Footprint of recycled water subsidies downwind of Lake Michigan

Continental evaporation is a significant and dynamic flux within the atmospheric water budget, but few methods provide robust observational constraints on the large-scale hydroclimatological and hydroecological impacts of this ‘recycled-water' flux. We demonstrate a geospatial analysis that provides such information, using stable isotope data to map the distribution of recycled water in shallow aquifers downwind from Lake Michigan. The δ2H and δ18O values of groundwater in the study region decrease from south to north, as expected based on meridional gradients in climate and precipitation isotope ratios. In contrast, deuterium excess (d = δ2H − 8 × δ18O) values exhibit a significant zonal gradient and finer-scale spatially patterned variation. Local d maxima occur in the northwest and southwest corners of the Lower Peninsula of Michigan, where ‘lake-effect' precipitation events are abundant. We apply a published model that describes the effect of recycling from lakes on atmospheric vapor d values to estimate that up to 32% of recharge into individual aquifers may be derived from recycled Lake Michigan water. Applying the model to geostatistical surfaces representing mean d values, we estimate that between 10% and 18% of the vapor evaporated from Lake Michigan is re-precipitated within downwind areas of the Lake Michigan drainage basin. Our approach provides previously unavailable observational constraints on regional land-atmosphere water fluxes in the Great Lakes Basin and elucidates patterns in recycled-water fluxes that may influence the biogeography of the region. As new instruments and networks facilitate enhanced spatial monitoring of environmental water isotopes, similar analyses can be widely applied to calibrate and validate water cycle models and improve projections of regional hydroecological change involving the coupled lake-atmosphere-land system. Read More: http://www.esajournals.org/doi/abs/10.1890/ES12-00062.1

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Stand scale variability of topsoil organic matter composition in a high-elevation Norway spruce forest ecosystem

Our knowledge about the effect of single-tree influence areas on the physicochemical properties of the underlying mineral soil in forest ecosystems is still limited. This restricts our ability to adequately estimate future changes in soil functioning due to forest management practices. We studied the stand scale spatial variation of different soil organic matter species investigated by 13C NMR spectroscopy, lignin phenol and neutral sugar analysis under an unmanaged mountainous high-elevation Norway spruce (Picea abies L.) forest in central Europe. Multivariate geostatistical approaches were applied to relate the spatial patterns of the different soil organic matter species to topographic parameters, bulk density, oxalate- and dithionite-extractable iron, pH, and the impact of tree distribution. Soil samples were taken from the mineral top soil. Generally, the stand scale distribution patterns of different soil organic matter compounds could be divided into two groups: Those compounds, which were significantly spatially correlated with topography/altitude and those with small scale spatial pattern (range ≤ 10 m) that was closely related to tree distribution. The concentration of plant-derived soil organic matter components, such as lignin, at a given sampling point was significantly spatially related to the distance of the nearest tree (p ≤ 0.05). In contrast, the spatial distribution of mainly microbial-derived compounds (e.g. galactose and mannose) could be attributed to the dominating impact of small-scale topography and the contribution of poorly crystalline iron oxides that were significantly larger in the central depression of the study site compared to crest and slope positions. Our results demonstrate that topographic parameters dominate the distribution of overall topsoil organic carbon (OC) stocks at temperate high-elevation forest ecosystems, particularly in sloped terrain. However, trees superimpose topography-controlled OC biogeochemistry beneath their crown by releasing litter and changing soil conditions in comparison to open areas. This may lead to distinct zones with different mechanisms of soil organic matter degradation and also stabilization in forest stands.