found to be most sensitive in the studied lowland catchments and they turned out to be the most influential factors on simulated water discharge. The most sensitive parameter was the threshold water level in shallow aquifer for baseflow (GWQMN). In contrast, many studies of mountainous or low mountain range catchments show that the most sensitive parameters were the surface runoff parameters.

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