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Abstract. Stable isotopic analysis of water in plant, soil, and hydrological studies often requires the extraction of water from plant or soil samples. Cryogenic vacuum extraction is one of the most widely used and accurate extraction methods to obtain such water samples. Here, we present a new design of a cryogenic vacuum extraction system with 18 extraction slots and an innovative mechanism to aerate the vacuum system after extraction. This mobile and extendable multi-port extraction system overcomes the bottleneck of time required for capturing unfractionated extracted water samples by providing the possibility to extract a larger number of samples per day simultaneously. The aeration system prevents the loss or mixture of water vapor during defrosting by purging every sample with high-purity nitrogen gas. A set of system functionality tests revealed that the extraction device guarantees stable extraction conditions with no changes in the isotopic composition of the extracted water samples. Surprisingly, extractions of dried and rehydrated soils showed significant differences of the isotopic composition of the added water and the extracts. This observation challenges the assumption that cryogenic extraction systems to fully extract soil water. Furthermore, in a plant water uptake study different results for hydrogen and oxygen isotope data were obtained, raising problems in the definition from which depths plants really take up water. Results query whether the well-established and widely used cryogenic vacuum distillation method can be used in a standard unified method of fixed extraction times as it is often done.

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In this study, the Mean Transit Time and Mixing Model Analysis methods are combined to unravel the runoff generation process of the San Francisco River basin (73.5 km2) situated on the Amazonian side of the Cordillera Real in the southernmost Andes of Ecuador. The montane basin is covered with cloud forest, sub‐páramo, pasture and ferns. Nested sampling was applied for the collection of streamwater samples and discharge measurements in the main tributaries and outlet of the basin, and for the collection of soil and rock water samples. Weekly to biweekly water grab samples were taken at all stations in the period April 2007–November 2008. Hydrometric data, Mean Transit Time and Mixing Model Analysis allowed preliminary evaluation of the processes controlling the runoff in the San Francisco River basin. Results suggest that flow during dry conditions mainly consists of lateral flow through the C‐horizon and cracks in the top weathered bedrock layer, and that all subcatchments have an important contribution of this deep water to runoff, no matter whether pristine or deforested. During normal to low precipitation intensities, when antecedent soil moisture conditions favour water infiltration, vertical flow paths to deeper soil horizons with subsequent lateral subsurface flow contribute most to streamflow. Under wet conditions in forested catchments, streamflow is controlled by near surface lateral flow through the organic horizon. Exceptionally, saturation excess overland flow occurs. By absence of the litter layer in pasture, streamflow under wet conditions originates from the A horizon, and overland flow. Copyright © 2011 John Wiley & Sons, Ltd.

An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.