Drought Visualization

The researchers of the DRINET project computed drought prediction data at 75 observation sites in Indiana for each month between December 1889 and December 2007. The model output was used in a student project in Purdue fall 2009 CS 530 (Introduction to Scientific Visualization). The goal of this project is to develop methods to visualize (1)the precipitation required for recovery from an existing drought and (2) probability of recovery from an existing drought for any given time point and prediction window (1-11 months).



Student Project 1 -- Indiana Drought Visualization

In this project, a system that seeks to intuitively visualize Indiana droughts is designed and implemented. To reconstruct continuous data from the discrete and scattered input, two nonlinear scattered data interpolation schemes are explored. Visualization of the data consists of rendering them in a height map and an isopleth/choropleth map with color coding.

droughtthumb1_1 droughtthumb1_2 droughtthumb1_3 droughtthumb1_4 droughtthumb1_5 droughtthumb1_6 droughtthumb1_7

 

 

Student Project 2 --3D Drought Visualization

 

 

In this project, a 3D based drought visualization is implemented using VTK and Flash Gadget.
droughtthumb2_1 droughtthumb2_2 droughtthumb2_3 droughtthumb2_4 droughtthumb2_5 droughtthumb2_5 droughtthumb2_7

 

drought_converted_gadget drought_converted_prep drought_converted_prob drought_converted_trend

 

 

 

Student Project 3 --Visualizing Drought Status using 3D Isocontour map

 

 

In this project, a 3D isocontour map is created to visualize current drought status over Indiana state.

thumbdrought3_1 thumbdrought3_2 thumbdrought3_3 thumbdrought3_4

 

drought3_1 drought3_2

 

 

 

Student Project 4 --Visualization of Drought in Indiana

 

 

The focus of this project was to visualize the temporal and spatial relationship of data collected at seventy five stations across Indiana. The goal was to visualize the full meaning of the data in a way that is simple and intuitive for the user. This was accomplished by cutting and filtering the data for simplification, and plotting the data with colorscales and spatial relationships that enhance the meaningful information included in the data.

thumbdrought4_1 thumbdrought4_2 thumbdrought4_3 thumbdrought4_4