Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1-2.6 but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2-4.5, SSP3-7.0, and SSP5-8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

Intercomparison of ten ISI-MIP models in simulating discharges along the Lancang-Mekong River basin

Water resources are of strategic importance for socioeconomic development. Many hydrological models (HMs) and land surface models (LSMs) have been developed for water resources assessment. However, systematic evaluation of discharge simulation from multiple models is still lacking in the Lancang-Mekong River basin. Here, we evaluated the performances of ten HMs and LSMs by evaluating their simulated discharge against observations at the basin scale. The selected models were within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2a) framework driven by Global Soil Wetness Project 3 (GSWP3) climate forcing data. Five discharge percentile series were used to evaluate the model performances for low, mean, and high flows. The intercomparison according to four statistical criteria revealed considerable differences exist in model performances for different discharge percentiles, indicating a large uncertainty caused by the choice of models with different degree of physical complexity and sensitivity to the quality of the input data. The models generally performed better for high flow than for low flow. Furthermore, the models generally performed better in downstream than in upstream, with the exception of close to the estuary, where complex processes involving interactions between freshwater and saline water are present. It is not surprising that the two calibrated model (WaterGAP2 and WAYS) are superior over the other models. This systematic intercomparison provides insights into the model behaviours and accuracies in discharges predicting with varying intensities, which can aid in quantifying uncertainties in water resources simulation at the basin scale.

Estimating land surface variables and sensitivity analysis for CLM and VIC simulations using remote sensing products

Assessment of Land Surface Models (LSMs) at heterogeneous terrain and climate regimes is essential for understanding complex hydrological and biophysical parameterization. This study utilized the two LSMs, Community Land Model (CLM 4.0) and three layer Variable Infiltration Capacity (VIC-3L), to estimate the interaction between land surface and atmosphere by means of energy fluxes including net radiation (R_N), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G). The modeled energy fluxes were analyzed at two sites: Freeman Ranch-2 (FR2) located in the lowland region of Texas (272m), and Providence 301 (P301) located on the mountains of Sierra Nevada in California (2015m) from 2003 to 2013. R_N was underestimated by CLM with bias -25.06Wm^-2 due to its snow hydrology scheme at P301. LE was overestimated by the VIC during summer precipitation and had a positive bias of 5.51Wm^-2, whereas CLM showed a negative bias of -6.58Wm^-2 at the FR2 site. G was considered as a residual term in CLM, which caused weak performance at P301, while VIC calculated G as a function of soil temperature, depth, and hydraulic conductivity. In addition, The MOD16 showed similar results with models at FR2; however, at P301, they yielded a correlation value of 0.85 and 0.21 for LSMs and MOD16, respectively. The later has lower correlation with in situ specifically in summer season caused by erroneous biophysical or meteorological inputs to the algorithms. The sensitivity analysis between soil moisture and turbulent fluxes, exhibited negative trend (especially for LE at P301) due to topography and snow cover. The results from this study are conducive to improvements in models and satellite based characterization of water and energy fluxes, especially at rugged terrain with high elevation, where observational experiments are difficult to conduct.