The recolonization of the river Elbe with benthic and hyporheic Ostracoda (Crustacea) after the reunion of Germany in 1989

2013 ◽  
Vol 98 (6) ◽  
pp. 305-312 ◽  
Author(s):  
Burkhard Scharf ◽  
Matthias Brunke
Keyword(s):  
River Elbe

Relocation of dredged material from Hamburg harbour in the River Elbe

1998 ◽  
Vol 37 (6-7) ◽  
pp. 241-248 ◽  
Author(s):  
A. Netzband ◽  
H. Christiansen ◽  
B. Maaß ◽  
G. Werner
Keyword(s):  
Water Quality ◽  
Benthic Community ◽  
Dredged Material ◽  
Far Field ◽  
Beneficial Use ◽  
River Elbe ◽  
Operating Strategies ◽  
Material Circulation

Besides the beneficial use of dredged material, sustainable relocation, which means keeping the sediments in the natural aquatic material circulation, is one goal for handling dredged material in the port of Hamburg. Decreasing contamination the River Elbe and new dredged material guidelines provide a basis for this. With comprehensive investigations, near- and far-field transport and the effects of relocation regarding the water quality and the benthic community were determined thus deveoloping conditions for future operating strategies.

Sources and transport of phosphorus in the Vltava river basin (Czech Republic)

1996 ◽  
Vol 33 (4-5) ◽  
pp. 137-144 ◽  
Author(s):  
Josef Hejzlar ◽  
Vojtech Vyhnálek ◽  
Jirí Kopácek ◽  
Jirí Duras
Keyword(s):  
Czech Republic ◽  
Population Density ◽  
Atmospheric Deposition ◽  
River Basin ◽  
Point Sources ◽  
Diffuse Sources ◽  
River Elbe ◽  
Municipal Wastewaters ◽  
Total P

Export and sources of P in the Vltava basin (subbasin of upper Elbe: total area – 28,093 km2; population density – 115 km−2; forests – 35%, farmland – 51%) were evaluated during 1972–1993. Annual export rates of total P from the basin to the river Elbe ranged between 38 and 68 kg km−2 a−1. Reservoirs with hydraulic retention times longer than 15 days were efficient traps for phosphorus retaining 20 to 30% of total P loading into the watercourses. Point sources (municipal wastewaters) were most important throughout the period and their share varied from approximately 60% in wet years to more than 90% in dry years. Export from diffuse sources (dominated by output from farmland) was highly dependent on discharge and fluctuaded between 5 and 40 kg km−2 a−1 in dry and wet years, respectively. Only about 2% of the P input into the basin from the fertilisation of farmland and from the atmospheric deposition was exported to the watercourses.

Reaction of fish larvae to the water of the River Elbe at Hamburg harbour

1993 ◽  
Vol 55 (3) ◽  
pp. 161-172 ◽  
Author(s):  
Andreas Rieckhoff ◽  
Walter Nellen
Keyword(s):  
Fish Larvae ◽  
River Elbe

scholarly journals Equilibrium sampling of polychlorinated biphenyls in River Elbe sediments – Linking bioaccumulation in fish to sediment contamination

Chemosphere ◽  
2015 ◽  
Vol 138 ◽  
pp. 856-862 ◽  
Author(s):  
Sabine Schäfer ◽  
Catherine Antoni ◽  
Christel Möhlenkamp ◽  
Evelyn Claus ◽  
Georg Reifferscheid ◽  
...  
Keyword(s):  
Polychlorinated Biphenyls ◽  
Sediment Contamination ◽  
River Elbe

scholarly journals Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

2018 ◽  
Vol 15 (9) ◽  
pp. 1881 ◽  
Author(s):  
Sina Keller ◽  
Philipp Maier ◽  
Felix Riese ◽  
Stefan Norra ◽  
Andreas Holbach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Green Algae ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Inland Waters ◽  
Learning Approaches ◽  
River Elbe ◽  
Regression Framework

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

Trace Metal Dynamics in Floodplain Soils of the River Elbe: A Review

2013 ◽  
Vol 42 (2) ◽  
pp. 622-622
Author(s):  
Christiane Schulz-Zunkel ◽  
Frank Krueger
Keyword(s):  
Trace Metal ◽  
Floodplain Soils ◽  
River Elbe

Small scale variability of chlorinated POPs in the river Elbe floodplain soils (Germany)

Chemosphere ◽  
2010 ◽  
Vol 79 (7) ◽  
pp. 745-753 ◽  
Author(s):  
Kristian Kiersch ◽  
Gerald Jandl ◽  
Ralph Meissner ◽  
Peter Leinweber
Keyword(s):  
Small Scale ◽  
Floodplain Soils ◽  
River Elbe
Sign in / Sign up

Export Citation Format

Share Document