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\begin{document}

\title{Quantifying streambed grain sizes and hydro-biogeochemistry using YOLO
and photos}



\author[1]{Yunxiang Chen}%
\author[2]{Jie Bao}%
\author[3]{Rongyao Chen}%
\author[4]{Bing Li}%
\author[5]{Yuan Yang}%
\author[2]{Lupita Renteria}%
\author[1]{Dillman Delgado}%
\author[1]{Brieanne Forbes}%
\author[2]{Amy E. Goldman}%
\author[6]{Manasi Simhan}%
\author[1]{Morgan Barnes}%
\author[1]{Maggi Laan}%
\author[1]{Sophia McKever}%
\author[2]{Zhangshuan Hou}%
\author[2]{Xingyuan Chen}%
\author[2]{Timothy D. Scheibe}%
\author[2]{James C Stegen}%
\affil[1]{Pacific Northwest National Laboratory}%
\affil[2]{Pacific Northwest National Laboratory (DOE)}%
\affil[3]{School of Petroleum Engineering, Yangtze University}%
\affil[4]{PNNL}%
\affil[5]{Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego}%
\affil[6]{Department of Geoscience, University of Wisconsin - Madison}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\selectlanguage{english}
\begin{abstract}
Streambed grain sizes and hydro-biogeochemistry (HBGC) control river
functions. However, measuring their quantities, distributions, and
uncertainties is challenging due to the diversity and heterogeneity of
natural streams. This work presents a photo-driven, artificial
intelligence (AI)-enabled, and theory-based workflow for extracting the
quantities, distributions, and uncertainties of streambed grain sizes
and HBGC parameters from photos. Specifically, we first trained You Only
Look Once (YOLO), an object detection AI, using 11,977 grain labels from
36 photos collected from 9 different stream environments. We
demonstrated its accuracy with a coefficient of determination of 0.98, a
Nash--Sutcliffe efficiency of 0.98, and a mean absolute relative error
of 6.65\% in predicting the median grain size of 20 testing photos. The
AI is then used to extract the grain size distributions and determine
their characteristic grain sizes, including the 5th, 50th, and 84th
percentiles, for 1,999 photos taken at 66 sites. With these percentiles,
the quantities, distributions, and uncertainties of HBGC parameters are
further derived using existing empirical formulas and our new
uncertainty equations. From the data, the median grain size and HBGC
parameters, including Manning's coefficient, Darcy-Weisbach friction
factor, interstitial velocity magnitude, and nitrate uptake velocity,
are found to follow log-normal, normal, positively skewed, near
log-normal, and negatively skewed distributions, respectively. Their
most likely values are 6.63 cm, 0.0339 s\selectlanguage{ngerman}·m-1/3, 0.18, 0.07 m/day, and
1.2 m/day, respectively. While their average uncertainty is 7.33\%,
1.85\%, 15.65\%, 24.06\%, and 13.88\%, respectively. Major uncertainty
sources in grain sizes and their subsequent impact on HBGC are further
studied.%
\end{abstract}\selectlanguage{ngerman}%



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