Download PDFOpen PDF in browser

Uncertainty Analysis of Watershed-Based Flow and Water Quality Modelling with Different DEM Data Sources

9 pagesPublished: September 20, 2018

Abstract

DEMs are important data required in watershed-based hydrological and water quality modeling since they are employed to derive critical characteristics of watershed through a watershed delineation process. This study aims to analyze the uncertainties associated with DEM sources in watershed modeling and compare them to DEM resolution-originated uncertainties. Toward this end, six different scenarios, involving 3 DEMs of 30-m resolution and 3 DEMs of 90-m resolution from NED, ASTER and SRTM sources, were developed using HSPF model for an agricultural watershed in Iowa, USA. The HSPF model was run for each scenario to produce simulated flow and loads of sediment, nitrate, and phosphorus. Results suggested that the level of uncertainty involved in the DEM sources was considerably (up to twofold) greater than those originated from decreasing DEM resolution. The finding is important to the proper selection of DEM data source and thereby to the reduction of uncertainties involved in watershed-based hydrological and water quality modelling.

Keyphrases: DEM sources, HSPF model, uncertainty analysis, Watershed modeling

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1778--1786

Links:
BibTeX entry
@inproceedings{HIC2018:Uncertainty_Analysis_of_Watershed_Based,
  author    = {Maryam Roostaee and Zhiqiang Deng},
  title     = {Uncertainty Analysis of Watershed-Based Flow and Water Quality Modelling with Different DEM Data Sources},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {1778--1786},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/NR7S},
  doi       = {10.29007/bm78}}
Download PDFOpen PDF in browser