CPP-based hydrological model parameter dynamic calibration method

Abstract

The invention relates to a CPP-based hydrological model parameter dynamic calibration method. The method comprises the following steps: S1, periodically dividing the rate into a plurality of sub-periods on an annual scale, and calculating meteorological clustering indexes and underlying surface clustering indexes of all the sub-periods; screening out candidate clustering indexes based on a nonlinear relationship between the clustering indexes and the flow; S2, eliminating multiple colinearity among clustering indexes by adopting a PCA algorithm; S3, averaging the clustering index values of each sub-period of all years; performing clustering operation twice in sequence according to the meteorological indexes and the underlying surface indexes, and finally dividing the hydrological process in the year into four sub-periods for calibration; S4, independently optimizing TOPMODEL model parameters in each sub-period by adopting an improved parallel calibration scheme, and combining the TOPMODEL model parameters to generate continuous flow sequence values; and S5, evaluating the simulation performance of the hydrological model under different flow conditions by using a multi-index comprehensive evaluation system. According to the invention, the prediction capability of the hydrological model in a changing environment is effectively improved.

Description

technical field [0001] The invention relates to the technical field of hydrological simulation and forecasting, and more specifically, to a method for dynamically calibrating hydrological model parameters based on CPP. Background technique [0002] Hydrological forecasting is widely used in fields such as flood control, drought resistance, water resources development and utilization, national economic construction and national defense, with huge economic benefits and many application units. Accurate hydrological forecasts provide valuable information for agricultural irrigation, ecosystem management, disaster management, hydropower generation, and rural and urban water supply. Therefore, more and more process-driven or data-driven hydrological models are designed and developed. The conceptual hydrological model expresses the hydrological process of the watershed in a generalized way. The model structure is simple, and the simulation results are sometimes unsatisfactory, but...

AI Technical Summary

Problems solved by technology

The conceptual hydrological model uses a generalized method to express the hydrological process of the watershed. The model structure is simple, and the simulation results are sometimes unsatisfactory, but it is practical

The distributed hydrological model can describe the hydrological process more accurately, but the required data are too detailed and the calculation process is too complicated

Regardless of the type of hydrological model, there are still many problems and challenges, such as the convergence of model parameters, parameter uncertainty, parameter sensitivity, model structure uncertainty, model complexity, model Robustness and the conflict of different traffic phase prediction accuracy, etc.

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