This paper presents key issues associated with uncertainty in flood inundation mapping. Currently flood inundation extent is represented as a deterministic map without consideration to the inherent uncertainties associated with various uncertain variables ͑precipitation, stream flow, topographic representation, modeling parameters and techniques, and geospatial operations͒ that are used to produce it. Therefore, it is unknown how the uncertainties associated with topographic representation, flow prediction, hydraulic model, and inundation mapping techniques are transferred to the flood inundation map. In addition, the propagation of these individual uncertainties and how they affect the overall uncertainty in the final flood inundation map is not well understood. By using a sample data set for Strouds Creek, N.C., this paper highlights key uncertainties associated with flood inundation mapping. In addition, the idea of a probabilistic flood inundation map is articulated, and an integrated framework approach that will connect data, models, and uncertainty analysis techniques in producing probabilistic flood inundation maps is presented.

Abstract. A method is proposed for routing spatially distributed excess precipitation over a watershed to produce runoff at its outlet. The land surface is represented by a (raster) digital elevation model from which the stream network is derived. A routing response function is defined for each digital elevation model cell so that water movement from cell to cell can be convolved to give a response function along a flow path and responses from all cells can be summed to give the outlet hydrograph. An example application of analysis of runoff on Waller Creek in Austin, Texas, is presented.

Tens of millions of people around the world are already exposed to coastal flooding from tropical cyclones. Global warming has the potential to increase hurricane flooding, both by hurricane intensification and by sea level rise. In this paper, the impact of hurricane intensification and sea level rise are evaluated using hydrodynamic surge models and by considering the future climate projections of the Intergovernmental Panel on Climate Change. For the Corpus Christi, Texas, United States study region, mean projections indicate hurricane flood elevation (meteorologically generated storm surge plus sea level rise) will, on average, rise by 0.3 m by the 2030s and by 0.8 m by the 2080s. For catastrophic-type hurricane surge events, flood elevations are projected to rise by as much as 0.5 m and 1.8 m by the 2030s and 2080s, respectively.

[1] Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the world's major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge.Citation: Zaitchik, B. F., M. Rodell, and F. Olivera (2010), Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme, Water Resour. Res., 46, W06507,

A unit hydrograph model is proposed in which the watershed is decomposed into subareas which are individual cells or zones of neighbouring cells. The unit hydrograph is found for each subarea and the response at the outlet to excess rainfall on each subarea is summed to produce the watershed runoff hydrograph. The cell to cell flow path to the watershed outlet is determined from a digital elevation model. A constant flow velocity is assigned to each cell and the time lag between subarea input and response at the watershed outlet is found by integrating the flow time along the path from the subarea to the outlet. The response function for a subarea is modelled as a lagged linear reservoir in which the flow time is equal to the sum of a time of translation and an average residence time in the reservoir. It is shown that the assumption of a spatially varying, but time-invariant, velocity field underlying this model produces a linear system model for all subareas whose outputs can be summed in the manner indicated. An example application is presented for the 8.70 km2 Severn watershed at Plynlimon in Wales using a 50 m digital elevation model in which the cell velocity is calculated by modifying an average watershed velocity according to the terrain slope and the drainage area of each cell. The resulting model reasonably reproduces the observed unit hydrograph.

This paper presents ArcGIS‐SWAT, a geodata model and geographic information system (GIS) interface for the Soil and Water Assessment Tool (SWAT). The ArcGIS‐SWAT data model is a system of geodatabases that store SWAT geographic, numeric, and text input data and results in an organized fashion. Thus, it is proposed that a single and comprehensive geodatabase be used as the repository of a SWAT simulation. The ArcGIS‐SWAT interface uses programming objects that conform to the Component Object Model (COM) design standard, which facilitate the use of functionality of other Windows‐based applications within ArcGIS‐SWAT. In particular, the use of MS Excel and MATLAB functionality for data analysis and visualization of results is demonstrated. Likewise, it is proposed to conduct hydrologic model integration through the sharing of information with a not‐model‐specific hub data model where information common to different models can be stored and from which it can be retrieved. As an example, it is demonstrated how the Hydrologic Modeling System (HMS) ‐ a computer application for flood analysis ‐ can use information originally developed by ArcGIS‐SWAT for SWAT. The application of ArcGIS‐SWAT to the Seco Creek watershed in Texas is presented.

[1] Including a global river network in the land component of global climate models (GCMs) is necessary in order to provide a more complete representation of the hydrologic cycle. The process of creating these networks is called river network upscaling and consists of lowering the resolution of already available fine networks to make them compatible with GCMs. Fine-resolution river networks have a level of detail appropriate for analysis at the watershed scale but are too intensive for global hydrologic studies. A river network upscaling algorithm, which processes fine-resolution digital elevation models to determine the flow directions that best describe the flow patterns in a coarser user-defined scale, is presented. The objectives of this study were to develop an algorithm that advances the previous work in the field by being applicable at a global scale, allowing for the upscaling to be performed in a projected environment, and generating evenly distributed flow directions.

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