Indian Forum for Water Adroit

Show Posts

This section allows you to view all posts made by this member. Note that you can only see posts made in areas you currently have access to.


Messages - ASHWATHI V K

Pages: [1]
1
Models / Re: Statistical downscaling of GCMs
« on: October 18, 2017, 05:08:49 PM »
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.
The following users thanked this post: ASHWATHI V K

2
Models / Re: Statistical downscaling of GCMs
« on: October 05, 2017, 05:38:50 PM »
Quote
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 2020-2100. 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.
The following users thanked this post: ASHWATHI V K

3
Models / Re: Statistical downscaling of GCMs
« on: October 04, 2017, 10:09:24 AM »
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
Quote
DOI 10.1007/s10584-011-0148-z

I have not worked much with temperature data, but this is what i feel.

Rohith A N
The following users thanked this post: ASHWATHI V K

4
Models / Re: Statistical downscaling of GCMs
« on: August 31, 2017, 04:17:57 PM »
You can use Grads to regrid and automate regriding for many variables/files.
The following users thanked this post: ASHWATHI V K

5
Models / Re: Statistical downscaling of GCMs
« on: August 11, 2017, 10:00:29 AM »
Not needed. After standardization, you can perform PCA.
The following users thanked this post: ASHWATHI V K

6
Models / Re: Statistical downscaling of GCMs
« on: July 14, 2017, 02:22:58 PM »
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.
The following users thanked this post: ASHWATHI V K

7
Models / Re: Statistical downscaling of GCMs
« on: July 08, 2017, 05:09:08 PM »
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)
The following users thanked this post: ASHWATHI V K

8
Models / Re: Statistical downscaling of GCMs
« 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.
The following users thanked this post: ASHWATHI V K

9
Models / Re: Statistical downscaling of GCMs
« 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).
The following users thanked this post: ASHWATHI V K

10
Hello,

IIRS is organizing online course on Remote Sensing and GIS Applications in Water Resources Management commencing from May 22, 2017. This online classes will be conducted every working day at 1600 hrs onward and only one class per day followed by an online exam on the last day of the course.
 
There is no course or registration fee.

For more information visit following links:

http://iirs.gov.in/Edusat-News
http://elearning.iirs.gov.in/edusat_lms/registration.php
The following users thanked this post: ASHWATHI V K

11
Models / Re: Statistical downscaling of GCMs
« 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.
The following users thanked this post: ASHWATHI V K

12
Models / Re: Statistical downscaling of GCMs
« 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
The following users thanked this post: ASHWATHI V K

13
Models / Re: Statistical downscaling of GCMs
« 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?
The following users thanked this post: ASHWATHI V K

14
Models / Re: Statistical downscaling of GCMs
« 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.
The following users thanked this post: ASHWATHI V K

Pages: [1]