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55 results for “waterway”
March 2002 CTD, PAR, oxygen and chlorophyll profiles for the Georgia Coastal Ecosystems Intracoastal Waterway transect
Four hydrographic surveys were performed from March 22 to March 23, 2002, along the Intracoastal Waterway between the Altamaha River south of Wolf Island and Sapelo Sound (Intracoastal Waterway Transect, GCE-IC). Vertical CTD profiles were collected at various intervals from 0km to 29km along the transect during various tidal regimes. Conductivity, temperature, pressure, optical back scatter, photosynthetically-available radiation, oxygen and chlorophyll a fluorescence were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected during the upcast were deleted. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
September 2002 bin-averaged CTD profiles for the Georgia Coastal Ecosystems Intracoastal Waterway transect
Three hydrographic surveys were performed from September 16 to September 18, 2002, along the Intracoastal Waterway between the Altamaha River south of Wolf Island and Sapelo Sound (Intracoastal Waterway Transect, GCE-IC). Vertical CTD profiles were collected at various intervals from 0km to 29km along the transect during various tidal regimes. Conductivity, temperature, pressure and optical backscatter were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected on the upcast were deleted, and the remaining data were averaged within 0.5m depth bins and interpolated to produce a smooth profile for contouring. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
September 2002 CTD, PAR, oxygen and chlorophyll profiles for the Georgia Coastal Ecosystems Intracoastal Waterway transect
Three hydrographic surveys were performed from September 16 to September 18, 2002, along the Intracoastal Waterway between the Altamaha River south of Wolf Island and Sapelo Sound (Intracoastal Waterway Transect, GCE-IC). Vertical CTD profiles were collected at various intervals from 0km to 29km along the transect during various tidal regimes. Conductivity, temperature, pressure, optical back scatter, photosynthetically-available radiation, oxygen and chlorophyll a fluorescence were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected during the upcast were deleted. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
<p>Inundation of coastal stormwater networks by tides is widespread due to sea-level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies are poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (1) documented temporal variation in concentrations of <em>Enterococcus spp</em>. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a two-month period and more intensively during two perigean spring tide periods, (2) measured ENT concentrations in roadway floodwaters during tidal floods, and (3) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving water body vary as a function of tidal stage and antecedent rainfall, but also site-specific characteristics of the stormwater network that drains to the waterbody. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceed thresholds for safe public use of recreational water. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR.</p>
mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. </p> <p>Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. </p> <p>In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download:</p> <p>• Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO)</p> <p>• RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM)</p> <p>• Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM)</p> <p>• Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD)</p> <p>• German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)</p>
mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)</p>
Container flows on road, rail and waterways along Rhine-Alpine corridor (Rhine section) at NUTS-2 level with cost-time-emissions estimates and accessibility-frequency-availability of modes
<p>The present dataset is used to estimate the heterogeneous mode choice preferences of shippers, that are presented in the following article :<br> "A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data"<br> (Nicolet, A., Negenborn, R. R. & Atasoy, B., A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data. IEEE Open Journal of Intelligent Transportation Systems, Vol. 3, 2022, pp. 650-661.)</p>
Figure 4 in Surveying seaweeds from the Ulvales and Fucales in the world's most frequently used artificial waterway, the Kiel Canal
Figure 4: Number of branches versus thallus height for Ulva intestinalis (A) and Ulva linza (B) collected in the Kiel Canal. Numbers indicate sampling sites and lines connect data in the sequence of sites along the canal (sites 1–16 for U. intestinalis and 9–16 for U. linza; compare Figure 1C).
Figure 3 in Surveying seaweeds from the Ulvales and Fucales in the world's most frequently used artificial waterway, the Kiel Canal
Figure 3: Morphology of material genetically identified as Ulva intestinalis collected from the Kiel Canal. Sampling sites with salinity recorded during collection are indicated. (A–B) Display the typical unbranched morphotype of U. intestinalis, whereas some specimens only exhibited branches at the thallus base (C–D); i is a close-up of thallus base of D. (E–F) Display branched forms of U. intestinalis with reduced thallus size encountered at low salinity sampling sites.
Figure 1 in Surveying seaweeds from the Ulvales and Fucales in the world's most frequently used artificial waterway, the Kiel Canal
Figure 1: Map of the study area and distribution of chemical parameters and detected species. (A) Map of the Kiel Canal (black line) in Northern Germany with location of sampling sites (arrowheads). (B) Salinity, dissolved inorganic nitrogen (DIN) and phosphate at the sampling sites 1–16 and the reference sites C1–C3 outside the canal, where only water parameters were measured. (C) Spatial distribution of Ulvales and Fucus species within the Kiel Canal. Arrows indicate major inflows of freshwater, names refer to larger towns or regions to facilitate orientation.
Figure 2 in Surveying seaweeds from the Ulvales and Fucales in the world's most frequently used artificial waterway, the Kiel Canal
Figure 2: Maximum likelihood tree inferred from tufA sequences, representing Ulvales species and their respective morphotypes, present in the Kiel Canal. Numbers at nodes refer to bootstrap values>70. Branch lengths are drawn proportionally to the amount of sequence change and GenBank accession numbers are given for all included samples. Clades containing specimens investigated within this study are highlighted in gray. Sample sites and their recorded salinity within the Kiel Canal are indicated. Samples marked with a solid circle are of unbranched morphology, those labeled with an asterisk are branched.
Figure 5 in Surveying seaweeds from the Ulvales and Fucales in the world's most frequently used artificial waterway, the Kiel Canal
Figure 5: Morphology of material genetically identified as Ulva linza collected from the Kiel Canal. Sampling sites with salinity recorded during collection are indicated. Branched and unbranched morphotypes of U. linza observed at two sampling sites with relatively high (A–B) and low (C–D) salinity are shown.
A Policy and Infrastructure Evaluation Model of Commodity Flows through Inland Waterway Ports (Dataset)
<p>The purpose of this project is to guide strategic investment into port capacity through the development of a policy and infrastructure evaluation model of inland waterway commodity flows. A multi-stage stochastic optimization model will be developed to evaluate tradeoffs in strategic, long-term port infrastructure investment with mid-term capacity expansion decisions and provision of complementary highway infrastructure made by public and private stakeholders, and shorter-term operational practices made by shippers and carriers. This work builds on prior MarTREC projects which developed a Multi-Commodity Assignment Problem to estimate annual commodity flows through inland waterway ports from truck Global Positioning System (GPS), marine Automatic Identification System (AIS), and the Lock Performance Management System (LPMS). The proposed project will explore critical extensions of the assignment model: 1) disaggregation of the temporal scope to reflect monthly seasonality among commodities, 2) incorporation of uncertainty related to observed vehicle and vessel movement data, and 3) inclusion of transportation costs. With these extensions the team expects to increase the accuracy and resolution of the commodity-based port throughput estimates and to allow the model to be used to not only describe the current system but to prescribe policy and project investment strategies for public and private sector transportation decision makers. Calibration and validation of the multi-stage optimization model will be done through two case studies. The regional-based study will use historical truck GPS, marine AIS, and LPMS datasets. The national-based study will use data from the Billion Ton Study led by the US Department of Energy. This will ensure a feasible and realistic base-case on which to compare future policy scenarios. This project aligns with MarTREC’s research focus area in Maritime and Multimodal Logistics Management by modeling commodity flows through ports that serve as critical connections for the multimodal freight supply chain.</p>
Data for The Unintended Consequences of Flood Mitigation along Inland Waterways
<p>The uploads contain a data description document and a spreadsheet containing the various data sets and links used in the project "<strong>The Unintended Consequences of Flood Mitigation along Inland Waterways – A Look at Resilience and Social Vulnerabilities through A Case Study Analysis".</strong></p>
AIS heatmap: North Sea and Dutch Inland Waterways for the months January, April, July, October in 2019
<p>This dataset contains information on vessel movements in the North Sea and Dutch Inland Waterways for the months January, April, July, and October in 2019. It provides a heatmap representation of vessel traffic density during these specific months, which can be useful for various maritime and environmental analyses.</p> <p>1. File Formats</p> <p>The dataset is provided in the following file formats:</p> <ul> <li>NetCDF : The primary data files are available in netcdf format. For each grid cell the variables sog (Speed Over Ground) and count (Number of AIS messages) are available</li> <li>GeoTIFF (Georeferenced Tagged Image File Format): Heatmap images are provided in GeoTIFF format, suitable for geographic visualization.</li> </ul> <p>The dataset is split into tiles. Each tile conforms to the <a href="https://wiki.openstreetmap.org/wiki/Tiles">OSM tiling</a> naming scheme.</p> <p>2. Variables </p> <p>The dataset includes the following key variables:</p> <ul> <li><strong>Speed Over Ground (SOG)</strong>: The average vessel's speed over the ground for all the messages.</li> <li><strong>Count</strong>: The number of AIS messages received in this location</li> </ul> <p>3. Data Collection Method </p> <p>The AIS data used in this dataset was collected from AIS transponders on vessels operating in the North Sea and Dutch Inland Waterways. These transponders transmit information such as vessel position, speed, and identification. The dataset aggregates this information to create heatmap images for analysis. We did this on all the messages. Some ships emit more messages than others. Ships emit messages at higher frequency when sailing than when stationary. </p> <p>4. Source of Original Data</p> <p>The original AIS data used to create this dataset was sourced from the AIS archive from Rijkswaterstaat. This dataset was analysed for the purpose of a <a href="https://ais-scrolly.netlify.app/">storymap</a>.</p> <p> </p> <p> </p>
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
Open the record for dataset details and reuse information.
SEAS5/System4-LARSIM_ME Seasonal Streamflow Forecasts for German Waterways
<p>The datasets provided were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em> “, work package 4, task 2 “<em>Analysis of the impact of changes in precipitation attributes from short to medium and climatic ranges</em>” and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Meißner et al. 2017, in Deliverable 4.2 „ <em>The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions</em> “ (Arnal et al. 2017) and in Deliverable 4.3 “<em>Forecast skill developments</em>” (Weerts et al. 2019). The aim was to evaluate the potential skill of seasonal streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As seasonal meteorological forecast data the reforecast dataset from ECMWF’s Seasonal Forecast System 4 (S4 hereafter) (Molteni et al. 2011) as well as from the fifth generation of ECMWF’s Seasonal forecasting system SEAS5 (ECMWF 2017, Johnson et al. 2018, Owens & Hewson 2018) of the period 1981 – 2016 were used.</p> <p>The horizontal resolution of System 4 is approximately 80 km. In operational mode the ensemble consists of 51 members generated by using an ensemble of initial conditions and by the use of stochastic physics. Re-forecasts starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast. For the period 1981 – 2011 the ensemble size varies between 15 members (initialization months January, March, April, June, July, September, October, December) and 51 members (for the remaining months). Since 2012, the ensemble size is 51 members all over the year (operational forecasts).</p> <p>The fifth generation of ECMWF seasonal forecasting system SEAS5 replaced System 4 in November 2017. The horizontal resolution of the model is 0.4°x0.4° (approx. 36 km). The ensemble consists of 51-members created using a combination of Sea Surface Temperature SST and atmospheric initial condition perturbations and the activation of stochastic physics (ECMWF 2017). Re-Forecasts with the ensemble size of 25 members starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>The precipitation and temperature data, used to force the hydrological model in simulation mode up to the initialization of the particular forecast, is taken from the E-OBS dataset, version 18 (Haylock et al. 2008). The downward surface solar radiation is extracted from the ERA-Interim reanalysis (Dee et al. 2011) for the period 1979-2018. For further details on data processing see Meißner et al. (2017).</p> <p>As meteorological seasonal forecasts tend to drift towards the model climate with increasing lead-time, the outputs daily total precipitation and air temperature from S4, interpolated to a 50 km x 50 km grid (multiple of the 5 km x 5 km model grid) and from SEAS5, interpolated to a 25 km x 25 km grid, respectively, were drift-corrected with the meteorological observation dataset used for the baseline simulation. As drift correction method the quantile-quantile method (Piani et al. 2010) was used. We corrected daily values of the different variables on a monthly basis, which means each daily value of the same month is corrected by the same scaling. Separate drift correction factors were estimated for each forecast initialization date (calendar month) and monthly lead time (month 1 to month 7) based on the reforecast datasets. In the final step the corrected precipitation and temperature were downscaled to the 5 km by 5 km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong><em>q_obs(time=24472, stations=8</em>)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_EOBS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the EOBS dataset and ERA-Interim stored as variable <em><strong>q_sim (time=13880, stations=8)</strong></em>. Period 1979-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=13880, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_System4_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System 4 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored as variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=51, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension stations.</p> <pre><code>loat q_fcast_ens(time=432, lead_time=215, realization=51, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_SEAS5_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System SEAS5 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=25, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension ensemble member, fourth dimension stations.</p> <pre><code>float q_fcast_ens(time=432, lead_time=215, realization=25, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Arnal, L., H. Cloke, L. Magnusson, B. Klein, D. Meissner, A. de Tomas, J. Hunink, I. Pechlivanidis, L. Crochemore, S. Suarez, A. Solera, J. Andreu, J. Knight, F. Liggins, A. Weerts, M. H. Ramos & G. Thirel (2017): The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions. Deliverable 4.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf</a></p> <p>Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Thepaut & F. Vitart (2011): The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137(656), 553-597</p> <p>ECMWF (2017): SEAS5 user guide - Version 1.1. ECMWF, Reading, UK</p> <p>Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones & M. New (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 113(D20), D20119</p> <p>Johnson, S. J., T. N. Stockdale, L. Ferranti, M. A. Balmaseda, F. Molteni, L. Magnusson, S. Tietsche, D. Decremer, A. Weisheimer, G. Balsamo, S. Keeley, K. Mogensen, H. Zuo & B. Monge-Sanz (2018): SEAS5: The new ECMWF seasonal forecast system. Geosci. Model Dev. Discuss. 2018, 1-44</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401-6423</p> <p>Molteni, F., T. Stockdale, M. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer & F. Vitart (2011): The new ECMWF seasonal forecast system (System 4). ECMWF Research Department Technical Memorandum n. 656, Shinfield Park, Reading</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Weerts, A., F. Silvestro, L. Magnusson, B. Klein, I. Pechlivanidis, F. Wetterhall, D. Lavers, E. Gascon, J. Day, S. Hagelin, M. Lindskog & B. van Osnabrugge (2019): Forecast skill developments. Deliverable 4.3, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811</p>
RCP8.5-ECEARTH-RACMO-LARSIM_ME Climate Flow Projection Data for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 „<em>Improving prediction on the climate scale</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 4.4 „<em>Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale</em>“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube.</p> <p>To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018).</p> <p>To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017).</p> <p>The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border).</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <em><strong>q_obs(time=24472, stations=8)</strong></em>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <pre><code>float q_obs(time=24472, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "observed streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_HYRAS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable <em><strong>q_sim (time=23741, stations=8)</strong></em>. Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=23741, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc:</strong></p> <p>Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable <em><strong>q_sim(time=54787, realization=16, stations=8)</strong></em>, first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=54787, realization=16, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "projected streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766</p> <p>Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284</p> <p>Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf">https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf</a></p> <p>Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256</p>
ClepsHresEns-HbvRhein134-SbkReRhein Medium Range Waterlevel Ensemble Forecasts for Waterway Rhine
<p>The datasets provided here were produced as part of the IMPREX project for work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.2 “<em>Framework for the assessment of forecast quality and value in the navigation sector</em>“(Klein & Meißner 2017) and Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim of the dataset was to apply the statistical post-processing method Ensemble Model Output Statistics EMOS (Gneiting et al. 2005) to estimate the predictive uncertainty of the waterlevel ensemble forecasts, in order to provide probabilistic water level forecasts to the end users (Klein & Meißner 2019).</p> <p>Meteorological forcing data used to calculate water-level forecasts with an extended forecast horizon are based on a 68 member multi-model ensemble: 51 ensemble members from ECMWF ENS (1 control forecast and 50 perturbed members) as well as the control forecast ECMWF HRES with a higher spatial resolution (Leutbecher & Palmer 2008, Owens & Hewson 2018), and the 16 members of the limited-area ensemble prediction system by the consortium for small-scale modelling COSMO LEPS (Montani et al. 2011, Marsigli et al. 2014). Archived meteorological real-time forecasts of the period January 2008 to December 2015 have been used to produce this comprehensive water level re-forecast data set.</p> <p>The conceptual, semi-distributed rainfall-runoff model HBV-96 (Bergström 1995, Lindstrom et al. 1997) is applied to calculate the flow forecasts used as boundary conditions and lateral inflows of the hydrodynamic model SOBEK (Deltares 2012) used to calculate water level forecasts along the river Rhine. The river Rhine basin is divided into 134 subbasins which are further subdivided into hydrological response units (HRU) according to land use and elevation classes. The flow formation processes are calculated on those HRUs. The model calculates flow with a temporal resolution of 1 h using temperature and precipitation fields that have been interpolated over the subbasins as meteorological input.</p> <p>The hydrodynamic model suite SOBEK is used as one-dimensional model, which uses cross-section information of the River Rhine as well as its main tributaries. The distance between the cross-sections, which cover the river bathymetry as well as its floodplains, is non-equidistant and ranges between 100 m and 800 m. As the main tributaries of the River Rhine are impounded rivers (e.g. Moselle, Main) the SOBEK-model includes several weirs with their specific control rules in order to simulate the real behaviour of these elements, too.</p> <p>The flow and water level forecasts were initialized each day at 06:00 UTC, which means that observed real-time meteorological data, interpolated to the subbasins of the hydrological model, up to the forecast date were used as forcings of the hydrological model and observed flow was used as input for the hydrodynamic model to initialize the model states. For the forecast period meteorological ensemble runs from the different Numerical Weather Prediction (NWP) models interpolated to the subbasins were used as forcings of the hydrological model. Flow forecasts of the large tributaries of the river Rhine simulated with HBV were then used as input for the hydrodynamic model. To reduce the error of the input to the hydrodynamic model autoregressive error correction models (Broersen & Weerts 2005) was applied using the differences between the simulation of the model using meteorological observations as forcings and the actually past flow observations as training data. This error correction reduces the error of the hydrological model at the forecast initialization time to zero. To reduce the error of the waterlevel forecasts obtained by running the hydrodynamic model, again autoregressive error correction models were applied using the differences between the water level simulation using observed flow as input and the water-level observations of the past.</p> <p><strong>Dataset H_OBS_RHINE.nc:</strong></p> <p>Hourly observed water levels of the gauges Kaub, Koeln, Ruhrort / Rhine for the period 2008–2016 stored as variable <em><strong>h_obs(time=78912, stations=3).</strong></em></p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).</p> <pre><code>float h_obs(time=78912, stations=3); :units = "cm"; :_FillValue = -9999.0f; // float :long_name = "observed waterlevel"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset H_MM_HBV134_SOBEK.nc</strong></p> <p>Hourly forecasted water level of the hydrodynamic mode SOBEK forced by flow forecasts of the hydrological model HBV134 forced by a multi-model meteorological ensemble. Daily forecasts initialized at 06:00 UTC of the period 2008-01-01 to 2015-12-31 with a lead time of 240 hours. Gauges Kaub, Koeln, Ruhrort / Rhine.</p> <p>Forecast values are stored in the variable <em><strong>h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station. ECMWF-HRES first realization, COSMO-LEPS realization 2 – 17, ECMWF-ENS realization 18- 68.</p> <pre><code>float h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3); :_FillValue = -9999.0f; // float :long_name = "forecast waterlevel ensemble"; :units = "cm"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Bergström, S. (1995): The HBV model. In: V. P. Singh (Ed.): Computer models of watershed hydrology. Water Resources Publications, Colorado, USA, 443-476</p> <p>Broersen, P. & A. Weerts (2005): Automatic Error Correction of Rainfall-Runoff models in Flood Forecasting Systems. Conference Proceedings: IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, 17-19 May 2005.</p> <p>Deltares (2012): Technical Reference SOBEK-RE. Deltares, Delft, The Netherlands</p> <p>Gneiting, T., A. E. Raftery, A. H. Westveld & T. Goldman (2005): Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133(5), 1098-1118</p> <p>Klein, B. & D. Meissner (2017): Framework for the assessment of forecast quality and value in the navigation sector. Deliverable 9.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf</a></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Leutbecher, M. & T. N. Palmer (2008): Ensemble forecasting. Journal of Computational Physics 227(7), 3515-3539</p> <p>Lindstrom, G., B. Johansson, M. Persson, M. Gardelin & S. Bergstrom (1997): Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201(1-4), 272-288</p> <p>Marsigli, C., A. Montani & T. Paccagnella (2014): Perturbation of initial and boundary conditions for a limited-area ensemble: multi-model versus single-model approach. Quarterly Journal of the Royal Meteorological Society 140(678), 197-208</p> <p>Montani, A., D. Cesari, C. Marsigli & T. Paccagnella (2011): Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus Series a-Dynamic Meteorology and Oceanography 63(3), 605-624</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
ENS-LARSIM_ME Monthly Streamflow Forecasts for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim was to evaluate the potential skill of monthly streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As meteorological forcing data to calculate monthly flow forecasts the extended-range forecasts from ECMWF-ENS are applied. Twice a week (Monday and Thursday), the ENS model is extended to a lead time up to 46 days by ECMWF. The horizontal resolution for the first 15 days is 0.2°x0.2° (approx. 18 km) and from day 15 to day 46 it is 0.4°x0.4° (approx. 36 km). The ensemble consists of 1 control forecasts and 50 perturbed members, made from slightly different initial atmospheric and oceanic conditions (Owens & Hewson 2018). Re-Forecasts are generated with the same model as used for the operational forecasts for the past 20 years, starting on the same day and month as each real time forecast. The ensemble size is 11-member, which means that in total 20 years x 11 members = 220 forecasts are available for each real time forecast date. The re-forecasts are also created twice a week (Mondays and Thursdays) and are available a week in advance.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>Real-time meteorological station data (precipitation, temperature and global radiation) was interpolated to the 5 km x 5 km model grid and used as meteorological forcing to initialize LARSIM-ME at the forecast date.</p> <p>Re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 generating re-forecasts of the last 20 years were used. As station density of real-time meteorological station data is limited before 2000, only reforecasts with a forecast date after 1999 were considered. Daily total precipitation, daily mean air temperature and global radiation of the reforecast dataset of ECMWF-ENS were interpolated to the 5kmx5km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong>q_obs(time=24472, stations=8)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_SYNOP_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from real-time meteorological station data stored as variable float <em><strong>q_sim(time=6210, stations=8)</strong></em>. Period 2000-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=6210, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_ENS_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature, precipitation and global radiation of the ECMWF ENS re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 with a lead time of 46 days. Forecast dates 2nd January 2000 to 9th March 2016, in total 1699 forecasts. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=1699, lead_time=46, realization=11, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station.</p> <pre><code>float q_fcast_ens(time=1699, lead_time=46, realization=11, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
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