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LiDAR based hydro-conditioned hydrological modeling for enhancing precise conservation practice placement in agricultural watersheds

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Abstract

High resolution Light Detection and Ranging (LiDAR)-derived Digital Elevation Models (DEMs) improve hydrologic modeling and aid in identifying the targeted locations of best conservation practices (CPs) in agricultural watersheds. However, the inability of LiDAR data to represent the conveyance of water under or through the surfaces (i.e., bridges or culverts) impedes the simulated flow, resulting in false upstream pooling. Improper flow simulation affects the accuracy of pollutant load estimations and targeted locations delineated by watershed models or models built upon hydro-conditioned DEMs (hDEM). We propose a novel approach of Hydro-conditioning to modify LiDAR imagery through breach lines, which is essential to accurately replicate the landscape hydrologic connectivity. We compared variations in outcomes of Agricultural Conservation Planning Framework (ACPF), based on manual and automated hDEMs for Plum Creek watershed, Minnesota. The derived flow network, catchment boundaries, drainage areas, locations/number of practices depend on the chosen hDEM. Locations, size and shape of bioreactors, drainage management, farm ponds, nutrient removal wetlands, riparian buffers are severely affected by hydro-conditioning. Shuttle Radar Topography Mission (SRTM) validation of hDEMs showed that Mean Average Percentage Deviation (MAPE) for automated and manual hDEMs is 1.34 and 0.998 respectively. Also, proximity analysis with a buffer of 200 m showed that CPs’ locations delineated by manual hDEM match better with the existing ones as compared to automated hDEM. Results indicate that coupled approach of using automated and manual ‘hDEM’ is best suited for guiding stakeholders towards the field-scale planning in a cost-saving manner.

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Data Availability

Data could be made available on request. The data can be accessed at this site:

https://respec.sharefile.com/share/getinfo/s28fe301dd9248a8a.

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Acknowledgements

The authors are thankful to University of Minnesota, and Board of Water and Soil Resources, Minnesota, U.S.A., for providing necessary funding and facilities to carry out this research work. USDA-ARS provided the software ACPF. Authors appreciate Houston Engineering Inc. and Brian Gelder,, Iowa State University for sharing relevant material concerning manual and automated hDEMs. Authors also acknowledge BITS Pilani, India for proving Research Initiation Grant for the research. We also express our sincere thanks to the anonymous reviewers and editors.

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Authors and Affiliations

  1. Birla Institute of Technology and Science, Pilani, Rajasthan, India

    Rallapalli Srinivas & Ajit Pratap Singh

  2. Minnesota Board of Water and Soil Resources, Saint Paul, USA

    Matt Drewitz

  3. Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, USA

    Rallapalli Srinivas & Joe Magner

  4. School of Engineering and Technology, BML Munjal University, Haryana, India

    Harish Puppala

  5. Department of Civil and Architectural Engineering, Qatar University, Doha, Qatar

    Riyadh Ibrahim Al-Raoush

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  1. Rallapalli Srinivas
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  2. Matt Drewitz
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  3. Joe Magner
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  4. Harish Puppala
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  5. Ajit Pratap Singh
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  6. Riyadh Ibrahim Al-Raoush
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Contributions

RS: Conceptualization, Methodology, Data curation, Software, Writing- Original draft preparation, Investigation. MD: Conceptualization, Data curation, Writing- Reviewing and Editing Visualization, Supervision, JM: Conceptualization, Writing- Reviewing and Editing Visualization, Supervision. HP: Writing- Reviewing and Editing, Conceptualization, Visualization, APS: Conceptualization, Writing- Reviewing and Editing, Visualization RAR: Writing- Reviewing and Editing, Visualization. All authors read and approved the final manuscript.

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Correspondence to Rallapalli Srinivas.

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Srinivas, R., Drewitz, M., Magner, J. et al. LiDAR based hydro-conditioned hydrological modeling for enhancing precise conservation practice placement in agricultural watersheds. Water Resour Manage 36, 3877–3900 (2022). https://doi.org/10.1007/s11269-022-03237-7

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