Drought Monitoring and Forecasting across Turkey: A Contemporary Review

Abstract

One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

1. Introduction

Water demand is increasing worldwide, mainly owing to the growing population and industrial development [1]. Excessive population growth [2], accompanied by rapid industrialization and urbanization [3], has produced exceptionally high levels of water demand in developing countries. Climate change is another crucial issue that has affected water resources throughout the entire world. This is especially true for Mediterranean countries such as Turkey, as one of the world’s climate hotspots that will most likely become progressively drier and drastically warmer at higher levels of global warming [4]. Recent catastrophic heat waves, e.g., the blistering summer 2021 wave that resulted in wildfires, raged for nearly two months along the Akdeniz coast [5], decreasing river flows and reservoir levels [6], the emerging tragedy of degraded and drying lakes/wetlands, such as Lake Akgöl in eastern Turkey [7], Tuz Lake in central Anatolia plateau [8], and the Amik in southern Turkey [9], together with successive dry years, so that most of the country receives below average rainfall, all are the signals indicating that Turkey experiences intense drought [10]. Accordingly, many studies have investigated the spatial and temporal variation of drought hazards in Turkey in recent decades [10,11]. Overall, the studies confirm the fact that Turkey has been exposed to drought hazards rather frequently (every four or five years since the late 1980s). Intensive drought periods in 1971–1974, 1983–1984, 1989–1990, 1996–2001, 2007–2008, 2013–2014, and 2020–2021 have been reported [10,11,12], and projections have revealed increasing drought severity and frequency under the postulate climate change scenarios across the country [13,14,15]. In addition, an increasing number of studies have also attempted to develop statistical or dynamic models to anticipate droughts’ onset and severity, mostly in catchment scale. While some of these studies emphasize the interconnections between large-scale climate variables and severe drought events [16], the others highlight the capabilities of machine learning (ML) techniques to fit accurate nonlinear curves on the historical drought time series.

Considering the increasing attention of both scientific and political communities to drought hazards and their vital role in sustainable development, many studies have investigated drought events from different perspectives in recent years [17]. Thus, an endeavor is required to summarize/review these studies to facilitate access to the main outcomes of these studies. Therefore, in this study, we aimed to review and highlight the main findings of the recent scholarly articles focused on drought monitoring, assessment, and prediction in Turkey. Our ultimate goals were to (i) recognize the spatiotemporal patterns of drought events (of any type) in the country, (ii) reveal existing gaps and (iii) determine directions for future studies required for drought mitigation and management plans in the country. Although a few studies are available in the literature that attempted to reflect historical drought conditions on a countrywide scale (e.g., [12,13,18]), to the best of the authors’ knowledge, this is the first study that reviews both drought assessment and prediction studies collectively. It is hoped that the findings enhance the associated societies’ awareness of tackling the impact of droughts.

This article was organized as Section 2 presents an overview of different drought types, associated indices, and the framework that we used to accomplish this review paper. In Section 3 and Section 4, drought monitoring and forecasting studies are reviewed, respectively. Finally, the main conclusions are discussed and described in Section 5 and Section 6, respectively.

2. Materials and Methods

As illustrated in Figure 1, a systematic review of drought assessment/monitoring and prediction studies was conducted to (i) determine trends in different drought types, including (meteorological drought (hereafter MD), agricultural drought (hereafter AD), hydrological drought (hereafter HD), and socioeconomic drought (hereafter SD)) across Turkey, (ii) figure out the most popular drought indices and types that are explored in the country, and (iii) discover the main source of data (remote sensing (RS) or in situ measurements) used to derive drought indices. To this end, the relevant documents were chosen from Web of Science (WoS) and Scopus databases considering two search items: drought and Turkey. The adopted inclusion criteria were peer-reviewed articles, review articles, and a book chapter. The exclusion criteria were conference papers, notes, letters, opinion pieces, and non-peer-reviewed literature. The search engine was filtered by the publication period (2010 and 2022). As a result, a total of 71 documents were collected (Figure 2), and then an initial assessment was done to categorize the documents into articles, book chapters, and conference papers. Figure 2 shows a significant increasing trend in the number of studies relevant to droughts in Turkey. Among the collected documents, conference papers (two papers) and those that were irrelevant to the scope of the present review (22 papers) were neglected, and the rest (47 papers) were reviewed.

As illustrated in Figure 3, Turkey consists of 25 main drainage basins. In some of the collected articles, the whole of Turkey and even Northern Cyprus were considered as the study area. Most of the studies merely monitored drought in a drainage basin by calculating various drought indices and analyzing historical drought events to figure out droughts’ features such as duration, severity, and occurrence.

2.1. Overview of Drought Indices

Drought is the consequence of a significant decline in the hydrological variables such as precipitation, soil moisture, and streamflow that undesirably affects all living beings. It is typically divided into four groups: MD, AD, HD, and SE [19,20]. All types start with MD, which is the result of precipitation deficiency. Dry spells in which rainfall amount is below the long-term precipitation average in a region lead to MD. MD causes the least disaster compared to the other types. For example, in case of insufficient rainfall, natural water sources such as lakes, wetlands, and groundwater reservoirs can be employed for water supply. HD is created by a decrease in precipitation and surface flow that might yield a decline in groundwater level. Since HD is closely related to water demand, it is crucial for urban catchments, industrialized regions, and agricultural activities. The AD, which is related to soil water storage and existing moisture capacity, is essential for crop growth and food security. In case of insufficient soil moisture for average crop growth and yields, AD may lead to crop failure, reduced range of productivity, livestock, and famine. The SD arising from the consequences of other types of droughts has important effects on society, and the economy can be expressed as a situation in which the demand for water exceeds the supply [19].

There are various indices for drought monitoring and assessment [20] that can identify the characteristics of drought, such as magnitude, severity, and duration. They are obtained from hydro-meteorological indicators, such as precipitation, temperature, runoff, soil moisture, reservoir storage, and groundwater level. Some indices are more appropriate than others for certain circumstances, such as the location of the study area, drought type, and availability of data. With the development of meteorological satellites and RS technology on the one hand and the emergence of data-mining techniques on the other hand, a lot of current research has been conducted in the field of drought monitoring and forecasting (DMF) using these technologies. However, the relevant literature proved that the use of these tools is still not well known in some regions, and most of the studies related to DMF in those countries are based on observational ground data.

2.2. Meteorological Drought Indices

The Standardized Precipitation Index (SPI) suggested by McKee et al. [21] is a widely used MD index to assess and forecast precipitation deficit and surplus because of simplicity. The frequency of dry and wet spells at a specified time scale for any location can be determined by utilizing only long-term precipitation data (usually 30 years or more [22,23]. It is mainly advantageous that SPI can be calculated for diverse accumulation timescales (1, 3, 6, 9, 12, 24, and 36 months), so drought effect on the water resources depends on these timescales. For example, SPI at short timescales (1 to 3 months) may be associated with soil moisture, as SPI at relatively long timescales (>6 months) may be connected with groundwater, streamflow, and storage in reservoirs. SPI at a long timescale over 6 months can be a sign of hydrologic drought [24,25,26].

SPI calculation begins with the determination of the probability density function for long-term precipitation data by considering distribution fitting for each time scale. Although gamma distribution is generally used for SPI calculation, different distributions are used in the literature [27,28]. The most appropriate distribution can be selected considering the L-coefficients, skewness, and kurtosis [29]. The distribution function is calculated and normalized to obtain the standard normal random variable, in other words, the SPI value [19]. Although SPI values can be evaluated considering the different classifications, dry and wet periods are symmetric for all the classifications because of normalized SPI values [23]. Drought classes based on SPI include extremely wet (2.0 ≤ SPI), severely wet (2.0 < SPI < 1.5), moderately wet (1.49 < SPI < 1.0), near normal (−1.0 < SPI < 1.0), moderate drought (−1.49 < SPI < −1.0), severe drought (−2 < SPI < −1.5), extreme drought (SPI ≤ −2.0) [21].

The Standardized Precipitation Evapotranspiration Index (SPEI) is a rather newly developed MD index [30] based on both precipitation and evapotranspiration. Although SPEI calculation is as simple as SPI calculation, it is a multi-scalar index including climatic water balance, which is the difference between precipitation and evapotranspiration. Thus, it is more sensitive to changes in temperature [31]. Although evapotranspiration is usually calculated by the Thornthwaite method because of its simplicity and requirement for few data such as latitude and daily temperature, as was suggested in the original SPEI calculation, some research indicated that this method underestimated or overestimated the evapotranspiration. So, the Penman–Monteith or the Hargreaves equations can be used to obtain evapotranspiration. While daily maximum and minimum temperature data is needed for the Hargreaves equation, the Penman–Monteith requires relatively more long-term data such as temperature, wind speed, relative humidity, and solar radiation. A comparison of drought classes between SPI and SPEI was presented by Danandeh Mehr et al. [15].

The De Martonne Aridity Index (DAI) is calculated utilizing monthly temperature and precipitation data. Annual values are also used to classify regional climate and temporal changes while defining aridity as the ratio of precipitation (mm) to mean temperature (°C) [32]. The DAI is always positive, and the seven drought classes according to DAI are defined: arid (DAI < 10), semi-arid (10 ≤ DAI < 20), Mediterranean (20 ≤ DAI < 24), semi-humid (24 ≤ DAI < 28), humid (28 ≤ DAI < 35), very humid (35 ≤ DAI ≤ 55), and extremely humid (DAI > 55) [33]. The De Martonne–Gottman index (DMGI) is a modified DAI index that is obtained using the precipitation and temperature at the driest month as well as mean annual temperature and total annual precipitation. Climate characteristics based on DMGI are indicated: polar, desert, steppe semi-arid, between steppe and moist, steppe semi-humid, humid, very humid, wet for the DAI values of below 0, 0–5, 5–10, 10–20, 20–28, 28–35, 35–55, and higher than 55, respectively [34].

The Palmer drought index (PDSI), which was developed by Palmer [35] and Alley [36], is based on a soil–water balance equation. Thus rainfall, runoff, soil moisture, and potential evaporation are required. PDSI is very closely affected by calibration periods. The availability of calibration is limited outside the calibration area. This index is not spatially comparable. Due to the disadvantages of PDSI mentioned in the previous sentence, the Self-calibrated Palmer Drought Severity Index (sc-PDSI), which is spatially comparable, has been developed, but even so, the major disadvantages associated with fixed temporal scale and exposure from the cases up to the preceding four years have not been overcome. PDSI values may usually change between −6 and +6, and the drought classification is as follows: near normal (−0.5 < PDSI < 0), incipient drought (−1.0 < PDSI < −0.5), mild drought (−2.0 < PDSI < −1.0), moderate drought (−3.0 < PDSI < −2.0), severe drought (−4.0 < PDSI < −3.0), extreme drought (PDSI ≤ −4.0) [37]. If soil moisture and/or the lakes, rivers, and reservoirs are significantly different from normal levels after drought termination or the end of the wet spell, it can be more appropriate to use Palmer Hydrological Drought Index (PHDI) depending on precipitation and soil moisture storage instead of meteorological index, PDSI [38]. Since only the rain is accepted as precipitation, not snowfall, snow cover, and frozen ground for PDSI and PDHI, this situation may lead to the wrong timing of the indices.

The China Z index (CZI) was introduced by the National Climate Centre of China for drought monitoring. CZI only requires monthly precipitation data and is recommended over SPI because it is easier to calculate in case of a lack of precipitation data [39]. The Pearson Type III distribution is the best-suited distribution for precipitation. Hence CZI depends on the Wilson–Hilferty cube-root transformation from the chi-square variable to the Z-scale [40]. The Modified Chinese Z index (MCZI) uses the data’s median for precipitation rather than the mean difference from the CZI calculation [41].

The Rainfall Anomaly Index (RAI) is a calculated comparison between each precipitation data and the ten lowest or ten highest precipitation data considering the long-term mean of precipitation [42]. The RAI can be used to determine various accumulation times, such as 1, 3, 6, 9, and 12 months and be used under circumstances in which 30 years of precipitation data are not available, and precipitation is not normally distributed [43].

The Deciles Index (DI) only requires precipitation data. Precipitation data should be normalized if it does not fit the normal distribution. Precipitation data sorted in ascending order is separated into ten groups, normal distribution deciles distribution, and cumulative frequency distribution is obtained. The first decile consists of the precipitation data less than 10% of the total amount of precipitation. The next decile involves the data from 10% up to 20%, and so on for the other deciles. DI is calculated by dividing the cumulative probabilities into five classes which consist of 20% (two deciles) in ascending sort: much below the normal, below the normal, near the normal, above the normal, and much above the normal, respectively. Drought severity is calculated by comparing the precipitation of a specified month with long-term cumulative distribution precipitation [41,42,43,44].

The Reconnaissance drought index (RDI) is stated as the initial value (α), normalized RDI (RDI_n), and standardized RDI (RDI_st). The initial value is obtained as the ratio of total precipitation to total potential evapotranspiration, RDI_n is computed using the arithmetic average of the initial values, and RDI_st is generated by the same method as SPI, so thresholds are the same for both indices [45]. Since the formulation of RDI_st consists of the natural logarithm of the initial value, RDI_st cannot be calculated if the total precipitation is zero. For this situation, different procedures have been suggested [46]. Because RDI does not depend on the potential evapotranspiration (PET) calculation method, RDI can be reliably evaluated even if only precipitation and temperature data are available [47].

The Percent of Normal Index (PNI) is calculated by the percentage ratio of the actual precipitation average of the period [48]. This period can be a month, rainfall season, or 12 months (annually). It is necessary to have at least 30-year precipitation data [49]. Besides, there are drawbacks that precipitation does not fit the normal distribution, and PNI depends on the location and season, so it is effective for a single region or season [50].

The Z-Score Index (ZSI) is obtained by the ratio of subtraction of the average from the actual precipitation to the standard deviation. Higher values mean a higher severe drought [40]. Since it does not need to adjust any distribution, such as Pearson III or Gamma, for the ZSI calculation, it may not be representative of shorter timescales compared to SPI. In addition, missing values in precipitation data are not a problem in this method, such as CZI [39].

The Erinç drought index (EDI)is calculated by dividing total precipitation by the average maximum temperature. Then, it is classified according to the areal distribution of vegetation formations in Turkey, as follows: severe arid (<8), arid (8–15), semi-arid (15–23), sub-humid (23–40), humid (40–55), per humid (>55) [51]. In order not to predict a more humid climate than it is in continental, cold winter or very cold climatic regions such as the northeastern Anatolia (Erzurum-Kars) part of Turkey, Erinç has adapted to use the average maximum temperature values instead of the mean temperature in the calculation of the index. Erinç suggested that the index should not be used in months when the mean maximum temperature value falls below 0 °C when evapotranspiration cannot occur [52].

Table 1. Comparison of meteorological drought index.

Index	Input Parameters	Features
SPI	P *	Highlighted by the WMO as a starting point for MD monitoring
SPEI	P, PET	Similar to SPI but with a temperature component. Sensitive to PET calculation method
DAI	P, T	Can also be used in climate classifications
IDMG	P, T	Can also be used in climate classifications
PDSI and ScPDSI	P, T, AWC	Calculation is complex and needs serially complete data
CZI and MCZI	P	Intended to improve upon SPI data
DI	P	Easy to calculate
RDI	P, T	Similar to SPEI
PNI	P	Suitable for drought assessment in a single region or season.
ZSI	P	Easy to calculate
EDI	P, T	A simple drought indicator for duration separation purposes, Suitable for arid/humid areas such as Turkey

* P: Precipitation, T: Temperature.

summarizes the main features of each index and the parameters required for its calculation.

2.3. Agricultural Drought Indices

The Normalized Difference Vegetation Index (NDVI) was suggested by Rouse [53] in 1974 as a vegetation index based on data from RS. NDVI is used to quantify vegetation greenness and vegetation density and assess changes in plant health. Visible wavelengths between 390 nm and 700 nm, especially the red wavelengths between 620 nm and 700 nm, are absorbed by the pigments in plant leaves during photosynthesis, while the near-infrared wavelengths within the range from 760 nm to 900 nm are reflected by spongy mesophyll in the plant. Considering the normalization of this difference for different ranges using satellite images enable us to find the NDVI [54,55]. The pattern of the vegetation change can be determined by comparing the NDVI values obtained from satellite images at different times. High values of NDVI, ranging from −1 to 1, account for higher biomass, vegetation, and vegetation density. While low vegetation and bare soil correspond to a negative value or close to zero, water, clouds, and snow indicate low values of NDVI. The areas with a low NDVI value where agriculture is intensive indicate poor plant growth owing to several causes, for instance, redundant moisture, drought, pests, or disease [55].

The Vegetation Condition Index (VCI) is based on NDVI images considering vegetation cover. VCI is expressed by a percentage by dividing the subtraction of the minimum value from the value of VCI at a given pixel by the difference between the maximum and minimum of VCI and multiplying by 100. While VCI higher than 50% represents more plant growth, dry conditions, and low vegetation growth are associated with VCI values less than 50 and approaching zero [56].

2.4. Hydrological Drought Indices

The most commonly utilized HD indices are the Standard Streamflow Index (SSI), Streamflow Drought Index (SDI), and Standardized Runoff Index (SRI). The main difference between the SSI calculation and the SPI calculation is that flow is employed in place of precipitation. Drought classification based on the thresholds is the same as SPI [57]. Since the mean and standard deviation of the standardized variable SSI are 0 and 1, respectively, SSI can be evaluated temporally and spatially [58]. Using the same methodology as SPI calculation, the SDI [59] is computed for the hydrological year starting in October and ending in September. However, cumulative streamflow volume rather than rainfall data is utilized for SDI. Negative SDI values are evaluated as dry periods, the classification based on SDI as follows: mild drought (−1.00 ≤ SDI < 0.0), moderate drought (−1.5 ≤ SDI < −1.0), severe drought (−2.00 ≤ SDI < −1.5), and extreme drought (SDI < −2.0). The SRI [60] used runoff data for the calculation, the same as the SPI, and can be investigated by the HD temporally and spatially. The drought classification of the SRI values is the same as the classification of SPI [61].

2.5. Socioeconomic and Ecological Drought Indices

The Multivariate Standardized Reliability and Resilience Index of Socioeconomic Drought (MSRRI) proposed by Mehran et al. [62] is combined the inflow-demand reliability (IDR) index and the water storage resilience (WSR) index by a statistical approach. While IDR is based on the top-down methodology evaluated with available water resources corresponding to the water demand, WSR is related to bottom-up as sufficient reservoir storage for the upcoming water demand.

The socioeconomic drought index (SEDI) is determined according to water shortage level at reservoirs and drought duration [63]. It has four levels: Level 1 for slight, 2 for moderate, 3 for severe, and 4 for extreme. Since SEDI considers the inconsistent situation with the reality that all prior water deficits must be made up with surplus water later, SEDI overestimated the impact of the socioeconomic drought. Therefore, the water resources system resilience (WRSR) index was suggested that considers the percentage of recoverable part of antecedent water deficit from excess water in later periods [64]. As the WRSR value, which ranges from 0 to 1, increases, a more severe socioeconomic drought can be expected.

Standardized supply and demand water index (SSDWI) is another socioeconomic drought index [65] that is calculated by the difference function using monthly water supplies and demands within a basin, run theory, and copula functions for analyzing drought characteristics at different timescales.

3. Review of Drought Monitoring Studies

Table 2 lists the studies that were conducted to assess drought events and their trend in Turkey during the period of 2010–2022. The articles are summarized in this section to answer the following questions:

• Which watershed or drainage basin was selected for the study area?
• Which drought indices and types were selected to monitor drought events?
• What methodology and modeling techniques were followed to reach drought monitoring or assessment findings?
• What were the study’s findings, and how did the authors interpret these findings?
• What kind of data (RS or in situ measurements) was used to derive a drought index?

Table 2. List of the papers that assess drought events and trends across Turkey.

Authors	Drought Type	Utilized Indices	Study Area
Afshar et al. [14]	MD	SPI	Ankara Province
Danandeh Mehr et al. [15]	MD	SPI, SPEI	Ankara Province
Dabanlı et al. [18]	MD	SPI	Turkey
Dursun and Babalık [34]	MD	SPI, DMGI	Isparta
Dikici [55]	MD, AD, HD	NDVI, VCI, DI, SPI, SPEI, SRI	Seyhan basin
Bacanli et al. [66]	MD	PDSI, EDI, DMI	CAR
Yıldız [67]	MD	SPI	CAR
Karabulut [68]	MD	SPI	Antakya-Kahramanmaraş
Topçu and Seçkin [69]	MD	SPI	Seyhan basin
Tosunoğlu and Kisi [70]	HD	AMS, AMD	Çoruh basin
Gumus and Algin [71]	MD and HD	SPI, SDI	Seyhan and Ceyhan River basins
Bacanlı [72]	MD	SPI	Aegean region
Payab and Turker [73]	MD	SPI	Turkish Republic of Northern Cyprus
Kumanlıoğlu [74]	MD and HD	SPI, SPEI, SRI	Gediz River basin
Bacanlı and Akşan [75]	MD	SPEI	Mediterranean region
Payab and Türker [76]	MD	SPI, RDI, SZS, CZI, SDDI, CZI-SDDI	Northern Cyprus
Cavus and Aksoy [77]	MD	SPI	Seyhan river basin
Altın et al. [78]	HD	SDI	Eastern Mediterranean basin
Katipoğlu et al. [79]	MD	SPI, SPEI, ZSI, RAI, RDI	Euphrates basin
Danandeh Mehr and Vaheddoost [80]	MD	SPI, SPEI	Ankara Province
Eris et al. [81]	MD and HD	SPI, SPEI, DPAI	Küçük Menderes River basin
Gümüş et al. [82]	MD	SPI	Southeastern Anatolia Project region
Yüce and Eşit [83]	MD	SPI, SPEI, scPDSI, CZI, MCZI, RAI, RDI, DI, PNI, ZI	Ceyhan basin
Altın and Altın [84]	MD and HD	SPI, SSI	Seyhan and Ceyhan River basins
Yılmaz et al. [85]	MD and HD	SPI, SSI	Upper Çoruh basin
Rolbiecki et al. [86]	MD	SPI	Çukurova region (Adana, Mersin, and Osmaniye)
Khorrami and Gunduz [87]	MD, AD, HD	SPI, SPEI, WSDI, SRI, GDSI	Turkey
Alkan and Tombul [88]	MD	SPI, SPEI	Seyhan and Ceyhan Basin
Akşan and Bacanli [89]	MD	CZI, EDI, PNPI, RAI, SPI, WASP, Z-Score	Southeastern Anatolia Region
Avsaroglu and Gumus [90]	HD	SDI	Tigris Basin
Molavizadeh et al. [91]	MD and AD	TCI, VCI, VHI	Turkey
Ozkaya and Zerberg [92]	HD	SDI	Tigris Basin

Table 2. List of the papers that assess drought events and trends across Turkey.

Authors	Drought Type	Utilized Indices	Study Area
Afshar et al. [14]	MD	SPI	Ankara Province
Danandeh Mehr et al. [15]	MD	SPI, SPEI	Ankara Province
Dabanlı et al. [18]	MD	SPI	Turkey
Dursun and Babalık [34]	MD	SPI, DMGI	Isparta
Dikici [55]	MD, AD, HD	NDVI, VCI, DI, SPI, SPEI, SRI	Seyhan basin
Bacanli et al. [66]	MD	PDSI, EDI, DMI	CAR
Yıldız [67]	MD	SPI	CAR
Karabulut [68]	MD	SPI	Antakya-Kahramanmaraş
Topçu and Seçkin [69]	MD	SPI	Seyhan basin
Tosunoğlu and Kisi [70]	HD	AMS, AMD	Çoruh basin
Gumus and Algin [71]	MD and HD	SPI, SDI	Seyhan and Ceyhan River basins
Bacanlı [72]	MD	SPI	Aegean region
Payab and Turker [73]	MD	SPI	Turkish Republic of Northern Cyprus
Kumanlıoğlu [74]	MD and HD	SPI, SPEI, SRI	Gediz River basin
Bacanlı and Akşan [75]	MD	SPEI	Mediterranean region
Payab and Türker [76]	MD	SPI, RDI, SZS, CZI, SDDI, CZI-SDDI	Northern Cyprus
Cavus and Aksoy [77]	MD	SPI	Seyhan river basin
Altın et al. [78]	HD	SDI	Eastern Mediterranean basin
Katipoğlu et al. [79]	MD	SPI, SPEI, ZSI, RAI, RDI	Euphrates basin
Danandeh Mehr and Vaheddoost [80]	MD	SPI, SPEI	Ankara Province
Eris et al. [81]	MD and HD	SPI, SPEI, DPAI	Küçük Menderes River basin
Gümüş et al. [82]	MD	SPI	Southeastern Anatolia Project region
Yüce and Eşit [83]	MD	SPI, SPEI, scPDSI, CZI, MCZI, RAI, RDI, DI, PNI, ZI	Ceyhan basin
Altın and Altın [84]	MD and HD	SPI, SSI	Seyhan and Ceyhan River basins
Yılmaz et al. [85]	MD and HD	SPI, SSI	Upper Çoruh basin
Rolbiecki et al. [86]	MD	SPI	Çukurova region (Adana, Mersin, and Osmaniye)
Khorrami and Gunduz [87]	MD, AD, HD	SPI, SPEI, WSDI, SRI, GDSI	Turkey
Alkan and Tombul [88]	MD	SPI, SPEI	Seyhan and Ceyhan Basin
Akşan and Bacanli [89]	MD	CZI, EDI, PNPI, RAI, SPI, WASP, Z-Score	Southeastern Anatolia Region
Avsaroglu and Gumus [90]	HD	SDI	Tigris Basin
Molavizadeh et al. [91]	MD and AD	TCI, VCI, VHI	Turkey
Ozkaya and Zerberg [92]	HD	SDI	Tigris Basin

Dabanlı et al. [18] examined the spatial and temporal patterns of drought to monitor the variability of drought across Turkey. To this end, precipitation records were obtained from 250 meteorological stations for the period between 1931 and 2010 (80 years). The drought analysis was performed using SPI values at 1-, 3-, 6-, 9-, and 12-month timescales. The SPI time was divided into two groups based on principal component analysis PCA, and then Turkey was classified into four regions based on the classified SPI groups. The temporal variability of drought events and their cyclical patterns were examined in each region. The results showed that widespread drought events were experienced in 1970, 1990, 1973, and 1957 for four regions of Turkey based on SPI-3, whereas drought events were observed in 1990, 2000, 1957, and 1955 for four regions of Turkey based on SPI-12. Moreover, the periodic characteristic of drought patterns showed that the frequency of short-term drought ranges between 2 and 5 years across all regions. However, the frequency of long-lasting drought varies between 10 and 20 years.

Dursun and Babalık [34] investigated the historical drought events in Isparta, Antalya. The precipitation and temperature observations were gathered from Atabey, Eğirdir, Isparta, Senirkent, Uluborlu, and Yalvaç meteorological stations between 1990 and 2020. DMGI and SPI series were calculated to monitor drought events in the region. The findings demonstrated that near-normal droughts were identified at all the chosen locations. The longest drought duration was detected between September 2004-March 2010 (67 months) in Yalvaç station, considering SPI-12 values. The authors demonstrated that SPI and DMGI provide similar results for their study area.

Dikici [55] investigated the drought events using vegetation indices such as NDVI and VCI to monitor drought in the Seyhan basin. NDVI and VCI values were attained using RS data. In addition, three MD indices, including DI, SPI, and SPEI, and one HD index (i.e., SRI) were adopted for comparison with AD. The results of the vegetation indices showed that the Seyhan basin experienced drought events in 1973–1974, 1989, 2001, 2007–2008, 2014, and 2016. An increasing trend in drought events was detected in the region. It was observed that other indices, such as DI, SPI, SPEI, and SRI, had been determined to be more accurate and to have a significant correlation with each other based on historical data.

Bacanlı et al. [66] investigated MD events regarding PDSI, Erinc, and DMI indices methods for the Central Anatolia Region (CAR), which contains Kızılırmak, Sakarya, and Konya basins. Thirteen meteorological stations provided the long-term monthly precipitation and temperature data for the years 1965 to 2006. The relative frequency values of PDSI revealed that severe and extreme drought events mostly appeared in Nevşehir, Niğde, and Sivas cities. Moderate drought events were observed in the Upper Sakarya basin. Konya closed basin, including Konya, Aksaray, and Karaman cities, witnessed moderate drought events in almost 20% of the study period. In addition, moderate drought events were observed more than 30% of the time at Aksaray station. Overall, the authors concluded that PDSI values imply a more humid condition than the other indices. The results of the DMI assessment showed that the Upper Sakarya and Kızılırmak basins are in a semi-dry and the Konya basin is in a dry region. The relative frequency results revealed that Konya had the driest period at 70%, whereas Çankırı and Eskişehir had semi-dry periods at 74% and 70%, respectively, according to the compared findings. Based on the Erinc and DMI values, the authors stressed that the Central Anatoli region is still at risk of a persistent drought.

Yıldız [67] investigated spatiotemporal drought variation over the CAR for the period of 1953–2004. To this end, historical SPI time series from 27 meteorology stations were used. A drought intensity–areal extent frequent curve for the area was created using monthly SPI values. Severe drought events were seen in the area during the years 1956, 1961, 1964, 1973, 1977, 1984, 1989, 1993–1994, 199–2001, and 2004. It was found that historical droughts of 1956, 1964, 1984, 1993, 2001, and 2004 have a range of return periods ranging from 2 to 25 years. More than 40% of severe droughts were observed between 1956 and 2001 in the CAR. Moderate drought events occurred during all drought years for more than half of the region.

Karabulut [68] used historical (1975–2010) SPI-1 and SPI-12 series from four observatory stations (Kahramanmaraş, İslahiye, Antakya, and Samandağ) to investigate the MD events in Antakya-Kahramanmaraş region. The author reported that the region frequently experienced moderate and extreme drought events, particularly in the years 1977 and 1980, 1981 and 1985, and 1989 and 1995, which resulted in a decline in the water levels of dams located around the study area.

Topçu and Seçkin [69] assessed MD records in the Seyhan basin using 20-year SPI series obtained from 11 meteorology stations in the study area. The results indicated that the most severe drought events occurred in 1973, 1988, 2001, and 2008. However, extreme wet years were observed in 1979, 1981, and 1994. The most severe drought periods happened at Adana station (1997–2005), Karaisalı station (2003–2009), Karataş station (2003–2007), Sarız station (1999–2003), Tufanbeyli station (2003–2008), and Ulukışla station (2004–2007). Many of these stations are situated in the Seyhan basin’s southern region. The results showed that most of the least SPI values are observed in a 6-month time scale.

Gumus and Algin [71] studied both MD and HD events in Seyhan and Ceyhan River basins using SPI and SDI. The drought analysis was done by observing monthly streamflow and precipitation data gathered from 14 meteorological stations and 12 streamflow gauge stations from 1970 to 2005. The SDI and SPI values were computed for the 3-, 6-, and 12-month timescales to monitor long-term historical drought events. The temporal variation of SPI values shows the most extreme events occurred in 1972, 1988, and 2000. However, HD episodes occurred at all the stations in 1985, 1991, and 2001. Inverse distance weighting was then used to prepare the spatial distribution maps. The highest extreme HD event occurred in 2001. The study indicated that a one-year lag exists between HD and MD events.

Bacanlı and Akşan [75] investigated MD events in the Mediterranean region of Turkey. Monthly precipitation and temperature measurements were collected from eight meteorology stations located in Adana, Antalya, Burdur, Hatay, Isparta, Kahramanmaraş, Mersin, and Osmaniye. SPEI values were then computed for each location over timescales of 1, 3, 6, 9, and 12 months. The study indicated that every station in the area is between mild dry and near normal. It was observed that Mersin has the highest percentage value for dry conditions compared with other stations.

Kumanlıoğlu [74] examined MD and HD occurrences in the Gediz River basin during the 1970 to 2013 period. The monthly temperature and precipitation data from seven meteorological stations in the study area were used to compute SPI and SPEI series. The monthly streamflow data from four stream gauges in the Gediz River basin were also used to compute the SRI series and assess HD events. The authors explained that wet periods were more frequent than dry periods before 1984, while dry periods in the region became dominant after this year. It was observed that current and impending dry conditions are likely due to the MD and HD trends. Based on the results of 12-month drought indices, a one-month lag period between MD and HDs was identified.

Cavus and Aksoy [77] presented a framework for the spatial analysis of drought using frequency analysis in the Seyhan river basin. Monthly precipitation data were gathered from 19 meteorological stations throughout the study area to evaluate SPI values for a 12-month timescale. It was observed that the precipitation measurement periods of the mentioned meteorological stations varied between 1962 and 2016. Frequency analyses were performed to evaluate precipitation deficit and mapped 1-, 3-, 6-, and 12-month drought durations with different return periods at a 12-month timescale. The results showed that the Seyhan River basin experiences mild and severe drought events regarding precipitation deficit. The authors indicated that the basin’s southern region is susceptible to drought for all return periods, while the northern part can be less affected by the drought.

Katipoğlu et al. [79] assessed the benefits and drawbacks of MD indices considering drought monitoring using correlation and linear regression (LR) analysis. The Euphrates basin of Turkey was chosen as a study area. The required precipitation and temperature data were gathered from Erzincan meteorological station covering 52 years (1966–2017). Then, values for long- and short-time steps such as 1, 3, 6, 9, 12, and 24 months were determined for SPI, ZSI, RAI, SPEI, and RDI. Root Mean Square Error (RMSE), R, and R² indicators were used to evaluate and compare the chosen indices. The authors mainly concentrated on comparing MD indices, instead of tracking drought events in the basin. The results showed that precipitation-based SPI and ZSI have similar trends, and temperature-affected SPEI and RDI indices showed similar patterns. The authors concluded that RAI is more suitable than the others for determining extreme events such as dry or wet.

Eris et al. [81] suggested a dimensionless precipitation anomaly index (DPAI) and used = SPI (monthly scale) and SPEI (monthly scale) to study the spatiotemporal variability of drought over the Küçük Menderes River basin. The data on monthly precipitation and temperature were gathered from five meteorological stations with varying record lengths between 12 and 59 years. A monthly-scale drought index was constructed for timescales of 1, 3, 6, 9, 12, 24, and 48 months. The MD events were tracked using short-scale drought indices. However, HD events were detected using long-scale drought indices. The SPI and SPEI results showed that the main drought periods in the area occurred in 1972, 1992, and 1994.

Yüce and Eşit [83] utilized different drought indices to track drought in the Ceyhan basin. Eight meteorological stations with various record years provided the monthly precipitation and temperature data. Then, SPI, SPEI, scPDSI, CZI, MCZI, RAI, RDI, DI, PNI, and ZI were calculated for different timescales. The comparison of the selected indices showed that apart from scPDSI, all other indices gave similar results and showed substantial correlation for a 1-month scale. There was a significant association between the SPI, SPEI, and RDI values for higher timescales such as 3, 6, 9, and 12 months. For the yearly maximum, minimum, and average temperature, however, substantial increasing trends were found across all stations. The authors indicated that the Ceyhan basin would unavoidably experience drought.

Altın and Altın [84] collected data on streamflow and precipitation from seven stream gauging stations and five meteorological stations over the period from 1967 to 2017. The MD and HD occurrences were investigated by using SPI and SSI indices for the eastern Mediterranean basin. The findings indicated that extreme and severe drought events based on SPI-12 were experienced in 2013–2014, whereas drought events based on SSI-12 were experienced in 2014–2015. Therefore, it has been observed that there is a 1-year lag between hydrological and MD, confirming the results of previous studies. In addition, uninterrupted MD events were observed in 2002–2003 and 2008–2009, considering SPI-12 values. However, a strong positive correlation was detected between SPI and SSI for 9-, 12-, and 24-month timescales. However, a negative correlation was found between them for 3-month timescales.

On the other hand, in recent years, different approaches have been employed for drought monitoring. While most of these studies considered trend analysis using a variety of methods such as LR, Mann–Kendall test, Spearman’s rho test, and Şen’s method, some others consider climate change scenarios such as gas emission scenarios of RCP 4.5 and RCP 8.5 to project both near and far and future drought conditions. Focusing on trend analysis for drought monitoring, our review revealed that there are a few applications in the pertinent literature. For example, Tosunoğlu and Kisi [70] applied several methods such as Mann–Kendall, modified Mann–Kendall, and innovative trend analysis to investigate the trends of HD variables in the Çoruh basin. To this end, daily streamflow records were collected from nine-gauge stations across the study area. Time series of annual maximum duration in terms of days and annual maximum severity in terms of m³/s days for each station were calculated using obtained daily streamflow data. The findings indicated that the Mann–Kendall test detected no significant trend across all stations, whereas the modified Mann–Kendall detected decreasing trend for the severity series at two stations. The modified Mann–Kendall test showed no significant trend for the seven stations, while Şen’s innovative trend analysis showed positive and negative trends at these stations. When the results of the study were assessed considering drought in the Çoruh basin, the authors stated that some HD events or water stress could be developed in the future.

Güner Bacanlı [72] analyzed the precipitation and MD trends in the Aegean region using LR, Mann–Kendall test, Şen method, and Spearman’s rho method. The monthly precipitation observations were collected from eight meteorological stations which are in different watershed areas in the Aegean region, such as Büyük Menderes, Küçük Menderes, Gediz, and West Mediterranean. Then, SPI values for 1-, 3-, 6-, 9-, and 12-month timescales were calculated to perform trend analysis. Decreasing trends were observed for monthly precipitation in December, January, February, and March in all stations. The authors indicated that drought is more frequent but shorter in a short time, while the duration of drought increases and the frequency decreases for long periods.

Payab and Türker [73] analyzed spatiotemporal features of drought events in the Turkish Republic of Northern Cyprus. To this end, SPI was estimated at several timescales, including 3, 6, and 12 months using the monthly precipitation data gathered from nine meteorological stations for the period 1977–2013. The authors showed that the northern part of Cyprus experienced three significant long-term drought events based on SPI-12 values. These drought events occurred between 1981–1985, 1995–1999, and 2005–2009. On the other hand, the authors analyzed the temperature and precipitation trends using the Mann–Kendall trend test for the region. The results showed that no significant precipitation trend was detected. However, an increase in the temperature trend was detected in most of the areas. The authors also compared many drought indices for monitoring the impact of drought and evaluated their drought monitoring performance in the northern part of Cyprus [76]. To this end, monthly precipitation and temperature data gathered from nine meteorological stations, together with crop data and groundwater level, were used to calculate SPI, RDI, statistical Z-score (SZS), CZI, Supply and Demand Drought Index (SDDI), and combined CZI and SDDI (CZI-SDDI) values. The indices were analyzed relative to each other at various timescales, including 3, 6, and 12 months. The results showed that all indices are strongly correlated with each other, with a minimum correlation of 0.982 and a maximum correlation of 0.998. The Mann–Kendall test was established for detecting drought severity trends. The results showed insignificant trends at all the stations. The authors concluded that none of the calculated indices could be accepted as the most appropriate index for monitoring drought in the case of northern Cyprus.

Altın et al. [78] studied to monitor HD events in Seyhan and Ceyhan River basins. To this end, SDI values were calculated for 3-, 6-, 9-, and 12-month timescales (complete hydrological year) using the historical river flow data that was measured from eight hydrometric stations across the study area. The extent, trend, and evolution of the drought were identified using the Mann–Kendall test and statistical analysis with a Gaussian filter. Mann–Kendall test results showed that between the end of the 1990s and the beginning of the 2000s, drought events began at approximately all stations. The authors indicated that the most extreme HD was experienced during 2007–2008 and 2014–2015 at the Tacin station.

The duration, severity, and trend of an MD over Ankara, Turkey’s capital city, were examined by Danandeh Mehr and Vaheddoost [80]. SPI and SPEI values for 3, 6, and 12-month timescales were computed using monthly temperature and precipitation data. The required data were gathered from six meteorological stations distributed across the study area. The findings indicated that between 1971 and 2016, there were five extreme droughts in the study area. On the other hand, the well-documented Spearmen rank-order correlation coefficient, Şen’s innovative trend analysis, and innovative trend analysis were used to identify probable drought patterns at each station. A slightly declining trend was seen in the area based on SPEI values. However, the authors indicated that SPI results do not fit the same pattern as SPEI.

The Southeastern Anatolia Project region underwent a spatiotemporal drought trend study by Gümüş et al. [82]. The monthly precipitation data were gathered from 15 meteorological stations. SPI values were calculated for 3-, 6-, and 12-month timescales for drought monitoring in the region. Considering SPI-12 values, the mild drought season occurrence in Diyarbakır, Kilis, Birecik, Akçakale, Şanlıurfa, and Siverek stations was found to be higher than other stations. On the other hand, Mann–Kendall and Mann–Kendall rank correlation tests were applied to detect monotonic drought trends. To this end, serial correlation from time series is eliminated using the pre-whitened approach. In addition, Sen’s slope and Inverse Distance Weighting methods were performed for the spatial analysis of drought. According to the findings, almost all the timescales in the region showed decreasing trends.

Recent studies have shown that various climate change scenarios were considered to reveal the future projections of drought events. For example, Danandeh Mehr et al. [15] investigated the historical (1971–2000) and near-future (2016–2040) characteristics of drought events in Turkey’s capital city of Ankara. Six meteorological stations located across the study area provided long-term data on precipitation and temperature. Then, SPI and SPEI values for 3-, 6-, and 12-month timescales were computed to monitor MD events. Six severe and two extreme drought events were observed between 1971 and 2000, according to SPEI-6 patterns. The results showed that the summer and autumn seasons are the main contributors to extreme drought events. Moreover, it was observed that severe and extreme drought events occurred mainly between 1989 and 1994. The results of the near-future projections under RCP4.5 and RCP8.5 scenarios revealed that no potential extreme drought events are expected. According to the authors’ analysis, dry spells will predominate in the second half of the near-future era under the RCP4.5 scenario, whereas under the RCP8.5 scenario, dry spells will be anticipated throughout the whole near-future period.

The impact of climate change on MD events over Turkey’s capital city, Ankara Province, was also evaluated by Afshar et al. [14]. Monthly precipitation measurements were gathered from five meteorological stations for the period between 1984 and 2018. To calculate SPI values for the time periods 1986–2018 and 2018–2050, the outputs of several climate models (RCP 4.5 and RCP 8.5) that have been downscaled and adjusted were employed. The results of the climate projections indicated a potential rise in the length of mild droughts. In addition, the authors indicated that extreme droughts in the region could be experienced with longer duration and higher severity.

Yılmaz et al. [85] evaluated the effects of climate change using downscale outputs of RCP4.5 and RCP 8.5 emission scenarios into the Soil and Water Assessment Tool model in the Upper Çoruh basin. For this purpose, climatic variables, catchment variables, morphological variables, and streamflow measurements were used to develop the hydrologic model. The huge set of different variables such as temperature, precipitation, solar radiation, relative humidity, wind speed, and river flow observations was collected from several stations distributed in the study area for a different period varying between 1971 and 2010. SPI and SSI values were calculated to perform quantitative hydro-MD analysis in the region. The effects of climate change on MD and HD were projected from 2030 to 2059 and 2070–2099. The authors indicated that drought durations’ frequency is expected to increase under two emission scenarios for the period of 2030–2059. It was observed that high-severity drought will be more likely under two emission scenarios during the period of 2030–2059 than 2070–2099.

4. Review of Drought Forecasting Studies

In this section, drought forecasting studies across Turkey were reviewed. The list of the selected papers was tabulated in

Table 3. List of papers that forecast drought across Turkey.

Authors	Drought Type	Method	Study Area
Başakın et al. [93]	MD	SVM, KNN	Kayseri
Tufaner and Özbeyaz [94]	MD	LR, ANN, SVM, DT	Adıyaman
Başakın et al. [95]	MD	ANFIS, EMD-ANFIS	Adana
Özger et al. [96]	MD	ANFIS, SVM, DT	Antalya and Adana
Danandeh Mehr et al. [97]	MD	FRF, FDT	Antalya
Mehdizadeh et al. [98]	MD	AR, BL, W-GEP	Ankara
Danandeh Mehr and Attar [99]	MD	GBT	Antalya and Ankara
Danandeh Mehr [100]	MD	DT, GP, GBT	Ankara and Antalya
Danandeh Mehr et al. [101]	MD	DT, GP, AR, WPGP	Ankara
Gholizadeh et al. [102]	MD	BAT-ELM	Ankara
Danandeh Mehr et al. [103]	MD	RF, ELM, Bat-ELM, GARF	Ankara
Citakoglu and Coşkun [104]	MD	ANN, ANFIS, GPR, SVR, KNN	Sakarya
Katipoğlu [105]	HD	SVR, GPR, RT, ET	Yesilirmak
Durdu [106]	MD	ARIMA, SARIMA	Büyük Menderes river basin

with their basic features.

Drought forecasting is a crucial part of managing and planning water resources. Although it is extremely difficult to predict when the drought will begin and end, numerous droughts forecasting models have been created to increase the ability to predict droughts. [107,108]. The LR analysis and artificial intelligence-based models (e.g., Artificial Neural Networks (ANN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Interface System (ANFIS), Decision Tree (DT), Random Forest (RF), Genetic Programming (GP)) are of the most widely used ML methods for drought forecasting. Hybrid models, which are the combinations of more than one ML technique, are another useful drought forecasting methodology. Several drought forecasting models across Turkey have been published by researchers in the relevant literature. For instance, Başakın et al. [93] developed SVM and K-nearest neighbor (KNN) models to forecast one, three, and six-month ahead PDSI values which were acquired from https://crudata.uea.ac.uk/cru/data/drought/ (accessed on 25 September 2018). In addition, the wavelet transform (WT) technique was integrated with each modeling approach to improve forecasting accuracy. To this end, 116 years of PDSI values were collected for Kayseri Province. RMSE and Nash–Sutcliffe Efficiency (NSE) were used to evaluate the generated model’s performance. The results showed that SVM and KNN models could be used for drought forecasting. However, it was observed that wavelet transform integrated with these models could improve the forecasting performance substantially.

Tufaner and Özbeyaz [94] collected monthly averages of temperature, pressure, relative humidity, wind speed, runoff, and total precipitation data for Adıyaman Province. The study made use of the Adıyaman Province’s meteorological data covering the period from 1980 to 2011. Then, PDSI values were calculated and used for drought forecasting. LR, DT, SVM, and ANN models were developed to predict PDSI values. Several performance evaluation criteria were adopted to compare the forecast performance of developed models. For PDSI forecasting, the study showed that the ANN model performs better than the others.

In another study, Başakın et al. [95] developed ANFIS and hybrid empirical mode decomposition-ANFIS (EMD-ANFIS) models to forecast scPDSI values with 1-, 3-, and 6-month lead times. The monthly scPDSI for Adana Province for the period of 1900–2016 was retrieved from the CRU high-resolution surface climate data set (https://crudata.uea.ac.uk/cru/data/drought/ (accessed on 10 January 2019)). The mean square error and NSE indicators were used for the performance evaluation of the ML model. The findings indicated that the EMD-ANFIS outperformed the standalone ANFIS. Özger et al. [96] compared the performance of several drought forecasting models, including three different standalone models and six hybrid models with two different decomposition techniques, namely EMD and WT. The scPDSI values retrieved from the CRU were used for 1, 3, and 6-month advance MD forecasting in Antalya and Adana provinces. The proposed models were compared using mean square error, NSE, and correlation coefficient (R²) indicators. The authors demonstrated that standalone models such as SVM, ANFIS, and M5-tree could not produce accurate results for mid-term (3-month) scPDSI forecast. However, integration of EMD and WT techniques with standalone models produced more accurate results for 1-, 3-, and 6-month predictions. In addition, the authors indicated that WD is superior to EMD.

Danandeh Mehr et al. [97] presented a hybrid fuzzy random forest (FRF) model to forecast one-month ahead SPEI classifications in the central Antalya region. Monthly SPEI data were gathered from the global SPEI database for the period 1961–2015 from four grid points. Then, a fuzzy inference system was applied to SPEI values to derive crisp and fuzzified values for an ungauged catchment. The SPEI values were converted into five classes: extremely dry, dry, near normal, wet, and extremely wet. A random forest algorithm was used to forecast SPEI classes one month ahead. The new model’s performance was evaluated using total accuracy, misclassification, and Kappa indicators. In addition, the FRF model was compared with the fuzzy decision tree (FDT) model for cross-validation purposes. The results showed that the FRF model is more accurate in predicting SPEI classes in the central Antalya region than the FDT model. The authors highlighted that global SPEI data could be used as an alternative for drought monitoring forecasting in ungagged catchments.

Mehdizadeh et al. [98] developed several drought modeling approaches using classic time series models such as linear autoregressive and nonlinear bi-linear in Turkey. Moreover, hybrid models were created by a combination of WT and gene expression programming (GEP) using five different mother wavelets. Three different timescales of SPEI, such as SPEI-3, SPEI-6, and SPEI-12, were calculated using the data gathered from six meteorology stations in Ankara. The created model’s performance was evaluated in terms of mean absolute error, RMSE, and R indicators. The findings of the model comparison revealed that bi-linear models are more accurate than autoregressive. The authors indicated that all hybrid models are superior to standalone GEP.

Danandeh Mehr and Attar [99] presented a gradient boosting regression tree (GBT) model for 1- and 3-month ahead SPEI class prediction in Antalya and Ankara. To this end, SPEI data were gathered from the global SPEI database for four grid points in Antalya and four grid points in Ankara. The SPEI-6 series’ arithmetic means were used to determine MD variation in each city. SPEI-6 values were classified into five different categories as extremely dry, dry, near normal, wet, and extreme wet. The model performance was evaluated using Kappa, overall accuracy, misclassification rate, class recall, precision, and F1-score. The results showed that the GBT model performs accurately, with total accuracy ranging from 72% and 83% for each city. In addition, the authors compared the GBT performance with tree-based models such as DT and RF. It was found that GBT is superior to its counterparts in the forecasting of extreme dry events. In another study, Danandeh Mehr [100] compared the classification and prediction performance of DT, GP, and GBT models for one month ahead of SPI-6/SPEI-6 in Ankara and Antalya province. For this purpose, precipitation data were gathered from six meteorological stations, and SPI values were calculated for Ankara Province. On the other hand, grid-based SPEI-6 values were obtained for CAR. The drought events were classified into three classes: wet, normal, and dry. Each developed model’s performance was tested considering average accuracy, KA, and classification error. The findings demonstrated that the GBT model outperformed DT and GP models in Ankara, while the GP model provided better performance in Antalya.

Danandeh Mehr et al. [101] presented a new wavelet packet-genetic programming (WPGP) model for MD prediction. For this purpose, precipitation and temperature measurements were collected from two stations in Ankara. SPEI values were calculated at each station. The SPEI series was divided into stochastic and deterministic sub-signals using a wavelet transform. Then, GP was used to develop deterministic sub-signals considering its effective lags, whereas different theoretical distribution functions were applied to determine stochastic sub-signals. Different models, such as autoregressive, GP, and RF, were also developed for cross-validation of the new model. NSE and RMSE were used to assess the model’s performance. The authors indicated that the new WPGP model is superior to other classic models used in this study for SPEI prediction.

Gholizadeh et al. [102] compared the prediction performance of ANN, an extreme learning machine (ELM), and a new hybrid model referred to as Bat-ELM. The Bat-ELM was introduced as a hybrid model that integrates the Bat optimization algorithm with ELM techniques. The SPEI-3 and SPEI-6 data were obtained using in situ observations at two meteorology stations in Ankara. The forecasting outcomes demonstrated that Bat-ELM had greater accuracy than the two standalone models. In addition, the authors indicated that the proposed Bat-ELM model could improve the forecasting accuracy of SPEI by 20% and 15% compared with ANN and ELM, respectively.

More recently, Danandeh Mehr et al. [103] presented a new forecasting model referred to as GARF, which is the integration of genetic algorithm and RF. SPEI values with 3- and 6-month timescales, which were calculated for Nallihan and Beypazari stations, were used to examine the GARF model forecast capabilities. In addition, the authors compared the developed model with classic RF, standalone ELM, and a hybrid Bat-ELM. RMSE and NSE were used to assess model performance. The finding demonstrated that the newly introduced GARF model is better than its counterparts for the prediction of SPEI-3 and SPIE-6 and improves forecasting accuracy by up to 40%.

5. Discussion

Inasmuch as Turkey is in the Mediterranean macroclimate region in the sub-tropical zone, huge precipitation differences can be seen over the country each year. Accordingly, many recent studies have investigated historical drought issues across the country. This review article showed that local and widespread drought events with various severity were reported across the country. Thus, drought should be considered one of the major challenges in front of the sustainable development of the country.

Regarding the monitoring studies, this review indicated that most of the articles have focused on MD events that were computed using in situ measurements. Surprisingly, the number of studies focused on the Anatolia region, particularly Ankara province, is strikingly higher than other basins. Further studies on the western black sea region and Istanbul province are suggested. The implemented indices showed that most of the recent DMF studies have focused on MD, followed by HDs. Among various indices, SPI has received the greatest attention. This is consistent with the findings of the recent review paper that demonstrated SPI as the index receiving the most attention from drought modelers across the globe [107]. A few studies have explored AD, and no study was found about SD and ecological drought in the past two decades. Thus, future studies need to further assess SD and ED across the country. In addition, this review revealed that only a few studies have focused on drought features under climate change scenarios [14,15,85]. Considering the increasing worldwide number of climate change impact assessment studies, one crucial direction for future work is the evaluation of the impact of global warming and recent climate change scenarios on various drought types, particularly AD, in the country.

Even though a variety of RS-based hydroclimatic data sources are available, only a few studies implemented these databases for DMF. This review indicated that when the use of soil/surface characteristics is required, researchers referred to RS data. For example, to estimate scPDSI and NDVI, satellite data were used by researchers. Absolutely, additional DMF studies based on RS technology/data will enable the local researchers and decision-makers to thoroughly investigate drought conditions across the country. As suggested in the literature, RS-based data not only provides opportunities to detect drought conditions in ungagged catchments, but they can also be used to fill the gaps (missing data) or correct the incorrect records that commonly exist in situ measurements.

6. Conclusions

This article provided an overview of drought indices and systematically reviewed the papers (2010–2022) that have attempted to either assess/monitor drought occurrences or develop a drought prediction/forecasting model focused on Turkey. The achieved concluding remarks are listed below.

• Drought has occurred many times in the country, and their adverse consequences are likely to increase in the future. In general, the potential rise in the length of MD and HD was reported. More studies are required to consider climate change impacts on basins’ hydrology.
• The majority of current studies have focused on historical MD events analyzing the spatiotemporal variation of both short- and long-term SPI time series at the catchment scale. Overall, the recurrence interval of MD events was reported to be between two to four years. Some of the studies have indicated the existence of one year lag between MD and HD events. Further studies on AD, HD, and SC at the catchment scale are highly required.
• In the domains of DMF, most of the current works are still based on merely observatory gaging stations. Satellite-driven data and RS technology were rarely implemented so far. There is a need to improve drought literature using emerging technologies.
• ML techniques, particularly hybrid models, have increased the accuracy of drought forecasting tools significantly. The behavioral patterns monitored during past droughts and large-scale climatic patterns are the mostly implemented predictors. Among a variety of ML techniques, ANNs followed by ANFIS and SVM were applied more frequently for drought prediction in Turkey. However, there is still a gap between research and practice in the usage of ML-based drought prediction models. Further studies on developing a robust drought prediction tool are needed for both short- and long-term drought forecasting and mapping potential drought hazards across the country.