A Method of Evapotranspiration Data Assimilation Based on Distributed Time-varying Gain Hydrological Model

Abstract

The invention discloses an evapotranspiration data assimilation method based on a distributed time varying gain hydrological model. The method includes the following steps that first, data preparation is conducted, and daily evapotranspiration is obtained based on a remote sensing model and flux observation data to be used for 'observation'; second, an initial ambient field is generated and model simulation is driven; third, when 'observation' data occur, an observation field and a prediction field are obtained; fourth, an analysis field is acquired in an assimilation mode through an ensemble Kalman filtering algorithm; fifth, the ambient field is updated. According to the method, through the data assimilation technology, an evapotranspiration result with high precision is used as observation information, the hydrological model is adjusted to operate, error accumulation is reduced, and an evapotranspiration sequence with high precision and continuous time is simulated. Compared with the prior art, the evapotranspiration can be assimilated directly through the method, the model nondeterminacy can be lowered, operating is easy, a water cycle physical mechanism is realized, and the method can be widely applied to accurately estimating and continuously acquiring region evapotranspiration.

Description

technical field [0001] The invention relates to the field of evapotranspiration estimation, in particular to an evapotranspiration data assimilation method based on a distributed time-varying gain hydrological model. Background technique [0002] The key link of the water cycle in the evapotranspiration (ET) basin determines the interaction and feedback of "geosphere-biosphere-atmosphere" in the earth system. Accurate estimation of evapotranspiration is crucial for studying global or regional water and energy balance, and the relationship between to climate change, water resource management, crop yield estimation, and environmental protection. Therefore, the research on evapotranspiration estimation methods has always been an important topic of common concern in the fields of agronomy, meteorology, hydrology, and geography. [0003] At present, the main ways to estimate regional evapotranspiration are: empirical formula method, hydrological model and remote sensing ET model...

AI Technical Summary

Problems solved by technology

The water balance method can only calculate regional evapotranspiration on a relatively long-term scale, and cannot reflect the temporal and spatial variation characteristics of evapotranspiration; the hydrothermal coupling equation method can easily calculate regional annual evapotranspiration, but it is difficult to measure large areas; the complementary relationship method Vulnerable to the influence of the underlying surface and the atmospheric system, the relationship between actual evaporation and potential evaporation is unstable

Generally speaking, the calculation results using empirical formulas have greater uncertainty, lower precision, and it is difficult to reflect regional variability

The hydrological model takes the entire water cycle system of a closed basin as the research object, divides the area into several hydrological units (grids), and obtains the evapotranspiration distribution of different hydrological units (grids). The problem is that when the simulation period is long , the accuracy will accumulate with the forward simulation error

This research is mainly based on the calibration of empirical parameters, using evapotranspiration observations to interpolate the model, which cannot achieve the optimal probability; Qin et al. [2] The extended Kalman filter is used to assimilate the ET retrieved by the one-layer remote sensing evapotranspiration model SEBS and the ET simulated by the distributed hydrological model. However, since ET is only a diagnostic variable in the hydrological model, simply updating ET cannot feed back the assimilation effect to the model. Hydrological series are not optimized as a whole and can only be equivalent to simple interpolation; Xie and Zhang [3] Using the flow assimilation SWAT model of the watershed control section, the accuracy of runoff and soil moisture has been improved, but the accuracy of evapotranspiration is not ideally improved under flow-based assimilation due to the influence of many factors; Lei et al. [4] Using SWAT to assimilate surface soil moisture, the results show that the simulation accuracy of deep soil moisture, surface runoff and lateral flow is improved, while evapotranspiration is still underestimated; Trudel et al. [5] Using the CATHY model to assimilate the flow and soil moisture at different depths only improves the simulation accuracy of soil moisture, but does not improve the accuracy of evapotranspiration series

[0005] There are still some bottlenecks in the above research, and the evapotranspiration assimilation system based on the hydrological model in the true sense has not been established.

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