Soil water content measurements deliver reliable estimates of water fluxes: A comparative study in a beech and a spruce stand in the Tharandt forest (Saxony, Germany)

Previous studies have shown that soil water content can vary considerably within homogeneous sites. This small-scale variability of soil water is often neglected when studying water and carbon fluxes in forest ecosystems. In this paper, the small-scale variability of soil water was analyzed at two contrasting eddy-flux sites, a Norway spruce forest and a European beech forest. Simultaneous measurements of precipitation, eddy covariance, and sap flow, from soil water content readings, were used to answer the question of how representative soil water gain is during rainfall and evapotranspiration is during dry periods.Our study demonstrates that the spatial and temporal variability in soil water under spruce and beech was mainly due to the differences in soil properties and root intensity. This can be concluded from the fact that the pattern of soil moisture distribution and flow paths under the trees were generally stable throughout the season. As a tendency, areas with preferred accumulation of rainwater were mainly characterized by maximum soil water depletion. Therefore, the density of the installed water content sensors should correspond to the variability of soil properties as well as rooting intensity. Based on previous studies and our own results, it can be concluded that a horizontal and vertical distance between 10 and 30 cm is best suited for water content sensors to detect preferential flow paths and deliver reliable estimates of soil water balance.Despite the occurrence of preferential flow, we found that the soil water increase during rainstorm periods and the soil water depletion during dry periods can be estimated relatively well when the small-scale variability of soil properties is considered in the experimental setup. In general, the evaporation estimates based on eddy covariance, sap flow, and soil water balance were consistent. However, compared to the spruce site, at the beech site the gap between the evapotranspiration estimates based on eddy covariance and soil water balance were often relatively large. Differences in the spatial extent of these methods can only explain these discrepancies to a certain extent. We suggest that this might be mainly due to the lack of water content sensors in the immediate vicinity of the beech tree trunk. Thus, stemflow-induced wetting and subsequent drying around the trunk could not be monitored in our study. This may result in an underestimation of evaporation from the soil under beech using the soil water balance method compared to the eddy covariance method. Finally, soil water depletion under spruce led to a significant reduction of transpiration when the actual available plant soil capacity (AWC) was <40% of the potential AWC. In contrast to the spruce stand, a reduction of transpiration of beech due to water shortage was not observed.     

The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data

We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO₂ and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals <110 gC m⁻² year⁻¹ for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence intervals on annual NEE increased by 30% when observed data were used instead of synthetic data, reflecting and quantifying the addition of model error. Finally, our analyses indicated that incorporating additional constraints, using data on C pools (wood, soil and fine roots) would help to reduce uncertainties for model parameters poorly served by eddy covariance data.