[1] Surface microtopography affects overland flow, infiltration, soil erosion, pollutant transport, and other fundamental hydrologic and environmental processes across scales. Under the influence of surface depressions, overland flow essentially features a series of progressive puddle-to-puddle (P2P) filling, spilling, merging, and splitting processes. The objectives of this study are to characterize puddles and their hierarchical relationships and model the microtopography-controlled P2P processes. We proposed a new modeling framework for simulating the P2P overland flow dynamics through cell-to-cell (C2C) and P2P routing for a set of puddle-based units (PBUs) in a well-delineated, cascaded P2P drainage system. Testing of the P2P model demonstrated its potential to improve overland flow modeling and hydrologic connectivity analysis by explicitly incorporating the hydrologic roles of depressions and quantifying the real microtopography-controlled P2P dynamics.Citation: Chu, X., J. Yang, Y. Chi, and J. Zhang (2013), Dynamic puddle delineation and modeling of puddle-to-puddle fillingspilling-merging-splitting overland flow processes, Water Resour. Res., 49,[3825][3826][3827][3828][3829]

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Surface microtopography plays an important role in overland flow generation and soil erosion processes. Characterization of surface depressions and delineation of the entire watershed are critical to watershed modeling and management. In most hydrologic models, however, surface depression storage is estimated indirectly and inputted as a known value. In addition, it is often assumed that overland flow initiates after all surface depressions are fully filled. In reality, surface microtopography may control overland flow generation, surface runoff, soil erosion, and other hydrologic processes in a dynamic manner. The role of depressions that have various spatial scales and distribution characteristics is far beyond the functions of storing and detaining/retaining surface runoff. In this study, an improved surface delineation method was proposed to identify surface depressions and their relationships, precisely quantify the surface microtopography, and compute the maximum depression storage based on high-resolution DEM data obtained by using an instantaneous-profile laser scanner. Furthermore, a user-friendly, Windows-based software package was developed to facilitate the associated computations and visualization. The delineation method and the related software were tested using various scale DEM data. It is demonstrated that the new delineation approach is effective and efficient.

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Digital elevation models (DEMs) are commonly utilized for characterizing surface topography in watershed modeling. More often, DEMs can be the sole information that is used for watershed delineation, determination of flow directions and accumulations, and identification of subbasin boundaries. Thus, the resolution or grid size of the DEM data is critical. Surface depression storage is one of the primary topographic attributes and an essential hydrologic variable in watershed hydrologic modeling. Efforts have been made to evaluate the effects of grid spacing of DEMs on topographic attributes and hydrologic analyses. However, previous studies showed varied relationships between grid spacing and surface depression storage. The objective of this study is to quantitatively evaluate the effects of DEM resolutions on the computed maximum depression storage (MDS) and maximum ponding area (MPA). Six surfaces that possess varying spatial scales and microtopographic features are used in the discussion. In addition, six interpolation methods are selected and their influences on MDS also are evaluated. It is found from this in-depth study that grid pacing of DEMs affects MDS and MPA differently, depending on the characteristics of surfaces, delineation methods, and interpolation approaches used for generating the DEM data for various spatial scales.