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This paper describes the FEWS-Waterways system for navigation that has been set up for optimizing inland water transport. The operational system computes water depths, flow velocities, and air clearances below bridges. These data are input for a trip advisor (so-called Economy Planner) that determines and visualizes the maximum laden draught or container height for which it is possible to pass the critical points on the route in addition to the optimal track in the navigation channel and ship speed during the trip to minimize fuel consumption. Accurate prediction of water depth in rivers and canals, air clearance under bridges and flow velocities enables maximizing the cargo volume during a trip, but also enables sailing with the optimal ship speed, in order to (a) arrive in time at the destination, and (b) reduce the fuel consumption and the carbon emission. Optimization becomes more important if the role of inland transport will increase. In the Netherlands this is the actual poli...

Floods in the Rhine and Meuse basins are generally induced by persistent rainfall over a multi-day period. The response to rainfall strongly varies over time. This variation can largely be ascribed to the variability in initial moisture conditions. With well-established remote sensing techniques large scale information on the surface soil moisture can be retrieved. For the application in the flood forecasting system of the Rhine and Meuse we follow two approaches in incorporating remotely sensed soil moisture information: by visualizing the soil moisture conditions over the Rhine and Meuse basins, which contributes to the value of the forecast; by focussing on the comparison between the satellite soil moisture data and the operational used hydrological model for the Rhine and its consequences for assimilation. In addition the satellite data are compared with soil moisture measurements from two ground-stations in the Rhine basin. After the implementation operational flood forecasting...

Streamflow forecasts from operational hydrologic models can be converted into forecasts of flood-inundation extent using either physically based hydraulic models or simpler terrain-based approaches. Two factors that influence simulated flood-inundation extent are spatial resolution of topographic data and in-channel and overland-flow roughness characterized by the Manning's n parameter. Here, AutoRoute, a raster-based flood-inundation model, was used to simulate two recent flood events in Florida (a forested floodplain) and Texas (an urban floodplain) using two different topographic resolutions and a range of Manning's n values. The AutoRoute-simulated flood-inundation extents were evaluated using observed extents from remotely sensed imagery. For comparison, the same flood events were also simulated using a one-dimensional Hydrologic Engineering Center River Analysis System (HEC-RAS) model. Results indicated that model performance was much improved with higher topographic r...

The Sistan plain is located in Sistan-Baluchistan in the south-east of Iran, bordering Afghanistan. The plain is home to approximately 350,000 people and supports 230,000 ha of irrigated agriculture fed by distributaries of the Hirmand River. The Hirmand River drains a 178,000 km2 catchment that is primarily located in Afghanistan. The Sistan plain is regularly affected by devastating flood events that usually result from the sudden melting of snow covering the Kuh-i-Baba mountains in Afghanistan several hundred kilometres away from ...

ABSTRACT The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely difficult to easily share data among scientists representing these communities without performing some cumbersome conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood forecasting systems are discussed.

Crop growth and production can be simulated by models for the whole canopy as a function of intercepted radiation, water availability, air temperature and nitrogen availability. Simulation models supply quantitative outputs starting from quantitative inputs and they need quite ...