Abstract
In recent years, Mekong Delta of Vietnam is severely affected by salinity intrusion and water scarcity due to climate variability. In this study, a comprehensive analysis of meteorology drought was conducted to detect drought events using the Standardized Precipitation Index at 3-, 6-, 9- and 12-month time scale based on monthly precipitation data from 46 precipitation gauge stations for a period of 1984–2015. The aim of the study is to assess the degree of meteorology drought from 1984 to 2015 in potential crop growing areas to provide early warnings and monitor drought events to minimize their negative effects. The results indicated that meteorological drought occurred at the central provinces of the study area in the period 1985–1994, the northeastern and northwestern provinces in the period 1995–2004 and 10 recent years (2005–2014) meteorological drought shifted toward southern coastal provinces. The analyzed results also showed a tendency to decrease in frequency of drought is recorded while a tendency to increase in the spatial distribution of drought with moderate and severe droughts is recorded. Among the major droughts, 1990–1992 was evaluated the most extreme drought with 85% of the study area covered by the extreme drought with peak value of − 2.63 recorded and lasting for 29 months.
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Introduction
In recent years, droughts appear frequently with high intensity (FAO 2016; RCSA 2016). These phenomena exacerbate in severity and impact negatively on agriculture, water resources as well as other sectors (Miyan 2015; IPCC 2007). The impact of drought affects the global ecosystem as whole but varies from region to region, and the impact are minimal in developed countries (Asadi et al. 2015; MNRE 2016). Specifically, Mekong Delta of Vietnam (MDV) is suffering from climate variability and facing serious socioeconomic problems especially in agricultural sectors due to increasing droughts which lead to water scarcity (APN 2010; RCSA 2016). In 2016, Vietnam recorded the worst drought event in 90 years and 52 out of 63 provinces having been affected by drought; 18 provinces were declared states of emergencies (FAO 2016; Vu et al. 2018). Agriculture contributes 14.62% of Vietnam’s GDP (RCSA 2016; Vo and Huynh 2014). But, 80% of Vietnam population depends on agriculture (FAO 2016; RCSA 2016).
Among all the natural hazards, drought ranked as first and affecting people directly (Miyan 2015; IPCC 2007). Miyan (2015) reported that climate change is the major cause of droughts worldwide. He stated that studies on historical drought events can help to give early awareness on negative impact in droughts. According to the report of Centre for Low Carbon Futures (CLCF), climate change has been impacted on many regions in Asia in 2020s year and will increase the drought severity compared with the period 1990–2005 years (Halwatura et al. 2016; RCSA 2016).
So far, many studies were conducted to measure and estimate meteorological droughts quantitatively by using drought index (Kwak et al. 2016; Yu et al. 2017) such as PDSI—Palmer Drought Severity Index (Liang et al. 2007; Yan et al. 2016), MSPI—Multivariate Standardized Precipitation Index (Bateni et al. 2018; Bazrafshan et al. 2014), SWSI—Surface Water Supply Index (Barua et al. 2009; Shafer and Dezman 1982), SRI—Standardized Runoff Index (Shukla and Wood 2008; Jang et al. 2017), RDI—Reconnaissance Drought Index (Asadi et al. 2015; Jamshidi et al. 2011), EDI—Evapotranspiration Deficit Index (Bayissa et al. 2018), SPEI—Standardized Precipitation Evapotranspiration Index (Mallyaa et al. 2016; Kumar et al. 2013) and SPI (Juliani and Okawa 2017; McKee et al. 1993). These common tools have been used to control and monitor droughts (Hao et al. 2016; Khedun et al. 2012).
Drought indices as mentioned above, the SPI was widely used in predicting and monitoring drought events (Bayissa et al. 2018; Jamshidi et al. 2011) around the world. For example, Rahmat et al. (2015) applied the SPI to investigate droughts in Victoria, Australia. They concluded that the SPI was shown to be satisfactory in assessing and monitoring droughts in Australia. Kwak et al. (2016) used the SPI to assess meteorological drought in the Nakdong River basins in South Korea. Suryabhagavan (2016) also applied the SPI to study potential crop growing areas in Ethiopia. Juliani and Okawa (2017) analyzed drought scales in Minas Gerais, Brazil, by applying the SPI. They concluded that the SPI could be used in the drought forecasting for other regions in Brazil. Yu et al. (2017) also applied the SPI to detect trends in precipitation in Hexi Corridor, northwest China. They reported that the SPI at 12-month time scale could be useful in agriculture production and rational use of water resources.
Therefore, studies on historical events of drought play an important role in controlling as well as minimizing damage caused by drought. This study therefore focused on drought trends in the MDV by using SPI to estimate the spatial and temporal distribution of the meteorological drought in the period of 1984–2014. The SPI has advantages compared to other tools such as it can be applied in different time scales to provide drought early warning (Bayissa et al. 2018; McKee et al. 1993). Another advantage is the input value only required precipitation data (Asadi et al. 2015; Jamshidi et al. 2011).
Materials and methods
Study area
The Mekong Delta (8°34′–11°10′N and 104°25′–106°48′E) is also known as the southern part of Vietnam and the largest rice warehouse in Vietnam (MDP 2013; RCSA 2016). It covers 1.7 million hectares of land with crops, the paddy rice area accounted for 53.93% and rice output accounted for 56.13% of Vietnam (Dan et al. 2015; RCSA 2016). The terrain of the study area is lowered from north to south with an average elevation of approximately 0.3–2.0 m above sea level (Vu et al. 2018; Vo and Huynh 2014) (Fig. 1).
Study area is located in monsoon tropics, and it is dominated by monsoon circulation which includes northeast monsoon from November to April of the next year and southwest monsoon from May to October. Rainy season coincides with the southwest monsoon, and it is characterized by hot, humid and heavy rain, while the dry season often coincides with northeast monsoon; it is characterized by dry, hot and a very little rainfall (Dinh et al. 2012; Vu et al. 2018). The climate brings its own nuances, which is a humid tropical monsoon; the high temperature throughout the year varies between 26 and 29 °C, and the mean annual rainfall varies 1370 mm at An Giang province and 2394 mm at Ca Mau province, of which about 85% falls in the rainy season (Table 1, Fig. 2). Precipitation has a strong variation seasonal and intensity; precipitation intensity increased from May to October and reaches its peak in October after intensity decreased from November to April of the next year.
Mean monthly precipitation dataset for 32 years (1984–2015) at 13 provinces in the MDV was represented by 46 precipitation gauge stations. The 46 precipitation gauge stations were selected to analyze, assemble and detect drought trends. The length of recorded precipitation data series in the study area was sufficiently covered to capture the fluctuations of precipitation with missing data of less than 10% to ensure the reliability of the statistical analysis (FAO 2016; Yu et al. 2017).
Methodology
The SPI was created by McKee et al. (1993), which is used to forecast meteorological drought (Teodoro et al. 2015; Svoboda et al. 2012). SPI has been successfully applied in many regions as South Korea, Ethiopia, Australia, and India (Bateni et al. 2018; Suryabhagavan 2016). In addition, at the Inter-Regional Workshop on Indices and Early Warning Systems for Drought in 2009, World Meteorological Organization (WMO) recommended the use of SPI for monitoring meteorological droughts (Bayissa et al. 2018; Hayes et al. 2011). The SPI then has been applied by National Weather Services of countries namely Argentina, Brazil, Chile, Paraguay, Uruguay, and China (Rivera and Penalba 2014). Specifically, precipitation data values are the transformation into standardized normal distribution. The cumulative probability distribution function is determined as the distribution of precipitation in the observation time series. The standard gamma probability distribution function then is defined by Eq. (1).
where β is a scale parameter, α is a shape parameter, x is the precipitation amount, and Γ(α) is the gamma function.
Where the parameter \(\alpha , \beta\) in Eq. (1) is calculated by Eqs. (2) and (3)
In Eq. (3), \(\bar{x}\) is the sample mean of the precipitation data series and \(\hat{\alpha }\) is a distribution parameter.
The factor A in Eq. (2) is calculated by Eq. (4)
In Eq. (4), n is number of precipitation observations.
We have a distribution function G(x), from which probabilities can be obtained from Eq. (5)
According to Wu et al. (2007) due to a precipitation, distribution may contain zeros; therefore, the mixed distribution function of zeros and continuous precipitation amounts are given by Eq. (6).
In Eq. (6), q is the probability of a zero, and it is determined by the ratio k/l, in which k is the number of zeros in a precipitation data series l.
Finally, the SPI is estimated by Eq. (7) for calculating the negative SPI values, while Eq. (8) applies the positive values.
Where t in Eqs. (7) and (8) is defined by Eqs. (9) or (10).
Where c0, c1, c2, d1, d2 and d3 are parameters and they are given as follows (Wu et al. 2007; Rahmat 2015) c0 = 2.515517; c1 = 0.802853; c2 = 0.010328; d1 = 1.432788; d2 = 0.189269; and d3 = 0.001308.
Accordingly, the positive and negative values of the SPI (Table 2) imply precipitation is higher and lower mean precipitation value. An area will be considered as drought if the SPI value of the area approaches − 1.0 or smaller (Asadi et al. 2015; Rahmat 2015).
Results and discussion
Temporal distribution of drought events
Figures 3, 4, 5 and 6 present the analyzed results of temporal drought characteristics over the study area. The SPI of 3-, 6-, 9 and 12-month time scales was analyzed to identify peak drought intensity, occurrence year and the longest duration of drought during period 1984–2015. Four significant drought events occurred including 1991–1993, 2002–2003, 2010–2011 and 2014–2015 years.
Among the events, drought in 1990–1992 was evaluated as the most extreme drought. Eleven out of 13 provinces the exception of Kien Giang and Ca Mau provinces (Table 3) occurred with a peak value of SPI-12-month drought scale was − 2.63 at Hau Giang province and duration of the drought was 29 months (Fig. 7).
Severe and extreme droughts were occurred in six provinces with risk peak value − 3.57 at Dong Thap province and lasting in 14 months at Tien Giang province for period of 2002–-2003, but for period 2010–2011, drought was occurred only in 6 out of 13 provinces where severe drought was occurred in An Giang, Kien Giang and Hau Giang provinces, while extreme drought was occurred in Vinh Long, Soc Trang and Ca Mau provinces with risk peak value − 2.30 and lasting in 11 months at Ca Mau province. For 2014–2015, in 12 out of 13 provinces (the exception of Tien Giang province) severe and extreme droughts were occurred with a peak value of − 2.39 and lasting for 24 months at Long An province (Fig. 7).
Figure 8 and Table 4 present the number of drought events occurred during period of 1984–2015 and their impacts over the entire study area. In the period of 1984–2015, moderate, severe and extreme droughts were occurred 33.69 times/32 years, 18.84 times/32 years and 7.30 times/32 years, respectively. Meanwhile, inland provinces recorded more extreme drought occurrence than the coastal provinces. The analyzed results showed that the moderate drought was occurred in study area more than once per year and extreme drought is less occurred than moderate and severe droughts. This confirms that inland provinces received less water vapor compared to coastal provinces, so extreme drought frequently occurred.
Temporal distribution of drought stages
Three drought stages including 1985–1994, 1995–2004 and 2005–2014 were also brought out to analyze precisely (Fig. 8, Table 5). In the period of 1985–1994, occurrence of moderate, severe and extreme droughts with average occurrences 14.8 time/10 years, 7.5 time/10 years and 3.5 time/10 years appears in the study area.
During this stage, the extreme drought did not occur at coastal provinces such as Kien Giang and Ca Mau. They are known as two coastal provinces with the most abundant moisture in the study area, and mean annual precipitation is relatively high, 2154 mm (at Kien Giang province) and 2394 mm (at Ca Mau). During 1995–2004, compared to 1985–1994, moderate, severe and extreme droughts were less occurred and only approximately 5.5 time/10 years, 3.2 time/10 years and 1.5 time/10 years were recorded, respectively, and extreme drought did not appear in the coastal provinces (Fig. 9), while for stage 2005–2014, moderate, severe and extreme droughts were more appeared than for stage 1995–2004 but also less than stage 1985–1994 with 8.9 time/10 years, 4.2 time/10 years and 1.8 time/10 years, respectively. For this stage, the extreme drought only found in coastal provinces namely Ben Tre, Soc Trang, Kien Giang and Ca Mau, while extreme drought did not occur in the most other provinces. Extreme drought occurred in the coastal provinces where the amount of moisture is very plentiful; in the past ten years (2005–2014), the occurrence of extreme drought is abnormal. Comparison with the pattern of their occurrence in the past, severe drought was recorded.
Spatial distribution of drought stages
The spatial distributions of SPI corresponding to 3-, 6-, and 9-month drought scales (not shown) and 12-month drought scale are divided into periods of 1985–1994, 1995–2004 and 2005–2014 as shown in Fig. 10. The analyzed results of drought risk using Arcview GIS software showed that moderate and severe drought occurred in the central provinces in the period of 1985–1994 only (Fig. 10a). This is explained by a mean annual precipitation at the central provinces decreased significantly compared to other regions and droughts, therefore, are occurred more drought than other regions.
For the period of 1995–2004, moderate and severe drought also appeared in the northeastern and northwestern provinces (Fig. 10b). The main reason is during the period of 1995–2004 mean annual precipitation at the northeastern and northwestern provinces also decreased significantly, while for the period of 2005–2014, the drought trends gradually shifted to southern coastal provinces, which can be clearly observed in Fig. 10c.
The analyzed results of precipitation data showed that mean annual precipitation in the period 2005–2014 at the coastal provinces significantly decreased and this explained why the coastal provinces are considered to have abundant precipitation, are occurred extreme drought.
Conclusion
The study analyzed drought time scales to estimate the spatial and temporal drought distributions in the period 1984–2015. Four main drought intervals were detected including 1990–1992, 2002–2003, 2010–2011 and 2014–2015 where in stage 1990–1992 the most extreme drought occurred at 11 out of 13 provinces with risk peak − 2.63 recorded and lasts for 29 months.
On average, the moderate, severe and extreme droughts appeared 33.69 times/32 years, 18.84 times/32 years and 7.30 times/32 years, respectively. Results also showed that the study area has become drier during stage 2005–2014 and the spatial distribution of drought has tended to shift to coastal provinces where they are considered to be abundant moisture content. It implies that the study area in the stage 2005–2014 received less precipitation than the stages 1985–1994 and 1995–2004.
The results have indirectly indicated that the southern and southeast coastal provinces of the study area where agricultural activities are mainly depending on precipitation are facing the threat of water shortage.
References
Asadi ZMA, Sivakumar B, Sharma A (2015) Droughts in a warming climate: a global assessment of SPI and RDI. J Hydrol 526:183–195
Asia-Pacific Network (APN) (2010) Climate change in Southeast Asia and assessment on impact, vulnerability and adaptation on rice production and water resource. Project Reference Number: CRP2008-03CMY-Jintrawet
Barua S, Perera BJC, Ng AWM (2009) A comparative drought assessment of Yarra River Catchment in Victoria, Australia. In: Interfacing modelling and simulation with mathematical and computational sciences: 18th IMACS World Congress, MODSIM09, Cairns, Australia 13-17 July 2009: proceedings
Bateni MM, Behmanesh J, Michele CD, Bazrafshan J, Rezaie H (2018) Composite agrometeorological drought index accounting for seasonality and autocorrelation. J Hydrol Eng ASCE. https://doi.org/10.1061/(asce)he.1943-5584.0001654 (in press)
Bayissa Y, Maskey S, Tadesse T, van Andel SJ, Moges S, van Griensven A, Solomatine D (2018) Comparison of the performance of six drought indices in characterizing historical drought for the upper Blue Nile Basin, Ethiopia. Geosciences. https://doi.org/10.3390/geosciences8030081
Bazrafshan J, Hejabi S, Rahimi J (2014) Drought monitoring using the Multivariate Standardized Precipitation Index (MSPI). J Water Resour Manag 28:1045. https://doi.org/10.1007/s11269-014-0533-2
Dan NH, Thoi NK, Dung BTN (2015) Evaluation of paddy land use in the Mekong River Delta. J Sci Dev 13(8):1435–1441
Dinh Q, Balica S, Popescu I, Jonoski A (2012) Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen Quadrangle in the Mekong Delta. Int J River Basin Manag 10(1):103–120
Food and Agriculture Organization (FAO) (2016) El Niño event in Viet Nam: agriculture, food security and livelihood need assessment in response to drought and salt water intrusion. Assessment Report
Halwatura D, McIntyre N, Lechner AM, Arnold S (2016) Reliability of meteorological drought indices for predicting soil moisture droughts. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hess-2016-467
Hao ZC, Hao FH, Vijay PS, Xia YL, Wei OY, Shend XY (2016) A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices. Adv Water Resour 92:240–247
Hayes M, Svoboda M, Wall N, Widhalm M (2011) The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bull Am Meteorol Soc 92:485–488
Intergovernmental Panel on Climate Change (IPCC) (2007) Contribution of working groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate, Geneva, Switzerland
Jamshidi H, Khalili D, Zadeh MR, Hosseinipour EZ (2011) Assessment and comparison of SPI and RDI meteorological drought indices in selected synoptic stations of Iran. In: World environmental and water resources congress, pp 1161–1173
Jang SH, Lee JK, Oh JH, Jo JW, Cho YH (2017) The probabilistic drought forecast based on the ensemble technique using the Korean surface water supply index. Nat Hazards Earth Syst Sci Discuss. https://doi.org/10.5194/nhess-2017-163
Juliani BHT, Okawa CMP (2017) Application of a Standardized Precipitation Index for meteorological drought analysis of the semi-arid climate influence in Minas Gerais, Brazil. Hydrology 4:26. https://doi.org/10.3390/hydrology4020026
Khedun CP, Chowdhary H, Mishra AK, Giardino JR, Singh VP (2012) Water deficit duration and severity analysis based on runoff derived from Noah Land Surface Model. J Hydrol Eng 18(7):817–833
Kumar KN, Rajeevan M, Pai DS, Srivastava AK, Preethi B (2013) On the observed variability of monsoon droughts over India. Weather Clim Extremes. https://doi.org/10.1016/j.wace.2013.07.006
Kwak JW, Kim SJ, Jung JW, Singh VP, Lee DR, Kim HS (2016) Assessment of meteorological drought in Korea under climate change. Adv Meteorol. https://doi.org/10.1155/2016/1879024
Liang E, Shao X, Liu H, Eckstein D (2007) Tree-ring based PDSI reconstruction since AD 1842 in the Ortindag Sand Land, east Inner Mongolia. Chin Sci Bull 52(19):2715–2721
Mallyaa G, Mishra V, Niyogi D, Tripathi S, Govindarajua RS (2016) Trends and variability of droughts over the Indian monsoon region. Weather Clim Extremes 12:43–68
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the eight conferences on applied climatology, Anaheim, CA, USA, American Meteorological Society, Boston, MA, pp 179–184
Mekong Delta Plan (MDP) (2013) Long-term vision and strategy for a safe, prosperous and sustainable delta, Royal Haskoning DHV. Wageningen University, Deltares, Rebel
Ministry of Natural Resources and Environment (MNRE) (2016) Climate change scenarios and sea level rise for Vietnam. Publishers resources, environment and map of Vietnam
Miyan MA (2015) Droughts in Asian least developed countries; vulnerability and sustainability. Weather Clim Extremes 7:8–23
Rahmat SN (2015) Methodology for development of drought severity–duration–frequency (SDF) curves. PhD thesis, School of Civil, Environmental and Chemical Engineering, RMIT University, Melbourne, Australia
Rahmat SN, Jayasuriya N, Bhuiyan M (2015) Assessing droughts using meteorological drought indices in Victoria, Australia. Hydrol Res. https://doi.org/10.2166/nh.2014.105
Research Centers in Southeast Asia (RCSA) (2016) The drought and salinity intrusion in the Mekong River Delta of Vietnam. Assessment Report
Rivera JA, Penalba OC (2014) Trends and spatial patterns of drought affected area in southern South America. Climate 2:264–278. https://doi.org/10.3390/cli2040264
Shafer BA, Dezman LE (1982) Development of surface water supply index-A drought severity indicator for Colorado. In: Proceeding of western snow conference, pp 164–175
Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett. https://doi.org/10.1029/2007gl032487
Suryabhagavan KV (2016) GIS-based climate variability and drought characterization in Ethiopia over three decades. Weather Clim Extremes. https://doi.org/10.1016/j.wace.2016.11.005
Svoboda M, Hayes M, Wood D (2012) Standardized Precipitation Index-User Guide. World Meteorological Organization (WMO), Geneva
Teodoro PE, Guedes CCC, Torres FE, Oliveira JJF, Silva JCA, Gois G, Coll DR (2015) Analysis of the occurrence of wet and drought periods using standardized. J Agron 14:80–86
Vo TD, Huynh VK (2014) Using a risk cost-benefit analysis for a sea dike to adapt to the sea level in the Vietnamese Mekong River Delta. Climate. https://doi.org/10.3390/cli2020078
Vu DT, Yamada T, Ishidaira H (2018) Assessing the impact of sea level rise due to climate change on seawater intrusion in Mekong Delta, Vietnam. Water Sci Technol. https://doi.org/10.2166/wst.2018.038
Wu H, Svoboda MD, Hayes MJ, Wilhite DA, Wen FJ (2007) Appropriate application of the Standardized Precipitation Index in arid locations and dry seasons. Int J Climatol 27:65–79
Yan H, Wang SQ, Wang JB, Lu HQ, Guo AH, Zhu ZC, Myneni RB, Shugart HH (2016) Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982–2011 by using PDSI indices and agriculture drought survey data. J Geophys Res Atmos 121:2283–2298
Yu XY, Zhao GX, Zhao WJ, Yan TT, Yuan XJ (2017) Analysis of precipitation and drought data in Hexi Corridor, Northwest China. Hydrology. https://doi.org/10.3390/hydrology4020029
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Lee, S.K., Dang, T.A. Spatio-temporal variations in meteorology drought over the Mekong River Delta of Vietnam in the recent decades. Paddy Water Environ 17, 35–44 (2019). https://doi.org/10.1007/s10333-018-0681-8
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DOI: https://doi.org/10.1007/s10333-018-0681-8