How to Clip the DEM by Polygon using ArcGIS

 This is a good example on how to clip the digital elevation model (DEM) or raster image based on a polygon shape using ArcGIS.

or can view by click here.

Master Proposal Presentation by Mr. Zahran

 Alhamdullah, calon sarjana pertama saya, Muhammad Zahran Syahmi telah membentangkan proposal. Terdapat banyak input dan penambahbaikan yang membina dari panel-panel iaitu Prof Dr Ayob (PPK Bioprocess), Dr Mahyun, and Dr Amirah. Terima kasih pada pengerusi iaitu Dr Norazian dan penyelaras, En Najmi dalam penganjuran perbentangan ini.

Nama: Muhammad Zahran Syahmi bin Armain
Tajuk: A new approach of coupled hydrologic-hydrodynamic and climate change modelling in simulating flood hazard risk
Tarikh Perbentangan: 3hb Sep 2020

NEX-GDDP Data

 One of the main limitations of SDSM is the lack of multiple GCMs for the AR5 RCPs. To gain insights into climate model uncertainty for different RCPs,we can deployed the NEX-GDDP data (https://cds.nccs.nasa.gov/nex-gddp/).

In NEX-GDDP data, different RCPs are deploy and can be compare to the projection from the SDSM model.

Reference:

Daksiya, V., Mandapaka, P., & Lo, E. Y. (2017). A comparative frequency analysis of maximum daily rainfall for a SE Asian region under current and future climate conditions. Advances in Meteorology, 2017.

Download Free Landsat 8 Satellite Imagery using Google Earth and Earth Explorer

 

 

Alternative Video

This video link (source) gives a good example on how to download free Landsat 8 by using Google Earth and EarthExplorer. The steps are as follows:

How to download free Landsat-8 satellite imagery using Google Earth and EarthExplorer from USGS:
1. Download Landsat 8 KML file from: https://landsat.usgs.gov/landsat/ge/
2. Drag and drop it in Google Earth.
3. Navigate to the desired area and select the scene(s).
4. Select your required data from the pop-up interface.
5. If you click on Download Additional Products, the EarthExplorer site will open in a browser window.
6. Sign in using your free registered account.
7. Click on the download option on the product information page.
8. Choose your desired data product.
9. Open the downloaded file in ERDAS Imagine seeing the imagery.

Merge raster images into a single image using Arcgis

 This video tutorial is a good example to merge or mosaic raster image.

Alternative Video

Supervised Image Classification in ArcGIS

 I obtained a good tutorial to supervised image classification using ArcMap. The tutorial as follows:

Alternative to the video

Update (2/5/2020)!
I found a good tutorial to classify the Landsat 8 images from the download step until the classification step. The tutorial video as follows:

Tidal Effect on the flood simulation

 This is a good explanation of considering the effect of tidal on the flood simulation using the HEC-RAS model.

Alternative Video

Emlid Reach RS+ (GPS)

 Finally, my research group is able to own a GPS instrument, Emlid Reach RS+. This instrument can find precis coordinate with the altitude. The instrument is also able to connect to JUPEM RTK to improve more the quality of the measurement.

Visiting the hydraulic lab at USM

 

How can I extract a time series of variable for a specific location (specific longitude and latitude) from a CMIP5 experiment netCDF data set? (Answer 1)

 Source of the discussion (Click Here)

Question:

I want to downscale temperature and precipitation output from some of the CMIP5 models for my country (Jordan) coordinates. but I failed until now to figure out how to extract a time series of precipitation and temperature for specific coordinates from the the NetCDF files that I downloaded form the CMIP5 data website.

Answer 1:

If you use Matlab you can use the commands ncdisp and ncread. With the command ncdisp you can obtain information about the file (attributes, variables, dimensions, units, etc.). And with the command ncread you can extract data for a given variable.

Otherwise, if you use R, there is a netcdf package (http://cran.r-project.org/web/packages/RNetCDF/index.html). Then, I think the commands to use would be file.inq.nc and var.get.nc. (NB: I haven’t used it so I cannot give you advice about it).

I show you an example using Matlab. I downloaded the file ‘pr_day_CNRM-CM5_rcp45_r1i1p1_20260101-20301231.nc’ from the project CMIP5 (precipitation in 2026-2030 according to the RCP4.5 scenario and CNRM-CM5 model).

I save the file name

>> file = ‘pr_day_CNRM-CM5_rcp45_r1i1p1_20260101-20301231.nc’;

Then, to display the file properties:

>> ncdisp(file)

Then you can know that the variables you are interested in are coded as lat (latitude), lon (longitude), time (time), and pr (precipitation). lat is a 128×1 vector, lon is a 256×1 vector, and time is a 1826×1 vector and pr is a 256x128x1826 array with the dimensions lon, lat, time.

With the following commands you can extract the longitude, latitude and time values of all the grid points:

>> longitude = ncread(file,’lon’);

>> latitude = ncread(file,’lat’);

>> time = ncread(file,’time’);

You can use the same instruction (with some additional input arguments) to extract precipitation data for a selection of coordinates and times:

vardata = ncread(source,varname,start,count,stride)

source is the file name

varname is the variable name

start is from where you want to start to read data for each dimension (in vector form)

count is how many data points do you want to read for each dimension (in vector form)

stride is the frequency of extraction data, i.e. each day, each two days, etc. (in vector form)

So, if you want to extract precipitation every day for the full period 2026-2030 between latitudes 39.9218ºN and 52.5286ºN, and between longitudes 0º and 11.25ºE you would have to type:

Precip = ncread(file,’pr’,[1 93 1], [9 10 Inf], [1 1 1]);

[1 93 1] is start, since longitude(1) = 0, latitude(93) = 39.9218, and you start at the first time measurement

[9 10 Inf] is count, since you want to extract data for 9 longitudes (longitude(9) = 11.25), for 10 latitudes (latitude(102) = 52.5286), and you use Inf to indicate you want all time points

[1 1 1] is stride, since you want to extract each longitude point, each latitude point and each time point

I hope this helps.

Simplest Way to Downscaling the GCMs

 A good idea on how to downscale the GCMs and how to interpolate GCM data is in Netcdf format as follows:

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Simplest way to downscaling is these steps:1- download your cmip5 data (is in Netcdf format)2- extract your time series with Netcdf-Extractor there is in: https://agrimetsoft.com3- Save your time series to excel(and historical data should be in excel file)4- calculate this Coefficient c=Mean of Historical data/Mean of GCM(CMIP5 data) data5- multiply all GCM(CMIP5 data) data by c CoefficientYour data downscaled

Source (Click Here)