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Messages - Pankaj Dey

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SM2RAIN is an algorithm, it is not complex or difficult to understand. It is based on a simple concept, i.e., when it rains, soil moisture increases. Therefore, simply relying on the inversion of the soil water balance equation (that is the equation governing the water fluxes between the atmosphere and the land surface), we estimate RAINFALL from SOIL MOISTURE observations: "Soil as a natural raingauge" (JGR 2014).

To read the remaining part, please follow the link:


Dear All,

GIAN, MHRD, Govt. of India sponsored International Course on  " ENVIRONMENTAL AND WATER RESOURCES DECISION MAKING USING INFORMATION THEORY UNDER CLIMATE AND ANTHROPOGENIC CHANGES" will be held during November 26–30, 2018 at College of Technology, GBPUAT, Pantnagar.
The International expert faculty will be Prof. Vijay P. Singh is a Distinguished Professor and Caroline & William N. Lehrer Distinguished Chair in Water Engineering, Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A & M University, College Station, Texas, USA.

His research interests include Surface-water Hydrology, Groundwater Hydrology, Hydraulics, Irrigation Engineering, Environmental Quality and Water Resources, and Hydrologic Impacts of Climate Change.
His professional heights include 850 papers published in refereed journals, 24 books, 57 edited books, 100 book chapters and many technical reports and special issues of journals. He is editor of many Journals. He has been awarded 2012 Texas A& M University Bush Excellence Award for Faculty in International Research; University Distinguished Professor Award 2013, Texas A & M University, 2013; and Lifetime Achievement Award, Environmental and Water Resources Institute, American Society of Civil Engineers, among more than 72 awards.
For registration visit

For course information visit

For updated information;

For GIAN information

Experts view:
Kindly circulate among your interested colleagues, research scholars, post graduate students, faculty, scientists, engineers.

Jakub Nowosad, a postdoc in the Space Informatics Lab at University of Cincinnati has develop these two courses on Spatial Analysis and GIS using R.

The relevant links are as follows:
Sample R Codes are also available with practical examples.

1. Introduction to Spatial Analysis using R.


2. GIS with R


3. Data Visualization and preprocessing

Link: 1.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

Link to paper:

Link to dataset:

Interesting information / History of Hydrology Interviews
« on: July 07, 2018, 12:20:56 PM »
This video series provides source material that will be of particular interest to scientists and instructors in the field of hydrology. Through in-depth interviews captured on film, eminent hydrologists discuss achievements in hydrological science that have occurred during their careers. These interviews offer valuable insight into the progression of research in the field of hydrology during the second half of the 20th century.

1. Interview with Prof. Eric F Wood by Prof. M. Sivapalan.

2. Interview with Prof. M. Sivapalan by Prof. Ross Woods

3. Interview with Prof. Keith J Beven.

4. Interview with Georgia Destouni

5. Interview with Mike J Kirkby

Tool to search for R packages on CRAN, based on their title, short and long descriptions. 'packagefinder' allows to search for multiple keywords at once and to combine the keywords with logical operators (AND/OR).

Link to manual:

Hydrological sciences / Community Water Model
« on: July 06, 2018, 09:06:40 PM »
Opensource model to examine how future water demand will evolve in response to socioeconomic change and how water availability will change in response to climate.


Calibrate and apply multivariate bias correction algorithms for climate model simulations of multiple climate variables. Three methods described by Cannon (2016)  and Cannon (2018)  are implemented: (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF transform (MBCn).

Main paper:

Package Manual:

Facilitates programmatic access to NASA Soil Moisture Active Passive (SMAP) data with R. It includes functions to search for, acquire, and extract SMAP data.

Link to manual:

Link to github page:

Hydrological sciences / 9 Free Global Land Cover / Land Use Data Sets
« on: June 15, 2018, 07:33:46 PM »
Please follow the link for more details:

Data Visualisation can be defined as representing numbers with shapes – and no matter what these shapes look like (areas, lines, dots), they need to have a color. Sometimes colors just make the shapes visible, sometimes they encode data or categories themselves. We’ll focus mostly on the latter in this article. But we’ll also take a general look at colors and what to consider when choosing them:

Link to the article:

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

Link to paper:

Link to data:

The package xplain is designed to help users interpret the results of their statistical analyses.
It does so not in an abstract way as textbooks do. Textbooks do not help the user of a statistical method understand his findings directly. What does a result of 3.14 actually mean? This is often hard to answer with a textbook alone because the book may provide its own examples but cannot refer to the specifics of the user’s case. However, as we all know, we understand things best when they are explained to us with reference to the actual problem we are working on. xplain is made to fill this gap that textbooks (and other learning materials) leave.
The basic idea behind xplain is simple: Package authors or other people interested in explaining statistics provide interpretation information for a statistical method (i.e. an R function) in the format of an XML file. With a simple syntax this interpretation information can reference the results of the user’s call of the explained R function. At runtime, xplain then provides the user with textual interpretation that really relates to his/her case.
Providing xplain interpretation information can be interesting for:R package authors who implement a statistical method
  • statisticians who develop statistical methods themselves
  • college and university teachers who want to make their teaching content more accessible for their students
  • everybody who enjoys teaching and explaining statistics and thinks he/she has something to contribute
  • xplain offers support for interpretation information in different languages and on different levels of difficulty.

link to the web page:

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