Abstract
Abstract. The simulation of rainfall-runoff process is essential for disaster emergency and sustainable development. One common disadvantage of the existing conceptual hydrological models is that they are highly dependent upon specific spatial-temporal contexts. Meanwhile, due to the inter-dependence of adjacent flow paths, it is still difficult for the RS or GIS supported distributed hydrological models to achieve high-performance application in real world applications. As an attempt to improve the performance efficiencies of those models, this study presents a high-performance rainfall-runoff simulating framework based on the flow path network and a separate particle system. The vector-based flow path lines are topologically linked to constrain the movements of independent rain drop particles. A separate particle system, representing surface runoff, is involved to model the precipitation process and simulate surface flow dynamics. The trajectory of each particle is constrained by the flow path network and can be tracked by concurrent processors in a parallel cluster system. The result of speedup experiment shows that the proposed framework can significantly improve the simulating performance just by adding independent processors. By separating the catchment elements and the accumulated water, this study provides an extensible solution for improving the existing distributed hydrological models. Further, a parallel modeling and simulating platform needs to be developed and validate to be applied in monitoring real world hydrologic processes.
Highlights
Surface runoff, known as overland flow, is one of most important hydrologic processes in water resource management and studies
There are essentially two main methods to model and simulate the rainfall-runoff process, the empirically based models and the physically based models. The former, the conceptual or the theoretical hydrological model, known as the so-called “black-box”, uses recorded data to predict surface runoff based on mathematical models such as regression analysis, support vector machine (SVM) (Lin et al, 2006), artificial neural network (ANN) (Wu et al, 2009), adaptive network-based fuzzy inference systems (ANFIS) (Talei et al, 2010) and particle swarm optimization (PSO) algorithm (Chou, 2012)
This study preliminarily explored a parallel framework for highperformance simulating surface rainfall-runoff dynamics over large areas with the assistance of the scale-adaptive flow path network and an independent particle system
Summary
Known as overland flow, is one of most important hydrologic processes in water resource management and studies. The runoff dynamics are susceptible to many parameters such as hydrologic, geologic and topographic factors (Li et al, 2014), the applications of these models are hampered by calibration and adaptable adjustment. The latter, the distributed hydrological model, increasingly supported by geographical information system (GIS) and remote sensing (RS) technology, tries to model the physical movements of water based on structured surface elements. These structured surface elements are created from cell or TIN based digital elevation model (DEM), such as the topography-based hydrological model (TOPMODEL), the Sys-
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