Topographic hydro-conditioning to resolve surface depression storage and ponding in a fully distributed hydrologic...

Area of Science:

• Hydrology and Geomorphology
• Remote Sensing and GIS
• Environmental Modeling

Background:

• Land surface depressions are critical for understanding rainfall-water interactions like ponding, infiltration, and runoff.
• Existing digital elevation models (DEMs) often fail to represent depressions at relevant scales for hydrologic modeling.
• Current DEM processing methods, including those using LiDAR, tend to smooth out depressions, impacting hydrological accuracy.

Purpose of the Study:

• To introduce a novel topographic conditioning workflow, the Depression-Preserved DEM Processing (D2P) algorithm.
• To develop a method that preserves physically meaningful surface depressions for improved hydrologic modeling.
• To enhance the integration of depression dynamics into efficient hydrologic simulations.

Main Methods:

• The D2P algorithm features adaptive screening for depression delineation.
• It incorporates filtering for anthropogenic features like bridges.
• D2P blends river smoothing with depression-resolving functionalities for comprehensive topographic conditioning.

Main Results:

• In a case study, D2P successfully identified 86% of ponds at a 10m DEM resolution.
• Compared to conventional methods, D2P reduced modified DEM cells by 51%, minimizing topographic alteration.
• Hydrologic simulations using D2P-processed DEMs showed improved surface water storage and attenuated, delayed peak streamflow.

Conclusions:

• The D2P algorithm offers a significant advancement in creating DEMs that accurately represent land surface depressions.
• Preserving depressions in DEMs leads to more robust and accurate hydrologic simulations of surface water dynamics.
• D2P facilitates better understanding and modeling of hydrological processes influenced by surface depressions.