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Messages - Subir Paul

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The work carried out by Chandan Banerjee along with Prof. D Nagesh Kumar titled "Assessment of Surface Water Storage trends for increasing groundwater areas in India" was recently published in the Journal of Hydrology.

Abstract: Recent studies based on Gravity Recovery and Climate Experiment (GRACE) satellite mission suggested that groundwater has increased in central and southern parts of India. However, surface water, which is an equally important source of water in these semi-arid areas has not been studied yet. In the present study, the study areas were outlined based on trends in GRACE data followed by trend identification in surface water storages and checking the hypothesis of causality. Surface Water Extent (SWE) and Surface Soil Moisture (SSM) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) respectively, are selected as proxies of surface water storage (SWS). Besides SWE and SSM, trend test was performed for GRACE derived terrestrial water storage (TWS) for the study areas named as R1, R2, GOR1 and KOR1. Granger-causality test is used to test the hypothesis that rainfall is a causal factor of the inter-annual variability of SWE, SSM and TWS. Positive trends were observed in TWS for R1, R2 and GOR1 whereas SWE and SSM show increasing trends for all the study regions. Results suggest that rainfall is the granger-causal of all the storage variables for R1 and R2, the regions exhibiting the most significant positive trends in TWS.

This work is also highlighted in "Research Matters":

Data / Game-changing Climate Data Store
« on: June 14, 2018, 07:03:55 PM »
Europe’s Climate Data Store (CDS), launched today, is a one-stop-shop for past, present and future climate information. The CDS greatly improves access to climate data and tools, is open and free for all to use, and will change the ways in which society can benefit from Earth observations and climate science.

Developed by the Copernicus Climate Change Service (C3S) at the European Centre for Medium-Range Weather Forecasts (ECMWF), the store draws on the vast wealth of Earth observation data collected day and night through the European Commission's Copernicus Programme.

The CDS is a cloud-based tool that allows policy-makers, businesses and scientists to browse and combine online petabytes of raw data, build their own applications, maps and graphs online in real time, and access all relevant climate information at the push of a button.

The CDS includes a toolbox enabling users to build their own web-based apps, and to analyse, monitor and predict changes in climate drivers – such as surface temperature and soil moisture – and their impact on business sectors such as energy, water management or tourism. Transforming data into climate-related information is therefore key added value of the Climate Data Store.

“The Climate Data Store gives a wide range of users easy access to good quality climate data in one place, and offers tools that will ultimately lead to better use of the data,” says Dick Dee, ECMWF’s Deputy Head of the Copernicus Climate Change Service.

The Climate Data Store (#ClimateDataStore) is freely accessible to everyone at

Hydrological sciences / GRACE-FO Will Help Monitor Droughts
« on: June 02, 2018, 09:59:51 AM »
The NASA/German Research Centre for Geosciences Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) mission launched onboard a SpaceX Falcon 9 rocket, Tuesday, May 22, 2018, from Space Launch Complex 4E at Vandenberg Air Force Base in California. The mission will measure changes in how mass is redistributed within and among Earth's atmosphere, oceans, land and ice sheets, as well as within Earth itself. GRACE-FO is sharing its ride to orbit with five Iridium NEXT communications satellites as part of a commercial rideshare agreement.

Data from the first NASA satellite mission dedicated to measuring the water content of soils is now being used operationally by the U.S. Department of Agriculture to monitor global croplands and make commodity forecasts.

The Soil Moisture Active Passive mission, or SMAP, launched in 2015 and has helped map the amount of water in soils worldwide. Now, with tools developed by a team at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, SMAP soil moisture data is being incorporated into the Crop Explorer website of the USDA’s Foreign Agricultural Service, which reports on regional droughts, floods and crop forecasts. Crop Explorer is a clearinghouse for global agricultural growing conditions, such as soil moisture, temperature, precipitation, vegetation health and more.

This dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system.

Announcements / Research Positions: SRA/RA Natural Sciences/Engg.
« on: May 14, 2018, 06:12:34 PM »
The SRAs/RAs recruited will work as part of an interdisciplinary team comprising economists and social scientists, but with distinct responsibilities.

Essential qualifications and skills

a) First class M.E/M. Tech/M.Sc (Water Resources Engineering/Environmental Engineering/Environmental Science)
b) Experience in Water quality modeling/Hydraulic Modeling
c) Ability to gather and analyze data, both qualitative and quantitative
d) Competence in English communication, both oral and written
e) Willingness to carry out extensive fieldwork in rural areas
f) Ability to network/liaison with local groups, organizations, agencies and the research team

Desirable qualifications
a) Knowledge of local language
b) Prior training or experience in working with issues relating to water and/or climate change

Gross salary: Rs. 26,000 to Rs. 35,000 per month depending upon qualifications and experience.

Duration of appointment: 2 years (to be confirmed after the first 3 months)

Interested people are requested to contact: Usha ( for natural science positions. Please furnish a current CV, a writing sample such as a research paper, project report or thesis chapter, and contact information of two referees.

Announcements / Position: Research Associates
« on: April 18, 2018, 09:24:58 PM »
ATREE invites applications for one Research Associate for a project 'Mapping ecosystems of the Northeast and Quantitative assessment and mapping of plant diversity and biological resources in Sikkim Himalaya. The project is funded by Department of Biotechnology, Government of India, New Delhi. The multi-partnership research project covers biosystematic & ecology studies of flowering and non-flowering plants, selected animal groups and documenting biological resources across the states of NE region that harbour two global hotspots of biodiversity.

Project Research staff are expected to curate the taxonomy and quantitative data of plants, biological resources, mapping their distribution patterns, write reports and prepare articles for peer-reviewed journals and reach-out to society through various media including Indian Bioresource Information Network (IBIN), DBT web portal. JRFs will be facilitated to register for PhD degree. We strongly encourage applicants from Northeast region states of India. The candidates should be self-driven, highly motivated and willing to undertake extensive fieldwork in remote field sites in the entire Northeast region.

Research Position: Research Associate (one)

Emoluments: Rs. 38000/- + 30% HRA for all three years, strictly as per DST guidelines*

Essential qualifications :

PhD in Remote Sensing / Geomatics / Geography / Environmental Science/ Natural resources management or closely related field
Experience in GIS/RS software such as ERDAS Imagine, Idrisi, QGIS & ArcGIS.
Experience with SQL, Python or R.
Preferable Qualification

Experience in automated image processing and analysis through custom scripts and applications (i.e. Python or R).
Experience in ArcGIS models and scripting.
Ability to work in interdisciplinary research teams
The candidate is required to provide complete services and assistance in:

Geospatial database development and management.
Conducting geospatial analysis for various research activities such as land cover mapping, modelling, image processing etc.
Assistance in training and outreach activities of the project, preparation of reports, manuscripts & organizing workshops

The India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001 is a compilation of the finest level of administrative boundaries in India (village/town-level) and over 200 socio-economic variables collected during the Indian Census in 1991 and 2001.
India Annual Winter Cropped Area, v1 (2001 – 2016):

Programming / Hydrological Models in an R Package (airGR)
« on: November 20, 2017, 05:27:06 PM »
airGR is a package which brings into the R software the hydrological modelling tools used and developed at the Catchment Hydrology Research Group of Irstea (France), including the GR rainfall-runoff models and a snowmelt and accumulation model, CemaNeige. The airGR package has been designed to fulfill two major requirements: to facilitate the use by non-expert users and to allow flexibility regarding the addition of external criteria, models or calibration algorithms.

FREEWAT is an HORIZON 2020 project financed by the EU Commission under the call WATER INNOVATION: BOOSTING ITS VALUE FOR EUROPE.
FREEWAT main result is an open source and public domain GIS integrated modelling environment (the FREEWAT platform) for the simulation of water quantity and quality in surface water and groundwater with an integrated water management and planning module.
If you are interested in water management and in simulation tools (and you are especially dealing with groundwater management) please visit the Software and Training page of this web site.
FREEWAT is conceived as a composite plugin for the well-known QGIS ( open source desktop software.

As composite plugin, FREEWAT is designed as a modular ensemble of different tools: some of them can be used independently, while some modules require the preliminary execution of other tools. In this framework, the following tool classifications can be defined:

Tools for the analysis, interpretation and visualization of hydrogeological and hydrochemical data and quality issues, also focusing on advanced time series analysis, embedded in akvaGIS module.

Simulation of models related to the hydrological cycle and water resources management:  flow models, transport models, crop growth models, management and optimization models (also related to irrigation management and rural issues).

Tools to perform model calibration, sensitivity analysis and uncertainty quantifications.

Additional tools for general GIS operations to prepare input data, and post-processing functionalities (module OAT – Observation and Analysis Tool).

SAC invites on-line applications for position of Junior Research Fellows (JRF) and Research
Associates (RA):

Data / 12 Sources to Download FREE Land Cover and Land Use Data
« on: October 25, 2017, 09:14:24 AM »
1. MODIS Global Land Cover
2. Global Land Survey (GLS)
3. Global Land Cover-SHARE (GLC-SHARE)
4. USGS – Global Land Cover Characterization (GLCC)
5. USGS Land Cover Institute (LCI)
6. Global Land Use Dataset
7. USGS Global Land Cover
8. Global Forest Change 2000–2015 Data
9. GlobCover
10. PALSAR Forest/Non-Forest map
11. MODIS-based Global Land Cover Climatology
12. GlobeLand30

Post your question/information / Water Balance App
« on: October 19, 2017, 06:18:41 PM »
This app is based on data from NASA’s Global Land Data Assimilation System (GLDAS-2.1), which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.

Programming / R Package: spm (Spatial Predictive Modeling)
« on: September 12, 2017, 05:11:10 PM »
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method.

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