[DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]
The effects of spatial reference systems on interpolations and accuracy are minimal, and lat and long can be used. Please see the following studies for details. Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to Spatial Reference Systems for Predicting Marine Environmental Variables, 20th International Congress on Modelling and Simulation: Adelaide, Australia, pp. 387-393. Jiang, W., Li, J., 2014. The effects of spatial reference systems on the predictive accuracy of spatial interpolation methods. Record 2014/01. Geoscience Australia: Canberra, pp 33. http://dx.doi.org/10.11636/Record.2014.001. Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference Systems on the Accuracy of Spatial Predictive Modelling along a Latitudinal Gradient, 22nd International Congress on Modelling and Simulation: Hobart, Tasmania, Australia, pp. 106-112. -----Original Message----- From: R-sig-Geo [mailto:r-sig-geo-bounces at r-project.org] On Behalf Of Dominik Schneider Sent: Wednesday, 14 February 2018 3:21 AM To: Stefano Sofia Cc: r-sig-geo at r-project.org Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital Elevation Model You can't use a lat/long coordinate system when kriging because the concept of distance is ambiguous. Convert all your data a UTM grid like you had in your first post and it should work. Another note, It looks like you are working at 0.01 deg which is on the order of 1km resolution so you may find other covariates such as aspect, slope, and wind sheltering/exposure, terrain roughness for estimating snow on the ground useful. see some of the earliest papers by Carroll, Cressie, and Elder. Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical methodologies used to estimate snow water equivalent, *JAWRA Journal of the American Water Resources Association*, *32*(2), 267?278, doi:10.1111/j.1752-1688.1996.tb03450.x. Carroll, S. S., and N. Cressie (1997), Spatial modeling of snow water equivalent using covariances estimated from spatial and geomorphic attributes, *Journal of Hydrology*, *190*(1-2), 42?59. Balk, B., and K. Elder (2000), Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed, *Water Resources Research*, *36*(1), 13?26, doi:10.1029/1999WR900251. Erxleben, J., K. Elder, and R. Davis (2002), Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains, *Hydrological Processes*, *16*(18), 3627?3649, doi:10.1002/hyp.1239. Erickson, T. A., M. W. Williams, and A. Winstral (2005), Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States, *Water Resour. Res.*, *41*(4), W04014, doi:10.1029/2003WR002973.
On Tue, Feb 13, 2018 at 3:45 AM, Stefano Sofia < stefano.sofia at regione.marche.it> wrote:
Dear Daniel and list users,
I tried to follow the instructions but I encountered two kinds of errors.
This is a reproducibile code:
------------------------------------------------------------
---------------------------------------------------
library(automap)
library(ggplot2)
library(gstat)
library(raster)
library(rasterVis)
library(rgdal)
library(maptools)
## LOADING DEM
ita_DEM <- getData('alt', country='ITA', mask=TRUE)
crs(ita_DEM) <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
+ellps=WGS84 +towgs84=0,0,0"
#ita_DEM <- as(ita_DEM, "SpatialGridDataFrame")
str(ita_DEM)
## LOADING RAINFALL DATA
rain_data <- data.frame(Cumulata=c(11.8, 9.0, 8.0, 36.6, 9.4),
Long_Cent=c(12.61874, 12.78690, 12.96756, 13.15599, 13.28157),
Lat_Cent=c(43.79447, 43.85185, 43.76267, 43.03470, 43.08003),
Altitude=c(112.20, 42.93, 36.14, 747, 465))
stations <- data.frame(rain_data$Long_Cent, rain_data$Lat_Cent)
rain_data <- SpatialPointsDataFrame(stations, rain_data,
proj4string=CRS("+init=epsg:4326"))
stations <- SpatialPoints(stations,
proj4string=CRS("+init=epsg:4326"))
## EXTRACT THE ELEVATION VALUES TO MY POINTS
rain_data$ExtractedElevationValues <- extract(x=ita_DEM, y=stations)
## CREATE GRID FOR KRIGING OUTPUT
minx <- rain_data at bbox[1,1]
maxx <- rain_data at bbox[1,2]
miny <- rain_data at bbox[2,1]
maxy <- rain_data at bbox[2,2]
pixel <- 0.01
grd <- expand.grid(x=seq(minx, maxx, by=pixel), y=seq(miny, maxy,
by=pixel))
coordinates(grd) <- ~x+y
gridded(grd) <- TRUE
proj4string(grd) <- CRS("+init=epsg:4326")
## KRIGING: autoKrige(YourMeasurements ~ YourExtractedElevationValues,
YourMeasurementLocations, TargetGrid) OK_snow <- autoKrige(Cumulata ~
rain_data$ExtractedElevationValues,
rain_data, grd)
------------------------------------------------------------
-------------------------------------------------------------------
The error I get is:
Error in autoKrige(Cumulata ~ rain_data$ExtractedElevationValues,
rain_data, :
Either input_data or new_data is in LongLat, please reproject.
input_data: +init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
+ellps=WGS84 +towgs84=0,0,0
new_data: +init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
+ellps=WGS84 +towgs84=0,0,0
but I did pay attention to have the same reference system in
rain_data, drg and the Digital Elevation Model.
Moreover, if I impose the class of the DEM to SpatialGridDataFrame
when I extraxt the elevation points from the DEM I get the following error:
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ?extract? for
signature ?"SpatialGridDataFrame", "SpatialPoints"?
Calls: extract -> <Anonymous>
Would you please somebody help to show me where is my mistake?
Thank you for all your attention
Stefano
(oo)
--oOO--( )--OOo----------------
Stefano Sofia PhD
Area Meteorologica e Area nivologica - Centro Funzionale Servizio
Protezione Civile - Regione Marche Via del Colle Ameno 5
60126 Torrette di Ancona, Ancona
Uff: 071 806 7743
E-mail: stefano.sofia at regione.marche.it
---Oo---------oO----------------
________________________________________
Da: R-sig-Geo [r-sig-geo-bounces at r-project.org] per conto di Daniel
Knitter [knitter at geographie.uni-kiel.de]
Inviato: luned? 12 febbraio 2018 9.16
A: r-sig-geo at r-project.org
Oggetto: Re: [R-sig-Geo] Interpolating snowfall values on a Digital
Elevation Model
...sorry, I missed one step: you need to extract the elevation values
to your points via extract function from the raster package:
YourPoints$YourExtractedElevationValues <- extract(x = DEM, y =
YourPoints)
autoKrige(YourMeasurements ~ YourExtractedElevationValues,
YourMeasurementLocations, TargetGrid)
Sorry for spamming.
Best,
Daniel
On Mon, 12 Feb 2018 08:03:41 +0000
Stefano Sofia <stefano.sofia at regione.marche.it> wrote:
Dear list users,
I have to produce rainfall maps. I know how to create a
(bi-dimensional)
grid and interpolate rainfall values (from automatic rain gauges) on
that grid using Kriging:
myinterpolation <- autoKrige(myrainfall_cumulate ~ 1, rain_data_UTM,
mygrid)
In reality I am dealing with snowfall values on mountain regions,
and in
this case altitude is an important factor, the use of a Digital
Elevation Model might make the difference.
Looking in the web I found some important material about accessing
elevation data in R with the "elevatr" package (by J.W.Hollister), I
am reading it.
My concern would then be to interpolate snowfall values on a Digital
Elevation Model. Did somebody already use R for these kinds of
calculations? Could somebody share with me some useful hints?
Thank you for your attention
Stefano Sofia
(oo)
--oOO--( )--OOo----------------
Stefano Sofia PhD
Area Meteorologica e Area nivologica - Centro Funzionale Servizio
Protezione Civile - Regione Marche Via del Colle Ameno 5
60126 Torrette di Ancona, Ancona
Uff: 071 806 7743
E-mail: stefano.sofia at regione.marche.it
---Oo---------oO----------------
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