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Investigating the appropriate model structure for simulation of a karst catchment from the aspect of spatial complexity
- Chang, Yong, Wu, Jichun, Jiang, Guanghui, Zhao, Xiaoer, Zhang, Qiang
- Environmental earth sciences 2019 v.78 no.1 pp. 13
- karsts, models, spatial variation, springs (water), watersheds
- Multi-model frameworks are widely used to identify the appropriate model structure for the study catchment. However, most frameworks mainly consider the process complexity of the model, and few of them consider the spatial complexity. In this paper, we investigated the appropriate model structure for a karst catchment from the aspect of spatial complexity. The purpose is twofold: (1) to investigate whether the spatial complexity is needed to simulate the spring discharge of this karst catchment and (2) to investigate whether the increase of model’s spatial complexity can make up its deficiency on the process complexity. Three simple lumped models with different process complexities were chosen to gradually increase the spatial heterogeneity of their parameters to investigate the appropriate model structure for simulating the discharge of a karst spring. The results show that the performances of three lumped models highly improve when adding the routing function to them. However, further considering the spatial parameter heterogeneity, only one model shows obvious performance improvement and other two models show limited improvement. Moreover, this model with relatively complex spatial parameter heterogeneity still shows worse performance than another lumped model. This indicates an increase of models’ spatial complexity cannot always make up their process deficiencies. The final comparison results indicated that the lumped model or their semi-lumped version with flexible process complexity is enough to simulate the discharge of this karst spring and no extra spatial complexity is needed. Our studies also indicated that the increase in spatial complexity of the model cannot always fully compensate its deficiency in process complexity.