SHIRIN STUDNICKA

I am a hydrologist with a strong interest in modeling hydrological time series and an aptitude for research, particularly in developing new AI/ML-based models. - My master’s thesis focused on evaluating an ensemble wavelon network (EWN) integrated with ensemble streamflow prediction (ESP) for monthly streamflow forecasting, using bootstrapped networks whose outputs were combined via an entropy-based approach. - In my PhD work, I critically examined the passive application of AI/ML in hydrology, explored a wide range of models including CNNs and LSTMs, and ultimately developed a semi-automated model that synthesizes monthly streamflow, outperforming both ARIMA and ANN under varying levels of colored noise. This model also generates potential monthly streamflow scenarios by encoding historical time series patterns into 8-bit images, capturing the textural structure of past flows.

• Studnicka, S., & Panu, U. (2025). Streamflow Synthesis Using an Encoded Textural Pattern Recognition System. I: Model Development. Journal of Hydrologic Engineering, 30(6), 04025039.
• Studnicka, S., & Panu, U. (2025). Streamflow Synthesis Using an Encoded Textural Pattern Recognition System. II: Model Applications. Journal of Hydrologic Engineering, 30(6), 04025040.
• Studnicka, S., & Panu, U. (2025). Techniques and Developments in Stochastic Streamflow Synthesis – A Comprehensive Review. Submitted to Encyclopedia. Resubmitted after revision and response to reviewers’ comments.
• Studnicka, S., & Panu, U. (2025). Exploring the Role of Coloured Noise in Modelling Short- and Long-term Dependence Structures in Monthly Streamflows Across Ontario. Journal of European Water (Manuscript under review).
• Studnicka, S., Li, D., & Panu, U. (2025). Extending Geometric Brownian Motion for Modelling Monthly Streamflow under Brown Noise Dominance over Other Coloured Noises. Journal of the American Water Resources Association (Manuscript under review).
• Studnicka, S., & Panu, U. (2023). Textural Image-Based Feature Prediction Model for Stochastic Streamflow Synthesis. Water Resources Management (Manuscript under review).
• Piran, S., & Panu, U. (2025). Developing a New Criterion to Evaluate Monthly Streamflow Modelling in the Frequency Domain. World Congress of the European Water Resources Association (EWRA 2025), June 28, 2025.
• Piran, S., & Panu, U. (2025). Investigations into the Relationships between Persistence, Complexity, and Scaling Behavior in Monthly Streamflow Across Ontario, Canada. Canadian Society for Civil Engineering (CSCE) Annual Conference, May 28, 2025.
• Piran, S., & Panu, U. (2024). Classifying Watersheds Based on Hydrologic Memory Using Noise Analysis of Monthly Streamflow in the St. Lawrence River Basin, Ontario, Canada. PEO 62nd Annual Engineering & Technology Conference, November 22, 2024.
• Piran, S., & Panu, U. (2023). Encoded-Streamflow Synthesis Using Textural Feature Recognition System. AGU Fall Meeting Abstracts, H44G-03P, December 15, 2023.

• Theories:
• Chaos Theory
• Bayesian Theory
• Information Theory
• Monte Carlo Algorithms
• Uncertainty Analysis
• Applications:
• AI and its applications in water resources management and hydrology
• Identifying hidden and underlying patterns in hydrologic time series
• Assessing climate change impacts on hydrological systems
• Rainfall–runoff modeling using physical, conceptual, and data-driven approaches
• Snow hydrology, snowmelt modeling, and remote sensing applications for snow water equivalent estimation
• Uncertainty analysis in water resources system operations