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Messages - Subir Paul

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Programming / Whitebox R Package
« on: June 28, 2019, 06:46:58 AM »
The whitebox R package is built on WhiteboxTools, an advanced geospatial data analysis platform developed by Prof. John Lindsay (webpage; jblindsay) at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. LiDAR point clouds can be interrogated (LidarInfo, LidarHistogram), segmented, tiled and joined, analyized for outliers, interpolated to rasters (DEMs, intensity images), and ground-points can be classified or filtered. WhiteboxTools is not a cartographic or spatial data visualization package; instead it is meant to serve as an analytical backend for other data visualization software, mainly GIS. Suggested citation: Lindsay, J. B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95, 75-84. doi:10.1016/j.cageo.2016.07.003.

https://github.com/giswqs/whiteboxR?fbclid=IwAR3fCaNX5iahf3PKOxlM11FhhwVhjXJIx3Dp5bXFgUUtzwqXGM8T1gUWtQ4

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Keep in mind that journal impact factor is just one metrics, so don’t take it too seriously! (Qiusheng Wu)
https://lidarblog.com/2019-06-20-journal-impact-factor?fbclid=IwAR1N4CNLH3PBBT4ZsyzfO6MTeFgeIpSKTi070l0dBkr7JJwu1xill6IOG-w

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Abstract: This paper is the outcome of a community initiative to identify major unsolved scientific problems
in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure
involved a public consultation through on-line media, followed by two workshops through which a
large number of potential science questions were collated, prioritised, and synthesised. In spite of
the diversity of the participants (230 scientists in total), the process revealed much about
community priorities and the state of our science: a preference for continuity in research questions
rather than radical departures or redirections from past and current work. Questions remain focussed
on process-based understanding of hydrological variability and causality at all space and time scales.
Increased attention to environmental change drives a new emphasis on understanding how change
propagates across interfaces within the hydrological system and across disciplinary boundaries. In
particular, the expansion of the human footprint raises a new set of questions related to human
interactions with nature and water cycle feedbacks in the context of complex water management
problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will
help guide research efforts for some years to come.
https://www.researchgate.net/publication/333685614_Twenty-three_Unsolved_Problems_in_Hydrology_UPH_-_a_community_perspective

You may find this article interesting!

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One major challenge in applying crop simulation models at the regional or global scale is the lack of available global gridded soil profile data. We developed a 10-km resolution global soil profile dataset, at 2 m depth, compatible with DSSAT using SoilGrids1km. Several soil physical and chemical properties required by DSSAT were directly extracted from SoilGrids1km. Pedo-transfer functions were used to derive soil hydraulic properties. Other soil parameters not available from SoilGrids1km were estimated from HarvestChoice HC27 generic soil profiles. The newly developed soil profile dataset was evaluated in different regions of the globe using independent soil databases from other sources. In general, we found that the derived soil properties matched well with data from other soil data sources. An ex-ante assessment for maize intensification in Tanzania is provided to show the potential regional to global uses of the new gridded soil profile dataset.
https://www.sciencedirect.com/science/article/pii/S1364815218313033

Availability: The soil dataset product is available at https://doi.org/10.7910/DVN/1PEEY0 under a Create Commons CC-BY-NC (Attribution, Non-commercial) 4.0 license via Dataverse.

Fortran codes for deriving soil parameters are also freely available via GitHub (https://github.com/Agro-Climate/Global-gridded-soil-data-for-DSSAT-at-10km).

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SM2RAIN-ASCAT is a new global scale rainfall product obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (Brocca et al., 2014). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over an irregular grid at 12.5 km on a global scale. The product represents the cumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (Wagner et al., 2013) for the period from January 2007 to December 2018 (12 years).

The rainfall dataset is provided in netCDF format. A total of 12 netCDF files, one per year, are provided. The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests.

A GeoTIFF version of the dataset is available here: https://zenodo.org/record/2615279

A sample dataset that can be used for testing SM2RAIN algorithm is available here: https://zenodo.org/record/2580285

Details on the dataset development and its assessment with ground and reanalysis observations are provided as:

Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W. (2019). SM2RAIN-ASCAT (2007–2018): global daily satellite rainfall from ASCAT soil moisture, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-48, in review.

https://www.earth-syst-sci-data-discuss.net/essd-2019-48/?fbclid=IwAR3u62jBm397QbhJIHjWUWYULK04yaO8U_pAtWXBc7XinQl8OJGEPXBScvA
https://zenodo.org/record/2591215#.XMRt8TAzaM8

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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

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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#!

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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/

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