A Rainfall-based, Sequential Depression-filling Algorithm and Applications on a Watershed in Northeastern Indiana, USA

Listed in Datasets

By Dennis Buckmaster1, Samuel Adam Noel1

Purdue University

Contents include script files for a sequential depression-filling algorithm developed in Matlab as well as elevation datasets used in a manuscript publication.

Version 1.0 - published on 30 Oct 2020 doi:10.4231/S2B6-J628 - cite this Archived on 30 Nov 2020

Licensed under Apache-2.0


The landscapes across much of the Midwestern United States are characterized by glacial activity that left water-holding kettles, depressions, and potholes. Until recently, traditional watershed algorithms assumed these depressions to be errors in the elevation data and filled them as a means of correction, when, in reality, many of these features may rarely fill and have the potential to dramatically affect surface flow patterns. These depressions play an important role in hydrology, water management, site planning, and agronomy.

An optimized sequential depression-filling algorithm (sdfa) was developed which fills these water-holding features sequentially based on their respective retention capacity and contributing area. Outputs reflect the state of connectivity following the application of a user-specified amount of rainfall excess (i.e., in excess of infiltration); depressions that have not been filled will remain as their own hydrologically common subcatchments. A performant algorithm is integral to future delivery and utilization by practitioners both in the office and in the field. The optimal set of subroutines were able to fill all of the depressions in a 239 km2 watershed in northeastern Indiana in 42 seconds and 1.5 hours on a consumer desktop computer for digital elevation models (DEMs) at 30-meter and 3-meter resolutions, respectively (O(n2) overall performance).

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