Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation
Introduction
Hydrological models are widely applied in water engineering for design and scenario impact investigations. Depending on the type of application, the catchment characteristics and the data availability, different spatial and temporal scales, different model conceptualizations and parameterizations are considered. In some cases, the most appropriate model is selected based on these criteria. However, in many applications the model selection seems subject to the common practice of the modeller. Rarely an objective model selection seems conducted (Najafi et al., 2011). Moreover, hydrological studies are often based on one particular hydrological model. The selected model structure might, however, strongly affect the study results, as was shown before by Breuer et al., 2009, Viney et al., 2009, Huisman et al., 2009, Ludwig et al., 2009, Maurer et al., 2010, Bae et al., 2011, Gosling et al., 2011, Smith et al., 2012, Van Steenbergen and Willems, 2012, Velázquez et al., 2012, among others. However, these studies did not draw much attention to the performance of the models under extreme conditions. The calibration was based on statistics evaluating the overall runoff performance, whereas it is known that this does not necessarily lead to good model performance for high and low flow extremes (Westerberg et al., 2011). It is more appropriate to consider multiple objectives that focus on the different aspects of the fit between simulated and observed discharges. Freer et al. (1996) used several performance measures in their Generalised Likelihood Uncertainty Estimation (GLUE) framework. Boyle et al., 2000, Madsen, 2000, Yu and Yang, 2000, Wagener et al., 2001, Wagener et al., 2003, Ferket et al., 2010, Zhang et al., 2011 applied performance measures on the subflow components of the runoff discharges or on periods covering different catchment response modes, e.g. wet periods, draining periods, dry periods; or high and low flows above or below a threshold. Westerberg et al. (2011) developed a calibration method including flow-duration curves. Model calibration based on multiple objectives is qualitatively more balanced but does not necessarily statistically perform the best (Westerberg et al., 2011, Willems, 2014). This might raise concern that uncertainty in the impact predictions is additionally induced by the calibration of the models.
Within this paper the influence of the model structure on the model performance for catchment runoff, including high and low flow conditions, is investigated by an ensemble of five hydrological models with different spatial resolutions and process descriptions. In order to obtain consistent and reliable models for use in water engineering (design) applications or scenario-based impact assessment, all models are consistently calibrated by a given systematic but time demanding calibration protocol. The protocol relies on information of runoff subflows and various types of runoff responses derived from the observed river flow, rainfall and potential evapotranspiration (ETo) time series. Explicit focus is given to the high and low flow extremes. It is analyzed whether the models produce reliable estimates of the flow regimes under the current climate and how well they simulate the changes in quick runoff coefficient under changing rainfall intensities. To cover a wide set of model complexities, the selected models in this study vary from the lumped conceptual models NAM, PDM and VHM, over the intermediate detailed and distributed model WetSpa, to the highly detailed and fully distributed model MIKE-SHE. The latter model simulates next to the catchment runoff also internal discharges and groundwater heads.
The Grote Nete catchment in Belgium is taken as case study. It is recognized that next to testing different model structures also different catchments with different meteorological and hydrological characteristics should be studied. Practical barriers, however, prevented us from repeating the approach on other catchments. High quality data and good knowledge on the case study processes and particularities are indeed required to make exhaustive studies on model structures behavior.
Section snippets
Study area
The Grote Nete catchment is located in the northeast of Belgium, with an area of 385 km2 at the outlet limnigraphic station of Geel-Zammel (Fig. 1). The long term mean annual precipitation in the catchment ranges from about 600 to 1100 mm with an areal average of 828 mm based on the years 2002–2008. The precipitation is almost equally distributed during the winter and summer periods. The long term average annual ETo is about 670 mm. The topography is flat, ranging from 12 m in the west to 69 m in the
Presentation of the calibration protocol
Given that this study explicitly focuses on the high and low flow extremes, the model calibration explicitly focused on these extremes rather than only on the simulation of the general runoff characteristics and response modes of the hydrographs. This requires multiple objectives to be considered. Multi-objective calibration strategies have been investigated and automated for conceptual rainfall–runoff models (Gupta et al., 1998, Madsen, 2000, Madsen, 2003, Wagener et al., 2001, Madsen et al.,
Results
The five hydrological models in this study were applied under the same meteorological and catchment conditions. They were run at an hourly time step, using the same precipitation and ETo inputs, and were calibrated against the same measured river flow data at the catchment outlet. The calibration data cover the period from September 2002 to the end of 2005 which covers a wide range of meteorological and hydrological conditions, including a very wet winter with several high peak flows and
Conclusions
Although the structure complexity (spatial resolution and process details) play a role on the model efficiency, all five models appear to be fairly adequate for the catchment. The overall runoff processes and streamflow dynamics are well captured by the models. Winter events are generally better estimated than the summer ones, and the lumped models perform better than the distributed ones. Also in terms of overall water balance and the subflows the lumped models are slightly superior with very
Acknowledgement
This research was supported by Flanders Hydraulics Research, Ministry of Mobility and Public Works, Flanders, Belgium.
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