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- W2318358857 abstract "Research Article| January 01, 2013 Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds Noah P. Snyder; Noah P. Snyder † 1Earth and Environmental Sciences Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, Massachusetts 02467, USA †E-mail: noah.snyder@bc.edu Search for other works by this author on: GSW Google Scholar Andrew O. Nesheim; Andrew O. Nesheim 1Earth and Environmental Sciences Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, Massachusetts 02467, USA Search for other works by this author on: GSW Google Scholar Benjamin C. Wilkins; Benjamin C. Wilkins 1Earth and Environmental Sciences Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, Massachusetts 02467, USA Search for other works by this author on: GSW Google Scholar Douglas A. Edmonds Douglas A. Edmonds 2Department of Geological Sciences, Indiana University, 1001 E. 10th Street, Bloomington, Indiana 47408, USA Search for other works by this author on: GSW Google Scholar GSA Bulletin (2013) 125 (1-2): 148–163. https://doi.org/10.1130/B30694.1 Article history received: 13 Mar 2012 rev-recd: 26 Jun 2012 accepted: 09 Jul 2012 first online: 08 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Noah P. Snyder, Andrew O. Nesheim, Benjamin C. Wilkins, Douglas A. Edmonds; Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds. GSA Bulletin 2013;; 125 (1-2): 148–163. doi: https://doi.org/10.1130/B30694.1 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGSA Bulletin Search Advanced Search Abstract Riverbed grain size controls suitability of spawning habitat for threatened fish species. Motivated by this relationship, we developed a model that uses digital elevation models (DEMs) to predict bed grain size. We tested the accuracy of our model and two existing models with channel measurements from high-resolution airborne light detection and ranging (LiDAR) DEMs. All three models assume that bed grain size is a function of reach-average high-flow channel hydraulics (measured by shear stress or stream power). Our test data are field measurements of median grain size (D50) at 276 stations along four rivers in Maine. Pleistocene continental glaciation strongly influences the longitudinal profiles, which have alternating steep and gradual segments. We exploit the resulting variations in sediment supply to understand the controls on model success or failure in predicting bed grain size. Results show that all three models have ∼70% success in predicting D50 within a factor of two overall, and better where the rivers are coarse gravel bedded (∼80% success where D50 ≥ 16 mm). This similarity is unsurprising given that the models primarily rely on channel gradient (S) and drainage area as inputs. Measurements of S from LiDAR DEMs yield only a modest improvement in model success over those from topographic maps. We find that our model works best in sediment-starved steep reaches. Model failures fall into two broad categories: (1) relatively fine-grained (D50 < 16 mm) depositional reaches where our assumption of a constant, bankfull threshold for bed mobilization may be invalid; and (2) reaches where local variations in hydraulic roughness and/or sediment supply control D50. We argue that models based on airborne infrared LiDAR DEMs may reach a maximum around 80%–85% accuracy due to these sub-reach-scale factors, which cannot be easily measured from DEMs. The overall success of the models in predicting grain size indicates that the morphology of these channels has adjusted to the imposed S and sediment load during the ∼15 k.y. since deglaciation and through the period of anthropogenic channel change over the past three centuries. You do not have access to this content, please speak to your institutional administrator if you feel you should have access." @default.
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- W2318358857 title "Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds" @default.
- W2318358857 cites W1496907329 @default.
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- W2318358857 cites W1896975493 @default.
- W2318358857 cites W1939879774 @default.
- W2318358857 cites W1964182441 @default.
- W2318358857 cites W1965014792 @default.
- W2318358857 cites W1969566724 @default.
- W2318358857 cites W1972951236 @default.
- W2318358857 cites W1978499331 @default.
- W2318358857 cites W1988519738 @default.
- W2318358857 cites W2001642050 @default.
- W2318358857 cites W2006085743 @default.
- W2318358857 cites W2007666156 @default.
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- W2318358857 cites W2035193798 @default.
- W2318358857 cites W2048511206 @default.
- W2318358857 cites W2069396152 @default.
- W2318358857 cites W2071127688 @default.
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- W2318358857 cites W2095876425 @default.
- W2318358857 cites W2101354433 @default.
- W2318358857 cites W2104714155 @default.
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