SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains

Description

SHIFT (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data.

Data Structure

We provide 2 resolutions of data for different needs.

• SHIFT_v2_90m: The original SHIFT data derived from MERIT-Hydro, currently lakes and reservoirs are not removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of ).
• SHIFT_v2_1km: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of ).

For more details, please refer to:

Zheng, K., Lin, P., and Yin, Z.: SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains, [Preprint], 29 December 2023.

Development Log

1. Changes in v2 compared to v1:
   1. Parameter 'b' Estimation: We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.
   2. Target Function for Parameter 'a': We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.

Contacts

• Kaihao Zheng, Mostaly@pku.edu.cn
• Peirong Lin, peironglinlin@pku.edu.cn