The influence of assimilating leaf area index in a land surface model on global water fluxes and storages

Abstract

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycles but also in the global water balance by controlling surface evapotranspiration (ET). Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water, energy, and carbon cycles. This study aims to assess the extent to which a land surface model (LSM) can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI into an LSM through an ensemble Kalman filter (EnKF) to estimate LAI, ET, canopy-interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework not only effectively reduces errors in LAI model simulations but also improves all the modeled water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet conditions). However, it tends to worsen some of the modeled water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the LSM is conservative, and the LAI assimilation introduces more vegetation, which requires more water than what is available within the soil.

Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

“…In this work, twenty ensemble members are generated by perturbing the atmospheric forcing. Following previous work (Kumar et al, 2019;Zhang et al, 2020), selected MERRA-2 atmospheric variables, such as shortwave and longwave radiation and precipitation, are perturbed hourly. Multiplicative perturbations are applied to the shortwave radiation and precipitation with a mean of 1 and standard deviations of 0.3 and 0.5, respectively.…”

“…They have used LDAS-Monde as the land surface model and assimilated ASCAT soil water index (SWI) and LAIGEOV1 LAI observation data within that model. Zhang et al (2020) proposed a global synthetic experiment to assimilate LAI within Noah-MP using an EnKF. They showed that LAI assimilation can improve global water fluxes and reduce the impact of high precipitation biases in the estimation of water variables.…”

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

“…The EnKF technique has flexibility in treating errors in model equations and parameters and is particularly suitable for nonlinear problems, such as soil dynamics [20,55]. Twenty ensemble members are generated by perturbing the atmospheric forcing following the same methodology presented in [24,26]. The precipitation and shortwave radiation are perturbed via multiplicative error models, whereas an additive perturbation is used for longwave radiation.…”

“…To determine if the DA improves or degrades the estimation of a specific variable with respect to the original (OL) model run, the Normalized Information Contribution (NIC; [24,26]) for NCRMSE is computed as follows:…”

“…The results demonstrated that the DA reduced the bias in LAI (from 5 m 2 /m 2 to ±1 m 2 /m 2 ). Two past studies proposed synthetic experiments to assimilate LAI alone within Noah-MP using an EnKF [26] and direct insertion (DI) [9]. The results showed an improvement in a subset of water and vegetation variables and a reduction in the impact of large biases in the model input precipitation.…”