search for: v_sum

...columns. The columns are the elevation, volume and the area of the cells (which were placed inside a polygon). I have extracted them from DEM raster to calculate the volume under polygon and the elevation for a specific volume of the reservoir. > head(x6,2) Elevation Vol Area V_sum A_sum 1 2145 13990.38 85.83053 13990.38 85.83053 2 2147 43129.18 267.88312 57119.56 353.71365 > tail(x6,2) Elevation Vol Area V_sum A_sum 158 2307 233.0276 233.02756 1771806968 15172603 159 2308 0.0000 71.65642 1771806968 15172674 I used a linear model to e...

...t;> x2 = sort(x2,'value') >> head(x2) >> x3 <- aggregate(Vol ~ value, data = x2, FUN = sum) >> x4 <- aggregate(coverage_area ~ value, data = x2, FUN = sum) >> x5 = cbind(x3, Area = x4[,2]) >> library(dplyr) >> x6 <- x5 %>% >> mutate(V_sum = cumsum(Vol)) %>% >> mutate(A_sum = cumsum(Area)) >> plot(x6$value~x6$V_sum) >> >> And I thought that it is possible to get the elevation for a specific >> volume by linear model between elevation and volume, as follow: >> >> # Get a linear model betw...

...) > >> head(x2) > >> x3 <- aggregate(Vol ~ value, data = x2, FUN = sum) > >> x4 <- aggregate(coverage_area ~ value, data = x2, FUN = sum) > >> x5 = cbind(x3, Area = x4[,2]) > >> library(dplyr) > >> x6 <- x5 %>% > >> mutate(V_sum = cumsum(Vol)) %>% > >> mutate(A_sum = cumsum(Area)) > >> plot(x6$value~x6$V_sum) > >> > >> And I thought that it is possible to get the elevation for a specific > >> volume by linear model between elevation and volume, as follow: > >> >...

John, Your reaction was what my original reaction was until I realized I had to find out what a DEM file was and that contains enough of the kind of depth-dimension data you describe albeit what may be a very irregular cross section to calculate for areas and thence volumes. If I read it correctly, this can be a very real-world problem worthy of a solution, such as in places like California