Abstract
Climatic variations cause droughts which badly affect the environment. The study focused on monitoring droughts to aid decision-making and mitigate their negative impacts on water availability for agriculture and livelihoods in the face of increasing water demand and climate change. To assess the agricultural droughts, a new agricultural Standardized Precipitation Index (aSPI) was calculated which is not used earlier in Balochistan. Widely recommended Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used for meteorological drought assessment. Drought indices comparison was also conducted to check the applicability. Rainfall, maximum temperature, and minimum temperature data (1992 to 2021) were utilized to calculate SPI, aSPI, and SPEI at different timescales (3, 6, 9, and 12 months) using DrinC software and SPEI calculator. Indices results revealed the following severe to extreme drought years: 1998, 1999, 2000, 2001, 2002, 2004, 2008, 2011, 2014, 2016, and 2017. It was determined that Dalbandin, Quetta, Sibi, Kalat, Khuzdar, and Zhob experienced higher extreme drought frequencies. Both long- and short-term drought durations were observed. Indices comparison showed that SPI is the most efficient drought index compared to aSPI and SPEI. This study offers valuable insights for managing water resources in the face of climate-induced droughts.
HIGHLIGHTS
This study reviews the literature studies on different drought indices.
Indices of aSPI, SPI, and SPEI were used for the drought assessment.
A comparison was made between the indices to select the best index.
INTRODUCTION
Pakistan was ranked 10, according to the Global Climate Risk Index (GCRI) (Eckstein et al. 2019), among the most affected countries by climate change with erratic rainfall patterns, extreme events (floods and droughts), sea-level rise, and increasing temperature. So, climate change has the potential to significantly impact agriculture, human health, and water resources (Krishna Kumar et al. 2011). Drought is characterized by a sequence of successive periods during which required water availability remains beneath the baseline of demands (Lanen et al. 2017). There are two kinds of definitions of drought: (i) conceptual definition is described as ‘a prolonged period of insufficient rainfall resulting in significant crop damage and reduced agricultural yield’ and (ii) operational definition is explicated as the starting, ending, geographic spread (extent), and intensity of the drought. The drought phenomenon is also known as the ‘creeping phenomenon’, which has a much bigger spatial and temporal scale as compared to floods (Mishra & Singh 2010). Natural disasters like floods and droughts occur globally and these disasters affect a large number of people (Kundzewicz & Kaczmarek 2009). Considering several angles and interests, Wilhite (2000) characterized droughts into four different classes: (i) Meteorological Drought (MD) is the deficiency of rainfall, normally if rainfall goes below the threshed hold value of a region by more than 25% then that specific region is in drought (Vidal & Wade 2009), (ii) hydrological drought (HD) is strongly associated with a surface or subsurface water deficiency due to a precipitation shortfall (Peters et al. 2006), (iii) agricultural drought (AD) is the difference between actual evapotranspiration (ET) capacity, soil water scarcity, and a decrease in groundwater or reservoir levels (Lanen et al. 2017), and (iv) socioeconomic drought (SD) arises when the available supply of economic products is less than its demand because of weather-related water supply shortages. So there is a need to perform such studies to mitigate adverse effects of droughts.
Arid regions of Balochistan have faced significant effects of droughts. This province has been affected by numerous severe droughts of 1967–1969, 1971, 1973–1995, 1994, and 1998–2002. These droughts resulted in the destruction of 80% of orchards (Ashraf & Routray 2015), the deprivation of 2 million animals (Jasveen 2008), and significant declines in the water table (Ahmad et al. 2020). Additionally, the region has experienced increased heatwaves, leading to more frequent and severe drought events (Zahid & Rasul 2012). These changes in climatic conditions are anticipated to have a substantial impact on Balochistan's hydrological cycle. Durrani et al. (2018) stated that drought is a continuously occurring natural phenomenon in Balochistan. Between 1950 and 2010, rainfall data revealed 18 drought events per 3.3 years, with mean rainfall deficiencies ranging from 20 to 40%. The most recent drought lasted from 1997 to 2003, and the longest recorded dry season lasted from 1945–1955. The normal precipitation values decreased from 0 to 21% throughout Balochistan. So, there is a need to monitor the droughts and their impacts (Garcia et al. 2022) in this province which could be helpful to prepare mitigation measures and climate change.
Drought indicators or indices are commonly used to monitor droughts by assessing variables like precipitation, temperature, streamflow, groundwater and reservoir levels, soil moisture, and snowpack, providing a quantitative assessment of the severity, location, timing, and duration of drought events (WMO 2016). The number of meteorological, agricultural, and HD indices were used by Xie et al. (2013), Alamgir et al. (2015), Jain et al. (2015), Jaagus et al. (2022), Pathak et al. (2016), Cassardo (2018), and Kavianpour et al. (2020) for the assessment of droughts in the subcontinent of Asia. Gadiwala (2013) used the standardized precipitation index (SPI) from 1951 to 2010 in the Sindh region and Naz et al. (2020) also used the SPI in Pakistan's dry provinces like Balochistan. Adnan et al. (2018) evaluated the performance of 15 drought indices, and their findings showed that SPI, standardized precipitation evapotranspiration index (SPEI), and reconnaissance drought index (RDI) demonstrated a good aptitude to monitor Pakistan's drought situation. Javed et al. (2021) studied characteristics of AD in China, using SPI, standardized soil moisture index (SSI), and multivariate standardized drought index (MSDI). Precipitation performs a fundamental role in drought monitoring (Bai et al. 2019; Zhong et al. 2019). So, to avoid uncertainties in precipitation measurement multiple satellite-based drought indices were used to monitor the droughts like microwave integrated drought index (MIDI), rapid change index (RCI), vegetation health index (VHI), temperature condition index (TCI), the energy-based water deficit index (EWDI), enhanced vegetation index (EVI), vegetation condition index (VCI), and the scaled drought condition index (SDCI) (Otkin et al. 2015; Sur et al. 2015; Park et al. 2018; García-León et al. 2019). Large-scale, multi-temporal coverage with a direct, integrated, and synoptic perspective of enormous areas are provided by satellite-based investigations (Nama et al. 2022).
Each drought index has limitations and varying applicability based on regional climate conditions, emphasizing the need for comprehensive drought indicators. Satellite-based indices are associated with the limitation of short time records of satellite data (AghaKouchak et al. 2015). Various studies were conducted in Balochistan to assess the droughts (Jamro et al. 2020; Naz et al. 2020; Qaisrani et al. 2021, 2022; Rafiq et al. 2022) by applying the different drought indices. However, these indices caused uncertainties during calculation because they did not use any particular drought monitoring model or software. So, to avoid these uncertainties the software of DrinC (Drought indices Calculator) was used in this study area. Batool et al. (2021) and Mahmood et al. (2023) elaborated on the excellent applicability of DrinC for drought assessment in Pakistan. Previous studies conducted in Balochistan about drought assessment did not use the aSPI (agricultural Standardized Precipitation Index) and also did not perform the analysis of drought indices comparison to assess their applicability. This study employed a novel agricultural index called aSPI which utilized effective precipitation to identify AD (Tigkas et al. 2019). The aSPI is a modified form of the SPI and this modified index better identifies AD and successfully links drought severity to its impact on vegetation, particularly in rainfed conditions. The utilization of multiple indices can provide a deeper understanding of drought conditions in a specific location (Aladaileh et al. 2019; Faye 2022). That's why in this study SPI, aSPI, and SPEI were utilized to monitor the droughts in drought-prone areas of Balochistan.
The main objective of this study is to assess and monitor the meteorological and agricultural droughts by employing the three indices of the SPI, aSPI, and SPEI, and also to perform the drought indices comparison. The results of the current study will provide direction for how water management strategies should be modified in the perspective of the shifting patterns of droughts in the Balochistan province as a result of climate change.
The paper is structured as follows: first, a brief overview of the study area is provided, including its climate, topography, and agricultural output. Next, the data, methods, and tools used are described, followed by the presentation of our results and discussion, and conclusion sections.
MATERIALS AND METHODS
Study area
Data collection
Metrological parameter data of precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) were collected for 10 metrological stations of Dalbandin, Jiwani, Kalat, Khuzdar, Lasbella, Panjgur, Pasni, Quetta, Sibi and Zhob in Balochistan from 1992 to 2021. This climatic parameter data were gathered from the Pakistan Meteorological Department (PMD).
Indices calculations
SPI and aSPI were calculated by using DrinC. DrinC (Drought Indices Calculator) software provides a simple boundary for calculating droughts. It is a public domain software which is freely available online (https://drought-software.com) and most widely used all over the world to perform drought-related studies. This software performs well in different applications for different sites, especially in dry and semi-arid regions, and it has become a beneficial research and practical device for drought investigation. The SPEI calculator tool was utilized to compute the SPEI. For this, only the rainfall and mean temperature databases were necessary.
Standardized Precipitation Index
The SPI was employed in this study due to its acceptance, usability, efficacy, and applicability. The major advantage of the SPI is that it is simple to use and calculate because it simply uses precipitation data. The SPI is appropriate in all climate regimes and for areas that might be data-poor or lack long-term data (WMO 2016). The SPI can be defined as the deviation of observed values from the long-term mean calculation by the number of standard deviations.
Agricultural Standardized Precipitation Index
Drought is regarded as a major natural hazard, particularly in the sector of agriculture, where it can cause considerable problems with food security and subsequent economic and social consequences (Zhao & Running 2010; Wegren 2011; Tigkas et al. 2019). AD, also known as vegetation-AD, is a word used to convey the extent of drought effects on vegetation, taking into consideration the plants' sensibility to climatic and hydrological circumstances (Wilhite 2005; Tsakiris et al. 2013).
Standardized Precipitation Evapotranspiration Index
Vicente-Serrano et al. (2010) created the SPEI, which considers the effect of high temperatures on drought. The SPEI calculation relies on fitting a probability distribution to a time series of differences (D) between precipitation (P) and ET.
The SPEI relies on both temperature and rainfall data and has the distinction of combining a multi-scalar character with the ability to integrate temperature fluctuation impacts on drought evaluation. It is theoretically identical to the SPI, but it takes temperature into account. The SPEI may be a reliable indicator of drought due to its capacity to integrate both precipitation and temperature (Gurrapu et al. 2014). In general, long-term rainfall data is first transformed to a probability distribution (e.g., gamma distribution) for the SPEI as well as for SPI and aSPI
For SPI, aSPI, and SPEI indices, droughts were categorized by using the index values given in Table 1.
Index values of drought (SPI) . | Drought class . |
---|---|
≤2.00 | Extremely wet |
(1.50–1.99) | Severely wet |
(1.00–1.49) | Moderate wet |
(0.00 to −0.99) | Mild drought |
(−1.00 to −1.49) | Moderate drought |
(−1.50 to −1.99) | Severe drought |
≤− 2.00 | Extreme drought |
Index values of drought (SPI) . | Drought class . |
---|---|
≤2.00 | Extremely wet |
(1.50–1.99) | Severely wet |
(1.00–1.49) | Moderate wet |
(0.00 to −0.99) | Mild drought |
(−1.00 to −1.49) | Moderate drought |
(−1.50 to −1.99) | Severe drought |
≤− 2.00 | Extreme drought |
Drought characteristics
A drought event's duration is equal to the number of months between its start and end month. It was calculated by adding up the total duration of an event. Short-term drought occurs when a meteorological pattern results in a precipitation shortfall that lasts for a few weeks or months. Long-term drought occurs when the pattern and precipitation shortages persist for longer than 6 months. Drought frequency is defined as the number of months in a given period. The number of drought events repeated in a given period, either annual or monthly, is called drought frequency.
RESULTS AND DISCUSSION
Data analysis
To assess the precipitation and temperature behavior or pattern concerning climate change, all data were divided into three climate periods: climate period 1 (from 1992 to 2001), climate period 2 (from 2002 to 2011), and climate period 3 (from 2012 to 2021) for all stations. The reason for this data segmentation is that most of the climatic and hydrological changes occurred during the last two to three decades (Ashfaq et al. 2014).
Rainfall analysis
Now another comparison was made between 2002–2011 and 2012–2021 to analyze the rainfall changing pattern. So, in the map of 2002–2011, the higher rainfall value ranged between 281 and 310 mm which completely disappeared in the map of 2012–2021. The lower rainfall value ranges of 50–90 mm and 90–120 mm have increased in the map of 2012–2021, which have less proportion in the map of 2002–2011. From the discussion, it was revealed that the study area of Balochistan has a decreasing trend of rainfall (Iqbal & Athar 2018). When the rainfall of a region has been decreased then there are the chances of droughts due to shortage of water. That's why Balochistan is facing the natural disaster situation of drought.
Temperature analysis
In Figure 4, the comparison of 1992–2001 and 2002–2011 presents that the map of 1992–2001 shows the lower Tmax value range between 30 and 31 °C which has been replaced by a higher Tmax value range of 32–33 °C in the map of 2002–2011. Now another comparison was made between 2002–2011 and 2012–2021. The map of 2002–2011 shows the lower Tmax value range between 26 and 27 °C which completely disappeared in the upstream area and also reduced in the central area of the map for 2012–2021. The higher value range of 33–34 °C has increased in the map of 2012–2021, by reducing the proportion of the lower value range of 32–33 °C in the map of 2002–2011. From the discussion, it was revealed that the study area of Balochistan has an increasing trend of maximum temperature.
In Figure 5, the comparison between 1992–2001 and 2002–2011 presents that the map of 2002–2011 shows the value ranges of 7–9 and 9–12 °C have increased which are not present in the map of 1992–2001. And a higher Tmin value range of 21–22 has increased in the map of 2002–2011 which is also not present in the map of 1992–2001. Now another comparison was made between 2002–2011 and 2012–2021 which shows that the map of 2002–2011 shows the lower Tmin value ranges of 5–7 and 7–9 °C have disappeared by increasing the proportion of higher Tmin values range of 13–14 in the map of 2012–2021. From the discussion, it was revealed that the study area of Balochistan has an increasing trend of minimum temperature.
Figures 4 and 5 show that both maximum and minimum temperature have an increasing trend in the region of Balochistan (Abbas et al. 2018). So, an increase in temperature is associated with an increase in the occurrence probability of droughts which is why Balochistan is facing drought conditions.
SPI calculation analysis
SPI analysis for 3-, 6-, 9- and 12-month timescales
For the SPI-3 results, extreme droughts were observed in the following regions and years; Dalbandin in 2001, Kalat in 2016, Khuzdar in 2002, 2003, 2015, and 2010, Quetta in 2001, 2005, 2009, and 2018, Sibi in 2003, and 2005, and Zhob in 2001, 2012, and 2017. However, no extreme drought events were observed in Jiwani, Lasbella, Pasni, and Panjgur according to SPI-3.
Regarding the SPI-6 results, the study highlighted extreme drought occurrences in Dalbandin (2001 and 2003), Kalat (2016), Khuzdar (2002, 2003, and 2015), Lasbella (2001 and 2003), Panjgur (2005, 2011, and 2019), Quetta (1994, 2001, 2005, and 2018), Sibi (2003 and 2005), and Zhob (2001, 2012, and 2017). However, Jiwani and Pasni did not experience extreme drought events according to the SPI-6 results.
For the SPI-9 results, extreme droughts were observed in the following regions and years; Dalbandin in 2001 and 2003, Kalat in 2016, Khuzdar in 2002, 2003, and 2016, Lasbella in 2001, 2002, and 2003, Panjgur in 2005, 2011, and 2019, Quetta in 2001, 2005, and 2019, Sibi in 2001, 2015, and 2019, and Zhob in 2001, 2012, 2013, and 2017. Stations located in Jiwani and Pasni show no extreme drought events according to SPI-9.
Regarding the SPI-12 results, the study highlighted extreme drought occurrences in Dalbandin (2001 and 2003), Jiwani (2012), Kalat (2016 and 2017), Khuzdar (2003 and 2004), Lasbella (2002, 2003, 2005, and 2011), Panjgur (2001, 2005, and 2019), Quetta (2001, 2002, 2005, and 2019), Sibi (2004 and 2006), and Zhob (2006, 2012, and 2013). The station located in Pasni shows no extreme droughts according to SP-12. So most significant drought events were observed at Dalbandin, Lasbella, Kalat, Sibi, and Khuzdar (Ahmed et al. 2016; Naz et al. 2020).
SPI drought frequency
SPI drought durations
Station . | Start – End . | Duration . | Station . | Start – End . | Duration . | Station . | Start – End . | Duration . |
---|---|---|---|---|---|---|---|---|
Dalbandin | Nov 2000 – Aug 2001 | 10 | Panjgur | Dec 1993 – Oct 1994 | 11 | Khuzdar | Jul 1999 – Jan 2000 | 6 |
Jan 2003 – Aug 2003 | 8 | Mar 2005 – Jan 2006 | 11 | Dec 2000 – May 2002 | 18 | |||
Jun 2014 – Aug 2015 | 15 | Dec 1998 – Aug 1999 | 9 | Oct 2004 – Jan 2000 | 16 | |||
Jiwani | Dec 2001 – May 2002 | 6 | Apr 2010 – Jun 2011 | 10 | Pasni | Dec 2000 – Sep 2001 | 10 | |
Jan 2003 – May 2003 | 5 | Dec 2018 – Sep 2019 | 10 | Mar 2005 – Jan 2006 | 11 | |||
Dec 2010 – May 2011 | 6 | Lasbella | May 1999 – Jan 2000 | 9 | Dec 2014 – Feb 2016 | 15 | ||
Kalat | Apr 1994 – Jun 1995 | 15 | Apr 2001 – Jun 2002 | 15 | Dec 2017 – Sep 2019 | 22 | ||
Aug 2000 – May 2002 | 21 | Nov 2004 – Dec2005 | 15 | Sibi | Mar 2005 – Jan 2006 | 11 | ||
Jul 2015- Mar 2017 | 21 | Jul 2018 – Sep 2019 | 15 | Nov 2014 – Sep 2015 | 11 | |||
Quetta | Dec 2000 – Sep 2002 | 20 | Nov 2002 – Oct 2003 | 12 | Apr 2010 – Jun 2011 | 15 | ||
Dec 2004 – Sep 2005 | 10 | Zhob | Oct 2001 – Oct 2002 | 13 | Jan 2003 – Jun 2004 | 18 | ||
Dec 2016 – Sep 2017 | 10 |
Station . | Start – End . | Duration . | Station . | Start – End . | Duration . | Station . | Start – End . | Duration . |
---|---|---|---|---|---|---|---|---|
Dalbandin | Nov 2000 – Aug 2001 | 10 | Panjgur | Dec 1993 – Oct 1994 | 11 | Khuzdar | Jul 1999 – Jan 2000 | 6 |
Jan 2003 – Aug 2003 | 8 | Mar 2005 – Jan 2006 | 11 | Dec 2000 – May 2002 | 18 | |||
Jun 2014 – Aug 2015 | 15 | Dec 1998 – Aug 1999 | 9 | Oct 2004 – Jan 2000 | 16 | |||
Jiwani | Dec 2001 – May 2002 | 6 | Apr 2010 – Jun 2011 | 10 | Pasni | Dec 2000 – Sep 2001 | 10 | |
Jan 2003 – May 2003 | 5 | Dec 2018 – Sep 2019 | 10 | Mar 2005 – Jan 2006 | 11 | |||
Dec 2010 – May 2011 | 6 | Lasbella | May 1999 – Jan 2000 | 9 | Dec 2014 – Feb 2016 | 15 | ||
Kalat | Apr 1994 – Jun 1995 | 15 | Apr 2001 – Jun 2002 | 15 | Dec 2017 – Sep 2019 | 22 | ||
Aug 2000 – May 2002 | 21 | Nov 2004 – Dec2005 | 15 | Sibi | Mar 2005 – Jan 2006 | 11 | ||
Jul 2015- Mar 2017 | 21 | Jul 2018 – Sep 2019 | 15 | Nov 2014 – Sep 2015 | 11 | |||
Quetta | Dec 2000 – Sep 2002 | 20 | Nov 2002 – Oct 2003 | 12 | Apr 2010 – Jun 2011 | 15 | ||
Dec 2004 – Sep 2005 | 10 | Zhob | Oct 2001 – Oct 2002 | 13 | Jan 2003 – Jun 2004 | 18 | ||
Dec 2016 – Sep 2017 | 10 |
aSPI calculation analysis
aSPI analysis for 3-, 6-, 9- and 12-month timescales
Regarding the aSPI-3 results, the study highlighted extreme drought occurrences in Khuzdar (2003, 2004, and 2005). The stations located in Dalbandin, Jiwani, Kalat, Lasbella, Quetta, Pasni, Sibi, Zhob, and Panjgur did not experience extreme drought events according to aSPI-3.
For the aSPI-6 results, extreme droughts were observed in the following regions and years; Zhob in 2001 and 2012. The stations located in Dalbandin, Jiwani, Kalat, Khuzdar, Lasbella, Quetta, Pasni, Sibi, Zhob, and Panjgur did not experience extreme drought events according to aSPI-6.
Regarding the aSPI-9 results, the study highlighted extreme drought occurrences in Khuzdar (2003 and 2004), Quetta (1997, 2001, and 2011), Sibi (2004), and Zhob (2001, 2002, and 2011). The stations located in Dalbandin, Kalat, Lasbella, Panjgur, Jiwani, and Pasni showed no extreme drought events according to aSPI-9.
For the aSPI-12 results, extreme droughts were observed in the following regions and years; Kalat in 2016, Khuzdar in 2003, 2004, and 2005, Quetta in 2001, 2002, and 2011, and Zhob in 2002 and 2013. The stations located in Dalbandin, Jiwani, Lasbella Panjgur, Pasni, and Sibi experienced no extreme drought events according to aSPI-12.
aSPI drought frequency
aSPI drought durations
Station . | Start – End . | Duration . | Station . | Start – End . | Duration . | Station . | Start – End . | Duration . |
---|---|---|---|---|---|---|---|---|
Quetta | Dec 1993 – May 1994 | 6 | Pasni | Jan 1997 – Mar 1997 | 3 | Lasbella | May 1999 – Oct 1999 | 6 |
Jan 2002 – Jun 2002 | 6 | Jan 2001 – Mar 2001 | 3 | May 2001 – Oct 2001 | 6 | |||
Dec 1996 – Jun 1997 | 7 | Jan 2002 – Mar 2002 | 3 | May 2003 – Oct 2003 | 6 | |||
Dec 2010 – Jun 2011 | 7 | Jan 2012 – Mar 2012 | 3 | May 2005 – Oct 2005 | 6 | |||
Zhob | Mar 2005 – Oct 2005 | 8 | Jan 2014 – Mar 2014 | 3 | May 2016 – Oct 2016 | 6 | ||
Feb 2017 – Sep 2017 | 8 | Jan 2016 – Mar 2016 | 3 | Dalbandin | Nov 2000 – Mar 2002 | 17 | ||
Feb 2018 – Sep 2018 | 8 | Jan 2017 – Mar 2017 | 3 | Jan 2003 – Aug 200 | 8 | |||
Feb 2002 – Oct 2002 | 9 | Jan 2018 – Mar 2018 | 3 | Dec 2010 – Aug 2011 | 9 | |||
Khuzdar | Jan 2019 – Sep 2019 | 9 | Jiwani | Jan 1995 – Mar 1995 | 6 | Jun 2014 – Mar 2015 | 9 | |
Feb 2005 - Oct 2005 | 9 | Jan 2000 – Mar 2000 | 6 | Apr 2018 – Apr 2019 | 13 | |||
Mar 1999 – Oct 1999 | 9 | Jan 2003 – Mar 2003 | 6 | Panjgur | Feb 1994 – Mar 1994 | 2 | ||
Jan 2003 – Jun 2004 | 18 | Jan 2008 – Mar 2008 | 6 | Feb 1995 – Mar 1995 | 2 | |||
Apr 2010 – Jul 2011 | 16 | Jan 2012 – Mar 2012 | 6 | Feb 2001 – Mar 2001 | 2 | |||
Mar 2007 – Oct 2007 | 18 | Jan 2013 – Mar 2013 | 6 | Feb 2002 – Mar 2002 | 2 | |||
Kalat | Jan 1995 – May 1995 | 5 | Jan 2017 – Mar 2017 | 6 | Feb 2003 – Mar 2003 | 2 | ||
Jan 2001 – May 2001 | 5 | Jan 2019 – Mar 2019 | 6 | Feb 2004 – Mar 2004 | 2 | |||
Jan 2002 – May 2002 | 5 | Sibi | May 2001 – Oct 2001 | 6 | Feb 2008 – Mar 2008 | 2 | ||
Jan 2004 – May 2004 | 5 | Feb 2001 – Jul 2001 | 6 | Feb 2009 – Mar 2009 | 2 | |||
Jan 2011 – May 2011 | 5 | Feb 2003 – Oct 2003 | 9 | Feb 2013 – Mar 2013 | 2 | |||
Jan 2016 – May 2016 | 5 | Feb 2015 - Oct 2015 | 9 | Feb 2016 – Mar 2016 | 2 | |||
Jan 2017 – May 2017 | 5 | Feb 2019 – Oct 2019 | 9 | Feb 2019 – Mar 2019 | 2 |
Station . | Start – End . | Duration . | Station . | Start – End . | Duration . | Station . | Start – End . | Duration . |
---|---|---|---|---|---|---|---|---|
Quetta | Dec 1993 – May 1994 | 6 | Pasni | Jan 1997 – Mar 1997 | 3 | Lasbella | May 1999 – Oct 1999 | 6 |
Jan 2002 – Jun 2002 | 6 | Jan 2001 – Mar 2001 | 3 | May 2001 – Oct 2001 | 6 | |||
Dec 1996 – Jun 1997 | 7 | Jan 2002 – Mar 2002 | 3 | May 2003 – Oct 2003 | 6 | |||
Dec 2010 – Jun 2011 | 7 | Jan 2012 – Mar 2012 | 3 | May 2005 – Oct 2005 | 6 | |||
Zhob | Mar 2005 – Oct 2005 | 8 | Jan 2014 – Mar 2014 | 3 | May 2016 – Oct 2016 | 6 | ||
Feb 2017 – Sep 2017 | 8 | Jan 2016 – Mar 2016 | 3 | Dalbandin | Nov 2000 – Mar 2002 | 17 | ||
Feb 2018 – Sep 2018 | 8 | Jan 2017 – Mar 2017 | 3 | Jan 2003 – Aug 200 | 8 | |||
Feb 2002 – Oct 2002 | 9 | Jan 2018 – Mar 2018 | 3 | Dec 2010 – Aug 2011 | 9 | |||
Khuzdar | Jan 2019 – Sep 2019 | 9 | Jiwani | Jan 1995 – Mar 1995 | 6 | Jun 2014 – Mar 2015 | 9 | |
Feb 2005 - Oct 2005 | 9 | Jan 2000 – Mar 2000 | 6 | Apr 2018 – Apr 2019 | 13 | |||
Mar 1999 – Oct 1999 | 9 | Jan 2003 – Mar 2003 | 6 | Panjgur | Feb 1994 – Mar 1994 | 2 | ||
Jan 2003 – Jun 2004 | 18 | Jan 2008 – Mar 2008 | 6 | Feb 1995 – Mar 1995 | 2 | |||
Apr 2010 – Jul 2011 | 16 | Jan 2012 – Mar 2012 | 6 | Feb 2001 – Mar 2001 | 2 | |||
Mar 2007 – Oct 2007 | 18 | Jan 2013 – Mar 2013 | 6 | Feb 2002 – Mar 2002 | 2 | |||
Kalat | Jan 1995 – May 1995 | 5 | Jan 2017 – Mar 2017 | 6 | Feb 2003 – Mar 2003 | 2 | ||
Jan 2001 – May 2001 | 5 | Jan 2019 – Mar 2019 | 6 | Feb 2004 – Mar 2004 | 2 | |||
Jan 2002 – May 2002 | 5 | Sibi | May 2001 – Oct 2001 | 6 | Feb 2008 – Mar 2008 | 2 | ||
Jan 2004 – May 2004 | 5 | Feb 2001 – Jul 2001 | 6 | Feb 2009 – Mar 2009 | 2 | |||
Jan 2011 – May 2011 | 5 | Feb 2003 – Oct 2003 | 9 | Feb 2013 – Mar 2013 | 2 | |||
Jan 2016 – May 2016 | 5 | Feb 2015 - Oct 2015 | 9 | Feb 2016 – Mar 2016 | 2 | |||
Jan 2017 – May 2017 | 5 | Feb 2019 – Oct 2019 | 9 | Feb 2019 – Mar 2019 | 2 |
SPEI calculation analysis
SPEI analysis for 3-, 6-, 9- and 12-month timescales
Regarding the SPEI-3 results, the study highlighted extreme drought occurrences in Dalbandin (2010), Jiwani (2010, 2016, 2017, and 2019), Kalat (2015 and 2016), Khuzdar (2003 and 2016), Lasbella (2006, 2018), Pasni (2005, 2016, and 2017), Panjgur (2010, 2015, and 2019), Quetta (1993), Sibi (2014), and Zhob (2001 and 2017).
For the SPEI-6 results, extreme droughts were observed in the following regions and years; Dalbandin in 2007, Jiwani in 2017 and 2019, Kalat in 2015, Khuzdar in 2003, Lasbella in 2019, Pasni in 2017, Panjgur in 2010, 2015, 2018, and 2019. The stations located in Quetta, Sibi, and Zhob didn't show any extreme drought events according to SPEI-6.
Regarding the SPEI-9 results, the study highlighted extreme drought occurrences in Dalbandin (2006 and 2007), Jiwani (2018), Kalat (2015), Khuzdar (2003), Lasbella (2019), Pasni (2017), Panjgur (2010 and 2019), and Sibi (2003 and 2004). The stations located in Quetta and Zhob show no extreme drought events according to SPE-9.
For the SPEI-12 results, extreme droughts were observed in the following regions and years; Jiwani in 2018, Kalat in 2016, Pasni in 2017, Panjgur in 2019, and Sibi in 2004. The stations located in Dalbandin, Khuzdar, Lasbella, Quetta, and Zhob have not experienced any extreme drought according to SPE-12. Most significant droughts were experienced in Kalat, Khuzdar, Dalbandin, Sibi and Lasbella (Naz et al. 2020; Qaisrani et al. 2021).
SPEI drought frequency
SPEI drought durations
Station . | Start – End . | Duration . | Station . | Start – End . | Duration . | Station . | Start – End . | Duration . |
---|---|---|---|---|---|---|---|---|
Dalbandin | Jan 1999 – Oct 1999 | 10 | Panjgur | Mar 2000 – Apr 2002 | 26 | Labella | Nov 1998 – Oct 1999 | 12 |
Nov 2004 – Aug 2005 | 10 | Jul 2002 – Mar 2004 | 21 | Feb 2000 – Mar 2004 | 26 | |||
Feb 2000 – Mar 2001 | 14 | Dec 2017 – Nov 2019 | 24 | Nov 2004 – Oct 2005 | 12 | |||
Feb 2012 – Apr 2013 | 15 | Pasni | Mar 1999 – Oct 1999 | 8 | Mar 2015 – Mar 2018 | 37 | ||
Mar 2015 – May 2016 | 15 | Jul 2002 – Feb 2003 | 8 | Jun 2018 – Oct 2019 | 17 | |||
Sep 2001 – Sep 2003 | 25 | Oct 2004 – Jun 2005 | 12 | Khuzdar | Jul 2002 – Mar 2004 | 21 | ||
Jiwani | Jul 2002 – Jan 2003 | 7 | Oct 2011 – Sep 2012 | 12 | Jan 2000 – Apr 2002 | 29 | ||
Sep 2006 – Aug 2007 | 12 | Mar 2016 – May 2019 | 39 | Mar 2015 – Mar 2018 | 37 | |||
Jul 2011 – Dec 2013 | 31 | Quetta | Aug 1995 – Nov 1997 | 28 | Jun 2018 – Oct 2019 | 17 | ||
Mar 2016 – May 2019 | 39 | Oct 2001 – Feb 2002 | 5 | Sibi | Jan 1999 – May 2001 | 29 | ||
Kalat | Oct 2000 – May 2001 | 8 | May 2015 – Jul 2016 | 15 | Sep 2001 – May 2004 | 33 | ||
Oct 2001 – Jun 2003 | 21 | Zhob | May 1999 – Nov 2002 | 43 | Mar 2006 – Jun 2007 | 16 | ||
Apr 2015- Sep 2017 | 30 | Nov 2016 – Jul 2018 | 21 | Nov 2018 – Oct 2019 | 12 |
Station . | Start – End . | Duration . | Station . | Start – End . | Duration . | Station . | Start – End . | Duration . |
---|---|---|---|---|---|---|---|---|
Dalbandin | Jan 1999 – Oct 1999 | 10 | Panjgur | Mar 2000 – Apr 2002 | 26 | Labella | Nov 1998 – Oct 1999 | 12 |
Nov 2004 – Aug 2005 | 10 | Jul 2002 – Mar 2004 | 21 | Feb 2000 – Mar 2004 | 26 | |||
Feb 2000 – Mar 2001 | 14 | Dec 2017 – Nov 2019 | 24 | Nov 2004 – Oct 2005 | 12 | |||
Feb 2012 – Apr 2013 | 15 | Pasni | Mar 1999 – Oct 1999 | 8 | Mar 2015 – Mar 2018 | 37 | ||
Mar 2015 – May 2016 | 15 | Jul 2002 – Feb 2003 | 8 | Jun 2018 – Oct 2019 | 17 | |||
Sep 2001 – Sep 2003 | 25 | Oct 2004 – Jun 2005 | 12 | Khuzdar | Jul 2002 – Mar 2004 | 21 | ||
Jiwani | Jul 2002 – Jan 2003 | 7 | Oct 2011 – Sep 2012 | 12 | Jan 2000 – Apr 2002 | 29 | ||
Sep 2006 – Aug 2007 | 12 | Mar 2016 – May 2019 | 39 | Mar 2015 – Mar 2018 | 37 | |||
Jul 2011 – Dec 2013 | 31 | Quetta | Aug 1995 – Nov 1997 | 28 | Jun 2018 – Oct 2019 | 17 | ||
Mar 2016 – May 2019 | 39 | Oct 2001 – Feb 2002 | 5 | Sibi | Jan 1999 – May 2001 | 29 | ||
Kalat | Oct 2000 – May 2001 | 8 | May 2015 – Jul 2016 | 15 | Sep 2001 – May 2004 | 33 | ||
Oct 2001 – Jun 2003 | 21 | Zhob | May 1999 – Nov 2002 | 43 | Mar 2006 – Jun 2007 | 16 | ||
Apr 2015- Sep 2017 | 30 | Nov 2016 – Jul 2018 | 21 | Nov 2018 – Oct 2019 | 12 |
Performance of drought indices with respect to rainfall pattern
The Dalbandin station's drought indices indicate that the SPI-12 lies under the category of extreme drought during the years 2000 and 2002 and moderate drought during the year 2001. These findings align with the observed deficiency in annual rainfall during the years 2000, 2001, and 2002. While aSPI and SPEI lie under the category of moderate drought during the same years but annual rainfall is less than the normal values.
The Jiwani station's drought indices indicate that the SPI-12 lies under the severe drought category during the years 2000, 2001, and 2002 and extreme drought during the year 2018. The annual rainfall pattern also shows the same pattern with the deficiency of rainfall during the years 2000, 2001, 2002, and 2018. While aSPI and SPEI lie under the category of moderate drought during the same years but annual rainfall is below the normal values.
The Kalat station's drought indices indicate that the SPI-12 lies under the severe drought category during the years 2001 and extreme drought during the years 2014 and 2015. These findings align with the observed deficiency in annual rainfall during the years 2001, 2014, and 2015, while aSPI and SPEI lie the under category of moderate drought during the same years.
The Khuzdar station's drought indices indicate that the SPI-12 lies under the extreme drought category during the years of 2002 and under the category of severe drought during the year of 2018. The annual rainfall pattern is aligned with the deficiency of rainfall during the years 2002 and 2018, while aSPI and SPEI lie under the category of moderate drought during the same years.
The Lasbella station's drought indices indicate that the SPI-12 lies under the severe drought category during the years 2000 and extreme drought during the years 2002 and 2004. Annual rainfall pattern also shows a deficiency of rainfall during the years 2000, 2002, and 2018, while aSPI and SPEI lie under the category of moderate drought during the same years.
The Pasni station's drought indices indicate that the SPI-12 lies under the severe drought category during the years 2000 and 2001. The annual rainfall pattern also shows the same pattern with the deficiency of rainfall during the years 2000 and 2001, while aSPI and SPEI lie under the category of moderate drought during the same years.
The Panjgur station's drought indices indicate that the SPI-12 lies under the severe drought category during the year 2000 and extreme drought during the year 2018. These findings align with the observed deficiency in annual rainfall during the years 2000 and 2018, while aSPI and SPEI lie under the category of moderate drought during the same years.
The Quetta station's drought indices indicate that the SPI-12 lies under the extreme drought category during the years 2000 and 2010. The annual rainfall pattern also shows the same pattern as the deficiency of rainfall during the years 2000 and 2010, while aSPI and SPEI lie under the category of moderate drought during the same years.
The Sibi station's drought indices indicate that the SPI-12 lies under the extreme drought category during the years of 2002 and severe drought during the year of 2018. The annual rainfall pattern also shows the same pattern with the deficiency of rainfall during the years 2002 and 2018, while aSPI and SPEI lie under the category of moderate drought during the same years.
The Zhob station's drought indices indicate that the SPI-12 lies under the extreme drought category during the years 2001, 2011, and 2016. These findings align with the observed deficiency in annual rainfall during the years 2001, 2011, and 2016, while aSPI and SPEI lie the under category of moderate to severe drought during the same years.
From the above analysis, it can be stated that the index of SPI for the 12-month timescale more accurately followed the annual rainfall pattern for all the stations of the study area of Balochistan. So, the performance of the SPI index is greater, followed by aSPI and SPEI.
Comparison of SPI, aSPI and SPEI on the basis of number of drought events
So, the comparison was made based on the number of droughts of different severities for the timescales of 3, 6, 9, and 12 months between the SPI, aSPI and SPEI which is described as follows.
Comparison of SPI, aSPI and SPEI for 3-month timescale
The following regions were identified as more extreme drought-prone areas based on the results of these indices: (i) SPI results: Khuzdar, Quetta, Zhob, and Dalbandin, (ii) aSPI results: no region, and (iii) SPEI results: Panjgur, Pasni, Jiwani, and Kalat. Furthermore, the following regions were identified as more severe drought-prone areas; (i) SPI results: Zhob, Sibi, Quetta, Lasbella, (ii) aSPI results: Zhob, Sibi, and, Quetta, and (iii) SPEI results: Dalbandin. Additionally, the following regions were identified as moderate drought-prone areas; (i) SPI results: Zhob, Kalat, Quetta, and Lasbella, (ii) aSPI results: Zhob, Sibi, Quetta, and Dalbandin, and (iii) SPEI results: Zhob, Sibi, and Lasbella. Finally, all regions were classified as experiencing mild drought conditions.
Comparison of SPI, aSPI and SPEI for 6-month timescale
The following regions were identified as more extreme drought-prone areas based on the results of these indices: (i) SPI results: Sibi, Quetta, Zhob, and Dalbandin, (ii) aSPI results: Sibi, Quetta, Zhob, Khuzdar, and (iii) SPEI results: Additionally, the following regions were identified as more severe drought-prone areas; (i) SPI results: Zhob, Sibi, Kalat, Quetta, Lasbella, (ii) aSPI results: Zhob, Quetta, Khuzdar, Kalat, and (iii) SPEI results: Pasni, Quetta, Sibi. Furthermore, the following regions were identified as moderate drought-prone areas; (i) SPI results: Dalbandin, Quetta, Lasbella, (ii) aSPI results: Panjgur, Zhob, and (iii) SPEI results: Jiwani, Zhob, Sibi, Lasbella. All regions lay under the category of mild droughts.
Comparison of SPI, aSPI and SPEI for 9-month timescale
The following regions were identified as more extreme drought-prone areas based on the results of these indices: (i) SPI results: Lasbella, Panjgur, Sibi, Quetta, Zhob, and Dalbandin, (ii) aSPI results: Quetta and Zhob, and (iii) SPEI results: Panjgur, Pasni, and Khuzdar. Additionally, the following regions were identified as more severe drought-prone areas; (i) SPI results: Zhob, Sibi, Kalat, Khuzdar, Quetta, Dalbandin, and Lasbella, (ii) aSPI results: Zhob, Quetta, Khuzdar, and Lasbella, and (iii) SPEI results: Pasni, Lasbella Quetta, and Sibi. Furthermore, the following regions were identified as moderate drought-prone areas; (i) SPI results: Pasni, Jiwani, and Dalbandin, (ii) aSPI results: Khuzdar, Quetta, Sibi, and Zhob, and (iii) SPEI results: Zhob, Khuzdar, and Dalbandin. All regions lay under the category of mild droughts.
Comparison of SPI, aSPI and SPEI for 12-month timescale
Comparison with other studies
The current study found the most extreme and severe droughts in the years 1998, 1999, 2000, 2001, 2002, 2004, 2008, 2011, 2014, 2016, and 2017 for all three indices. Studies conducted by Qaisrani et al. (2021), Naz et al. (2020) and Rafiq et al. (2022) found similar results. Long-term durations of drought events were observed for Kalat, Khuzdar and Dalbandin stations and Naz et al. (2020) found similar long-term durations of drought. Indices comparison found that SPI predicted a higher number of droughts and had a better performance in predicting drought, so SPI is the best index to monitor droughts. Studies conducted by Sims et al. (2002), Quiring & Papakryiakou (2003), Gadiwala (2013), Naz et al. (2020) and Adnan et al. (2018) also declared the SPI as the best drought index because the SPI based solely on rainfall allows drought assessment even without other measurements. It quantifies precipitation deficit for various timescales, enabling assessment in meteorological, hydrological, and agricultural contexts. Standardization ensures a consistent frequency of extreme drought events at any location and timescale.
CONCLUSION
Agro-MD insight is crucial for the early warning and mitigation process. The drought assessment in the current study exposed that preliminary analysis of rainfall and temperature data showed the decreasing and increasing trend of rainfall and temperature, respectively, in the Balochistan. Drought characteristics of duration, severity and frequency were investigated with the help of SPI, aSPI, and SPEI indices. Also, a comparison was made between the indices to check their applicability. The SPI analysis revealed that extreme drought events were observed in the years 1996, 1998, 2000, 2001, 2003, 2004, 2008, 2012, 2014, and 2017, while severe drought events occurred in 2002, 2005, 2009, 2011, 2013, 2019, and 2021. The aSPI analysis revealed that extreme droughts were faced in 1997, 2001, 2003, 2005, 2013, and 2021, while severe drought events occurred in 1994, 1995, 1997, 2002, 2004, 2005, 2012, 2016, and 2019. The SPEI analysis identified that extreme drought events were observed in the years 1993, 2000, 2005, 2006, and 2015, 2017, 2018, and 2019, while severe drought events occurred in 1993, 1994, 1995, 1996, 1997, 2003, 2004, 2007, 2011, 2016. Drought duration analysis of SPI determined that Kalat experienced a drought event lasting 21 months while Khuzdar and Sibi had drought periods lasting 18 months. Dalbandin experienced a drought event lasting for 17 months, while 18 months of drought were observed for Khuzdar according to aSPI. According to the SPEI, Khuzdar experienced a drought period of 29 months, Kalat endured a drought for 30 months, Sibi faced a drought for 33 months, and Zhob suffered the longest with a drought lasting 43 months. From drought frequency analysis it was determined that all ten stations have mild drought frequencies, while Dalbandin, Quetta, Sibi, Kalat, Khuzdar, and Zhob demonstrated extreme drought frequencies. The assessment also involved comparing the number of drought events, which revealed that the SPI identified a higher number of drought events compared to the other two indices. Furthermore, it was observed that SPI was closely aligned with the rainfall pattern in comparison to the other indices. So, it can be stated that SPI determined better results as compared to aSPI and SPEI and it is a more efficient index for drought assessment in drought-prone areas of Balochistan. This study will be helpful for water management strategies with regards to drought mitigation in the context of climate change.
This study provides future direction for how water management strategies should be modified in respect to the shifting patterns of droughts as a result of climate change. This study will provide assistance to stakeholders, including Climate Change Experts, Hydrologists, Agronomists, and Water Resource Managers, in developing an effective drought mitigation policy. It will contribute to the identification, evaluation and implementation of water resources and agricultural management strategies that utilize rainwater harvesting, smart irrigation techniques, and appropriate tools.
Limitations and prospects for future research
This study investigated the historical droughts by using the historical data of climatic parameters. The study does not take into account the influence of ocean–atmospheric drivers of regional phenomena or the impact of physical drivers on severe drought. These are the limitations of this study. Future studies should include the Global Climate Models (GCMs) for future drought monitoring. There is a need to incorporate this study with groundwater drought as another important type of drought because groundwater plays a major role in measuring agricultural droughts. The water demand of the region needs to be incorporated in drought indices due to fast-growing populations.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.