Integrating python into ArcGIS: creating ice-sheet accumulation rates from radar echograms
For more than 50 years, scientists have retrieved ice cores from the world's ice sheets to study ice dynamics as well as past and present climatic and atmospheric conditions, including the accumulation rate. The ice-sheet accumulation rate is not only an important climate indicator but also a significant component of ice-sheet mass balance, which is the total mass gained or lost over a prescribed period of time. Snow accumulation is the primary mass contributor while ice-flux into the ocean and surface melting are the primary mass loss mechanisms. As concern over sea-level change and ice-sheet stability increases, more accurate and spatially complete estimates of the accumulation rate are required. Therefore, the sparse point estimates of the accumulation rate (i.e., ice cores) no longer give sufficient data for regional mass balance estimates because of their limited spatial coverage, but remain important paleoclimate records due to exceptional temporal resolution. In order to improve current mass balance estimates at the regional scale, improvement in the spatial resolution of accumulation rate estimates is necessary.
Taking advantage of a recent technological advance, we map and date subsurface layers using airborne radar and ice cores. Using the radar echograms, we manually digitize the firn layers, which are then dated where the layer intersects a dated core site. Because these layers are within the firn column, density variations are considered as well. The process is equivalent to taking several ice cores along the radar flight lines with coarse temporal accumulation rates but at a high-spatial sampling frequency. The accumulation rate data is now spatio-temporal in nature. Just as ice cores require significant time-series analysis, the airborne accumulation rate data requires complex spatial analysis, which is most appropriately accomplished in a Geographic Information System. However, the out-of-the-box tools available in ArcGIS are not able to accomplish the required processing steps and calculations to make accumulation rate estimates from radar echograms. Development of python scripts allowed us to create custom processing tools and calculations unique to our problem while remaining in the ArcGIS work environment.
Integrating python scripts into ArcGIS is beneficial because it allows us to view our data along with existing ArcGIS layers and access the spatial analysis tools developed by ESRI but at the same time allow for customization. The python language was straightforward to learn, making it an excellent language to learn for GIS experts looking to generate more complex tools. Additionally, users of the python scripts can have a range of python knowledge as the scripts can be run in the command line or as a standalone tool in ArcGIS. Python is a suitable choice for geoprocessing in ArcGIS since it is expandable as many modules are available to the user, adding functionality without cost.
The accumulation-rate python scripts developed are unique as they allow data management and calculations while working in a software environment developed specifically for spatial data. The scripts transform digitized internal layers from radar space (i.e., radar travel time and flight time) to geographic space (i.e., latitude, longitude, and depth). Point features are generated along the flight path at a user determined sampling frequency. The horizontal geographic information is stored in that point feature, but the depths of each firn layer are stored as an attribute to the point. This feature is important as it allows us to have a single point on the surface represent multiple depths for each layer. An additional shapefile is created that creates point features with 3D geometries, allowing for 3-dimensional visualization of the internal layers. Finally, accumulation rates are estimated for each point feature across the entire flight line. With the resulting dataset, we can now generate regional accumulation rate grids for different time periods (layers) with unprecedented spatial resolution.