The impact of water storage capacity curve based on SMAP data on the simulation effect of Hymod model

Yifei Tian; Tao Zhang

doi:10.1117/12.3023998

21 February 2024 The impact of water storage capacity curve based on SMAP data on the simulation effect of Hymod model

Yifei Tian, Tao Zhang

Proceedings Volume 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023); 1298805 (2024) https://doi.org/10.1117/12.3023998
Event: Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 2023, Xi’an, China

Abstract

This study attempts to use the spatial distribution information provided by SMAP remote sensing soil moisture data to estimate the water storage capacity curve parameters in the Hymod model, and study the impact of this method on the simulation effect of the hydrological model. Then, based on the calculated soil water storage capacity, the empirical distribution curve of soil water storage capacity can be obtained. After testing the goodness of fit of Pareto distribution and Lognormal distribution, this study proposes to use Lognormal distribution to describe the space of water storage capacity. distributed. Finally, the Hymod model with different water storage capacity curves was used to conduct hydrological simulations in the four study basins, and the simulation results of the models were compared and analyzed. Judging from the runoff simulation effect of the model on the four study watersheds, the Hymod model using Lognormal distribution is better than the results using Pareto distribution in both the rate period and the inspection period, with the improvement effect in the Zishui watershed being the most obvious.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifei Tian and Tao Zhang "The impact of water storage capacity curve based on SMAP data on the simulation effect of Hymod model", Proc. SPIE 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 1298805 (21 February 2024); https://doi.org/10.1117/12.3023998

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KEYWORDS

Soil moisture

Data modeling

Calibration

Soil science

Data storage

Remote sensing

Spatial resolution

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