ABSTRACT
Integrating connectivity theory within watershed modelling is one solution to overcome spatial and temporal shortcomings of sediment transport prediction, and Part I and II of these companion papers advance this overall goal. In Part I of these companion papers, we present the theoretical development of probability of connectivity formula considering connectivity's magnitude, extent, timing and continuity that can be applied to watershed modelling. Model inputs include a high resolution digital elevation model, hydrologic watershed variability, and field connectivity assessments. We use the model to investigate the dependence of the probability of connected timing and spatial connectivity on sediment transport predictors. Results show the spatial patterns of connectivity depend on both structural and functional characteristics of the catchment, such as hillslope gradient, upstream contributing area, soil texture, and stream network configuration (structural) and soil moisture content and runoff generation (functional). Spatial connectivity changes from catchment-to-catchment as a function of soil type and drainage area; and it varies from event-to-event as a function of runoff depth and soil moisture conditions. The most sensitive connected pathways provide the stencil for the probability of connectivity, and pathways connected from smaller hydrologic events are consistently reconnected and built upon during larger hydrologic events. Surprisingly, we find the probability of connected timing only depends on structural characteristics of catchments, which are considered static over the timescales analyzed herein. The timing of connectivity does not statistically depend on functional characteristics, which relaxes the parameterization across events of different magnitudes. This result occurs because the pathway stencil accumulates sediment from adjacent soils as flow intensity increases, but this does not statistically shift the frequency distribution.
ABSTRACT
Integrating connectivity theory within watershed modelling is one solution to overcome spatial and temporal shortcomings of sediment transport prediction, and Part I and II of these companion papers advance this overall goal. In Part II of these companion papers, we investigate sediment flux via connectivity formula discretized over many catchments and then integrated via sediment routing; and we advance model evaluation technology by using hysteresis of sensor data. Model evaluation with hysteresis indices provides nearly a 100% increase in model statistics. Hysteresis loop evaluation shows a shift from near linear behavior at low to moderate events and then clock-wise loops for larger events indicating the importance of proximal sediment sources. Catchment-scale sediment flux varies as function of the probability of timing and extent of connectivity of an individual catchment. Watershed-scale sediment flux shows self-similarity for the main stem of the river channel as the 181 catchments are integrated moving down gradient. Sediment flux varies from event-to-event as a function of the most sensitive connected pathways, including ephemeral gullies and roadside ditches in this basin. These sensitive pathways contribute disproportionately large amounts to overall sediment yield regardless of the total rainfall depth. Prediction requires the connectivity formula, erosion formula and sediment routing formula; and the probability of connectivity alone was a poor predictor for sediment transport. The result highlights the importance of coupling connectivity simulations with sediment transport formula, and our method provides one such approach.
