Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000–2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg⁻¹H₂O) > shrublands (2.00 gC kg⁻¹H₂O) > croplands (1.76 gC kg⁻¹H₂O) > grasslands (1.04 gC kg⁻¹H₂O). During the period 2000–2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management.

The interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible (H) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H, LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H, LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS.

In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a priori knowledge. Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (EM) methods. ET observations from eddy covariance towers (ET_EC) at AmeriFlux sites and ET values from the water balance method (ET_WB) are used to evaluate the BTCH- and EM-integrated ET estimates. Results indicate that BTCH performs better than EM and all the individual parent products. Moreover, the trend of BTCH-integrated ET estimates, and their influential factors (e.g., air temperature, normalized differential vegetation index, and precipitation) from 1982 to 2011 are analyzed by the Mann–Kendall method. Finally, the 30-year (1982 to 2011) total water storage anomaly (TWSA) in the Mississippi River Basin (MRB) is retrieved based on the BTCH-integrated ET estimates. The TWSA retrievals in this study agree well with those from the Gravity Recovery and Climate Experiment (GRACE).