Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling.     

Depressional runoff cascade networks of the Des Moines Lobe of Iowa

In this study, we develop a general mathematical framework and algorithm for routing cumulative precipitation excess through depressional fill–spill cascade networks in a landscape using only information about depression morphology, local contributing areas, and potential overland flow pathways. The framework also allows for the classification of depressions according to their landscape position within a network, and calculation of precipitation- and non-precipitation-dependent network properties, including measures of network complexity and runoff connectivity. To demonstrate its use, we applied our framework to the 167,287 drained depressions of the Des Moines Lobe of Iowa, a sub-region of the Prairie Pothole Region of North America, over a large range of historically observed precipitation amounts for scenarios both neglecting and incorporating infiltration in runoff generation. Our results show that 85.3% of depressions in this region form 18,851 unique depressional runoff cascade networks, with the remainder being disjunct features. Most of the properties of the region's networks appear to conform to either a truncated power-law or lognormal distribution. For a given rainfall amount, surface runoff connectivity between depressions within networks, and between networks and off-network areas, is controlled primarily by available aggregate depressional volumetric storage and contributing area, and to a lesser degree, network complexity.     

MODELING TRANSIENT pH DEPRESSIONS IN COASTAL STREAMS OF BRITISH COLUMBIA USING NEURAL NETWORKS1

ABSTRACT: Transient events in water chemistry in small coastal watersheds, particularly pH depressions, are largely driven by inputs of precipitation. While the response of each watershed depends upon both the nature of the precipitation event and the season of the year, how the response changes over time can provide insight into landscape changes. Neural network models for an urban watershed and a rural‐suburban watershed were developed in an attempt to detect changes in system response resulting from changes in the landscape. Separate models for describing pH depressions for wet season and dry season conditions were developed for a seven year period at each watershed. The neural network models allowed separation of the effects of precipitation variations and changes in watershed response. The ability to detect trends in pH depression magnitudes was improved by analyzing neural network residuals rather than the raw data. Examination of sensitivity plots of the models indicated how the neural networks were affected by different inputs. There were large differences in effects between seasons in the rural‐suburban watershed whereas effects in the urban watershed were consistent between seasons. During the study period, the urban watershed showed no change in pH depression response, while the rural‐suburban watershed showed a significant increase in the magnitude of pH depressions, likely the result of increased urbanization.