A New 3-D Simulating Digital Lake Bathymetry Method

The surface water volume is one of the most important component of global water cycle,and the lake volume accounts for the vast majority of total surface water volume,so researcher and studies on lake volume are getting more attention.The availability of lake bathymetry maps is imperative to estimate lake water volumes and their variability.It has been difficult to obtain all the bathymetric measurements of thousand lakes across the globe,due to costly labors and/or harsh topographic regions.There are also some other methods to estimate the bathymetry or storage,but they all have limitations.Such as spectrum method will fail in deep or turbid lakes;equation method can not generate the whole bathymetry;airborne gravity method also costs much when measuring globally;spline method needs field measured data as input and so on.In this study,we develop a new digital lake bathymetry model(DLBM)using the step-wise water recession method(WRM)and morphologic function module(MFM)to generate 3-Dimensional lake bathymetric and estimate the lake volume based on digital elevation model(DEM).There are two assumptions: 1)the lake's bathymetry was formed and shaped by the similar geological processes as the surrounding landmass,and 2)the agent of water(the thickness of the sedimentary deposit proportional to lake water depth)was uniform.DLBM is programed by Matlab for batch processing,and it can search the location then calculate the lake bathymetry automatically.Ontario Lake and Namco Lake are used as examples to demo the model development,calibration,and refinement.Finally we conclude that DLBM can basically express some topographical features;when compared with water element curves,although area curve is not satisfied,the volume fitting is acceptable;and in the statistical analysis with the data from Hydro LAKES,the final R-squared of our model is 0.627 which is better than the result 0.588 from the equation method.DLBM will work when the lake boundary is complicated without internal measured data.It's a bold attempt in this field with satisfied result,but there is still room for developing and improving.