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Messages - Subir Paul

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Identifying floodplain boundaries is of paramount importance for earth, environmental and socioeconomic studies addressing riverine risk and resource management. However, to date, a global floodplain delineation using a homogeneous procedure has not been constructed. In this paper, we present the first, comprehensive, high-resolution, gridded dataset of Earth’s floodplains at 250-m resolution (GFPLAIN250m). We use the Shuttle Radar Topography Mission (SRTM) digital terrain model and set of terrain analysis procedures for geomorphic floodplain delineations. The elevation data are processed by a fast geospatial tool for floodplain mapping available for download at https://github.com/fnardi/GFPLAIN. The GFPLAIN250m dataset can support many applications, including flood hazard mapping, habitat restoration, development studies, and the analysis of human-flood interactions. To test the GFPLAIN250m dataset, we perform a consistency analysis with floodplain delineations derived by flood hazard modelling studies in Europe.

https://www.nature.com/articles/sdata2018309?fbclid=IwAR3MHI8XxKnlhlBjiEHOm-roCgMULRh9EdDAmfw_8lqm90aiBzhYHyX8GX0

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Air temperature at 2 m above the land surface is a key variable used to assess climate change. However, observations of air temperature are typically only available from a limited number of weather stations distributed mainly in developed countries, which in turn may often report time series with missing values. As a consequence, the record of air temperature observations is patchy in both space and time. Satellites, on the other hand, measure land surface temperature continuously in both space and time. In order to combine the relative strengths of surface and satellite temperature records, we develop a dataset in which monthly air temperature is predicted from monthly land surface temperature for the years 2003 to 2016, using a statistical model that incorporates information on geographic and climatic similarity. We expect this dataset to be useful for various applications involving climate monitoring and land-climate interactions.

https://www.nature.com/articles/sdata2018246

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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 iisc.sonali@gmail.com

https://www.int-res.com/prepress/c01530.html

5
The work is carried out by ChandraRupa along with Prof. P.P.Mujumdar titled "Dependence structure of urban precipitation extremes" is recently published in the Advances in Water Resources.

Abstract: Addressing spatial variation of extreme precipitation in urban areas is important for urban hydrologic designs. Climatology of urban areas is, in general, different from that of its surroundings and the spatial variation of extreme precipitation within the city exhibits shorter spatial range, especially for short duration events. This work aims at understanding the dependence structure of extreme precipitation within an urban area and its surrounding non-urban areas at various durations. The spatial dependence of precipitation is analysed with three different measures for examining dependence, considering observations from pairs of stations. Further, the spatial precipitation extremes are modelled with the max-stable process to include the dependence structure of spatial extremes. The City of Berlin, Germany, with surrounding non-urban area is considered to demonstrate the methodology. For this case study, the hourly precipitation shows weaker dependence within the city even at small distances, whereas the daily precipitation shows a high degree of dependence. This dependence structure of the daily precipitation gets masked as more and more surrounding non-urban areas are included in the analysis. Further, the extreme precipitation at different durations are modelled considering max-stable process. Different geographical and climatological covariates are considered in the modelling of location and scale parameters of the Generalized Extreme Value distribution. The geographical covariates are seen to be predominant within the city and the climatological covariates prevail when non-urban areas are added. These results suggest the importance of quantification of dependence structure of spatial precipitation at the sub-daily timescales, as well as the need to more precisely model spatial extremes within the urban areas.

https://www.sciencedirect.com/science/article/pii/S030917081730564X

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This two-week programme jointly organized by the Ashoka Trust for Research in Ecology and the Environment (ATREE) and Rajiv Gandhi University (RGU) in Rono Hills, Doimukh, Arunachal Pradesh, delves into various concepts about ecology, climate change, interaction of climate change and biodiversity, ecosystem services and functions as well as social science.
Current post-graduate students, early career professionals working with or with experience of working with non-profits, government agencies, research institutions and academic centres in Northeastern India. Age limit: 35 years.

Deadline to apply is 15th September, 2018.
Selected applicants will be notified directly by 21st September, 2018. Our decisions will be final.

There is no fee for the workshop. All participants will be reimbursed for travel from their departure location to the RGU campus, and back (train (3AC), bus, shared taxi). Accommodation at the RGU hostels and food will be covered. Stationary and equipment for the field exercises and mini-projects will be provided by the organizers.

COURSE DURATION: October 22nd – November 3rd, 2018

VENUE: Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh

APPLY ONLINE: http://bit.ly/EH_Apply

https://www.atree.org/acc_eh_2018

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The work is carried out by B.N.Priyanka along with Prof. M.S. Mohan Kumar titled "Estimating anisotropic heterogeneous hydraulic conductivity and dispersivity in a layered coastal aquifer of dakshina kannada district, karnataka" is recently published in the Journal of Hydrology.

Abstract: The solution for the inverse problem of seawater intrusion at an aquifer scale has not been studied as extensively as forward modeling, because of the conceptual and computational difficulties involved. A three-dimensional variable-density conceptual phreatic model is developed by constraining with real-field data such as layering, aquifer bottom topography and appropriate initial conditions. The initial aquifer parameters are layered heterogeneous and spatially homogeneous that are based on discrete field measurements. The developed conceptual model shows poor correlation with observed state variables (hydraulic head and solute concentration), signifying the importance of spatial heterogeneity in hydraulic conductivity and dispersivity of all the layers. The conceptual model is inverted to estimate the anisotropic spatially varying hydraulic conductivity and the longitudinal dispersivity at the pilot points by minimizing the least square error of state variables across the observation wells. The inverse calibrated model is validated for the hydraulic head at validation wells and the solute concentration is validated with equivalent solute concentration derived from the electrical resistivity, which shows good results against the field measurements. The verification of estimated anisotropic hydraulic conductivity with the electrical resistivity tomography image shows good agreement. This investigation gives an insight about constraining the highly parameterized inverse model with real-field data to estimate spatially varying aquifer parameters for an effective simulation of the seawater intrusion in a layered coastal aquifer.

https://www.sciencedirect.com/science/article/pii/S0022169418306280#!

9
The work carried out by H. R. Shwetha along with Prof. D Nagesh Kumar titled "Performance evaluation of satellite-based approaches for the estimation of daily air temperature and reference evapotranspiration" was recently published in the Hydrological Sciences Journal.

Abstract: Different satellite-based radiation (Makkink) and temperature-based (Hargreaves-Samani, Penman Monteith Temperature, PMT) reference evapotranspiration (ETo) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (Tmax) in the ETo models was estimated using temperature/vegetation index (TVX) and advanced statistical approach (ASA), and evaluated with observed Tmax obtained from automatic weather stations; minimum air temperature (Tmin) was estimated using ASA; and solar radiation data was obtained from the Kalpana-1 satellite. Land surface temperature was employed in the ETo models in place of air temperature (Ta) to check the potency of its applicability. The results suggest that the PMT model with Ta as input performed better than the other ETo models, with correlation coefficient (r) averaged root mean square error (RMSE), and mean bias error (MBE) of 0.77, 0.80 mm d−1 and –0.69 for all land cover classes. The ASA yielded better Tmax and Tmin values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).

https://sciencetrends.com/satellites-to-estimate-reference-evapotranspiration-and-air-temperature/

10
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.

https://link.springer.com/epdf/10.1007/s11269-018-2070-x?author_access_token=YxxTcjx7d12KvZnS_Ggkofe4RwlQNchNByi7wbcMAY6E3fu4PE4tR_0dFoWrWP5OVtTWI4aUyFDT1hFxFyDq2y_v6UkKX4zm5AR57ce_khbfc4vO7nLCK4iMvzwGtGiod4m3KNP-STeHcQ4s58e1Iw%3D%3D

11
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.

https://www.iitism.ac.in/index.php/empreg/Registration
https://www.iitism.ac.in/uploads/adv/Faculty-AD-2018.pdf

12
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.

https://www.hydrol-earth-syst-sci.net/22/1095/2018/

13
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.

https://www.sciencedirect.com/science/article/pii/S0924271618300303?via%3Dihub

14
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.

https://www.tandfonline.com/doi/abs/10.1080/01431161.2018.1448480?journalCode=tres20

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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.

https://www.sciencedirect.com/science/article/pii/S0022169418303780?via%3Dihub

This work is also highlighted in "Research Matters":
https://researchmatters.in/news/study-shows-increase-surface-water-peninsular-india#.WzTlnlw8x_Y.facebook

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