Matthias Cuntz

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Water labeled with stable isotopes provides a conservative tracer, being neither produced nor consumed, for water flowpaths within soils and root systems. We added a strong, evenly distributed ² HHO label to one m ² of soil surface and continuously monitored its passage downward into the soil and upward into the stems of surrounding trees, with the objective of illuminating spatiotemporal lateral root water uptake and overlap. The study was conducted during the historic drought of 2018 in a mature Scots pine ( Pinus sylvestris ) forest growing on sandy soil in northern Sweden. Continuous in situ isotopic measurements of tree xylem water evidenced root system overlap of six trees within the labeled square meter. This result is consistent with previous estimates from labelled nutrient uptake measurements at this site. However, label uptake differed sharply among trees, even within the same radius; 90 % of the label was taken up by one of the two trees closest to the labelled plot. Horizontal transport rates in tree roots averaged 0.17±0.05 m d ⁻¹ , meaning that the arrival of label pulse in tree stems was delayed by 6–33 d from first tree to last. Root water uptake by trees appeared restricted to the upper 60 cm of mineral soil, even at the peak of the drought; the volume-weighted average depth of uptake was 30.6±1.2 cm throughout the study period. Label intensity of the mineral soil weakened throughout the drought, consistent with the notion that the label was being dispersed or diluted into pre-event water. Our data provide a daily and three-dimensional description of the passage of a labeled water pulse, highlighting the heterogeneity in horizontal water distribution by roots and the uneven partitioning of label among individual trees in a boreal forest.

Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

The lack of energy balance closure in Eddy‐Covariance (EC) measurements is a well‐known, still unresolved challenge in micrometeorology, with energy balance closure (EBC) rates typically ranging between 60% and 80%. While numerous hypotheses have been proposed to explain this imbalance, the relative contributions of neglected energy storage terms, data quality and flux processing options remain insufficiently disentangled. Using standardized ICOS and NEON datasets, we show that a significant portion of the observed energy imbalance can be attributed to overlooked or inconsistently handled energy components and turbulent flux quality control. Using data drawn from 84 sites, we show that comprehensive energy accounting—including soil heat flux, storage terms (soil, air, biomass), photosynthetic energy demand, and strict quality filtering of turbulent fluxes—improved EBC by 16% on average, with site‐specific gains up to 40%. However, we also identify a persistent residual imbalance that is unlikely to be resolved through methodological refinements or additional measurements alone, pointing to fundamental physical processes that are not accounted for in the standard measurement and processing. We argue that this unresolved imbalance should be explicitly acknowledged and bounded, rather than implicitly absorbed into correction schemes, and we outline practical guidance for diagnosing and interpreting EBC in standardized flux networks. This perspective evaluates methodological advances and residual uncertainties, providing an actionable framework for the appropriate use of EC energy fluxes in carbon, water, and climate research.

Summary: The study of seasonal xylem hydraulics has predominantly focused on embolism‐induced losses, whereas growth‐driven increases in hydraulic capacity have received little attention. We assessed the intra‐annual dynamics of xylem formation and gain of conductivity in the current‐year ring of three species with contrasting tree‐ring structure, sessile oak ( Quercus petraea , ring‐porous), European beech ( Fagus sylvatica , diffuse‐porous) and Norway spruce ( Picea abies , conifer), using micro‐cores collected over 3 yr in north‐eastern France. Ring conductivity loss (indicated by tylose‐occluded vessels) was also examined in both angiosperms. Due to the differences in water‐transport efficiency and cavitation vulnerability between earlywood and latewood vessels, oak presented distinct intra‐annual dynamics of current‐ring basal area increment (BAI) and the gain and loss of conductive area (CA), theoretical hydraulic conductivity ( K _h ) and specific hydraulic conductivity ( K _s ). Conversely, the intra‐annual gain in BAI, CA and conductivity was largely coordinated in beech and spruce. Our results imply that the proportional gain in ring hydraulic capacity could potentially be estimated as a delayed BAI curve in diffuse‐porous and conifer species, but not in ring‐porous species; and the comparison of native conductivity measurements within or across species should account for their timing within the growing season, particularly in ring‐porous species.

Land-atmosphere exchange shifts from energy-limited to water-limited regime at a critical soil moisture, which marks a fundamental transition in the Earth system. Estimates of the critical threshold vary a lot across studies despite its importance for the mechanistic understanding of soil moisture limitation on transpiration and plant productivity.We introduce a novel, model-based diagnostic approach — the Normalized Transpiration Deficit (NTD) method — and demonstrate that it yields results highly consistent with observational methods such as finding breakpoints in the evaporative fraction. Using a hydraulically-enabled version of the CABLE-POP land surface model, we conducted a factorial experiment across various soil textures, climate regimes, and plant hydraulic parameters. It suggests that the critical threshold occurs at a broadly similar soil matric potential (ψcrit) across soil types, resulting in a quasi-linear relationship between the critical volumetric soil moisture (θcrit) and sand content, as observed in earlier studies. The dependency of θcrit on soil type vanished when it was normalised by field capacity, which yielded hence also a universal threshold of relative extractible water REWcrit, as found empirically for forest ecosystems.Most of the variance of θcrit, 86%, came from soil texture in the factorial experiment, while the variances of ψcrit and REWcrit were largely explained by plant hydraulic traits, accounting for 87% and 77% of total variance, respectively. Within the plant hydraulic traits, the P50-values of stomatal conductance (ψ50,l) and of xylem conductance (ψ50,x) showed the strongest correlations with the critical thresholds, indicating that vulnerability to hydraulic dysfunction plays a key role in shaping plant responses to soil drying. There was, however, no direct effect of climate on any of the critical thresholds, i.e. the thresholds remained invariant across climates for given soil and vegetation types. This suggests that apparent climate dependencies reported in observational studies may be artifacts due to limited soil moisture ranges at each observational site, or they represent biological adaptation and acclimation that is currently not captured in our static model parameters.These findings highlight the necessity of incorporating ecosystem-scale hydraulic regulation in biosphere models to reconcile divergent estimates of critical thresholds and to improve predictions of drought impacts on water and carbon fluxes.