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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 "Analyzing Large-Scale Hydrologic Processes Using GRACE and Hydrometeorological Datasets" was recently published in the Water Resources Management.

Abstract: Water demand in India is growing due to its increasing population, economic growth and urbanization. Consequently, knowledge of interdependencies of large-scale hydrometeorological processes is crucial for efficient water resources management. Estimates of Groundwater(GW) derived from Terrestrial Water Storage (TWS) data provided by Gravity Recovery andClimate Experiment (GRACE) satellite mission along with various other satellite observations and model estimates now facilitates such investigations. In an attempt which is first of its kind in India, this study proposes Independent Component Analysis (ICA) based spatiotemporal analysis of Precipitation (P), Evapotranspiration (ET), Surface Soil Moisture (SSM), RootZone Soil Moisture (RZSM), TWS and GW to understand such interdependencies at intra- and interannual time scales. Results indicate that 84–99% of the total variability is explained by the first 6 ICs of all the variables, analyzed for a period 2002–2014. The Indian Summer monsoon rainfall (ISMR) is the causative-factor of the first component describing 61–82% of the total variability. The phase difference between all seasonal components of P and that of RZSM and ET is a month whereas it is 2 months between the seasonal components of P and that of SSM,TWS and GW. ET is observed to be largely dependent on RZSM while GW appears as themajor component of TWS. GW and TWS trends of opposite nature were observed in northernand southern part of India caused by inter-annual rainfall variability. These findings when incorporated in modelling frameworks would improve forecasts resulting in better water management.

Announcements / Rolling advertisement for All Faculty Positions
« on: July 02, 2018, 03:59:52 PM »
Applications are invited from Indian nationals including Persons of Indian Origins (PIOs) and Overseas Citizens of India (OCIs) and Foreign Nationals* to the posts of Assistant Professor/Associate Professor/Professor, in the Departments of Applied Chemistry, Applied Geology, Applied Geophysics, Applied Mathematics, Applied Physics, Chemical Engineering, Civil Engineering, Computer Science & Engineering, Electrical Engineering, Electronics Engineering, Environmental Science and Engineering, Fuel and Mineral Engineering, Humanities and Social Sciences, Management Studies, Mechanical Engineering, Mining Machinery Engineering, Mining Engineering, and Petroleum Engineering.

The work carried out by Ila Chawla along with Prof. P.P. Mujumdar titled "Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin" was published in the Hydrology and Earth System Sciences.

Abstract: Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

The work carried out by Subir Paul along with Prof. D Nagesh Kumar titled "Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach" was published in the ISPRS Journal of Photogrammetry and Remote Sensing.

Abstract: Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

The work carried out by H. R. Shwetha along with Prof. D Nagesh Kumar titled "Estimation of daily vegetation coefficients using MODIS data for clear and cloudy sky conditions" was published in the International Journal of Remote Sensing.

Abstract: Spatially distributed vegetation coefficients () data with high temporal resolution are in demand for actual evapotranspiration estimation, crop condition assessment, irrigation scheduling, etc. Traditional remotely sensed based  data application gets hindered because of two main reasons i.e 1) spectral reflectance based  accounts only for transpiration factor, but fails to account for total evapotranspitration. 2) required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the  data. Hence there is a necessity of a model which accounts for both transpiration and evpaoration factors and also for a gap filling method, which can produce accurate continuous quantification of  values. Therefore, in this study, different combinations of enhanced vegetation index (EVI), global vegetation moisture index (GVMI) and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain the best model. To fill the gaps in the data, initially, temporal fitting of  values have been examined using Savitsky-Goley (SG) filter for 3 years of data (2012–2014), but this fails when sufficient high quality  values are unavailable. In this regard, three gap filling techniques namely regression, artificial neural networks (ANN) and interpolation techniques have been employed over Cauvery basin. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate  values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations with correlation coefficient (r) and root mean square error (RMSE) values of 0.824 and 0.204 respectively. Furthermore, the results indicated that SG filter can be used for temporal fitting and for gap filling regression technique performed better than other techniques with the r and RMSE values of 0.68 and 0.25 for Berambadi station.

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.

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