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
]. Droughts can be broadly divided into four types: (a) meteorological; (b) agricultural; (c) hydrological; and (d) socioeconomic [2
]. 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
]. Besides, it can also be applied to reflect flood conditions in addition to drought monitoring [9
]. 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
], spatiotemporal drought analysis [15
], 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
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
]. 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
]. 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
] 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
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
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
]. 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 monitoring in LMB is of primary necessity for water planning and management to mitigate their detrimental impacts on the society, economy and environment. However, the ground-based precipitation gauges are very limited or unavailable for common users.
In this study, the performance of CHIRPS for precipitation capturing and drought monitoring is firstly evaluated through a direct quantitative comparison based on ground observations and an indirect comparison by using surface soil moisture (SM). Based on long-term CHIRPS product and SPI drought index, the primary objective of this study is to describe the spatiotemporal characteristics of meteorological droughts in LMB at multiple time scales. In addition, the drought impacts on vegetation are also analyzed. This is one early study to assess and apply CHIRPS for drought monitoring over Lower Mekong Basin, spanning the time from January 1981 to July 2016. The results of this paper will provide useful insights on the stability of CHIRPS for drought monitoring and contribute to a more comprehensive understanding of historical droughts over LMB, which are very important for the water resources management and design of contingency plans to reduce the impacts of droughts.
LMB experiences concurrent drought events. The representativeness problem of rain gauges greatly limits the study of drought characteristics at the local scale. Availability of the latest monthly, 0.05° satellite-based CHIRPS provides an opportunity for long-term assessments of droughts at a finer resolution than previously possible. Based on a comparison of precipitation and SPI between CHIRPS and observed values, this paper focuses on monitoring drought conditions in Lower Mekong Basin from January 1981 to July 2016 using CHIRPS, which has a 36-year record of data. Two groups of rain gauges with different availability periods (20 gauges for the period of 1981–2016 and 38 gauges for the period of 1999–2016) were used as the reference to validate the stability of CHIRPS. SPIs at four different time scales (i.e., SPI1, SPI3, SPI6, and SPI12) were used to evaluate the drought conditions over LMB. The main findings of this study are as follows:
CHIRPS shows reasonable ability to identify and characterize drought events. In comparison to rain gauges, CHIRPS performs well in estimating both monthly precipitation with high CC (0.98) and low RMSE (<13 mm/month) and SPI at different timescales. The results of three-month SPI (SPI3) have the best agreements while the relatively worse performance was detected at 12-month (SPI12) with slight overestimation in comparison with gauges. The SPI values at three-month timescale also exhibit high consistency with the variation of SAI_SM for the drought evolution.
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%).
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.
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.