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Against the background of the rising sea level and land subsidence, protecting the progressively eroding coast along the Vietnam Mekong Delta becomes of tremendous importance. Within the presented work, design conditions for breakwaters were derived from offshore climate reanalysis data (ERA5), which were transferred to the nearshore by two numerical approaches, i.e. SwanOne and Delft3D, for different average and extreme wave and weather conditions. Within this process, design wave heights and periods at the nearshore could be determined for 10- to 100-year recurrence intervals. Both models thereby showed sufficient accuracy according to measurements in the field. Limitations must be made regarding the available spatio-temporal resolution, where reanalysis data showed a lack of short but high peak values compared to the observed measurements. Both numerical approaches showed their capabilities, where SwanOne offers a simple and fast calculation method, while it lacks of continuous effects like wind-generated swell or bottom friction. The Delft3D software on the other hand provides a more complete representation, not only of wave but also current dynamics, while it requires a much broader amount of input parameters and more complex boundary conditions. Within this study, the advantages and disadvantages of both models could be demonstrated, whereas for the final calculation of nearshore wave characteristics, only SwanOne was applicable based on the input parameters extracted from statistical analysis of long term ERA5 data. [ABSTRACT FROM AUTHOR]

In rain‐fed farming systems, the rainy season onset date (RSOD) is of utmost importance. RSODs are often defined by applying fixed thresholds to station data or by quantifying changes in the large‐scale atmospheric circulation between the dry and wet seasons. In this study, a new improved approach is proposed and applied to determine RSODs for Vietnam based on daily rainfall station data for the period 1979–2019. In this approach, the inter‐annual similarity between station‐based RSODs and regional‐scale RSOD signal, obtained by applying principal component analysis to multiple fields, was examined. The method allows to quantify the sensitivity of RSODs to three thresholds chosen; the rainfall amount of an initial wet spell (P), the number of wet days (N), and duration of control period (D). It is demonstrated that the sensitivity to the choice of the thresholds varies across stations and sub‐regions. In particular, P is a principal parameter to determine RSODs with the significant control at 66.4% stations over entire Vietnam. While D plays an important role at 55.7% stations, and N seems to be less important, which influences only 31.3% stations. The results also reflected that the selected optimal criteria for each station captured very well the rainy seasonal cycle of Vietnam. Trends towards earlier onset dates are found for large parts of central Vietnam, whereas several northern stations show a trend towards later onsets. The new method can be applied to other monsoon regions to define more spatially coherent thresholds to define local, agriculturally relevant rainy season onsets. [ABSTRACT FROM AUTHOR]

The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Subseasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic subregions of Vietnam. The results show that the ECMWF model reproduces well the observed interannual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all subregions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all subregions indicate that the model outperforms the observed climatology in predicting the RSOD at subseasonal lead times (∼28–35 days). However, the model is overall more skillful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analyzing the highest skill score for each subregion at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers. [ABSTRACT FROM AUTHOR]

In this study, different criteria to determine the rainy season onset date (RSOD) and its predictability in the Central Highlands (CH) of Vietnam are investigated. Using daily rainfall amounts from 10 meteorological stations for the period 1981–2015, four criteria to determine the RSOD were tested in order to select the one that most reasonably depicts the climatology of the RSOD over the CH. Results show that the RSOD varies strongly from year to year. In the long‐term mean, the onset starts first in southern parts of the CH and then progresses to the northern parts. The earliest onset date is around the beginning of April, and the latest in the second half of May. The average RSOD is on 28 April with a standard deviation of 14 days. Thus, the RSOD is distinct from the summer monsoon onset, namely, leading the mean summer monsoon onset by about 3 weeks in some years. In terms of remote influences, the RSOD in the CH has a high correlation with the El Niño–Southern Oscillation phenomenon, with most RSODs being later during El Niño years, while being earlier during La Niña years. The RSOD in the CH also shows high correlations with sea surface temperatures (SSTs), 850‐hPa zonal winds (U850) and mean sea level pressures (PMSL) over certain regions of the Pacific and Indian Oceans. Building on these relationships, the RSOD can be successfully predicted using large‐scale fields of SST, U850 and PMSL as predictors in two different approaches, namely the field mean and principal component analysis. This suggests that the overall approach can also be applied in predicting the RSOD in the CH on sub‐seasonal to seasonal timescales. [ABSTRACT FROM AUTHOR]