Abstract
The coupled routing and excess storage (CREST) distributed hydrological model has been applied regionally and globally for years. With the development of remote sensing, requirements for data assimilation and integration have become new challenges for the CREST model. In this paper, an improved CREST model version 3.0 (Tsinghua University and China Institute of Water Resources and Hydropower Research, Beijing, China) is proposed to enable the use of remotely-sensed data and to further improve model performance. Version 3.0 model’s runoff generation, soil moisture, and evapotranspiration based on three soil layers to make the CREST model friendly to remote sensing products such as soil moisture. A free water reservoir-based module which separates three runoff components and a four mechanism-based cell-to-cell routing module are also developed. Traditional CREST and CREST 3.0 are applied in the Ganjiang River basin, China to compare their simulation capability and applicability. Research results indicate that CREST 3.0 outperforms the traditional model and has good application prospects in data assimilation, flood forecasting, and water resources planning and management applications.
Highlights
China to test the characteristics of model calibration, simulation, and validation. These improvements make the coupled routing and excess storage (CREST) model easier to combine with the remotely-sensed data such as soil moisture and actual evapotranspiration to decrease the uncertainty problems in distributed hydrological model calibration and validation, and provide the possibility to improve the model simulation capability in regional and global applications
This indicates that CREST 2.x may underestimate the total soil moisture of the vadose zone due to the exclusion of the deep soil layer
CREST 2.x separates runoff into overland flow and interflow according to saturated hydraulic conductivity, and can be recognized as only considering the upper and lower layer soil moisture
Summary
Hydrological models have been successfully invented and applied for decades worldwide. The first is to construct the model using conceptually-based modules such as water storage capacity distribution curve method for runoff generation computation, infiltration curve method for runoff component separation, experience equation-based evapotranspiration calculation, linear reservoir method for flow concentration, etc. The second way is to construct the model mainly based on the partial differential equations (PDEs) that finely describe the dynamics of the hydrological processes [15] These equations usually include the Richards equation for runoff generation computation, Darcy’s law for soil water movement and transportation, and shallow water equations (typically Saint-Venant equations) for overland flow and channel routing. The conceptually-based models can fully adopt the useful information contained in the remote sensing retrieved big data with larger spatiotemporal resolutions, and usually perform satisfactorily in global water cycle modeling and simulation [20,21,22]. The flow concentration module of CREST 2.x does not consider the ground water routing, and may tend to under-estimate the water quantity in long-term and long computational timestep hydrological simulations
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