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Messages - Karthikeyan L

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1
A work carried out by Sonali Pattanayak titled "Linkage between global sea surface temperature and hydroclimatology of a major river basin of India before and after 1980" got published recently in the Environmental Research Letters.

Abstract: Frequent occurrence of flood and drought worldwide has drawn attention to assess whether the hydroclimatology of major river basins has changed. Mahanadi river basin (MRB) is the major source of fresh water for both Chattisgarh and Odisha states (71 million population approximately) of India. The MRB (141,600 km2 area) is one of the most vulnerable to climate change and variations in temperatures and precipitation regions. In the recent years, it has been repetitively facing the adverse hydrometeorological conditions. Large-scale ocean-atmospheric phenomena have substantial influence on river hydroclimatology. Hence Global sea surface temperature (SST) linkage with precipitation and surface temperature of MRB is analyzed over period 1950-2012. Significant changes in seasonal correlation patterns are witnessed from 1950-1980 (PR-80) to 1981-2012 (PO-80) periods. The correlation is higher during PR-80 compared to PO-80 between Niño region SST versus maximum temperature (Tmax) in all seasons except pre monsoon season and minimum temperature (Tmin) in all seasons except monsoon season. However, precipitation correlation changes are not prominent. Like SST, correlation patterns of sea level pressure with precipitation, Tmax and Tmin are shifted conspicuously from PR-80 to PO-80. These shifts could be related to change in Pacific decadal SST patterns and human induced anthropogenic effects. Fingerprint-based detection and attribution analysis revealed that the observed changes in Tmin (pre monsoon and monsoon season) during second half of the 20th century cannot be explained solely by natural variability and these changes can be attributed to human induced anthropogenic effect.

Sonali can be contacted at iisc.sonali@gmail.com

2
Recently, I have published a couple of papers related to satellite microwave soil moisture research in the Advances in Water Resources Journal.

The process of retrieving soil moisture from satellite microwave sensors depends on the type of sensor i.e., active and passive microwave sensors. Over the past four decades, the microwave community has progressed in terms of improving the sensor design and retrieval algorithm so as to achieve accurate global scale soil moisture observations.

The first paper titled "Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms" gives a comprehensive overview of the developments that took place in the retrieval algorithms over the past four decades. The paper discusses the algorithmic developments of both active as well as passive sensors. We have also, for the first time, summarized the literature in the form of figures, one each for active and passive microwave soil moisture research (PFA). This review also discusses the latest developments in components of the algorithms, and also discusses the challenges that need attention in future. I can say that this paper serves as a starting point for someone who wants to venture into the field of microwave soil moisture research.

The second paper titled "Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons" is focused on assessing the accuracy of soil moisture products, which were developed over the past forty years. We have considered eight passive (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS, and SMAP), two active (ERS-Scatterometer, and MetOp-ASCAT), and one active-passive combined (ESA-CCI combined product) soil moisture products for the analysis. The Contiguous United States (CONUS) is considered as a case study. The validation is carried out using the data pertaining to 1058 stations over the CONUS, along with model soil moisture simulations obtained from the VIC land surface model. We analyzed these products in terms of daily coverage (a figure in this context is atteched with this post), temporal performance, and spatial performance. We also carried out inter-satellite comparisons to study the roles of sensor design and algorithms on the retrieval accuracy. The Part 1 serves as a prelude to this paper. Through these papers, one can get an idea about how well the satellites along with algorithms have progressed over the four decades and significantly improved the accuracy of soil moisture retrievals.

Part 1 can be downloaded for free from here: https://authors.elsevier.com/a/1VjkU16J1mlNrZ (until November 4, 2017)
Part 2 can be downloaded for free from here: https://authors.elsevier.com/a/1VoHy16J1mlNra (until November 17, 2017)

Here are the permanent links:
Part 1: http://www.sciencedirect.com/science/article/pii/S0309170817301859
Part 2: http://www.sciencedirect.com/science/article/pii/S0309170817301860

You can write an email to me at karthik120120@gmail.com for full-text requests or any other query related to papers.

3
The work carried out by Ila Chawla along with Prof. P.P. Mujumdar titled "Partitioning Uncertainty in Streamflow Projections under Nonstationary Model Conditions" was recently published in the Advances in Water Resources Journal.

In this work, the authors, in a novel attempt, have addressed the possibility of nonstationary hydrological model (here they have used the VIC land surface model coupled with a routing model). They found that the hydrological model parameters, which are influenced by climate variables, vary over time, and thus may not be assumed to be stationary. Further, the work also involves attribution the total uncertainty in the streamflow projections to multiple effects such as, model parameters, GCM simulations, emission scenarios, land use scenarios, the assumption of hydrological model stationarity, along with internal variability of the model. The Upper Ganga Basin (UGB) is considered as a case study for this analysis.

Further details on the work can be found here: http://www.sciencedirect.com/science/article/pii/S0309170817300179

Authors are glad to share their article with interested readers.

Correspondence to: pradeep@iisc.ac.in

4
Programming / Re: Exporting High Resolution figures from 'R'
« on: July 06, 2017, 05:28:03 PM »
And I forgot about 'Cairo' error. With the current version of Cairo, you cannot create tiff file. Type Cairo.capabilities() to see what all formats are supported. To make this work, you need to edit the source code of Cairo library using the modified code put up by the Cairo developers on this github page https://github.com/s-u/Cairo/tree/master/R. If you wan to avoid all of it, then go with default function tiff. Apart from smoother plots, the only other advantage with Cairo is the option of enabling background transparency.

5
Programming / Re: Exporting High Resolution figures from 'R'
« on: July 06, 2017, 04:59:58 PM »
Give the width and height units in inches. It will be easier for you to understand and manipulate the values. Plot error regarding margins could be due to insufficient space in the RStudio window. I know its silly, but you can increase the space for the plot by dragging with mouse. Also, I can see that you are not using dev.off() command, you should include that too. Forget the pointsize for now, increase the resolution, specify width and height in inches, and let us know what you have got!

Code: [Select]
tiff('test.tiff', width=20, height=20, res=300)----> The default units are pixels, so you are giving the size as 20 x 20 pixels, which is definitely too small for the figure to plot, isn't it? So, my advice is to change the units to inches and then keep playing with widths and heights

If you are still unable to plot, please provide data here, we can try to help you with that!

6
Programming / Re: Exporting High Resolution figures from 'R'
« on: July 06, 2017, 12:21:18 AM »
We can create high resolution plots in two ways: 1) using the default functions; 2) using the 'cairo' package. Say you want to create a tiff file. Both methods have the following syntax,

Code: [Select]
tiff (filename='filename.tiff',  width=5, height=4,units="in",pointsize=12,res=72)
$$your plot command$$
dev.off ()
In case of cairo, load the library first, instead of tiff we use:
Code: [Select]
cairo(filename='filename.tiff', type="tiff", width=5, height=4,units="in",pointsize=12,res=72)Cairo is used because it produces much smoother plots.

Say you want to increase the resolution to 300 dpi from existing 72 dpi. Apart from changing 'res' in the function input, you should also increase the pointsize. The default pointsize correspond to 1/72 of inch. The pointsize should be increased according to this formula: 12×(new resolution)/72. In case of 300 dpi, it will be 12×300/72=50.

In case your output seems to be clumsy, increase width and height parameters.

7
Models / Re: I need help to run Skehekin sample with VIC 5.0
« on: June 24, 2017, 05:36:34 PM »
Also, please look into the other threads posted in this forum regarding the VIC model. To avoid redundancy, start new thread only when you are not able to find answer to your question in our older threads.

Cheers!

8
Models / Re: I need help to run Skehekin sample with VIC 5.0
« on: June 24, 2017, 05:34:05 PM »
You will have to compile the model first. You can find detailed explanations regarding setting up the VIC model in the following links:
https://vic.readthedocs.io/en/master/
http://www.hydro.washington.edu/Lettenmaier/Models/VIC/Overview/ModelOverview.shtml

9
Post your question/information / Re: Indexed image in Matlab
« on: May 11, 2017, 11:48:36 AM »
The example that I have given towards the end is for the indexed image. 'corn.tif' is an indexed image.

10
Post your question/information / Re: Indexed image in Matlab
« on: May 10, 2017, 10:23:00 AM »
An indexed image is useful to represent the image which has limited number of colors in it so as to reduce the space consumption (of the image). As the name suggests, an indexed image is a matrix of indexes with each number pointing to the color in the colormap matrix.

Say you have an image with only three colors red, green and blue and no other color, its indexed image X (assuming it to be a 4x4 matrix image) could look something like this:
Code: [Select]
X=[3,2,2,1;1,3,1,2;1,2,1,2];And the corresponding colormap matrix will be:
Code: [Select]
map=[1,0,0;0,1,0;0,0,1];Here map will be 3x3 matrix with rows indicating number of colors (which is three in this example) and columns indicating R,G,B (the numbers will be varying between [0,1]).
For example, 3 in X simply points to 3rd row in map matrix and takes its corresponding color (which is blue).

Once you obtain index and colormap from an image, you can use them to get an RGB image by using ind2rgb
 (http://in.mathworks.com/help/matlab/ref/ind2rgb.html) command. This command simply maps indexes to the colormap matrix and creates a 3D matrix where the third dimension contains the RGB colors. You can run following lines in matlab for better understanding:

Code: [Select]
[X,map] = imread('corn.tif');
RGB = ind2rgb(X,map);
imagesc(RGB);

11
Programming / Re: BIAS CORRECTION BY R PROGRAMMING
« on: April 10, 2017, 10:26:29 AM »
You can do a simple google search and find out for yourself the answer to your question. For your assistance, here is the link of google search results:
https://www.google.co.in/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=bias+correction+R+package
Let us know what you found from the search and then we can discuss further.

12
If you are dealing with huge datasets that are saved in .mat format, loading them to workspace eats lot of RAM. So, matlab has an option to read part of such files without loading them to memory. Have a look at this link:

https://in.mathworks.com/help/matlab/ref/matfile.html

13
Interesting information / A lecture by Prof. Fawwaz Ulaby
« on: January 28, 2017, 11:40:36 PM »
In this lecture Prof. Fawwaz T Ulaby, a pioneer in Radar Remote Sensing talks in a fascinating way about how Radar technology was developed in 1970s. You can see the first half of the video.

https://www.youtube.com/watch?v=VSEggEb8yXk

14
Programming / Colormaps in MATLAB
« on: January 28, 2017, 11:36:51 PM »
Toolbox below provides users to view images in MATLAB with wider range of colorbars.

http://in.mathworks.com/matlabcentral/fileexchange/45208-colorbrewer--attractive-and-distinctive-colormaps

15
Work carried out by Ms. Buvaneshwari has been published in Science of the Total Environment.

Title: Groundwater resource vulnerability and spatial variability of nitrate contamination: Insights from high density tubewell monitoring in a hard rock aquifer

Description: Agriculture has been increasingly relying on groundwater irrigation for the last decades, leading to severe groundwater depletion and/or nitrate contamination. Understanding the links between nitrate concentration and groundwater resource is a prerequisite for assessing the sustainability of irrigated systems. The Berambadi catchment (ORE-BVET/Kabini Critical Zone Observatory) in Southern India is a typical example of intensive irrigated agriculture and then an ideal site to study the relative influences of land use, management practices and aquifer properties on NO3 spatial distribution in groundwater. The monitoring of > 200 tube wells revealed nitrate concentrations from 1 to 360 mg/L. Three configurations of groundwater level and elevation gradient were identified: i) NO3 hot spots associated to deep groundwater levels (30–60 m) and low groundwater elevation gradient suggest small groundwater reserve with absence of lateral flow, then degradation of groundwater quality due to recycling through pumping and return flow; ii) high groundwater elevation gradient, moderate NO3 concentrations suggest that significant lateral flow prevented NO3 enrichment; iii) low NO3 concentrations, low groundwater elevation gradient and shallow groundwater indicate a large reserve. We propose that mapping groundwater level and gradient could be used to delineate zones vulnerable to agriculture intensification in catchments where groundwater from low-yielding aquifers is the only source of irrigation. Then, wells located in low groundwater elevation gradient zones are likely to be suitable for assessing the impacts of local agricultural systems, while wells located in zones with high elevation gradient would reflect the average groundwater quality of the catchment, and hence should be used for regional mapping of groundwater quality. Irrigation with NO3 concentrated groundwater induces a “hidden” input of nitrogen to the crop which can reach 200 kgN/ha/yr in hotspot areas, enhancing groundwater contamination. Such fluxes, once taken into account in fertilizer management, would allow optimizing fertilizer consumption and mitigate high nitrate concentrations in groundwater.

Citation:
Buvaneshwari, S., Riotte, J., Sekhar, M., Kumar, M. M., Sharma, A. K., Duprey, J. L., ... & Durand, P. (2017). Groundwater resource vulnerability and spatial variability of nitrate contamination: Insights from high density tubewell monitoring in a hard rock aquifer. Science of The Total Environment, 579, 838-847.

Buvaneshwari can be reached at buvanasriramulu@gmail.com

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