Method and system for estimating surface water resource quantity of watershed in area without data

Abstract

The invention provides a method and system for estimating the surface water resource quantity of a watershed in a region without data, and the method comprises the following steps: obtaining measured rainfall data and remote sensing rainfall product data of a monthly-scale site of a watershed to be estimated, and fusing the two types of data to obtain surface average rainfall data; calculating monthly scale surface average potential evapotranspiration data of the drainage basin according to actually measured meteorological data of meteorological stations in the drainage basin; obtaining basin monthly scale surface actual evapotranspiration data and land water reserve change data in the remote sensing product; establishing a WBM-DP two-parameter hydrological model, determining an objective function, optimizing the objective function, calibrating the parameters, and then returning an optimal value to the WBM-DP two-parameter hydrological model; and calculating the monthly runoff of the drainage basin to be estimated according to the output parameters of the two-parameter hydrological model. The redundancy in the data acquisition process is solved, the certainty and reliability of the result are improved, and meanwhile, the limitation that the result difference of different traditional parameter transplantation methods is large is overcome.

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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