A method and system for estimating surface water resources in watershed areas without data

Abstract

The present invention proposes a method and system for estimating the amount of surface water resources in a watershed area without data, including the following steps: obtaining the actual precipitation data and remote sensing precipitation product data of the monthly scale station in the watershed to be estimated, and merging the two types of data to obtain the surface average Precipitation data; calculate the monthly-scale surface average potential evapotranspiration data of the watershed based on the measured meteorological data of meteorological stations in the watershed; obtain the monthly-scale surface actual evapotranspiration data of the watershed and land water storage change data in the remote sensing products; establish the WBM‑DP two-parameter hydrological model , and determine the objective function, optimize the objective function, calibrate the parameters, and then return the optimal value to the WBM-DP two-parameter hydrological model; calculate the monthly runoff of the watershed to be estimated according to the output parameters of the two-parameter hydrological model. The invention solves the redundancy of the data collection process, increases the certainty and reliability of the results, and at the same time, overcomes the limitation that the results of different methods of traditional parameter transplantation are quite different.

Description

technical field [0001] The invention relates to a method and system for estimating the amount of surface water resources in a watershed area without data, in particular to the G06F field. Background technique [0002] Hydrological research in areas with no data or data shortage is one of the hot and difficult issues in modern international hydrology and water resources research. Among them, the traditional method for predicting surface water resources in areas without data is mainly the parameter transplantation method, that is, by selecting representative watersheds or watersheds similar to the underlying surface and climatic conditions of the watershed to be predicted, the flow generation and confluence parameter calibration work is carried out. And the runoff parameters of the basin are transplanted, so as to obtain the quantified parameter transplantation indicators of the areas without data, so as to achieve the purpose of estimating the amount of surface water resource...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, on the one hand, the traditional parameter transplantation method has a large demand for underlying surface data, such as elevation, land type, net rainfall, topographic index, etc., which requires a lot of data processing work, resulting in cumbersome calculations and easy omissions; On the other hand, the selection of parameter transplantation methods is also diverse, such as multiple regression method, spatial neighbor method, and physical feature similarity method. The realization of this method needs to find one or several areas with data that are "close in geographical location", "similar in climate type", and "close in the characteristics of the underlying surface" for parameter calibration and transplantation, which will undoubtedly increase the redundancy of data processing degree

[0004] In summary, the traditional parameter transplantation method undoubtedly requires a large amount of data collection and processing work and complicated processing procedures, and the result may not be ideal. Therefore, the traditional parameter transplantation method is difficult to ensure the accuracy of surface water resource estimation in areas with no data or data shortage. sex

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