Flood early warning systems (FLEWSs) contribute remarkably to reducing economic and life losses during a flood. The theory of critical slowing down (CSD) has been successfully used as a generic indicator of early warning signals in various fields. A new tool called persistent homology (PH) was recently introduced for data analysis. PH employs a qualitative approach to assess a data set and provide new information on the topological features of the data set. In the present paper, we propose the use of PH as a preprocessing step to achieve a FLEWS through CSD. We test our proposal on water level data of the Kelantan River, which tends to flood nearly every year. The results suggest that the new information obtained by PH exhibits CSD and, therefore, can be used as a signal for a FLEWS. Further analysis of the signal, we manage to establish an early warning signal for ten of the twelve flood events recorded in the river; the two other events are detected on the first day of the flood. Finally, we compare our results with those of a FLEWS constructed directly from water level data and find that FLEWS via PH creates fewer false alarms than the conventional technique.

The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall’s tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.

Flooding is an environmental hazard that occurs almost everywhere around the world. Analysis of streamflow data can give us important climatic information for flooding events. Persistent homology (PH), a new analysis tool in topological data analysis (TDA) offers a new way to look at the information in a data set using qualitative approach. PH uses topology to extract topological features such as connected components and cycles that exist in the data set. In this paper, we present a new approach for streamflow data analysis for flood detection by using PH. An analysis was conducted at Sungai Kelantan, Malaysia. The result shows that PH gives different pattern of topological features for dry and wet periods. In particular, there are more persistent topological features in the form of connected components and cycles in the wet periods compared to the dry periods. We observed that the time series of the distance measure corresponding to the evolution of the components is consistent with the time series of the streamflow data. As a conclusion, this study suggests that the time series of the distance measure corresponding to the evolution of the components can be used for flood detection at Sungai Kelantan, Malaysia.

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