Median bed-material sediment particle size across rivers in the contiguous US
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Published:2022-02-24
Issue:2
Volume:14
Page:929-942
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Abeshu Guta Wakbulcho, Li Hong-YiORCID, Zhu ZhenduoORCID, Tan ZeliORCID, Leung L. RubyORCID
Abstract
Abstract. Bed-material sediment particle size data, particularly the median sediment particle size (D50), are critical for understanding and modeling
riverine sediment transport. However, sediment particle size observations are primarily available at individual sites. Large-scale modeling and
assessment of riverine sediment transport are limited by the lack of continuous regional maps of bed-material sediment particle size. We hence
present a map of D50 over the contiguous US in a vector format that corresponds to approximately 2.7 million river segments (i.e., flowlines) in the
National Hydrography Dataset Plus (NHDPlus) dataset. We develop the map in four steps: (1) collect and process the observed D50 data from 2577
U.S. Geological Survey stations or U.S. Army Corps of Engineers sampling locations; (2) collocate these data with the NHDPlus flowlines based on
their geographic locations, resulting in 1691 flowlines with collocated D50 values; (3) develop a predictive model using the eXtreme Gradient
Boosting (XGBoost) machine learning method based on the observed D50 data and the corresponding climate, hydrology, geology, and other attributes
retrieved from the NHDPlus dataset; and (4) estimate the D50 values for flowlines without observations using the XGBoost predictive model. We expect
this map to be useful for various purposes, such as research in large-scale river sediment transport using model- and data-driven approaches,
teaching environmental and earth system sciences, planning and managing floodplain zones, etc. The map is available at https://doi.org/10.5281/zenodo.4921987
(Li et al., 2021a).
Funder
U.S. Department of Energy
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference38 articles.
1. Ackers, P. and White, W. R.:
Sediment Transport: New Approach and Analysis,
J. Hydraul. Div.,
99, 2041–2060, https://doi.org/10.1061/JYCEAJ.0003791, 1973. 2. Afan, H. A., El-shafie, A., Mohtar, W. H. M. W., and Yaseen, Z. M.:
Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction,
J. Hydrol.,
541, 902–913, https://doi.org/10.1016/j.jhydrol.2016.07.048, 2016. 3. Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.:
Optuna: A Next-generation Hyperparameter Optimization Framework,
in: Proc. 25th ACM SIGKDD Int.
Conf. Knowl. Discov. Data Min., July 2019, 2623–2631, https://doi.org/10.1145/3292500.3330701, 2019. 4. An, C., Gong, Z., Naito, K., Parker, G., Hassan, M. A., Ma, H., and Fu, X.:
Grain Size-Specific Engelund-Hansen Type Relation for Bed Material Load in Sand-Bed Rivers, With Application to the Mississippi River,
Water Resour. Res.,
57, e2020WR02751, https://doi.org/10.1029/2020WR027517, 2021. 5. Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., and Cox, D. D.:
Hyperopt: A Python library for model selection and hyperparameter optimization,
Comput. Sci. Discov.,
8, 014008, https://doi.org/10.1088/1749-4699/8/1/014008, 2015.
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