Point data of an irrigation command area in Haryana has been collected on water depths (during dry and wet season) and electrical conductivity. Part of the area is negatively effected by salinity and waterlogging. The objective of the study is to analyze the causes for these common problems in command areas.
First, the outcome of various interpolation procedures of the point data has to be evaluated before relations between EC and distance from canal, EC and vegetation index, water table fluctuations and distance from canal can be analyzed. The findings of the study has to be put in output maps, graphs and tables. The case study is based on actual data collected by the Central Soil Salinity Research Institute in Karnal, Haryana, India.

Introduction and basic data

To familiarize yourself with the area, you will display the SPOT bands, the polygon map of the area, and inspect the point data.
Then, you will create a false color composite and an Normalized Difference Vegetation Index (NDVI) image.

For the data points in the area, the following data have been collected:

• elevation,
• pH,
• height of watertable in October,
• height of watertable in January,
• ECE values.

Point interpolation

You will create a number of raster maps which contain interpolated values from the point data of the watertable height in October, using the following point interpolation methods:

• Moving Average (method linear decrease, limiting distance 1400m),
• Moving Average (method linear decrease, limiting distance 700m),
• Trend Surface (plane),
• Trend Surface (second order surface),
• Trend Surface (sixth order surface).

When you like, you can also do Moving Average using the inverse distance weight function (limiting distance 1400m), and/or Moving Surface with various options.

The output raster maps of these interpolations are supposed to represent the watertable height in October (meters below surface).

As another map is available with interpolated elevations (meters above sea level), maps with groundwater levels above sea level can be calculated. Subsequently, a groundwater fluctuation map is calculated. These calculations are performed using simple MapCalc statements.

You will use the Spatial Correlation operation on the point data, to find out whether the values of points that are close to one another have a higher autocorrelation than the points that are at larger distances from one another. You will do this both for the October watertable height measurements, as well as for the Ece measurements.

Data analyses

With Distance calculation, you will create a map with distances towards the canal. Then, by using Cross, you will try to find relations between:

• Distance to canal and groundwater fluctuation,
• NDVI and Ece,
• Ece and Distance to canal.

Mind: In the case study it is described to cross value maps with one another.
It might be simpler however to:

• Rasterize your point map Data;
• Cross this rasterized point map with the distance map;
• Join the distance map values into the attribute table of the point map.

Furthermore, in the same way, you can:

• Cross the rasterized point map with the NDVI map;
• Join the NDVI values into the attribute table of the point map.
• Cross the rasterized point map with the Groundwater fluctuation map;
• Join the Groundwater fluctuation values into the attribute table of the point map.

You can then directly assess relations between all parameters within the attribute table.

Finally, you can investigate any relations by creating graphs from the columns in the attribute table.

For more information on this case study, contact:

A.M. van Lieshout
Department of Water Resources,
International Institute for Geo-Information Science and Earth Observation (ITC),
P.O. Box 6, 7500 AA Enschede, The Netherlands.
Tel: +31 53 4874306, Fax: +31 53 4874336, e-mail: lieshout@itc.nl