Kommula et al. (2025) Improving micro rainwater harvesting site selection with high-resolution LiDAR DEMs: A GIS-based multi-criteria approach

Identification

• Journal: Remote Sensing Applications Society and Environment
• Year: 2025
• Date: 2025-12-19
• Authors: Sri Priyanka Kommula, Bharat Lohani, Dongryeol Ryu, Stephan Winter
• DOI: 10.1016/j.rsase.2025.101839

Research Groups

• Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
• Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, Australia

Short Summary

This study develops a GIS-based multi-criteria framework using LiDAR DEMs at varying resolutions (1 meter to 30 meters) to improve micro rainwater harvesting (RWH) site selection, demonstrating that high-resolution LiDAR DEMs significantly enhance accuracy compared to satellite-derived CartoDEM. The 1-meter LiDAR DEM achieved the highest overall accuracy of 0.87, outperforming the 30-meter CartoDEM (0.62) for identifying suitable RWH sites.

Objective

• To investigate the impact of using high-resolution LiDAR Digital Elevation Models (DEMs) on the accuracy of suitable micro rainwater harvesting (RWH) site identification, particularly in forested areas, compared to commonly used satellite-derived DEMs (CartoDEM).
• To determine the appropriate spatial resolution for siting RWH structures with widths ranging from 5 meters to 30 meters.

Study Configuration

• Spatial Scale: A watershed area of 18 square kilometers (1800 hectares) in the Gurgaon and Faridabad districts of Haryana, India.
• Temporal Scale: Rainfall data analyzed over a 20-year period.

Methodology and Data

• Models used:
  • GIS-based multi-criteria decision analysis (MCDA) using the Analytical Hierarchy Process (AHP).
  • Soil Conservation Service (SCS) method for runoff estimation.
  • ArcGIS Pro (ESRI Inc., Version 2.9.0) for all GIS operations.
• Data sources:
  • Elevation:
    • CartoDEM (30-meter resolution) from the Indian Space Research Organization (ISRO).
    • LiDAR DEM (0.3-meter native resolution, resampled to 1-meter, 5-meter, 10-meter, and 30-meter) derived from aerial LiDAR point clouds (10 points per square meter) captured by Geokno India Pvt. Ltd. for the Forest Department, Haryana, India.
  • Rainfall: Indian Meteorological Department (IMD), Pune (0.25° by 0.25° resolution).
  • Land Use/Land Cover (LULC): Prepared by Geokno India Pvt. Ltd. from aerial photographs.
  • Soil: FAO digital soil map of the world (1:5 million scale).
  • Geomorphology: Bhukosh gateway, Geological Survey of India (GSI).
  • Lithology: Bhukosh gateway, Geological Survey of India (GSI).
  • Validation Data: 116 expert-verified field locations (58 suitable, 58 unsuitable) provided by Water and Power Consultancy Services Limited (WAPCOS, Ltd.), India.

Main Results

• The 1-meter resolution LiDAR DEM achieved the highest performance for RWH site identification with an Overall Accuracy (OA) of 0.87, Precision of 0.98, and Recall of 0.98 when the "moderately suitable" class (SC3) was used as the threshold.
• The 30-meter CartoDEM yielded an OA of 0.62 (with SC3 threshold). The 30-meter LiDAR DEM also showed an OA of 0.62, suggesting that at this resolution, the limited validation data did not fully capture LiDAR's accuracy advantage.
• High-resolution LiDAR DEMs (1-meter and 5-meter) significantly improved the delineation of slope and flow accumulation, leading to more cohesive and accurate mapping of suitable RWH sites along stream channels compared to coarser DEMs.
• Visual comparison revealed that CartoDEM-derived stream networks exhibited an offset of approximately 30 meters from actual channel centerlines, while LiDAR DEMs aligned closely.
• Introducing a 15-meter buffer around validation points improved the OA for finer resolutions, particularly for the strictest suitability class (SC4), where the 1-meter LiDAR DEM achieved an OA of 0.97.

Contributions

• Systematically evaluates the utility of high-resolution LiDAR DEMs for micro-scale rainwater harvesting (RWH) site selection, addressing a gap in existing literature regarding appropriate grid size relative to structure dimensions.
• Demonstrates that high-resolution LiDAR DEMs significantly enhance the accuracy of RWH site identification, especially in complex and forested terrains where traditional DEMs struggle.
• Provides a practical and transferable GIS-based multi-criteria framework for watershed managers to design micro RWH structures.
• Highlights the importance of high-quality validation data for robust quantitative assessment of DEM performance in RWH siting.

Funding

• Melbourne India Postgraduate Academy (scholarship within the Joint Ph.D. program between The University of Melbourne and IIT Kanpur).
• Haryana Forest Department, India (for sharing aerial LiDAR data and validation data).

Citation

@article{Kommula2025Improving,
  author = {Kommula, Sri Priyanka and Lohani, Bharat and Ryu, Dongryeol and Winter, Stephan},
  title = {Improving micro rainwater harvesting site selection with high-resolution LiDAR DEMs: A GIS-based multi-criteria approach},
  journal = {Remote Sensing Applications Society and Environment},
  year = {2025},
  doi = {10.1016/j.rsase.2025.101839},
  url = {https://doi.org/10.1016/j.rsase.2025.101839}
}