Faranak Tootoonchi

Part of both Plant production and Plan ecology groups. Expert in applying various process-based and statistical methods to large-scale climate data, for assessment of climate change impacts. Skilled in time-series and multivariate analysis, bias correction, downscaling, and modeling (hydrological and crop). Experienced with ANN, random forest, and wavelet-based methods, as well as drought and flood quantification. Proficient in handling gridded datasets (e.g., NETCDF) and high-performance computing in super computers.

Crop modeling: Response of crops to climatic variabilities; Linear mixed effect models

Hydrological modeling: Semi-distributed models (HBV); Black-box models (ANN-based models); Assessment of changes in Runoff indicators

Downscaling and working with climate models: Handling large datasets, gridded data (e.g., NETCDF format); Assessment of different climate models’ properties (CMIP5- GCM, CORDEX-RCM); Uni/Multivariate methods (QDM, Copula-based methods); Assessment of nonstationary behavior; Projection of future climate features under climate change scenarios

For a full list of publication visit my google scholar and researchgate.

Postdoc: Swedish university of agricultural sciences (SLU) - Sweden (2023-2025) Focus:  Impact of climate change on crop production
PhD candidate in environmental impact assessment: Uppsala University –  Sweden (2018- 2022). Thesis: Reducing uncertainties in climate change impact studies through uni- and multivariate methods: a Nordic perspective
Post-Graduate studies: Tehran polytechnic – Iran (2015- 2018)
Research focus: Hydroclimatic change detection in desiccating lakes: A case study of Urmia Lake - Iran