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Models / Re: Anybody working with VIC model
« Last post by Diwan on Today at 11:08:16 AM »
Kindly provide sufficient information while posting your query. So that people can try to address it.
Models / Re: Anybody working with VIC model
« Last post by hsalehi on October 13, 2018, 11:16:37 PM »
hi to all
I am running the vic model using Python Codes.
I have a sample and I'm training with that model
I'm have problems with the rout and the codes are not responding.
Does anyone with Python run the model ?
Post your question/information / Re: Need advice for purpose of pan card
« Last post by Sat Kumar Tomer on October 13, 2018, 12:10:53 PM »
How is it relevant to this forum?
Please send us a copy of your institute ID card to within 3 days. Otherwise, your account will be deleted.
Models / Re: Anybody working with VIC model
« Last post by hsalehi on October 12, 2018, 04:30:20 AM »
hi to all
I am running the vic model using Python Codes.
I have a sample and I'm training with that model
I'm having problems with the rout and the codes are not responding.
Does anyone with Python run the model ?
HydroSight is a highly flexible statistical toolbox for quantitative hydrogeological insights. It comprises of a powerful groundwater hydrograph time-series modelling and simulation framework plus a data quality analysis module. Multiple models can be built for one bore, allowing statistical identification of the dominant processes, or 100’s of bores can be modelled to quantify aquifer heterogeneity. This flexibility allows many novel applications such as:Separations of the impacts from pumping and drought over time.
  • Probabilistic estimation of aquifer hydraulic properties.
  • Estimation of the impacts of re-vegetation on groundwater level.
  • Exploration of groundwater management scenarios.
  • Interpolation and extrapolation irregularly observed hydrograph at a daily time-step.
The toolbox can be used from a highly flexible and stand-alone graphical user interface ( or programmatically from within Matlab 2014b (or later).
Interesting information / Targeting 1.5 °C
« Last post by Pankaj Dey on October 10, 2018, 07:24:26 PM »
In December 2015, representatives from 195 nations met in Paris to negotiate an international agreement to combat climate change. The resulting ‘Paris Agreement’ codified an aspiration to limit the level of global temperature rise to 1.5 °C above pre-industrial levels —  lower than the previously generally agreed target of 2 °C. From a research standpoint, a more ambitious temperature target poses many questions that could draw scientific and intellectual attention and resources. Furthermore, the timescales in which researchers must decide how to engage with this new policy context is very short.
The Intergovernmental Panel on Climate Change has agreed to publish a special report on the costs and implications of the 1.5 °C target in 2018. In order to inform that process, researchers must decide which efforts to prioritise and begin work almost immediately. But deciding what can and should be delivered is far from trivial. This evolving collection draws together content from Nature Climate Change, Nature Geoscience, Nature Communications and Nature to provide comment on how research might best inform decisions about limiting climate warming as well as presenting pertinent new research that adressess this very question.

In regression, we assume noise is independent of all measured predictors. What happens if it isn’t?

A number of key assumptions underlie the linear regression model – among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model.


Dear Sir/Madam,

As part of the SMART training and outreach activities of Space Applications Centre (SAC), ISRO, Ahmedabad,  four days training programme on ‘Satellite based Sounding of the Atmosphere: Techniques and Applications’ is planned during 27-30 November 2018 at SAC, Ahmedabad.

I hereby enclose the details of this training programme and the application form. Request you to forward the details to interested scientists/students working in your organisation.
Thanking you,

Warm regards,

V. Sathiyamoorthy
Dr. V. Sathiyamoorthy
Scientist-SG & Head
Space Applications Centre, Bopal Campus
Indian Space Research Organisation
BOPAL, Ahmedabad -380058
Phone: 079-26916112

Fax: 079-26916127
The work is carried out by Dr. Sonali Pattanayak along with Prof. Ravi S. Nanjundiah and Prof. D. Nagesh Kumar titled "Detection and attribution of climate change signal in South India maximum and minimum temperatures" got published in the Climate Research.

Abstract: South India has seen significant changes in climate. Previous studies have shown that southern part of India is more susceptible to impact of climate change than the rest of the country. A rigorous climate model-based detection and attribution analysis is performed to determine the root cause of the recent changes in climate over South India using fingerprint analysis. Modified Mann-Kendall test signalized non-stationariness in Tmax and Tmin in most of the season during the period 1950-2012. The diminishing cloud cover trend might be inducing significant changes in temperature during the considered time period. Significant downward trends in RH during most of the season could act as an evidence of the recent significant warming. The observed seasonal Tmax, Tmin change patterns are strongly associated with El Niño Southern Oscillation. Significant positive associations between South India temperatures and Niño3.4 are found in all the seasons. Deployment of fingerprint approach indicated that the natural internal variability obtained from 14 climate model control simulations could not explain these significant changes in Tmax (post-monsoon) and Tmin (pre- monsoon and monsoon) of South India. Moreover the experiment simulating natural external forcings (solar and volcanic) do not coincide with the observed signal strength. The dominant external factor leading to climate change is GHGs and its impact is eminent compared to other factors such as, land use change and anthropogenic aerosols. Anthropogenic signals are identifiable in observed changes in Tmax and Tmin of South India and these changes can be explained only when anthropogenic forcing are involved.

Sonali can be contacted at
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