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Large-Watershed Flood Forecasting with High-Resolution Distributed Hydrol. Earth Syst. Sci., 21, 735–749, 2017 www.hydrol-earth-syst-sci.net/21/735/2017/ doi:10.5194/hess-21-735-2017 © Author(s) 2017. CC Attribution 3.0 License. Large-watershed flood forecasting with high-resolution distributed hydrological model Yangbo Chen, Ji Li, Huanyu Wang, Jianming Qin, and Liming Dong Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China Correspondence to: Yangbo Chen ([email protected]) Received: 17 September 2016 – Published in Hydrol. Earth Syst. Sci. Discuss.: 26 September 2016 Accepted: 7 January 2017 – Published: 3 February 2017 Abstract. A distributed hydrological model has been suc- cell, but the model spatial resolution with a 200 m × 200 m cessfully used in small-watershed flood forecasting, but there grid cell is recommended in this study to keep the model at a are still challenges for the application in a large watershed, better performance. one of them being the model’s spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model’s computation efficiency in a large watershed, the other is implementing the model on a high- 1 Introduction performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of Flooding is one of the most devastating natural disasters in the Liujiang River basin in south China. Terrain data digital the world, and huge damages have been caused (Krzmm, elevation model (DEM), soil and land use are downloaded 1992; Kuniyoshi, 1992; Chen, 1995; EEA, 2010). Flood fore- from the website freely, and the model structure with a high casting is one of the most widely used flood mitigation mea- resolution of 200 m × 200 m grid cell is set up. The initial surements, and the watershed hydrological model is the ma- model parameters are derived from the terrain property data, jor tool for flood forecasting. Currently the most popular and then optimized by using the Particle Swarm Optimiza- hydrological model for watershed flood forecasting is still tion (PSO) algorithm; the model is used to simulate 29 ob- the lumped model (Refsgaard et al., 1997), which averages served flood events. It has been found that by dividing the the terrain property and precipitation over the watershed, as river channels into virtual channel sections and assuming the well as the model parameters. Hundreds of lumped models cross section shapes as trapezoid, the Liuxihe model largely have been proposed and widely used, such as the Sacramento increases computation efficiency while keeping good model model proposed by Burnash et al. (1995), the Tank model performance, thus making it applicable in larger watersheds. proposed by Sugawara et al. (1995), the Xinanjiang model This study also finds that parameter uncertainty exists for proposed by Zhao (1977), and the ARNO model proposed physically deriving model parameters, and parameter opti- by Todini (1996), to name a few among others. It is widely mization could reduce this uncertainty, and is highly recom- accepted that the precipitation for driving the watershed hy- mended. Computation time needed for running a distributed drological processes is usually unevenly distributed over the hydrological model increases exponentially at a power of watershed, particularly for the large watershed; therefore, the 2, not linearly with the increasing of model spatial resolu- lumped model could not easily forecast the watershed flood- tion, and the 200 m × 200 m model resolution is proposed ing of large watersheds. Furthermore, due to the inhomo- for modeling the Liujiang River basin flood with the Liuxihe geneity of terrain property over the watershed, which is true model in this study. To keep the model with an acceptable even in very small watershed, the watershed flood forecasting performance, minimum model spatial resolution is needed. could not be forecasted accurately if the model parameters The suggested threshold model spatial resolution for model- are averaged over the watershed. For this reasons, new mod- ing the Liujiang River basin flood is a 500 m × 500 m grid els are needed to improve the watershed flood forecasting capability, particularly for large-watershed flood forecasting. Published by Copernicus Publications on behalf of the European Geosciences Union. 736 Y. Chen et al.: Large-watershed flood forecasting with high-resolution distributed hydrological model Development of distributed hydrological models in the The distributed hydrological model derives model parame- past decades has provided the potential to improve the wa- ters physically from the terrain property data, and is regarded tershed flood forecasting capability. One of the most impor- not needed to calibrate the model parameter, and therefore it tant features of the distributed hydrological model is that it could be used in data-poor or ungauged basins. This feature divides watershed terrain into grid cells, which are regarded of distributed hydrological model allowed it to be applied to have the same meaning of a real watershed; i.e., the grid widely in evaluating the impacts of climate changes and ur- cells have their own terrain properties and precipitation. Hy- banization on hydrology (Li et al., 2009; Ott and Uhlenbrook, drological processes are calculated at both the grid cell scale 2004; VanRheenen et al., 2004; Olivera and DeFee, 2007). and the watershed scale, and the parameters used to calcu- But it also was found that this feature caused parameter un- late hydrological processes are also different at different grid certainty due to the lack of experiences and references in cells. This feature enables it to describe the inhomogene- physically deriving model parameters from the terrain prop- ity of both the terrain property and precipitation over water- erty; therefore it could not be used in fields that required high shed. The distributed feature of the distributed hydrological flood-simulated accuracy, including watershed flood fore- model is a very important feature compared to the lumped casting. It was realized that parameter optimization for a model, which could help it better simulate the watershed hy- distributed hydrological model is also needed to improve drological processes at all scales, small or large. The inho- the model’s performance, and a few methods for optimiz- mogeneity of precipitation over a watershed could also be ing parameters of the distributed hydrological model have well described in the model, this is very helpful in modeling been proposed. For example, Vieux and Vieux (2003) tried a large-watershed hydrological processes, particularly in trop- scalar method to adjust the model parameters, and the model ical and sub-tropical regions where the flooding is driven by performance was found to be largely improved. Madsen et heavy storms. For this reason, the distributed hydrological al. (2003) proposed an automatic multi-objective parameter model is usually regarded as having the potential to better optimization method with the Shuffled Complex Evolution simulate or forecast the watershed flood (Ambroise et al., (SCE) algorithm for the SHE model, which also improved 1996; Chen et al., 2016). Employing a distributed hydrolog- the model performance. Shaffi and De Smedt (2009) pro- ical model for watershed food forecasting has been the new posed a multi-objective genetic algorithm for optimizing pa- trend (Vieux et al., 2004; Chen et al., 2016; Cattoën et al., rameters of the WetSpa model; the improved model result 2016; Witold et al., 2016; Kauffeldt et al., 2016). is regarded to be reasonable. Xu et al. (2012a) proposed an The blueprint of a distributed hydrological model is re- automated parameter optimization method with the Shuffled garded to be proposed by Freeze and Harlan (1969); the Complex Evolution method developed at The University of first distributed hydrological model was the SHE model pro- Arizona (SCE-UA) algorithm for the Liuxihe model, which posed by Abbott et al. (1986a, b). The distributed hydro- improved the model performance in small-watershed flood logical model requires different terrain property data for ev- forecasting. Chen et al. (2016) proposed an automated pa- ery grid cells to set up the model structure; therefore, it is rameter optimization method based on the Particle Swarm data-driven model. In the early stage of distributed hydro- Optimization (PSO) algorithm for Liuxihe model water- logical modeling, this posted great challenge for the appli- shed flood forecasting, and tested it in two watershed: one cation of distributed hydrological model, as the data were small watershed and one large watershed. The results sug- not widely available and inexpensively accessible. With the gested that the distributed hydrological model should op- development of remote sensing sensors and techniques, ter- timize model parameters even if there is only little avail- rain data covering global range with high resolution has be- able hydrological data, while the derived model parameters come readily available and could be acquired inexpensively. from the terrain property could physically serve as initial pa- For example, the digital elevation model (DEM) at 30 m rameters. The above progresses in the distributed hydrolog- grid cell resolution with global coverage could be freely ical model’s parameter optimization have matured, and will downloaded (Falorni et al., 2005; Sharma and Tiwari, 2014), largely improve the performance of the distributed hydrolog- which largely enhances the development and application of cial
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