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Messages - Pankaj Dey

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Programming / HYDROMAD: Hydrological Model Assessment and Development
« on: January 30, 2017, 04:43:57 PM »
hydromad is an R package (i.e. a software package for the R statistical computing environment). It provides a modelling framework for environmental hydrology: water balance accounting and flow routing in spatially aggregated catchments. It supports simulation, estimation, assessment and visualisation of flow response to time series of rainfall and other drivers.
[/size]A minimal unit hydrograph framework is used, where areal rainfall is passed through a soil moisture accounting (SMA) model to estimate effective rainfall; this is then passed through a routing model to estimate streamflow. Included are several implementations of models consistent with this framework, notably the IHACRES CWI and CMD soil moisture accounting models, and unit hydrograph transfer functions for the routing.

Programming / RQGIS: Integrating R with QGIS
« on: January 30, 2017, 03:30:35 PM »

Establishes an interface between R and 'QGIS', i.e. it allows the user to access 'QGIS' functionalities from the R console. It achieves this by using the 'QGIS' Python API via the command line. Hence, RQGIS extends R's statistical power by the incredible vast geo-functionality of 'QGIS' (including also 'GDAL', 'SAGA'- and 'GRASS'-GIS among other third-party providers). This in turn creates a powerful environment for advanced and innovative (geo-)statistical geocomputing. 'QGIS' is licensed under GPL version 2 or greater and is available from <>.

Link :

The differential warming of land and ocean leads to many continental regions in the Northern Hemisphere warming at rates higher than the global mean temperature. Adaptation and conservation efforts will, therefore, benefit from understanding regional consequences of limiting the global mean temperature increase to well below 2°C above pre-industrial levels, a limit agreed upon at the United Nations Climate Summit in Paris in December 2015. Here, we analyze climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to determine the timing and magnitude of regional temperature and precipitation changes across the contiguous United States (US) for global warming of 1.5 and 2°C and highlight consensus and uncertainties in model projections and their implications for making decisions. The regional warming rates differ considerably across the contiguous US, but all regions are projected to reach 2°C about 10-20 years before the global mean temperature. Although there is uncertainty in the timing of exactly when the 1.5 and 2°C thresholds will be crossed regionally, over 80% of the models project at least 2°C warming by 2050 for all regions for the high emissions scenario. This threshold-based approach also highlights regional variations in the rate of warming across the US. The fastest warming region in the contiguous US is the Northeast, which is projected to warm by 3°C when global warming reaches 2°C. The signal-to-noise ratio calculations indicate that the regional warming estimates remain outside the envelope of uncertainty throughout the twenty-first century, making them potentially useful to planners. The regional precipitation projections for global warming of 1.5°C and 2°C are uncertain, but the eastern US is projected to experience wetter winters and the Great Plains and the Northwest US are projected to experience drier summers in the future. The impact of different scenarios on regional precipitation projections is negligible throughout the twenty-first century compared to uncertainties associated with internal variability and model diversity.
[/size]paper link:

First year of data from SMAP satellite provides new insights for weather, agriculture, and climate.
[/size]Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it dicult to quantify the global distribution and dynamics of surface soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA’s Soil Moisture Active Passive mission to show that surface soil moisture—a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces—plays a significant role in the water cycle. Specifically, we find that surface soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface soil moisture storage layer at the land surface.
[/size]link to the paper:

Programming / Lyx: Easy LaTeX document Processor
« on: January 06, 2017, 10:52:55 AM »
LyX is an open sourcedocument processor based on top of the LaTeXtypesetting system. Unlike most word processors, which follow the WYSIWYG ("what you see is what you get") paradigm, LyX has a WYSIWYM ("what you see is what you mean") approach, where what shows up on the screen is only an approximation of what will show up on the page.

Links to download Lyx:

Using Lyx, we can easily prepare LaTeX documents without worrying too much about different technical stuffs.

Feature Selection, also known as variable selection, is the process of selecting a subset of relevant features/variables/predictors.  Method for feature selection is an ever-expanding area of research. Filter type feature selection methods typically process features one at a time whereas wrapper methods consider subsets of features, and evaluate model selection criteria for each subset. Embedded methods inherently perform variable  selection as part of the learning process and include various regularization methods (such as the Lasso).  We propose a Bayesian method  for variable selection. While this is a general statistical approach, the  motivation comes from the problem of selecting relevant biomarkers  associated with (a) lymph node metastasis and (b) improved survival from Non-Small Cell Lung Cancer. Both of these are non-linear models which adds substantial complexity to the feature selection problem.
[/size]DAY & DATE:                Tuesday, 3rd January 2017
[/size]TIME:                        4.00 PM (Tea/Coffee at 5.00 PM)
[/size]VENUE:                Faculty Hall,Main Building, IISc.

Streamflow time series often contain gaps of varying length and location. However, the influence of these gaps on trend detection is poorly understood and cannot be estimated [/size]a priori[/color] in trend detection studies. We simulated the effects of varying gap size (1, 2, 5, and 10 years) and location (one quarter, one third, and half of the way) on the detection rate of significant monotonic trends in annual maxima and peaks-over-threshold, based on the most commonly-used trend tests in time series of varying length (from 15 to 150 years) and trend magnitude ([/font][/color][/size]β[/size][size=0.688em][/color]1[/size][/color]). Results show that, in comparison with the complete time series, the loss in trend detection rate tends to grow with (1) increasing gap size, (2) increasing gap distance from the middle of the time series, (3) decreasing [/font][/color][/size]β[/size][size=0.688em][/color]1[/size][/color] slope, and (4) decreasing time series length. Based on these findings, we provide objective recommendations and cautionary remarks for maximal gap allowance in trend detection in extreme streamflow time series.[/font][/color][/size][/color][/size]link to paper : [/color][/size][/color]

Post your question/information / Interview with Prof. V.P.Singh
« on: December 23, 2016, 03:27:41 PM »
This is a wonderful interview where Professor shares his experiences on  his journey.
Please follow the link and enjoy the reading

Courtesy : Swapan Kumar Masanta

Name of the Speaker : Dr Munir Ahmad Nayak Post Doctoral Fellow Cornell University, USA.
Date & Time : January 09, 2017 - 4.00 PM Venue :       Conference Room [first floor] Civil Engineering Department Atmospheric rivers (ARs) are long and narrow river-like features in the lower troposphere that carry most of the atmospheric water vapor fluxes from the tropics to the mid-latitudes. Because of the large amount of moisture transported by these storms, they can cause heavy rainfall and major flooding events, such as the Midwest floods of 1993 and 2008. The overarching theme of this thesis is to understand the impacts of ARs on extreme precipitation and floods over the central United States.

Programming / An Interactive Tool for Using Landsat 8 Data in MATLAB
« on: December 11, 2016, 04:57:39 PM »

URL to the video:  [/size][/color][/size]

This video demonstrates how to use an interactive tool in MATLAB® for selecting, accessing, processing, and visualizing Landsat 8 data.
[/size]With this tool, you can:[/color][/size]Create a map display of scene locations with markers that contain each scene’s metadata.Access Landsat 8 data hosted by Amazon Web Services.Combine and enhance individual Landsat 8 spectral bands in a variety of typical approaches.Create image and map displays of processed results.

You can download the code from the following link[/size]

Divecha Centre is organizing TWAS Young Scientists conference for young scientists in Central & South Asian Region titled "Frontiers in Earth, Climate and Ocean Sciences", supported by the Ministry of Earth Sciences and jointly organized by TWASROCASA at Jawaharlal Nehru Centre for Advanced Scientific Research and Divecha Centre for Climate Change, Indian Institute of Science. More than 30 foreign young scientists and an equal number of Indian young scientists (< 40 years) are attending (in addition to local participants from CAOS, DCCC, JNCASR and other departments/centres of IISc). The conference is scheduled to be conducted at Divecha Centre for Climate Change during December 5-7, 2016. Prof. R. Narasimha has kindly agreed to inaugurate the conference and Dr. M. Rajeevan, Secretary, MoES has agreed to be the chief guest.

There is a very good paper by Yue and Wang (2004) for determination of effective sample size for S to eliminate the influence of serial correlation on the MK test by Monte Carlo simulation. Hope this will be useful for you.
Please find the link to access the paper:

DOI: 10.1023/B:WARM.0000043140.61082.60

Study material / Trend Assessment by the Innovative-Şen Method
« on: November 21, 2016, 03:11:56 PM »
The MK test gives a holistic monotonic trend without any categorization of the time series into a set of clusters, but the innovative-Şen method is based on cluster and provides categorical trend behavior in a given time series. The main purpose of this paper is to provide important differences between these two approaches and their possible similarities.

Please follow the link to access the paper:

DAY & DATE:                Friday, 18th November 2016 TIME:                        2.30 PM (Tea/Coffee after the talk) VENUE:                Seminar Hall, Department of Management Studies, IISc. 

The presentation will focus on: -GeoIntelligence: Patterns, correlations and insights into economic indicators, market trends, macro-level changes and customer behaviour -Characteristics of Satellites & Satellite Data

Using Typeset you can write your content or upload your MS Word document, choose a relevant template and get the content formatted in seconds! The access is free to all students from IISc. All your documents would reside encrypted on the platform and only you will be able to access them. Also, you can export your documents any time into PDF/ LaTex/ Zip format. We have set up an exclusive access for IISc students. Click the following link to access Typeset and start progressing on your research today. (Platform can be accessed only via this link.) For any help or feedback, you can reach out to - Saikiran chandha(founder of Typeset) at"

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