Abstract
Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model’s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models’ NSE were all classified as “very good” based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model.
Highlights
A hydrologic model is a simplified representation of a complex system that facilitates the understanding of the hydrologic cycle in a simplified manner
This study investigates a hybrid RR model that uses intermediate state variables obtained from a conceptual model within an least squared support vector machine (LSSVM)-based regression framework
The tank model is a conceptual RR model that requires a small amount of data and relatively few model parameters
Summary
A hydrologic model is a simplified representation of a complex system that facilitates the understanding of the hydrologic cycle in a simplified manner. RR modeling began with conceptual models from the 1960s to the 2000s, followed by recent advances using data-driven approaches, mainly through the use of machine learning techniques. Conceptual models, such as the Sacramento model [16], IHACRES [6], PDM model [17], and HBV model [18], typically require fewer parameters compared to physically-based models (e.g., MIKE-SHE [19], SWAT [20], and TOPMODEL [15]). This study investigates a hybrid RR model that uses intermediate state variables obtained from a conceptual model (i.e., the tank model) within an LSSVM-based regression framework.
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