ALBERT

Description: The fields of eco-hydrological modelling and extreme flow prediction and management demand detailed information of streamflow intermittency and its corresponding landscape controls. Innovative sensing technology for monitoring of streamflow intermittency in perennial rivers and intermittent reaches improves data availability, but reliable maps of streamflow intermittency are still rare. We used a large dataset of streamflow intermittency observations and a set of spatial predictors to create logistic regression models to predict the probability of streamflow intermittency for a full year as well as wet and dry periods for the entire 247 km2 Attert catchment in Luxembourg. Similar climatic conditions across the catchment permit a direct comparison of the streamflow intermittency among different geological and pedological regions. We used 15 spatial predictors describing land cover, track (road) density, terrain metrics, soil and geological properties. Predictors were included as local-scale information, represented by the local value at the catchment outlet and as integral catchment information calculated as the mean catchment value over all pixels upslope of the catchment outlet. The terrain metrics catchment area and profile curvature were identified in all models as the most important predictors, and the model for the wet period was based solely on these two predictors. However, the model for the dry period additionally comprises soil hydraulic conductivity and bedrock permeability. The annual model with the most complex predictor set contains the predictors of the dry-period model plus the presence of tracks. Classifying the spatially distributed streamflow intermittency probabilities into ephemeral, intermittent and perennial reaches allows the estimation of stream network extent under various conditions. This approach, based on extensive monitoring and statistical modelling, is a first step to provide detailed spatial information for hydrological modelling as well as management practice.

Description: Sustainable water resources management needs to be based on sound process understanding. This is especially true in a changing world, where boundary conditions change and models calibrated to the status quo are no longer helpful. There is a general agreement in the hydrologic community that we are in need of a better process understanding and that one of the most promising ways to achieve this is by using nested experimental designs that cover a range of scales. In the here presented study we argue that while we might be able to investigate a certain process at a plot or hillslope in detail, the real power of advancing our understanding lies in site intercomparison and if possible knowledge transfer and generalization. The experimental design of the CAOS observatory is based on sensor clusters measuring ground-, soil and stream water, sap flow and climate variables in 45 hydrological functional units which were chosen from a matrix of site characteristics (geology, land use, hillslope aspect, and topographic positions). This design allows for site intercomparisons that are based on more than one member per class and thus does not only characterize between class differences but also attempts to identify within-class variability. These distributed plot scale investigations offer a large amount of information on plot scale processes and their variability in space and time (e.g. water storage dynamics and patterns, vertical flow processes and vadose zone transit times, transpiration dynamics and patterns). However, if we want to improve our understanding of runoff generation (and thus also of nutrient and contaminant transport and export to the stream) we need to also understand how these plots link up within hillslopes and how and when these hillslopes are connected to the stream. And certainly, this is again most helpful if we do not focus on single sites but attempt experimental designs that aim at intercomparison and generalization. At the same time, the investigation of hillslope-stream connectivity is extremely challenging due to the fact that there is a high 4-dimensional variability of the involved processes and most of them are hidden from view in the subsurface. To tackle this challenge we employed a number of different field methods ranging from hillslope scale irrigation and flow-through experiments, to in depth analyses of near stream piezometer responses and stream reach tracer experiments, and then moving on to the mesoscale catchment with network wide investigations of spatial patterns of stream temperature and electric conductivity as well as of the expansion and shrinkage of the network itself. In this presentation we will provide an overview of the rationale, approach, experimental design and ongoing work, the challenges we encountered and a synthesis of exemplary results.

Description: Intermittent streams represent a substantial part of the total stream network, and their occurrence is expected to increase due to climate change. Thus, it is of high relevance to provide detailed information on the temporal and spatial controls of streamflow intermittency to support management decisions. This study presents an event-based analysis of streamflow responses in intermittent streams in a mesoscale catchment with a temperate climate. Based on the streamflow responses, precipitation events were classified into flow or no-flow classes. Response controls like precipitation, soil moisture, and temperature were used as predictors in a random forest model to identify the temporally changing factors that explain streamflow intermittency at the event scale. Soil moisture was the most important predictor, but the predictor importance varied with the geology in the catchment. Streamflow responses in the slate geology were controlled by soil moisture in the shallow and deep soil layers, while streamflow in the marl geology was primarily controlled by soil moisture in the upper soil layer. Streamflow responses in catchments underlain by both marl and sandstone were dependent on soil moisture, whereas streamflow in the only catchment with a pure sandstone geology depended on precipitation characteristics. In all slate and marl catchments, streamflow intermittency also varied with soil temperature, which is probably a proxy for seasonal changes in evapotranspiration and an indicator of freezing conditions. Our findings underline the importance of using high temporal resolution data and tailored event definitions that account for the fast changes between flow/no flow in intermittent streams to identify streamflow controls at the event scale.