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In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities. 

Satellite observations show widespread increasing trends of leaf area index (LAI), known as the Earth greening. However, the biophysical impacts of this greening on land surface temperature (LST) remain unclear. Here, we quantify the biophysical impacts of Earth greening on LST from 2000 to 2014 and disentangle the contributions of different factors using a physically based attribution model. We find that 93% of the global vegetated area shows negative sensitivity of LST to LAI increase at the annual scale, especially for semiarid woody vegetation. Further considering the LAI trends ( P ≤ 0.1), 30% of the global vegetated area is cooled by these trends and 5% is warmed. Aerodynamic resistance is the dominant factor in controlling Earth greening’s biophysical impacts: The increase in LAI produces a decrease in aerodynamic resistance, thereby favoring increased turbulent heat transfer between the land and the atmosphere, especially latent heat flux. 

Abstract. Water management substantially alters natural regimes ofstreamflow through modifying retention time and water exchanges amongdifferent components of the terrestrial water cycle. Accurate simulation ofwater cycling in intensively managed watersheds, such as the Yakima River basin (YRB) in the Pacific Northwest of the US, faces challenges inreliably characterizing influences of management practices (e.g., reservoiroperation and cropland irrigation) on the watershed hydrology. Using the Soiland Water Assessment Tool (SWAT) model, we evaluated streamflow simulationsin the YRB based on different reservoir operation and irrigation schemes.Simulated streamflow with the reservoir operation scheme optimized by theRiverWare model better reproduced measured streamflow than the simulationusing the default SWAT reservoir operation scheme. Scenarios with irrigationpractices demonstrated higher water losses through evapotranspiration (ET)and matched benchmark data better than the scenario that only consideredreservoir operations. Results of this study highlight the importance ofreliably representing reservoir operations and irrigation management forcredible modeling of watershed hydrology. The methods and findings presentedhere hold promise toenhance water resources assessment that can be applied to other intensively managed watersheds. 

Density effect corrections (DECs) are applied to adjust raw CO₂fluxes measured by eddy covariance (EC) systems with open‐path gas analyzers. DEC is also required for adjusting the measured CO₂concentration fluctuations to obtain the adjusted CO₂concentrations for analyzing turbulent statistics or quantifying fluxes. However, our data show that the power spectra of the DEC‐adjusted CO₂concentrations are distorted in the high‐frequency range, as compared with the corresponding spectra of temperature and water vapor density. This contradicts the similarity behavior of scalars, as suggested by Monin‐Obukhov similarity theory. It is demonstrated that such a distortion is caused by the DEC‐induced spikes in the DEC‐adjusted CO₂, altering turbulent statistics of CO₂and scalar similarity between CO₂and other scalars. Our results suggest that CO₂fluxes are overestimated by applying DEC especially under high Bowen ratio conditions, potentially leading to substantial uncertainties in long‐term ecosystem carbon exchange in dry regions.

 

In tropical forests, both vegetation characteristics and soil properties are important not only for controlling energy, water, and gas exchanges directly but also determining the competition among species, successional dynamics, forest structure and composition. However, the joint effects of the two factors have received limited attention in Earth system model development. Here we use a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM‐FATES, to explore how plant traits and soil properties affect tropical forest growth and composition concurrently. A large ensemble of simulations with perturbed vegetation and soil hydrological parameters is conducted at the Barro Colorado Island, Panama. The simulations are compared against observed carbon, energy, and water fluxes. We find that soil hydrological parameters, particularly the scaling exponent of the soil retention curve (B_sw), play crucial roles in controlling forest diversity, with higherB_swvalues (>7) favoring late successional species in competition, and lowerB_swvalues (1 ∼ 7) promoting the coexistence of early and late successional plants. Considering the additional impact of soil properties resolves a systematic bias of FATES in simulating sensible/latent heat partitioning with repercussion on water budget and plant coexistence. A greater fraction of deeper tree roots can help maintain the dry‐season soil moisture and plant gas exchange. As soil properties are as important as vegetation parameters in predicting tropical forest dynamics, more efforts are needed to improve parameterizations of soil functions and belowground processes and their interactions with aboveground vegetation dynamics.

 

The nonclosure of surface energy balance remains an outstanding problem in eddy covariance (EC) measurements of land‐surface fluxes of heat, water vapor, and CO₂. Here data collected from an EC tower over a semiarid sagebrush ecosystem indicate that under unstable atmospheric conditions, the nonclosure becomes increased with increasing instability, consistent with many other studies. It is demonstrated that the increased nonclosure is not caused by the inadequate sampling of large‐scale turbulent motions using a 30‐min averaging interval, though the scales of turbulent motions dominating sensible and latent heat fluxes become enlarged with increasing instability. Quadrant analysis is then used to reveal that the flux contributions from ejections remain nearly constant with increasing instability, whereas the flux contributions from sweeps are reduced and their time fractions increase. Our results imply that the increased nonclosure of surface energy balance is associated with changes in turbulent structures including their dominant time scales and flux contributions of ejections and sweeps as the atmospheric instability increases, which require further studies using vertically distributed observations and/or large eddy simulations.

 

Prior research indicates that land use and land cover change (LULCC) in the central United States has led to significant changes in surface climate. The spatial resolution of simulations is particularly relevant in this region due to its influence on model skill in capturing mesoscale convective systems (MCSs) and on representing the spatial heterogeneity. Recent advances in Earth system models (ESMs) make it feasible to use variable resolution (VR) meshes to study regional impacts of LULCC while avoiding inconsistencies introduced by lateral boundary conditions typically seen in limited area models. Here, we present numerical experiments using the Community Earth System Model version 2–VR to evaluate (1) the influence of resolution and land use on model skill and (2) impacts of LULCC over the central United States at different resolutions. These simulations are configured either on the 1° grid or a VR grid with grid refinement to 1/8° over the contiguous United States for the period of 1984–2010 with two alternative land use data sets corresponding to the preindustrial and present day states. Our results show that skill in simulating precipitation over the central United States is primarily dependent on resolution, whereas skill in simulating 2‐m temperature is more dependent on accurate land use. The VR experiments show stronger LULCC‐induced precipitation increases over the Midwest in May and June, corresponding to an increase in the number of MCS‐like features and a more conductive thermodynamic environment for convection. Our study demonstrates the potential of using VR ESMs for hydroclimatic simulations in regions with significant LULCC.

 

Insufficiently calibrated forest parameters of the Soil & Water Assessment Tool (SWAT) may introduce uncertainties to water resource projections in forested watersheds. In this study, we improved SWAT forest parameterization and phosphorus cycling representations to better simulate forest ecosystems in the St. Croix River basin, and we further examined how those improvements affected model projections of streamflow, sediment, and nitrogen export under future climate conditions. Simulations with improved forest parameters substantially reduced model estimates of water, sediment, and nitrogen fluxes relative to those based on default parameters. Differences between improved and default projections can be attributed to the enhanced representation of forest water consumption, nutrient uptake, and protection of soil from erosion. Better representation of forest ecosystems in SWAT contributes to constraining uncertainties in water resource projections. Results of this study highlight the importance of improving SWAT forest ecosystem representations in projecting delivery of water, sediment, and nutrients from land to rivers in response to climate change, particularly for watersheds with large areas of forests. Improved forest parameters and the phosphorus weathering algorithms developed in this study are expected to help enhance future applications of SWAT to investigate hydrological and biogeochemical consequences of climate change.