The southeast Mediterranean region of the Gaza Strip is showing traces of evolving drought patterns driven by the impacts of climate change. The analysis of rainfall data at eight meteorological stations for a period extending over 48 years from 1974 to 2021 shows obvious variations in the spatiotemporal distribution of the rainfall over the Gaza Strip. The total monthly rainfall trend analysis for the wet months indicates a decreasing trend during February, March, April, and November with a ratio ranging between −16 and −62% and an increasing trend of about 35 and 141% through October at each of the meteorological stations. However, January shows an increasing trend of about 1–27% at all meteorological stations except Nussirat where a decline of about −7% is recorded while December refers to an increasing trend of about 5–27% in the north area of Gaza and declining trend of −2 to −17% over the southern region of the Gaza Strip. The drought analysis using the SPI indicator refers to a significant development of drought during the years 1990, 1999, 2010, and 2014 in the Gaza Strip with a major incident of occurrence where specifically, the monthly drought in terms of SPI-1 is identified as moderate and mild by about 15–21 and 27–56%, respectively. The agricultural drought of SPI-3 is nominated by severe, moderate, and mild severity with an incident reaching 11%, 8–11%, and 21–52%, respectively. However, the hydrological droughts demonstrated by the 9-month SPI-9 and 12-month SPI-12 potentially occur in extreme, severe, moderate, and mild with a probability of incident defined by up to 4, 13, 15, and 28–40%, respectively, for SPI-9 and by up to 4, 6, 11 and 38% extreme, severe, moderate, and mild, respectively, in case of SPI-12.

  • Drought is a dominant climate changes consequence in the Mediterranean zone.

  • The agricultural droughts occur at an incident between 11 and 52% in the Gaza Mediterranean area.

  • The hydrological droughts are recorded at a probability between 4 and 40% in the Gaza Mediterranean area.

Graphical Abstract

Graphical Abstract
Graphical Abstract

The anthropogenic climate change impacts, identified by the anomalous disturbances in the spatiotemporal distribution of the rainfall patterns and the increase in the global temperature expected by 1.5–2 °C by the end of the century, are reported by frequent and extreme incidences of drought events where more than 65% of the total world population might endure increasingly periodic drought that is anticipated to double in one-third of the world zones (Cammarano et al. 2019; Hameed et al. 2020; Sardans et al. 2020; IPCC 2022). The climate system in the southeastern Mediterranean region tends to respond quickly to rising levels of global warming in the form of accelerating desertification, decreasing water availability, irreversibly reducing terrestrial biodiversity, and negatively impacting the capacity of the Mediterranean ecosystem to store carbon (Malhi et al. 2021; Vogel et al. 2021). Therefore, the development of drought in the southeast Mediterranean is notably recorded with diverse and inconsistent spatial distribution influenced by the extreme variability of rainfall, the unbalanced seasonal distribution, and the intricate dynamic atmospheric processes (Michaelides et al. 2018). The drought is handled by developing a comparative scale to observe the change in the meteorological time series, such as the Standardized Precipitation Indicator (SPI), which is the most widely used drought index based on changes in precipitation patterns over various periods (McKee et al. 1985, 1993, 1995). The innovative polygon trend analysis (IPTA), a nonparametric technique, is mostly used to analyze drought trends based on rainfall data (Mann 1945; Sen 1968;,Kendall 1975; Ali & Abubaker 2019; Sen et al. 2019; Al-Najjar et al. 2022). However, according to scenarios depicting greenhouse gas concentration and emission, pollutants, and changes in land use, the future patterns of drought are predicted by projecting the atmospheric variables of the future period using General Circulation Models (GCMs) outputs (Sharafati et al. 2020; Salman et al. 2021). Numerous types of research showed the effectiveness of precipitation indices and nonparametric methods for interpreting drought episodes (Ali & Abubaker 2019; Sen et al. 2019; Al-Najjar et al. 2020, 2022; Zhang et al. 2020). Floods, on the other hand, are considered the drought counterpart that results from surplus water conditions of climate change that are statistically analyzed in terms of frequency using the models of cluster analysis such as finite mixture models (Ahani et al. 2020a, 2020b, 2022), hybrid regionalization method (Ahani et al. 2019a, 2019b), feature weighting and selection method (Ahani et al. 2019a, 2019b), feature selection and clustering algorithms (Ahani et al. 2019a, 2019b). Regionally on a local scale, several studies indicate evident signs of drought in the southeastern Mediterranean countries demonstrated by the significant decline in the number of rainy events. The Middle East drought probability increases from less than 20% during the 1970s to more than 80% in the period 2010–2019 (Al-Najjar et al. 2020). Behzadi et al. (2022) used the GCMs projections and SPI to predict the upcoming drought in Iran from 2021 to 2050 where according to the predictions, almost 70% of the future will be dry, and throughout 2030–2040, drought will affect almost all of the country's territories, as well as severe long drought episodes, are addressed to last for 14 years. In Jordan, the annual rate of rainfall drop is evaluated by about 1.8 mm with a drought likelihood of occurring every two to three years (Mustafa & Rahman 2018; Aladaileh et al. 2019). The count of extreme dry days is anticipated to increase twice between 2040 and 2090 in Lebanon (Mahfouz et al. 2016). Syria lives considerable increases in the prevalence and magnitude of droughts, with two severe drought events occurring between the years 1999–2001 and 2007–2010, respectively, which significantly decreased the yields of wheat and barley crops (Mohammed et al. 2020). Drought is a recorded occurrence in Palestine that has an impact on the recharging of the coastal aquifer and the viability of life. The 2007–2008 drought, which resulted in total rainfall that was just 67% of the region's annual average, had a direct negative impact on agricultural productivity, costing 114 million USD, and harmed almost 200,000 different types of cattle (MoA 2008). The likelihood of a drought occurring in Palestine is roughly 60–80% every three months, and it occurs more than 80% of the time each year. The intensity of the drought is growing where the aridity index, which runs from 0.16 to 0.3, is predicted to decline as a result of climate change-related factors like reduced precipitation and rising temperatures. Fiscally, the agricultural industry employs 4.7% of the workforce, whereas fishing employs 10% of the workforce. A greater portion of the workforce is anticipated to lose their jobs due to the drought intensity, especially in the agricultural sector, where the unemployment rate is now over 41% (Al-Najjar et al. 2020). Therefore, focusing on the assessment of the drought induced by rainfall pattern variations in the region of the Gaza Strip to overarch the goal of characterizing the historical drought conditions across the coastal zone of the southeastern Mediterranean using a daily long-term rainfall dataset for the past 48 years (1974–2021) is an urgent issue to deal with the drought episodes alarming and to avoid them in advance the potential drought damages where the study provides the agricultural drought oscillation every 3 months as well as the hydrological drought events every 9–12 months so that to enable the agricultural as well as the coastal aquifer managers for preparing efficient and on time mitigation measures.

Study area and dataset

The scope of the study area is determined by the boundaries of the Gaza Strip located southeast of the Mediterranean region. The Gaza Strip, shown in Figure 1, covers an area of roughly 365 km2 residing about 2.2 million people (PCBS 2022). In this study, the daily rainfall datasets gathered throughout 48 years (1974–2021) have been obtained from eight meteorological stations run by the Palestinian ministry of agriculture.
Figure 1

Study area of the Gaza Strip.

Figure 1

Study area of the Gaza Strip.

Close modal

The daily rainfall recordings were statistically processed and assembled into sets at each of the nominated meteorological stations to represent the total monthly rainfall. As a result, the trend was examined using Mann–Kendell and Sen slope tests on a monthly and annual basis for the statistical analysis of the rainfall dataset. The Innovative Trend Analysis (ITA) method was used to refine the monthly trend analysis for greater accuracy. The Standardized Precipitation Index (SPI), which measures dryness on a scale of 1, 3, 6, 9, and 12 months, was used to estimate the drought evolving in terms of the agricultural and hydrological dimensions. Hence, the parametric strategy might not be able to identify the trend in the dataset; however, following the nonparametric approach can show a higher potential for the data trend. Accordingly, the SPI is shown to be a useful index for revealing the evolution of a drought event based on a precipitation time series. The index also gives classifications for the severity of the drought, which encourages decision-makers to plan for drought or flood mitigation measures.

Mann–Kendell and Sen slope tests

The parametric Mann–Kendell and Sen slope tests are tests based on null hypotheticals. According to the null hypothesis, which states that there is no trend, and the alternative hypothesis, which states that there is a significant rising or falling trend in the precipitation, the Mann–Kendell trend test is used to detect statistically significant decreasing and increasing trends in long-term temporal data. Sen's slope is frequently used to determine the sloping trend of the time series. Positive slopes reflect a rising trend, whereas growing slopes are indicated by positive slopes, and zero slope values indicate no trend at all (Mann 1945; Sen 1968; Kendall 1975).

Innovative trend analysis

The monthly time scale of the rainfall dataset was divided into two equal portions, and the analysis of the rainfall trends was conducted using the IPTA. The endpoint of the polygon created for each month is formed by plotting the first data set and the second data set on the coordinate x and y axes, respectively. Here, the 45° (1:1) line depicts the non-trend region and divides the area into an upper region with an increasing trend and a lower region with decreasing trend (Sen 2014, 2017). The 3D (dimensional) IPTA, which divides the rainfall time series into three dataset units, is suggested by this study. A deeper assessment of the changes in the rainfall pattern enabling a more precise estimation of the drought patterns from the past to the present over wider subperiods is introduced by the developed 3D-IPTA approach.

Standardized precipitation index

The standardized precipitation index (SPI), classified in Table 1, is the most extensively used and recognized drought indicator for describing climatic drought. The SPI is considerably defined for various timescales 1, 3, 6, 12, 24, and 48 months which is calculated by a probability density function, shown in Equation (1), of the gamma distribution (McKee et al. 1993, 1995).
(1)
where f (x) is the gamma distribution function, x is the amount of rainfall (x > 0), α is the shape parameter (α > 0), and β is the scale parameter (β > 0).
Table 1

Classification of drought based on SPI (Mckee et al. 1993)

SPI valueCategory of droughtProbability of occurrence (%)
0 to −0.99 Near normal or mild 34.1 
−1 to −1.49 Moderate 9.2 
−1.5 to −1.99 Severe 4.4 
−2 and less Extreme 2.3 
SPI valueCategory of droughtProbability of occurrence (%)
0 to −0.99 Near normal or mild 34.1 
−1 to −1.49 Moderate 9.2 
−1.5 to −1.99 Severe 4.4 
−2 and less Extreme 2.3 

In this study, the shifting SPI for the timescales of 3, 6, 9, and 12 months were performed for the daily rainfall dataset at each of the eight stations to detect the frequency pattern of the drought cycles from 1974 through 2021.

Analysis of rainfall

In the Gaza Strip of the southeast Mediterranean region, the months of January through April and September through December are regarded as the monsoon seasons. The analysis of the total monthly rainfall at eight meteorological stations, depicted in Figure 2 and Table 2, between the years 1974 and 2021 shows that the average total monthly rainfall in the north and south of the Gaza Strip, respectively, ranges from 31 to 17 mm, with a standard deviation ranges between 59 and 33 mm. The trend analysis for the total monthly total rainfall indicates no trend pattern based on the Mann–Kendell and Sen slope tests.
Table 2

Monthly and annual statistical analysis of rainfall 1974–2021

StationBeit HanonBeit LahiaShatiRemalNussiratDeir Al-BalahKhanyounisRafah
Latitude (°N) 31.54 31.56 31.54 31.52 31.43 31.41 31.34 31.27 
Longitude (°E) 34.54 34.47 43.45 34.44 34.39 34.34 34.31 34.26 
Monthly analysis 
Min rainfall (mm) 
Avg rainfall (mm) 31.21 31.73 28.33 28.06 25.88 23.94 20.91 17.42 
Max rainfall (mm) 445.40 461.50 409.7 429 327 354.6 278.5 275.5 
St. Deviation 57.77 58.77 51.43 51.57 46.90 44.07 39.76 33.46 
Mann–Kendell (p-value) 0.94 0.71 0.69 0.79 0.84 0.70 0.52 0.53 
Trend No trend No trend No trend No trend No trend No trend No trend No trend 
Sen's Slope 
Trend No trend No trend No trend No trend No trend No trend No trend No trend 
Annual analysis 
Min rainfall (mm) 17.90 18.00 25.70 29.50 33.70 10.80 25.00 11.00 
Avg rainfall (mm) 374.54 380.74 339.93 336.72 310.56 287.32 250.95 209.06 
Max rainfall (mm) 831.50 876.50 752.30 655.10 678.50 683.00 571.50 492.80 
St. Deviation 186.04 195.21 163.27 153.12 148.83 144.33 136.02 111.20 
Mann–Kendell (p-value) 0.92 0.78 0.79 0.95 0.45 0.58 0.61 0.55 
Trend No tend No tend No tend No tend No tend No tend No tend No tend 
Sen's Slope 0.27 −0.49 −0.41 0.16 −1.17 −0.89 −0.91 −0.91 
Trend Increasing trend Decreasing trend Decreasing trend Increasing trend Decreasing trend Decreasing trend Decreasing trend Decreasing trend 
StationBeit HanonBeit LahiaShatiRemalNussiratDeir Al-BalahKhanyounisRafah
Latitude (°N) 31.54 31.56 31.54 31.52 31.43 31.41 31.34 31.27 
Longitude (°E) 34.54 34.47 43.45 34.44 34.39 34.34 34.31 34.26 
Monthly analysis 
Min rainfall (mm) 
Avg rainfall (mm) 31.21 31.73 28.33 28.06 25.88 23.94 20.91 17.42 
Max rainfall (mm) 445.40 461.50 409.7 429 327 354.6 278.5 275.5 
St. Deviation 57.77 58.77 51.43 51.57 46.90 44.07 39.76 33.46 
Mann–Kendell (p-value) 0.94 0.71 0.69 0.79 0.84 0.70 0.52 0.53 
Trend No trend No trend No trend No trend No trend No trend No trend No trend 
Sen's Slope 
Trend No trend No trend No trend No trend No trend No trend No trend No trend 
Annual analysis 
Min rainfall (mm) 17.90 18.00 25.70 29.50 33.70 10.80 25.00 11.00 
Avg rainfall (mm) 374.54 380.74 339.93 336.72 310.56 287.32 250.95 209.06 
Max rainfall (mm) 831.50 876.50 752.30 655.10 678.50 683.00 571.50 492.80 
St. Deviation 186.04 195.21 163.27 153.12 148.83 144.33 136.02 111.20 
Mann–Kendell (p-value) 0.92 0.78 0.79 0.95 0.45 0.58 0.61 0.55 
Trend No tend No tend No tend No tend No tend No tend No tend No tend 
Sen's Slope 0.27 −0.49 −0.41 0.16 −1.17 −0.89 −0.91 −0.91 
Trend Increasing trend Decreasing trend Decreasing trend Increasing trend Decreasing trend Decreasing trend Decreasing trend Decreasing trend 
Figure 2

Mapping of the total monthly rainfall pattern for the period 1974–2021.

Figure 2

Mapping of the total monthly rainfall pattern for the period 1974–2021.

Close modal
On an annual basis, the total annual rainfall in the area ranges between 18 and 832 mm with a standard deviation of between 195 and 111 mm. The trend analysis using Mann–Kendell test reveals no trend patterns in all of the stations; however, standing on the Sen slope trend analysis, the stations of Beit Hanon and Remal show increasing trend patterns while the other station exhibit declining trends. The spatial distribution of the average total monthly rainfall, given in Figure 3, shows that during the wet months, the average total rainfall ranges between 25 and 125 mm. However, the dry months demonstrate low rainfall of an average of less than 25 mm.
Figure 3

Spatial distribution of the average total monthly rainfall.

Figure 3

Spatial distribution of the average total monthly rainfall.

Close modal
The findings of the comparative trend analysis using the IPTA approach for the period of 1998–2021 in comparison to the base timeframe of 1974–1997, given in Figure 4, show remarkable variations in total rainfall amount.
Figure 4

IPTA method for total monthly rainfall for meteorological stations of (a) Beit Hanon, (b) Beit Lahia, (c) Shati, (d) Remal, (e) Nussirat, (f) Deir Al-Balah, (g) Khanyounis, (h) Rafah.

Figure 4

IPTA method for total monthly rainfall for meteorological stations of (a) Beit Hanon, (b) Beit Lahia, (c) Shati, (d) Remal, (e) Nussirat, (f) Deir Al-Balah, (g) Khanyounis, (h) Rafah.

Close modal

Significantly, there is no change detected in the dry months extending from May to August which record a total monthly rainfall of zero at each of the eight meteorological stations. In comparing the prior period of 1998–2021 with the former period of 1974–1997, the wet months of January, October, and December reveal abundant total monthly rainfall at the meteorological stations of Beit Hanon, Beit Lahia, Shati, and Remal. The rainfall data at the metrological station of Nussirat show an incremental rainfall increase in the wet months of October and December while at the remaining stations of Deir Al-Balah, Khanyounis, and Rafah, the increasing trend in the rainfall is detected in January and October. However, the wet months of February, March, April, September, and November show a declining trend at all of the eight meteorological stations.

In the same context, the 3D (dimensional) IPTA method suggested by this study introduces focus scope changes on the rainfall parameter over three frame times. Hence, the outputs of comparative trend analysis using the 3D-IPTA approach for the period of 1974–1989, 1990–2005, and 2006–2021, shown in Figure 5, show remarkable variations in total monthly rainfall quantities. While a declining pattern in the total monthly rainfall is shown in 2006–2021 compared to the period of 1990–2005, the trend analysis points to a rising tendency for the period of 1990–2005 relative to 1974–1989. In contrast, a rising tendency for rainfall amount in January is recorded during 2006–2021 when related to the period years of 1974–1989.
Figure 5

Mapping of the 3D-IPTA method for total monthly rainfall.

Figure 5

Mapping of the 3D-IPTA method for total monthly rainfall.

Close modal

In general, it can be concluded from Table 3 that the rainfall trend at all of the stations is in a downward pattern from 2006 to 2021, and a drought state dominated during this time.

Table 3

Trend assessment for monsoon months using 3D and 2D IPTA approaches

 
 

The analysis of meteorological drought in the Gaza Strip was analyzed using the SPI method with daily rainfall time series data (1974–2021), and the results show that considering extreme and severe droughts, several drought events occurred at different time scales. Scoping of drought by using the SPI-1 shown in Figure 6, the SPI-3 shown in Figure 7, the SPI-6 shown in Figure 8, the SPI-9 shown in Figure 9, and the SPI-12 shown in Figure 10 indicates that the years 1975, 1976, 1978, 1980, 1981, 1985, 1990, 1994, 1995, 1999, 2009, 2010, 2011, 2012, 2014, and 2015 were the most drought-affected in the Gaza Strip at the southeast coast of Mediterranean.
Figure 6

Analysis of drought using 1-month SPI-1.

Figure 6

Analysis of drought using 1-month SPI-1.

Close modal
Figure 7

Analysis of drought using 3-month SPI-3.

Figure 7

Analysis of drought using 3-month SPI-3.

Close modal
Figure 8

Analysis of drought using 6-month SPI-6.

Figure 8

Analysis of drought using 6-month SPI-6.

Close modal
Figure 9

Analysis of drought using 9-month SPI-9.

Figure 9

Analysis of drought using 9-month SPI-9.

Close modal
Figure 10

Analysis of drought using 12-month SPI-12.

Figure 10

Analysis of drought using 12-month SPI-12.

Close modal
The analysis of the drought frequency by the SPI-1 shown in Figure 11, the SPI-3 shown in Figure 12, the SPI-6 shown in Figure 13, the SPI-9 shown in Figure 14, and the SPI-12 shown in Figure 15 refers to the drought probability of occurrence reaching more than 51%.
Figure 11

Incidents of drought frequency based on the 1-month SPI-1 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Figure 11

Incidents of drought frequency based on the 1-month SPI-1 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Close modal
Figure 12

Incidents of drought frequency based on the 3-month SPI-3 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Figure 12

Incidents of drought frequency based on the 3-month SPI-3 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Close modal
Figure 13

Incidents of drought frequency based on the 6-month SPI-6 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Figure 13

Incidents of drought frequency based on the 6-month SPI-6 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Close modal
Figure 14

Incidents of drought frequency based on the 9-month SPI-9 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Figure 14

Incidents of drought frequency based on the 9-month SPI-9 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Close modal
Figure 15

Incidents of drought frequency based on the 12-month SPI-12 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Figure 15

Incidents of drought frequency based on the 12-month SPI-12 for: (a) January, (b) February, (c) March, (d) April, (e) November, and (f) December.

Close modal
For the management of crop planting and irrigation patterns, as well as the recharge processes of the Gaza groundwater of the coastal aquifer, mapping the agricultural and hydrological drought are an urgent issue. Therefore, the pattern of drought cycling is shown in Figure 16 for the 3-month SPI-3 in January and April. However, Figure 17 demonstrates the annual fluctuation of drought using SPI-12 in December during the critical years.
Figure 16

Spatial pattern of SPI for 3-month (a) January (b) April.

Figure 16

Spatial pattern of SPI for 3-month (a) January (b) April.

Close modal
Figure 17

Spatial pattern of SPI for 12 months in December.

Figure 17

Spatial pattern of SPI for 12 months in December.

Close modal

Assessing the effects of climate change in Mediterranean regions is crucial to anticipate potential adverse repercussions on agriculture and water supplies. The area of the Gaza Strip forms a representative case study to analyze the climate drought evolution in the Mediterranean region for the reason of its location in between the arid and semiarid zone.

In the southeast Mediterranean region of Gaza, the rainfall patterns throughout 1974–2021 distribute locally over the area with high rainfall intensity in the north decreasing toward the south where the total monthly rainfall ranges between 0 and 445 mm with an average value of between 17 and 32 mm; however, on an annual basis, the total rainfall is between 11 and 877 mm with an average value of between 209 and 381 mm. The monthly trend analysis shows no trend pattern at all of the metrological stations; however, by assessing the annual total rainfall, the trend shows a rising pattern in Beit Hanon and Remal stations while the other stations are experiencing a declining trend and this might be related to the regionalization effects. In this regard, by comparing the former period of 1974–1997 with the posterior period of 1998–2021, a decreasing tendency is observed in February, March, April, and November according to the trend analysis for the rainfall factor during the rainy months over the whole area of the Gaza Strip while January and October typically show a gradual increase in the amount of rainfall. However, the northern part of the Gaza Strip reported an excess of rainfall during December, while the central and southern parts recorded a decline in rainfall. The abundance of rainfall amounts reaches 27% in January and 142% in October, respectively. In December, the north receives a 27% increase, while the south has a −17% reduction. However, the rainfall shortfall over the remaining wet months ranges from −16 to −62%. The incidence of extreme, severe, moderate, and mild droughts per year would be up to 4, 6, 11, and 38% hence the worst drought episodes were detected in 1990, 2010, and 2014.

Based on that, the southeastern Mediterranean region of the Middle East experiences greater precipitation loss than other places, which prolongs the drought in the coastal region due to the influence of the Sinai and the Sahel Desert. The area of the Gaza Strip is vulnerable to drought events with an occurrence incidence of about 59% that potentially affects the agricultural production and the recovery of the Gaza coastal aquifer where according to the reports the year 2010 showed the most significant drought period. The agricultural drought occurs at a maximum of once every 5 years while the hydrological drought occurs every two years; therefore, the northern Gaza Strip has an adequate water balance, which encourages agricultural activity there while forcing other areas to reorganize the irrigation and crop watering schedules.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Ahani
A.
,
Mousavi Nadoushani
S. S.
&
Moridi
A.
2019a
A homogeneity-based feature selection algorithm for regionalization of watersheds
.
Hydrological Sciences Journal
.
doi:10.1080/02626667.2019.1686638
.
Ahani
A.
,
Mousavi Nadoushani
S. S.
&
Moridi
A.
2019b
A hybrid regionalization method based on canonical correlation analysis and cluster analysis: a case study in northern Iran
.
Hydrology Research
50
(
4
),
1076
1095
.
doi:10.2166/nh.2019.105
.
Ahani
A.
,
Mousavi Nadoushani
S. S.
&
Moridi
A.
2020a
Regionalization of watersheds based on the concept of rough set
.
Natural Hazards
104
,
883
899
.
https://doi.org/10.1007/s11069-020-04196-1
.
Ahani
A.
,
Mousavi Nadoushani
S. S.
&
Moridi
A.
2020b
Regionalization of watersheds by finite mixture models
.
Journal of Hydrology
583
,
124620
.
doi:10.1016/j.jhydrol.2020.124620
.
Ahani
A.
,
Mousavi Nadoushani
S. S.
&
Moridi
A.
2022
A ranking method for regionalization of watersheds
.
Journal of Hydrology
609
.
https://doi.org/10.1016/j.jhydrol.2022.127740
.
Aladaileh
H.
,
Al Qinna
M.
,
Karoly
B.
,
Al-Karablieh
E.
&
Rakonczai
J.
2019
An investigation into the spatial and temporal variability of the meteorological drought in Jordan
.
Climate
7
(
6
),
82
.
doi:10.3390/cli7060082
.
Ali
R. O.
&
Abubaker
S. R.
2019
Trend analysis using Mann-Kendall, sen's slope estimator test and innovative trend analysis method in Yangtze River basin, China: review
.
International Journal of Engineering & Technology
8
,
110
119
.
Al-Najjar
H.
,
Ceribasi
G.
,
Dogan
E.
,
Abualtayef
M.
,
Qahman
K.
&
Shaqfa
A.
2020
Stochastic time-series models for drought assessment in the Gaza Strip (Palestine)
.
Journal of Water and Climate Change
11
(
S1
),
85
114
.
https://doi.org/10.2166/wcc.2020.330
.
Al-Najjar
H.
,
Purunam
A.
,
Özkan
K.
&
Abualtayef
M.
2022
Analysis of extreme rainfall trend and mapping of the Wadi pluvial flood in the Gaza coastal plain of Palestine
.
Acta Geophysica
70
,
2135
2147
.
https://doi.org/10.1007/s11600-022-00890-9
.
Behzadi
F.
,
Yousefi
H.
,
Javadi
S.
,
Moridi
A.
,
Mehdy
S.
,
Shahedany
H.
&
Neshat
A.
2022
Meteorological drought duration–severity and climate change impact in Iran
.
Theoretical and Applied Climatology
149
,
1297
1315
.
https://doi.org/10.1007/s00704-022-04113-5
.
Cammarano
D.
,
Ceccarelli
S.
,
Grando
S.
,
Romagosa
I.
,
Benbelkacem
A.
,
Akar
T.
,
Al-Yassin
A.
,
Pecchioni
N.
,
Francia
E.
&
Ronga
D.
2019
The impact of climate change on barley yield in the Mediterranean basin
.
European Journal of Agronomy
106
,
1
11
.
Hameed
M.
,
Ahmadalipour
A.
&
Moradkhani
H.
2020
Drought and food security in the Middle East: an analytical framework
.
Agricultural and Forest Meteorology
281
,
107816
.
IPCC
2022
Climate Change 2022 Impacts, Adaptation and Vulnerability
.
Intergovernmental Panel on Climate Change
,
Switzerland
.
Kendall
M. G.
1975
Rank Correlation Methods
.
Griffin, Oxford, UK.
Mann
H. B.
1945
Nonparametric tests against trend
.
Econometrica
13
,
245
259
.
MoA 2008 Agricultural water scarcity. Position Paper of the MoA. Issued 22 July 2008, MoA, Palestine.
Mahfouz
P.
,
Mitri
G.
,
Jazi
M.
&
Karam
F.
2016
Investigating the temporal variability of the standardized precipitation index in Lebanon
.
Climate
4
(
2
),
27
.
doi:10.3390/cli4020027
.
Malhi
Y.
,
Girardin
C.
,
Metcalfe
D. B.
,
Doughty
C. E.
,
Aragão
L. E. O. C.
,
Rifai
S. W.
,
Oliveras
I.
,
Shenkin
A.
,
Aguirre-Gutiérrez
J.
&
Dahlsjö
C. A. L.
2021
The global ecosystems monitoring network: monitoring ecosystem productivity and carbon cycling across the tropics
.
Biological Conservation
253
,
108889
.
McKee
T. B.
,
Doesken
N. J.
&
Kleist
J.
1985
Drought monitoring with multiple time scales
. In
Proceedings of the 9th Conference on Applied Climatology
.
American Meteorological Society
:
Dallas, TX
,
USA
, pp.
233
236
.
McKee
T. B.
,
Doesken
N. J.
&
Kleist
J.
1993
The relationship of drought frequency and duration to time scales
. In
Proceedings of the Eighth Conference on Applied Climatology
.
American Meteorological Society
:
Boston, MA
.
McKee
T. B.
,
Doesken
N. J.
&
Kleist
J.
1995
Drought monitoring with multiple time scales
. In
Proceedings of the Ninth Conference on Applied Climatology
.
American Meteorological Society
:
Boston, MA
.
Michaelides
S.
,
Karacostas
T.
,
Sanchez
J. L.
,
Retalis
A.
,
Pytharoulis
I.
,
Homar
V.
,
Romero
R.
,
Zanis
P.
,
Giannakopoulos
C.
&
Bühl
J.
2018
Reviews and perspectives of high impact atmospheric processes in the Mediterranean
.
Atmospheric Research
208
,
4
44
.
Mohammed
S.
,
Alsafadi
K.
,
Al-Awadhi
T.
,
Sherief
Y.
,
Harsanyie
E.
&
El Kenawy
A. M.
2020
Space and time variability of meteorological drought in Syria
.
Acta Geophysica Volume
68
,
1877
1898
.
doi:10.1007/s11600-020-00501-5
.
Mustafa
A.
&
Rahman
G.
2018
Assessing the spatio-temporal variability of meteorological drought in Jordan
.
Earth Systems and Environment
2
(
2
),
247
264
.
doi:10.1007/s41748-018-0071-9
.
PCBS 2022 Estimated population in the Palestinian Territory mid-year by governorate, 1997-2021. IOP Publishing Physics Web. Available from: http://www.pcbs.gov.ps/Portals/_Rainbow/Documents/%D8%A7%D9%84%D9%85%D8%AD%D8%A7%D9%81%D8%B8%D8%A7%D8%AA%20%D8%A7%D9%86%D8%AC%D9%84%D9%8A%D8%B2%D9%8A%2097-2017.html (accessed 25 September 2022).
Salman
S. A.
,
Shahid
S.
,
Sharafati
A.
,
Salem
G. S. A.
,
Bakar
A. A.
,
Farooque
A. A.
,
Chung
E.-S.
,
Ahmed
Y. A.
,
Mikhail
B.
&
Yaseen
Z. M.
2021
Projection of agricultural water stress for climate change scenarios: a regional case study of Iraq
.
Agriculture
11
,
1288
.
https://doi.org/10.3390/agriculture11121288.
Sardans
J.
,
Urbina
I.
,
Grau
O.
,
Asensio
D.
,
Ogaya
R.
&
Peñuelas
J.
2020
Long-term drought decreases ecosystem C and nutrient storage in a Mediterranean holm oak forest
.
Environmental and Experimental Botany
177
,
104135
.
https://doi.org/10.1016/j.envexpbot.2020.104135
.
Sen
Z.
,
Sisman
E.
&
Dabanli
I.
2019
Innovative Polygon Trend Analysis (IPTA) and applications
.
Journal of Hydrology
575
,
202
210
.
Sharafati
A.
,
Pezeshki
E.
,
Shahid
S.
&
Motta
D.
2020
Quantification and uncertainty of the impact of climate change on river discharge and sediment yield in the Dehbar river basin in Iran
.
J Soils Sediments
20
,
2977
2996
.
https://doi.org/10.1007/s11368-020-02632-0.
Vogel
J.
,
Paton
E.
,
Aich
V.
&
Bronstert
A.
2021
Increasing compound warm spells and droughts in the Mediterranean Basin
.
Weather and Climate Extremes
32
,
100312
.
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