Drought is a natural phenomenon caused by extreme and persistent precipitation shortage. This shortfall causes impacts on hydrology, agriculture, and the economy of a country. Secondly, drought/dryness has certain unique characteristics (severity, duration) among natural hazards which makes it difficult to classify the persistent and subjective network of impacts. Drought classification is important for managing drought, allowing both quantitative evaluation and potential risk assessment planning. The simpler approach of drought indices has made it easier for various researchers and organizations to classify drought. Several drought indices have been proposed at the national and global level to characterize hydrological, meteorological and agricultural droughts. Until now, there has been no widely agreed drought index among researchers. Therefore, researchers are trying to modify and reconstruct a simple, complete, and robust drought index for effective use and planning of the management of water resources. Due to the complex terrestrial ecosystem, researchers have integrated multiple drought indices for evaluation and monitoring of regional drought conditions. The reviewed composite or aggregated indices revealed that researchers are mainly focused on regional climatic and environmental conditions, and differences of theoretical backgrounds while integrating a drought index. There is a lack of performance evaluation of these indices because usually the comparative analysis between the integrated index and earlier developed composite indices is not performed. Secondly, the developer researchers did not mention limitations such as data, which is considered a paramount issue while applying these indices in other regions. Therefore, there is still comprehensive work needed for the simple integration of drought indices for general applications.

  • Drought indices are selected to study the limitations in indices.

  • Complex terrestrial ecosystem, researchers used to integrate/aggregate multiple drought indices.

  • Integrated/aggregated drought indices are lacking in performance evaluation.

     
  • PDSI

    Palmer Drought Severity Index

  •  
  • FIDI

    Fuzzy Integrated Drought Index

  •  
  • EAPI

    Evapotranspiration Anomaly Percentage Index

  •  
  • PAPI

    Precipitation Anomaly Percentage Index

  •  
  • SMAI

    Soil Moisture Anomaly Percentage Index

  •  
  • RAI

    Runoff Anomaly Percentage Index

  •  
  • SPI

    Standardized Precipitation Index

  •  
  • SPEI

    Standardized Precipitation Evapotranspiration Index

  •  
  • SWSI

    Surface Water Supply Index

  •  
  • WMO

    World Meteorological Organization

  •  
  • MDIs

    Meteorological Drought Indices

  •  
  • Z-index

    Z Index Palmer

  •  
  • RAI

    Rainfall Anomaly Index

  •  
  • KBDI

    Keetch–Byram Drought Index

  •  
  • PMDI

    Palmer Modified Drought Index

  •  
  • DSI

    Drought Severity Index

  •  
  • GRI

    Groundwater Resource Index

  •  
  • DFI

    Drought Frequency Index

  •  
  • EDI

    Effective Drought Index

  •  
  • RDI

    Reconnaissance Drought Index

  •  
  • RDI

    Reclamation Drought Index

  •  
  • WBDI

    Water Balance Derived Drought Index

  •  
  • ETDI

    Evapotranspiration Deficit Index

  •  
  • CMI

    Crop Moisture Index

  •  
  • CSDI

    Crop Specific Drought Index

  •  
  • RSM

    Relative Soil Moisture

  •  
  • DTx

    Agricultural Drought Index

  •  
  • VegOut

    Vegetation Outlook

  •  
  • VCI

    Vegetation Condition Index

  •  
  • PHDI

    Palmer Hydrological Drought Index

  •  
  • WBDI

    Water Balance Derived Drought Index

  •  
  • SDI

    Sperling Drought Index

  •  
  • NDVI

    Normalized Difference Vegetation Index

  •  
  • CI

    Composite Index

  •  
  • CDI

    Composite Drought Index

  •  
  • ADI

    Aggregate Drought Index

  •  
  • IDCI

    Integrated Drought Condition Index

  •  
  • MCDIs

    Multivariable Composite Drought Index

  •  
  • MIDI

    Microwave Integrated Drought Index

  •  
  • PADI

    Process-based Accumulated Drought Index

  •  
  • NADI

    Nonlinear Aggregated Drought Index

  •  
  • MIDI

    Multivariate Integrated Drought Index

Drought occurrence has become very frequent globally (Dai 2011; Trenberth et al. 2014). Although there are tangible debates on the perspectives and definitions of drought, there exists no accepted global standard explanation. Briefly, drought can be defined as a deficiency or shortfall of precipitation by an estimated mean within a period over a region (Dracup et al. 1980; Correia et al. 1991; González & Valdés 2006; Mishra & Singh 2010, 2011; Azmi et al. 2016). Therefore, the impacts expressed by drought are essential in realizing the context of drought. In the literature, several drought realizations have identified by Wilhite & Glantz (1985). From the research, four major types of drought are classified as meteorological, agricultural, hydrological, and socioeconomic droughts (Figure 1). Meteorological and hydrological droughts are linked with a reduction in precipitation. In these droughts, meteorological droughts have purely occurred when there is a precipitation reduction while hydrological droughts have happened when runoff or streamflow has declined due to less precipitation or other human activities such as the upstream build-up of hydrological structures. Likewise, agricultural droughts have occurred when there is a deficit of soil moisture while socioeconomic droughts have been the result of ecological water deficit or human water use (Huang et al. 2016; Faiz et al. 2018b, 2020; Mukherjee et al. 2018; Yihdego et al. 2019).

Figure 1

General diagram of different types of droughts.

Figure 1

General diagram of different types of droughts.

Close modal

All the above-mentioned types of drought begin as a result of a deficiency in precipitation over a period of time or space. Early stages of drought start as a result of an accumulation of a deficiency in precipitation, which is commonly known as meteorological drought. Deficiency in precipitation is caused by the persistence of high temperatures above normal, high winds, and relatively low humidity over time. Therefore, there are great environmental and socioeconomic impacts as a result of drought. Different regions have different patterns concerning the climatic conditions; thus, meteorological droughts are stated as a change in the hydro-meteorological and local geographical conditions. Hence, meteorological drought is mainly defined by the difference in the geographical, hydro-meteorological, and climatological conditions. There is a high possibility of the rapid development of meteorological drought, although this drought can end easily if the precipitation deficits are small. Meteorological droughts can also stay longer than usual or can develop into other drought types. For example, meteorological drought can also develop into agricultural drought in this case, if there are precipitation deficits during the crop-growing season resulting in the restrained growth and development of plants (Narasimhan & Srinivasan 2005; Eslamian 2014). Therefore, agricultural drought can be regarded as the next phase resulting from meteorological droughts. Agricultural drought, therefore, refers to a drought whereby there is an extension of its characteristics for a while, leading to effects on a region's agricultural demand as a result of lack of soil moisture. Agricultural drought has the potential to extend beyond the growing seasons, but there may exist a natural break between seasons in the case of no agricultural activities taking place. There are also instances whereby agricultural drought precedes a growing season in the case of situations that do not favor planting.

Hydrological droughts are drought events that result in the reduction of water availability in water resources (Eslamian 2014). Meteorological droughts may last for longer periods leading to a lack of surface flow due to soil dryness, thereby affecting the hydrological situation of a certain region. It is hard to monitor the hydrological impacts of drought in a certain region just after initiation. Therefore, an extension of the dry period in a certain region could result in a decline in the streamflow, soil moisture, or subsurface recharge. During the winter season, frozen precipitation is for future runoff, while in the case of a dry winter, there may be an emergence of hydrological drought in the preceding months. Despite the existence of precipitation events, dry soil is a great inhibitor of substantial runoff. Also, dryness and heat can lead to a reduction in the availability of water in a hydrological system. Water can be withheld by water managers in some hydrological systems in the case that there is a concern of hydrologic drought, to assist in moderating its future impacts. Although the precipitation may return to normal, the hydrology of a region may be affected by long-term hydrological drought.

The socioeconomic meanings of drought definition may differ from the other types of drought as the occurrence of this drought is dependent on the process of economic goods and supply and demand which are related to agricultural and meteorological elements (Wilhite & Glantz 1985; Arab et al. 2010; Shi et al. 2018; Guo et al. 2019). Climate and weather conditions can be used to explain the scarcity of certain goods and to describe why the demand for certain goods exceeds their supplies. Development of socioeconomic drought impacts may be observed immediately in a region and can extend over time, depending on the severity and impacted value of the good. Likewise, ecological drought is referred to as a prolonged deficiency in the availability of natural water supplies either in natural or managed hydrology. For example, ecological drought may affect the ecological water level (the lowest level of water required for the natural functioning of an ecosystem in a lake) of a certain lake (Wantzen et al. 2008; Liu et al. 2012; Crausbay et al. 2017).

Drought characteristics and monitoring

Due to the difficulties in the existing definitions of drought, their development is dependent on the indicators or indices which help in determining this phenomenon. Therefore, to study drought, primary information on the characteristics of weather and climate in a specific region must first be gathered to define the probability of a past or an ongoing drought event. A regular regional climatological behavior may be a sign of drought initiation in another region. Proper planning may be a possibility of mitigating the impacts of probable drought in this case if it is assumed that a unique dry pattern exists in the region. Monitoring the drought conditions may be conducted by early warning systems that might be helpful for adequate preparation of any drought event (Liu & Kogan 1996; Wilhite & Svoboda 2000; Paulo et al. 2005; Heim & Brewer 2012; Wilhite 2016). Adequate preparation is critical to mitigating drought consequences, which may lead to worse outcomes in the region.

Reliable and long-term precipitation data is very critical for basic drought characteristics. Some fundamental drought characteristics such as timing, duration, severity, magnitude, intensity, frequency, and predictability are defined and used by Yevjevich (1967), Dracup et al. (1980) and Salas (1993). For example, the duration of drought depends upon its dynamic nature and may be for a week or year. The accumulated water deficit in the form of runoff, soil moisture, and precipitation below the defining threshold during the drought period is called the magnitude. Similarly, the ratio of magnitude and duration is defined as drought intensity, and severity is related to precipitation deficit degree. Based on these characteristics, drought early warning systems are developed through comparisons of archives with current weather events. Precipitation is known to be the cornerstone indicator of many droughts, but other indicators are critical in assessing the severity of the drought. In a study area, one should ideally monitor rivers and streams, storage of water, moisture in the soil and crop production, in addition to any other indicator that is perilous in understanding the availability of water. The quality and quantity of information available in many regions do not warrant a determination of whether the region is ravaged by drought because it may not be possible to assess each indicator. However, in verifying the existence of drought severity, multiple indicators should be evaluated. For accurate analysis of the current and historical conditions, reliable and longest gap-free records are vital. Drought should be understood as a feature category whereby a region's dry side is expressed in temporal and spatial existence of precipitation. For an accurate statistical definition, extreme events are better understood if they fall within a sample size. A previous study documented that at least 50 years of precipitation records should be available for accurate definition and analysis of seasonal and long-term drought periods (Guttman 1999). For example, if we use the standardized precipitation index (SPI), then more data is vital when drought covers multiple years. On the other hand, some indicators which are associated with remote sensing data may not have long records. For proper monitoring of drought, enough time and energy should be dedicated to reconstructing and developing datasets that have many points. Nevertheless, the loss of information during the reconstruction of data is an issue that should be taken care of while the data is developed. Upon completion of the research, tracking conditions not only add to the recording period but also enable scientists and researchers to gain near insight into the environment and climate in the area. Therefore, it is critical to understand the monitoring of current conditions to know about the amount of precipitation that is expected over a period. Furthermore, the beginning of a drought cannot necessarily be characterized by a precipitation decrease for a typical seasonal precipitation duration of a region. Therefore, the determination of precipitation is vital for any area.

Indicators/indices

An indicator is defined as a scale variable in hydrological-, meteorological-, socioeconomic-, and agricultural-related studies indicating a deficiency or a drought-related potential (Svoboda & Fuchs 2016; Eslamian et al. 2017). On the other hand, an index determination is defined as the derivation method through value-added data related to drought through the comparison of the historical data with the current conditions (Heim 2002; Zargar et al. 2011; Eslamian 2014; Hao & Singh 2015). Indices are used in quantifying droughts and their magnitude while at the same time being used as indicators. A comparison of the long-term average with the actual precipitation is regarded as the quickest and easiest way for drought determination. Computation with the percent normal methods is also possible, but it has drawbacks because the calculation is based on the difference of median and means of a dataset and it can produce a difference for shorter periods. For long-term climatic records, the means and median precipitation are often not the same because the value of precipitation occurrence exceeds by 50 percent what usually occurs in monthly or seasonal precipitation. A percent normal index follows a normal distribution, and seasonal or monthly precipitation scales do not have a normal distribution. Therefore, comparing the differences of the normal or mean may give misleading results. Thus, researchers need to find a better way of computing the moments of distribution related to precipitation by using some historical context. For this objective, drought indices were established to express drought in such a way that they can provide extra information rather than just giving the comparison of the current situation with the historical average and water shortage identification related to the duration and intensity of the event. Heim & Brewer (2012) demonstrated the early 1900s drought indices that became the US standard drought monitor in 1999. These indices were Munger's Index, Kincer's Index, Marcovitch's Index, Blumenstock's Index, Antecedent Precipitation Index, Moisture Adequacy Index, Palmer's Index, Crop Moisture Index, Keetch–Byram Drought Index, Surface Water Supply Index, and Drought Monitor, respectively. These indices were developed based on regional and local definitions of droughts. For example, 15 consecutive no-rain days, rainfall less than 87% of normal expectancy, and precipitation for more than 21 days with less than 1/3 normal expectancy. Heim & Brewer (2012) also used these indices for comparison purposes and monitoring of drought. During the study, he found Blumenstock and Munger's indices best in measuring drought in the short term, and also it was comprehended that different realizations were produced from different indices. Several drought indices have been developed for drought monitoring, which can be found in the published literature; for example, meteorological drought indices (Palmer Drought Severity Index (Palmer 1965); Deciles (Gibbs & Maher 1967), US Drought Monitor (Svoboda et al. 2002), Standardized Precipitation Index (McKee et al. 1993), and agricultural drought indices (Crop Moisture Index (Palmer 1968), Crop Specific Drought Index (Meyer et al. 1993, Soil Moisture Deficit Index (Narasimhan & Srinivasan 2005).

The progression of the indices’ development is aimed at finding a dimensionless value with a meaning to express the severity of the drought. Some of the drought indices are strictly focused on agricultural issues, while others are concerning about the supply and availability of water in a region. Palmer (1965) incorporated regional water balance and developed the Palmer Drought Severity Index (PDSI) for the identification of agricultural and meteorological drought episodes. After that, more indices were developed based on the context of recent drought realization. For example, a multivariate standardized drought index was introduced by Hao & AghaKouchak (2013) which was based on the copula concept. The index combines a standardized soil moisture index and Standardized Precipitation Index (SPI) to characterize drought probabilistically. A recent study conducted by Nasab et al. (2018) used a machine-learning algorithm and developed an integrated drought index called the Fuzzy Integrated Drought Index (FIDI) and compared it with the Evapotranspiration Anomaly Percentage Index (EAPI), Precipitation Anomaly Percentage Index (PAPI), Soil Moisture Anomaly Percentage Index (SMAI) and Runoff Anomaly Percentage Index. They found the Fuzzy Integrated Drought Index to be a relatively convenient index.

Advantages and limitations of some popular indices

In this section, some popular drought indices like Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Deciles, and Surface Water Supply Index (SWSI)) that are mainly used for the monitoring and forecasting of dry conditions in different parts of the world are discussed. Due to their prevalence, they need discussion before the debate on the integration or composite side of indices.

SPI

SPI, developed by McKee et al. (1993), received global attention before the World Meteorological Organization's, 2009 recommendations because many countries around the globe needed to calculate and track drought conditions. SPI makes use of historical records of precipitation to establish a probability for the different time lengths. The SPI intensity scale has positive and negative values that are linked with surplus and deficit events. When the SPI value is below (−1.0) for a certain period, it is characterized as an initiating drought event. The drought event will persist until the SPI turns positive. SPI is flexible and makes it easier for the computation of both short- and long-term periods through the definition of various intervals of time. SPI also makes it appealing because the index can be computed even where data is missing. Distribution can also be developed and used because of the way SPI is computed. In the case of missing data, the result is null, and the computer will move to the SPI value that is next if the available data is enough. SPI is primarily calculated for between one month and 72 months, but its use is usually for 24 months or less. The flexibility of SPI allows perfect monitoring of agricultural, hydrological, and meteorological droughts that have various impacts on different time scales. Impacts of time on precipitation deficit gradually affect water resources. SPI of multitude durations are helpful in showing different water feature changes (Zargar et al. 2011). SPI is frequently used in different studies globally (Lloyd-Hughes & Saunders 2002; Ahmad et al. 2004; Pai et al. 2011; Raziei et al. 2013; Vu-Thanh et al. 2014; Wang et al. 2015a, 2015b; Faiz et al. 2018a; Khan et al. 2018). Furthermore, the World Meteorological Organization Workshop in 2009, Beijing, China, was under the theme of developing the standards for drought early warning, and the recommendations of different experts were to develop the standards and identify the methods for agricultural drought indices. For this scenario, the Lincoln workshop in multiple sessions was held under the objectives of limitations, drawbacks, and advantages of already developed indices. The main finding of the workshop was that SPI could be used for early drought warnings and also a request was made to the World Meteorological Organization for implementation of this recommendation (Hayes et al. 2011).

Limitations. SPI only uses one climate variable (precipitation), which makes it less connected with the ground conditions. Some studies mentioned that potential evapotranspiration should be included because it is a good indicator to assess the ground condition related to the regional climate (Vicente-Serrano et al. 2010). Secondly, long-term and accurate precipitation data, as well as some information about local climatological conditions, are necessary for accurate identification of regions that have greater drought tendency.

SPEI

SPEI is based on SPI. The only difference is that in this index, the author adds a component of temperature for capturing a climate water balance (Vicente-Serrano et al. 2010; Faiz et al. 2020). In Thornthwaite (1948) potential evapotranspiration is used to calculate climatic water balance. It has been established that various meteorological parameters can be used in obtaining good PET (potential evapotranspiration) estimates, but in a drought index context, a general water balance estimation is adequate. Estimation of water balance helps in keeping calculations parsimonious and also gives extra data requirements that are useful in actual evapotranspiration determination. Previous studies documented that SPEI can be efficiently used for agricultural drought tracking. SPEI drought studies have been conducted by Nguyen et al. (2015), Tajbakhsh et al. (2015), Alam et al. (2017), Ertugrul et al. (2017), Khan et al. (2017), Zhao et al. (2017), Faiz et al. (2018c), Mitra et al. (2018) and Manzano et al. (2019).

Limitations. A simple climatic water balance may overestimate or underestimate the drought conditions. Secondly, some researchers mentioned that SPEI acts similarly to SPI when potential evapotranspiration becomes zero (Song et al. 2015; Faiz et al. 2018c). For example, in colder regions where winter temperatures are mostly below zero the application of SPEI is not suitable.

PDSI

PDSI was developed by Wayne Palmer in the 1960s (Palmer 1965) primarily to assess agricultural drought. PDSI uses the moisture availability of a region and monitors drought by the use of a water balance equation. PDSI, unlike SPI, has a component of temperature and soil moisture. The temperature data is used for the estimation of PET through the utilization of the Thornthwaite approach and default information on soil moisture is derived from soil information (Palmer 1965). Incorporation of soil information is rendered more challenging as a result of the other variables used to define PDSI. Both SPI and PDSI have a categorization of wet and dry conditions with most values ranging between +4 and −4. The use of both scales allows users to be familiar with the response of PDSI to the events of precipitation, giving a better realization of the reaction of the index for different regions. PDSI applications have been widely used in the literature (Zhai et al. 2010; Vicente Serrano et al. 2011; Ram 2012; Darand 2015; Jamro et al. 2019; Dehghan et al. 2020; Faiz et al. 2020).

Limitations. PDSI has a time scale, which is approximately nine months, that causes a lag in drought conditions due to the simplified component of soil moisture calculations. A lag application can be used for several months, leading to a drawback in the identification of a drought situation that is rapidly emerging. PDSI also has seasonal applications because it does not account for satisfactory precipitation and frozen soils. Therefore, precipitation events are assumed to be liquid precipitation. The main limitation of PDSI is that it was primarily developed to use in the Midwest of the USA for initiating the identification of agricultural droughts. Documentation regarding PDSI limitations can be found in the following studies: Willeke et al. (1994), McKee et al. (1995), Guttman (1998). Secondly, due to its wide acceptance, PDSI results are still considered approximations due to the simple form of calculation of potential evapotranspiration.

Deciles

To identify and classify droughts, deciles of precipitation are used. In deciles, the historical records are fragmented into multiple portions that represent ten percent of data. When precipitation falls in the 10% driest record it will refer to the first decile while the median will be the fifth decile. The straightforward approach helps researchers in defining the dryness conditions of a region and supports them in understanding the interaction point of the current precipitation regime. A decile approach can be used in monitoring any drought type due to its flexibility of threshold that is established based on the region's climate.

Limitation. The drawback of using percent normal calculations is that the precipitation can get classified against historical data.

SWSI

As earlier mentioned, one of the major drawbacks of PDSI is mis-considering the calculations of frozen precipitation. The drawback was addressed by Shafer & Dezman (1982) in SWSI in the 1980s. SWSI was aimed at use in hydrological drought monitoring and addressing the disadvantages of PDSI due to not taking into account frozen precipitation. The disadvantages were addressed through the consideration of the mountainous region's snowpack together with the subsequent runoff of melting snow that ends in the river streams. For calculation of SWSI, four inputs are required such as streamflow, precipitation, snowpack, and reservoir storage. Weighted values were given based on total water balance contributions in the basin, while the scaling ranged from +4.2 to −4.2, which is very similar to the PDSI. Some authors applied SWSI in their studies for hydrological drought conditions (Kwon & Kim 2010; Steinemann et al. 2015).

Limitations. Although SWSI has many advantages over PDSI, it has drawbacks that limit its widespread use. One of the issues limiting its application is that many inputs are required for SWSI calculation. Therefore, there is an addition or omission of data points over the basin for the assignment of weight to be readjusted. There exists a challenge in comparing different case studies because the calculations are unique for every area of study. SWSI is also not applicable or has limited application in basins due to management practices and water retention in the basins.

As reviewed above, drought indices are extensively used for drought monitoring, dry-spell analysis, and performance evaluation around the globe. For example, Wang et al. (2015a, 2015b) used meteorological drought indices (MDIs) like PDSI, SPI, SPEI, deciles, Z-index, and scPDSI (Self-Calibrated Palmer Drought Severity Index) as indicators in northeastern China to assess soil moisture conditions. Similarly, Liu et al. (2018) (North China Plain), Faiz et al. (2018a) (Songhua River Basin), Liu et al. (2016) (Mongolian Plateau), and Li et al. (2012) (South China) used MDIs for drought monitoring and their characteristics. Hänsel et al. (2019) used the water balance anomaly index and a combination of different precipitation indices over Central Europe to analyze the seasonal drought trends and variations. Different studies have also been carried out to assess drought characteristics and dry spells over the Czech Republic, Poland, and Germany (Potop et al. 2014; Osuch et al. 2016; Lüttger & Feike 2018). Yaltı & Aksu (2019) and Eris et al. (2020) used MDIs for drought analysis in Turkey. Their application of MDIs suggested that selected indices are best to explain the spatio-temporal variability of a river basin or a site. In the south-central United States, Tian et al. (2018) used six MDIs for agricultural drought monitoring and found that multiple drought indices are necessary for agricultural drought monitoring. Although these studies confirm the applicability of single variable or multiple variable drought indices, many researchers have pointed out the drawbacks and reformulated some MDIs with more data input for better assessment of drought characteristics. For instance, Kim et al. (2009) modified the effective drought index to calculate drought characteristics. For that purpose, the author used individual drought event duration and severity, runoff, and accumulated negative effective drought index. Likewise, Li et al. (2017) reformulated SPEI with pan evaporation data, and single variable SPEI is the best option for measuring dry spells when a large amount of data to calculate evapotranspiration is not available for SPEI.

To validate a new drought index, a comparison of traditional drought indices with the new index is necessary to assess its performance (Azmi et al. 2016). One of the main issues in several countries for monitoring drought is missing data because long-term, reliable and consistent records of precipitation are not available. In cases where data breaks exist, SPI can get favorable information. When using SPI, it is possible with time intervals that can make sense of the severity and type of drought likely with the area of study. SPI is an index based on precipitation, and it tends to be applied more often in the identification of hydrological and meteorological drought episodes with no component of water balance. SPI is useful in the identification and development of drought situations for agricultural drought. Therefore, on a short time scale, SPI responds quickly to find drying conditions. Hence, SPI has flexibility for computation at any period. SPEI helps in updating weekly by use of a moving time window. SPEI also uses the difference between potential evapotranspiration and precipitation in determining a wet or dry period. The flexible nature of SPEI enables it to have a capacity for utilization in monitoring the different drought types as a result of incorporating water balance.

In a changing climate, drought indices change the ways of addressing and monitoring dryness conditions, therefore a comprehensive debate and discussions are necessary for the literature. The indices consider paleoclimatic data apart from written records of climate in understanding the past characteristics of drought. Drought is a constant phenomenon that has different episodes that occur regularly in history. Changing climate context is an indication of the continued occurrence of droughts because droughts are natural cycles of climate around the globe. Although drought has been known to be caused by different triggers, some scientists link more severe droughts to changes in climate. The emission of greenhouse gases into the atmosphere causes an increase in air temperature leading to the evaporation of moisture from the land and water resources ceasing. Temperatures that make the environment warmer also lead to an increase in evaporation in plant soils affecting the life of plants and leading to a reduction in rainfall events. Drought-stricken areas may not experience rainfall, therefore, leading to low absorption of water, which can increase flash flood likelihood. Increased temperatures and uncertain amounts of precipitation distribution can increase the frequency, duration, and intensity of droughts at several locations (Easterling et al. 2000; IPCC 2014).

According to many studies, there is no specific drought index that has the capability of performing appropriately under all circumstances. The studies have also concluded that most drought indices cannot comprehensively evaluate the stress conditions of water for a specific ecosystem. Due to these limitations, it has been recommended in many studies that an approach such as aggregation or combination should be used for the derivation of new drought indices that would enhance the accuracy and reliability of these drought indices (Eklund & Seaquist 2015; Hao & Singh 2015; Faiz et al. 2018c; Flint et al. 2018; Shen et al. 2019). The most common aggregation or combination is based on the uses of the blending of objective and subjective drought indices. Secondly, other aggregation and combination approaches may be used for multivariate drought indices. Specific drought indices give different information if not conflicting under different conditions of climate, application perspective, and use of land. In conclusion, there exists no drought index fitting all the various circumstances. Keeping in mind this scenario regarding the application of drought indices, we also assess the pattern and focus of authors when they developed and applied drought indices based on the combining or aggregation of drought indices. For this purpose, we take the data from the web of science and analyze the author's keywords and try to understand their research pattern. Firstly, a simple drought index keyword was searched in the web of science from 1986 to 2019. Two common keywords that were found in the database were ‘monitoring’ and ‘climate change’ and their main focus was on crop yield and dryness conditions (Figure 2(a) and 2(b)).

Figure 2

(a) Application of drought indices based on authors' keyword selection obtained from the web of science core collection record from 1986–2019. WOS document information was imported into VOSviewer to generate bibliometric networks. (b) Authors' focus based on keyword selection obtained from the web of science core collection record from 1986–2019.

Figure 2

(a) Application of drought indices based on authors' keyword selection obtained from the web of science core collection record from 1986–2019. WOS document information was imported into VOSviewer to generate bibliometric networks. (b) Authors' focus based on keyword selection obtained from the web of science core collection record from 1986–2019.

Close modal

Likewise, aggregation-based drought indices have keywords like subjectivity analysis, limited mathematical, and statistical etc., keywords found in the web of science database that were used by researchers for their research objectives to develop aggregated or composite drought indices. Therefore, we further reviewed the composite and aggregated drought indices for assessing the researchers' focus.

Given the author's perspective and web of science database, we have reviewed composite and aggregated drought indices that have been developed and applied in different regions of the world and tried to find what were the reasons behind the aggregation of those drought indices. For this purpose, the web of science database was searched for composite, aggregation, combined, modification, and reformulation keywords and then reviewed and the main points of the developed composite or aggregated drought indices were highlighted. The basic concept behind integration or the composite index is presented in Figure 3.

Figure 3

The basic concept behind the integration of drought indices. Note: Z-index Palmer (Palmer 1965); (RAI) Rainfall Anomaly Index (van Rooy 1965), (KBDI) Keetch–Byram Drought Index (Keetch & Byram 1968), (PMDI) Palmer Modified Drought Index (Palmer 1965), (DSI) Drought Severity Index (Bryant et al. 1992), (DFI) Drought Frequency Index (González & Valdés 2006), (EDI) Effective Drought Index (Byun & Wilhite 1999), (RDI) Reconnaissance Drought Index (Tsakiris & Vangelis 2005), (RDI) Reclamation Drought Index (Wagner et al. 2003), (GRI) Groundwater Resource Index (Mendicino et al. 2008), (WBDI) Water Balance Derived Drought Index (Vasiliades et al. 2011), (ETDI) Evapotranspiration Deficit Index (Narasimhan & Srinivasan 2005), (CMI) Crop Moisture Index (Palmer 1968), (CSDI) Crop Specific Drought Index (Meyer & Hubbard 1995), (RSM) Relative Soil Moisture (Thornthwaite & Mather 1955), (DTx) Agricultural Drought Index (Matera et al. 2007), (VegOut) Vegetation Outlook (Tadesse & Wardlow 2007), (VCI) Vegetation Condition Index (Thenkabail et al. 1994).

Figure 3

The basic concept behind the integration of drought indices. Note: Z-index Palmer (Palmer 1965); (RAI) Rainfall Anomaly Index (van Rooy 1965), (KBDI) Keetch–Byram Drought Index (Keetch & Byram 1968), (PMDI) Palmer Modified Drought Index (Palmer 1965), (DSI) Drought Severity Index (Bryant et al. 1992), (DFI) Drought Frequency Index (González & Valdés 2006), (EDI) Effective Drought Index (Byun & Wilhite 1999), (RDI) Reconnaissance Drought Index (Tsakiris & Vangelis 2005), (RDI) Reclamation Drought Index (Wagner et al. 2003), (GRI) Groundwater Resource Index (Mendicino et al. 2008), (WBDI) Water Balance Derived Drought Index (Vasiliades et al. 2011), (ETDI) Evapotranspiration Deficit Index (Narasimhan & Srinivasan 2005), (CMI) Crop Moisture Index (Palmer 1968), (CSDI) Crop Specific Drought Index (Meyer & Hubbard 1995), (RSM) Relative Soil Moisture (Thornthwaite & Mather 1955), (DTx) Agricultural Drought Index (Matera et al. 2007), (VegOut) Vegetation Outlook (Tadesse & Wardlow 2007), (VCI) Vegetation Condition Index (Thenkabail et al. 1994).

Close modal

Composite index (CI)

CI was firstly developed by the National Climate Center of China for meteorological drought monitoring by the combination of SPI at one- and three-month scales and evapotranspiration (Li et al. 2009; Qian et al. 2011). The application of CI is limited in northeast China because CI takes the previous winter's accumulated precipitation while spring drought in the region is related to spring precipitation as well as the previous winter's soil water storage. Therefore, CI failed to represent the occurrence of spring droughts (Song et al. 2014). Thus, CI was reformulated for the spring season by considering the previous autumn's precipitation. Authors compared spring CI with original CI, scPDSI, and SPI and concluded that spring CI was better than these indices while also documenting several uncertainties that are related to soil moisture data, and spring disaster records, etc. (Song et al. 2014). In spring CI, the authors concluded that SPI which was based on the previous year's (Sep–Nov) precipitation and April or May precipitation instead of taking one- and three-month SPI is better for detecting droughts in colder environments.

Composite drought index (CDI)

Waseem et al. (2015) established CDI based on the possible direct and wettest conditions and individual variable entropy to reflect agricultural, meteorological, and hydrological anomalies. The developmental concept of CDI is to provide an effective methodology that can present drought dynamics without considering transformations, assumptions, feature extraction techniques, and cumbersome empirical derivations. Variables such as precipitation, streamflow, land surface temperature, and normalized difference vegetation index were used to assess the drought conditions in South Korea (Waseem et al. 2015). CDI has been compared well with ADI (Bazrafshan et al. 2014). The authors documented that CDI is temporally flexible and physically sound and associated with climatic conditions and possible variants of the study area, but the CDI application in any other region is not found which can warrant the mentioned pros of CDI.

Aggregate drought index (ADI)

ADI was developed based on PDSI limitations such as empirical formulation and geographical location of a specific area (US Midwestern states), and snowfall process, and SWSI shortfalls in considering soil moisture and evaporation. ADI uses five to six variables such as snow water content, evapotranspiration, reservoir storage, soil moisture, streamflow, and precipitation (Keyantash & Dracup 2004). ADI's direct mathematical approach can assess the agricultural water shortage, and hydrological and meteorological droughts in aggregated perspectives. A comparison of ADI with PDSI revealed a satisfactory correlation (between 0.78 and 0.65). Wang et al. (2018) developed ADI using evapotranspiration, potential evapotranspiration, SPI, normalized difference precipitation index, soil moisture, and SPEI based on the argument that drought had larger spatial heterogeneity. The author got the best results for different growth stages of winter wheat but did not make comparison with earlier developed ADI and other indices. Secondly, more data is required for the application of ADI (developed by Wang et al. 2018), which may limit its applicability.

Integrated drought condition index (IDCI)

Shen et al. (2019) used vegetation conditions, soil moisture, and SPEI on a three-month scale to integrate a drought index. The authors argued that there is a knowledge gap for agricultural drought hazards but the study did not evaluate and analyze the risk assessment of hazards. IDCI was compared with the scaled crop yields index, soil moisture condition index, and vegetation condition index. IDCI performs better than these indices and IDCI can be used for agricultural drought monitoring.

Multivariable composite drought index (MCDIs)

A remote sensing approach is used to develop MCDIs (Liu et al. 2020). Normalized difference vegetation index, soil moisture, and satellite precipitation are integrated for MCDIs. MCDIs show good agreement with moisture index, SPI, and SPEI at different time scales. The index is effective for assessing meteorological and agricultural drought in semi-tropical monsoon climate regions. The concept behind MCDIs is that indices are region-dependent and cannot reflect and capture the role of a single variable in drought formation.

Microwave integrated drought index (MIDI)

Zhang et al. (2017a) developed MIDI using remote sensing data to overcome the spatial coverage limits in an area that has high spatial variability of in situ stations. MIDI was integrated with land surface temperature, soil moisture, and precipitation for short-term droughts in a semi-arid region of China. MIDI and SPI reflect similar temporal changes and spatial patterns at one- and three-month scales. MIDI is best for cropland and grassland areas to measure short-term meteorological droughts.

Process-based accumulated drought index (PADI)

PADI was integrated with vegetation conditions, soil moisture, precipitation, and crop growth stages (Zhang et al. 2017b). The authors provide solid arguments about univariate and bivariate analyses used in the drought system. Both analyses have shortcomings therefore the multivariate idea is used to develop PADI. PADI was compared with PDSI and SPI and a satisfactory correlation was found among them. PADI was also correlated with wheat yield loss and a good correlation was found.

Nonlinear aggregated drought index (NADI)

NADI was developed based on the limitation that linear principal component analysis represents less variance in the data (Barua et al. 2012). Several hydro-meteorological variables (streamflow, rainfall, storage reservoir volume, potential evapotranspiration, soil moisture content) are tested as input in NADI. The primary purpose of NADI was to enhance ADI methodology (Keyantash & Dracup 2004) and forecasting capabilities.

Multivariate integrated drought index (MIDI)

Chang et al. (2016) constructed MIDI to investigate drought risk by the integration of SPI, modified Palmer drought severity index, runoff anomaly percentage, and precipitation anomaly percentage. The basis of MIDI is that drought conditions may reflect different situations on a six-month scale compared with a one-month scale because the cumulative effect of water shortage also has an impact on droughts in different periods.

Identification and monitoring of droughts using diverse indices are very important for the proper management of impending droughts. Until now there has been no widely agreed drought index among researchers. Therefore, researchers are trying to modify and reconstruct a complete, simple, and robust drought index for effective use and planning of the management of water resources. However, in the terrestrial ecosystem, it is much more complex to assess water stress and monitor dry conditions using a single variable or sole drought indicator. Hence, researchers have used different drought indices to construct an integrated or composite drought index for evaluation and monitoring of drought.

The reviewed composite or aggregated indices revealed that authors are mainly focused on regional climatic, environmental conditions, data use, and differences of theoretical backgrounds in the development of indices. Researchers are mostly engrossed in drought impact on agriculture and crop yield and soil moisture. They have not compared developed indices with other composite or integrated indices. Mostly the indices were compared with an early developed single or multiple variable drought index (SPEI, SPI, PDSI), while the developer authors did not mention limitations such as data, which is a big problem while applying these indices in other regions. Therefore, there is still comprehensive work needed for the simple integration of drought indices for general applications.

This study was supported by the CAS President's International Fellowship Initiative (PIFI) (Project No. 2020PE0048), the CAS Talents Program, and the CAS-CSIRO drought propagation collaboration project. We appreciate anonymous reviewers and editors for their critical and constructive comments for improving the paper quality.

The authors declare no potential conflict of interest.

Data cannot be made publicly available; readers should contact the corresponding author for details.

Ahmad
S.
,
Hussain
Z.
,
Qureshi
A. S.
,
Majeed
R.
&
Saleem
M.
2004
Drought Mitigation in Pakistan: Current Status and Options for Future Strategies
.
IWMI
,
Colombo, Sri Lanka
.
Alam
N. M.
,
Sharma
G. C.
,
Moreira
E.
,
Jana
C.
,
Mishra
P. K.
,
Sharma
N. K.
&
Mandal
D.
2017
Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India
.
Physics and Chemistry of the Earth, Parts A/B/C
100
,
31
43
.
Arab
D.
,
Elyasi
A.
,
Tavakolifar
H.
&
Karamouz
M.
2010
Developing an integrated drought monitoring system based on socioeconomic drought in a transboundary river basin: a case study
. In:
World Environmental and Water Resources Congress 2010: Challenges of Change
(R. N. Palmer, ed.)
,
ASCE
,
Reston, VA, USA
, pp.
2754
2761
.
Azmi
M.
,
Rüdiger
C.
&
Walker
J. P.
2016
A data fusion-based drought index
.
Water Resources Research
52
(
3
),
2222
2239
.
Barua
S.
,
Ng
A. W. M.
&
Perera
B. J. C.
2012
Artificial neural network-based drought forecasting using a nonlinear aggregated drought index
.
Journal of Hydrologic Engineering
17
(
12
),
1408
1413
.
Bazrafshan
J.
,
Hejabi
S.
&
Rahimi
J.
2014
Drought monitoring using the multivariate standardized precipitation index (MSPI)
.
Water Resources Management
28
(
4
),
1045
1060
.
Bryant
S.
,
Arnell
N. W.
&
Law
F. M.
1992
The long-term context for the current hydrological drought
. In:
Institute of Water and Environmental Management (IWEM) Conference on the Management of Scarce Water Resources
,
13
14
October, Brighton, UK
.
Byun
H.-R.
&
Wilhite
D. A.
1999
Objective quantification of drought severity and duration
.
Journal of Climate
12
(
9
),
2747
2756
.
Correia
F. N.
,
Santos
M. A.
&
Rodrigues
R. R.
1991
Reliability in regional drought studies
. In:
Water Resources Engineering Risk Assessment
(J. Ganoulis, ed.)
,
Springer
,
Berlin, Germany
, pp.
43
62
.
Crausbay
S. D.
,
Ramirez
A. R.
,
Carter
S. L.
,
Cross
M. S.
,
Hall
K. R.
,
Bathke
D. J.
,
Betancourt
J. L.
,
Colt
S.
,
Cravens
A. E.
,
Dalton
M. S.
,
Dunham
J. B.
,
Hay
L. E.
,
Hayes
M. J.
,
McEvoy
J.
,
McNutt
C. A.
,
Moritz
M. A.
,
Nislow
K. H.
,
Raheem
N.
&
Sanford
T.
2017
Defining ecological drought for the twenty-first century
.
Bulletin of the American Meteorological Society
98
(
12
),
2543
2550
.
Dai
A.
2011
Drought under global warming: a review
.
Wiley Interdisciplinary Reviews: Climate Change
2
(
1
),
45
65
.
Darand
M.
2015
Drought monitoring in Iran by Palmer severity drought index (PDSI) and correlation with oceanic atmospheric teleconnection patterns
.
Geographical Researches Quarterly Journal
29
(
4
),
67
82
.
Dracup
J. A.
,
Lee
K. S.
&
Paulson
E. G.
Jr
1980
On the definition of droughts
.
Water Resources Research
16
(
2
),
297
302
.
Easterling
D. R.
,
Meehl
G. A.
,
Parmesan
C.
,
Changnon
S. A.
,
Karl
T. R.
&
Mearns
L. O.
2000
Climate extremes: observations, modeling, and impacts
.
Science
289
(
5487
),
2068
2074
.
Eris
E.
,
Cavus
Y.
,
Aksoy
H.
,
Burgan
H. I.
,
Aksu
H.
&
Boyacioglu
H.
2020
Spatiotemporal analysis of meteorological drought over Kucuk Menderes River Basin in the Aegean region of Turkey
.
Theoretical and Applied Climatology
142
(
3
),
1515
1530
.
Ertugrul
M.
,
Varol
T.
,
Kaygin
A. T.
&
Ozel
H. B.
2017
The relationship between climate change and forest disturbance in Turkey
.
Fresenius Environmental Bulletin
26
(
6
),
4064
4074
.
Eslamian
S.
2014
Handbook of Engineering Hydrology: Modeling, Climate Change, and Variability
.
CRC Press
,
Boca Raton, FL, USA
.
Eslamian
S.
,
Ostad-Ali-Askari
K.
,
Singh
V. P.
,
Dalezios
N. R.
,
Ghane
M.
,
Yihdego
Y.
&
Matouq
M.
2017
A review of drought indices
.
International Journal of Constructive Research in Civil Engineering (IJCRC)
3
(
4
),
48
66
.
Faiz
M. A.
,
Liu
D.
,
Fu
Q.
,
Sun
Q.
,
Li
M.
,
Baig
F.
,
Li
T.
&
Cui
S.
2018a
How accurate are the performances of gridded precipitation data products over northeast China?
Atmospheric Research
211
,
12
20
.
Faiz
M. A.
,
Liu
D.
,
Fu
Q.
,
Uzair
M.
,
Khan
M. I.
,
Baig
F.
,
Li
T.
&
Cui
S.
2018b
Stream flow variability and drought severity in the Songhua River Basin, northeast China
.
Stochastic Environmental Research and Risk Assessment
32
(
5
),
1225
1242
.
Faiz
M. A.
,
Liu
D.
,
Fu
Q.
,
Wrzesiński
D.
,
Baig
F.
,
Nabi
G.
,
Khan
M. I.
,
Li
T.
&
Cui
S.
2018c
Extreme precipitation and drought monitoring in northeastern China using general circulation models and pan evaporation-based drought indices
.
Climate Research
74
(
3
),
231
250
.
Faiz
M. A.
,
Liu
D.
,
Fu
Q.
,
Naz
F.
,
Hristova
N.
,
Li
T.
,
Niaz
M. A.
&
Khan
Y. N.
2020
Assessment of dryness conditions according to transitional ecosystem patterns in an extremely cold region of China
.
Journal of Cleaner Production
255
,
120348
.
Flint
L. E.
,
Flint
A. L.
,
Mendoza
J.
,
Kalansky
J.
&
Ralph
F. M.
2018
Characterizing drought in California: new drought indices and scenario-testing in support of resource management
.
Ecological Processes
7
,
1
.
Gibbs
W. J.
&
Maher
J. V.
1967
Rainfall Deciles as Drought Indicators
.
Bulletin No. 48, Bureau of Meteorology
,
Melbourne, Australia
.
González
J.
&
Valdés
J. B.
2006
New drought frequency index: definition and comparative performance analysis
.
Water Resources Research
42
,
W11421
.
Guo
Y.
,
Huang
S.
,
Huang
Q.
,
Wang
H.
,
Fang
W.
,
Yang
Y.
&
Wang
L.
2019
Assessing socioeconomic drought based on an improved multivariate standardized reliability and resilience index
.
Journal of Hydrology
568
,
904
918
.
Guttman
N. B.
1998
Comparing the Palmer drought index and the standardized precipitation index
.
JAWRA Journal of the American Water Resources Association
34
(
1
),
113
121
.
Guttman
N. B.
1999
Accepting the standardized precipitation index: a calculation algorithm
.
JAWRA Journal of the American Water Resources Association
35
(
2
),
311
322
.
Hänsel
S.
,
Ustrnul
Z.
,
Łupikasza
E.
&
Skalak
P.
2019
Assessing seasonal drought variations and trends over Central Europe
.
Advances in Water Resources
127
,
53
75
.
Hao
Z.
&
AghaKouchak
A.
2013
Multivariate standardized drought index: a parametric multi-index model
.
Advances in Water Resources
57
,
12
18
.
Hao
Z.
&
Singh
V. P.
2015
Drought characterization from a multivariate perspective: a review
.
Journal of Hydrology
527
,
668
678
.
Hayes
M.
,
Svoboda
M.
,
Wall
N.
&
Widhalm
M.
2011
The Lincoln Declaration on Drought Indices: universal meteorological drought index recommended
.
Bulletin of the American Meteorological Society
92
(
4
),
485
488
.
Heim
R. R.
Jr
2002
A review of twentieth-century drought indices used in the United States
.
Bulletin of the American Meteorological Society
83
(
8
),
1149
1166
.
IPCC
2014
Climate Change 2014 – Mitigation of Climate Change: Summary for Policymakers
.
Intergovernmental Panel on Climate Change, Cambridge University Press
,
Cambridge, UK
.
Keetch
J. J.
&
Byram
G. M.
1968
A Drought Index for Forest Fire Control
.
Research Paper SE-38
,
US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station
,
Asheville, NC, USA
.
Khan
M. I.
,
Liu
D.
,
Fu
Q.
,
Saddique
Q.
,
Faiz
M. A.
,
Li
T.
,
Qamar
M. U.
,
Cui
S.
&
Cheng
C.
2017
Projected changes of future extreme drought events under numerous drought indices in the Heilongjiang province of China
.
Water Resources Management
31
(
12
),
3921
3937
.
Li
S. Y.
,
Liu
R. H.
,
Shi
L. K.
&
Ma
Z. G.
2009
Analysis on drought characteristic of Henan in recent 40 years based on meteorological drought composite index
.
Journal of Arid Meteorology
27
(
2
),
97
102
.
Li
W.
,
Hou
M.
,
Chen
H.
&
Chen
X.
2012
Study on drought trend in south China based on standardized precipitation evapotranspiration index
.
Journal of Natural Disasters
21
(
4
),
84
90
.
Li
B.
,
Liang
Z.
,
Zhang
J.
&
Wang
G.
2017
A revised drought index based on precipitation and pan evaporation
.
International Journal of Climatology
37
(
2
),
793
801
.
Liu
W. T.
&
Kogan
F. N.
1996
Monitoring regional drought using the vegetation condition index
.
International Journal of Remote Sensing
17
(
14
),
2761
2782
.
Liu
H.
,
Wang
Y.
&
Guan
X.
2012
Research on suitable ecological water level in Poyang Lake wetland: a case study in Xingzi Hydrological Station
.
Journal of Nanchang Institute of Technology
31
(
3
),
46
50
.
Liu
X.
,
Zhu
X.
,
Pan
Y.
,
Bai
J.
&
Li
S.
2018
Performance of different drought indices for agriculture drought in the North China Plain
.
Journal of Arid Land
10
(
4
),
507
516
.
Liu
Q.
,
Zhang
S.
,
Zhang
H.
,
Bai
Y.
&
Zhang
J.
2020
Monitoring drought using composite drought indices based on remote sensing
.
Science of The Total Environment
711
,
134585
.
Lloyd-Hughes
B.
&
Saunders
M. A.
2002
A drought climatology for Europe
.
International Journal of Climatology: A Journal of the Royal Meteorological Society
22
(
13
),
1571
1592
.
Lüttger
A. B.
&
Feike
T.
2018
Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany
.
Theoretical and Applied Climatology
132
(
1–2
),
15
29
.
Manzano
A.
,
Clemente
M. A.
,
Morata
A.
,
Luna
M. Y.
,
Beguería
S.
,
Vicente-Serrano
S. M.
&
Martín
M. L.
2019
Analysis of the atmospheric circulation pattern effects over SPEI drought index in Spain
.
Atmospheric Research
230
,
104630
.
Matera
A.
,
Fontana
G.
,
Marletto
V.
,
Zinoni
F.
,
Botarelli
L.
&
Tomei
F.
2007
Use of a new agricultural drought index within a regional drought observatory
. In:
Methods and Tools for Drought Analysis and Management
(G. Rossi, T. Vega & B. Bonaccorso, eds),
Springer
,
Dordrecht, The Netherlands
, pp.
103
124
.
McKee
T. B.
,
Doesken
N. J.
&
Kleist
J.
1993
The relationship of drought frequency and duration to time scales
. In:
Proceedings of the 8th Conference on Applied Climatology
, 17–22 January, Anaheim, CA, USA, pp.
179
183
.
McKee
T. B.
,
Doesken
N. J.
&
Kleist
J.
1995
Drought monitoring with multiple time scales
. In:
Proceedings of the 9th Conference on Applied Climatology
, 15–20 January, Dallas, TX, USA, pp. 233–236.
Mendicino
G.
,
Senatore
A.
&
Versace
P.
2008
A groundwater resource index (GRI) for drought monitoring and forecasting in a Mediterranean climate
.
Journal of Hydrology
357
(
3–4
),
282
302
.
Meyer
S. J.
&
Hubbard
K. G.
1995
Extending the crop-specific drought index to soybean
. In:
Proceedings of the 9th Conference on Applied Climatology, 15–20 January, Dallas, TX, USA
, pp.
258
259
.
Meyer
S. J.
,
Hubbard
K. G.
&
Wilhite
D. A.
1993
A crop-specific drought index for corn: I. Model development and validation
.
Agronomy Journal
85
(
2
),
388
395
.
Mishra
A. K.
&
Singh
V. P.
2010
A review of drought concepts
.
Journal of Hydrology
391
(
1–2
),
202
216
.
Mishra
A. K.
&
Singh
V. P.
2011
Drought modeling – a review
.
Journal of Hydrology
403
(
1–2
),
157
175
.
Mitra
S.
,
Srivastava
P.
&
Lamba
J.
2018
Probabilistic assessment of projected climatological drought characteristics over the southeast USA
.
Climatic Change
147
(
3–4
),
601
615
.
Mukherjee
S.
,
Mishra
A.
&
Trenberth
K. E.
2018
Climate change and drought: a perspective on drought indices
.
Current Climate Change Reports
4
(
2
),
145
163
.
Nasab
A. H.
,
Ansary
H.
&
Sanaei-Nejad
S. H.
2018
Analyzing drought history using fuzzy integrated drought index (FIDI): a case study in the Neyshabour basin, Iran
.
Arabian Journal of Geosciences
11
(
14
),
390
.
Nguyen
L. B.
,
Li
Q. F.
,
Ngoc
T. A.
&
Hiramatsu
K.
2015
Drought assessment in Cai river basin, Vietnam: a comparison with regard to SPI, SPEI, SSI, and SIDI
.
Journal of the Faculty of Agriculture, Kyushu University
60
,
417
425
.
Osuch
M.
,
Romanowicz
R. J.
,
Lawrence
D.
&
Wong
W. K.
2016
Trends in projections of standardized precipitation indices in a future climate in Poland
.
Hydrology and Earth System Sciences
20
(
5
),
1947
1969
.
Palmer
W. C.
1965
Meteorological Drought
.
Research Paper No. 45, US Department of Commerce, US Weather Bureau
,
Washington, DC, USA
.
Paulo
A. A.
,
Ferreira
E.
,
Coelho
C.
&
Pereira
L. S.
2005
Drought class transition analysis through Markov and Loglinear models, an approach to early warning
.
Agricultural Water Management
77
(
1–3
),
59
81
.
Potop
V.
,
Boroneanţ
C.
,
Možný
M.
,
Štěpánek
P.
&
Skalák
P.
2014
Observed spatiotemporal characteristics of drought on various time scales over the Czech Republic
.
Theoretical and Applied Climatology
115
(
3
),
563
581
.
Qian
W.
,
Shan
X.
&
Zhu
Y.
2011
Ranking regional drought events in China for 1960–2009
.
Advances in Atmospheric Sciences
28
(
2
),
310
321
.
Raziei
T.
,
Bordi
I.
&
Pereira
L. S.
2013
Regional drought modes in Iran using the SPI: the effect of time scale and spatial resolution
.
Water Resources Management
27
(
6
),
1661
1674
.
Salas
J. D.
1993
Analysis and modelling of hydrologic time series
. In:
Handbook of Hydrology
(D. R. Maidment, ed.), McGraw-Hill, New York, USA, Chapter 19.
Shafer
B. A.
&
Dezman
L. E.
1982
Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas
. In:
Proceedings of the 50th Annual Western Snow Conference
, Reno, NV, USA, pp. 164–175.
Shen
Z.
,
Zhang
Q.
,
Singh
V. P.
,
Sun
P.
,
Song
C.
&
Yu
H.
2019
Agricultural drought monitoring across Iinner Mongolia, China: model development, spatiotemporal patterns and impacts
.
Journal of Hydrology
571
,
793
804
.
Song
X.
,
Li
L.
,
Fu
G.
,
Li
J.
,
Zhang
A.
,
Liu
W.
&
Zhang
K.
2014
Spatial–temporal variations of spring drought based on spring-composite index values for the Songnen Plain, northeast China
.
Theoretical and Applied Climatology
116
(
3–4
),
371
384
.
Song
X.
,
Song
S.
,
Sun
W.
,
Mu
X.
,
Wang
S.
,
Li
J.
&
Li
Y.
2015
Recent changes in extreme precipitation and drought over the Songhua River Basin, China, during 1960–2013
.
Atmospheric Research
157
,
137
152
.
Steinemann
A.
,
Iacobellis
S. F.
&
Cayan
D. R.
2015
Developing and evaluating drought indicators for decision-making
.
Journal of Hydrometeorology
16
(
4
),
1793
1803
.
Svoboda
M.
&
Fuchs
B.
2016
Handbook of Drought Indicators and Indices
.
World Meteorological Organization and Global Water Partnership
,
Geneva, Switzerland
.
Svoboda
M.
,
LeComte
D.
,
Hayes
M.
,
Heim
R.
,
Gleason
K.
,
Angel
J.
,
Rippey
B.
,
Tinker
R.
,
Palecki
M.
,
Stooksbury
D.
,
Miskus
D.
&
Stephens
S.
2002
The drought monitor
.
Bulletin of the American Meteorological Society
83
(
8
),
1181
1190
.
Tadesse
T.
&
Wardlow
B.
2007
The Vegetation Outlook (VegOut): a new tool for providing outlooks of general vegetation conditions using data mining techniques
. In:
Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)
,
IEEE
,
Piscataway, NJ, USA
, pp.
667
672
.
Tajbakhsh
S.
,
Eisakhani
N.
&
Kazemi
A. F.
2015
Assessment of meteorological drought in Iran using standardized precipitation and evapotranspiration index (SPEI)
.
Journal of the Earth and Space Physics
41
(
2
),
313
321
.
Thenkabail
P. S.
,
Ward
A. D.
&
Lyon
J. G.
1994
Landsat-5 thematic mapper models of soybean and corn crop characteristics
.
Remote Sensing
15
(
1
),
49
61
.
Thornthwaite
C. W.
1948
An approach toward a rational classification of climate
.
Geographical Review
38
(
1
),
55
94
.
Thornthwaite
C. W.
&
Mather
J. R.
1955
The water balance
.
Publications in Climatology
8
,
1
104
.
Tian
L.
,
Yuan
S.
&
Quiring
S. M.
2018
Evaluation of six indices for monitoring agricultural drought in the south-central United States
.
Agricultural and Forest Meteorology
249
,
107
119
.
Trenberth
K. E.
,
Dai
A.
,
van der Schrier
G.
,
Jones
P. D.
,
Barichivich
J.
,
Briffa
K. R.
&
Sheffield
J.
2014
Global warming and changes in drought
.
Nature Climate Change
4
(
1
),
17
22
.
Tsakiris
G.
&
Vangelis
H.
2005
Establishing a drought index incorporating evapotranspiration
.
European Water
9
(
10
),
3
11
.
Van Rooy
M.
1965
A rainfall anomaly index independent of time and space
.
Notos
14
,
43
48
.
Vasiliades
L.
,
Loukas
A.
&
Liberis
N.
2011
A water balance derived drought index for Pinios river basin, Greece
.
Water Resources Management
25
(
4
),
1087
1101
.
Vicente-Serrano
S. M.
,
Beguería
S.
&
López-Moreno
J. I.
2010
A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index
.
Journal of Climate
23
(
7
),
1696
1718
.
Vicente Serrano
S. M.
,
Beguería
S.
&
López-Moreno
J. I.
2011
Comment on ‘Characteristics and trends in various forms of the Palmer Drought Severity Index (PDSI) during 1900–2008’ by Aiguo Dai
.
Journal of Geophysical Research: Atmospheres
116
, D19112.
Vu-Thanh
H.
,
Ngo-Duc
T.
&
Phan-Van
T.
2014
Evolution of meteorological drought characteristics in Vietnam during the 1961–2007 period
.
Theoretical and Applied Climatology
118
(
3
),
367
375
.
Wagner
W.
,
Scipal
K.
,
Pathe
C.
,
Gerten
D.
,
Lucht
W.
&
Rudolf
B.
2003
Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data
.
Journal of Geophysical Research: Atmospheres
108
(
D19
),
4611
.
Wang
H.
,
Rogers
J. C.
&
Munroe
D. K.
2015a
Commonly used drought indices as indicators of soil moisture in China
.
Journal of Hydrometeorology
16
(
3
),
1397
1408
.
Wang
W.
,
Zhu
Y.
,
Xu
R.
&
Liu
J.
2015b
Drought severity change in China during 1961–2012 indicated by SPI and SPEI
.
Natural Hazards
75
(
3
),
2437
2451
.
Wantzen
K. M.
,
Rothhaupt
K.-O.
,
Mörtl
M.
,
Cantonati
M.
,
Tóth
L. G.
&
Fischer
P.
2008
Ecological effects of water-level fluctuations in lakes: an urgent issue
. In:
Ecological Effects of Water-Level Fluctuations in Lakes
(K. M. Wantzen, K.-O. Rothhaupt, M. Mörtl, M. Cantonati, L. G. Tóth & P. Fischer, eds),
Springer, Dordrecht, The Netherlands
, pp.
1
4
.
Waseem
M.
,
Ajmal
M.
&
Kim
T.-W.
2015
Development of a new composite drought index for multivariate drought assessment
.
Journal of Hydrology
527
,
30
37
.
Wilhite
D. A.
2016
Managing drought risk in a changing climate
.
Climate Research
70
(
2–3
),
99
102
.
Wilhite
D. A.
&
Glantz
M. H.
1985
Understanding: The drought phenomenon: the role of definitions
.
Water International
10
(
3
),
111
120
.
Wilhite
D. A.
&
Svoboda
M. D.
2000
Drought early warning systems in the context of drought preparedness and mitigation
. In:
Early Warning Systems for Drought Preparedness and Drought Management
(D. A. Wilhite, M. V. K. Sivakumar & D. A. Wood, eds), World Meteorological Organization, Geneva, Switzerland, pp.
1
21
.
Willeke
M.
,
Hosking
J. R. M.
,
Wallis
J. R.
&
Guttman
N. B.
1994
The National Drought Atlas
. Institute for Water Resources Report 94-NDS-4,
Army Corps of Engineers, Fort Belvoir
, VA, USA.
Yaltı
S.
&
Aksu
H.
2019
Drought analysis of Iğdır Turkey
.
Turkish Journal of Agriculture – Food Science and Technology
7
(
12
),
2227
2232
.
Yevjevich
V.
1967
An Objective Approach to Definitions and Investigations of Continental Hydrologic Droughts
. Hydrology Papers No. 23, Colorado State University, Fort Collins, CO, USA.
Yihdego
Y.
,
Vaheddoost
B.
&
Al-Weshah
R. A.
2019
Drought indices and indicators revisited
.
Arabian Journal of Geosciences
12
(
3
),
69
.
Zargar
A.
,
Sadiq
R.
,
Naser
B.
&
Khan
F. I.
2011
A review of drought indices
.
Environmental Reviews
19
,
333
349
.
Zhai
J.
,
Su
B.
,
Krysanova
V.
,
Vetter
T.
,
Gao
C.
&
Jiang
T.
2010
Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China
.
Journal of Climate
23
(
3
),
649
663
.
Zhang
L.
,
Jiao
W.
,
Zhang
H.
,
Huang
C.
&
Tong
Q.
2017a
Studying drought phenomena in the continental United States in 2011 and 2012 using various drought indices
.
Remote Sensing of Environment
190
,
96
106
.
Zhang
X.
,
Chen
N.
,
Li
J.
,
Chen
Z.
&
Niyogi
D.
2017b
Multi-sensor integrated framework and index for agricultural drought monitoring
.
Remote Sensing of Environment
188
,
141
163
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).