The effects of topographic depressions on multiscale overland flow connectivity: A high‐resolution spatiotemporal pattern analysis approach based on connectivity statistics

Abstract

In watershed modelling, the traditional practice of arbitrarily filling topographic depressions in digital elevation models has raised concerns. Advanced high‐resolution remote sensing techniques, including airborne scanning laser altimetry, can identify naturally occurring depressions that impact overland flow. In this study, we used an ensemble physical and statistical modelling approach, including a 2D hydraulic model and two‐point connectivity statistics, to quantify the effects of depressions on high‐resolution overland flow patterns across spatial scales and their temporal variations in single storm events. Computations for both models were implemented using graphic processing unit‐accelerated computing. The changes in connectivity statistics for overland flow patterns between airborne scanning laser altimetry‐derived digital elevation models with (original) and without (filled) depressions were used to represent the shifts of overland flow response to depressions. The results show that depressions can either decrease or increase (to a lesser degree and shorter duration) the probability that any two points (grid locations) are hydraulically connected by overland flow pathways. We used macro‐connectivity states (Φ) as a watershed‐specific indicator to describe the spatiotemporal thresholds of connectivity variability caused by depressions. Four states of Φ are identified in a studied watershed, and each state represents different magnitudes of connectivity and connectivity changes (caused by depressions). The magnitude of connectivity variability corresponds to the states of Φ, which depend on the topological relationship between depressions, the rising/recession limb, and the total rainfall amount in a storm event. In addition, spatial distributions of connectivity variability correlate with the density of depression locations and their physical structures, which cause changes in streamflow discharge magnitude. Therefore, this study suggests that depressions are “nontrivial” in watershed modelling, and their impacts on overland flow should not be neglected. Connectivity statistics at different spatial scales and time points within a watershed provide new insights for characterizing the distributed and accumulated effects of depressions on overland flow.

Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics

“…They are small yet abundant at the catchment or larger scales (Wu et al., 2019). The temporally stored water in these surface depressions thus plays an important role in catchment hydrological processes, including evaporation (Alexander et al., 2018; Cohen et al., 2016; Golden et al., 2017; Lane et al., 2018; Rajib et al., 2020; Yu & Harbor, 2019). The transpiration component of initial vaporization corresponds to the fast transpiration, which only draws on the upper 50cm of the soil layer where most root biomass is located, and most transpiration occurs (Savenije, 2004).…”

Performances of the New HEC-RAS Version 5 for 2-D Hydrodynamic-Based Rainfall-Runoff Simulations at Basin Scale: Comparison with a State-of-the Art Model

“…It should be observed that the new HEC-RAS 5.0.7 has been developed also to deal with a challenging issue for both scientist and technicians, related to the use of the SWEs for the description of the hydrodynamic-based surface runoff computations in rainfall-runoff simulations at the catchment scale. This approach, sometimes referred as direct rainfall method [36,40], consists in the application of rainfall to each cell of the two-dimensional domain. The consequent runoff is then routed by the 2D hydrodynamic model.…”

Comparison of observed and DEM-driven field-to-river routing of flow from eroding fields in an arable lowland catchment

“…It is not easy, however, to distinguish between real and artefactual blind pits in DEMs (Callaghan and Wickert, 2019). If DEM smoothing pre-infills any real-world blind pits, this has the undesirable effect of artificially increasing the connectivity of the DEM (Yu and Harbor, 2019) and increasing field-to-river flow speeds (i.e. decreasing travel times) for flowlines which pass through these filled blind pits.…”