Improving predictions of evapotranspiration by integrating multi-source observations and land surface model

Xinlei He, Shaomin Liu, Tongren Xu, Kailiang Yu, Pierre Gentine, Zhe Zhang, Ziwei Xu, Dandan Jiao and Dongxing Wu

Agricultural Water Management, 2022, vol. 272, issue C

Abstract: Accurate estimation of evapotranspiration (ET) is essential for understanding terrestrial energy, water, and carbon cycles. This study proposes a hybrid model integrating in-situ and remote sensing-derived soil moisture (SM) observations and remote sensing leaf area index (LAI) with the Noah-MP model. The ensemble Kalman filter (EnKF) approach updates the leaf biomass and specific leaf area (SLA) by assimilating the remotely sensed LAI. A machine learning (ML) surrogate model is used to integrate multi-site SM profile observations and remote sensing SM products to estimate the three-layer SM. An iterative coupling of two parts implements the hybrid model: optimization of leaf biomass and SLA by assimilation of LAI in the Noah-MP model and simulation of three-layer SM in the ML surrogate model. The performance of the hybrid model is evaluated in the Heihe River Basin (HRB) in northwest China. The estimated ET from the hybrid model is compared with observations from the large aperture scintillometer (LAS) at the Arou, Daman, and Sidaoqiao sites and up-scaled watershed ET over the HRB. The findings indicate that the hybrid model performs well in vegetated areas but underestimates ET in extreme arid deserts. The three-site unbiased root mean squared errors (ubRMSEs) of ET estimates from the hybrid model are 29.06%, 42.76%, and 50.00% lower than Noah-MP at the Arou, Daman, and Sidaoqiao sites, respectively. The coupling of data assimilation (DA) and ML methods can improve vegetation dynamics and SM transport estimation in the Noah-MP model. The hybrid model can take advantage of DA and ML methods and integrate multi-source observations to improve the accuracy of ET estimation. The results also indicate that the ET predictions are more sensitive to root zone SM (0–40 cm) over croplands, grasslands, and shrublands, while the ET simulations are more affected by deeper rooting depths SM (0–100 cm) and groundwater over forests.

Keywords: Hybrid model; Multi-source observations; Ensemble Kalman filter method; Machine learning method; Heihe River Basin (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:272:y:2022:i:c:s0378377422003742

DOI: 10.1016/j.agwat.2022.107827

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