Predicting hydropeaking effects on spatiotemporal macroinvertebrate distribution by using machine learning algorithms
Creators: name: Scheib, Mirjam
Publisher: name: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Year: 2024
Subjects: 006: Spezielle Computerverfahren
333.79: Energie

description: A significant proportion of electricity in Switzerland is generated through high-head storage hydropower plants, modifying natural flow patterns downstream and inducing non-natural sub-daily flow fluctuations known as hydropeaking. Effects of hydropeaking on macroinvertebrates (MI) are diverse, complex, and highly taxon- and trait-specific.
This master's thesis investigates the ecological impact of hydropeaking on MI by comparing advanced machine learning (ML) models, specifically Boosted Regression Trees (BRT) and Random Forests (RF). Furthermore, this thesis aims to optimize ML habitat models (HMs), considering spatial autocorrelation and evaluating the influence of the number of environmental predictors used on ML predictive performance. Finally, using the best-performing ML model this thesis evaluated the ecological impacts on MI by comparing study areas with natural/near-natural (reference) and hydropeaking conditions.
Using data from 31 study areas across 11 Swiss rivers, ML models predicted MI distribution using four different MI indices (k-index, Shannon index, absolute and relative abundance), considering diverse hydrological regimes. Using the best-performing ML model with the highest predictive performance, hydropeaking impacts on MI distribution were assessed under different discharge scenarios.
The results highlight slightly superior predictive performance of RF over BRT. Environmental predictor analysis reveals improved model performance with increased environmental predictors used. In the comparative analysis between hydropeaking and reference study areas, significantly higher simulated MI index values were observed in reference study areas compared to hydropeaking study areas. Spatio-temporal analysis further revealed varied MI index distribution patterns across different discharge scenarios.
Comparing ML algorithms for hydropeaking impact assessment revealed nuances in model performances and highlighted the need to account for spatial autocorrelation. An evaluation of the impact of the number of environmental predictors used in ML models stresses the relevance of meticulous environmental predictor selection. This thesis highlights the suitability of the k-index as an ecological metric for assessing hydropeaking effects, incorporating multiple metrics indicative of hydropeaking impacts, such as diversity and taxa richness. The relationship between model complexity and uncertainty is intricate, with a call to leverage deep learning and ensure clear guidelines for stream health modelling.
In conclusion, this thesis introduces a pioneering data-based ML framework for estimating hydropeaking impacts on MI distribution. This thesis emphasizes transparent decision-making, rigorous evaluations, and the relevance of the type of environmental predictors included in ML models. The findings provide valuable insights for science- and data-based management of hydropeaking river ecosystems.