• Lake depth and volume are important in biogeochemical and thermal processes1-5

• Current climate models often ignore lake depth6

• Depth is not known for most lakes; it must be measured on site by boat

• Prior research has predicted approximate lake depth from DEM data on regional scale1,2,7

• Get mean depth values for 330 lakes from ILEC encyclopedia of lakes; match them in ArcGIS to polygons from the Global Lake and Wetland Database (GLWD)8

• Construct buffers around lakes; calculate ASTER GDEM9 elevation statistics (mean, min, max, several percentiles) within each buffer in Google Earth Engine

• Use multiple linear regression in R to model depth from elevation statistics and location; choose best model with two predictors

• Check choice of predictors with cross-validation and by applying model to SRTM DEM10

• Apply model to 242,197 GLWD lakes to create 1-degree gridded maps of predicted volume and average depth, adding in large lakes with known depth

• Use Monte Carlo error propagation for gridded predictions

p50 and p10 are the 50th and 10th percentiles of elevation in the bufferglacial = 1 if lake is in LGM ice sheet area, 0 otherwise11

Predicting Lake Depth from Topography to Map Global Lake VolumeNataniel Holtzman Tamlin Pavelsky

natan.holtzman@gmail.com pavelsky@unc.eduDepartment of Geological Sciences , University of North Carolina at Chapel Hill

• High depth in mountain areas, especially at high latitudes

• High volume in northern Canada, west coast of North America, southern Andes, Scandinavia, southern Tibetan Plateau, northeast and north central Siberia

• Excluding known large lakes, 50% prediction interval width for volume is less than 50% of estimate in 33% of grid cells, making up 33% of volume, found in lake-dense regions like Canada

• ASTER’s predictions are 2% higher on average than SRTM’s (not shown; they exclude the Arctic); SRTM’s are higher than ASTER’s near coasts

• Improve resolution of gridded product by using lake databases beyond GLWD12, which may become available in the near future

• Combined with SWOT water surface elevations, depth estimates could help constrain total lake volume

• Get more lakes with known depth, to study regional differences in topography-depth relationship

• Include additional predictor variables

DEM, resolution

b1(topographycoefficient)

b2(glacial

coefficient)

b0(intercept)

r2Number of

lakes

10^ (leave-one-out RMSE)

ASTER, 100m

0.94 ± 0.04 0.18 ± 0.04 -0.51 ± 0.07 0.66 327 2.37

SRTM, 90m

0.87 ± 0.04 0.19 ± 0.04 -0.36 ± 0.06 0.66 318 2.37

H21F-1491

• Lake depth mirrors topographic relief on global scale, and is roughly predictable from 100m DEM

• Glacial lakes are deeper• Our gridded product can be used in

models of climate and biogeochemical cycling

• Our predictive model can be used for regional studies of water resources

Cumulative distribution of GWLD lake abundance, area, and predicted volume

by elevation (after Verpoorter et al.12)

Lakes used in model

Distribution of model residuals, with those of steppe-climate lakes shown below

Model residual by lake circularity index, with smoother

pred.

References1. Hollister JW, Milstead WB, Urrutia MA. 2011. Predicting Maximum Lake Depth from Surrounding Topography. Schumann GJ-P, editor.

PLoS ONE 6:e25764. 2. Sobek S, Nisell J, Fölster J. 2011. Predicting the depth and volume of lakes from map-derived parameters. Inland Waters 1:177–184.3. Long Z, Perrie W, Gyakum J, Caya D, Laprise R. 2007. Northern Lake Impacts on Local Seasonal Climate. Journal of Hydrometeorology

8:881–8964. Martynov A, Sushama L, Laprise R, Winger K, Dugas B. 2012. Interactive lakes in the Canadian Regional Climate Model, version 5: the role

of lakes in the regional climate of North America. Tellus A 64. 5. Rouse WR, Oswald CJ, Binyamin J, Spence C, Schertzer WM, Blanken PD, Bussières N, Duguay CR. 2005. The Role of Northern Lakes in a

Regional Energy Balance. Journal of Hydrometeorology 6:291–305. 6. Subin ZM, Riley WJ, Mironov D. 2012. An improved lake model for climate simulations: Model structure, evaluation, and sensitivity

analyses in CESM1. Journal of Advances in Modeling Earth Systems 4. 7. Heathcote AJ, del Giorgio PA, Prairie YT, Brickman D. 2015. Predicting bathymetric features of lakes from the topography of their

surrounding landscape. Canadian Journal of Fisheries and Aquatic Sciences 72:643–650. 8. Lehner B, Döll P. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296:1–22. 9. Hulley GC, Hook SJ, Abbott E, Malakar N, Islam T, Abrams M. 2015. The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s

emissivity at 100 meter spatial scale. Geophysical Research Letters 42:7966–7976. 10. Jarvis A, Reuter HI, Nelson A, Guevara E. 2008. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m

Database (http://srtm.csi.cgiar.org).11. Ray N, Adams JM. 2001. A GIS-based Vegetation Map of the World at the Last Glacial Maximum (25,000-15,000 BP). Internet Archaeology

11. 12. Verpoorter C, Kutser T, Seekell DA, Tranvik LJ. 2014. A global inventory of lakes based on high-resolution satellite imagery. Geophysical

Research Letters 41:6396–6402.

GWLD lake area and predicted volume by latitude (1-degree totals and 3-degree moving averages

Background

Methods

Results: Model

Results: Gridded Maps

Conclusions

Future Directions

Construct buffer as in Heathcote et al.7

Get DEM elevations in buffer Record 10th and 50th percentiles of elevation; use difference between them in model

Cumulative distribution of DEM elevation in bufferExample: Lácar Lake, Argentina