Water Adroit Forum
Materials and Methods => Models => Topic started by: ASHWATHI V K 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 (CNRMCM5, MRI CGCM3, MIROC ESM, CESMCAM5, 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

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 nonlinear) 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.

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 NCEPNCAR reanalysis data and precipitation as predictand (station data) for same period. Then find the relation between predictand and predictor variable in NCEPNCAR 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?

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 NCEPNCAR reanalysis data and precipitation as predictand (station data) for same period. Then find the relation between predictand and predictor variable in NCEPNCAR 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. NCEPNCAR, 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?

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 NCEPNCAR reanalysis data. Is this procedure right?
There are a lot of files with different variable in NCEPNCAR 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

Hello,
"There are a lot of files with different variable in NCEPNCAR 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. NCEPNCAR 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

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

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.

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

Hai,
In procedure to do statistical downscaling of precipitation, I have selected 8 variables from NCEPNCAR 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

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

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 NCEPNCAR 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

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

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).

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, CMCCCMs at 0.74*0.75 degree and ECEARTH provides at 1.11*1.125 degree.

Hai,
Thankyou Rohith.
Do you have any material on quantile mapping method of downscaling?
With regards
Ashwathi V K

You will find many materials and papers if you just google.
you can also have look at old discussions in the group about quantile mapping.
just follow the link.
(http://wateradroit.com/forum/index.php/topic,384.msg1269.html#msg1269) (http://wateradroit.com/forum/index.php/topic,979.msg2823.html#msg2823)

Hai,
I have downloaded some predictor variables from GFDL climate model of CMIP5 project which is in netcdf format. It has four dimension (lat, lon, time, and pressure levels). I want extract data for one pressure level (500Hpa). Is there any method to extract the values? I have tried with R program. But i have failed. Is there any method. Please help me.
With regards
Ashwathi V K

hey you can use Matlab functions (ncinfo or ncdisp to know the contents and units of the variable in nc file ) and ncread to extract the data.

Hai,
While doing statistical downscaling, after selecting predictor variables of all the surrounding grids of the study area, should i take the average of all the grid for each of these variables before doing Principal component Analysis?
Please help me..
With Regards
Ashwathi V K

Not needed. After standardization, you can perform PCA.

Hai,
I have a netcdf file( GCM data) with 2.5*2 degree resolution which is of 4 dimension (lat, lon, time and pressure level) and has 1 variable (Example  uwind). I also have Era Interim climate data which is of 2.5*2.5 degree resolution. I want change resolution of GCM data to that of Era Interim data so that points of both the file overlap and then extract data from resampled GCM data for points in between Latitude 7.5 degree N and 15 degree N and Longitude 72.5 and 80 degree East for 500Hpa and 750Hpa pressure levels and convert it to csv file, so that i can use data directly for PCA. Likewise I have to perform these for about 100 files. Is there any way to do this for all the files together? I tried with R program. But I failed. Please help me.
With Regards
Ashwathi V K

You can use Grads to regrid and automate regriding for many variables/files.

Hai,
I have a doubt on results of PCA regression statistical downscaling of temperature. I have downscaled GFDL CM3 and MRICGCM3 of CMIP5 project for Scenarios RCP2.6, 4.5, 8.5 and found temperature for a period of 2020 to 2100 for different stations. I have used ERA INTERIM daily data for Calibration. Correlation coefficient of Calibration and Validation results for different stations ranges between 0.650.8.
But in the result it was found that 50% of downscaled temperature data has lesser value for higher RCP. i.e, temperature of RCP 8.5 is lower than that of RCP2.6 for certain days in the period between 20202100. I have checked the procedure that i have done. I am not able solve the above mentioned problem.
Can you please suggest any solution for the above mention problem?
With Regards
Ashwathi V K

hi
I think it can be possible in the near future (maybe till 2050s), because rcp2.6 is a "peak and decline" scenario, which reaches the peak around 2050 and declines towards the end of century. While rcp8.5 is raising scenario towards end of the century.
You can refer this
DOI 10.1007/s105840110148z
I have not worked much with temperature data, but this is what i feel.
Rohith A N

But in the result it was found that 50% of downscaled temperature data has lesser value for higher RCP. i.e, temperature of RCP 8.5 is lower than that of RCP2.6 for certain days in the period between 20202100. I have checked the procedure that i have done. I am not able solve the above mentioned problem.
Can you please suggest any solution for the above mention problem?
My view :
50% of downscaled temperature data has lesser value for higher RCP is "an observation based on analysis performed and not a problem" . Also it doesn't say anything about trends (which may be present and should be look into) in the results obtained. Check for the frequency of events (above certain threshold) obtained in results.

Hai Alok and Rohith,
But my doubt is that , for RCP 8.5, most of the temperature should be greater than that of RCP 2.6 because of higher emission in RCP8.5? And average of daily downscaled temperature for RCP2.6,4.5,8.5 over a period from 2020 to 2100 is excatly same.
With Regards
Ashwathi V K

Hi,
The problem you are facing needs thorough look into data used, model created and the results obtained. It will be very difficult for anyone to give satisfactory answer to this problem without checking all the steps followed. I would suggest plotting the raw data of variables of all grid points, bias corrected data, and the downscaled data and then comparing the all the plots for better understanding.

Hai,
Can anybody tell the function in r program to get values of optimum parameters after performing support vector regression?
With regards
Ashwathi V K

Hai,
Please find the following procedure that i have used for downscaling:
Downscaling Method:
GCM GFDL CM3, MRI CGCM 3 from CMIP5 Project for RCP2.6, 4.5, 8.5
Predictor variables  Sea level pressure, Surface air temperature, Zonal wind speed at 500Hpa, Zonal wind speed at 850Hpa, Meridonal wind speed at 500Hpa, Meridonal wind speed at 850Hpa, Zonal wind speed at surface, Meridonal wind speed at surface, Geopotential height at 500Hpa, Geopotential height at 850Hpa.
Calibration data  ERA INTERIM Daily data of all the predictor variables.
Procedure
 Downloaded daily data of above mentioned predictor variables from ERA INTERIM website for a period from 1979
2012 (for GFDL  2.5 degree resolution data and for CGCM3 1.125 degree resolution data). Data include all the
surrounding grids of study area.
 Extracted data from netcdf files to excel files using R program.Removed Feb 29 data of leap year..
 Standardised all the data of predicted variable for all the grids.
 Done Principal Component Analysis using SPSS software and selected 50 Principal components which give 97% variance.
 Divided data into Calibration and validation datasets.
 Done Multiple linear regression between 50 Principal Components in calibration data set and observed station
temperature using SPSS statistical software. And found correlation coeficients.
 Using the coefficients from regression during calibration, station temperature was found from 50 Principal Components
in Validation Period and found Correlation coefficients.
 Calibration and Validation results are attached (PFA).
 Downloaded daily data of above mentioned predictor variables of GFDL CM3 and CGCM3 model from CMIP5 project
for three different scenarios for a period from 2020 to 2100.
 Extracted and resampled the data of all the predictor variables of GCM using R program so that final selected grid are
same as that of ERA INTERIM datasets and converted to excel format. This was done for each scenario for each GCM
separately.
 Done Standardisation of all the variables
 Done Principal component Analysis of all standardised variables and selected 50 Principal Components which give 97%
variance
 Using the coefficients obtained during Multiple linear regression during Calibration, temperature for each station was
found.
After following above procedure if am not getting a proper downscaled value. I wanted to check how my result changes when i include bias correction.
How can i include bias correction in the above procedure? can i apply bias correction directly to the downscaled result??
Please help me.
With Regards
Ashwathi V K

Hai,
Can any one explain the procedure to do "change factor method of statistical downscaling"?
With Regards
Ashwathi