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Article

Salinity Intrusion Trends under the Impacts of Upstream Discharge and Sea Level Rise along the Co Chien River and Hau River in the Vietnamese Mekong Delta

by
Tuu Nguyen Thanh
1,*,
Hiep Huynh Van
2,
Hoang Vo Minh
3 and
Van Pham Dang Tri
4
1
Institue of Environmental Science and Technology, Tra Vinh University, 126, Nguyen Thien Thanh, Tra Vinh City 87000, Vietnam
2
Department of Civil engineering, School of Engineering and Technology, Tra Vinh University, 126, Nguyen Thien Thanh, Tra Vinh City 87000, Vietnam
3
School of Agriculture and Aquaculture, Tra Vinh University, 126, Nguyen Thien Thanh, Tra Vinh City 87000, Vietnam
4
Research Institute for Climate Change, Can Tho University, 3/2 Street, Ninh Kieu District, Can Tho City 94000, Vietnam
*
Author to whom correspondence should be addressed.
Climate 2023, 11(3), 66; https://doi.org/10.3390/cli11030066
Submission received: 18 January 2023 / Revised: 9 March 2023 / Accepted: 11 March 2023 / Published: 13 March 2023
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Abstract

:
A one-dimensional hydraulic HEC-RAS model was developed to forecast the change in salinity in the tributaries of the Co Chien and Hau Rivers in Tra Vinh province, Vietnam. The boundary data includes river discharge at Can Tho and My Thuan, water levels, and salinity at coastal monitoring stations. Six monitoring stations along the Co Chien River and Hau River were selected to study salinity changes. Four scenarios for the period 2020–2050 were selected, including SLR17, SLR22, SLR26L, and SLR26H, corresponding to sea level rise (17, 22, and 26 cm) and upstream river discharge decrease (in the ranges of 100–128% and 80–117% at Can Tho and My Thuan, respectively) in the dry season based on new climate change scenarios in Vietnam and previous studies. The results highlight that when the average discharge at Can Tho and My Thuan reduces, the salinity increases more significantly than the impact of sea level rise. Salinity at the monitoring stations in Tra Vinh province is projected to increase within the ranges of 4–21% and 3–29% along the Co Chien River and Hau River, respectively. In addition, sea level rise is seen to affect the discharge distribution into the Co Chien River. It suggests an urgent need to raise farmers’ awareness of climate change adaptation, investment in production equipment, and appropriate regulation of riverbed mining and activities upstream in the Mekong River.

1. Introduction

The Vietnamese Mekong Delta (VMD) is widely recognized as one of the most vulnerable deltas to the impacts of climate change [1]. Decision No. 417/QD-TTg issued by the Vietnamese prime minister in 2019 [2] underpinned the importance of evidence-ready datasets and updated forecasts to support planning, as well as spatial organization and the transformation of agricultural production models. In addition, a master plan for sustainable development to adapt to climate change is needed (Decision No. 287/QD-TTg of the Vietnamese prime minister: approving the master plan for the Mekong Delta in the period 2021–2030, with a vision to 2050 [3]). These documents stipulated the improved data needs for both the application of smart technologies in agricultural production and simultaneously adapting to climate change. This in turn will require the improved monitoring of resources and environmental changes in the event of droughts and salinity variability due to changes in both climate and sea level. This is because such forecasting and warning systems for the early prediction of changes need to be developed.
During the 2015–2016 dry season, salt water intruded more than 90 km inland and caused severe crop damage in 11 of the 13 provinces (including 09 coastal provinces) within the VMD [4,5,6]. An estimated two million people directly lost income from agricultural production, while two million people also faced shortages in drinking water and domestic water supplies due to drought and salinity intrusion. The Tra Vinh coastal area of the VMD is located between two main tributaries of the Mekong River (the Co Chien River and Hau River) [7]. The projected sea level rise by 2030 could impact over 24 thousand ha of agricultural land [8] through salinity intrusion and significantly impact the local water resources necessary for most sectors of life and production.
Moreover, upstream hydropower dam construction and operation could be affecting the hydrological system of the Lower Mekong River Basin, especially in the VMD. The number of operational hydroelectric stations increased by 183% from 2000 to 2010; meanwhile, the total water storage capacity has increased fourfold since the 1990s [9]. Hydropower dams are most likely the main cause of the reported shrinking capacity of Tonle Sap Lake in Cambodia where the minimum surface water area has been observed to be decreasing at a rate of 7.33 km2/year [9].
Representative Concentration Pathways (RCPs) were described as consistent predictive scenarios consisting of components of radiative forcing in the year 2100 that serve as inputs for climate modeling [10]; in which, four climate change scenarios were based on greenhouse gas emissions RCP2.6 (low emission), RCP4.5 (medium emission), RCP6.0 (high emission), and RCP8.5 (very high emission). According to [11], from 2016 onward, the global sea level has tended to increase by approximately 0.34 cm per year. The recent national report on climate change and sea level rise scenarios [12] issued by the Ministry of Natural Resources and Environment of Vietnam (MONRE) presented that sea level was projected to increase by 24, 23, and 28 cm under RCP2.6, RCP4.5, and RCP8.5, respectively, by the year 2050 compared to the period 1986–2005. A study [13] showed that discharge at a hydrological station upstream in the Mekong River in Cambodia (Figure 1a) tended to change from −20% to +10% and a sea level rise of 35 cm leading to a decrease in river discharge at Can Tho and My Thuan (Figure 1b) during the dry season in the period 2036–2065 compared to 2009–2011. Moreover, in recent years, the effects of climate change have become more apparent, and in particular, the water source for agricultural production. For example, the coastal areas in Cu Lao Dung district, Soc Trang province are gradually shifting from sugarcane to shrimp farming to adapt to climate change [14]. Risk mapping methods highlight that the effects of climate change reduce the area of rice cultivated land by 39% (if floods persist) and 44% (if saline intrusion persists) [15]. An important factor in agricultural production is meeting water demand. Therefore, solutions are proposed to assess the problem of water excess and shortage through geospatial analysis and mathematical modeling.
Mathematical modeling methods are typically applied to climate change research and run simulations to forecast future phenomena through defined scenarios [16,17,18,19,20]. A one-dimensional (1-D) hydraulic model is a mathematical model that simulates the flow properties in a river network [21]. In many coastal areas of the world, the applications of 1-D hydraulic models include flood impact assessment (in combination with mapping) [22,23,24,25,26], analysis of sediment transport [27,28,29], water quality [30], and salinity [31,32,33,34,35]. Important properties include river flow, water level, wet cross-sectional area, and roughness coefficient. In addition, the tool of water quality analysis is also included in the simulation of salinity intrusion [36,37]. Models have been developed based on sea level rise and discharge changes in the Sebou River in Morocco [38], Shatt Al-Arab River in Iraq [39], and Bahmanshir River in Iran [40]. Thus, 1-D hydraulic models were applied to the simulation of salinity intrusion not only in the VMD, but also in various coastal areas with similar scenario development methods. The difference is that the scenarios could depend on the human activities in local and upstream areas.
In Vietnam, previous studies using the 1-D MIKE model as a salinity simulation tool in the Mekong Delta have given acceptable accuracy [41,42,43]. A further study demonstrated that a 1-D hydraulic analysis model (that is the Hydrologic Engineering Center’s River Analysis System (HEC-RAS)) can provide informative estimates of water quality responses to sea level rise scenarios for Ho Chi Minh City in Vietnam [44]. The advantage of HEC-RAS is that it is free software, requires simple data input, exhibits an easy-to-use interface [45,46], and can simulate salinity using the module of water quality analysis [47]. Other models applied HEC-RAS to tidal wave propagation and numerically modeled salinity transport [48]. The 1-D hydraulic mathematical model is reliable in that a salinity variation over time is performed at a specific location, but it requires more data and time to perform [38,48]. The modeling tool is therefore useful for water managers and engineers to make rough estimates of saline intrusion along the estuary axis even during extreme events. Previous studies have developed hydraulic and salinity models in HEC-RAS to evaluate the variables that govern both the flow and salinity within a river system with highly reliable data [36,49]. It was indicated that the 1-D model HEC-RAS is qualified for the simulation and prediction of saline intrusion. As such, this study aims to (i) develop a 1-D hydraulic model using HEC-RAS, and (ii) forecast the changes in salinity in Tra Vinh province, a coastal province of the VMD, under the impacts of sea level rise and reduced upstream flow volumes. Our results will aid local managers and policymakers in planning salinity prevention via an increased understanding of salinity trends and the mechanisms at work.

2. Materials and Methods

2.1. Boundary Conditions

Hydraulic and salinity data were collected from Tra Vinh provincial hydro-meteorological stations in February 2020 at peak salinity conditions, including (1) boundary conditions (hourly river discharge at Can Tho and My Thuan; and hourly water levels at coastal stations including Vam Kenh, Binh Dai, An Thuan, Ben Trai, and Tran De) and (2) model calibration data (hourly water level at Tra Vinh and Cau Quan, and hourly salinity at Lang The, Tra Vinh, Hung My, Duong Duc, Cau Quan, and Tra Kha). The stations My Thuan, Lang The, Tra Vinh, Hung My, and Ben Trai are located along the Co Chien River; the stations Can Tho, Duong Duc, Cau Quan, Tra Kha, and Tran De are located on the Hau River; and the stations Vam Kenh, Binh Dai, and An Thuan are in the coastal area of Ben Tre province (located on the Tien River and Ham Luong River) (Figure 1).

2.2. Hydraulic Simulation

The dispersion of salinity is influenced by the hydraulic regime and highly dependent on estuary morphology. In the hydraulic module, HEC-RAS solves the following 1-D continuity and momentum equations, known as the Saint-Venant equations [21,50,51,52,53].
∂ A ∂ t + ∂ A u ∂ x = 0
∂ u ∂ t + u · ∂ u ∂ x + g · ∂ ε ∂ x = − P A · τ ρ
where x is river length (m); t is time (s); A is wet cross-section area (m2); u is flow velocity (m/s); ε is water level (m); τ is shear stress of water; P is wet cross-section perimeter (m); ρ is the specific weight of water (kg/m3); and g is gravity acceleration (m/s2). In addition, discharge was calculated based on riverbed roughness, slope, and wet cross-section area (the Manning equation).
Q = 1 n · A · R 2 / 3 · S 1 / 2
where Q is river discharge (m3/s); R is hydraulic radius (m); S is riverbed slope; and n is riverbed roughness.

2.3. Salinity Simulation

The water quality analysis module of the HEC-RAS model system was used to simulate salinity. The salinity boundary conditions were observed hourly salinity at coastal monitoring stations on the Tien River, Ham Luong River, Co Chien River, and Hau River. In order to simulate the salinity propagation, salinity dispersion coefficients were required [36,54,55]. In the VMD, the dispersion coefficients of salinity were determined to be 500 m2/s at Can Tho and My Thuan, and gradually increased at the estuaries (approximately 7500 m2/s) found through calibration and validation methods.

2.4. Model Calibration and Validation

The most recent year (2020) was selected for the baseline scenario to compare with 2050; therefore, the water level and salinity data were calibrated for 2020 and then validated for another year (2015). For hydraulic calibration and validation, Manning’s n coefficients (the riverbed roughness) (Equation (3)) were adjusted so that the simulated and observed water levels are approximately the same [22]. The Nash–Sutcliffe efficiency (NSE) (Equation (4)) was used to test the reliability of the hydraulic models [13,42,56,57]. NSE of the Tra Vinh and Cau Quan stations (Figure 1b) were calculated based on the comparison of observed and simulated water levels for the years 2020 and 2015, respectively, to calibrate and validate the hydraulic model. For salinity calibration and validation, the NSE and correlation coefficients (R2) of 6 stations (Figure 1b) were used to calibrate and validate the salinity [42,58] by adjusting the dispersion coefficients of each river section so that the measured and simulated salinity values were equivalent. R2 was calculated using the CORREL function for observed and simulated salinity values in Microsoft Excel software. NSE and R2 close to 1 showed a high-reliability model [59].
N S E = 1 − ∑ t = 1 n S i m t − O b s t 2 ∑ t = 1 n O b s t − O b s ¯ 2
where t is time; n is the total number of simulated time steps; S i m t is simulated data at time t; O b s t is observed data at time t; and O b s ¯ is average observed data [60].

2.5. Scenario Development

Future scenarios were developed based on the analysis of changes in upstream discharge and water levels by referencing the latest official report by MONRE and previous studies [12,13,42,61]. According to monitoring data, during the period 2015–2020, the average hourly river discharge in the dry season at Can Tho and My Thuan decreased by 417 m3/s and 426 m3/s, respectively. Furthermore, it was estimated that the average hourly river discharge in the dry season at Can Tho and My Thuan will change by −128% and −117% during the period 2020–2050, respectively (Table 1). A previous study indicated that the average flow from Can Tho and My Thuan was projected to change from −80% to −100% with a sea level rise of 35 cm in the dry season between periods 2009–2011 and 2036–2065 [13]. It can be estimated that the average sea level rise could be about 0.88 cm per year (i.e., 26 cm between 2020 and 2050) in the VMD. In addition, the sea level in the VMD was projected to increase from 22 cm to 26 cm in the 2050s compared to 2013 (i.e., 0.59 to 0.74 cm per year) under the climate change scenarios RCP4.5 and RCP8.5 [42]. Other studies suggest that the water level could increase by 0.55 cm per year [61,62]; therefore, it is reasonable to assume that the sea level in 2050 could increase by approximately 17 cm relative to 2020 levels. However, according to MONRE [12], sea level rise was projected to range from 23 to 28 cm by 2050 compared to the baseline period (1986–2005) under RCP2.6, RCP24.5, and RCP8.5. Thus, it is estimated that by mid-century, sea level rise would range between 0.46 and 0.56 cm per year (between 14 and 17 cm in the period 2020–2050). In this study, reliable scenarios were developed to assess the impacts of sea level rise and upstream discharge reduction during the dry season, including sea level increases of 17, 22, and 26 cm combined with discharge reduction ranging from 80% to 128% (Table 1).

3. Results

3.1. Hydraulic Calibration and Validation

Manning’s roughness coefficient of the VMD was found in a range from 0.014 to 0.022 in the coastal area [63]. According to the hydraulic results, the roughness coefficient of the river and canal system of Tra Vinh tended to increase gradually from the estuary to the upstream, ranging from 0.014 to 0.021. There were two stations selected to calibrate and validate the water level which were Tra Vinh and Cau Quan stations (Figure 1b). Calibration and validation results under the comparison between observed and simulated water levels at Tra Vinh and Cau Quan stations showed high reliability. In 2020, NSEs were 0.94 and 0.88 at Tra Vinh and Cau Quan stations, respectively (Figure 2a,b). In addition, NSEs in 2015 were estimated to be 0.99 and 0.92 at Tra Vinh and Cau Quan stations, respectively (Figure 1b and Figure 2c,d).

3.2. Peak Salinity Delay

The delay of peak salinity is the time for the peak salinity to intrude inland where the monitoring station records. Peak salinity moving from the estuary upstream has a delay time based on distance [64]. For the Co Chien River, the delay time from the estuary to Hung My, Tra Vinh, and Lang The stations was estimated to be 01, 24, and 25 h, respectively. As for the Hau River, the delay time was 01, 31, and 30 h from the estuary to Tra Kha, Cau Quan, and Duong Duc stations, respectively.

3.3. Salinity Calibration and Validation

The salinity data during the period of occurrence of the maximum value (that is from 0:00 on 1 February 2020 to 23:00 on 20 February 2020) were selected to calculate the reliability of the model. R2 at Hung My, Tra Vinh, Lang The, Tra Kha, Cau Quan, and Duong Duc stations ranges from 0.86 to 0.92 in 2015, and from 0.80 to 0.96 in 2020 (Table 2). The study focused on calibrating salinity for 2020 by adjusting the dispersion coefficients. These coefficients probably had large errors if applied to 2015 (low NSE at Hung My and Tra Kha). When considering NSE, two stations showed high reliability, which were Tra Vinh station (NSEs were 0.73 and 0.81 in 2020 and 2015, respectively) and Cau Quan station (NSEs were 0.73 and 0.53 in 2020 and 2015, respectively) (Table 2 and Figure 3).

3.4. Distribution of Average Discharge into the Co Chien River

At My Thuan, the discharge is divided into two tributaries, which are the Tien River tributaries (including the total discharge of the Tien River and Ham Luong River) and the Co Chien River tributary (Figure 1b). In this study, during February, the discharge distribution ratio to the Co Chien River was 53% and 59% in 2015 and 2020, respectively. When discharge at My Thuan changes under future scenarios (Table 1), the distribution of the flow through the two tributaries also changes proportionally (Table 3). Discharge distribution into the Co Chien River tends to increase in proportion to the increase in sea level (from 69% to 86%).

3.5. Salinity Changes under Future Scenarios

3.5.1. Impacts of Sea Level Rise under Climate Change Scenarios in Vietnam and Extreme Decrease of Upstream River Discharge

Figure 4a highlights that the average change in salinity varies from +3% (+0.3 g/L) to +28% (+1.0 g/L) under the impacts of a sea level rise of 17 cm and the reduction in upstream river discharge (SLR17) (Table 4). The minimum change in salinity is −10% (−1.1 g/L) at Hung My, and the maximum increase is +70% (+2.2 g/L) at Duong Duc. The average salinity in the Co Chien River tends to increase between +8% (+0.7 g/L) and +21% (+0.8 g/L) more than that of the Hau River despite the same changes in river discharge. When the water level increases by 5 cm (SLR22) for both tributaries (from 17 cm to 22 cm), there is no significant difference compared to the rise of 17 cm; however, the salinity at the monitoring stations along the Tien River tends to decrease slightly (−1%). The lowest drop at Hung My would be −11% (−1.2 g/L), and the largest increase in Duong Duc would be +74% (+2.5 g/L) (Figure 4b). In a comparison of the four scenarios, it can be seen that the salinity increases significantly when the upstream river discharge decreases sharply (Figure 4).

3.5.2. Impact of Extreme Sea Level Rise

The average changes in salinity vary from +3% (+0.3 g/L) to +26% (+1.0 g/L) under the impacts of a sea level rise of 26 cm and a reduction in upstream river discharge (Figure 4c,d, and Table 4). The minimum change in salinity is −16% (−1.6 g/L) at Hung My and the maximum increase is +65% (+2.3 g/L) at Duong Duc. Salinity in the Co Chien tributary tends to slightly increase (+1%) while it is almost unchanged in the Hau River tributary when the discharge decreased (Figure 4c,d).

4. Discussion

According to the report by the Mekong River Commission, the discharge distribution ratio from My Thuan to the Co Chien River was in the range of 31–56% from 1974 to 1993 [65,66]. In this study, in the years 2015 and 2020, the discharge distribution to the Co Chien River tended to increase compared to the past period. This demonstrated the future impact of sea level rise on the discharge distribution ratio from My Thuan to the Co Chien River in the future (Table 3). The greater discharge could be a reason for the decrease in salinity at Lang The, Tra Vinh, and Hung My stations along the Co Chien tributary under the scenario of a sea level rise of 22 cm compared to 17 cm (Figure 1b and Figure 4a,b). However, this is only one of the reasons; the primary consequences we disregarded were those caused by humans, such as the sinking of the deltaic system and modifications to river cross-sections (due to, for instance, sand mining and lack of upstream sediments [9,67,68,69,70,71,72,73]). As such, it is necessary to have appropriate policies regulating riverbed sand mining [74,75] and the operation of upstream hydropower dams [76,77,78,79].
Scenarios were selected on the basis of previous studies and calculated from historical data. SLR17 and SLR22 were two low sea level rise scenarios with a sharp decrease in upstream river discharge. In contrast, SLR26L and SLR26H were two high sea level rise scenarios with slight changes in river discharge (Table 2). This suggests that a decrease in upstream discharge [80,81,82,83,84,85] is the main cause of the increase in salinity in the main tributaries (the Co Chien River and Hau River). The decrease in discharge was assessed as a result of reduced precipitation [81,85,86,87] together with the increase in irrigation demand in the dry season [13,88] as well as the operation of upstream hydropower plants [9,61,69,89].
There were many solutions to adapt to salinity intrusion proposed or implemented for the VMD. The appropriate solutions for Tra Vinh included (i) sluice gate systems for salinity prevention (currently in operation) [90,91], (ii) changing crop structure to adapt to drought and salinity (for example, salinity-/drought-resistant varieties) [7,76,92,93], (iii) reducing the number of crops per year (from three crops/year to two or one crop yearly) [7,94,95], desalination for domestic water use [96], (iv) monitoring, forecasting and early warning of salinity intrusion [97,98], (v) climate-smart agriculture (rice-shrimp) [92,99,100], and (vi) improving agro-fishery value chains [101,102,103,104]. However, despite the vast array of strategies, there still remain implementation challenges, such as farmers’ low awareness, lack of investment in production equipment [105], and the complicated socio-hydrological factors in the region.
The developed model excluded changes in the riverbed geomorphology [80,85,106] for all scenarios; therefore, the changes in salinity on the river systems in the study area are only assessed according to the changes in the upper and lower boundary conditions for the dry season (i.e., sea level and upstream discharge volumes). Moreover, due to only focusing on the main river system, anthropogenic impacts were overlooked. The model used river network data and cross-sections of previous years to simulate the year 2020 and future years. Since the model only focuses on Tra Vinh province, the discharge losses to other areas, such as Can Tho and Ben Tre, were not included.
Within the scope of our study, some uncertainties and limitations exist. The river network data in the same year were applied in the simulation for different years; however, the errors were within the acceptable range. At some time points, the salinity at Hung My and Tra Kha (Figure 4) decreased due to model errors; however, the average value at all stations increased. The peak salinity value could not be predicted accurately because it occurs at various times during the dry season (that is from January to April) [107,108]; moreover, hourly salinity fluctuates continuously, and the peak salinity value is transient and changes immediately.
Further research could include the impacts of irrigation constructions and operations (sluice gate systems for salinity prevention) and other human activities (sand mining, landslide, and subsidence). Moreover, two-dimensional hydraulic models to assess salinity intrusion and dispersal in different dimensions and hydrological models (including temperature, rainfall, and evapotranspiration) should be developed.

5. Conclusions

In general, the applied 1-D hydraulic HEC-RAS model demonstrated reliable accuracy through calibration and validation of water level and salinity in the coastal VMD. The model focused on studying a small area (Tra Vinh province) with more up-to-date scenarios of the world and of the VMD. The results highlight the important impacts of sea level rise compounded with decreases in upstream river discharge volumes (greater impact) on salinity intrusion in the dry season. These can inform the discussions of local managers and policymakers involved in decision-making. In addition, the impacts of sea level rise on the discharge distribution within the two tributaries were recognized as a natural environment finding. Input data and future scenarios were based on the measurement and verification data from previous studies. The model indicated a trend in future salinity; however, this needs to be further studied based on many different factors and applied to various coastal regions in the VMD.

Author Contributions

Conceptualization, T.N.T. and H.H.V.; methodology, T.N.T.; software, T.N.T.; validation, T.N.T. and V.P.D.T.; formal analysis, T.N.T., H.H.V. and V.P.D.T.; investigation, T.N.T.; resources, V.P.D.T., T.N.T. and H.H.V.; data curation, T.N.T.; writing—original draft preparation, T.N.T. and V.P.D.T.; writing—review and editing, V.P.D.T., H.V.M. and H.H.V.; visualization, T.N.T.; supervision, V.P.D.T.; project administration, T.N.T.; funding acquisition, T.N.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Tra Vinh University, contract number 302/2020/HĐ.HĐKH&ĐT-ĐHTV.

Data Availability Statement

Not applicable.

Acknowledgments

We are grateful to Tra Vinh University for providing supporting materials and equipment for the study. This research was funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) and the UK Natural Environment Research Council (NERC) under grant number NE/S002871/2 for providing updated input data on boundary conditions and river bathymetry.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. IPCC. Working Group II: Impacts, Adaptation and Vulnerability; The Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2007. [Google Scholar]
  2. Axmann, H.B.; Soethoudt, J.M.; Thúy, V.D. Roadmap Post-Harvest Loss Reduction in Selected Vietnamese Value Chains: Phase 2; Wageningen Food & Biobased Research: Wageningen, The Netherlands, 2022. [Google Scholar]
  3. Truong, C.Q.; Drogoul, A.; Gaudou, B.; Taillandier, P.; Quang, N.H.; Brugière, A.; Nguyen, H.T.T.; Minderhoud, P.; Espagne, E. LUCAS-GEMMES: Integrated dynamics of adaptation strategies in the Vietnamese Mekong Delta. In The Mekong Delta Emergency Climate and Environmental Adaptation Strategies to 2050; Agence Française de Développement: Paris, France, 2022. [Google Scholar]
  4. Hoan, N.X.; Khoi, D.N.; Trung, L.D. Assessing the adaptive capacity of farmers under the impact of saltwater intrusion in the Vietnamese Mekong Delta. J. Environ. Plan. Manag. 2019, 62, 1619–1635. [Google Scholar] [CrossRef]
  5. Nguyen, N.A. Historic drought and salinity intrusion in the Mekong Delta in 2016: Lessons learned and response solutions. Vietnam. J. Sci. Technol. Eng. 2017, 59, 93–96. [Google Scholar] [CrossRef] [PubMed]
  6. CGIAR Research Program on Climate Change, Agriculture and Food Security. The Drought and Salinity Intrusion in the Mekong River Delta of Vietnam: Assessment Report; CGIAR: Washington, DC, USA, 2016. [Google Scholar]
  7. Dang, K.K.; Do, T.H.; Le, T.H.L.; Le, T.T.H.; Pham, T.D. Impacts of farmers’ adaptation to drought and salinity intrusion on rice yield in Vietnam’s Mekong Delta. J. Agribus. Dev. Emerg. Econ. 2021, 11, 27–41. [Google Scholar] [CrossRef]
  8. Tran, P.H. Assessment of Changes in the Structure Land Use in Tra Vinh Province Under the Scenarios of Climate Change and Sea Level Rise. Vietnam J. Sci. Technol. 2020, 58, 70. [Google Scholar] [CrossRef] [Green Version]
  9. Lin, Z.; Qi, J. Hydro-dam–A nature-based solution or an ecological problem: The fate of the Tonlé Sap Lake. Environ. Res. 2017, 158, 24–32. [Google Scholar] [CrossRef] [PubMed]
  10. Pachauri, R.K.; Allen, M.R.; Barros, V.R.; Broome, J.; Cramer, W.; Christ, R.; Church, J.A.; Clarke, L.; Dahe, Q.; Dasgupta, P. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014; ISBN 9291691437. [Google Scholar]
  11. Lindsey, R. Climate Change: Global Sea Level. Available online: https://www.climate.gov/news-features/understanding-climate/climate-change-global-sea-level (accessed on 14 August 2020).
  12. MONRE (Ministry of Natural Resources and Environment of Viet Nam). Climate Change Scenarios (Kịch bản biến đổi khí hậu); MONRE (Ministry of Natural Resources and Environment of Viet Nam): Ha Noi, Vietnam, 2021. [Google Scholar]
  13. Anh, D.T.; Hoang, L.P.; Bui, M.D.; Rutschmann, P. Modelling seasonal flows alteration in the Vietnamese Mekong Delta under upstream discharge changes, rainfall changes and sea level rise. Int. J. River Basin Manag. 2019, 17, 435–449. [Google Scholar] [CrossRef]
  14. Khai, H.V.; Trang, N.T.; Tu, V.H.; Hong, N.B. Environmental efficiency of transformed farming systems: A case study of change from sugarcane to shrimp in the vietnamese mekong delta. For. Res. Eng. Int. J. 2018, 2, 54–60. [Google Scholar]
  15. Wassmann, R.; Phong, N.D.; Tho, T.Q.; Hoanh, C.T.; Khoi, N.H.; Hien, N.X.; Vo, T.B.T.; Tuong, T.P. High-resolution mapping of flood and salinity risks for rice production in the Vietnamese Mekong Delta. Field Crops Res. 2019, 236, 111–120. [Google Scholar] [CrossRef]
  16. Chen, Z.; Liu, J.; Qian, Z.; Li, L.; Zhang, Z.; Feng, G.; Ruan, S.; Sun, G. Monitoring Land Degradation through Vegetation Dynamics Mathematical Modeling: Case of Jornada Basin (in the US). Remote Sens. 2023, 15, 978. [Google Scholar] [CrossRef]
  17. Wang, T.; Tu, X.; Singh, V.P.; Chen, X.; Lin, K.; Lai, R.; Zhou, Z. Socioeconomic drought analysis by standardized water supply and demand index under changing environment. J. Clean. Prod. 2022, 347, 131248. [Google Scholar] [CrossRef]
  18. Kim, S.; McDonald, B.C.; Seo, S.; Kim, K.; Trainer, M. Understanding the paths of surface ozone abatement in the Los Angeles Basin. J. Geophys. Res. Atmos. 2022, 127, e2021JD035606. [Google Scholar] [CrossRef]
  19. Mel, R.A.; Lo Feudo, T.; Miceli, M.; Sinopoli, S.; Maiolo, M. A coupled modelling system to assess the effect of Mediterranean storms under climate change. Nat. Hazards Earth Syst. Sci. Discuss. 2022, 1–31. [Google Scholar] [CrossRef]
  20. Nandi, S.; Gonela, V. Rainwater harvesting for domestic use: A systematic review and outlook from the utility policy and management perspectives. Util. Policy 2022, 77, 101383. [Google Scholar] [CrossRef]
  21. Brunner, G.W. HEC-RAS River Analysis System-Hydraulic Reference Manual, Version 5.0; USACE: Washington, DC, USA, 2016. [Google Scholar]
  22. Malik, S.; Pal, S.C. Potential flood frequency analysis and susceptibility mapping using CMIP5 of MIROC5 and HEC-RAS model: A case study of lower Dwarkeswar River, Eastern India. SN Appl. Sci. 2021, 3, 31. [Google Scholar] [CrossRef]
  23. Ghimire, E.; Sharma, S.; Lamichhane, N. Evaluation of one-dimensional and two-dimensional HEC-RAS models to predict flood travel time and inundation area for flood warning system. ISH J. Hydraul. Eng. 2022, 28, 110–126. [Google Scholar] [CrossRef]
  24. Ngo, H.; Bomers, A.; Augustijn, D.C.M.; Ranasinghe, R.; Filatova, T.; van der Meulen, B.; Herget, J.; Hulscher, S.J.M.H. Reconstruction of the 1374 Rhine river flood event around Cologne region using 1D-2D coupled hydraulic modelling approach. J. Hydrol. 2023, 617, 129039. [Google Scholar] [CrossRef]
  25. Malik, S.; Pal, S.C.; Chowdhuri, I.; Chakrabortty, R.; Roy, P.; Das, B. Prediction of highly flood prone areas by GIS based heuristic and statistical model in a monsoon dominated region of Bengal Basin. Remote Sens. Appl. Soc. Environ. 2020, 19, 100343. [Google Scholar] [CrossRef]
  26. Ruidas, D.; Saha, A.; Islam, A.R.M.T.; Costache, R.; Pal, S.C. Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river, India. Environ. Sci. Pollut. Res. 2022, 1–16. [Google Scholar] [CrossRef]
  27. Lee, J.; Ahn, J. Analysis of Bed Sorting Methods for One Dimensional Sediment Transport Model. Sustainability 2023, 15, 2269. [Google Scholar] [CrossRef]
  28. Salheddine, M.; André, P.; Mahmoud, H. A coupled 1-D/2-D model for simulating river sediment transport and bed evolution. J. Hydroinform. 2020, 22, 1122–1137. [Google Scholar] [CrossRef]
  29. Joshi, N.; Lamichhane, G.R.; Rahaman, M.M.; Kalra, A.; Ahmad, S. Application of HEC-RAS to study the sediment transport characteristics of Maumee River in Ohio. In Proceedings of the World Environmental and Water Resources Congress 2019: Hydraulics, Waterways, and Water Distribution Systems Analysis, Pittsburgh, PA, USA, 19–23 May 2019; American Society of Civil Engineers Reston: Reston, VA, USA, 2019; pp. 257–267. [Google Scholar]
  30. Gordillo, G.; Morales-Hernández, M.; García-Navarro, P. Finite volume model for the simulation of 1D unsteady river flow and water quality based on the WASP. J. Hydroinform. 2020, 22, 327–345. [Google Scholar] [CrossRef]
  31. Haddout, S.; Priya, K.L.; Adarsh, S. Investigation and comparison of one-dimensional (1-D) analytical models prediction for salt intrusion condition in two selected estuaries. Mar. Georesour. Geotechnol. 2020, 38, 374–384. [Google Scholar] [CrossRef]
  32. Nizar, Y.; Touazit, A.; Igouzal, M. Predicting the thermal regime of the Sebou river estuary (morocco) using a one-dimensional model (HEC-RAS 5.0). Nat. Environ. Pollut. Technol. 2022, 21, 883–890. [Google Scholar] [CrossRef]
  33. Pereira, D.A.; Shah, A.; Hasan, M.M.; Ahmad, A.; Alwi, A.T.; Affan, S.; Lazim, Z.M. Saline water intrusion through Rajang river network due to sea level rise. J. Mech. Civ. Eng. IOSR-JMCE 2019, 16, 55–65. [Google Scholar]
  34. Bui, L.T.; Le, D.T.M. Application of MIKE/SWAT for simulation the salt intrusion–a case study in Ve river, Quang Ngai province. Lowl. Technol. Int. 2020, 22, 258. [Google Scholar]
  35. Ho, B.Q.; Nguyen, K.D.; Vu, K.H.N.; Nguyen, T.T.; Nguyen, H.T.T.; Ngo, D.D.N.; Tran, H.T.H.; Le, P.H.; Nguyen, Q.H.; Ngo, Q.X. Apply MIKE 11 model to study impacts of climate change on water resources and develop adaptation plan in the Mekong Delta, Vietnam: A case of Can Tho city. Environ. Monit. Assess. 2022, 194, 765. [Google Scholar] [CrossRef] [PubMed]
  36. Haque, A.; Hossain, K.M.A.; Shadia, N. Coupled 1D hydrodynamic-water quality model of Kobadak-Sibsa River system for salinity. Int. J. Environ. Sci. Dev. 2021, 12, 311–315. [Google Scholar] [CrossRef]
  37. Islam, M.T. Environmental Flow Assessment of Gorai-Madhumati-Kaliganga-Balaswar River System. Master’s Thesis, Department of Water Resources Engineering (WRE), BUET, Dhaka, Bangladesh, 2019. [Google Scholar]
  38. Haddout, S.; Maslouhi, A. One-Dimensional Hydraulic Analysis of the Effect of Sea Level Rise on Salinity Intrusion in the Sebou Estuary, Morocco. Mar. Geod. 2018, 41, 270–288. [Google Scholar] [CrossRef]
  39. Hamdan, A.N.A.; Al-Mahdi, A.A.J.; Mahmood, A.B. Modeling the effect of sea water intrusion into Shatt Al-Arab River (Iraq). J. Univ. Babylon Eng. Sci. 2020, 28, 210–224. [Google Scholar]
  40. Farahani, N.D.; Shahraiyni, H.T.; Sheikhi, R. Water quality modeling for determination of suitability of water for shrimp farming in tidal rivers. Water Qual. Res. J. 2019, 54, 34–46. [Google Scholar] [CrossRef] [Green Version]
  41. Tran Anh, D.; Hoang, L.P.; Bui, M.D.; Rutschmann, P. Simulating future flows and salinity intrusion using combined one-and two-dimensional hydrodynamic modelling—The case of Hau River, Vietnamese Mekong delta. Water 2018, 10, 897. [Google Scholar] [CrossRef] [Green Version]
  42. Ha, N.T.T.; Trang, H.T.; Vuong, N.V.; Khoi, D.N. Simulating impacts of sea level rise on salinity intrusion in the Mekong Delta, Vietnam in the period 2015–2100 using MIKE 11. Naresuan Univ. Eng. J. 2016, 11, 21–24. [Google Scholar]
  43. Lam, N.T. Real-Time prediction of salinity in the Mekong River Delta. In Proceedings of the International Conference on Asian and Pacific Coasts, Hanoi, Vietnam, 25–28 September 2019; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1461–1468. [Google Scholar]
  44. Thuy, N.T.D.; Phung, N.K.; Hoan, N.X.; Khoi, D.N. Assessing the impacts of the changes in upstream flow and sea level rise due to climate change on seawater intrusion in Ho Chi Minh City using the HEC-RAS 1D model. Vietnam. J. Hydrometeorol. 2018, 1, 64–69. [Google Scholar]
  45. Helal, E.; Taher El-Gamal, T.; Ezzat Zidane, A. Improving quantitative and qualitative equity of water distribution systems. Ain Shams Eng. J. 2023, 14, 101969. [Google Scholar] [CrossRef]
  46. Ismail, H.; Kamal, M.R.; Shanono, N.J.; Amin, S.A. Application of HEC-RAS model for adaptive water allocation in a Large-Scale Rice Irrigation Scheme. Alger. J. Eng. Technol. 2021, 5, 19–29. [Google Scholar]
  47. Pasquier, U.; He, Y.; Hooton, S.; Goulden, M.; Hiscock, K.M. An integrated 1D–2D hydraulic modelling approach to assess the sensitivity of a coastal region to compound flooding hazard under climate change. Nat. Hazards 2019, 98, 915–937. [Google Scholar] [CrossRef] [Green Version]
  48. Haddout, S.; Igouzal, M.; Maslouhi, A. Analytical and numerical study of the salinity intrusion in the Sebou river estuary (Morocco) – Effect of the “super Blood Moon” (total lunar eclipse) of 2015. Hydrol. Earth Syst. Sci. 2016, 20, 3923–3945. [Google Scholar]
  49. Saran, S.H.; Rahman, A.; Yunus, A. Simulation of Flow and Salinity in Rupsha-Passur River System. In Proceedings of the 4th International Conference on Civil Engineering for Sustainable Development (ICCESD 2018), Khulna, Bangladesh, 9–11 February 2018. [Google Scholar]
  50. Minh, H.V.T.; Tri, V.P.D.; Ut, V.N.; Avtar, R.; Kumar, P.; Dang, T.T.T.; Van Hoa, A.; Van Ty, T.; Downes, N.K. A Model-Based Approach for Improving Surface Water Quality Management in Aquaculture Using MIKE 11: A Case of the Long Xuyen Quadangle, Mekong Delta, Vietnam. Water 2022, 14, 412. [Google Scholar] [CrossRef]
  51. Montazeri, A.; Mazaheri, M.; Morid, S.; Mosaddeghi, M.R. Effects of upstream activities of Tigris-Euphrates River Basin on water and soil resources of Shatt al-Arab Border River. Sci. Total Environ. 2023, 858, 159751. [Google Scholar] [CrossRef]
  52. Alabyan, A.M.; Krylenko, I.N.; Lebedeva, S.V.; Panchenko, E.D. World Experience in Numerical Simulation of Flow Dynamics at River Mouths. Water Resour. 2022, 49, 766–780. [Google Scholar] [CrossRef]
  53. Darji, J.; Lodha, P.; Tyagi, S. Assimilative capacity and water quality modeling of rivers: A review. AQUA—Water Infrastruct. Ecosyst. Soc. 2022, 71, 1127–1147. [Google Scholar] [CrossRef]
  54. Haque, A.; Hossain, K.M.A.; Student, M.S.E.; Author, C. Salinity Modeling of Kobadak- Sibsa River System By Hec-RAS. In Proceedings of the 5th International Conference on Civil Engineering for Sustainable Development (ICCESD2020), Khulna, Bangladesh, 7–9 February 2020; pp. 1–11. [Google Scholar]
  55. Kulmart, K.; Pochai, N. Numerical simulation for salinity intrusion measurement models using the MacCormack finite difference method with lagrange interpolation. J. Interdiscip. Math. 2020, 23, 1157–1185. [Google Scholar] [CrossRef]
  56. Mohammadi, M.; Mohtadi, M. On the Hydraulic Simulation of River Simineh using HEC-RAS and ArcGIS Software. Hydrogeomorphology 2022, 9, 103–187. [Google Scholar]
  57. Vashist, K.; Singh, K.K. Minimisation of Overestimation of River Flows in 1D-hydrodynamic Modeling; Department of Civil Engineering, NIT: Kurukshetra, India, 2021. [Google Scholar]
  58. Doan, Q.T.; Nguyen, C.D.; Chen, Y.C.; Pawan, K.M. Modeling the influence of river flow and salinity intrusion in the Mekong River Estuary, Vietnam. Lowl. Technol. Int. 2014, 16, 14–25. [Google Scholar] [CrossRef] [Green Version]
  59. Yazdi, J.; Moridi, A. Interactive reservoir-watershed modeling framework for integrated water quality management. Water Resour. Manag. 2017, 31, 2105–2125. [Google Scholar] [CrossRef]
  60. Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
  61. Kantoush, S.; Van Binh, D.; Sumi, T.; Trung, L.V. Impact of Upstream Hydropower Dams and Climate Change on Hydrodynamics of Vietnamese Mekong Delta. J. Jpn. Soc. Civ. Eng. Ser. B1 (Hydraul. Eng.) 2017, 73, I_109–I_114. [Google Scholar] [CrossRef] [Green Version]
  62. Doyle, T.W.; Day, R.H.; Michot, T.C. Development of Sea Level Rise Scenarios for Climate Change Assessments of the Mekong Delta, Vietnam; USGS: Reston, VA, USA, 2016. [Google Scholar]
  63. Vu, H.T.D.; Trinh, V.C.; Tran, D.D.; Oberle, P.; Hinz, S.; Nestmann, F. Evaluating the impacts of rice-based protection dykes on floodwater dynamics in the vietnamese mekong delta using geographical impact factor (Gif). Water 2021, 13, 1144. [Google Scholar] [CrossRef]
  64. Kuo, A.Y.; Fang, C.S. A mathematical model for salinity intrusion. Coast. Eng. Proc. 1972, 126, 2275–2289. [Google Scholar]
  65. WUP-JICA. Vol. II: Supporting report, Paper VII: Maintenance of flows on the Mekong Mainstream. In The Study on Hydro-Meteorological Monitoring for Water Quantity Rules in Mekong River Basin; Mekong River Commission: Vientiane, Laos, 2004; Volume II, 184p. [Google Scholar]
  66. Nguyen, A.D.; Savenije, H.H.G.; Pham, D.N.; Tang, D.T. Using salt intrusion measurements to determine the freshwater discharge distribution over the branches of a multi-channel estuary: The Mekong Delta case. Estuar. Coast. Shelf Sci. 2008, 77, 433–445. [Google Scholar] [CrossRef]
  67. Brunier, G.; Anthony, E.J.; Goichot, M.; Provansal, M.; Dussouillez, P. Recent morphological changes in the Mekong and Bassac river channels, Mekong delta: The marked impact of river-bed mining and implications for delta destabilisation. Geomorphology 2014, 224, 177–191. [Google Scholar] [CrossRef]
  68. Jordan, C.; Tiede, J.; Lojek, O.; Visscher, J.; Apel, H.; Nguyen, H.Q.; Quang, C.N.X.; Schlurmann, T. Sand mining in the Mekong Delta revisited-current scales of local sediment deficits. Sci. Rep. 2019, 9, 17823. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Van Binh, D.; Kantoush, S.; Sumi, T. Changes to long-term discharge and sediment loads in the Vietnamese Mekong Delta caused by upstream dams. Geomorphology 2020, 353, 107011. [Google Scholar] [CrossRef]
  70. Gruel, C.-R.; Park, E.; Switzer, A.D.; Kumar, S.; Ho, H.L.; Kantoush, S.; Van Binh, D.; Feng, L. New systematically measured sand mining budget for the Mekong Delta reveals rising trends and significant volume underestimations. Int. J. Appl. Earth Obs. Geoinf. 2022, 108, 102736. [Google Scholar] [CrossRef]
  71. Vasilopoulos, G.; Quan, Q.L.; Parsons, D.R.; Darby, S.E.; Tri, V.P.D.; Hung, N.N.; Haigh, I.D.; Voepel, H.E.; Nicholas, A.P.; Aalto, R. Establishing sustainable sediment budgets is critical for climate-resilient mega-deltas. Environ. Res. Lett. 2021, 16, 064089. [Google Scholar] [CrossRef]
  72. Loc, H.H.; Van Binh, D.; Park, E.; Shrestha, S.; Dung, T.D.; Son, V.H.; Truc, N.H.T.; Mai, N.P.; Seijger, C. Intensifying saline water intrusion and drought in the Mekong Delta: From physical evidence to policy outlooks. Sci. Total Environ. 2021, 757, 143919. [Google Scholar] [CrossRef]
  73. Rentier, E.S.E.; Cammeraat, L.H.E. The environmental impacts of river sand mining. Sci. Total Environ. 2022, 838, 155877. [Google Scholar] [CrossRef]
  74. Hui, T.R.; Park, E.; Loc, H.H.; Tien, P.D. Long-term hydrological alterations and the agricultural landscapes in the Mekong Delta: Insights from remote sensing and national statistics. Environ. Chall. 2022, 7, 100454. [Google Scholar] [CrossRef]
  75. Tri, V.P.D.; Trung, P.K.; Trong, T.M.; Parsons, D.R.; Darby, S.E. Assessing social vulnerability to riverbank erosion across the Vietnamese Mekong Delta. Int. J. River Basin Manag. 2022, 1–12. [Google Scholar] [CrossRef]
  76. Le Huy, B.; Le, H.; Xuan, H.N. The Harmful Effect of the Hydro-Electric Dams Upstream of the Mekong River: Effect on the Ecosystems and Livelihoods of People in Mekong Delta, Vietnam. Water Conserv. Sci. Eng. 2022, 7, 1–20. [Google Scholar] [CrossRef]
  77. Tran, T.A.; Tortajada, C. Responding to transboundary water challenges in the Vietnamese Mekong Delta: In search of institutional fit. Environ. Policy Gov. 2022, 32, 331–347. [Google Scholar] [CrossRef]
  78. Park, E. Environmental issues in the Mekong Delta: Drivers, consequences, and management outlooks. Geogr. Dig. 2022, 1, 1. [Google Scholar]
  79. Feng, W.; Lei, Z.; Zhou, Z. Mekong River Basin International Cooperation: The Organizational and Geopolitical Mobilizations. In Proceedings of the 2021 International Conference on Public Art and Human Development (ICPAHD 2021), Kunming, China, 24–26 December 2021; Volume 638, pp. 323–328. [Google Scholar]
  80. Jordan, C.; Visscher, J.; Dung, N.V.; Apel, H.; Schlurmann, T. Impacts of human activity and global changes on future morphodynamics within the tien river, vietnamese mekong delta. Water 2020, 12, 2204. [Google Scholar] [CrossRef]
  81. Park, E.; Loc, H.H.; Van Binh, D.; Kantoush, S. The worst 2020 saline water intrusion disaster of the past century in the Mekong Delta: Impacts, causes, and management implications. Ambio 2022, 51, 691–699. [Google Scholar] [CrossRef]
  82. Van Tho, N. Salinity Intrusion in the Vietnamese Mekong Delta, a Threat: Possible Causes, Effects on People’s Life and Production, and Temporary Solutions and Adaptable Strategies. In Sustainable Development of Water and Environment; Springer: Berlin/Heidelberg, Germany, 2022; pp. 1–10. [Google Scholar]
  83. Pham, T.T.B.; Nguyen, V.T.; Pham, B.T.; Tran, H.D.; Duong, M.B.; Ngo, T.T.M.; Mai, T.D.; Pham, Q.M. Seawater Intrusion Processes Along the Tien River Mouth in the Period 2000–2020. In Proceedings of the International Conference on Geo-Spatial Technologies and Earth Resources, Hanoi, Vietnam, 13–14 October 2022; Springer: Berlin/Heidelberg, Germany, 2023; pp. 19–34. [Google Scholar]
  84. Le, H.-M.; Ludwig, M. The Salinization of Agricultural Hubs: Impacts and Adjustments to Intensifying Saltwater Intrusion in the Mekong Delta. In Beiträge zur Jahrestagung des Vereins für Socialpolitik 2022: Big Data in Economics; ZBW-Leibniz Information Centre for Economics: Kiel, Hamb, 2022. [Google Scholar]
  85. Chowdhury, M.M.A. Assessment of Sediment and Salinity in the Lower Mekong River Basin. Master’s Thesis, Virginia Tech, Blacksburg, VA, USA, 2023. [Google Scholar]
  86. Kang, H.; Sridhar, V.; Mainuddin, M.; Trung, L.D. Future rice farming threatened by drought in the Lower Mekong Basin. Sci. Rep. 2021, 11, 9383. [Google Scholar] [CrossRef]
  87. Lavane, K.; Kumar, P.; Meraj, G.; Han, T.G.; Ngan, L.H.B.; Lien, B.T.B.; Van Ty, T.; Thanh, N.T.; Downes, N.K.; Nam, N.D.G. Assessing the Effects of Drought on Rice Yields in the Mekong Delta. Climate 2023, 11, 13. [Google Scholar] [CrossRef]
  88. Van Binh, D.; Kantoush, S.A.; Saber, M.; Mai, N.P.; Maskey, S.; Phong, D.T.; Sumi, T. Long-term alterations of flow regimes of the Mekong River and adaptation strategies for the Vietnamese Mekong Delta. J. Hydrol. Reg. Stud. 2020, 32, 100742. [Google Scholar] [CrossRef]
  89. Kang, H.; Sridhar, V. A near-term drought assessment using hydrological and climate forecasting in the Mekong River Basin. Int. J. Climatol. 2021, 41, E2497–E2516. [Google Scholar] [CrossRef]
  90. Truong, Q.C.; Nguyen, T.H.; Tatsumi, K.; Pham, V.T.; Tri, V.P.D. A Land-Use Change Model to Support Land-Use Planning in the Mekong Delta (MEKOLUC). Land 2022, 11, 297. [Google Scholar] [CrossRef]
  91. Diep, N.T.H.; Trung, P.K.; Nhung, D.T.C.; Huong, N.T.T.; Vu, P.T.; Tuan, L.A. Adaptation to saline intrusion for agriculture farming transformation in the coastal Ben Tre province, Vietnam. In Proceedings of the 2nd International Conference on Environment, Resources and Earth Sciences (ICERES 2021), Ho Chi Minh City, Vietnam, 29 October 2021; Volume 964. [Google Scholar]
  92. Yen, B.T.; Son, N.H.; Amjath-Babu, T.S.; Sebastian, L. Development of a participatory approach for mapping climate risks and adaptive interventions (CS-MAP) in Vietnam’s Mekong River Delta. Clim. Risk Manag. 2019, 24, 59–70. [Google Scholar] [CrossRef]
  93. Yen, B.T. Protocol for Mapping Climate-Risks and Associated Adaptation Plans under Asian Mega-Deltas Initiative; IRRI: Hanoi, Vietnam, 2022. [Google Scholar]
  94. Tuu, N.T.; Lim, J.; Kim, S.; Pham Dang Tri, V.; Kim, H.; Kim, J. Surface water resource assessment of paddy rice production under climate change in the Vietnamese Mekong Delta: A system dynamics modeling approach. J. Water Clim. Chang. 2019, 11, 514–528. [Google Scholar] [CrossRef] [Green Version]
  95. Nguyen, T.T.V.; Nguyen, M.T.; Tran, H.H.; Loi, L.T.; Tri, V.P.D. Effectiveness of surface water resources management for agriculture and aquaculture in the context of saline intrusion in Ben Tre province. Can Tho Univ. J. Sci. 2021, 57, 82–92. [Google Scholar]
  96. Thanh, T.; Chi Sy, P.; Van, H.T.T.; Quy, N.D.; Buu, L.T.H.; Van Tan, L. Technology selection and potential application of IOT technology in the desalination system to supply domestic water to people in saline intrusion areas, Ben Tre provine. Vietnam J. Hydrometeorol. 2022, EME4, 370–381. [Google Scholar]
  97. Apel, H.; Khiem, M.; Quan, N.H.; Toan, T.Q. Brief communication: Seasonal prediction of salinity intrusion in the Mekong Delta. Nat. Hazards Earth Syst. Sci. 2020, 20, 1609–1616. [Google Scholar] [CrossRef]
  98. Do, P. Application of GIS to Evaluate Saltwater Intrusion on Agricultural Production in the Mekong Delta. Bachelor’s Thesis, University of Applied Sciences, Kouvola, Finland, 2021. [Google Scholar]
  99. Dangc, K.K.; Doan, M.T.; Le, T.H.L.; Nguyen, T.T.N.; Pham, D.T.; Do, H.T.; Ngo, S.D.; Vu, T.B.N.; Nguyen, P.A. Assessing the Performance of Climate Smart Rice Production Systems in the Upper Part of the Vietnamese Mekong River Delta. Asian J. Agric. Dev. 2021, 18, 16–29. [Google Scholar]
  100. Le, T.T.; Bui, T.Y.; Van Pham, T.; Nguyen, V.K.; Tran, T.M.H.; Nguyen, V.H.; Bernardo, E.; Celeridad, R. Guidebook on the Development of Climate-Smart Maps and Adaptation Plans (CS-MAP) for Rice Production in Viet Nam; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Hanoi, Vietnam, 2021. [Google Scholar]
  101. Van Tinh, T.; Ngoc Vang, N. The domestic rice value chain in the mekong delta. In White Gold: The Commercialisation of Rice Farming in the Lower Mekong Basin; Springer: Berlin/Heidelberg, Germany, 2020; pp. 375–395. [Google Scholar]
  102. Veliu, A.; Gessese, N.; Ragasa, C.; Okali, C. Gender Analysis of Aquaculture Value Chain in Northeast Vietnam and Nigeria. In Gender Analysis of Aquaculture Value Chain in Northeast Vietnam and Nigeria; World Bank: Washington, DC, USA, 2009. [Google Scholar]
  103. Lim, G. Value chain upgrading: Evidence from the Singaporean aquaculture industry. Mar. Policy 2016, 63, 191–197. [Google Scholar] [CrossRef]
  104. Quyen, N.T.K.; Hien, H.V.; NH, H. Value Chain Analysis in Domestic Aquaculture: Case Study of Swamp Eel (Monopterus Albus) Culture in An Giang Province, Vietnam. Int. J. Sci. Res. Publ. 2019, 9, 638–646. [Google Scholar]
  105. Tran, D.D.; Park, E.; Tuoi, H.T.N.; Thien, N.D.; Tu, V.H.; Ngoc, P.T.A.; Van, C.T.; Long, P.K.; Ho, H.L.; Quang, C.N.X. Climate change impacts on rice-based livelihood vulnerability in the lower Vietnamese Mekong Delta: Empirical evidence from Can Tho City and Tra Vinh Province. Environ. Technol. Innov. 2022, 28, 102834. [Google Scholar] [CrossRef]
  106. Van Binh, D.; Kantoush, S.A.; Ata, R.; Tassi, P.; Nguyen, T.V.; Lepesqueur, J.; Abderrezzak, K.E.K.; Bourban, S.E.; Nguyen, Q.H.; Phuong, D.N.L. Hydrodynamics, sediment transport, and morphodynamics in the Vietnamese Mekong Delta: Field study and numerical modelling. Geomorphology 2022, 413, 108368. [Google Scholar] [CrossRef]
  107. Ngo, K.D.; Lechner, A.M.; Vu, T.T. Land cover mapping of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery. Remote Sens. Appl. Soc. Environ. 2020, 17, 100272. [Google Scholar] [CrossRef]
  108. Nguyen Thanh, T.; Tri, V.P.D.; Kim, S.; Phuong, T.N.; Mong, T.L.; Tuan, P.V. A Subregional Model of System Dynamics Research on Surface Water Resource Assessment for Paddy Rice Production under Climate Change in the Vietnamese Mekong Delta. Climate 2020, 8, 41. [Google Scholar] [CrossRef] [Green Version]
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Figure 1. Location of the study area: (a) location of Tra Vinh province in the VMD, in which the (b) river network and monitoring stations Tra Vinh and Cau Quan (underlined) were selected for hydraulic calibration and validation; six stations including Lang The, Tra Vinh, Hung My, Duong Duc, Cau Quan, and Tra Kha were selected for salinity calibration and validation; and other stations are boundary stations.
Figure 1. Location of the study area: (a) location of Tra Vinh province in the VMD, in which the (b) river network and monitoring stations Tra Vinh and Cau Quan (underlined) were selected for hydraulic calibration and validation; six stations including Lang The, Tra Vinh, Hung My, Duong Duc, Cau Quan, and Tra Kha were selected for salinity calibration and validation; and other stations are boundary stations.
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Figure 2. Water level calibration in 2020 at (a) Tra Vinh station with NSE = 0.94 and (b) Cau Quan station with NSE = 0.88, and validation in 2015 at (c) Tra Vinh station with NSE = 0.99 and (d) Cau Quan station with NSE = 0.92. The mean sea level was 0 m, and water levels above and below the mean sea level had positive and negative values, respectively.
Figure 2. Water level calibration in 2020 at (a) Tra Vinh station with NSE = 0.94 and (b) Cau Quan station with NSE = 0.88, and validation in 2015 at (c) Tra Vinh station with NSE = 0.99 and (d) Cau Quan station with NSE = 0.92. The mean sea level was 0 m, and water levels above and below the mean sea level had positive and negative values, respectively.
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Figure 3. Calibration of salinity at (a) Tra Vinh station with NSE = 0.73 and (b) Cau Quan station with NSE = 0.73. Validation of salinity at (c) Tra Vinh station with NSE = 0.73 and (d) Cau Quan station with NSE = 0.53.
Figure 3. Calibration of salinity at (a) Tra Vinh station with NSE = 0.73 and (b) Cau Quan station with NSE = 0.73. Validation of salinity at (c) Tra Vinh station with NSE = 0.73 and (d) Cau Quan station with NSE = 0.53.
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Figure 4. Salinity changes under scenarios (a) SLR17, (b) SLR22, (c) SLR26L, and (d) SLR26H in 2050 compared to 2020. The average change is the average of the hourly changes in salinity, and the maximum and minimum changes are the greatest and lowest values of the hourly changes in salinity in the simulation period (from 0:00 on 1 February to 23:00 on 20 February), respectively.
Figure 4. Salinity changes under scenarios (a) SLR17, (b) SLR22, (c) SLR26L, and (d) SLR26H in 2050 compared to 2020. The average change is the average of the hourly changes in salinity, and the maximum and minimum changes are the greatest and lowest values of the hourly changes in salinity in the simulation period (from 0:00 on 1 February to 23:00 on 20 February), respectively.
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Table
Table 1. Scenarios of sea level rise and river discharge change in 2050 compared to base year 2020.
Table 1. Scenarios of sea level rise and river discharge change in 2050 compared to base year 2020.
ScenariosSea Level RiseAverage River Discharge Change References
Can ThoMy Thuan
SLR1717 cm−128% (−2501 m3/s) −117% (−2557 m3/s) [12,42,61]
SLR2222 cm−128% (−2501 m3/s)−117% (−2557 m3/s)[12,42,61]
SLR26L26 cm−100% (−1123 m3/s) −80% (−1069 m3/s) [13,42]
SLR26H26 cm−100% (−1123 m3/s)−83% (−1109 m3/s) [13,42]
Note: Discharge volumes from estuaries to the upstream were considered negative.
Table
Table 2. Salinity calibration and validation.
Table 2. Salinity calibration and validation.
Station2020 (Calibration)2015 (Validation)
R2NSER2NSE
Lang The0.800.620.880.56
Tra Vinh0.860.730.920.81
Hung My0.920.790.88−0.30
Duong Duc0.800.600.820.53
Cau Quan0.870.730.860.53
Tra Kha0.960.790.860.48
Table
Table 3. Distribution ratios of river discharge from My Thuan to the Co Chien River under sea level rise and upstream river discharge changes.
Table 3. Distribution ratios of river discharge from My Thuan to the Co Chien River under sea level rise and upstream river discharge changes.
Scenario2020SLR17SLR22SLR26LSLR26H
Distribution ratio (%)5969718683
Table
Table 4. Average salinity changes under future scenarios.
Table 4. Average salinity changes under future scenarios.
StationScenario
SLR17SLR22SLR26LSLR26H
Lang The+21% (+0.8 g/L)+20% (+0.8 g/L)+12% (+0.5 g/L)+13% (+0.5 g/L)
Tra Vinh+15% (+0.8 g/L)+14% (+0.8 g/L)+8% (+0.4 g/L)+9% (+0.5 g/L)
Hung My+8% (+0.7 g/L)+7% (+0.6 g/L)+4% (+0.4 g/L)+5% (+0.4 g/L)
Duong Duc+28% (+1.0 g/L)+29% (+1.1 g/L)+26% (+1.0 g/L)+26% (+1.0 g/L)
Cau Quan+14% (+0.9 g/L) +14% (+0.9 g/L)+13% (+0.8 g/L)+13% (+0.8 g/L)
Tra Kha+3% (+0.3 g/L)+3% (+0.3 g/L)+3% (+0.3 g/L)+3% (+0.3 g/L)
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Thanh, T.N.; Huynh Van, H.; Vo Minh, H.; Tri, V.P.D. Salinity Intrusion Trends under the Impacts of Upstream Discharge and Sea Level Rise along the Co Chien River and Hau River in the Vietnamese Mekong Delta. Climate 2023, 11, 66. https://doi.org/10.3390/cli11030066

AMA Style

Thanh TN, Huynh Van H, Vo Minh H, Tri VPD. Salinity Intrusion Trends under the Impacts of Upstream Discharge and Sea Level Rise along the Co Chien River and Hau River in the Vietnamese Mekong Delta. Climate. 2023; 11(3):66. https://doi.org/10.3390/cli11030066

Chicago/Turabian Style

Thanh, Tuu Nguyen, Hiep Huynh Van, Hoang Vo Minh, and Van Pham Dang Tri. 2023. "Salinity Intrusion Trends under the Impacts of Upstream Discharge and Sea Level Rise along the Co Chien River and Hau River in the Vietnamese Mekong Delta" Climate 11, no. 3: 66. https://doi.org/10.3390/cli11030066

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