diff --git a/manuscript/Makefile b/manuscript/Makefile index cbd4ce3..135dbf8 100644 --- a/manuscript/Makefile +++ b/manuscript/Makefile @@ -3,7 +3,7 @@ manuscript.pdf: agujournaltemplate.pdf wrr_submission.pdf @echo wordcount: @~/.TinyTeX/bin/x86_64-linux/texcount agujournaltemplate.tex -sum -sub=section -appendix.pdf: si_template_2019.pdf +appendix.pdf: si_template_2019.pdf si_template_2019.tex cp si_template_2019.pdf appendix.pdf agujournaltemplate.pdf: agujournaltemplate.tex lagosdepth.bib diff --git a/manuscript/agujournaltemplate.tex b/manuscript/agujournaltemplate.tex index b7dee0d..8d78bee 100644 --- a/manuscript/agujournaltemplate.tex +++ b/manuscript/agujournaltemplate.tex @@ -234,9 +234,9 @@ \subsection{Data description} We used our generated bathymetry surfaces to find the location of the deepest point in the lake and we resolved ties by choosing the deepest point that was closest to the center of the lake. We calculated the center of the lake not as its centroid but rather by finding the point farthest from the lake shoreline (i.e. its “visual distance to lake center”). For these calculations, we used the \texttt{polylabelr} R package \cite{larssonPolylabelrFindPole2019}, which interfaces with the Mapbox pole of inaccessibility algorithm \cite{agafonkinJSLibraryFinding2019}. We calculated in-lake slope as maximum lake depth divided by the distance to the deepest point and we calculated nearshore land slope for each lake by computing the slope within a 100-m buffer using data from a high resolution digital elevation model ($\sim$15x15m grain) accessed using the \texttt{elevatr} R package \cite{hollisterElevatrAccessElevation2017} and computed using the terrain function in the \texttt{raster} R package \cite{hijmansRasterGeographicData2019}. -We categorized lakes based on their cross-section shape and reservoir class. For cross-section shape, we categorized lakes as either convex or concave following the method of \citeA{hakansonLakeFormLake1977} by computing normalized lake depth-area relationships (i.e. hypsographic curves) and assigning class membership based on whether a lake’s curve falls above or below that of a simple straight-sided cone (Figure S3). We further classified lakes using data from \citeA{polus2020}, which uses the output of a machine learning algorithm to assign a probability to each lake as to whether it is a reservoir or a natural lake. For our purposes, we determined a lake to be a reservoir if the classification probability was 0.75 or greater. The \citeA{polus2020} data product defines reservoirs as any permanent waterbody that has a water control structure likely to significantly impact flow or pool water, beyond simply controlling water level. It makes no distinction between different dam types or dam heights. +We categorized lakes based on their cross-section shape and reservoir class. For cross-section shape, we categorized lakes as either convex or concave following the method of \citeA{hakansonLakeFormLake1977} by computing normalized lake depth-area relationships (i.e. hypsographic curves) and assigning class membership based on whether a lake’s curve falls above or below that of a simple straight-sided cone (Figure S3). We further classified lakes using the output of a machine learning algorithm to assign a probability to each lake as to whether it is a reservoir or a natural lake. For our purposes, we determined a lake to be a reservoir if the classification probability was 0.75 or greater. Our reservoir classification data defines reservoirs as any permanent waterbody that has a water control structure likely to significantly impact flow or pool water, beyond simply controlling water level. It makes no distinction between different dam types or dam heights. -Covariates used in random forest modeling (Table S1, Equation \ref{eq3}) for lake elevation, area, island area, perimeter, shoreline development, watershed to lake area ratio, and hydrologic subbasin (i.e. HUC4s), were obtained from the LAGOS-US LOCUS database \cite{smith2020}. One such measure, that of shoreline development, is a measure of lake perimeter shape defined as: +Covariates used in random forest modeling (Table S1, Equation \ref{eq3}) for lake elevation, area, island area, perimeter, shoreline development, watershed to lake area ratio, and hydrologic subbasin (i.e. HUC4s), were obtained from the LAGOS-US LOCUS database. One such measure, that of shoreline development, is a measure of lake perimeter shape defined as: \begin{linenomath*} \begin{equation} @@ -325,7 +325,7 @@ \subsection{Representativeness of proxy measures of lake geometry} \noindent In comparing among lake geometry measures, our analysis suggests that proxy distance to lake center is representative of true distance to the deepest point of the lakes but that proxy nearshore land slope is not representative of true in-lake slope. A simple indication of this non representativeness is that proxy nearshore land slope was often (in $>$ 74\% of cases) steeper than true in-lake slope. This finding is consistent with \citeA{heathcotePredictingBathymetricFeatures2015} whos results suggest that in-lake slopes are shallower compared to the surrounding land. The shallow nature of in-lake slopes is likley a function of erosion and sediment transport processes \cite{hakansonLakeBottomDynamics1981, johanssonNewApproachesModelling2007}. -One surprising finding with respect to the relationship between true and proxy geometry measures when examined by lake class was the fact that there was no greater difference between proxy and true distances in reservoirs compared to natural lakes. This is contrary to the idea that most reservoirs are drowned river valleys where the deepest point is close to the edge at the end of the reservoir (i.e. next to the dam) rather than in the center of the reservoir \cite{lanza1985interactions}. One possible explanation is that the reservoir classification data from \citeA{polus2020} uses a more general definition of a reservoir (i.e. any permanent waterbody that has a water control structure likely to significantly impact flow or pool water) compared to that of conventional classifications that are tied to specific dam types or dam heights. Another possible explanation is that conventional reservoir classifications are conceptually biased towards more southern areas with few natural lakes (Figure S2). +One surprising finding with respect to the relationship between true and proxy geometry measures when examined by lake class was the fact that there was no greater difference between proxy and true distances in reservoirs compared to natural lakes. This is contrary to the idea that most reservoirs are drowned river valleys where the deepest point is close to the edge at the end of the reservoir (i.e. next to the dam) rather than in the center of the reservoir \cite{lanza1985interactions}. One possible explanation is that our reservoir classification data uses a more general definition of a reservoir (i.e. any permanent waterbody that has a water control structure likely to significantly impact flow or pool water) compared to that of conventional classifications that are tied to specific dam types or dam heights. Another possible explanation is that conventional reservoir classifications are conceptually biased towards more southern areas with few natural lakes (Figure S2). We found other differences among lake geometry measures according to lake cross-section shape. One finding was that convex lakes, when compared to concave lakes, had longer distances to lake centers relative to corresponding distances to the deepest point of lakes. In addition, convex lakes often had steeper in-lake slopes relative to nearshore land slopes as compared to concave lakes. Finally, it was notable that convex lakes were deeper than concave lakes despite having similar distributions of lake surface area (Figure S7). The underlying cause of these differences is unknown but one possibility is that geometry is tied to the circumstances of lake formation whereby the formation of concave lakes were a result of more intense glacial scouring compared to that of convex lakes \cite{gorhamPhysicalLimnologyNorthern1958}. While our findings provide some evidence in support of this idea, namely that there is a geographic hotspot of concave lakes associated with the glaciated “prairie pothole region” \cite{hayashiSimpleEquationsRepresent2000}, the overall geographic distribution of lake cross-section shapes does not support this idea. Instead of a concentrated area of concave lakes in formerly glaciated regions, there appears to be a fairly even mix of concave and convex lakes distributed amongst the northern (i.e. glaciated) and southern (non-glaciated) portions of our study area (Figure S2). diff --git a/manuscript/appendix.pdf b/manuscript/appendix.pdf index 2531026..d9faaf7 100644 Binary files a/manuscript/appendix.pdf and b/manuscript/appendix.pdf differ diff --git a/manuscript/combined.pdf b/manuscript/combined.pdf index bcdcc2f..4534f25 100644 Binary files a/manuscript/combined.pdf and b/manuscript/combined.pdf differ diff --git a/manuscript/si_template_2019.tex b/manuscript/si_template_2019.tex index ccc2ce6..af55a8e 100644 --- a/manuscript/si_template_2019.tex +++ b/manuscript/si_template_2019.tex @@ -263,7 +263,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % -\bibliography{lagosdepth} +% \bibliography{lagosdepth} % % no need to specify bibliographystyle % @@ -337,7 +337,7 @@ \begin{figure} \begin{center}\includegraphics[width=0.45\textwidth]{../figures/gg_effort-1}\end{center} - \caption{Comparison between characteristics of lakes with bathymetry data against lakes with depth from other sources in the LAGOSUS-Depth product \cite{stachelek2020}. The distance to urban area metric is calculated using data from the 2018 US Census Urban and Rural Classification.}\label{figs7} + \caption{Comparison between characteristics of lakes with bathymetry data against lakes with depth from other sources in the LAGOSUS-Depth product. The distance to urban area metric is calculated using data from the 2018 US Census Urban and Rural Classification.}\label{figs7} \end{figure} \clearpage @@ -350,13 +350,13 @@ \begin{figure} \begin{center}\includegraphics[width=0.73\textwidth]{../figures/lgnemanual-vs-bathy-depth-1}\end{center} - \caption{Comparison between reported depth and depth extracted from bathymetry surfaces by US State where reported depths come from the LAGOSUS-Depth product \cite{stachelek2020}. For this figure, no reported depth values originated from the same source as its corresponding bathymetry-derived value.}\label{figs2} + \caption{Comparison between reported depth and depth extracted from bathymetry surfaces by US State where reported depths come from the LAGOSUS-Depth product. For this figure, no reported depth values originated from the same source as its corresponding bathymetry-derived value.}\label{figs2} \end{figure} \clearpage \begin{table} -\caption{Summary of lake characteristics for the present study (and for lakes in the contiguous United States from \citeA{stachelek2020}). Predictor variables for computing random forest offsets (Equation 2) are printed in bold face. Dashes (-) indicate an identical sample size among this study and that of the contiguous United States.} \label{table1} +\caption{Summary of lake characteristics for the present study (and for lakes in the contiguous United States). Predictor variables for computing random forest offsets (Equation 2) are printed in bold face. Dashes (-) indicate an identical sample size among this study and that of the contiguous United States.} \label{table1} \centering \setlength\tabcolsep{1.5pt} % default value: 6pt \begin{tabular}{lllll}