Potential impact of diversion canals and retention areas as climate change adaptation measures on flood risk reduction: A hydrological modelling case study from the Chao Phraya River Basin, Thailand

Effect of monsoonal rainfall and tides on salinity intrusion and mixing dynamics in a macrotidal estuary

Studies on the temporal and spatial variations of estuarine hydrodynamics, particularly those focusing on the combination of extreme and transition periods in the estuary with seasonal fluctuation of discharge, are rarely reported. Due to its importance, this study investigates the effect of rainfall and tide on the salinity intrusion and mixing conditions in the monsoon-affected macrotidal Tanintharyi River estuary (TRE), Myanmar, during the period of 2017-2019. The maximum salinity intrusion and partially mixed to well-mixed conditions were found during the neap-spring tidal cycles of dry seasons. The minimum salinity intrusion, along with partially mixed to stratified conditions, was found during spring to neap tidal cycles of wet seasons. Further, the transitional periods before and after the wet season have different salinity intrusion and mixing conditions based on the different discharge fluctuations in the past months. The salinity intrusion and mixing conditions in the TRE were largely influenced by the lack of rainfall during the dry season, whereas the combined effect of rainfall and the tidal range dominated during the wet season. Finally, the salinity interface gradient (SIG10) index was found to be the convenient index to examine the mixing condition of a large area compared to the traditional indices because of the minimum data requirement with easiness of calculation by using the reference figures from the published articles.

Analysing the performance of the NARX model for forecasting the water level in the Chikugo River estuary, Japan

Estuaries are dynamic environments which are driven by various natural processes like river discharge, tides, waves, influx of saline water and sediments, etc. These ecosystems are the most sensitive to sea level rise and fluctuations in river discharge associated with climate change. A direct response of sea level rise and river discharge can be observed in the water level of estuaries. However, existing models have not considered these parameters for forecasting water level. This paper focuses on developing a water level forecast model for the Chikugo River estuary in Japan using Nonlinear Autoregressive with Exogenous inputs (NARX Model). NARX neural network was used to do the one-step-ahead prediction of water level considering the various parameters that can very well be influenced by climate change: previous water level, river discharge, and salinity. Accordingly, three models were developed: (i) Model I considering previous water level; (ii) Model II additionally considering river discharge; and (iii) Model III additionally considering salinity. All the models showed appreciable performance in forecasting the water level. Model III had the best correlation with the water level with a cross-correlation value of 0.6030, while the river discharge had only a cross-correlation of 0.1113 indicating that the Chikugo River estuary is tide-dominated. The model was trained using different combinations of available data - previous water level, river discharge, and salinity. Cross-correlation results showed a better correlation between water level and salinity than various other combinations trained. Therefore, tidal intrusion influences the water level in the estuary, thereby depicting that sea level rise can affect the water level, and its influence can be well predicted by the developed model. The water level significantly affects the flora and fauna and the predictability of future estuarine floods can help in taking necessary mitigation strategies.