The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of coupled cloud physics and land-surface models. Here, we use functional decomposition of satellite-derived SM (SMAP/L4) and cloud vertical profiles (CVP: GPM/DPR/L2A) in the central US to quantify the relationship between SM and the vertical distribution of cloud water. High-dimensional model representation disentangles the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show the sign and strength of this feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in the antecedent 7-hour SM and land-surface temperature can increase reflectivity up to 4 dBZ in the lower atmosphere (1-3 km above the surface). The presented approach brings new insights into observational understanding of SM-precipitation feedback and possesses the potential for diagnosing cloud models regarding land-atmosphere coupling representation.

The linear relationship between gross primary productivity (GPP) and evapotranspiration (ET), evidenced by site-scale observations, is well recognized as an indicator of the close interactions between carbon and hydrologic processes in terrestrial ecosystems. However, it is not clear whether this relationship holds at the catchment scale, and if so, what are the controlling factors of its slope and intercept. This study proposes and examines a generalized GPP-ET relationship at 380 near-natural catchments across various climatic and landscape conditions in the contiguous U.S., based on monthly remote sensing-based GPP data, vegetation phenology, and several hydrometeorological variables. We demonstrate the validity of this GPP-ET relationship at the catchment scale, with Pearson’s r ≥ 0.6 for 97% of the 380 catchments. Furthermore, we propose a regionalization strategy for estimating the slope and intercept of the generalized GPP-ET relationship at the catchment scale by linking the parameter values a priori with hydrometeorological data. We validate the monthly GPP predicted from the relationship and regionalized parameters against remote-sensing based GPP product, yielding Kling-Gupta Efficient (KGE) values ≥ 0.5 for 92% of the catchments. Finally, we verify the relationship and its parameter regionalization at 35 AmeriFlux sites with KGE ≥ 0.5 for 25 sites, demonstrating that the new relationship is transferable across the site, catchment, and regional scales. The relationship will be valuable for diagnosing coupled water–carbon simulations in land surface and Earth system models and constraining remote-sensing based estimation of monthly ET.

Net photosynthesis (AN) is a major component of the global carbon cycle, with significant feedback to decadal-scale climate change. Although plant acclimation to environmental changes can modify AN, traditional vegetation models in Earth System Models (ESMs) often rely on plant functional type (PFT)-specific parameter calibrations or simplified acclimation assumptions, both of which lacked generalizability across time, space and PFTs. In this study, we propose a differentiable photosynthesis model to learn the environmental dependencies of Vc,max25, as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of Vc,max25, learning the environment dependencies of key photosynthetic parameters improves model spatiotemporal generalizability. Applying environmental acclimation to Vc,max25 led to substantial variation in global mean AN, calling for the attention to acclimation in ESMs. The model effectively captured multivariate observations (Vcmax25, stomatal conductance gs, and AN) simultaneously and, in fact, multivariate constraints further improved model generalization across space and PFTs. It also learned sensible acclimation relationships of Vc,max25 to different environmental conditions. The model explained more than 54%, 57% and 62% of the variance of AN, gs, and Vcmax25, respectively, presenting a first global-scale spatial test benchmark of AN and gs. These results highlight the potential of differentiable modeling to enhanced process-based modules in ESMs and effectively leverage information from large, multivariate datasets.

Western U.S. conifer forests harbor diverse ecological strategies that enable species to persist across a wide range of hydroclimate conditions, along with wildfire and eruptive insect outbreaks. Assessing climate influences on future forest composition and carbon sequestration requires vegetation process models that have sufficient ecological resolution to simulate this range of ecological variability. Here we present progress towards incorporating multiple shade and drought tolerance strategies in a vegetation demographic model parameterized for Western U.S. forests. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) to simulate a mixed conifer forest dominated by ponderosa pine and incense cedar in the Sierra Nevada Mountains of California. FATES resolves plant growth and respiration at the level of cohorts, defined by size and plant functional type. Incense cedar is shade and drought tolerant, while ponderosa pine is shade intolerant and the canopy dominant. We synthesized literature values of plant traits that correspond to important physiological and allometric parameters in FATES and conducted a sensitivity analysis within the observed parameter ranges with respect to carbon and water fluxes. Model output was benchmarked against carbon flux, water flux, and leaf area index measurements from the Critical Zone Observatory/AmeriFlux CZ2 site during 2010-2012. Specific leaf area, Vcmax, rooting distribution, and allometric equations had the most influence on simulated carbon and water fluxes. Final simulated average annual gross primary production (GPP) over 2010-2012 (1156 +- 79.2 gC/m2/yr) was 3.8% lower than observed GPP (1202 +-138.2 gC/m2/yr). Simulated evapotranspiration (ET, 373 +- 25 mm/yr) was 62% lower than measured ET (993 +-158 mm/yr). Simulated leaf area index (LAI, 1.2) was within the range of measured LAI (0.5-1.5). Preliminary analysis indicates underestimation of ET is likely due to an overestimation of soil water drainage. Our final parameter set allows pine and cedar coexistence to emerge from a bare ground initialization, and additional sensitivity testing of parameters important for coexistence are in progress. Clearly, observationally constrained parameters are critical for simulating ecosystem dynamics in Western U.S. forests.

A growing number of coastal eco-geomorphologic modeling studies have been conducted to understand coastal marsh evolution under sea level rise (SLR). Although these models quantify marsh topographic change as a function of sedimentation and erosion, their representations of vegetation dynamics that control organic sedimentation differ. How vegetation dynamic schemes and parameter values contribute to simulation outcomes is still not quantified. Additionally, the sensitivity of modeling outcomes on parameter selection in the available formulations has not been rigorously tested to date, especially under the influence of an accelerating SLR. This knowledge gap severely limits modeling accuracy and the estimation of the vulnerability of coastal marshes under SLR. In this paper, we used coastal eco-geomorphologic models with different vegetation dynamic schemes to investigate the eco-geomorphologic feedbacks of coastal marshes and parametric sensitivity under SLR scenarios. We found that marsh accretion rate near the seaward boundary can keep pace with moderate and high rates of SLR, while interior marsh regions are vulnerable to a high rate of SLR. The simulations with different vegetation schemes exhibit diversity in elevation and biomass profiles and parametric sensitivity. We also found that the model parametric sensitivity varies with rates of future SLR. Vegetation-related parameters and sediment diffusivity, which are not well measured or discussed in previous studies, are identified as some of the most critical parameters. Our findings provide insights to appropriately choose modeling presentations of key processes and feedbacks for different coastal marsh landscapes under SLR, which has practical implications for coastal ecosystem management and protection.

Coastal saltwater intrusion (SWI) is one key factor affecting the hydrology, nutrient transport, and biogeochemistry of coastal marsh landscapes. Future climate change, especially intensified sea level rise (SLR), is expected to trigger SWI to encroach coastal freshwater aquifers more intensively. Numerous studies have investigated decadal/century scale SWI under SLR by assuming a static coastal landscape topography. However, coastal marshes are highly dynamic systems in response to SLR, and the impact of coastal marsh evolution on SWI has received very little attention. Thus, this study investigated how coastal marsh evolution affects future SWI with a physically-based coastal hydro-eco-geomorphologic model, ATS (Advanced Terrestrial Simulator). Our synthetic modeling experiments showed that it is very likely that the marsh elevation increases with future SLR, and a depression zone is formed due to the different marsh accretion rates between the ocean boundary and the inland. We found that, compared to the cases without marsh evolution, the marsh accretion may significantly reduce the surface saltwater inflow at the ocean boundary, and the evolved topographic depression zone may prolong the residence time of surface ponding saltwater, which causes distinct subsurface salinity distributions. We also found that the marshland may become more sensitive to the upland groundwater table that controls the freshwater flux to the marshes, compared with the cases without marsh evolution. This study demonstrates the importance of marsh evolution to the freshwater-saltwater interaction under sea level rise and can help improve our predictive understanding of the vulnerability of the coastal freshwater system to sea level rise.