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We particpated to the training in Wageningen and went through the tutorial ,then, after discussion with one Client ( case study on River water balance) we found out that the most effective way to let the Client accept and understand the uncertainty in the results was:
– first reduce the number of possible combinations of Climate change projections of used variables by identifyng the most suitable idrological model among the 3 available one for the area of interest (comparison of hydrological modelled discharges Vs. recorded values for actual period)
– present final results using probabilities (i.e. probaility that the choosen output indicator in Climate change conditions falls in a range of values or in another)May 2, 2017 at 7:40 pm in reply to: Downscaling data/indicators to higher resolution for test cases. #2115Hi René
Thank you for your reply, we have normalized the FDCs dividing them by average flow and our catchments are indeed relatively small (roughly 50 to 600 km2)
for this case we will then go for catchment level discharge data from EHYPE 3.1.Kind regards
Paolo This reply was modified 2 years, 7 months ago by Paolo GECOS.
April 14, 2017 at 8:24 am in reply to: Downscaling data/indicators to higher resolution for test cases. #2071Hi
We use ECV (daily river flow 0.5 deg and catchments) to get seasonal flow duration curves in C.C: conditions
Particularly we apply delta change method to local observed flow duration curves (available for period 19912001) in order to correct percentiles in C.C. conditions by taking changes of percentiles in swicca modeled FDC and applying this changes to observed FDC.We guess if we can exclude one or more hydrological models (EHYpe, VIC 421, LISFLOOD), in order to reduce possible output combinations, making some assumptions on how good they are in describing most relevant mechanisms in discharge generation for the area of interest.
We guess that this is possible by examinating shape of flow duration curve: we proceeded this way– we have local studies that give for this area two adimensional Flow Duration Curves (A_FDC, normalized by mean discharge over observed discharge time series) for catchments area above and below 100 km2.
– We compared this two representative curves with A_FDC from the three hydrological models in the reference period 19712001 for every hydrological model, and every input/forcing both for 0.5 deg data and catchment scale data (the latest only for EHYPE21)
– we found quite different behavior of the three models and particularly found out a better fit between observed A_FDC and modeled A_FDC for EHype21 at catchment level.
a few graphs showing this are available in this link
GDOC_ FDCThe question is : can we assume from this comparison that hydrological model EHYpe21 at catchment scale is more suitable for the area of interest and exclude other models, then apply delta change method in order to modify actual observed FDC to obtain Climate change FDC?
many thanks for the help
best
Paolo This reply was modified 2 years, 8 months ago by Paolo GECOS.
April 29, 2016 at 5:34 pm in reply to: Downscaling data/indicators to higher resolution for test cases. #1214We are interested in downscaling to finer resolution actual information you provided at 0.5 ° cell size in the demonstrator, however requirements are different for the two case studies.
For case study that uses indicators such as the change in Flow duration curves percentiles, we think we can use them as they are provided now. We don’t need to downscale a model time series of discharge (Ronald showed in his example it could be tricky indeed), we are fine with percentiles variations that represent average upstream catchment behavior. Provided resolution, even if a ittle too coarse, is still usable as one ore more cells may fit inside a catchment, so using the average values from involved cells should work.
For case study that uses indicators such as precipitation and temperature we need finer resolution (roughly 2X2 km2) time series of 10 days values in C. C. If we got your suggestion right we could do this taking the local time series for every station and trying to modify them using the modelled indicators.
In the case of mentioned 10 days PT variables , for example we cuold use the 10 days modelled time series of “changes in the variable” and just apply it to every local stations data belonging tho that cell.

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