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Messages - Pankaj Dey

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1
bigleaf is an R package for the calculation of physical (e.g. aerodynamic conductance, surface temperature) and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. All calculations are based on a 'big-leaf' representation of the vegetation and return representative bulk ecosystem/canopy variables.


Link to R Manual:
CitationKnauer J, El-Madany TS, Zaehle S, Migliavacca M (2018) Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLoS ONE

2
Hydrological sciences / Creating aesthetically pleasing plots in MATLAB
« on: August 16, 2018, 11:08:56 PM »
Dear All,


Most of us use MATLAB as one of the programming tools as well as for data visualization. Different kind of plots such as Probability Density Function (PDF), Boxplot, Heatmap, creating beautiful confidence interval around statistic, are often used in Hydrology and Water Resources Engineering.


Here, I am sharing some of the links from Github to do all kinds of plots mentioned above. If you have a Github account, fork them for future reference. All the functions are explained step by step.


1. Probability Density Function: https://github.com/ahmedaq/Making-elegant-Matlab-figures#generatepdf


2. Boxplots:  https://github.com/ahmedaq/Making-elegant-Matlab-figures#boxplot
                   https://github.com/kakearney/boxplot2-pkg
3. Heatmaps: https://github.com/ahmedaq/Making-elegant-Matlab-figures#heatmap


4. Confidence Interval or Bounded Lines: https://github.com/kakearney/boundedline-pkg


Thank you very much.

3
Interesting information / The Effects of Tropical Vegetation On Rainfall
« on: August 14, 2018, 10:58:27 PM »
Vegetation modifies land-surface properties, mediating the exchange of energy, moisture, trace gases, and aerosols between the land and the atmosphere. These exchanges influence the atmosphere on local, regional, and global scales. Through altering surface properties, vegetation change can impact on weather and climate. We review current understanding of the processes through which tropical land-cover change (LCC) affects rainfall. Tropical deforestation leads to reduced evapotranspiration, increasing surface temperatures by 1–3 K and causing boundary layer circulations, which in turn increase rainfall over some regions and reduce it elsewhere. On larger scales, deforestation leads to reductions in moisture recycling, reducing regional rainfall by up to 40%. Impacts of future tropical LCC on rainfall are uncertain but could be of similar magnitude to those caused by climate change. Climate and sustainable development policies need to account for the impacts of tropical LCC on local and regional rainfall.


Link to paper: https://www.annualreviews.org/doi/abs/10.1146/annurev-environ-102017-030136

4
In recent decades India has undergone substantial land use/land cover change as a result of population growth and economic development. Historical land use/land cover maps are necessary to quantify the impact of change at global and regional scales, improve predictions about the quantity and location of future change and support planning decisions. Here, a regional land use change model driven by district-level inventory data is used to generate an annual time series of high-resolution gridded land use/land cover maps for the Indian subcontinent between 1960–2010. The allocation procedure is based on statistical analysis of the relationship between contemporary land use/land cover and various spatially explicit covariates. A comparison of the simulated map for 1985 against remotely-sensed land use/land cover maps for 1985 and 2005 reveals considerable discrepancy between the simulated and remote sensing maps, much of which arises due to differences in the amount of land use/land cover change between the inventory data and the remote sensing maps.


Link to paper: https://www.nature.com/articles/sdata2018159


Link to data: https://figshare.com/collections/A_spatio-temporal_land_use_land_cover_reconstruction_for_India_1960_2010_/3967329

5
While analyzing geospatial data, easy visualization is often needed that allows for quick plotting, and simple, but easy interactivity. Additionally, visualizing geospatial data in projected coordinates is also desirable. The 'quickmapr' package provides a simple method to visualize 'sp', 'sf' (via coercion to 'sp'), and 'raster' objects, allows for basic zooming, panning, identifying,labeling, selecting, and measuring spatial objects. Importantly, it does not require that the data be in geographic coordinates.


Link: https://cran.r-project.org/web/packages/quickmapr/quickmapr.pdf

7
Reliable meteorological data are a basic requirement for hydrological and ecological studies at the landscape scale. Given the large spatial variation of meteorology over complex terrains, meteorological records from a single weather station are often not representative of entire landscapes. Studies made on multiple sites over a landscape require different meteorological series for each site; and other studies may require meteorological data series for all grid cells of a landscape, in a continuous way. In these cases, spatial correlation between the meteorology series of different sites or cells must be taken into account. For example, the sequence of days with rain of contiguous cells will normally be the same or very similar, even if precipitation amounts may differ. Finally, studies addressing the impacts of climate change on forests and landscapes require downscaling coarse-scale predictions of global or regional climate models to the landscape scale. When downscaling predictions for several locations in a landscape, spatial correlation of predictions is also important.


With the aim to assist research of climatic impacts on forests, the R package meteoland provides utilities to estimate daily weather variables at any position over complex terrains:


 1. Spatial interpolation of daily weather records from meteorological stations.


 2. Statistical correction of meteorological data series (e.g. from climate models).


 Spatial interpolation is required when meteorology for the area and period of interest cannot be obtained from local sensors. The nearest weather station may not have data for the period of interest or it may be located too far away to be representative of the target area. Correcting the biases of a meteorological data series containing biases using a more accurate meteorological series is necessary when the more accurate series does not cover the period of interest and the less accurate series does. The less accurate series may be at coarser scale, as with climate model predictions or climate reanalysis data. In this case one can speak of statistical correction adn downscaling. However, one may also correct the predictions of climate models using reanalysis data estimated at the same spatial resolution.


Link to paper: https://www.sciencedirect.com/science/article/pii/S1364815217309830


Link to R Manual: https://cran.r-project.org/web/packages/meteoland/meteoland.pdf


Link to User Guide: https://cran.r-project.org/web/packages/meteoland/vignettes/Meteorology.pdf

8
Gramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab.


Link: https://in.mathworks.com/matlabcentral/fileexchange/54465-gramm-complete-data-visualization-toolbox-ggplot2-r-like


Link: https://github.com/piermorel/gramm

9
Hydrological sciences / El Niño–Southern Oscillation complexity
« on: July 26, 2018, 08:45:25 PM »
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.


Link to paper: https://www.nature.com/articles/s41586-018-0252-6

10
The Parallel Data Assimilation Framework - PDAF - is a software environment for ensemble data assimilation. PDAF simplifies the implementation of the data assimilation system with existing numerical models. With this, users can obtain a data assimilation system with less work and can focus on applying data assimilation.
PDAF provides fully implemented and optimized data assimilation algorithms, in particular ensemble-based Kalman filters like LETKF and LSEIK. It allows users to easily test different assimilation algorithms and observations. PDAF is optimized for the application with large-scale models that usually run on big parallel computers and is applicable for operational applications. However, it is also well suited for smaller models and even toy models.
PDAF provides a standardized interface that separates the numerical model from the assimilation routines. This allows to perform the further development of the assimilation methods and the model independently. New algorithmic developments can be readily made available through the interface such that they can be immediately applied with existing implementations. The test suite of PDAF provides small models for easy testing of algorithmic developments and for teaching data assimilation.
PDAF is an open-source project. Its functionality will be further extended by input from research projects. In addition, users are welcome to contribute to the further enhancement of PDAF, e.g. by contributing additional assimilation methods or interface routines for different numerical models.


Link: http://pdaf.awi.de/trac/wiki/WikiStart

11
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:
https://www.linkedin.com/pulse/do-you-know-sm2rain-read-here-its-short-story-luca-brocca/

13

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. http://baen.tamu.edu/people/singh-vijay/
For registration visit
http://www.gian.iitkgp.ac.in/GREGN

For course information visit
http://www.gbpuat.ac.in/gian/index.html

For updated information;
https://sites.google.com/site/pantnagar2018/

For GIAN information http://www.gian.iitkgp.ac.in/

Experts view:

https://youtu.be/A7-Mc0udPUU
Kindly circulate among your interested colleagues, research scholars, post graduate students, faculty, scientists, engineers.

14
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.


Link: https://nowosad.github.io/presentations/2017/intro_to_spatial_analysis/
Slides: https://cdn.rawgit.com/Nowosad/Intro_to_spatial_analysis/05676e29/Intro_to_spatial_analysis.html#1


2. GIS with R


Link: https://nowosad.github.io/presentations/2017/gis_with_r_start/
Slides: https://cdn.rawgit.com/Nowosad/gis_with_r_how_to_start/aea08f46/gis_with_r_start.html#1


3. Data Visualization and preprocessing


Link: 1. https://nowosad.github.io/presentations/2017/intro_to_data_visulalization/
        2. https://cdn.rawgit.com/Nowosad/Intro_to_data_processing/5d0da6a7/Intro_to_data_processing.html#1




15
Abstract.
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 https://doi.org/10.1594/PANGAEA.885492 (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: https://www.earth-syst-sci-data.net/10/1237/2018/


Link to dataset: https://doi.pangaea.de/10.1594/PANGAEA.885492

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