Improving micro rainwater harvesting site selection with high-resolution LiDAR DEMs: A GIS-based multi-criteria approach

Accurate identification of micro-surface rainwater harvesting (RWH) sites depends on the quality of topographic data. Commonly used DEMs such as SRTM, ASTER, and CartoDEM are limited by their coarse resolution and often fail to capture the fine-scale geomorphic features required for identifying these structures. Their radar- and optical-based acquisition methods also struggle in hilly and densely vegetated terrains, further restricting their ability to represent terrain accurately. To address these limitations, this study develops a GIS-based decision-support framework using the Analytical Hierarchy Process (AHP) to compare satellite-derived CartoDEM (30-m) with LiDAR DEMs at 30-m, 10-m, 5-m, and 1-m resolutions. Seven parameters—runoff, slope, land use/land cover, soil, lithology, flow accumulation, and geomorphology—were integrated to generate suitability maps for gabions and loose stone check dams. Validation against 116 expert-verified sites demonstrates that the 1-m LiDAR DEM achieves the highest performance (OA = 0.87, Precision = 0.98, Recall = 0.98), substantially outperforming CartoDEM (OA = 0.62). While discrepancies existed between CartoDEM and LiDAR DEM at 30-m resolution, these differences were not reflected in OA values, likely due to the limited validation dataset. High-resolution LiDAR DEMs significantly improve the delineation of slope and flow accumulation, enabling more reliable micro-RWH site identification. The proposed framework provides a practical and transferable method for watershed managers designing micro RWH structures in complex terrains. • Siting rainwater harvesting structures was attempted using CartoDEM and LiDAR DEM. • Superior efficacy of LiDAR DEM over CartoDEM was demonstrated. • Siting performance improved with DEM resolution from 30 m to 1 m. • 1-m and 5-m LiDAR DEMs showed best performance for micro RWH site identification.