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Statistical downscaling of GCMs

ASHWATHI V K

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Statistical downscaling of GCMs
« on: April 07, 2017, 02:39:35 PM »
Hai,

I want to find impact of climate change on water balance components in a small basin. I am using SWAT hydrological model. To find impact of climate change, i have selected 5 GCMs (CNRM-CM5, MRI- CGCM3, MIROC- ESM, CESM-CAM5, NorESM- M) of CMIP5 project based on literature. Scenarios selected are RCP 2.6,4.5 and 8.5. I have following doubts regarding climate model:
1. Can the GCMs of CMIP5 project be statistically downscaled using SDSM downscaling model? (In example which in the model webpage, they have used only HADCM3)
2. Metereological parameter of SWAT are precipitation, min and max temperature, wind speed, humidiy and solar radiation. Is it necessary to downscale all parameters from GCMs to use it in SWAT? Or can we use only precipitation and temperature?
3. When downloading GCM output is it necessary to download files of all variables? Or need to download only precipitation and min and max temperature?
4. Which is the best and simple method to statistically downscale GCM output?
5. If we are using SDSM downscaling model, on what basis we have to select predictor variables?

Being new to climate models, I have only little idea on climate models. What is the best method to find impact of climate change on hydrology with hydro logical model selected being SWAT?
 
Please help me in clearing my doubts.

With Regards
Ashwathi


 
 

Alok Pandey

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Re: Statistical downscaling of GCMs
« Reply #1 on: April 07, 2017, 03:18:40 PM »
My view :

1. Can the GCMs of CMIP5 project be statistically downscaled using SDSM downscaling model? (In example which in the model webpage, they have used only HADCM3)

-> Yes.

2. Metereological parameter of SWAT are precipitation, min and max temperature, wind speed, humidiy and solar radiation. Is it necessary to downscale all parameters from GCMs to use it in SWAT? Or can we use only precipitation and temperature?

->  Since all these parameters are also provided by the GCM, it is advisable or preferred (not necessarily imposed) to use  them in analysis. However if any other model provides these parameters as long term forecasts, it can be used to enhance our perspectives on uncertainties associated with the models.

3. When downloading GCM output is it necessary to download files of all variables? Or need to download only precipitation and min and max temperature?

->  Better download only those files which will be used for analysis.

4. Which is the best and simple method to statistically downscale GCM output?

->  Every method has its pros and cons. "Best" word is very subjective here. Complexity of downscaling methods can indeed be discussed. Useful Link : http://wateradroit.com/forum/index.php/topic,706.msg2064.html#msg2064

5.  If we are using SDSM downscaling model, on what basis we have to select predictor variables?

->  Predictor variables are selected based on their strong relationships (linear and/or non-linear) with predictand. High Correlation (also supported by physics of nature) is one widely accepted measure.

P.S. Researchers are encouraged to add and/or correct points mentioned in this post.
« Last Edit: April 07, 2017, 03:28:51 PM by Alok Pandey »
Alok Pandey,
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Water Resources and Environmental Engineering,
IISc Bangalore
 
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ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #2 on: April 11, 2017, 03:08:25 PM »
Hai,

Thank you Alok for your reply.

Based on my understanding from literature, in statistical downscaling, if we want future precipitation, first we have to select predictor variables (pressure, humidity,air temp etc) from NCEP-NCAR reanalysis data and precipitation as predictand (station data) for same period. Then find the relation between predictand and predictor variable in NCEP-NCAR data. Then calibrate the model and then select same predictor variables from GCM (pressure, humidity,air temp etc) and find predictand (future station data). Is this true? Or is there any change from this?

Or should we relate predictor variable in NCEP- NCAR data and predictor variables in GCM?
 

Alok Pandey

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Re: Statistical downscaling of GCMs
« Reply #3 on: April 12, 2017, 04:17:09 PM »
Hello,

"Based on my understanding from literature, in statistical downscaling, if we want future precipitation, first we have to select predictor variables (pressure, humidity,air temp etc) from NCEP-NCAR reanalysis data and precipitation as predictand (station data) for same period. Then find the relation between predictand and predictor variable in NCEP-NCAR data. Then calibrate the model and then select same predictor variables from GCM (pressure, humidity,air temp etc) and find predictand (future station data). Is this true? Or is there any change from this?"
-> The above mentioned approach is widely used. Also many agencies now provide reanalysis data (i.e. NCEP-NCAR, ERA, JRA). The main assumptions behind this approach is that the relationship between reanalysis data (predictor) and station data (predictand) will be preserved (i.e. stationary relationship) in future and thus can be used as it is on GCM data to obtain final future projections of predictand.

"Or should we relate predictor variable in NCEP- NCAR data and predictor variables in GCM?"
-> I did not understand the rationale behind this? Where will we use this relationship in the analysis?
Alok Pandey,
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ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #4 on: April 13, 2017, 11:41:56 AM »
Hai,

Actually i want to find impact of climate change on water balance components in SWAT hydrological model. To find climate change, i have selected some GCMs. To use GCM i need to statistically downscale it using SDSM downscaling model. To statistically downscale GCM,  i have to select NCEP-NCAR reanalysis data. Is this procedure right?

There are a lot of files with different variable in NCEP-NCAR reanalysis data archive? Should i download entire files? How will we obtain NCEP -NCAR reanalysis data for the grid containing my study area. Being a fresher in Climate modelling, i am bit confused. Is there any link to obtain NCEP -NCAR reanalysis data.
Please help me.

With Regards
Ashwathi
 

Alok Pandey

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Re: Statistical downscaling of GCMs
« Reply #5 on: April 14, 2017, 09:01:26 AM »
Hello,

"There are a lot of files with different variable in NCEP-NCAR reanalysis data archive? Should i download entire files? How will we obtain NCEP -NCAR reanalysis data for the grid containing my study area."
-> No need to download entire files. NCEP-NCAR provides freedom to download specific variable (pressure, humidity,air temp etc.) of particular time scales (daily, monthly etc.) corresponding to region of interest. more information on below links :
1. http://wateradroit.com/forum/index.php/topic,47.msg98.html#msg98
2. http://wateradroit.com/forum/index.php/topic,485.msg1584.html#msg1584
Alok Pandey,
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ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #6 on: April 27, 2017, 12:23:37 PM »
Hai,

If there is only one grid of reanalysis data in the study area, is it necessary to do principal component analysis before doing regression?


With regards
Ashwathi
 

Alok Pandey

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Re: Statistical downscaling of GCMs
« Reply #7 on: April 27, 2017, 03:34:25 PM »
Hello,

In downscaling, we usually take grids which encompass the whole study area and not just the grid which falls into it. This way the minimum number of grids for the study comes out to be 4 (for one climate variable). Thus more number of variables necessitates use of PCA.
Alok Pandey,
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Water Resources and Environmental Engineering,
IISc Bangalore
 
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ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #8 on: May 03, 2017, 01:45:16 PM »
Hai,

I have NCEP/NCAR reanalysis data in netcdf format. I want to convert it into csv file. I have a script in R programming to convert netcdf to csv. But since the data in netcdf file is too large which exceeds number columns in csv file, i am not able get entire data in csv format. How can i split the data in nc file to multiple sheets of csv file.

Script is as follows:

library(ncdf4)
getwd()
workdir <- "I:\\NCEP\\"
setwd(workdir)
ncin <- nc_open("X27.251.228.22.106.3.5.59.nc")
print("ncin")
dname <- "hgt"
lon <- ncvar_get(ncin, "lon")
nlon <- dim(lon)
head(lon)
lat <- ncvar_get(ncin, "lat", verbose = F)
nlat <- dim(lat)
head(lat)
print(c(nlon, nlat))
t <- ncvar_get(ncin, "time")
tunits <- ncatt_get(ncin, "time", "units")
nt <- dim(t)
tmp.array <- ncvar_get(ncin, dname)
dlname <- ncatt_get(ncin, dname, "long_name")
dunits <- ncatt_get(ncin, dname, "units")
fillvalue <- ncatt_get(ncin, dname, "_FillValue")
dim(tmp.array)
title <- ncatt_get(ncin, 0, "title")
institution <- ncatt_get(ncin, 0, "institution")
datasource <- ncatt_get(ncin, 0, "source")
references <- ncatt_get(ncin, 0, "references")
history <- ncatt_get(ncin, 0, "history")
Conventions <- ncatt_get(ncin, 0, "Conventions")
# split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth = as.integer(unlist(tdstr)[2])
tday = as.integer(unlist(tdstr)[3])
tyear = as.integer(unlist(tdstr)[1])
chron::chron(t, origin = c(tmonth, tday, tyear))
tmp.array[tmp.array == fillvalue$value] <- NA
length(na.omit(as.vector(tmp.array[, , 1])))
m <- 1
tmp.slice <- tmp.array[, , m]
lonlat <- expand.grid(lon, lat)
tmp.vec <- as.vector(tmp.slice)
length(tmp.vec)
tmp.df01 <- data.frame(cbind(lonlat, tmp.vec))
names(tmp.df01) <- c("lon", "lat", paste(dname, as.character(m), sep = "_"))
head(na.omit(tmp.df01), 20)
csvfile <- "cru_tmp_1.csv"
write.table(na.omit(tmp.df01), csvfile, row.names = FALSE, sep = ",")
tmp.vec.long <- as.vector(tmp.array)
length(tmp.vec.long)
tmp.mat <- matrix(tmp.vec.long, nrow = nlon * nlat, ncol = nt)
dim(tmp.mat)
head(na.omit(tmp.mat))
# create a dataframe
lonlat <- expand.grid(lon, lat)
tmp.df02 <- data.frame(cbind(lonlat, tmp.mat))
options(width = 110)
head(na.omit(tmp.df02, 20))
csvfile <- "cru_tmp_2.csv"
write.table(na.omit(tmp.df02), csvfile, row.names = FALSE, sep = ",")

In the case of GCMs, i tried downloading a GCM output. But it took nearly 10 hours for a single variable in my laptop. Is there any requirement for laptops and internet connections to download and use GCM data?

Please give me solution.

With regards
Ashwathi
 

ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #9 on: May 09, 2017, 05:07:14 PM »
Hai,

In procedure to do statistical downscaling of precipitation, I have selected 8 variables from NCEP-NCAR reanalysis data and have done principal component analysis using XLSTAT. I have got some results after PCA (attached). Based on what value of  the result  will we have to select important variables out of 8 variables? I am totally confused. Please help me.

With regards
Ashwathi 
 

Alok Pandey

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Re: Statistical downscaling of GCMs
« Reply #10 on: May 09, 2017, 07:54:09 PM »
Hello,

Eigenvalues of the covariance matrix of data matrix is used to select the number of newly formed variable (without any physical significance).
To learn more about PCA, go through this educational video :
Lecture series on Stochastic Hydrology by Prof. P. P. Mujumdar, Department of Civil Engineering, IISc Bangalore
https://www.youtube.com/watch?v=TqCnD1avqMY
Alok Pandey,
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Water Resources and Environmental Engineering,
IISc Bangalore
 

ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #11 on: May 25, 2017, 12:04:08 PM »
Hai,

In downscaling, i have selected predictor variables such as air temperature at 2m, air temperature at 250Hpa, u wind speed at 250Hpa, V wind speed at 250Hpa, geopotential height at 500Hpa, mean sea level pressure, specific humidity at 2m and vertical wind velocity from NCEP-NCAR reanalysis data. I have standardized the data and done Principal component analysis using XLSTAT. I got 5 principal components. Then from observed station data (predictand) i have deducted mean and then performed multiple regression using XLSTAT.  After regression my correlation is found to be very poor approximately 0.2. Is this acceptable? How can i improve correlation? Is there any mistake in this? Please help me.


With regards
Ashwathi V K
 

ASHWATHI V K

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Re: Statistical downscaling of GCMs
« Reply #12 on: May 29, 2017, 04:19:30 PM »

Hai

Is there GCMs of finer resolution (1 degree x 1 degree) under CMIP5 project with RCP 2.6 scenario available?

With Regards
Ashwathi V K
 

Rohith Kannada

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Re: Statistical downscaling of GCMs
« Reply #13 on: June 21, 2017, 08:24:49 PM »
Hey

I suggest you, to try change factor or quantile mapping methods to downscale. They are simple and many people are using these methods (am not sure in case of SWAT).
Rohith A N
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Rohith Kannada

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Re: Statistical downscaling of GCMs
« Reply #14 on: June 21, 2017, 08:31:41 PM »

Is there GCMs of finer resolution (1 degree x 1 degree) under CMIP5 project with RCP 2.6 scenario available?

Model CCSM4 provide projections at 0.94*1.25 degree, CMCC-CMs at 0.74*0.75 degree and EC-EARTH provides at 1.11*1.125 degree.
« Last Edit: June 26, 2017, 12:04:14 AM by Rohith Kannada »
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