Resumen
Flooding is a recurring natural disaster worldwide; developing countries are particularly affected due to poor mitigation and management strategies. Often discharge is used to inform the flood forecast. The discharge is usually inferred from the water level via the rating curve because the latter is relatively easy to measure compared to the former. This research focuses on Cambodia, where data scarcity is prevalent, as in many developing countries. Thus, the rating curve has not been updated, making it difficult to effectively evaluate the performance of the global streamflow services, such as the Global Flood Awareness System (GloFAS) and Streamflow Prediction Tool (SPT), whose longer lead time can benefit the country in taking early action. In this study, we used time series of water level and discharge data to understand the changes in the flood plain to generate a data-derived rating curve for fifteen stations in Cambodia. We deployed several statistical and data-driven techniques to derive a generalized, scalable, and region-agnostic method. We further validated the process by applying it to ten stations in the US and found similar performance. In Cambodia, we obtained an average Kling Gupta Efficiency (KGE) of ~99% & an average Relative Root Mean Squared Error (RRMSE) of 12% with an average Mean Absolute Error (MAE) of 200 m3" role="presentation">33
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/s. In the US, overall KGE was 97%, with an average RRMSE of 17% and an average MAE of 32 m3" role="presentation">33
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/s. The results indicated that the distribution of the dataset was key in deriving a good rating curve and that the stations with a low flow stations generally had higher errors than the high flow stations. The time series approach was shown to have more probability in capturing the high-end and low-end events compared to traditional method, where usually fewer data points are used. The study demonstrates that time series of data has valuable information to update the rating curve, especially in a data-scarce country.