SEFS graduate student Sofia Saenz Kruszka has been awarded the Integral Big Data Research Fund Award. This fund supports graduate students who are incorporating a big data approach to their scholarly work. In this context, a big data project is defined as one where the student is focused on extracting information from large datasets through the use of interesting, innovative computational and analytic approaches, and where the object of the work is an exploration of emergent patterns and/or relationships of scientific interest.

Before embarking on her graduate degree, Kruszka was working as a research scientist in UW’s Harvey Lab. She was tasked with getting the landscape simulation model, iLand, up and running in Washington and Oregon. For her graduate research Kruszka, Harvey, and collaborators at the USFS will be using iLand in a watershed on the eastside of the Cascades in Washington to look at the future trajectories of forest structure, composition and function. Her work will forecast how the landscape’s  forests will change over time based on climate change, fire disturbance and land management.

The simulation program allows users to simulate individual tree growth, competition, regeneration, and mortality across the landscape and the output is an extremely detailed, spatially explicit dataset that can characterize forests to the level of individual trees . Running these simulations allows scientists and land managers to project the future range of variability of forest structure, composition, and function across young and old forests over a predetermined span of time – years, decades and in some instances hundreds of years. These projections give scientists and forest managers a better idea of what the long term effects of their land management prescription will do given climate change, fire disturbance and human management of the forests.

Kruszka will use the Integral Big Data Research Fund Award to run the simulations and analysis using the university’s high powered supercomputer. On demand computing allows the output of this data to be determined more quickly and efficiently.