sustainability

Article Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product

Hao Guo 1,2,3,4, Anming Bao 1,4,*, Tie Liu 1,4, Felix Ndayisaba 1,2,5, Daming He 6, Alishir Kurban 1,4 and Philippe De Maeyer 3,4 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; [email protected] (H.G.); [email protected] (T.L.); [email protected] (F.N.); [email protected] (A.K.) 2 University of Chinese Academy of Sciences, Beijing 100039, China 3 Department of Geography, Ghent University, Gent 9000, Belgium; [email protected] 4 Sino-Belgian Joint Laboratory of Geo-information, Urumqi 830011, China and Ghent 9000, Belgium 5 University of Lay Adventists of Kigali (UNILAK), 6392 Kigali, Rwanda 6 Asian International Rivers Center, Yunnan University, Kunming 650091, China; [email protected] * Correspondence: [email protected]; Tel.: +86-0991-788-5378

Academic Editor: Audrey Mayer Received: 9 May 2017; Accepted: 25 May 2017; Published: 29 May 2017

Abstract: Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1–12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991–1994 for 38 months and the driest one in 2015–2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition.

Keywords: drought; SPI; CHIRPS; remote sensing; Lower Mekong Basin

1. Introduction Drought is considered as one of the costliest and most damaging as well as one of the most complex and least understood natural hazards due to its high heterogeneity in space and variability in time [1–3]. Droughts can be broadly divided into four types: (a) meteorological; (b) agricultural; (c) hydrological; and (d) socioeconomic [2,4]. Meteorological drought occurs first as a consequence of precipitation deficit followed by agricultural and hydrological drought because all other three drought types are mainly driven by the continuous precipitation deficit [5]. SPI is the most popular and commonly used precipitation deficit index to reflect drought condition. Developed by McKee et al. [6], SPI is a simply computed drought index which is only based on long-term precipitation records. The most attractive feature of SPI is its suitability to different regions at flexible timescales [7,8]. Besides, it can also be applied to reflect flood conditions in addition to

Sustainability 2017, 9, 901; doi:10.3390/su9060901 www.mdpi.com/journal/sustainability Sustainability 2017, 9, 901 2 of 21 drought monitoring [9,10]. Based on the above advantages, the World Meteorological Organization (WMO) suggests SPI as the reference drought index [11]. The SPI has been widely applied to study the characteristics of drought, such as drought forecasting [12], drought frequency analysis [13,14], spatiotemporal drought analysis [15,16], drought period and severity [17], and climate change impact studies [18]. Therefore, SPI is used in this study as the drought monitoring index. Conventional drought monitoring depends on ground observations which have relatively high accuracy and long-term records. However, the ground-based precipitation observation networks are sparse and nonhomogeneous or not available for common users in many regions around the world such as Mekong River Basin [19]. Due to the limited and variable representativeness, drought monitoring based on ground observations is subject to limitations and drawbacks. Spatial interpolation may be one possible solution, however, it will introduce high uncertainty, especially for the mountainous regions with sparse and uneven gauges [20,21]. With the development of remote sensing technique, a variety of satellite-based precipitation retrieval algorithms have been produced by combining both Infrared (IR) and passive microwave (PMW) estimates from different sensors in recent years, such as Tropical Rainfall Measuring Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA) [22], Climate Prediction Center morphing technique (CMORPH) [23], Global Satellite Mapping of Precipitation (GSMaP) [24], NRL-Blend satellite rainfall estimates from the Naval Research Laboratory (NRL) [25] and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [26,27]. Based on their advantages of large-scale coverage, high spatiotemporal resolution and public accessibility, satellite precipitation estimates are playing increasingly important role in providing supplemental data resources for different meteorological and hydrological applications such as flood and landslide monitoring [28–30]. Though some evaluation works have been reported for drought monitoring by using short-term satellite precipitation products, the error from the limited data record should be studied further [15,31,32] because the time series of most products are too short for confident drought monitoring, which should be based on historical data with at least 30 years length of record [33,34]. PERSIANN Climate Data Record (PERSIANN-CDR) [35] and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) [36] are two newly developed long-term satellite-based precipitation products which can meet the demand of precipitation characteristics and drought monitoring at a climate time scales for more than 30 years. The two satellite-based precipitation products with long-term records provide valuable data resources for studying the spatial and temporal characteristics of increasingly frequent droughts. Due to their late availability, few efforts have been made to investigate the performance of PERSIANN-CDR and CHIRPS products to estimate the amount and spatial distribution of precipitation, as well as their potential for being applied in drought monitoring. Miao et al. [19] evaluated PERSIANN-CDR’s performance in capturing the behavior of daily extreme precipitation events in China from 1983 to 2016 and found that the performance of PERSIANN-CDR is good in most parts of China. Based on gridded gauge dataset, the performance of PERSIANN-CDR for monitoring meteorological drought events was also evaluated from 1983 to 2014 over Mainland China. It was found that PERSIANN-CDR showed reasonable performance for drought monitoring over most of China [17]. In comparison to a dense and reliable gauge network from 1981 to 2010, CHIRPS showed good correlation over Cyprus [37] and CHIRPS also gave a promising performance in capturing precipitation extremes [38]. When applied as input precipitation information in streamflow simulation in Italy, CHIRPS also suggested satisfactory performance [39]. However, little has been done to evaluate the performance and the possibility for application of CHIRPS data in drought monitoring. While contrasting PERSIANN-CDR and CHIRPS for drought monitoring over Chile, it was found that CHIRPS had a better fit with in-situ observations than PERSIANN-CDR and were more applicable for regional drought monitoring with higher spatial resolution (0.05◦) and longer periods of data records (36 years till now) [40]. Sustainability 2017, 9, 901 3 of 21

The Mekong River basin is one of the most important transboundary river basins in Southeast Asia [41]. Over the last decades, LMB has experienced frequent drought events. For example, severe drought events occurred during 1992–1993, 1998–1999, 2003–2005 and 2010–2011 [42–44]. The worst drought in decades occurred beginning from late 2015 and continued through July 2016, with impacts spread over Vietnam, Cambodia, Laos and Thailand. These droughts have great impacts on the local agriculture, forestry, water resources, industry, and the environment [44]. Therefore, drought monitoringSustainability 2017 in, 9, LMB 901 is of primary necessity for water planning and management3 of 21 to mitigate their detrimental impacts on the society, economy and environment. However, the ground-based precipitationdrought gauges events are occurred very limited during 1992–1993, or unavailable 1998–1999, for 2003–2005 common and users. 2010–2011 [42–44]. The worst drought in decades occurred beginning from late 2015 and continued through July 2016, with In thisimpacts study, spread the performanceover Vietnam, Cambodia, of CHIRPS Laos forand precipitationThailand. These capturingdroughts have and great drought impacts on monitoring is firstly evaluatedthe local agriculture, through aforestry, direct water quantitative resources, comparison industry, andbased the environment on ground [44]. observations Therefore, and an indirect comparisondrought monitoring by using in LMB surface is of soilprimary moisture necessit (SM).y for water Based planning on long-term and management CHIRPS to mitigate product and SPI drought index,their detrimental the primary impacts objective on the ofsociety, this econom study isy and to describeenvironment. the However, spatiotemporal the ground-based characteristics of precipitation gauges are very limited or unavailable for common users. meteorologicalIn droughts this study, in the LMB performance at multiple of CHIRPS time scales.for precipitation In addition, capturing the droughtand drought impacts monitoring on vegetation are also analyzed.is firstly evaluated This is through one early a direct study quantitative to assess comparison and apply based CHIRPS on ground for observations drought monitoring and an over Lower Mekongindirect Basin,comparison spanning by using the surface time soil from moistu Januaryre (SM). 1981Based toon Julylong-term 2016. CHIRPS The resultsproduct ofand this paper will provideSPI usefuldrought insightsindex, the primary on the objective stability of ofthis CHIRPS study is to fordescribe drought the spat monitoringiotemporal characteristics and contribute to a of meteorological droughts in LMB at multiple time scales. In addition, the drought impacts on more comprehensivevegetation are understandingalso analyzed. This of is historical one early droughtsstudy to assess over and LMB, apply which CHIRPS are for very drought important for the water resourcesmonitoring over management Lower Mekong and Basin, design spanning of contingency the time from plansJanuary to 1981 reduce to July the 2016. impacts The results of droughts. of this paper will provide useful insights on the stability of CHIRPS for drought monitoring and 2. Study Region,contribute Datasets to a more andcomprehensive Methods understanding of historical droughts over LMB, which are very important for the water resources management and design of contingency plans to reduce the impacts 2.1. Studyof Area droughts. As the2. 12thStudy longest Region, Datasets river in and the Methods world and longest river in South Asia, Mekong River has a length of 4880 km [43,45]. The Lower Mekong Basin (LMB) is the downstream part of Mekong River Basin, 2.1. Study Area which encompasses parts of four countries: Laos, Thailand, Cambodia, and Vietnam (Figure1a). As the 12th longest river in the world2 and longest river in South Asia, Mekong River has a This regionlength covers of 4880 approximately km [43,45]. The618,783 Lower Mekong km with Basin forest(LMB) is and the agriculturaldownstream part land of Mekong accounting River to nearly 35% and 40%,Basin, respectively. which encompasses The topography parts of four ofcountries: the LMB Laos, is Thailand, mainly organized Cambodia, and by theVietnam Northern (Figure Highlands, Khorat Plateau,1a). This Tonle region Sap covers Basin, approximately and Mekong 618,783 Delta km2 (Figure with forest1b) and from agricultural north to land south. accounting Plains (lowerto than 200 m) mainlynearly limited35% and in40%, Thailand, respectively. Cambodia, The topography and of Vietnamese the LMB is mainly Mekong organized Delta by andthe Northern high mountains Highlands, Khorat Plateau, Tonle Sap Basin, and Mekong Delta (Figure 1b) from north to south. located mostlyPlains in(lower Laos than are 200 the m) two mainly main limited terrain in Thailand, feathers Cambodia, [42]. The and LMB Vietnamese is a tropical Mekong monsoon Delta region ◦ with a monthlyand high mean mountains temperature located mostly of 20 inC Laos and are annual the two accumulated main terrain feathers precipitation [42]. The ofLMB about is a 1000 mm. This area experiencestropical monsoon two region distinctly with a different monthly mean seasons: temperature the wet of summer 20 °C and season annual fromaccumulated May to October influencedprecipitation by the moist of about Southwest 1000 mm. Monsoon This area and experiences the dry two winter distinctly season different from seasons: November the wet to the April summer season from May to October influenced by the moist Southwest Monsoon and the dry influencedwinter by the season dry from Northeast November Monsoon to the April [46 influenced]. by the dry Northeast Monsoon [46].

Figure 1. Maps of the Mekong Basin: (a) river network; (b) topography; and (c) two group gauges Figure 1. Maps of the Mekong Basin: (a) river network; (b) topography; and (c) two group gauges based on the availability of observations (20 gauges for January 1981–July 2016 and 38 gauges for based on theJanuary availability 1999–July 2016) of observations in the Lower Mekong (20 gauges Basin. for January 1981–July 2016 and 38 gauges for January 1999–July 2016) in the Lower Mekong Basin. Sustainability 2017, 9, 901 4 of 21

2.2. Datasets

2.2.1. The Observed Precipitation Gauge For validating CHIRPS product, the precipitation observed records were obtained from the Global Summary of the Day (GSOD) which is released by the National Oceanic and Atmospheric Administration’s (NOAA’s) National Climatic Data Center (NCDC) [47]. In order to ensure a high quality of gauges, a systematic quality control procedure was carried out based on several criteria: Firstly, a filtering method was used to select the gauges with least missing data. The gauges with more than 30% missing values of time series records were not considered. In addition, rain gauges were removed when the negative values were found or daily rainfall values were larger than twice the annual average value [48]. Finally, the Normal Ratio Method [49] is applied to fill the left missing values for each selected gauge by using the two or three closest gauges. Due to data availability limitations and for reasons relating to data quality issues, the precipitation gauge data are not limited to the scope of LMB. To ensure the data length requirement (>30 years) of computing SPI [34] and the quality of validation for CHIRPS, the gauges after quality control are divided into two groups: the first group includes 38 gauges marked with black triangles in Figure1c with the period from January 1999 to July 2016. It is mainly used to check the performance of CHIRPS in capturing the behaviors of monthly precipitation. The second group includes 20 gauges marked with green circles in Figure1c (from January 1981 to July 2016), whose length of the records is the same as CHIRPS’. It is applied to identify the possible error of CHIRPS for drought monitoring.

2.2.2. CHIRPS Satellite Precipitation Dataset Developed by the U.S. Geological Survey (USGS) and the Climate Hazards Group at the University of California, Santa Barbara, CHIRPS [50] is a new land-only IR-based climatic precipitation data with high spatial resolution (0.05◦ × 0.05◦) and long-term records (1981-present). Global climatology, satellite estimates and gauge observations are integrated into this datasets. CHIRPS has a relatively high resolution (0.05◦) than other satellite-based precipitation products which commonly have a spatial resolution of 0.25◦ or 0.1◦. Three different temporal resolutions, pentadal, decadal, and monthly, are available online from 1981 to near-present. Here, the monthly CHIRPS version 2.0 from January 1981 to July 2016 is used, and CHIRPS version 2.0 was referred to as CHIRPS in this study for brevity.

2.2.3. AVHRR Vegetation Health Product In order to analyze the impacts of meteorological drought on vegetation, the Vegetation Health Index (VHI) dataset collected from Advanced Very High Resolution Radiometer (AVHRR) imagery is also used in this paper. It is released by the NOAA Center online [51]. The VHI dataset has been widely applied for early drought warning, monitoring of crop yield and production and assessment of irrigated areas and excessive wetness [52–56]. VHI is a weighted average of two sub-indices: the Vegetation Condition Index (VCI) calculated from Normalized Difference Vegetation Index (NDVI) and the Temperature Condition Index (TCI) computed from brightness temperature (TB) data. Both VCI and TCI are based on AVHRR data and are averaged with uniform weighting to form the empirical VHI, which can reflect both vegetation cover and temperature anomalies [55]. The weekly VHI product with 4 km spatial resolution in GEO-TIFF format is used in this study for the period 1981 (Week 35)–2016 (Week 37). In order to check the possible impact of drought on vegetation, the weekly VHI is integrated into monthly, and the standardized anomalies index (SAI) is applied to the weekly VHI as Equation (1).

VHI − VHI SAI_VHI = i (1) σVHI where SAI_VHI represents the VHI anomalies, and VHI and σVHI are the average value and standard deviation of the VHI series, respectively. Sustainability 2017, 9, 901 5 of 21

2.2.4. Soil Moisture Dataset The soil moisture dataset (SM) from the Noah model of Global Land Data Assimilation System (GLDAS) is used to cross-validate the accuracy of CHIRPS for drought monitoring. The 0–10 cm soil moisture product from 2000 to 2016 at monthly −0.25◦ resolution is used in this study. The soil moisture dataset is obtained from the Goddard Earth Sciences Data and Information Services Center (GES DISC) [57]. Similar to VHI, the SAI of SM is also calculated according to Equation (2).

SM − SM SAI_SM = i (2) σSM where SAI_SM means SM anomaly series, and SM and σSM are the average value and standard deviation of the SM series, respectively.

2.3. Methods

2.3.1. Statistical Evaluation Metrics Several traditional statistical metrics, including Relative bias (RB), Pearson linear correlation coefficient (CC) and root mean square error (RMSE), are used for the validation of CHIRPS. Fractional RMSE (FRMSE) is also adopted in this study to avoid the possible rainfall volume dependent errors. The equations and optimal values of RB, CC, RMSE and FRMSE are given in Table1.

Table 1. List of the traditional metrics used in validation of CHIRPS product.

Statistical Metric Equation Optimal Value N N 1 Relative Bias (RB) RB = ∑ (Si − Gi)/ ∑ (Gi) 0 = = i 1 s i 1 s Pearson linear correlation N N 2 N 2 1 CC = ( ∑ (Si − S)(Gi − G))/( ∑ (Si − S) ∑ (Gi − G) ) 1 coefficient (CC) i=1 i=1 i=1 s N 1 1 2 Root Mean Square Error (RMSE) RMSE = N ∑ (Si − Gi) 0 i=1 s N 1 1 2 Fractional RMSE (FRMSE) FRMSE = N ∑ (Si − Gi) /G 0 i=1 1 S stands for satellite-based CHIRPS product and G represents the gauge observations. RB, CC and FRMSE are dimensionless, and RMSE is in mm/month.

2.3.2. The Standardized Precipitation Index (SPI) Developed by McKee et al. [6], the Standardized Precipitation Index (SPI) is developed to describe precipitation deficit to reflect meteorological drought condition. Robust SPI computation should be based on long-term precipitation records (>30 years) which is accumulated over a specified timescale [58]. Generally, precipitation is on non-normal stable distribution, the long-term precipitation records are firstly fitted to a gamma distribution and then the normal distribution is transformed into a Gaussian distribution by using equal probability transformation. The most attractive feature of SPI is that drought and flood conditions can be directly compared in different regions over flexible timescales [59]. In the present paper, one-month, three-month, six-month and 12-month time scales (SPI1, SPI3, SPI6 and SPI12) are chosen to represent short- and long-term droughts. Although SPI is a kind of meteorological drought index, SPI different timescales can also be used to reflect the different drought conditions like agricultural and hydrological drought conditions [60]. The range of SPI scope is from −3 to +3. The positive and negative values indicate wet and dry conditions, respectively. Specific drought classifications based on SPI values are given in Table2. In this study, the latest SPI program (SPI_SL_6) from the National Drought Mitigation Centre [61] is used to compute SPI for each gauge and grid over LMB through 427 months (more than 36 years) at different time scales. Sustainability 2017, 9, 901 6 of 21

Centre [61] is used to compute SPI for each gauge and grid over LMB through 427 months (more than 36 years) at different time scales. Sustainability 2017, 9, 901 6 of 21 Table 2. Drought classification according to SPI values [6].

Table 2. DroughtSPI classification Value according Category to SPI values [6]. 2.0 and above Extremely wet SPI Value Category 1.5 to 1.99 Severely wet 2.0 and above Extremely wet 1.0 to 1.49 Moderately wet 1.5 to 1.99 Severely wet 1.0 to−0.99 1.49 to 0.99 Near Moderately normal wet −0.99− to1.0 0.99 to −1.49 Moderately Near dry normal −1.0 to −1.49 Moderately dry −1.5 to −1.99 Severely dry −1.5 to −1.99 Severely dry −2.0 and−2.0 lessand less Extremely Extremely dry dry

2.3.3. Drought IdentificationIdentification and Characteristics According to McKee et al. [6], [6], a drought event is defineddefined as a period when SPI is continuously below 00 withwith thethe lowest lowest SPI SPI value value less less than than−1.0. −1.0. Only Only the the droughts droughts persistent persistent at least at least continuous continuous two monthstwo months are considered. are considered. Once droughtOnce drought events areevents identified, are identified, the drought the indicatorsdrought indicators can be calculated can be bycalculated run theory by (Figurerun theory2)[ 62 (Figure]. These 2) indicators [62]. These include indicators drought include duration drought (DD), duration drought (DD), intensity drought (DI), droughtintensity severity(DI), drought (DS)and severity drought (DS) area and (DA).drought DD area is the (DA). number DD is of the months number in which of months SPI values in which are negativeSPI values for are a drought negative event for [ 63a ].drought The absolute event sum [63] of. allThe SPI absolute values duringsum of a droughtall SPI eventvalues is during regarded a asdrought drought event severity is regarded (DS) and as DA dr isought defined severity as the (DS) area percentageand DA is betweendefined maximumas the area drought percentage area andbetween total maximum study area. drought DS is calculated area and astotal follows: study area. DS is calculated as follows: DD DSSPI DD  i (3) DS i=1 ∑ SPIi (3) i=1 where i is a month; SPIi is the SPI value in month i; and DD and DS are the duration and severity of wherea droughti is aevent, month; respectively.SPIi is the SPI value in month i; and DD and DS are the duration and severity of a drought event, respectively.

Figure 2. The run theory map of drought event and drought characteristics for drought duration (DD), Figure 2. The run theory map of drought event and drought characteristics for drought duration drought intensity (DI1 and DI2) and drought severity (DS). (DD), drought intensity (DI1 and DI2) and drought severity (DS).

DI has two kindskinds ofof definition:definition: the lowest SPI valuevalue [[63]63] and severityseverity divided by durationduration during the drought period period [64]. [64]. Both Both drought drought intensities intensities are are ad adoptedopted as as DI1 DI1 and and DI2 DI2 in inthis this paper. paper. The The larger larger of ofDIs DIs are, are, the the more more severe severe the the drought. drought. In In addition, addition, the the total total drought duration (TDD) expressed by drought month number with SPI value less than −1 through the study period is also used to assess the drought vulnerability. Sustainability 2017, 9, 901 7 of 21

3. Results

3.1. Evaluation of CHIRPS Using Rain Gauges The satellite products should be validated before being applied in drought monitoring because the drought monitoring may be affected by their potential errors. In this section, CHIRPS product is evaluated by using selected rain gauges and gridded surface soil moisture to shed light on the capability of CHIRPS in drought monitoring.

3.1.1. Precipitation Comparison Figure3 gives the annual cycle of monthly precipitation and annual scatter plots based on 38 gauges from 1999 to 2016 (Figure1c). CHIRPS performs well in capturing the annual monthly precipitation cycle in conformity with the gauge observations. However, CHIRPS seems to overestimate monthly precipitation in the rainy season and a slight underestimation is found during the dry season. The scatter plot of monthly mean precipitation for the whole year suggests that CHIRPS has an excellent agreement with observations: the scatter points are close to the 1:1 line with high CC (0.98), and low positive RB (2.68%), RMSE (12.31 mm/month) and FRMSE (0.09) (Figure2b). The same high CC values (0.98) are also found in both dry and rainy season. The slight overestimation in the rainy season and underestimation in the dry season with low RB values (4.26% and −4.18%, respectively) exhibit consistency with the comparison of annual monthly precipitation in Figure2a. The low FRMSE value (0.10) in rainy season indicates that the relatively high RMSE is caused by the high rainfall volume. Seasonally, CHIRPS has a better performance in the rainy season than that in the dry season with higher CC values and lower FRMSE values for most gauges. This may be related to the common season-dependent errors that satellite sensors perform well in detecting strong and convective rainfall events, but show difficulty in identifying shallow and warm rainfall events [65,66], because the CHIRPS algorithm incorporates different satellite estimates (e.g., TRMM 3B42) and depends on 0.25◦ TRMM training data. Generally, CHIRPS gives outstanding performance in capturing monthly rainfall compared with in-situ observations over LMB and its surrounding areas.

3.1.2. SPI Comparison To further validate the suitability of CHIRPS for drought monitoring applications, the domain-averaged time series SPIs at different timescales (one-month, three-month, six-month and 12-month) have been calculated for the period from January 1981 to July 2016 (Figure4). Because one needs at least 30 years of monthly values for robust SPI computation, only 20 gauges with monthly records more than 30 years are included for robust SPI calculation in this section (Figure1c). It should be mentioned here that the SPI values shown in Figure4 are suppressed by the spatial averaging process, while, in fact, the spatially distributed SPI values may be as low as −3 in some individual grids during drought period. In addition, the results of this part are limited to gauge stations, most of which is outside the LMB area. In other words, the SPI comparison has been employed as a proxy to assess the accuracy of CHIRPS for drought monitoring but does not reflect the drought conditions over the LMB. Sustainability 2017, 9, 901 8 of 21 Sustainability 2017, 9, 901 8 of 21

Figure 3. 3. (a)( aAnnual) Annual cycle cycle of monthly of monthly precipitation precipitation and annual and precipitation annual precipitation scatter plots scatter with fitted plots linear with fittedline and linear confidence line and bounds confidence of 95% bounds based ofon 95% precipitation based on derived precipitation from 38 derived gauges from and 38corresponding gauges and b d correspondinggrids of CHIRPS grids from of 1999 CHIRPS to 2016. from (b 1999–d) Scatter to 2016. plots ( – of) Scatter monthly plots mean of monthly precipitation mean for: precipitation (b) the whole for: (b) the whole year; (c) the dry season (November to April); and (d) the rainy season (May to October). year; (c) the dry season (November to April); and (d) the rainy season (May to October).

As can be observed in Figure4 4,, CHIRPSCHIRPS performsperforms wellwell inin capturingcapturing thethe time-seriestime-series SPISPI patternpattern and showsshows goodgood consistencyconsistency inin bothboth frequencyfrequency andand intensityintensity withwith thethe gauge-derivedgauge-derived SPIs.SPIs. CHIRPSCHIRPS based SPI cancan capturecapture droughtdrought tendenciestendencies andand characteristicscharacteristics of drought variation at differentdifferent time scales. TheThe CCsCCs between between SPIs SPIs of of CHIRPS CHIRPS and and gauges gauges for allfor time all scalestime scales are higher are higher than 0.85 than and 0.85 RMSEs and areRMSEs lower are than lower 0.4. than According 0.4. According to the statistics to the statis (CCtics and (CC RMSE), and RMSE), CHIRPS CHIRPS and rain and gauge rain SPIs gauge exhibit SPIs betterexhibit agreements better agreements at both at three-month both three-month and six-month and six-month time scalestime scales (SPI3 (SPI3 and SPI6)and SPI6) with with higher higher CC (0.89)CC (0.89) and and lower lower RMSE RMSE (0.34 (0.34 and and 0.35, 0.35, respectively), respectively), while while SPIs SPIs at at 12-month 12-month time time scalescale exhibitexhibit lessless agreement in terms of the lowerlower CCCC (0.85)(0.85) andand higherhigher RMSERMSE (0.39).(0.39). The amplitudeamplitude of fluctuationsfluctuations for SPIs derivedderived fromfrom CHIRPS CHIRPS is is slightly slightly larger larger than than that that from from gauges, gauges, which which indicates indicates thatCHIRPS that CHIRPS tends totends overestimate to overestimate severity severity for both for flood both periodsflood peri andods drought and drought periods. periods. This phenomenon This phenomenon can be can more be easilymore easily interpreted interpreted from thefrom comparison the comparison at longer at lo timenger scales time (Figurescales (Figure4c,d). However, 4c,d). However, CHIRPS CHIRPS based SPIsbased seem SPIs toseem underestimate to underestimate the drought the drought severity severity during during 2001–2003 2001–2003 at six-month at six-month and 12-month and 12-month scales. scales. Sustainability 2017, 9, 901 9 of 21 Sustainability 2017, 9, 901 9 of 21

Figure 4.4. Spatial averaged time series SPIs for CHIRPS and rain ga gaugesuges from 1981 to July 2016 at different time scales:scales: (a)) one-month;one-month; ((b)) three-month;three-month; ((cc)) six-month;six-month; andand ((dd)) 12-month.12-month.

3.1.3. Cross Validation ofof CHIRPSCHIRPS Given the fact that surface soil moisture is sensitive to drought condition, one indirect cross validation isis proposedproposed using using GLDAS GLDAS soil soil moisture moistu datasetre dataset (0–10 (0–10 cm). cm). Here, Here, the three-month the three-month SPI (SPI3) SPI is(SPI3) used, is knowingused, knowing in advance in advance that surface that surface moisture moisture is sensitive is sensitive to short-term to short-term droughts. droughts. The spatial The averagedspatial averaged SAI_SM SAI_SM and SPI3 and are SPI3 compared are compared in Figure in 5Figure. Due 5. to Due the availabilityto the availability of GLDAS of GLDAS SM data, SM thedata, comparison the comparison is during is during 2000–2016 2000–2016 and the and 2015–2016 the 2015–2016 drought drought event is markedevent is bymarked scattered by rectangle.scattered rectangle. Sustainability 2017, 9, 901 10 of 21

Sustainability 2017, 9, 901 10 of 21 Sustainability 2017, 9, 901 10 of 21

Figure 5. Time series spatial averaged SPI and SAI_SM from 2000 to 2016. The typical drought event is marked by scattered rectangle. Figure 5. Time seriesseries spatialspatial averagedaveragedSPI SPI and and SAI_SM SAI_SM from from 2000 2000 to to 2016. 2016. The The typical typical drought drought event event is marked by scattered rectangle. Asis marked can be by seen scattered that the rectangle. SPI3 remains in good agreement with SAI_SM with CC as high as 0.74. The variation of SPI3 is almost identical for the variation of SAI_SM with values lower than 0 for As can be seen that the SPI3 remains in good agreement with SAI_SM with CC as high as 0.74. droughtAs canperiods be seen and that positive the SPI3 values remains for wet in goodperiods. agreement For the severe with SAI_SM drought with events CC at as three-month high as 0.74. The variation of SPI3 is almost identical for the variation of SAI_SM with values lower than 0 for Thetime variationscale during of SPI3 2000–2016, is almost such identical as the short-term for the variation drought of in SAI_SM 2005 and with drought values event lower 4 thanoccurring 0 for drought periods and positive values for wet periods. For the severe drought events at three-month droughtduring 2015–2016 periods and (marked positive by values a rectangle), for wet periods.both SPI3 For and the SAI_SM severe drought are below events to the at three-month normal and time the time scale during 2000–2016, such as the short-term drought in 2005 and drought event 4 occurring scaledrought during evolutions 2000–2016, are suchalso similar. as the short-term For instance, drought the drought in 2005 and double-peak drought event shape 4 occurringis well captured during during 2015–2016 (marked by a rectangle), both SPI3 and SAI_SM are below to the normal and the 2015–2016by both SPI3 (marked and SAI_SM by a rectangle), at the same both SPI3time andwith SAI_SM similar are evolution below to shape the normal during and the the 2015–2016 drought drought evolutions are also similar. For instance, the drought double-peak shape is well captured evolutionsdrought event. are alsoThe similar.results suggest For instance, that CHIRPS the drought could double-peak effectively capture shape isthe well drought captured events by bothand by both SPI3 and SAI_SM at the same time with similar evolution shape during the 2015–2016 SPI3drought and evolution. SAI_SM at the same time with similar evolution shape during the 2015–2016 drought event. Thedrought results event. suggest The thatresults CHIRPS suggest could that effectively CHIRPS capturecould effectively the drought capture events the and drought drought events evolution. and 3.2.drought Drought evolution. Monitoring Based on CHIRPS 3.2. Drought Monitoring Based on CHIRPS 3.2.1.3.2. Drought Temporal Monitoring Analysis Based on CHIRPS 3.2.1. Temporal Analysis To suggest the temporal evolution of droughts, the Hovmöller diagram was generated to 3.2.1.To Temporal suggest theAnalysis temporal evolution of droughts, the Hovmöller diagram was generated to provide provide a visualization of the temporal evolution of the SPI calculated at time scales from 1- to a visualizationTo suggest of the the temporal evolutionevolution ofof thedroughts, SPI calculated the Hovmöller at time scales diagram from 1-was to 12-monthgenerated forto 12-month for the LMB (Figure 6). A drought episode is assumed as being a number of consecutive theprovide LMB a (Figure visualization6). A drought of the episode temporal is assumed evolution as of being the aSPI number calculated of consecutive at time scales months from in which 1- to months in which the SPI values remain less than −1. It should be noted that these SPI values can the12-month SPI values for the remain LMB less(Figure than 6).− 1.A Itdrought should episod be notede is thatassumed these as SPI being values a number can only of represent consecutive the only represent the average conditions at LMB scale, so higher or lower values may be found in averagemonths conditionsin which the at LMBSPI values scale, so remain higher less or lower than values−1. It should may be be found noted in individualthat these gridsSPI values at smaller can individual grids at smaller spatial scale. These values are hard to observe because the time series spatialonly represent scale. These the average values are conditions hard to observeat LMB because scale, so the higher time seriesor lower SPI values aremay aggregated be found toin SPI values are aggregated to LMB scale. The results show that drought episodes are frequent in LMBindividual scale. grids The results at smaller show spatial that drought scale. episodesThese values are frequent are hard in to LMB. observe The mainbecause persistent the time drought series LMB. The main persistent drought episodes identified are 1983, 1991–1994, 1998–1999, 2005 and episodesSPI values identified are aggregated are 1983, to 1991–1994,LMB scale. 1998–1999, The results 2005 show and that 2015–2016, droughtwhile episodes the continuousare frequent wet in 2015–2016, while the continuous wet periods are during 1999–2001, 2007–2010 and 2011–2013. The periodsLMB. The are main during persistent 1999–2001, drought 2007–2010 episodes and id 2011–2013.entified are The 1983, longest 1991–1994, drought 1998–1999, is identified 2005 for and the longest drought is identified for the period of 1991–1994. There is one short-term but severe drought period2015–2016, of 1991–1994. while the There continuous is one short-termwet periods but are severe during drought 1999–2001, in 2005, 2007–2010 and the drought and 2011–2013. for 2015–2016 The in 2005, and the drought for 2015–2016 seems to be the severest one in terms of drought intensity. seemslongest to drought be the severest is identified one infor terms the period of drought of 199 intensity.1–1994. There is one short-term but severe drought in 2005, and the drought for 2015–2016 seems to be the severest one in terms of drought intensity.

Figure 6. The Hovmöller diagram of SPI based on CHIRPS from 1- to 12-month time scales for the period of 1981–July 2016 over LMB. Figure 6. The Hovmöller diagram of SPI based on CHIRPS from 1- to 12-month time scales for the period of 1981–July 2016 over LMB. Sustainability 2017,, 9,, 901901 11 of 21

Figure 7 shows the temporal evolution of SPI based on all grids over LMB, and the positive blue Figurearea and7 shows the negative the temporal red area evolution represent of SPI the based wet and on all dry grids periods over LMB,based andon average the positive monthly blue areaSPI. andAs the negativetime scale red increases, area represent the theamplitud wet ande dryof SPI periods values based and on the average frequency monthly of SPI. temporal As the timevariability scale increases,in time series the amplitude decrease. ofBasically, SPI values SPI andvalues the frequencyat shorter oftime temporal scales variabilitycan provide in early time serieswarning decrease. of drought Basically, and SPIhelp values to evaluate at shorter drought time scales intensity can providewith the early lowest warning SPI values of drought (DI1). and In helpFigure to 6a, evaluate there is drought a strong intensity temporal with fluctuation the lowest for SPI SPI1 values with (DI1). alternating In Figure periods6a, there of dryness is a strong and temporalwetness. Because fluctuation SPI1 for reflects SPI1 with short alternating period drought periods conditions of dryness and and its wetness. application Because is mainly SPI1 reflectsrelated shortto meteorological period drought drought conditions along andwith its short- applicationterm soil is moisture mainly related and crop to meteorological stress [34]. As droughtthe timealong scale withincreases short-term and reaches soil moisture cumulative and crop durations stress [34 of]. As12 themonths time scale(Figure increases 7d), the and separation reaches cumulative between durationscontinuing of dryness 12 months and (Figure wetness7d), becomes the separation much more between evident, continuing which drynesscan have and a good wetness indication becomes of mucha long-term more evident,drought whichperiod. can have a good indication of a long-term drought period.

Figure 7.7.The The temporal temporal evolution evolution of SPI of values SPI valu at differentes at different time scales: time (a) one-month;scales: (a) (one-month;b) three-month; (b) (three-month;c) six-month; ( andc) six-month; (d) 12-month. and The(d) gray12-month. columns The highlighted gray columns in different highlighted plots representin different the plots four mostrepresent severe the drought four most listed seve inre Table drought3. listed in Table 3. Sustainability 2017, 9, 901 12 of 21

Sustainability 2017, 9, 901 12 of 21 There are fewer drought events detected during the period after 2000 than that for the period before 2000.There It isare noted fewer thatdrought the droughtevents detected in 2005 during was quitethe period short after but 2000 severe than with that large for the negative period SPI valuesbefore for the2000. short It is timenoted scale.that the However, drought in the 2005 SPI was values quite atshort 12-month but severe time with scale large are negative much SPI smaller. Becausevalues SPI for values the short at longer time timescale. scalesHowever, are accumulatedthe SPI values fromat 12-month SPI values time at scale a shorter are much duration, smaller. which couldBecause be negative SPI values or positive, at longer the time SPIs scales values are at longeraccumulated timescales from SPI tend values to lean at toa zero,shorter except duration, when an extraordinarywhich could drought be negative or flood or positive, takes place.the SPIs The values drought at longer events time detectedscales tend in to this lean part to zero, are except consistent with thewhen drought an extraordinary period shown drought in Figureor flood6. takes Moreover, place. theThe resultsdrought of events SPI values detected over in LMBthis part are are also in accordanceconsistent with with those the drought of SPI values period basedshown onin Figure gauges, 6. Moreover, which can the indirectly results of proveSPI values the over confidence LMB of are also in accordance with those of SPI values based on gauges, which can indirectly prove the CHIRPS for drought monitoring over LMB. confidence of CHIRPS for drought monitoring over LMB. The timeThe time series series of spatially of spatially distributed distributed SPI SPI values values can can be be applied applied toto evaluateevaluate the the land land fraction fraction for regionsfor underregions drought under drought conditions conditio [15].ns Figure [15].8 Figuredisplays 8 displays the temporal the temporal variability variability of area percentageof area affectedpercentage by moderate affected drought by moderate (areas drought in yellow), (areas severe in yellow), drought severe (areas drought in red), (areas and extremein red), droughtand (areasextreme in dark drought red) for (areas different in dark time red) scales for differ (1-,ent 3-, time 6-, and scales 12-month). (1-, 3-, 6-, and 12-month).

FigureFigure 8. 8.The The temporal temporal evolutionevolution of of drought drought area area at different at different time timescales: scales: (a) one-month; (a) one-month; (b) three-month; (c) six-month; and (d) 12-month. The Orange, vermillion and red colors indicate (b) three-month; (c) six-month; and (d) 12-month. The Orange, vermillion and red colors indicate moderate, severe and extreme dry area ratio, respectively. The gray columns highlighted in different moderate, severe and extreme dry area ratio, respectively. The gray columns highlighted in different plots represent the four most severe droughts. plots represent the four most severe droughts. Sustainability 2017, 9, 901 13 of 21

The monthly SPI12 values during the study period were used to define drought events according to the definition of drought described in Section 2.3.2. LMB experienced four most severe droughts since 1981, which are listed in Table3 with start and end time, drought duration (DD), drought severity (DS), and three drought intensity indexes (lowest SPI value, DI1; averaged DS, the maximum ratio of drought affected area, DI3). The gray rectangles in different plots in Figures7 and8 represent the four selected drought events listed in Table3. The maximum drought area of these four drought events can cover up to more than 60%, which can also be seen in Figure8.

Table 3. Characteristics of four most severe drought events at 12-month time scale over LMB.

Index Start-End DD DS DI1 DI2 DA (%) D1 October 1982–March 1984 18 9.62 1.09 0.53 61.5 D2 May 1991–June 1994 38 33.61 1.33 0.88 67.5 D3 September 1997–April 1999 20 16.55 1.23 0.83 67.9 D4 April 2015–July 2016 16 15.50 1.45 0.97 75.6

According to drought duration (DD) and drought severity (DS), the 1991–1994 drought is the longest drought event for a drought period of 38 months with largest drought severity (33.61). While the drought in 2015–2016 is the most severe drought with lowest SPI value (−1.45) and the maximum drought affected area of 75.6%.

3.2.2. Total Drought Duration Analysis In this study, the spatial distributed total drought duration at various time scales are adopted to study the drought occurrences with different intensity in LMB. The aim of this part is to identify regions susceptible to drought at different time scales based on the drought month number. When SPI is calculated at short time scales in areas with low seasonal precipitation, it could misleadingly lead to large positive or negative values [34,67] and LMB has quite low precipitation in the dry season (Figure2a). Therefore, one-month SPI (SPI1) is ignored to avoid the possible misleading interpretation and the spatial distributions of SPI3, SPI6, and SPI12 are analyzed henceforth. Figure9 gives the spatial distribution of drought month number for different drought categories (i.e., total, moderate, severe and extreme) at three timescales (3-, 6-, and 12-month). The spatial distribution of TDD indicates that droughts tend to occur in the northern highlands and southern Tonle Sap with a TDD value greater than 80 at three-month time scale, while the most parts of central Knorat Plateau are characterized by lower frequencies (<60) (Figure9a,e,i). As the time scale increases to longer time scales (six-month and 12-month), no major changes are observed in most parts of LMB, rather there is an increased frequency of droughts over southeastern LMB. The TDDs of moderate drought account for the majority of total droughts, and they occurred more frequently over the northern and southern parts of LMB. For 3-, 6-, and 12-month time scales, the patterns of TDD are similar except for the reduction of TDD in the north part of LMB (i.e., north highlands) when studied at longer time scales. The TDD of severe drought account for about one-quarter of TDD for droughts and has similar spatial distribution for different time scales. However, the TDDs of extreme drought have a quite distinctive spatial pattern at varying time scales. It is noted that extreme droughts are more typical in Mekong Delta region for 12-month time scales. As the time scale increases, the TDD of extreme drought in central LMB and Mekong Delta region increases as well. The southeastern part of LMB has relatively small TDD of extreme drought. SustainabilitySustainability 20172017,, 99,, 901901 14 of 21

Figure 9. Spatial distribution of total drought duration (TDD) at different time scales: (a–d) three-month; Figure 9. Spatial distribution of total drought duration (TDD) at different time scales: (a–d) (e–h) six-month; and (i–l) 12-month for different drought severities: TDD of all droughts (SPI < −1), three-month; (e–h) six-month; and (i–l) 12-month for different drought severities: TDD of all TDD of moderate drought (−1.5 < SPI < −1), TDD of severe drought (−2 < SPI < −1.5), and TDD of droughts (SPI < −1), TDD of moderate drought (−1.5 < SPI < −1), TDD of severe drought (−2 < SPI < extreme drought (SPI < −2) from January 1981 to July 2016. −1.5), and TDD of extreme drought (SPI < −2) from January 1981 to July 2016.

3.2.3. SpecificSpecific Drought EventsEvents AnalysisAnalysis As itit isis widelywidely acknowledged,acknowledged, the spatialspatial distributiondistribution is criticalcritical to understandunderstand the droughtdrought events [[68].68]. CHIRPS has a relatively higher spatial resolution which can provide more detaildetail spatialspatial distributions. Spatial distributions of drought indicatorsindicators (i.e., DS, DI1, DI2, and max DA) for the four severest droughtdrought eventsevents listedlisted inin TableTable3 3are are shown shown in in Figure Figure 10 10.. The fourfour droughtsdroughts havehave differentdifferent spatialspatial characteristics.characteristics. The The severity of D1 is the smallestsmallest (Figure(Figure 1010a),a), andand itit mainlymainly isis restrictedrestricted toto thethe easterneastern partpart ofof LMB,LMB, especiallyespecially forfor thethe northeastnortheast andand southeast withwith relatively relatively high high drought drought intensity. intensity. The The maximum maximum DA (61.5%)DA (61.5%) is also is the also smallest the smallest among fouramong drought four drought events. Itevents. is obvious It is thatobvious the D2 that drought the D2 is drought the severest is the drought severest according drought to according the high DS to valuesthe high covering DS values most covering parts of most LMB parts except of the LMB Delta except region the (Figure Delta region10e–h). (Figure The large 10e–h). value The of DSlarge is mainlyvalue of attributed DS is mainly to the attributed longer drought to theduration. longer drought The value duration. of DI1 isThe quite value different of DI1 from is quite DI2 althoughdifferent thefrom patterns DI2 although of them the are patterns similar. of With them respect are similar. to D3, theWith drought respect severity, to D3, the as welldrought as drought severity, intensity as well foras drought both DI1 intensity and DI2, for mainly both focused DI1 and on DI2, the Mekongmainly focused Delta region on the with Mekong high DSDelta values. region According with high to DS values. According to the values of DS and DIs, the Mekong Delta region was greatly affected by Sustainability 2017, 9, 901 15 of 21

Sustainability 2017, 9, 901 15 of 21 the values of DS and DIs, the Mekong Delta region was greatly affected by the drought that occurred fromthe 1997 drought to 1999. that The occurred latest from drought 1997 eventto 1999. occurred The latest from drought late 2015event to occurred 2016 (D4). from It late seems 2015 that to the drought2016 severity (D4). It ofseems this that event the was drought high, severity while drought of this event duration was high, was relativelywhile drought short duration compared was to the threerelatively other events. short compared However, to D4 the is three the most other intenseevents. However, drought eventD4 is the with most high intense values drought of DI1 event and DI2, as wellwith as high the highestvalues of maximum DI1 and DI2, DA as up well to as 75.6%. the highest maximum DA up to 75.6%.

FigureFigure 10. Spatial 10. Spatial distribution distribution of droughtof drought severity severity (DS), (DS), drought drought intensity intensity (DI1 (DI1 and and DI2) DI2) and and SPI SPI value for thevalue month for the with month maximum with maximum drought areadrought (DA) area for (DA) four for most four severe most droughtsevere drought events events over LMB over from 1981 toLMB 2016: from (a 1981–d) to D1, 2016: (e– (ha)–d D2,) D1, (i –(el–)h D3) D2, and (i–l ()m D3– pand) D4. (m– Allp) D4. the All statistics the statistics are basedare based on on SPI12. SPI12. Sustainability 2017, 9, 901 16 of 21

3.3. DroughtSustainability Impacts 2017, 9, on901 Vegetation 16 of 21 The3.3. Drought drought Impacts characteristics on Vegetation could help to understand the vegetation vulnerability to droughts, conversely,The the drought vegetation characteristics response could also help can beto unders helpfultand to assessthe vegetation the accuracy vulnerability of drought to droughts, monitoring by usingconversely, CHIRPS. the Itvegetation should be response noted that also vegetation can be helpful can only to beassess used the as accuracy a surrogate of drought for drought monitoringmonitoring when by thereusing CHIRPS. are no artificial It should means be noted introducing that vegetation water can to only the be plants, used as as a vegetationsurrogate for could thrivedrought even during monitoring a drought when with there timing are no irrigation. artificial Here, means the introducing spatial averaged water SAI_VHIto the plants, and SPI3as are comparedvegetation in Figure could 11 thrive. It is welleven observedduring a drought that SAI_VHI with timing and SPI3 irriga havetion. a Here, relatively the spatial agreeable averaged evolution with aSAI_VHI correlation and SPI3 coefficient are compared value of in 0.45. Figure Though 11. It is the well correlation observed that coefficient SAI_VHI value and isSPI3 not have very a high, relatively agreeable evolution with a correlation coefficient value of 0.45. Though the correlation the variation of SAI_VHI generally corresponds well to the drought conditions with negative (positive) coefficient value is not very high, the variation of SAI_VHI generally corresponds well to the SAI_VHIdrought value conditions for dry with (wet) negative periods. (positive) It is indicatedSAI_VHI value that for the dry meteorological (wet) periods. It droughts is indicated have that great impactsthe onmeteorological vegetation health. droughts Conversely, have great the correspondingimpacts on vegetation variation betweenhealth. theConversely, vegetation the health conditioncorresponding and drought variation condition between also the indirectly vegetation reflect health the condition fair capability and drought of CHIRPS condition for also drought monitoringindirectly in reflect LMB region.the fair capability of CHIRPS for drought monitoring in LMB region.

FigureFigure 11. Time 11. Time series series spatial spatial averaged averaged SPI3 SPI3 andand SAI_VHISAI_VHI from from 1981 1981 to to2016. 2016. The The four four most most severe severe droughtdrought events events are markedare marked by by scattered scattered rectangles. rectangles.

Specifically, the relationship between region averaged SAI_VHI, SPI value and drought area at Specifically,three-month scale the relationshipduring four drought between periods region ar averagede also compared SAI_VHI, and SPIshown value in Figure and drought 12. The area at three-monthcorresponding scale periods during have four also droughtbeen marked periods by scattered are also rectangles compared and andlabels shown (D1, D2, in D3 Figure and 12. The correspondingD4) in Figure 11. periods The spatial have alsoaveraged been SPI marked and drought by scattered area rectanglesat three-month and labelstime scale (D1, for D2, the D3 four and D4) in Figuredrought 11. Theevents spatial show averaged that the four SPI drought and drought events area have at different three-month drought time evolutions scale for with the fourdifferent drought eventsdrought show intensity, that the fourdrought drought severi eventsty and different have different drought drought duration. evolutions For example, with D1 different has only droughtone intensity,drought drought area peek severity with corresponding and different lowest drought SPI duration. value. D4 Forhas two example, drought D1 area has peaks only while one droughtD2 has variable drought area evolution with four drought area peaks. D3 has continuous mild drought area peek with corresponding lowest SPI value. D4 has two drought area peaks while D2 has variable area evolution with small fluctuation. The four distinctive drought events could help us to droughtthoroughly area evolution test the drought with four impacts drought on vegetation area peaks. and D3 indirectly has continuous reflect the mild accuracy drought of the area CHIRPS evolution with smallfor drought fluctuation. monitoring. The four distinctive drought events could help us to thoroughly test the drought impacts onAs vegetation can be obviously and indirectly observed reflect in Figure the accuracy12, the SAI_VHI of the CHIRPS exhibit identical for drought consistency monitoring. with Asdrought can be area obviously for four observed much different in Figure drought 12, the even SAI_VHIts. For the exhibit short-term identical drought consistency event (D1) with with drought area forone four drought much peak, different SAI_VHI drought also responds events. Forwith the one short-term valley. When drought the drought event area (D1) reaches with one a peak drought peak,with SAI_VHI lowest also SPI respondsvalue in April with 1983, one valley.SAI_VHI When value the drops drought to the area negative reaches minimum. a peak with Moreover, lowest SPI valueSAI_VHI in April also 1983, can SAI_VHI respond well value to dropsthe small to thevariation negative of drought minimum. area, like Moreover, the small SAI_VHI drought area also can respondpeak well in December to the small 1982 variation and September of drought 1983, area, as well like as the the small relatively drought wet areaperiod peak from in DecemberOctober to 1982 December 1983. Comparison with other drought events (D2, D3 and D4) shows good matches and September 1983, as well as the relatively wet period from October to December 1983. Comparison between the temporal variation of SAI_VHI and drought area. Quantitatively, there are some withdifferences other drought between events the (D2,domain-averaged D3 and D4) SPI3 shows and good SAI_VHI matches values. between The reasons the temporalfor this difference variation of SAI_VHIcan be and complex, drought such area. as the Quantitatively, influence of irrigation there are, somethe drought differences resistance between of plants the domain-averagedwith different SPI3root and system, SAI_VHI the values.possible near-surface The reasons aquifer for this and differencethe spatially can averaged be complex, process of such SPI. as Overall, the influence the of irrigation,above results the droughtsuggest that resistance the vegeta oftion plants health with is sensitive different to the root drought system, condition; the possible in other near-surface words, aquifer and the spatially averaged process of SPI. Overall, the above results suggest that the vegetation health is sensitive to the drought condition; in other words, the droughts have great impacts on Sustainability 2017, 9, 901 17 of 21 Sustainability 2017, 9, 901 17 of 21 thevegetation droughts health have condition. great impacts The resultson vegetation could also health be indirect condition. proof The for results the capability could also of CHIRPSbe indirect for proofdrought for monitoring.the capability of CHIRPS for drought monitoring.

Figure 12.12. TheThe relationship relationship between between SAI_VHI SAI_VHI and and drought drought indicators indicators (SPI3 (SPI3 and drought and drought area) during area) duringfour specific four specific drought drought events: (events:a) D1, ( b(a)) D2,D1, ( c(b)) D3 D2, and (c) ( dD3) D4. and The (d) eventsD4. The are events also marked are also in marked Figure 11in Figureby scattered 11 by rectangles.scattered rectangles.

4. Conclusions LMB experiences concurrent drought events. Th Thee representativeness problem problem of rain gauges greatly limits the study of drought drought characteristics characteristics at at the the local local scale. scale. Availability Availability of of the the latest latest monthly, monthly, 0.05°0.05◦ satellite-based CHIRPSCHIRPS provides provides an an opportunity opportunity for for long-term long-term assessments assessments of droughts of droughts at a finerat a finerresolution resolution than previouslythan previously possible. possible. Based onBased a comparison on a comparison of precipitation of precipitation and SPI and between SPI between CHIRPS CHIRPSand observed and values,observed this values, paper focusesthis paper on monitoringfocuses on droughtmonitoring conditions drought in Lowerconditions Mekong in Lower Basin Mekongfrom January Basin 1981 from to January July 2016 1981 using to CHIRPS,July 2016 which using has CHIRPS, a 36-year which record has of a data. 36-year Two record groups of of data. rain Twogauges groups with of different rain gauges availability with different periods availability (20 gauges periods for the period(20 gauges of 1981–2016 for the period and 38of gauges1981–2016 for andthe period38 gauges of 1999–2016) for the period were of used 1999–2016) as the reference were us toed validate as the reference the stability to ofvalidate CHIRPS. the SPIsstability at four of CHIRPS.different timeSPIs at scales four (i.e.,different SPI1, time SPI3, scales SPI6, (i.e., and SPI1 SPI12), SPI3, were SPI6, used and to evaluateSPI12) were the used drought to evaluate conditions the droughtover LMB. conditions The main over findings LMB. of The this main study findings are as follows:of this study are as follows:

(1) CHIRPSCHIRPS shows reasonable abilityability toto identifyidentify and and characterize characterize drought drought events. events. In In comparison comparison to torain rain gauges, gauges, CHIRPS CHIRPS performs performs well well in estimating in estimating both both monthly monthly precipitation precipitation with highwith CChigh (0.98) CC (0.98)and low and RMSE low (<13RMSE mm/month) (<13 mm/month) and SPI at and different SPI timescales.at different The timescales. results of three-monthThe results SPIof three-month(SPI3) have the SPI best (SPI3) agreements have the while best theagreemen relativelyts while worse the performance relatively wasworse detected performance at 12-month was detected(SPI12) with at 12-month slight overestimation (SPI12) with inslight comparison overestimation with gauges. in comparison The SPI valueswith gauges. at three-month The SPI valuestimescale at three-month also exhibit hightimescale consistency also exhibit with thehigh variation consistency of SAI_SM with the for variation the drought of SAI_SM evolution. for the drought evolution. (2) In the period of January 1981–July 2016, LMB experienced several severe drought events such as November 1982–February 1984, June 1991–May 1994, September 1997–April 1999, and April Sustainability 2017, 9, 901 18 of 21

(2) In the period of January 1981–July 2016, LMB experienced several severe drought events such as November 1982–February 1984, June 1991–May 1994, September 1997–April 1999, and April 2015—July 2016. The drought event from May 1991 to June 1994 is found as the longest one with drought duration of 38 months, and the drought of 2015–2016 is the most intense one with the lowest SPI value (−1.45), the highest averaged drought severity (0.97) and the largest drought affected area (75.6%). (3) The total drought duration analysis further reveals that droughts occurred more frequently in the northern and southern LMB. Southwestern part of LMB and Mekong Delta region are more susceptible to severe drought events with long-term durations. (4) The comparison between SAI_VHI and SPI3 shows that vegetation health is greatly influenced by droughts in LMB.

This study has demonstrated that CHIRPS is a valuable dataset for drought monitoring in Lower Mekong Basin. Results showed that the SPI derived from CHIRPS could detect drought events well by describing their temporal evolution, occurrence, and spatial distribution. Based on the advantage of high-resolution and long-term records of CHIRPS, it is found that four severe droughts occurred in Lower Mekong Basin with the longest one during 1991–1994 and the most intense one during 2015–2016 with drought affected area up to 75.6%. The analysis shows that the southwestern LMB and Mekong Delta region are more drought sensitive than other regions, which may be helpful to farmers for agricultural planning and drought management. In addition, the recurring droughts could have great impacts on vegetation conditions in LMB. The results of this study could provide valuable information for the improvement of sustainable water resource management.

Acknowledgments: This work was jointly funded by the National Key Research and Development Program of China (2016YFA0601601) and One Thousand Youth Talents Plan of China (Xinjiang Project: Y374231001). H.G. was supported by a grant from the program of China Scholarships Council (201604910968) during his stay in Ghent University, Gent, Belgium. Author Contributions: Anming Bao conceived and designed the framework of this study. Hao Guo and Tie Liu processed the data, and analyzed the results under the supervision of Daming He, Alishir Kurban and Philippe De Maeyer. Hao Guo wrote the paper with the help from Felix Ndayisaba and Philippe De Maeyer. All authors contributed to the final version of the manuscript by proofreading and bringing in constructive ideas. Conflicts of Interest: The authors declare no conflict of interest.

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