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Project

Measuring and Modelling Hydrological Surface Connectivity

Hydrological connectivity describes the internal linkages between runoff generation in upper parts of the catchment and the receiving water. It is quantified as the ratio of the runoff reaching the catchments outlet and the total internal runoff generation. It thus effectively bridges the gap between the point-scale separation of rainfall into soil water storage and (sub-)surface runoff as opposed to what we see as response at the hillslope or catchment scale. In between both, significant water redistribution from runoff source areas into sinks may occur such that isolated active areas in upslope regions may become disconnected and may not contribute to the actual outflow. This pattern-process interaction is one of the main reasons why hydrological observations at laboratory or plot scale are inadequate to explain the phenomena witnessed on hillslopes and in catchments and is why hydrological connectivity has become, in the last decade, a central concept in hydrology, particularly in semi-arid environments. The overall purpose of this dissertation is to identify which factors control the connectivity of a hillslope and to discuss how this reflects on data collection and modelling.

While traditionally hydrological variables are estimated through point observations dynamically varying in time, connectivity stresses the need to also account for space. It is the spatiotemporal heterogeneity in infiltration capacity and rainfall intensity that determines connectivity or disconnectivity with complex and spatially varying thresholds governing which parts of the domain are active and which contributing and which dominant processes affect storage, redistribution and connectivity. Once established that it is the spatial pattern of heterogeneous runoff generation and abstraction and not merely the statistical distribution of this heterogeneity, we ask ourselves which aspects of pattern determine the connectivity and which are negligible. We test a number of hypotheses by opposing landscape metrics with the modelled hydrograph of a virtual hillslope. Each metric describes a different characteristic of the pattern and their variable correlation with connectivity can thus supply an objective criterion to ascertain what controls connectivity. The aggregation of runoff source areas in interaction with the flow distance to the outlet dominate in the presence of a mosaic of runoff sinks and sources. When the area reacts more homogeneous, e.g. due to high rainfall intensity, the pattern disappears and it becomes mainly the travel time distribution that governs connectivity. While the used landscape metrics could only partially predict the connectivity, they are insightful tools for hypothesis testing and, we believe, can reversely be used in basin classification and the identification of dominant processes. If we establish a link between a certain metric and a particular basin functioning, we may also decide upon a range of values of that metric that a basin should fulfil in order to be classified in a particular class.

We propose randomness of pattern as an important characteristic of heterogeneity to distinguish two classes: when the heterogeneity is random, its particular configuration becomes superfluous and only the statistical distribution of its properties remains of interest. If, on the other hand, the heterogeneity expresses clear spatial configuration or gradients, accounting for this configuration is indispensible. This has important implications for modelling as well as for data collection. If the particular spatial configuration has no influence on the outcome, it also does not need to be explicitly modelled. The degrees of freedom of the model can therefore be reduced to a smaller number of parameters that set the statistical distribution rather than having one uncorrelated parameter for each spatial element in the modelling space which would clearly lead to the problem of equifinality. If we can only parameterise those spatial aspects that matter, we effectively reduce the overparamaterisation that plagues modern distributed modelling. As for data collection: since the exact spatial configuration of random patterns is redundant knowledge, a sparse data collection that settles the statistical distribution of heterogeneity suffices in that case; while in the presence of significant configuration, data sampling strategies should be tailored to capture the spatial patterns.


If we want to gain a better understanding of processes, we need to build models that can test hypotheses and collect data that allows to falsify the hypotheses. Both the reconfirmation of space as a crucial dimension beside time and the observation that non-random pattern matters, emphasises the need in connectivity related problems for data that can sample dense spatial variations in hydrological state variables and processes. We explore a combination of Electrical Resistivity Tomography (ERT) and Time Domain Reflectometry (TDR): ERT samples spatial averages and can be translated to a dense 3D resistivity distribution. TDR, on the other hand, is a point-scale measurement and is used to supply a transformation between resistivity and water content, the so-called pedophysical relation. By collecting data both before and after a concentrated flow experiment in a semi-arid gully, we map the total infiltration that occurred by taking the difference between the estimated water distributions before and after. The straightforward three-step scheme (ERT inversion, transformation to water content, subtraction) returns, however, an invalid solution. To improve the outcome, we developed an alternative inversion that constrains the outcome to comply to our knowledge of the experiment and that jointly inverses ERT and TDR while optimising the pedophysical parameters. Although these alterations effect significant improvements, the signal-to-noise ration in the data and the poor resolution of the outcome undermine the reliability of the obtained infiltration map. We were therefore unable to utilise it in further hypothesis testing or modelling. Nonetheless, we believe that ERT and TDR sampling in combination with our proposed inversion scheme can potentially give insightful results.

Date:1 Oct 2008 →  9 Nov 2012
Keywords:Connectivity, Land use, Hydrological
Disciplines:Soil sciences, challenges and pollution, Agriculture, land and farm management, Physical geography and environmental geoscience
Project type:PhD project