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Post your question/information / Quantile mapping
« on: March 26, 2018, 04:55:56 PM »
Dear All,

I am trying to do statistical downscale precipitation of CMIP5 GCM output using Bias correction(quantile mapping) Spatial disaggregation method (with reference to a paper(attached)). I have following doubt:

1. In this method, is it okay to use only one GCM output predictor variable (precipitation) instead of group of predictor variables such as geopotential height, u-wind, v-wind etc?

2. After selecting predictor variable, should we divide the data for each month and perform analysis?

3. In the paper, it was mentioned that for each day we have to find moving window of +/- 15 days and then find CDF. Can anybody guide me how to perform this and find cdf ?

I am little confused. Please guide me..

With Regards
Ashwathi V K

Post your question/information / Calibration of SWAT model
« on: June 29, 2017, 02:36:34 PM »
I have simulated streamflow  using QGis interface of SWAT model in a semiarid catchment that has forest and agricultural ecosystem for total 13years (3 years warmup, 8 years calibration, 2 years validation). Area of catchment is 1200 My warmup period is 3 years. After simulation R^2 value and NS was found to be 0.78 and -0.21.

I tried calibrating the model using SWATCUP , by following the paper for calibration period. In SWATCUP, initially I set the result of initial model, with period of simulation as calibration period.. Then my NS is found to be -1.16 and R^2 is found to be 0.54.

Since overall flow overestimates the observed flow, in first two simulation i have decreased CN, increased sol_AWC, ESCO and  in the third and fourth simulation i have increased GW_revap, GW_qmn, GW_Delay and decreased Revapmn, Alpha_BF.  I have done 4 iteration of 150 simulation but there is no much improvement in the performance. I have attached my output flow from QSWAT model. I request you to suggest parameter that is to be needed to change during calibration, so that i get a good performance.

With Regards
Ashwathi V K

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

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


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