Process oriented insights from interpretable machine learning - what influences flood generating processes?
Abstract
Hydroclimatic flood generating processes, such as saturation excess rain, short -duration rain, long -duration rain, snowmelt and rain-on-snow, underpin our understanding of current and future flood behaviour . Knowledge about flood generating processes helps to improve modelling decisions, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the distribution of flood generating processes. With this study we aim to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchment in the conterminous United States) and test attribute influence on flood processes with two complementary approaches: firstly, a data-based approach which compares attribute probability distributions of different flood processes, and secondly, a random forest model in combination with an interpretable machine learning approach ( A ccumulated L ocal E ffects - ALE ). This machine learning technique is new to hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality and mean precipitation) to be most influential on flood process distribution. However, attribute influence varies both with process and climate type. The implication of these findings is that flood processes should be considered for future climate change impact studies, as impact will vary between processes. Further analysis in this direction is essential, since most recent studies do not distinguish flood events in that regard.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH203...04S
- Keywords:
-
- 1817 Extreme events;
- HYDROLOGY;
- 1821 Floods;
- HYDROLOGY;
- 1847 Modeling;
- HYDROLOGY;
- 1873 Uncertainty assessment;
- HYDROLOGY