The Mekong Delta is the most important food production
area in Vietnam, but salinity intrusion during the dry season poses a
serious threat to agricultural production and livelihoods. A seasonal
forecast of salinity intrusion is required in order to mitigate the negative
effects. This communication presents a statistical seasonal forecast model
based on logistic regression using either the ENSO34 index or streamflow as
a predictor. The model is able to reliably predict the salinity intrusion up to 9
months ahead (receiver operating characteristic (ROC) scores: >0.8). The model can
thus be used operationally as a basis for timely adaptation and mitigation
planning.
Problem setting
The Mekong Delta (MKD) is the most important food production area in
Vietnam and is responsible for about 56 % of the rice production of Vietnam and
20 % of the agricultural exports of Vietnam as a whole (source: General
Statistics Office of Vietnam, GSO, http://www.gso.gov.vn, last access: March 2020). As a low-lying
coastal area in a monsoonal climate with distinct wet and dry season, it is
naturally prone to salt-water intrusion into the river and channel network
during the dry low-flow season. Sea level rise and climate change aggravate
this problem, causing more severe, longer-lasting, and more frequent
droughts, with the consequence of more severe (longer-lasting and higher
salinity levels) and more frequent salinity intrusions during the dry season
(Smajgl et al., 2015). The current agricultural
production systems and livelihoods have adapted to a given intensity of
salinity intrusion over time, but these changes pose a serious threat to the
agricultural production and livelihood of the population. A drastic example
of the immense negative effects of a strong salinity intrusion is the dry
season of 2015/2016. This unprecedented high-salinity intrusion – which
manifested in the earliest onset of high-salinity levels, the highest
observed salinity measurements in many places, the longest duration, and the
deepest penetration of saline water in the river system ever observed –
caused widespread crop loss throughout the delta. Of the 13 provinces in
the Mekong Delta, 9 were affected by severe salinity intrusion, and all
provinces were affected by water shortage (Nguyen, 2017). Approximately
400 000 ha of cropland was subject to saline irrigation water, of which
238 276 ha was paddy rice fields. The salinity intrusion also affected
6575 ha of vegetables, 29 277 ha of fruit trees, and 79 000 ha of
aquaculture, affecting mainly brackish water shrimp. Figure 1 provides an overview
of the land use in the Mekong Delta as observed and classified in 2010 by
satellite remote sensing (Leinenkugel et al., 2013). It
roughly illustrates the main affected cropping types in the coastal areas of
the delta. The overall economic damages amounted to VND 5.5 trillion,
equivalent to about USD 236 million and 0.74 % of the national gross
domestic product of the agriculture, fishery, and forestry sectors in 2016
(source: GSO). Furthermore the Vietnamese National Steering Center for
Natural Disaster Prevention and Control reported that 194 000 households
lacked fresh water for domestic use in the Mekong Delta (VDMA,
2016).
The main damaging effect is the lack of fresh water required for irrigation
of rice paddies but also of fruit orchards and vegetable farming. The crops
are damaged or even die either by lack of water or by the adverse effects
of high salt concentration in the irrigation water. Because of these
effects, salinity intrusion in the Mekong Delta can also be termed as
agricultural drought following the general definition of agricultural
drought as a situation in which plant water demands cannot be satisfied by
water availability (Mannocchi et al., 2004; Mishra and Singh, 2010).
By terming salinity intrusion an agricultural drought we hereby extend the
definition of water availability from a pure physical, quantitative view to
a water quality perspective. This agricultural drought is a serious hazard
for large parts of the population for which irrigation-based agriculture is
still the basis of livelihoods. An important factor for the large damages
was the lack of appropriate mitigation plans and a timely warning of
serious salinity intrusion in order to prepare and adapt
agricultural practices for damage reduction.
However, studies and publications dealing with direct forecasts or early
warning of salinity intrusion in the Mekong Delta do not exist. This holds
true for both short-term and seasonal forecasts. The presented study is thus
a novel work in this regional context but also beyond. Publications about
forecast models of salinity intrusion are rather scarce in general. There
are just a few papers dealing with this issue. All of them apply different
methods than the method presented in this study. The approaches of other
studies are (a) hydrodynamic modelling of salinity intrusion and the coupling
of these models with meteorological and tidal forecast models
(Risley et al., 1993), (b) the use of artificial neural
networks (Lu and Chen, 2010; Roehl et al.,
2013), (c) kernel-based support vector machines (Rohmer and Brisset,
2017), and (d) power law models derived from hydrodynamic models
(Etemad-Shahidi et al., 2008). The approaches applied in these
studies are quite complex and require an extensive set of data and models.
Although there is this clear gap in the literature, operational forecasts of
salinity intrusion in the Mekong Delta are made by the National Centre for
Hydro-Meteorological Forecasting (NCHMF) and the Southern Institute of
Water Resources Research (SIWRR) under the authority of the Ministry of
Agriculture and Rural Development (MARD). The forecasts are provided to MARD
and distributed among governmental agencies and provincial governments in
the MKD. Both forecasts are based on a complex chain of hydrological and
hydraulic models, which are fed by precipitation and tidal monitoring data
and forecasts. The forecasts of the NCHMF are short-term, i.e. with a lead
time of 10 d, and are issued on a regular basis every 5–10 d. The forecasts of SIWRR also cover longer lead times up to a maximum of 2
months. The core of the forecast model of SIWRR is described in
Toan (2014). This lead time is, however, too short to plan and
adapt the cropping system well ahead of the drought event. Forecasts with
lead times of several months are required to change the cropping system and
to prepare for crop planting during the dry season (December to April).
A drought like the one in 2016 is expected to occur more often in the future
as negative rainfall anomalies occurring with El Niño events are
expected to occur more frequently (Azad and Rajeevan, 2016).
Moreover, sea levels around the Mekong Delta are rising and are expected to
rise further in future (Smajgl et al., 2015).
Rising sea levels cause increasing backwater effects and reverse flow in the
river channels, thus promoting salinity intrusion during the dry season.
The 2016 event was a wake-up call for society and officials in Vietnam
as it proved that large structural problems in drought management and
mitigation exist. In order to support disaster management this study aims to
develop a reliable and simple salinity intrusion forecast system
enabling lead times of several months and thus better early-warning capabilities as well as adaptation to and mitigation of the adverse impacts of salinity intrusion
and agricultural droughts in the Mekong Delta.
Hydrology, data, and method
The hydrology of the Mekong Delta and Mekong basin is dominated by a
monsoonal climate, separating the hydrological year into distinct rainy/high-flow and dry/low-flow seasons. The hydrological regime lags behind the climate
regime depending on the location in the basin. In the Mekong Delta this lag
is most noticeable due to the time required for transforming rainfall in the
approximately 800 000 km2 basin to river discharge and routing the
discharge to the delta. The lag time in the delta between the onset and end of
the rainy and flood season can be up to 2 months. Moreover, the dry-season
discharge in the Mekong Delta crucially depends on the discharge generated
in the Mekong basin, which in turn depends on the amount of rainfall in
the basin during the monsoon period. Discharge in the Mekong basin and
delta is therefore highly correlated with the monsoon intensity (Delgado et al.,
2012; Räsänen and Kummu, 2013). The South East Asian monsoon intensity is
itself determined by the periodically changing sea surface temperatures in
the western central Pacific Ocean (West Pacific Warm Pool) that are associated with
the El Niño–Southern Oscillation (ENSO). Of particular importance for
the monsoon strength is the situation of ENSO in winter and spring prior to
the monsoon season, when the general circulation and moisture fluxes for the
monsoon season are initiated (Ju and Slingo, 1995). Strong South East Asian monsoons are associated with La Niña events, while
weak monsoons and thus higher chances of salinity intrusion in the dry
season are associated with El Niño events (Ju and Slingo,
1995). Therefore, a general causal chain for dry-season discharge and thus
salinity intrusion in the Mekong Delta can be formulated as follows: ENSO
determines the intensity of the South East Asian monsoon, the monsoon intensity
determines the rainfall amount over the Mekong basin, the rainfall amount
determines the flood-season discharge, the flood-season discharge is itself
indicative of the following dry-season discharge, and the dry-season
discharge controls the salinity intrusion in the Mekong Delta.
This general causal chain forms the basis for the simple salinity intrusion
forecast model presented in this study. Firstly, an early forecast will be
attempted utilizing an ENSO index as a predictor. Secondly, an additional
forecast will be tested using flood-season and early dry-season discharge as
a predictor. Monthly ENSO indexes were collected from the Physical Sciences
Division (PSD) of the Earth System Research Laboratory (ESRL) of the
National Oceanic and Atmospheric Administration (NOAA; https://www.esrl.noaa.gov/psd/data/climateindices/list/, last access: May 2019). Furthermore,
monthly discharge data were collected from the Southern Regional
Hydrometeorological Center (SRHMC) for the gauging station Tan Chau, located
at the Mekong (Tien in Vietnam) branch of the Mekong in the delta (Fig. 1). The salinity of surface water in the MKD is also measured by the SRHMC but
during the dry season only. Salinity is monitored at 39 locations in the
MKD and is determined by collecting water samples mid-river at 20 %, 50 %
and 80 % of the water depth. The samples are analysed in the laboratory, and
the reported salinity is the mean of these three measurements. If the water
depth is below 3 m, only one sample is taken at mid-depth. Due to
constraints in personal and monetary resources, the monitoring is, however,
not time-continuous. The general scheme is to measure 2–3 d in a row at 2 h intervals (i.e. 12 measurements per day). This measurement period is
followed by 2–4 d without measurements, after which the monitoring
resumes. The salinity time series used in the model development was recorded
at gauge Son Doc in Ben Tre province in the estuary of the Mekong (Tien)
river branch. Son Doc is located about 175 km downstream of the gauge Tan
Chau and about 24 km upstream of the river mouth (Fig. 1). The salinity
measurements covered all dry seasons in the time span 1996–2016. The
temporal coverage of the salinity measurements at Son Doc is shown in
Supplement Sect. S1. In order to derive a meaningful predictand of salinity
intrusion, the mean salinity of February and March (February–March) was calculated
from the available salinity measurements. This aggregation time period was
chosen because it coincides with the vegetative stage of irrigated paddies
grown during the dry season, which is the most sensitive phase of paddy rice
to high salinity levels (Zeng et al., 2001).
The envisaged seasonal forecast of salinity intrusion does not aim to
forecast the exact mean salinity intrusion of this period but rather to
forecast the probability of exceedance of critical levels of salinity
intrusion. For paddy rice a salinity of the irrigation water exceeding 4 g L-1
is seen by the authorities in Vietnam as too high for the plants to survive
during the vegetative stage (cf. Zeng and Shannon, 2000). Therefore
a mean salinity of 4 g L-1 during February and March is adopted as the critical
salinity level for the forecast model. The mean salinity threshold during
this period means that this threshold will be exceeded 43 % of the
time, considering the negative exponential distribution of the data (cf.
Supplement Sect. S2) and assuming that the discontinuous measurements are
representative of the whole time period. In addition to the critical
salinity level a threshold of 3 g L-1 mean February–March salinity is used as
a predictand. This threshold is exceeded 53 % of the time in February and
March (cf. Supplement Sect. S2) and serves as a warning threshold, indicating a
strong salinity intrusion with chances of salinity also exceeding 4 g L-1 at
times. Rice irrigated with this salinity threshold might survive depending
on the duration of the irrigation with saline water, but losses in crop
yield have to be expected (Grattan et al., 2002; Zeng and
Shannon, 2000).
The forecast model is based on a logistic regression (LR), i.e. a linear
statistical model that relates categorized values to continuous, real-number
type predictors (Menard, 2009). LR has to our knowledge never been
used in forecasting salinity intrusion. Moreover, a seasonal forecast of
salinity intrusion, i.e. a forecast with several months lead time, has not
been published before. The presented work is thus also novel in this aspect.
Compared to the published approaches of salinity intrusion listed in the
introduction and the model chains used for the operational forecasts,
forecasting with LR is a very simple and data-sparse approach.
The categories to be predicted by the LR are the mean February–March salinity values
categorized in bins above or below the defined salinity thresholds (4
and 3 g L-1). The continuous predictors are monthly ENSO index and
standardized streamflow index (SSI) values. For this kind of regression LR
is the appropriate tool. LR is very flexible in its application because it
is not limited to normally distributed predictors as, for example, the possible
alternative method of linear discriminant analysis is (Pohar et al.,
2004). Regression models were developed with a single predictor using either
ENSO indexes or SSIs following the causal chain leading to salinity
intrusion in the MKD described above. The ENSO indexes tested were monthly
ENSO1, ENSO3, ENSO4, and ENSO34 indexes. All of these indexes aim to
represent the state of the El Niño–Southern Oscillation by
considering sea surface temperatures at different regions of the Pacific
Ocean. Among these indexes ENSO34 is regarded as the most appropriate sea
surface temperature index representing the general state of ENSO
(Bamston et al., 1997). The testing of different indexes aims
to identify the most robust predictor for salinity intrusion in
the MKD. All of the ENSO indexes used in the forecast models start in April
of the year before the dry season, i.e. with a lead time of up to 9 months
before the start of the forecasted February–March time period. For the streamflow
predictors, the monthly discharges recorded at the Tan Chau station were
transformed into SSIs. This transformation is similar to transforming
precipitation records into standardized precipitation indexes (SPIs), as
typically done in drought studies and drought definitions (Mishra and
Singh, 2010). SSIs have been applied as predictands in seasonal forecast
studies, e.g. for predicting streamflow in southern Africa
(Seibert et al., 2017). SSIs normalize the monthly
discharges by fitting a gamma distribution to the observed long-term record
of discharges (here from 1980 to 2016) and transferring it to a normal
distribution with a mean of 0. A SSI value of 0 thus indicates the normal
hydrological state, while negative values indicate a water deficiency. SSIs
have the advantage that a drought condition can be directly recognized by the
SSI value and that the prediction models can be easier transferred and
compared to other gauging stations with different streamflow magnitudes.
Another strength of SSIs is that they can be calculated for a variety of timescales. This is important because droughts usually manifest over extended
time periods. SSIs were thus derived for different timescales ranging from 1
month (SSI1) to 6 months (SSI6), each starting in June prior to the dry
season, i.e. with a maximum of 7 months lead time. The calculation of the SSIs
was performed with the R package SPEI (Vicente-Serrano et al., 2012).
Supplement Sect. S3 provides a list of all predictors used in the detection of the
best performing forecast models.
The logistic regression models were fitted by iteratively reweighted least
squares using the R function glm for a binomial model type, which represents
the logistic regression. A fitted LR model provides estimates of the
probability of exceedance of the defined salinity thresholds depending on
the predictor value. One model was fitted for all predictors and lead times,
and the best performing ENSO and SSI predictors for the different lead times
were manually selected according to the following criteria:
the receiver operating characteristic (ROC) score (Mason, 2008);
the Akaike information criterion (AIC) (Burnham and Anderson, 2004);
the Cragg and Uhlers (also known as Nagelkerke) pseudo-R2
(Nagelkerke, 1991), which is defined for categorical variables
analogously to the normal R2 for continuous variables;
the accuracy (rate of correct forecasts).
In order to test the robustness of the linear models a leave-one-out cross-validation (LOOCV) was also performed as applied in
Apel et al. (2018) for forecasting seasonal
streamflow in central Asia. A robust model is characterized by a model for
which the performance values of the LOOCV do not deteriorate compared to the
performance of the model using the full data set. Therefore, the ROC scores
and accuracies of the LOOCV were used in addition to the performance
criteria listed above for the selection of the best performing forecast
models.
Results and discussion
Best prediction models using the ENSO34 index (top row: a and b)
and SSI3 (bottom row: c and d) as predictors for forecasting exceedance
of the 3 g L-1 (left: a and c) and 4 g L-1 (right: b and d) salinity
threshold (“salthresh”). The top left insets of (a) to (d) show the ROC
curves with the following performance criteria: R2 is the Cragg and Uhlers
pseudo-R2; ROC is the ROC score; ROC_cv is the ROC score of
the LOOCV; Acc is the accuracy (fraction-correct predictions); and Acc_cv is the accuracy of the LOOCV. The top right insets of (a) to
(d) show the logistic regression results with the probabilities of
exceedance and non-exceedance of the salinity thresholds depending on
the predictor. The bottom insets of (a) to (d) show the observed mean
February–March salinity levels at Son Doc and the predictions in terms of
exceedance of the salinity thresholds.
The performance testing of the ENSO predictors identified the ENSO34 index
as the best performing ENSO index. The best forecast could be obtained with the
April index, i.e. with a lead time of 9 months. Figure 2a and b
illustrate the performance of the forecast model. Using only the ENSO34
index of April an ROC score of 0.98 and 0.8 and an accuracy of 95 % and
71 % could be achieved for the 3 and 4 g L-1 thresholds, respectively.
The pseudo-R2's of 0.89 (3 g L-1 threshold) and 0.4 (4 g L-1 threshold)
confirm this excellent performance, considering the typically lower values
of pseudo-R2 compared to normal R2 values. This performance is
exceptionally high for a seasonal forecast with such a long lead time. The
LOOCV resulted in similar high performances, thus indicating the robustness
of the statistical model. The logistic regression curves in the top right
insets show that the 3 g L-1 threshold can be very well discriminated by the
ENSO34 index. The steep slopes of the probabilities changing from drought
classification to non-drought classification illustrate this. For the 4 g L-1
threshold the slopes are more gentle, indicating a less pronounced
discrimination, which is expressed by the lower performance values. The
bottom insets in the figure panels show the observed drought events with
reported high-salinity intrusion and the forecasts of the model, including
the LOOCV forecasts. It can be seen that only 1 out of 21 dry seasons would
have been misclassified as drought for the 3 g L-1 threshold. For the 4 g L-1
threshold 6 out of 21 events would have been wrongly predicted. In this
context it has to be noted that the probability threshold for classifying a
forecast as a drought was set to 0.5. Different probability thresholds
for drought classification have been tested and resulted in different
classification errors, but the number of wrong classifications and thus the
performance values remained the same.
The forecast performance with ENSO decreases with decreasing lead times,
as shown in Fig. 3a by decreasing ROC scores and increasing AIC values.
This finding is in line with the causal chain explained above, where ENSO precedes the monsoon development. During the monsoon period ENSO changes
to a different state but with less of an impact on the monsoon
intensity (Ju and Slingo, 1995); thus the value of ENSO as
a predictor for salinity intrusion decreases.
Performance of logistic model with ENSO34 and SSI3 predictors at
different lead time in terms of (a) ROC score and (b) AIC. The months of
the x axis denote a forecast at the end of the indicated month prior to the
dry season. For the mean February–March salinity a forecast in December
means 1 month of lead time for the predicted season and in April a lead time of 9
months.
Opposite behaviour is observed for the SSI predictors (Fig. 3b).
These show in general an increasing performance with decreasing lead time.
This behaviour also reflects the hydrological system of the Mekong, where
the streamflow at the late flood (October–November) and early dry season
(December) is an aggregated measure of the total monsoonal rainfall amount
over the Mekong basin, which is more reliable compared to streamflow during
the early and high flood season. The SSI3 predictor, i.e. an aggregated
index of 3 months of discharge, performs best but only slightly better
than SSI4 and SSI6 (not shown). The best forecasts were obtained for
November and December, i.e. with 1–2 month(s) lead time. Interestingly
the SSI forecasts were in general better for the 4 g L-1 threshold than for
the 3 g L-1 threshold (Fig. 3), which is opposite to the forecasts with
ENSO. The 4 g L-1 threshold exceedance can be forecasted with an ROC score of
0.85 and an accuracy of 85 % with SSI3 in December and November (Fig. 2c and d). Only 3 out of 21 events were wrongly classified for this
salinity threshold. This means that the early salinity intrusion forecast by
ENSO for the critical salinity threshold of 4 g L-1 can be further improved
with SSI forecasts a few months prior to the dry season, which is important
for the actual implementation of disaster mitigation plans.
However, in order to obtain a continuous, well-performing forecast model,
the forecasts during the flood season (July–October) need to be improved.
Suitable predictors during this period would be rainfall estimates over the
Mekong basin following the causal chain of salinity intrusion in the MKD
outlined in the method section. These rainfall predictors could be derived
from the real-time monitoring rainfall network in the Mekong basin or
near-real-time satellite-based rainfall products such as the TRMM-based
multi-satellite precipitation analysis (TMPA/3B4x) with its latency of 1–2
months. This needs to be tested during further developments of the model.
Conclusions and outlook
The proposed simple linear seasonal forecasting model of salinity intrusion
in the Mekong Delta based on ENSO and SSI predictors proved to be a useful
tool for an early warning of salinity intrusion during the dry low-flow
season in the Mekong Delta. The exceedance and non-exceedance of critical
and high levels of salinity at the Son Doc gauging station could be
forecasted with high probabilities. Combining the ENSO and SSI forecast
models results in a forecasting system that could deliver an early warning
as early as 9 months prior to the period of the dry season most critical for
paddy rice but also for other crops and fruit orchards. The early forecasts
with ENSO in April before the actual flood and the following dry season
could serve as an early warning of a likely high-salinity intrusion. The
later forecasts based on SSIs would then provide more reliable forecasts of a
severe salinity intrusion that would cause high damages to and negative impacts
on the livelihood of the population in the coastal provinces of the Mekong
Delta if no mitigation action is initiated in time. Based on the long
lead times of the forecasts appropriate mitigation measures can be planned
as early as during the flood season, i.e. well ahead of the dry season, and then
activated if the early forecasts are confirmed by the late forecasts based
on SSIs, i.e. actual observed discharges. The proposed forecasting model
could thus be used as data-based support for disaster mitigation planning.
However, it could be further improved to obtain robust forecasts during the
flood season, i.e. lead times of 5–7 months, by testing rainfall estimates
over the Mekong basin as predictors.
Due to its simplicity the model can easily be transferred to other gauging
stations in the Mekong Delta. This requires mainly sufficiently long time
series of salinity measurements because long-term discharge records for the
calculation of SSIs are readily available for the main gauges in the Vietnamese Mekong Delta (VMD) and
the time series of ENSO indexes are available from public sources. As a rule
of thumb, salinity time series of about 15 years and longer should be
sufficient for fitting the model (cf. Apel et al.,
2018). While the studied gauge Son Doc can be regarded as representative of
the general salinity intrusion in the Mekong Delta, forecasts of a larger
number of stations would increase data-based evidence of an imminent
severe salinity intrusion affecting the whole delta. Moreover, due to the
simple model structure and data requirements, the model could be applied
beyond the Mekong Delta, in other coastal areas that drain larger basins in a
monsoonal climate, where similar hazards of dry-season salinity intrusion
exist.
After the model development, the model was validated by forecasting the
salinity intrusion in the 2020 dry season. It predicted the observed high-salinity intrusion with high confidence using both the ENSO34 predictor of
April 2019 and the SSI3 predictors of November and December 2019. This
means that the observed severe salinity intrusion in the dry season of 2020
could have been predicted with a lead time of 9 months. This lead time is
certainly sufficient for timely mitigation and adaptation planning. The
proposed model can therefore provide data-based support for decisions of
the Vietnamese government for future salinity intrusions during the dry
seasons. This could avoid severe damages and negative impacts like those in
2015/2016, when no preventive and mitigating action was taken. Mitigation
action should include not only local measures in the VMD but also
negotiations with the Mekong riparian countries with the aim of adapting the
operation schedule of reservoirs in the Mekong basin in order to maintain
sufficient flow during the dry season. This aspect in particular needs long-term preparation both in terms of reservoir operation planning and for the
required political discussions among the riparian countries.
Data availability
Data are available upon request
from the corresponding author (heiko.apel@gfz-potsdam.de).
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-20-1609-2020-supplement.
Author contributions
HA developed the method and wrote the
original draft of the paper. All authors contributed to the preparation of
this paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was performed as part of the research
project “Catch-Mekong” (https://catchmekong.eoc.dlr.de/, last access: March 2020) and the
project “Transforming agricultural livelihoods for climate change adaptation
in the Vietnamese Mekong Delta: A case study in Ben Tre province”.
Financial support
This research has been supported by the German Ministry of Education and Research (BMBF; grant no. 02WM1338C) and by the Vietnamese Ministry of Science and Technology (MOST;
grant no. KHCN-TNB-DT/14-19/C20).The article processing charges for this open-access publication were covered by a research centre of the Helmholtz Association.
Review statement
This paper was edited by Paolo Tarolli and reviewed by Anton Pijl and one anonymous referee.
ReferencesApel, H., Abdykerimova, Z., Agalhanova, M., Baimaganbetov, A., Gavrilenko, N., Gerlitz, L., Kalashnikova, O., Unger-Shayesteh, K., Vorogushyn, S., and Gafurov, A.: Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management, Hydrol. Earth Syst. Sci., 22, 2225–2254, 10.5194/hess-22-2225-2018, 2018.Azad, S. and Rajeevan, M.: Possible shift in the ENSO-Indian monsoon
rainfall relationship under future global warming, Scientific Reports, 6,
20145, 10.1038/srep20145, 2016.Bamston, A. G., Chelliah, M., and Goldenberg, S. B.: Documentation of a
highly ENSO-related sst region in the equatorial pacific: Research note,
Atmosphere-Ocean, 35, 367–383, 10.1080/07055900.1997.9649597, 1997.Burnham, K. P. and Anderson, D. R.: Multimodel Inference: Understanding AIC
and BIC in Model Selection, Sociol. Method. Res., 33, 261–304,
10.1177/0049124104268644, 2004.Delgado, J. M., Merz, B., and Apel, H.: A climate-flood link for the lower Mekong River, Hydrol. Earth Syst. Sci., 16, 1533–1541, 10.5194/hess-16-1533-2012, 2012.Etemad-Shahidi, A., Dorostkar, A., and Liu, W.-C.: Prediction of salinity
intrusion in Danshuei estuarine system, Hydrol. Res., 39, 497–505,
10.2166/nh.2008.107, 2008.Grattan, S. R., Zeng, L., Shannon, M. C., and Roberts, S. R.: Rice is more
sensitive to salinity than previously thought, Calif. Agr., 56,
189–198, 10.3733/ca.v056n06p189, 2002.Ju, J. and Slingo, J.: The Asian summer monsoon and ENSO, Q. J. Roy. Meteor. Soc., 121, 1133–1168, 10.1002/qj.49712152509,
1995.Leinenkugel, P., Kuenzer, C., Oppelt, N., and Dech, S.: Characterisation of
land surface phenology and land cover based on moderate resolution satellite
data in cloud prone areas - A novel product for the Mekong Basin, Remote
Sens. Environ., 136, 180–198, 10.1016/j.rse.2013.05.004, 2013.
Lu, J.-F. and Chen, Z.-S.: Salinity Prediction at Modaomen Waterway in
Estuary of Pearl River, Journal of China Hydrology, 5, 69–74, 2010.
Mannocchi, F., Francesca, T., and Vergni, L.: Agricultural drought: Indices,
definition and analysis, IAHS-AISH Publication, 246–254, 2004.Mason, S. J.: Understanding forecast verification statistics, Meteorol.
Appl., 15, 31–40, 10.1002/met.51, 2008.
Menard, S.: Logistic Regression: From Introductory to Advanced Concepts and
Applications, ISBN-13: 9781483351421, 392 pp., SAGE Publications, Thousand Oaks, California, USA, 2009.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J.
Hydrol., 391, 202–216, 2010.Nagelkerke, N. J. D.: A Note on a General Definition of the Coefficient of
Determination, Biometrika, 78, 691–692, 10.1093/biomet/78.3.691, 1991.Nguyen, N. A.: Historic drought and salinity intrusion in the Mekong Delta
in 2016: Lessons learned and response olutions, Vietnam Journal of Science,
Technology and Engineering – Environmental Sciences, 59, 93–96, 10.31276/VJSTE.59(1).93, http://vietnamscience.vjst.vn/index.php/VJSTE/article/view/32, 2017.Pohar, M., Blas, M., and Turk, S.: Comparison of Logistic Regression and
Linear Discriminant Analysis: A Simulation Study, Metodološki zvezki1 – Advances in Methodology and Statistics, 1,
143–161, 2004.
Räsänen, T. A. and Kummu, M.: Spatiotemporal influences of ENSO on
precipitation and flood pulse in the Mekong River Basin, J.
Hydrol., 476, 154–168, 10.1016/j.jhydrol.2012.10.028, 2013.Risley, J. C., Guertin, D. P., and Fogel, M. M.: Salinity-Intrusion
Forecasting System for Gambia River Estuary, J. Water Res.
Pl., 119, 339–352,
10.1061/(ASCE)0733-9496(1993)119:3(339), 1993.
Roehl Jr., E. A., Daamen, R. C., and Cook, J. B.: Estimating seawater
intrusion impacts on coastal intakes as a result of climate change, Journal
– AWWA, 105, E642–E650, 10.5942/jawwa.2013.105.0131, 2013.Rohmer, J. and Brisset, N.: Short-term forecasting of saltwater occurrence
at La Comté River (French Guiana) using a kernel-based support vector
machine, Environ. Earth Sci., 76, 246, 10.1007/s12665-017-6553-5,
2017.Seibert, M., Merz, B., and Apel, H.: Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods, Hydrol. Earth Syst. Sci., 21, 1611–1629, 10.5194/hess-21-1611-2017, 2017.Smajgl, A., Toan, T. Q., Nhan, D. K., Ward, J., Trung, N. H., Tri, L. Q.,
Tri, V. P. D., and Vu, P. T.: Responding to rising sea levels in the Mekong
Delta, Nature Clim. Change, 5, 167–174, 10.1038/nclimate2469, 2015.
Toan, T. Q.: 9 – Climate Change and Sea Level Rise in the Mekong Delta:
Flood, Tidal Inundation, Salinity Intrusion, and Irrigation Adaptation
Methods, in: Coastal Disasters and Climate Change in Vietnam, edited by:
Thao, N. D., Takagi, H., and Esteban, M., Elsevier, Oxford, 199–218, 2014.VDMA: Report on Drought, Salinization and Response Options, Vietnam Disaster
Management Authority (VDMA) – National Steering Center for Natural Disaster
Prevention and Control, available at: http://dmc.gov.vn/Uploads/Thong tin Thien tai - Disaster Information/2016/03.2016/Han han/Bai trinh bay bao cao han han_EN.pdf?lang=vi-VN (last access: March 2020), 2016.Vicente-Serrano, S. M., Lopez-Moreno, J. I., Begueria, S., Lorenzo-Lacruz,
J., Azorin-Molina, C., and Moran-Tejeda, E.: Accurate Computation of a
Streamflow Drought Index, J. Hydrol. Eng., 17, 318–332,
10.1061/(Asce)He.1943-5584.0000433, 2012.Zeng, L. and Shannon, M.: Salinity Effects on Seedling Growth and Yield
Components of Rice, Crop Science – CROP SCI, 40, 996–1003,
10.2135/cropsci2000.404996x, 2000.Zeng, L., Shannon, M. C., and Lesch, S. M.: Timing of salinity stress
affects rice growth and yield components, Agr. Water Manage., 48,
191–206, 10.1016/S0378-3774(00)00146-3, 2001.