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Distributed Hydrologie Modelling for Flood Forecasting

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Distributed Hydrologie Modelling for Flood Forecasting CIS and Remote Sensing in Hydrology, Water Resources and Environment (Piocccdines of 1CGRHWE held at the Three Gorges Dam, China, September 2003). IAHS Publ. 289, 2004 1 Distributed hydrologie modelling for flood forecasting BAXTER E. VIEUX School of Civil Engineering and Environmental Science, University of Oklahoma, 202 West Boyd Street, Norman, Oklahoma 73069, USA bvieux(5),ou.edu Abstract As more accurate precipitation measurements and terrestrial characteristics are built into hydrologie models, the foundation for making hydrologie predictions is undergoing substantial change. The recent decade has seen rapid development of sophisticated computer programs capable of using the rich information content of remotely sensed geospatial data describing vegetative cover or soil moisture; distributed maps of precipitation derived from gauges, radar, and satellite; and digital terrain maps representing the drainage network. The goal of distributed hydrologie modelling is to take into account the heterogeneity of the watershed with the aim of making more accurate and reliable hydrologie predictions. An important area of application for distributed models is flood forecasting in urban and rural areas. Distributed modelling is a growing field of application worldwide, with varying degrees of empiricism and physical basis. Distributed modelling is currently applied from small catchments to large river basins ranging in size from 100 km" to over 100 000 km2. Key words distributed hydrologie modelling; floods; forecasting; radar rainfall INTRODUCTION Mathematical modelling of watersheds began with Sherman's unit hydrograph method published in 1932. Since then many types of hydrologie models have been developed. An ongoing debate within the hydrology community is how to construct a model that best represents the Earth's hydrological processes. A central theme in this debate is how best to use spatially explicit representations of heterogeneous characteristics of a watershed. Model development is striving to keep pace with the explosive growth of online geospatial data sources and remote sensing of the land surface. Because the full capabilities of a distributed model cannot be realized without spatially representative rainfall, radar and satellite technology as measurement of precipitation is advancing the state of hydrologie modelling. Geospatial data and spatially representative precipi­ tation measurements offer new possibilities for distributed modelling. This paper is organized as a review of major themes in hydrologie modelling with an emphasis on distributed approaches, particularly physics-based models and their applications. Recent advances in technology in remote sensing, data telemetry, and computing have led to increased interest in developing improved model representations. Much of the growth of distributed modelling is attributable to the availability of geospatial data and geographic information systems (GIS) for analysis and manipulation of digital terrain, soils, and landuse/cover into hydrologie parameters. The digital revolution re­ sponsible for the growth of geospatial data has spurred hydrologie model development 2 Baxter E. Vieux in the recent decade (Singh & Woolhiser, 2002). Availability of accurate precipitation measurement that is representative over a watershed can be a major limitation to accurate hydrologie forecasts. Concurrent development in raingauge networks, radar, and satellite technology for precipitation measurement has made distributed hydrologie modelling feasible. Hydrodynamic models employ a spatial discretization of the catchment and numerical integration of equations of momentum and mass conservation. Such models provide a basis for full use of distributed information relevant to the physical processes in the catchment. Ready access to worldwide geospatial data facilitates the development and initial parameterization of distributed models. The HYDRO-IK geospatial dataset provides digital elevation model (DEM) data and derivative products including streams and watersheds at 1-km resolution. Figure 1 shows a subset of DEM and stream channel data for the Asian Continent. The streams derived from the elevation data are superimposed over a hillshade relief map. Data such as this provides drainage network topology, slope, and subbasin arrangement for distributed modelling. Higher-resolution geospatial data can be used to refine distributed models, particularly in smaller subbasins. The HYDRO-IK data has been used to construct a flash flood forecast model for the Yantze River basin controlled by the Three Gorges Dam (Chen et al, 2003). One of the principal parameters derived from a DEM is flow direction for runoff routing through a network of grid cells or subbasins. Hydrodynamic models By better representation of the spatio-temporal characteristics governing the transformation of rainfall into runoff, distributed hydrologie modelling seeks to reduce Fig. 1 HYDRO-IK geospatial data for Asia. Streams are derived from the elevation data, which is shown as a hillshade relief map. Data such as this provides drainage network topology, slope, and sub-basin arrangement for distributed modelling. Distributed hydrologie modelling for flood forecasting 3 prediction uncertainty. Approaches to flood forecasting range from simple empirical formulas to sophisticated models that utilize both conceptual and hydrodynamical mathematical relationships are described by Rodda & Rodda (1999). Distributed hydrologie modelling relies on geospatial data used to define topography, land-use/- cover, soils, and precipitation input. Distributed hydrologie modelling may be termed physics-based if it uses conservation of momentum, mass and energy to model the processes. Solution of flow analogies (e.g. kinematic, diffusive wave, or full dynamic) employs numerical methods with a discrete representation of the catchment as a finite difference grid or finite element mesh. Example models that can be classified as physics- or physically-based distributed models (PBD), include r.water.fea (Vieux & Gauer 1994; Vieux, 2001); a parallel version of r.water.fea called the distributed hydrologie model (DHM); Vflo™ distributed hydrologie model (Vieux & Vieux, 2002; Vieux et al, 2003); CASC2D (Julien & Saghafian, 1991; Ogden & Julien 1994); Système Hydrologique Européen (SHE) (Abbott etal, 1986a,b); the Distributed Hydrology Soil Vegetation Model (DHSVM) (Wigmosta etal, 1994); and the Large Scale Catchment Model (LASCAM) (Sivapalan et al, 2002). In the development of a distributed model of a watershed, issues related to para­ meter and input spatial resolution inevitably arise. Because a distributed model may rely on remotely sensed and geospatial data from various sources, each parameter may have a different resolution. The resolution that is necessary to capture the spatial varia­ bility is often not addressed in favour of using the finest resolution possible. However, from practical considerations, computer resources may be wasted if a coarser resolu­ tion would suffice, especially in real-time. The question of which resolution suffices for hydrologie purposes is answered in part by testing the quantity of information contained in a data set as a function of resolution (Vieux, 1993, 2001, 2004; Farajalla & Vieux, 1995). Slope derived from digital elevation data is resolution dependent, which can dramatically affect hydrologie modelling where landsurface slope is used in hydraulic equations of overland and channel flow. Grid based models that derive drainage direction and slope from digital elevation data show resolution-dependence as reported by Kojima & Takara (2003) for the 110-km2 Yada River basin in Japan. Similar to the resolution-dependent effects reported by Vieux (1993), they found that coarser resolution models, e.g. 250-m resolution, produce earlier and higher peaks than finer resolution (50-m) models. Precipitation input resolution can have significant effects on prediction accuracy of a distributed model. Shrestha et al. (2003) investigated the influence of forcing data resolution for various size basins. A distributed model was forced with different input resolution, and the sensitivity of predictions was tested for a range of basin sizes. The sensitivity of the model in relation to data resolution and basin size was found for the 132 350-km2 Huaihe, 29 844-km2 Wangjiaba, and 2093-km2 Suiping River basins in China. In their approach, a ratio between the input grid resolution and catchment size is formed, called the comparison index (CI). Marginal increases in performance were observed beyond a threshold, which indicates that input resolution has a more profound influence in smaller basins than in larger ones where some of the effects of input variability may be attenuated due to channel routing. The issue of subgrid parameterization of processes is beginning to be addressed in distributed modelling. Even though distributed models discretize the domain into 4 Baxter E. Vieux subareas, the sub-grid parameterization becomes an important consideration. When models of large watersheds are constructed at coarse resolution, e.g. 100-km grid cells, runoff generation at this scale requires a stochastic distribution of parameters within the grid cell in order to achieve a reasonable representation of rainfall-runoff. Spatial averaging of rainfall intensities and soil properties in large grids tends to underestimate runoff. Probability density functions for distribution of rainfall and hydraulic conduc­ tivity helps overcome the lack
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