Rainfall Trends and Extremes in Saudi Arabia in Recent Decades

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Article Rainfall Trends and Extremes in Saudi Arabia in Recent Decades

Mansour Almazroui Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, P. O. Box 80208, Jeddah 21589, Saudi Arabia; [email protected] Received: 24 July 2020; Accepted: 8 September 2020; Published: 10 September 2020

Abstract: The observed records of recent decades show increased economic damage associated with flash flooding in different regions of Saudi Arabia. An increase in extreme rainfall events may cause severe repercussions for the socio-economic sectors of the country. The present study investigated the observed rainfall trends and associated extremes over Saudi Arabia for the 42-year period of 1978–2019. It measured the contribution of extreme events to the total rainfall and calculated the changes to mean and extreme rainfall events over five different climate regions of Saudi Arabia. Rainfall indices were constructed by estimating the extreme characteristics associated with daily rainfall frequency and intensity. The analysis reveals that the annual rainfall is decreasing 1 (5.89 mm decade− , significant at the 90% level) over Saudi Arabia for the entire analysis period, while it increased in the most recent decade. On a monthly scale, the most significant increase 1 1 (5.44 mm decade− ) is observed in November and the largest decrease (1.20 mm decade− ) in January. The frequency of intense rainfall events is increasing for the majority of stations over Saudi Arabia, while the frequency of weak events is decreasing. More extreme rainfall events are occurring in the northwest, east, and southwest regions of Saudi Arabia. A daily rainfall of 26 mm is identified ≥ as the threshold for an extreme event. It is found that the contribution of extreme events to the total rainfall amount varies from region to region and season to season. The most considerable contribution (up to 56%) is found in the southern region in June. Regionally, significant contribution comes from the coastal region, where extreme events contribute, on average, 47% of the total rainfall each month from October to February, with the largest (53%) in November. For the entire country, extreme rainfall contributes most (52%) in November and least (20%) in July, while contributions from different stations are in the 8–50% range of the total rainfall.

Keywords: extreme rainfall events; frequency; extreme rainfall contribution; Saudi Arabia

1. Introduction Extreme rainfall events have severe socio-economic impacts, often resulting in droughts, strong winds, and flash floods, and disrupting the environment, including the human population [1,2]. Consequently, any changes in the frequency and intensity of such events are of significant interest. The Intergovernmental Panel on Climate Change (IPCC) has resolved with a high confidence level that the frequency of extreme rainfall events will increase under the influence of global warming in some parts of the world [3]. Climate change often appears as an increase in the intensity and frequency of extreme weather events. Additionally, the trends of climate variables in the historical period may be used as precursors of a changing climate [4]. Therefore, it is essential for us to complete historical trend assessments, including for extreme events [5]. The inter-annual variability of rainfall largely modulates the extreme events in semi-arid to arid regions such as Saudi Arabia, where such events make up a significant fraction of the total annual rainfall. The spatial distributions of inter-annual rainfall over the country stem from climate variability, which is further associated with droughts and disastrous

Atmosphere 2020, 11, 964; doi:10.3390/atmos11090964 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 964 2 of 26

floods. Therefore, the assessment of heavy rainfall events for specific return periods is often mandatory for the appropriate design of urban drainage and infrastructure, as well as for long-term planning. Substantial changes in the frequency of extremes can be associated with small changes in variability rather than any change in the mean [6]. The intensification and more frequent occurrence of extreme rainfall have been discussed in climate change studies, and public awareness of the issue has been raised [7]. However, it has not been investigated comprehensively with observational datasets, especially with data at the daily timescale. An evaluation of the trend in extremes on different timescales is vital to develop the adaptation policies that can address the future occurrence of extreme events for a country like Saudi Arabia. Saudi Arabia lies at the tropical latitudes of 18–32◦ N and longitudes of 35–55◦ E, and covers 80% of the Arabian Peninsula [8]. In 2019, the country’s population reached 34.22 million, up from 27.56 million in 2010 [9], which is a 24% increase in the total population during the last decade. This has imposed severe stress on the water and agricultural resources of the country. Therefore, an in-depth analysis of historical rainfall extreme records is necessary not only for developing policies linked with future planning for the water and agriculture sectors, but also to preserve national heritage sites, promote the tourism industry, and provide disaster resilience. The global warming phenomenon has restructured rainfall, resulting in frequent extreme events [10–12]. Several studies have been conducted on the trend of rainfall on different spatial and temporal scales to evaluate the potential impacts of climatic changes and variability [8,13–17]. For example, annual and seasonal rainfall along with related extremes over Egypt and the Nile river basin are analyzed using different Mann–Kendall versions for the period 1948–2010 [18,19]. Saudi Arabia is categorized as a semi-arid to arid climatic zone with a low annual rainfall, high temperatures, limited groundwater reserves, and no perennial rivers [20], where future extremes will likely increase [21]. Gauge-based rainfall and climate model-simulated rainfall over Saudi Arabia are assessed against the 53 observations for the period 1965–2005, which discussed the rainfall climatology and trends [22]. The annual mean rainfall over Saudi Arabia is scanty and displays a large spatial and temporal variability over the entire country. The southern parts of the country receive more rainfall compared to the central and northern regions. Saudi Arabia is strongly affected by both severe flash floods and droughts, and the impact of both have intensified in recent years [23,24]. Saudi Arabia has experienced more extreme flood events in the last decade, especially in the northern, eastern, and southwestern regions. An assumption can be made that these more frequent extreme rainfall events are a consequence of global warming, as simulated by the climate models [25]. Heavy rainfall events over Saudi Arabia commonly occur due to deep convection triggered jointly by Mediterranean storms and Active Red Sea Trough events [26]. These intense rainfall events have severe impacts on lives and livelihoods in the major cities of Saudi Arabia. Several extreme events caused floods in Saudi Arabia in recent years [27], such as the 74 mm event on 25 November 2009, and the 111 mm event on 26 January 2011, which both caused severe flooding in Jeddah [8]. Almazroui et al. [28] demonstrated the impact of climate change on the duration of monthly precipitation, both wet and dry spells, using historical records and regional climate model simulations for the Wadi Al Lith basin in western Saudi Arabia. The pattern of rainfall and its associated atmospheric circulation over Saudi Arabia displays strong seasonality, as the influence of both tropical and extra-tropical drivers (and the interactions between them) causes extreme rainfall events at different times through the annual cycle. During the dry season, the Arabian heat low is driven by the South Asian monsoon, causing middle and upper tropospheric subsidence over the East Mediterranean region and the Middle East [29–31], resulting in the suppression of rainfall over Saudi Arabia. However, mechanical lifting causes rainfall in the southwestern mountainous region due to the effect of the local topography [32]. During the wet season (October–May) the westerly jet passes over Saudi Arabia and interacts with Red Sea Trough (RST), which advects warm, moist tropical air from the south, enhancing the baroclinicity and thus the rainfall in the region [29,32–34]. Moreover, the El Niño Southern Oscillation (ENSO) significantly impacts the wet season precipitation variability over Saudi Arabia through large changes in the moisture Atmosphere 2020, 11, 964 3 of 26 availability, as well as variability in the westerly jet passing over the region. This results in anomalous conditions [35] and triggers rainfall extremes, which are more frequent during the positive phase of ENSO 4 [2]. In addition to the large-scale circulation, the local surface heating and topography also play a crucial role in determining the spatial distribution and intensity of extreme rainfall over Saudi Arabia. A large portion of the annual total rainfall comes from just a few extreme events, which are very rare over Saudi Arabia. In historical records, the extreme daily precipitation events contribute a large fraction (20–70%) of the total rainfall over the Arabian Peninsula [20]. This is likely to be further increased in the future [36]. However, little is still known about how rainfall extremes in different climatic regions and in different seasons contribute to the total rainfall over Saudi Arabia. The concept of a return period is easily inferred, where stationarity in the rainfall regime can be expected. Thus, it is important to establish whether rainfall records show any indication of trends over time [37]. In this study, the observed data from 25 meteorological stations are used to perform a detailed analysis of the temporal and spatial distribution of rainfall, its trend in the present climate, and the contribution of extreme events to total rainfall for a better understanding of the rainfall variability over Saudi Arabia. The paper is organized as follows: Section2 describes the data and methodology; Section3 describes the main results of the study. The summary and conclusion are discussed in Section4.

2. Data and Methods

2.1. Data The daily total rainfall records for the period 1978–2019 were obtained for 25 meteorological stations in Saudi Arabia from the General Authority of Meteorology and Environmental Protection (GAMEP) (Table1). The geographic locations of the meteorological stations over Saudi Arabia are shown in Figure1. The rainfall dataset was passed through the quality control test following Almazroui et al. [38] by performing a constancy check related to station relocations, instrument upgrades, and changes in the surrounding environment that were likely to create heterogeneity in the station dataset. The Climate Hazards Group Infrared Rainfall with Stations (CHIRPS; [39]) and Global Precipitation Climatology Centre (GPCC; [40]) gridded monthly rainfall datasets were used to construct the rainfall climatology. Atmosphere 2020, 11, 964 4 of 26

1 Table 1. Surface station information along with coordinates and elevations, the annual and seasonal rainfall (mm) climatology, and their trends (mm decade− ). The superscripts a, b, and c represent trend significance at the 90%, 95%, and 99% confidence level, respectively. Stations are arranged in five climate regions of the country.

Rainfall (mm) Trend (mm Decade 1) Region Sr. No. Station Code Station Name − Lat. (N) Lon. (E) Elevation (m) Annual Wet Dry Annual Wet Dry 1 40375 Tabuk 31.4 30.3 0.7 2 c 1 0 28.38 36.60 778 − − 2 40356 Turaif 84.9 83.6 0.3 14 b 13 b 0 31.68 38.73 855 − − 3 40360 Guriat * 48.7 48.0 0.4 0 0 0 31.40 37.28 509 4 40362 Rafha 80.6 79.4 0.1 7 7 0 29.62 43.48 449 − − 5 40357 Arar 59.7 58.4 0.1 3 3 0 30.90 41.13 555 Northern − − 6 40361 Al-Jouf 56.1 55.0 0.8 5 7 0 29.78 40.10 689 7 40394 Hail 97.4 96.5 0.4 30 a 28 a 0 27.43 41.68 1015 − − 8 40405 Gassim 131.3 130.2 0.2 19 c 17 c 0 26.30 43.77 648 − − 9 40437 Riyadh * 105.5 104.9 0.0 14 12 0 24.93 46.72 614 − − 10 40373 Al-Qaisumah 120.7 119.9 0.3 11 c 8 0 28.32 46.13 358 − − 11 40417 Dammam 87.4 88.9 0.2 16 9 0 26.45 49.82 22 Interior 12 40420 Al-Ahsa * 86.5 84.6 0.7 3 2 0 25.30 49.48 179 − − 13 40430 Madinah 61.0 56.3 4.3 5 4 0 24.55 39.70 654 − − 14 40400 Al-Wejh 31.1 31.0 0.1 6 7 0 26.20 36.48 20 15 40439 Yenbo 33.0 32.6 0.3 4 5 0 24.13 38.07 8 16 41024 Jeddah 51.2 50.2 0.9 1 2 0 21.70 39.18 15 c

Coastal 17 41030 Makkah * 98.1 86.5 11.6 13 8 1 21.43 39.77 240 − − 18 41140 Gizan 127.8 82.2 42.3 10 0 8 16.88 42.58 6 19 41036 Taif 162.6 132.0 29.1 5 4 1 21.48 40.55 1478 − − − 20 41055 Al-Baha * 131.5 102.3 28.9 19 b 11 7 c 20.30 41.65 1672 − − − 21 41114 Khamis Mushait 184.6 118.7 65.6 12 4 7 18.30 42.80 2066 − c − b − Highland 22 41112 Abha 221.2 165.3 55.7 25 28 4 18.23 42.65 2090 − − 23 41084 Bisha 86.4 79.8 6.2 8 6 2 19.98 42.63 1167 − − − 24 41128 Najran 60.3 47.4 12.9 9 7 2 17.62 44.42 1214

Southern 25 41136 Sharorah * 57.8 41.0 16.6 4 3 1 17.47 47.10 720 − − − Country 92.5 80.5 11.4 6 c 5 0 − − Note: Wet season is October–May, and dry season is June–September. The asterisk (*) with the station name represents data available from 1985, while all the others are from 1978, except for Dammam which starts at 1999. Atmosphere 2020, 11, 964 5 of 26 Atmosphere 2020, 11, x FOR PEER REVIEW 4 of 30

Figure 1. RegionalRegional map map with with surface surface elevation elevation (m) in and around Saudi Arabia. The The 25 25 meteorological meteorological station locations across Saudi Arabia (see Table 1 1)) areare shownshown withwith closedclosed circles.circles. TheThe solidsolid blueblue lineslines indicate the five ﬁve climatic regions regions—northern—northern (Turaif, (Turaif, Gurait, Gurait, Arar, Arar, Al Al-Jouf,-Jouf, Rafha, Rafha, Tabuk, Tabuk, and and Hail), Hail), coastal (Wejh,(Wejh, Yenbo, Yenbo, Jeddah, Jeddah, Makkah, Makkah, and and Gizan), Gizan), interior interior (Al-Qaysumah, (Al-Qaysumah, Gassim, Gassim, Dammam, Dammam, Al-Ahsa, Al- Ahsa,Madinah, Madinah, Riyadh), Riyadh), highland highland (Taif, Al-Baha, (Taif, Al Abha,-Baha, andAbha Khamis, and Khamis Mushait), Mushait), and southern and southern (Bisha, Najran,(Bisha, Najran,and Sharorah)—in and Sharorah) Saudi—in Arabia, Saudi adaptedArabia, adapted from Almazroui from Almazroui et al. [41 et]. al. [41].

2.2. Methods Methods The station station-based-based total total rainfall, rainfall, with with the the daily daily temporal temporal resolution, resolution, was was used used to explore to explore the frequencythe frequency and intensity and intensity of rainfall of rainfall extremes, extremes, along with along the withcontribution the contribution of extreme of events extreme to the events total rainfallto the total amount. rainfall Extreme amount. rainfall Extreme indices rainfall were indices generated were generatedusing the usingdaily therainfall daily time rainfall series time data series for thedata 42 for-year the 42-yearperiod 1978 period–2019. 1978–2019. The daily The rainfall daily rainfall data is data the is most the most reliable reliable way way to determine to determine the frequencythe frequency and and trend trend of extreme of extreme rainf rainfallall events events [42]. [42 ]. The entireentire timetime seriesseries is is divided divided into into two two equal equal periods, periods, and and the the trend trend for eachfor each half half period period is then is thencomputed computed along along withthe with decadal the decadal trends. trends. The frequency The frequency of rainy daysof rainy was days counted was at counted each station at each for several threshold values, such as 1, 5, and up to 50 mm, with intervals of 5 mm. In this article, station for several threshold values,≥ such≥ as ≥1, ≥5, and≥ up to ≥50 mm, with intervals of 5 mm. In this article,an extreme an extreme rainfall rainfall event is event defined is defined using the using daily the accumulated daily accumulated rainfall. rainfall. An extreme An extreme rainfall eventrainfall is eventdefined is asdefined “An event as “An having event at having least one at dayleast accumulated one day accumulated rainfall equal rainfall to or equal above to a specificor above threshold”. a specific Thethreshold rainfall”. The trends rainfall and their trends significance and their levels significance were calculated levels were using calculated the regression using the equation regression and equationF-test, respectively and F-test, [ 38respectively]. There is also[38]. non-parametricThere is also non trend-parametric analysis, trend such analysis, as the Mann–Kendall such as the Mann (MK)– Kendalltest [43,44 (MK)] with test the [43 MK,44] statistic, with the and MK the statistic, S test statisticand the [S45 test] to statistic obtain the[45] slope to obtain and significancethe slope and is significancealso used. is also used. The return period of rainfall occurrence is defineddefined as the time duration expectation over which there are no no extreme extreme event event occurrences occurrences on on the the average. average. In In practical practical works, works, gen generally,erally, this this duration duration is adaptedis adapted as 2 as-year, 2-year, 5-year, 5-year, 10-year, 10-year, 25-year, 25-year, 50-year, 50-year, or 100 or- 100-year,year, corresponding corresponding to 0.50, to 0.50,0.20, 0.20,0.10, 0.04, 0.10, 0.02,0.04, or 0.02, 0.01 or risk 0.01 levels, risk levels, respectively. respectively. As a method, As a method, the extreme the extreme event event occurrences occurrences are assumed are assumed to take to place independently from each other, and therefore the probability distribution function (PDF) models provide a scientific basis. There are three distinct approaches, including the (i) annual

Atmosphere 2020, 11, 964 6 of 26 take place independently from each other, and therefore the probability distribution function (PDF) models provide a scientific basis. There are three distinct approaches, including the (i) annual maxima, (ii) over given threshold extremes, and (iii) threshold selection, such that the number of years is equal to the number of extreme events over the threshold level [46]. Robust assessments can only be obtained from the analysis of the frequency of daily rainfall over a long time-series. The daily total rainfall time-series is analyzed with the help of the gamma distribution. The anticipated annual, wet-season, and dry-season maximum rainfall during the 42-year period is ascertained for different return periods at each station. The return periods are derived from the statistical distribution for every station from the annual extreme time-series; where the trend is obvious in the rainfall regime, a substitute for the concept of return period is suggested, based on the probability of occurrence of an extreme event of specified magnitude, during a period extending into the future during which the observed trend can be assumed to persist. The threshold for extreme events that contribute to the total rainfall is defined using percentile values. First, the cut-off daily rainfall for the 90th, 95th, and 99th percentiles are obtained. The rainfall amounts at the 90th, 95th, and 99th percentiles are calculated and compared with the thresholds of 10 mm up to 30 mm with intervals of 1 mm at each station and for the entire country. The amount of ≥ ≥ percentile-based rainfall equivalent to the threshold-based rainfall defines the extreme event threshold. For example, if the threshold value used to define an extreme event over Saudi Arabia is 26.5 mm day 1, the amount of rainfall 26.5 mm is equivalent to the amount of rainfall at a specific percentile − ≥ (e.g., the 95th percentile). Finally, the contribution of extreme events to the total rainfall at different stations is computed in order to understand the extent to which extreme events contribute to the total rainfall over specific climatic regions.

3. Results and Discussion

3.1. Rainfall Distribution and Climatology The heterogeneous spatial distribution of rainfall from north to south over Saudi Arabia is shown by the CHIRPS and GPCC data (Figure2). The wet season extends from October to May and the dry season from June to September. An understanding of the rainfall distribution on different timescales is necessary for climatic and hydrological studies because the amount of rainfall informs projects related to municipal civil works, flood control, agriculture management, and water reservoirs. The climate of Saudi Arabia is influenced by different weather systems due to its wide latitudinal extent. Both tropical and extra-tropical weather systems influence the climate of Saudi Arabia [47]. Seasonal rainfall analysis shows that most rainfall occurs during the spring and winter seasons, while the minimum contribution is from the summer season (Table1). During the summer season, strong surface heating causes a heat low that is associated with dry conditions over Saudi Arabia. Meanwhile, the Red Sea Trough (RST) extends from south of the Red Sea towards the north over the Eastern Mediterranean. The RST phenomenon favors the development of strong depressions over the central Red Sea, which can lead to heavy rainfall [48]. Furthermore, the remote influence of the ENSO region during its negative phase causes upper-level divergence, along with lower level convergence over the region that favors convective rainfall over the south and southwest of Saudi Arabia [47]. Mechanical lifting by local orography in the southwestern region enhances rainfall on the windward side of the mountain ranges along the Red Sea coast, as seen in Figure2. The Rub Al-Khali, or Empty Quarter, covering the southeastern region, shows the lowest rainfall totals, with almost no rainfall throughout the year. Both the CHIRPS and GPCC monthly rainfall datasets show homogeneous spatial rainfall distributions with little quantitative differences. The annual rainfall distribution shows maxima in the central and southwestern parts, with the greatest amounts over 200 mm in the southwestern region (Figure2a,b). The annual climatology of the CHIRPS dataset shows most rainfall (up to 245 mm) over the southwestern coastal belt and some regions of northeastern Saudi Arabia. Smaller amounts (35 to 70 mm) were observed over the northwestern and southeastern quadrants, while Atmosphere 2020, 11, 964 7 of 26 the central regions received 90 mm to 160 mm during the period 1978–2019. The annual climatology of the GPCC dataset shows a very similar rainfall pattern as in CHIRPS, with maximum rainfall (150–190 mm) occurring over the southwestern coastal regions along with parts of the northeastern region. Less rainfall (approx. 10–70 mm) was measured over the northwestern and southeastern regions, while the central regions acquired values of 60–125 mm. Comparatively, the annual climatology of GPCC shows a somewhat lower rainfall over the country. Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 30

Figure 2. Spatial distribution o off rainfall rainfall (mm) (mm) from from CHIRPS CHIRPS (a (a,c,)c )and and GPCC GPCC (b (,bd,)d for) for the the whole whole year year ((aa,,bb),), wetwet seasonseason (c,d), and dry season season ( (ee,,ff)) in in Saudi Saudi Arabia Arabia during during the the period period 1981 1981–2019.–2019.

3.2. RainfallThe wet Variability season rainfall and Trends spatial distribution patterns are influenced through the Mediterranean and central Asian troughs [49,50]. Wet season rainfall distributions show maxima in the central parts of SaudiThe Arabia,observed with mean values annual of rainfall more than in Saudi 150 mmArabia (Figure is 92.52 c,d).mm for Hot the and period dry 1978 conditions–2019 (Figure prevail during3a, Table the 1). summer The 42-year season time [51 seri].es However, of annual occasionally, mean rainfall Indian over monsoonSaudi Arabia effects shows can an lead apparent to deep convectiondecadal variability over the ( southwesternFigure 3a). The highlands. time series The regression summer analysis season rainfall shows distributiona decreasing shows trend maxima(−5.89 mm decade−1, significant at the 90% confidence level) for the entire period 1978–2019; it increased at in southwestern Saudi Arabia, with totals of more than 75 mm (Figure2e,f). Overall, the CHIRPS the rate of 21.02 mm decade−1 (90% significant level) in the first half (1978–1998), while it had an data show more specific and higher amounts of rainfall over Saudi Arabia as compared to the GPCC insignificant slow increase rate (3.86 mm decade−1) in the second half (1999–2019). However, the data. Surface rainfall measurements from the meteorological stations across Saudi Arabia show similar decadal trends vary from the first to the last decade. In the first decade (1980–1989), the annual rainfall values, as tabulated in Table1. decreased at the rate of −8.52 mm decade−1 (insignificant), while in the second decade (1990–1999) it 3.2.mostly Rainfall remained Variability above and normal Trends and increased at the rate of 32.09 mm decade−1 (insignificant). During the first decade, the maximum rainfall occurred in 1981 (approx. 175 mm), whereas the minimum rainfallThe spike observed (70 mm) mean happened annual rainfall in 1984. in In Saudi the second Arabia decade is 92.5 mm(1990 for–1999), the period the maximum 1978–2019 rainfall (Figure of3a, Tableabout1 ).165 The mm 42-year was observed time series in of1997. annual In the mean third rainfall decade over (2000 Saudi–2009) Arabia, the annual shows rainfall an apparent was mostly decadal 1 variability (Figure3a). The time series regression analysis shows a decreasing trend (−1 5.89 mm decade , below the 42-year mean, and again showed a decreasing trend (−30.70 mm decade −, significant at the− significant90% confidence at the level), 90% confidence while the level)last decade for the (2010 entire–2019) period had 1978–2019; an increasing it increased trend of at 36.49 the rate mm of decade 21.02 mm−1 1 decade(significan− (90%t at the significant 90% confidence level) in thelevel). first Note half that (1978–1998), the annual while rainfall it had in an 2017 insignificant was 70 mm, slow which increase is 1 ratelower (3.86 than mm that decade of the− previous) in the second year, 2016 half (99 (1999–2019). mm), and However,the following the decadalyear, 2018 trends (123 varymm). from However, the first in 2017 a daily rainfall of 93 and 56 mm fell on February 14 and 17, respectively, in Abha. In Khamis Mushait, a daily rainfall of 51, 63, and 36 mm fell on February 13, 14, and 17, respectively. These suggest that annual rainfall totals come from just a few days and a few stations—i.e., extreme events contribute strongly to the totals. Similar trend behavior was observed for the wet season rainfall data (Figure 3b). The wet season- observed mean rainfall is 80.5 mm over the full period. The entire period also shows an insignificantly decreasing trend (−4.83 mm decade−1), as in the annual rainfall observations. The wet season trends for the first and second half also follows the annual pattern. The wet season rainfall shows an

Atmosphere 2020, 11, 964 8 of 26 to the last decade. In the first decade (1980–1989), the annual rainfall decreased at the rate of 8.52 mm 1 − decade− (insignificant), while in the second decade (1990–1999) it mostly remained above normal and 1 increased at the rate of 32.09 mm decade− (insignificant). During the first decade, the maximum rainfall occurred in 1981 (approx. 175 mm), whereas the minimum rainfall spike (70 mm) happened in 1984. In the second decade (1990–1999), the maximum rainfall of about 165 mm was observed in 1997. In the third decade (2000–2009), the annual rainfall was mostly below the 42-year mean, and again showed 1 a decreasing trend ( 30.70 mm decade− , significant at the 90% confidence level), while the last decade − 1 (2010–2019) had an increasing trend of 36.49 mm decade− (significant at the 90% confidence level). Note that the annual rainfall in 2017 was 70 mm, which is lower than that of the previous year, 2016 (99 mm), and the following year, 2018 (123 mm). However, in 2017 a daily rainfall of 93 and 56 mm fell on February 14 and 17, respectively, in Abha. In Khamis Mushait, a daily rainfall of 51, 63, and 36 mm fell on February 13, 14, and 17, respectively. These suggest that annual rainfall totals come from just a few days and a few stations—i.e., extreme events contribute strongly to the totals. Similar trend behavior was observed for the wet season rainfall data (Figure3b). The wet season-observed mean rainfall is 80.5 mm over the full period. The entire period also shows an insignificantly decreasing trend ( 4.83 mm decade 1), as in the annual rainfall observations. − − The wet season trends for the first and second half also follows the annual pattern. The wet season 1 rainfall shows an insignificantly decreasing trend ( 18.02 mm decade− ) during the first decade, while − 1 the last decade (2010–2019) shows an insignificantly increasing trend (32.56 mm decade− ). The observed dry season mean total rainfall is 11.4 mm over Saudi Arabia, which accounts for only 12.36% of the annual rainfall during the period 1978–2019 (Figure3c). Rainfall trends were insignificant in the dry season during all the decadal slots. Overall, the annual and wet season rainfall patterns are quite similar; both depict the rainfall surplus decade of 1990–1999, while the same rainfall deficiency was found in the decade 2000–2009. On the other hand, the dry season rainfall pattern differs slightly from the annual and wet season patterns, since during the dry seasons the largest rainfall deficiency was observed in the decade 1980–1989. The relatively heterogeneous behavior of these trends indicates a large decadal variability in the rainfall over Saudi Arabia. The increasing and decreasing rainfall trends may be due to decadal variations in the large-scale ocean and atmospheric circulation patterns [52]. Because the rainfall data are neither independent nor normally distributed, therefore the slope and significance of trends are also tested using Mann–Kendall test. According to this test, the slopes show increasing and decreasing trends similar to regression analysis, with a slight variation in the magnitude. For example, the annual trends are 5.38, 3.17, 17.41, 32.94, and 55.62 mm decade 1 for the entire − − − − period, the first decade, the second decade, the third decade, and the last decade, respectively. During the wet season, the trends are 7.64, 27.08, 81.35, 57.07, and 40.64 mm decade 1 for the entire period, − − − − the first decade, the second decade, the third decade, and the last decade, respectively. In the dry season, the trends are 25.80, 16.45, and 12.42 mm decade 1 for the first, second, and third decade, − − respectively, while closer to the total period and last decade, which are too small. Note that all of them show an insignificant level of confidence in Mann–Kendall test. Different Mann–Kendall versions have almost no change in rainfall and its extremes, but however are very sensitive to temperature extremes [17]. As we are concerned with the precipitation and the Mann–Kendall test shows an almost similar trend to the regression analysis, the trends of the rainfall and extremes are therefore evaluated using the regression method for the rest of the analysis. Note that the rainfall trend for a longer period, such as 30 years or more, is climatologically reasonable for annual and seasonal scales. However, the decadal trends in this study are used to show the variation in different decades, which may not enough for climate study. Therefore, the rest of the study emphasized the use of the 42-year rainfall time series in the trend analysis, which may not sufficient for climate study. The analysis of climatological rainfall amounts at each station shows that the central and southwestern parts of Saudi Arabia receive the most rainfall, with significantly decreasing trends (Table1). However, the low rainfall regions such as the eastern, northwestern, and southern Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 30 insignificantly decreasing trend (−18.02 mm decade−1) during the first decade, while the last decade (2010–2019) shows an insignificantly increasing trend (32.56 mm decade−1). The observed dry season mean total rainfall is 11.4 mm over Saudi Arabia, which accounts for only 12.36% of the annual rainfall during the period 1978–2019 (Figure 3c). Rainfall trends were insignificant in the dry season during all the decadal slots. Overall, the annual and wet season rainfall patterns are quite similar; both depict the rainfall surplus decade of 1990–1999, while the same rainfall deficiency was found in the decade 2000–2009. On the other hand, the dry season rainfall pattern differs slightly from the annual and wet season patterns, since during the dry seasons the largest rainfall deficiency was observed in the decade 1980–1989. The relatively heterogeneous behavior of these trends indicates a large decadal variability in the rainfall over Saudi Arabia. The increasing and decreasing rainfall trends may be due to decadal variations in the large-scale ocean and atmospheric circulation patterns [52]. Because the rainfall data are neither independent nor normally distributed, therefore the slope and significance of trends are also tested using Mann–Kendall test. According to this test, the slopes show increasing and decreasing trends similar to regression analysis, with a slight variation in the magnitude. For example, the annual trends are −5.38, −3.17, 17.41, −32.94, and 55.62 mm decade−1 for the entire period, the first decade, the second decade, the third decade, and the last decade, respectively. During the wet season, the trends are −7.64, −27.08, 81.35, −57.07, and 40.64 mm decade−1 for the entire period, the first decade, the second decade, the third decade, and the last decade, respectively. In the dry season, the trends are 25.80, 16.45, and −12.42 mm decade−1 for the first, second, and third decade, respectively, while closer to the total period and last decade, which are too small. Note that all of them show an insignificant level of confidence in Mann–Kendall test. Different Mann–Kendall versions have almost no change in rainfall and its extremes, but however are very sensitive to temperature extremes [17]. As we are concerned with the precipitation and the Mann– Kendall test shows an almost similar trend to the regression analysis, the trends of the rainfall and extremes are therefore evaluated using the regression method for the rest of the analysis. Note that the rainfall trend for a longer period, such as 30 years or more, is climatologically reasonable for annual and seasonal scales. However, the decadal trends in this study are used to show the variation in different decades, which may not enough for climate study. Therefore, the rest of the study emphasized the use of the 42-year rainfall time series in the trend analysis, which may not sufficient for climate study. The analysis of climatological rainfall amounts at each station shows that the central and Atmospheresouthwestern2020, 11 parts, 964 of Saudi Arabia receive the most rainfall, with significantly decreasing trends9 of 26 (Table 1). However, the low rainfall regions such as the eastern, northwestern, and southern mountainous regions show (insignificant) increasing trends in rainfall over the annual cycle and mountainousduring the wet regions season show for the (insignificant) period 1978 increasing–2019. trends in rainfall over the annual cycle and during the wet season for the period 1978–2019.

180 1978—2019 160 (a) 1980—1989 1990—1999 140 2000—2009 2010—2019 120 Average 100 80 60 40

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FigureFigure 3.3. TimeTime sequence of the country country-averaged-averaged rainfall rainfall (mm) (mm) for for (a ()a the) the whole whole year year,, (b ()b wet) wet season, season, andand ((cc)) dry season season for for the the period period 1978 1978–2019.–2019. The Thedecadal decadal trends trends are also are shown also shown at each atpanel each for panel 1980– for 1980–1989,1989, 1990– 1990–1999,1999, 2000– 2000–2009,2009, and 2010 and–2019 2010–2019.. The horizontal The horizontal line indi linecates indicates the average the averagerainfall (mm): rainfall (mm):92.54 mm 92.54 for mm the for whole the wholeyear, 80.47 year, mm 80.47 for mm the forwet the season, wet season,and 11.44 and mm 11.44 for mmthe dry for theseason. dry Note season. Notethat the that vertical the vertical scales scales are not are the not same the samefor all fortheall panels. the panels.

Figure 4 shows the annual cycle of rainfall, the change with respect to the base period (1981– 2010), and the decadal trends over Saudi Arabia. The highest average rainfall (15.83 mm) occurs in April, while the lowest (1.46 mm) occurs in June. The highest positive change was observed during February (with the next highest changes in April and May), while the lowest negative change was observed during June (with the next lowest changes in November and December). There are decreasing trends in the annual rainfall cycle for all months, except for August and November. The highest increase (1.85 mm decade−1) was observed in November, and the greatest decrease (−2.83 mm decade−1) in March. On the other hand, rainfall changes with respect to the base period 1981 to 2010 show the most positive changes in February, April, May, September, and October, whereas the rainfall amounts were below the mean line in other months of the year. The maximum rainfall change occurred in February, and the minimum is in November. and December.

Atmosphere 2020, 11, 964 10 of 26

Figure4 shows the annual cycle of rainfall, the change with respect to the base period (1981–2010), and the decadal trends over Saudi Arabia. The highest average rainfall (15.83 mm) occurs in April, while the lowest (1.46 mm) occurs in June. The highest positive change was observed during February (with the next highest changes in April and May), while the lowest negative change was observed during June (with the next lowest changes in November and December). There are decreasing trends in the annual rainfall cycle for all months, except for August and November. The highest increase (1.85 mm decade 1) was observed in November, and the greatest decrease ( 2.83 mm decade 1) − − − in March. On the other hand, rainfall changes with respect to the base period 1981 to 2010 show the most positive changes in February, April, May, September, and October, whereas the rainfall amounts were below the mean line in other months of the year. The maximum rainfall change occurred inAtmosphere February, 2020 and, 11, x the FOR minimum PEER REVIEW is in November. and December. 11 of 30

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−1 1 FigureFigure 4.4. Annual cycle cycle of of rainfall rainfall (mm) (mm) along along with with trends trends (mm (mm decade decade) −and) and change change in rainfall in rainfall (mm) (mm) forfor thethe periodperiod 1978 1978–2019.–2019. Note Note that that the the change change in inrainfall rainfall is the is the with with respect respect to base to base period period 1981 1981–2010.–2010.

Further,Further, SaudiSaudi Arabia can can be be divided divided into into five ﬁve climatic climatic regions regions—i.e.,—i.e., northern, northern, coastal, coastal, interior, interior, highland,highland, and and southern—as southern—as described described in [41 in ]. [41 These]. These regions regions are categorized are categorized based on based their on topographic their characteristicstopographic characteristics and distinct and rainfall distinct patterns. rainfall The patterns. annual The cycle annual of rainfall cycle of during rainfall some during extreme some wet (1992,extreme 1997, wet 2018) (1992, and 1997, dry 2018)(2007, and 2008, dry 2012) (2007, years 2008, and 2012) for the years entire and period for the was entire examined period for was Saudi Arabiaexamined and for its Saudi ﬁve climatic Arabia and regions its five (Figure climatic5). regions It is important (Figure 5). to It mention is important that outto mention of three that extremely out wetof three years, extremely two (1992, wet 1997) years, were two strong(1992, 1997) to very were strong strong El to Nino very years, strong while El Nino two years, (2008, while 2012) two out of the(2008, three 2012) dry out years of the were thr weakee dry to years moderate were weak La Nina to moderate years [53 ].La In Nina 1992, years 52 mm [53] of. In rainfall 1992, 52 fell mm in of Gizan andrainfall 40 mm fell inin JeddahGizan and on2 40 and mm 11 in November, Jeddah on respectively. 2 and 11 November, In 1997, therespectively. total annual In rainfall1997, the came total from onlyannual a few rainfall days, came 78 (37),from 120only (41), a few and days, 105 78 (99)(37), mm 120 (41), on 24, and 25, 105 and (99) 29 mm March, on 24, respectively, 25, and 29 March, in Abha respectively, in Abha (Khamis Mushait). This indicates that, in Saudi Arabia, extreme events (Khamis Mushait). This indicates that, in Saudi Arabia, extreme events contribute strongly to the total contribute strongly to the total rainfall, as discussed later. rainfall, as discussed later. The monthly-average rainfall over Saudi Arabia is in the range of approximately 1 to 15 mm The monthly-average rainfall over Saudi Arabia is in the range of approximately 1 to 15 mm month−1, with the highest variability during the wet season (October–May). The country-average month 1, with the highest variability during the wet season (October–May). The country-average annual− cycle (black solid curve in Figure 5a) shows that the highest rainfall is observed during the annualwet season, cycle while (black the solid lowest curve is during in Figure the5 a)dry shows season. that The the selected highest years rainfall with is months observed (vertical during bars) the wet season,experienced while the the highest lowest and is duringlowest rainfall the dry amounts season. (Figure The selected 5a–f). From years the with analysis, months it (verticalis evident bars) experiencedthat the northern, the highest coastal, and and lowest interior rainfall regions amounts receive (Figure rainfall5 a–f).only Fromduring the the analysis, wet season, it is evidentand thatthat extreme rainfall events occur during the early three months of the wet season (Figure 5b–d). The highland and southern regions receive the highest amount of rainfall, and experience the most extreme rainfall events, during March, April, and May (Figure 5e,f). However, the highland and southern regions are also vulnerable to extreme rainfall events during the dry season, particularly during August.

Atmosphere 2020, 11, 964 11 of 26 the northern, coastal, and interior regions receive rainfall only during the wet season, and that extreme rainfall events occur during the early three months of the wet season (Figure5b–d). The highland and southern regions receive the highest amount of rainfall, and experience the most extreme rainfall events, during March, April, and May (Figure5e,f). However, the highland and southern regions are Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 30 also vulnerable to extreme rainfall events during the dry season, particularly during August.

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AUG NOV MAY MAR 1 Figure 5. Annual cycle of rainfall (mm month− ) for the (a) country, (b) northern, (c) coastal, (d) interior, (e) highland,Figure 5. andAnnual (f) southern cycle of rainfall regions (mm for month wet (1992,−1) for 1997,the (a and) country, 2018) ( andb) northern, dry (2007, (c) 2008,coastal, and (d) 2012) yearsinterior, along with(e) highland, the rainfall and ( climatologyf) southern regions (mm, for black wet solid(1992, curve)1997, and averaged 2018) and for dry the (2007, period 2008 1978–2019., and The vertical2012) years axis along scales with are the not rainfall the same climatology for all panels.(mm, black solid curve) averaged for the period 1978– 2019. The vertical axis scales are not the same for all panels. Various characteristics were observed in rainfall changes at 25 stations across Saudi Arabia with respect toVarious the base characteristics period. It was were found observed that the in rainfall changes decreased at 25 annually stations across at most Saudi of the Arabia stations, with while a rainfallrespect increase to the base was period. observed It was at some found stations. that the Forrainfall instance, decreased the rainfall-changeannually at most analysisof the stations, shows that while a rainfall increase was observed at some stations. For instance, the rainfall-change analysis Hail (Turaif) experienced the signiﬁcant lowest (highest) value of 164.9 (58.6) mm decade 1 of annual shows that Hail (Turaif) experienced the significant lowest (highest)− value of −164.9 (58.6)− mm rainfall changes (Figure6a). However, Yenbo and Wejh observed almost no changes. Similar behavior decade−1 of annual rainfall changes (Figure 6a). However, Yenbo and Wejh observed almost no of rainfallchanges. changes Similar over behavior the 25 of stationsrainfall changes was observed over theduring 25 stations the was wet observed season (Figure during 6theb). wet In thisseason season, a negligible(Figure 6b). change In this was season, observed a negligible over Jeddah, change Najran,was observed Yenbo, over Al-Jouf, Jeddah, and Najran, Dammam. Yenbo, Interestingly,Al-Jouf, the wetand season Dammam. and annualInterestingly, statistics the showwet season positive and changes annual overstatistics the northernshow positive parts, changes along withover negativethe northern parts, along with negative changes over the central and southwestern parts of Saudi Arabia. This behavior shows the dominant role of Mediterranean troughs during the wet season. During the

Atmosphere 2020, 11, 964 12 of 26

Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 30 changes over the central and southwestern parts of Saudi Arabia. This behavior shows the dominant role ofdry Mediterranean season, negligible troughs changes during were the observed wet season. over most During of Saudi the dryArabia, season, except negligible at some stations changes in were observedthe southwestern over most of mountainous Saudi Arabia, region except (Figure at some 6c). stations In this season, in the southwesternAbha was the mountainousonly station that region −1 (Figureexperienced6c). In this an season, increase Abha in rainfall was the of only around station 40 mm that decade experienced. Overall, an increase most stations in rainfall recorded of around a rainfall decrease,1 while a few recorded an increase. 40 mm decade− . Overall, most stations recorded a rainfall decrease, while a few recorded an increase.

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FigureFigure 6. Changes 6. Changes in rainfallin rainfall at at 25 25 stations stations and and overover the entire entire country country for for (a) ( aannual,) annual, (b) wet (b) wetseason season (October–May),(October–May), and and (c) dry(c) dry season season (June–September) (June–September) withwith r respectespect to to base base period period 1981 1981–2010–2010 averaged averaged for thefor period the period 1978–2019. 1978–2019 The. The blue blue and and red redbars bars indicate positive positive and and negative negative changes, changes, respectively. respectively. The scales on the vertical axes are not the same for all panels. The station locations in diﬀerent climate zone can be seen in Table1. Atmosphere 2020, 11, 964 13 of 26

3.3. Extreme Rainfall Events Frequency and Trends The mean climatology of the frequency of extreme rainfall events based on different thresholds ( 1, 5, 10, 20, 30, and 50 mm) is shown in Figure7. The spatial maps of rainfall frequency, ≥ ≥ ≥ ≥ ≥ ≥ calculated for each station, suggest that high-intensity events are observed mainly in the central and southwestern regions. However, some exceptions are reported in the northwestern and eastern regions, which usually have a low frequency of intense rainfall events. The high mountainous southwestern region experiences local convective activity due to orographic effects, which consequently cause rainfall extremes [54]. The spatial patterns of the frequency of daily rainfall for different thresholds show very similar behavior. The wet season rainfall is dynamical. A line of convergence often develops along with the central and southwestern parts of Saudi Arabia, which can trigger significant rainfall events. Therefore, during the wet season a lot of rain falls on the northeastern, central, and southwestern parts of Saudi Arabia. The spatial climatological maps of the frequency of extreme rainfall events show that intense rainfall events are most likely to occur in the southwestern region along the Red Sea coast (Figure7e,f). In addition to the annual frequency of extreme rainfall events with different thresholds, the trends of these frequencies are shown in Figure8 for the period 1978–2019. Figure8 shows significantly decreasing trends in the frequency of rainfall extremes in the central and southwestern parts of Saudi Arabia, which have the highest amount of annual rainfall. However, the northwestern, coastal, eastern, and southern stations all show increasing trends in the frequency of extreme rainfall events, particularly for intense events ( 30, 40, and 50 mm). Interestingly, more stations have increasing trends for ≥ ≥ ≥ intense rainfall events than for low rainfall events. Figure9 shows the time series of extreme rainfall event frequency for various thresholds in Saudi Arabia during the period 1978 to 2019. It is clear from the figure that the high and low peaks for the different thresholds line up almost vertically. The maximum extreme rainfall event frequency for all the thresholds happened in 1982 (followed by 1997 and 2018), while the frequency dropped to its lowest level in 2007. Note that 1992, 1997, and 2018 are all El Nino years. Additionally, note how the extreme rainfall event frequency drops as the threshold is increased. The different threshold frequency ranges are the following: 1 (9–20 day year 1), 5 (4–9 day year 1), 10 (2–5 day year 1), ≥ − ≥ − ≥ − and 20 mm (1–3 day year 1). For the thresholds 25 to 50 mm, the frequency remained under 2 day ≥ − ≥ ≥ year 1. Rainfall events with daily rainfall thresholds 1 and 5 mm are not necessarily extreme events. − ≥ ≥ Rainfall events with threshold values of 25 mm and above have the lowest occurrence rate during ≥ the period 1978–2019. Rainfall events with a threshold of 25 mm may be considered as genuine ≥ extremes [2], and are investigated further below. The frequency time series results are consistent with the spatial distribution, as shown in Figure7a–f. Interestingly, Figure9 shows that the frequency of extreme events at all thresholds decreased significantly after 2000, though it increased again in recent years after 2010. This analysis shows that the greatest increase is likely to occur in short-duration rainfall. However, the analysis is potentially suggesting an increase in the magnitude and frequency of flash floods in future years because of the increasing trend of intense rainfall events. AtmosphereAtmosphere 20202020,, 1111,, x 964 FOR PEER REVIEW 1514 of of 30 26

FigureFigure 7. 7. AnnualAnnual frequency frequency of rainfall of rainfall extremes extremes for thresholds: for thresholds: (a) 1, ((ab)) 1,5, (cb)) 10, 5, (cd)) 10,20, ( (ed)) 30, 20, and (e) ( 30,f) 50and mm (f) for 50 mmthe period for the 1978 period–2019 1978–2019..

In addition to the annual frequency of extreme rainfall events with different thresholds, the trends of these frequencies are shown in Figure 8 for the period 1978–2019. Figure 8 shows significantly decreasing trends in the frequency of rainfall extremes in the central and southwestern parts of Saudi Arabia, which have the highest amount of annual rainfall. However, the northwestern, coastal, eastern, and southern stations all show increasing trends in the frequency of extreme rainfall

Atmosphere 2020, 11, 964 15 of 26

Figure 8. Trends (mm decade 1) of rainfall extremes for thresholds: (a) 1, (b) 5, (c) 10, (d) 20, − ≥ ≥ ≥ ≥ (e) 30, and (f) 50 mm for the period 1978–2019. The upward (blue) and downward (red) triangles ≥ ≥ represent increasing and decreasing trends, respectively. Note that the scales of all the panels are not the same. Atmosphere 2020, 11, x FOR PEER REVIEW 17 of 30

20 1 mm 5 mm 18 10 mm 20 mm 30 mm 40 mm 16 50 mm 14 12 10

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1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1978 FigureFigure 9. Extreme 9. Extreme rainfall rainfall time time sequence sequence of event of event frequency frequency for di forﬀerent different thresholds thresholds in Saudi in ArabiaSaudi Arabia for thefor period the period 1978–2019. 1978–2019.

The observational data indicate that most stations show negative trends in the frequency of −1 extreme events that have rainfall thresholds of up1 to 50 mm day (Figure 10). For example, Dammam, Najran, and Gizan have significant positive trends in the frequency of less intense rainfall events, but the majority of stations showed significantly decreasing trends as the intensity increased. For the Abha, Al-Quasumah, and Gassim stations, the trends were negative for less intense rainfall but became positive for intense events. It is evident that rainfall events with light to moderate intensity show significantly decreasing trends over most of Saudi Arabia, but the majority of stations display positive trends for the most intense rainfall events. Overall, for the country average, trends in rainfall event frequency change from negative to positive as the event intensity increases. As the threshold increases, a large number of stations, which show a negative trend at a low threshold, move towards more positive trends, which is a clear indication that the country may experience more extreme rainfall events in the future.

Atmosphere 2020, 11, 964 16 of 26

The observational data indicate that most stations show negative trends in the frequency of 1 extreme events that have rainfall thresholds of up to 50 mm day− (Figure 10). For example, Dammam, Najran, and Gizan have significant positive trends in the frequency of less intense rainfall events, but the majority of stations showed significantly decreasing trends as the intensity increased. For the Abha, Al-Quasumah, and Gassim stations, the trends were negative for less intense rainfall but became positive for intense events. It is evident that rainfall events with light to moderate intensity show significantly decreasing trends over most of Saudi Arabia, but the majority of stations display positive trends for the most intense rainfall events. Overall, for the country average, trends in rainfall event frequency change from negative to positive as the event intensity increases. As the threshold increases, a large number of stations, which show a negative trend at a low threshold, move towards more positive trends, which is a clear indication that the country may experience more extreme rainfall Atmosphereevents in 20 the20, future.11, x FOR PEER REVIEW 18 of 30

2.5 Abha Khamis 2.0 Taif Gassim 1.5 Al-Baha Gizan Qaisumah 1.0 Riyadh Makkah 0.5 Hail Dhammam 0.0 Al-Ahsa Bisha -0.5 Turaif Rafha Trends (day/decade) Trends Madinah -1.0 Najran Arar -1.5 Sharorah Al-Jouf -2.0 Jeddah Gurait -2.5 Yenbo 1 5 10 15 20 25 30 35 40 45 50 Tabuk Daily Rainfall Threshold (mm) Wejh Country

Figure 10. 10. ExtremeExtreme rainfall rainfall trends trends for for different diﬀerent thresholds thresholds at eachat each station station in Saudi in Saudi Arabia Arabia for the for periodthe period 1978 1978–2019.–2019.

Statistical analysis analysis of of the annual frequency of rainfall events reveals the decadal behavior of extreme rainfall rainfall events events in in ﬁve five climatic climatic regions regions of Saudi of Saudi Arabia Arabia (Figure (Figure 11). There 11). is There a marginal is a marginal increase increasein the frequency in the frequency of extreme of rainfall extreme events rainfall in the events most in recent the most decade recent (Figure decade 11a). ( TheFigure frequency 11a). The of frequencyextreme events of extreme is highly events variable is highly from variable north tofrom south north and to has south changed and has over changed time during over time the during period the1978–2019. period The1978 stations–2019. The in the stations northern in the region northern show an region increase show in thean increase frequency inof the extreme frequency rainfall of extremeevents during rainfall the events most during recent decadethe most (Figure recent 11 decadeb). The (Figure stations 11b). in the The coastal stations and in highland the coastal regions and highlandshow the regions highest show frequency the highest of extreme frequency rainfall of extreme events as rainfall compared events to as other compared regions to (Figureother regions 11c,e), (andFigure this 11c,e), may be and due this to themay moisture be due convergenceto the moisture zones convergence that occur there.zones Thethat highestoccur there. frequency The highest of light frequencyto moderate of rainfall light to events moderate is observed rainfall in events the highland is observed region in (Figure the highland 11e). The region stations (Figure in the 11e). southern The stationsregion show in the relatively southern few region very show extreme relatively rainfalls few events very (Figureextreme 11 rainfallsf). The historicalevents (Figure register 11f). shows The historicalthat the frequency register shows of extreme that the rainfall frequency events of isextreme high in rainfall the highland, events is interior, high in andthe highland, coastal regions interior, as andcompared coastal toregions the other as compared two regions. to the From other the two analysis, regions. itFrom is concluded the analysis, that it the is concluded highland, that interior, the highland,and coastal interior, regions and will coastal experience regions more will frequent experience extreme more rainfall frequent events extreme in the rainfall near future. events in the near future.

16 16 (a) Country (b) Northern 14 14 12 12 10 10 8 8

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Frequency Frequency (days) Frequency Frequency (days) 4 4 2 2 0 0 1980—1989 1990—1999 2000—2009 2010—2019 1980—1989 1990—1999 2000—2009 2010—2019

Atmosphere 2020, 11, x FOR PEER REVIEW 18 of 30

Figure 10. Extreme rainfall trends for different thresholds at each station in Saudi Arabia for the period 1978–2019.

Statistical analysis of the annual frequency of rainfall events reveals the decadal behavior of extreme rainfall events in five climatic regions of Saudi Arabia (Figure 11). There is a marginal increase in the frequency of extreme rainfall events in the most recent decade (Figure 11a). The frequency of extreme events is highly variable from north to south and has changed over time during the period 1978–2019. The stations in the northern region show an increase in the frequency of extreme rainfall events during the most recent decade (Figure 11b). The stations in the coastal and highland regions show the highest frequency of extreme rainfall events as compared to other regions (Figure 11c,e), and this may be due to the moisture convergence zones that occur there. The highest frequency of light to moderate rainfall events is observed in the highland region (Figure 11e). The stations in the southern region show relatively few very extreme rainfalls events (Figure 11f). The historical register shows that the frequency of extreme rainfall events is high in the highland, interior, and coastal regions as compared to the other two regions. From the analysis, it is concluded that the highland, interior, and coastal regions will experience more frequent extreme rainfall events in the nearAtmosphere future.2020 , 11, 964 17 of 26

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6 6 Frequency Frequency (days) Frequency Frequency (days) 4 4 2 2 0 0 1980—1989 1990—1999 2000—2009 2010—2019 1980—1989 1990—1999 2000—2009 2010—2019

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8 Frequency Frequency (days) 6 Frequency (days) 4 4 2 2 0 0 1980—1989 1990—1999 2000—2009 2010—2019 1980—1989 1990—1999 2000—2009 2010—2019

Figure 11. Annual frequency (days) of rainfall extremes in different decades for (a) country average, Figure(b) northern, 11. Annual (c) coastal, frequency (d) interior, (days) (ofe) rainfall highland, extremes and (f) in southern different regions decades in Saudifor (a) Arabia. country The average vertical, (scaleb) northern, for all panels (c) coastal, is the same (d) interior, except for (e) the highland, highland. and (f) southern regions in Saudi Arabia. The vertical scale for all panels is the same except for the highland. The analysis of the time series of daily rainfall at 25 stations in Saudi Arabia showed a significantly decreasing trend in the frequency of light to moderate intensity rainfall events (Table2). The trends The analysis of the time series of daily rainfall at 25 stations in Saudi Arabia showed a in the extremes are observed to be homogeneous in the central and southwestern parts, with some significantly decreasing trend in the frequency of light to moderate intensity rainfall events (Table 2). differences in the significance levels. However, the daily rainfall data analysis shows increasing trends, The trends in the extremes are observed to be homogeneous in the central and southwestern parts, though insignificant, in the frequency of extreme rainfall events of 30 mm. Any positive or increasing with some differences in the significance levels. However, the daily≥ rainfall data analysis shows increasingtrend in the trends, frequency though of extremeinsignificant, rainfall in eventsthe frequency is a serious of extreme concern rainfall for the events regions of of ≥30 Saudi mm. Arabia Any positivevulnerable or toincreasing flash floods. trend These in the statistical frequency analyses of extreme can have rainfall severe events implications is a serious for theconcern probability for the of regioflashns flooding of Saudi events Arabia and vulnerabledroughts, which to flash can floods. be triggered These under statistical the influence analyses of can a future have warmersevere implicationsclimate [24]. for the probability of flash flooding events and droughts, which can be triggered under the influence of a future warmer climate [24].

Atmosphere 2020, 11, 964 18 of 26

1 Table 2. Rainfall trends (mm decade− ) at different station locations in Saudi Arabia. The superscripts a, b, and c represents trend significance at the 90%, 95%, and 99% confidence level, respectively.

Region Station 1 mm 5 mm 10 mm 15 mm 20 mm 25 mm 30 mm 35 mm 40 mm 45 mm 50 mm Tabuk 0.04 0.23 0.15 c 0.05 0.05 0.06 0.05 0.03 0.00 0.02 0.00 − − − − − Turaif 0.85 0.12 0.35 0.05 0.05 0.12 0.15 b 0.13 b 0.13 b 0.13 b 0.12 b − − − − − − − − − − − Gurait 0.43 a 0.24 a 0.03 0.10 0.03 0.04 0.01 0.03 0.04 0.00 a 0.00 a − − − − − Rafha 0.88 0.30 0.37 c 0.16 0.06 0.04 0.05 0.08 b 0.06 c 0.00 0.00 − − − − − − − − − Arar 0.72 0.36 0.17 0.04 0.07 0.00 0.08 b 0.07 b 0.03 0.00 0.00 Northern − − − − Al-Jouf 0.02 0.01 0.19 0.24 c 0.17 b 0.11 b 0.10 a 0.03 c 0.00 0.00 0.00 Hail 1.60 b 1.54 a 1.04 a 0.74 a 0.41 a 0.27 a 0.24 a 0.23 a 0.13 a 0.13 a 0.10 b − − − − − − − − − − − Gassim 1.74 b 1.21 b 0.57 c 0.26 0.10 0.10 0.11 0.11 0.15 b 0.10 c 0.05 − − − − − − − − − − − Riyadh 1.04 0.81 0.76 c 0.26 0.23 0.13 0.03 0.02 0.02 0.02 0.00 − − − − − − − − − − Al-Qaisumah 2.00 a 0.95 b 0.23 0.10 0.02 0.03 0.00 0.02 0.03 0.03 0.01 − − − − − − Dammam 2.06 0.29 0.22 0.26 0.32 0.29 0.14 0.04 0.04 0.12 0.12

Interior − − Al-Ahsa 0.01 0.11 0.21 0.18 0.08 0.00 0.03 0.08 0.08 0.08 0.05 − − − − − − − − Madinah 1.40 b 0.71 b 0.09 0.04 0.03 0.05 0.01 0.03 0.01 0.05 0.04 − − − Wejh 0.00 0.22 0.23 c 0.23 b 0.13 0.10 0.09 0.09 0.07 0.06 0.04 Yenbo 0.18 0.10 0.02 0.06 0.09 0.12 c 0.11 c 0.01 0.02 0.03 0.03 Jeddah 0.53 0.18 0.15 0.12 0.01 0.02 0.04 0.07 0.06 0.07 0.06 b c − − − − − − Coastal Makkah 0.83 0.85 0.38 0.38 0.11 0.03 0.02 0.02 0.00 0.02 0.03 − − − − − − − − Gizan 1.83 a 0.49 0.10 0.08 0.01 0.15 0.10 0.14 0.08 0.15 0.04 Taif 0.60 0.20 0.06 0.08 0.03 0.13 0.12 0.01 0.03 0.04 0.01 − − − − − − − − − Al-Baha 0.76 0.46 0.75 c 0.61 b 0.43 b 0.32 a 0.14 0.17 b 0.17 b 0.15 c 0.02 − − − − − − − − − − Khamis Mushait 0.58 0.67 0.52 0.45 c 0.30 0.22 0.10 0.03 0.08 0.12 0.12 c − a − b − b − − − − Highland Abha 2.17 0.99 0.82 0.50 0.37 0.32 0.24 0.18 0.13 0.08 0.06 − − − − − − − − − − − Bisha 1.21 c 0.29 0.47 c 0.15 0.04 0.03 0.01 0.04 0.01 0.01 0.01 − − − − − − − − − Najran 1.29 c 0.59 c 0.31 0.13 0.06 0.07 0.04 0.00 0.04 0.02 0.02 Sharorah 0.18 0.17 0.18 0.10 0.13 0.11 0.10 0.03 0.03 0.02 0.02 Southern − − − − − − − − − − Country 0.43 0.32 c 0.24 c 0.12 0.06 0.02 0.01 0.02 0.03 0.01 0.00 − − − − − − − − − −

For the lower event thresholds (1–20 mm), the rainfall trend is decreasing over many stations, while a few stations show an increasing trend (Figure 12). For the higher thresholds (25–50 mm), this scenario is reversed, and a large number of stations show an increasing trend of extreme rainfall, while a few stations have decreasing trends, except for the 45 mm threshold. Overall, there is an increase ≥ in the trend of extreme rainfall events with an intensity of 25 mm and above. For the threshold 50 mm, ≥ ≥ 15 stations (60%) show a positive trend, while 14 stations (56%) show a positive trend for the threshold 30 mm. The northern and coastal regions contributed 33% and 27%, respectively, of the 60% positive ≥ trend for the threshold 50 mm. For light rainfall, more stations show a decreasing trend than ≥ an increasing trend. In the case of the threshold 1 mm, 64% of the stations show a decreasing trend, ≥ of which the northern region contributes 38%, while the interior and highland regions contribute 25%. These are all evidence of an increasing trend in extreme rainfall events in Saudi Arabia. The extreme rainfall trends and events are essential for the deﬁnition of the regional climate. This kind of analysis will be helpful to understand the risk for the country and to take possible countermeasures. Atmosphere 2020, 11, 964 19 of 26 Atmosphere 2020, 11, x FOR PEER REVIEW 21 of 30

Increase Sig Increase Insig Decrease Sig Decrease Insig

10 Daily Rainfall Threshold (mm) Threshold Rainfall Daily

0 5 10 15 20 25 Number of Station

Figure 12. The number of stations with extreme rainfall trends increasing (blue) and decreasing (brown) atFigure diﬀerent 12. thresholdsThe number for of the stations period 1978–2019.with extreme rainfall trends increasing (blue) and decreasing (brown) at different thresholds for the period 1978–2019. 3.4. Rainfall Return Period 3.4. RainfallThe depth–duration–frequency Return Period (DDF) curves are plotted on a logarithmic scale for the vertical axis onlyThe depth (Figure–duration 13). Abha–frequency and Taif (DDF) receive curves almost are 100 plotted mm ofon rainfall a logarithmic every year,scale andfor the more vertical than 550axis mmonly is (Figure expected 13). to Abha occur and within Taif thereceive next almost 40-year 100 return mm periodof rainfall (Figure every 13 year,a). However, and more Sharorah, than 550 Jeddah,mm is expected and Yenbo to showed occur within that every the year next rainfall 40-year is return observed period below (Figure 1 mm, 13a). and withinHowever, the mentioned Sharorah, 40-yearJeddah, return and Yenbo period showed only 50 that mm every of rainfall year rainfallis expected is observed to happen. below For 1 the mm, wet and season within return the periodmentioned curves 40- (Figureyear return 13b), period the Abha, only Gassim, 50 mm andof rainfall Al-Qaisumah is expected stations to happen. observed For around the wet 20 season mm of rainfallreturn period every curves year, and (Figure within 13b), the the mentioned Abha, Gassim, 40-year and return Al-Qaisumah period, around stations 250 observed mm is expected around 20 to happen.mm of rainfall However, every Sharorah, year, and Jeddah, within Yenbo, the mentioned and Tabuk observed40-year return rainfall period, of below around 1 mm 250 each mm year, is andexpected within to happen. the mentioned However, 40-year Sharorah, return Jeddah, period Yenbo, 50 mm and of rainfallTabuk observed is expected rainfall to happen. of below Finally, 1 mm foreach the year, dry and season within return the mentioned period (Figure 40-year 13c), return Abha, period Al-baha, 50 mm Khamis of rainfall Mushait, is expected and Taif to typicallyhappen. observedFinally, for rainfall the dry of between season return 1 to 10 period mm, and (Figure a 100 13 mmc), rainfall Abha, Al event-baha, is expected Khamis toMushait, happen and at some Taif timetypically in the observed next 40 years.rainfall There of between are many 1 to stations 10 mm, whereand a 100 rainfall mm doesrainfall not event occur is every expected year to in happen the dry season.at some Intime such in the stations, next 40 some years. rainfall There may are many occur stations within awhere 5-year rainfall period. does For not example, occur every at Al-Jouf, year Turaif,in the dry and season. Yenbo, In a 1 such mm rainfallstations, event some is rainfall expected may to occur sometimewithin a 5- withinyear period. a 5-year For return exam period.ple, at Al-Jouf, Turaif, and Yenbo, a 1 mm rainfall event is expected to occur sometime within a 5-year return period.

AtmosphereAtmosphere2020 2020, 11, 1,1 964, x FOR PEER REVIEW 2220 of of 30 26

1000 (a) Annual

100

Annual Rainfall (mm) Rainfall Annual 0.1

0.01

1.02 1.05 1.08 1.10 1.13 1.16 1.19 1.23 1.26 1.30 1.34 1.39 1.43 1.48 1.54 1.59 1.65 1.72 1.79 1.87 1.95 2.05 2.15 2.26 2.39 2.53 2.69 2.87 3.07 3.31 3.58 3.91 4.30 4.78 5.38 6.14 7.17 8.60

14.33 21.50 43.00 10.75

1000 (b) Wet

100

0.1 Wet Season Rainfall (mm) Rainfall Season Wet

0.01

1.02 1.05 1.08 1.10 1.13 1.16 1.19 1.23 1.26 1.30 1.34 1.39 1.43 1.48 1.54 1.59 1.65 1.72 1.79 1.87 1.95 2.05 2.15 2.26 2.39 2.53 2.69 2.87 3.07 3.31 3.58 3.91 4.30 4.78 5.38 6.14 7.17 8.60

14.33 21.50 43.00 10.75

1000 Dammam Guriat (c) Dry Al-Ahsa Riyadh Al-Baha 100 Sharorah Makkah Abha Al-Jouf 10 Bisha Gassim Gizan Hail 1 Jeddah Khamis Madinah Najran Qaisumah 0.1 Rafha Tabuk

Dry Season Rainfall (mm) Rainfall Season Dry Taif Turaif 0.01 Wejh Yenbo

Arar

1.05 1.08 1.10 1.13 1.16 1.19 1.23 1.26 1.30 1.34 1.39 1.43 1.48 1.54 1.59 1.65 1.72 1.79 1.87 1.95 2.05 2.15 2.26 2.39 2.53 2.69 2.87 3.07 3.31 3.58 3.91 4.30 4.78 5.38 6.14 7.17 8.60

1.02 Country

14.33 21.50 43.00 Return Period (year) 10.75

FigureFigure 13. 13.The The depth–duration–frequency depth–duration–frequency curvescurves of the return periods for the ( a) annual, ( (b)) wet wet season,season, and and ( c()c dry) dry season season rainfall rainfall at at di differentﬀerent stations stations averaged averaged forfor thethe periodperiod 1978–2019.1978–2019. Rainfall amountsamounts on on the the vertical vertical axis axis are are in in a a logarithmic logarithmic scale. scale.

Atmosphere 2020, 11, 964 21 of 26

3.5. ContributionAtmosphere 20 of20 Extreme, 11, x FOR RainfallPEER REVIEW to the Total Rainfall 24 of 30 The contribution of extreme rainfall to the total rainfall is important for the development of early 3.5. Contribution of Extreme Rainfall to the Total Rainfall warning systems and for planning preventive measures against flash floods within the country. For the The contribution of extreme rainfall to the total rainfall is important for the development of early 90th, 95th, and 99th percentiles, it was found that the amount of rainfall at the 90th percentile was warning systems and for planning preventive measures against flash floods within the country. For greater, whilethe 90 itth, was 95th, less and for99th the percentiles, 99th percentile it was found as compared that the amount to the of threshold-basedrainfall at the 90th percentile rainfall (Figurewas 14a). For simplicity,greater, onlywhile threeit was less threshold-based for the 99th percentile rainfall as compared amounts to are the shownthreshold here.-based Therainfall threshold-based (Figure rainfall amounts14a). For and simplicity, percentile-based only three threshold amounts-based are di rainfallfferent amounts for diff areerent shown stations. here. However,The threshold therainfall amount atbased the 95thrainfall percentile amounts and is almost percentile equivalent-based amounts to rainfall are different with a for threshold differentof stations.26 mm However, at the country the rainfall amount at the 95th percentile is almost equivalent to rainfall with a threshold≥ of ≥26 mm scale. The scatter plot (Figure 14b) clearly shows that the country-averaged rainfall amounts for at the country scale. The scatter plot (Figure 14b) clearly shows that the country-averaged rainfall the 95th percentile and the 26 mm threshold lies along the 45-degree line of the plot. For the 95th amounts for the 95th percentile≥ and the ≥26 mm threshold lies along the 45-degree line of the plot. percentile,For the the threshold95th percentile, of the20 threshold mm allows of ≥20 toomm muchallows too rain, much while rain, thewhile threshold the threshold of of30 ≥30 mm mm permits ≥ ≥ too little.permits Therefore, too little. the Therefore, threshold the of threshold26 mm ofwas ≥26 mm chosen was chosen as the as extreme the extreme rainfall rainfall threshold threshold (close to ≥ the 95th percentile)(close to the 95 overth percentile) the country over asthe acountry whole. as a whole.

5000 (a) 99p 95p 90p 30 mm 4000 26 mm 20 mm

3000

2000 Rainfall (mm) Rainfall 1000

Abha

Bisha

Gizan

Turaif

Tabuk

Guriat

Yenbo

Riyadh

Jeddah

Madinah

Sharorah Dammam Qaisumah

4000 (b) 20 mm 26 mm 30 mm 3500

3000

2500

2000

1500

1000 Rainfall at Daily Threshold (mm) Threshold Daily at Rainfall 500

0 0 500 1000 1500 2000 2500 3000 3500 4000 Rainfall at 95p (mm)

Figure 14. (a) Amount of rainfall (mm) at the 90th, 95th, and 99th percentiles along with at

the 20, 26, and 30 mm thresholds in Saudi Arabia for the period 1978–2019. (b) Scatter plot ≥ ≥ ≥ of the percentile-based and threshold-based rainfall. The open-square, triangle, and circle represent station values, while the closed square, triangle, and circle represent the country average at 20, 26, ≥ ≥ and 30 mm, respectively. ≥ Atmosphere 2020, 11, x FOR PEER REVIEW 25 of 30

Figure 14. (a) Amount of rainfall (mm) at the 90th, 95th, and 99th percentiles along with at the ≥20, ≥26, and ≥30 mm thresholds in Saudi Arabia for the period 1978–2019. (b) Scatter plot of the percentile- based and threshold-based rainfall. The open-square, triangle, and circle represent station values, while the closed square, triangle, and circle represent the country average at ≥20, ≥26, and ≥30 mm, respectively.

Given the fact that extreme rainfall events contributed to the total rainfall in Saudi Arabia, a comprehensive analysis was performed on the daily rainfall data to explore the changes in the total amount of water as extreme rainfall as well as quantifying the contributions from changes in the intensity and the frequency (Figure 15). The investigation of extreme rainfall events shows that extreme rainfall events of ≥26 mm contributed between 8% (Arar) and 50% (Yenbo) of the total rainfall occurring in Saudi Arabia. Annually, less than 10% of extreme events contribute 26% of the total rainfall (24 mm), while more than 90% of the events contribute the other 74% (66 mm) for the entire country (Figure 15a). For the country average, rainfall occurred mostly in the wet season, and extreme events contributed the most (32% of the total) in November, followed by 30% in March. For the northern region, rainfall is almost zero during the dry season, and extreme events contribute the most (20%) in May, followed by 19% in January (Figure 15b). In the coastal region, extreme events contribute strongly, with the largest contributions in the October–February period Atmosphere(Figure2020 15c)., 11, In 964 this region, extreme events contribute, on average, 47% (43–53%) of the total in each22 of 26 month from October to February, with the highest (53%) in November. The inland region follows a similar pattern to the northern region (Figure 15d). For the highlands, rainfall occurs in almost all Given the fact that extreme rainfall events contributed to the total rainfall in Saudi Arabia, months, especially in March–May and July–August (Figure 15e). In this region, extreme rainfall a comprehensiveevents contribute analysis the most was (50% performed of the total) on the in dailyFebruary, rainfall followed data toby explore49% in March. the changes The rainfall in the total amountcharacteristics of water as in extreme the southern rainfall region as well are different as quantifying from in the other contributions regions, since from rainfall changes occurs in mainly the intensity andin the the frequency March–May (Figure period 15 ).(Figure The investigation 15f). In this region, of extreme extreme rainfall rainfall events has the shows highest that contribution extreme rainfall events(56%) of in26 June. mm Hence, contributed the contribution between of 8%extreme (Arar) events and to 50% the total (Yenbo) rainfall of theamount total fluctuates rainfall from occurring ≥ in Saudiregion Arabia. to region, Annually, with the lesshighest than contribution 10% of extreme (up to events56%) found contribute in the southern 26% of theregion total in rainfallJune, while (24 mm), whilethe more largest than contribution 90% of the from events the contribute coastal region the othercomes 74%between (66mm) October for theand entireFebruary. country Among (Figure the 15a). For thefive countryclimate regions, average, the rainfall coastal occurredregion is where mostly extreme in the wetevents season, comprise and the extreme largest eventsfractioncontributed of the total rainfall. the most (32% of the total) in November, followed by 30% in March.

60 60 (a) Country Rainfall ≥26 mm (b) Northern 50 50

40 40

30 30

20 20

Rainfall / Frequency RainfallFrequency / (%) RainfallFrequency / (%) 10 10

0 0

Atmosphere 2020, 11, x FOR PEER REVIEW 26 of 30

JUL

SEP

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SEP

FEB

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AUG

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Annual Annual

60 60 (c) Coastal (d) Interior 50 50

40 40

30 30

20 20

Rainfall / Frequency RainfallFrequency / (%) Rainfall / Frequency RainfallFrequency / (%) 10 10

0 0

JUL

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60 60 (e) Highland (f) Southern 50 50

40 40

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Rainfall / Frequency RainfallFrequency / (%) Rainfall / Frequency RainfallFrequency / (%) 10 10

0 0

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Annual Annual

Figure 15. Contribution of extreme rainfall to total amounts (%), and extreme event frequency (%) at the 26 mm threshold in Saudi Arabia and ﬁve climate regions for the period 1978–2019. Annually, ≥Figure 15. Contribution of extreme rainfall to total amounts (%), and extreme event frequency (%) at fewerthe than ≥26 10%mm threshold of extreme in eventsSaudi Arabia contributed and five 26% climate of the regions total rainfall for the (24period mm), 1978 while–2019 more. Annually, than 90% of eventsfewer contributed than 10% of the extreme other 74%events (66 contributed mm) for the 26% entire of the country. total rainfall (a) Country.(24 mm), while (b) Northern. more than ( c90%) Coastal. (d) Interior.of events ( econtributed) Highland. the (f )other Southern. 74% (66 mm) for the entire country. (a) Country. (b) Northern. (c) Coastal. (d) Interior. (e) Highland. (f) Southern.

Atmosphere 2020, 11, 964 23 of 26

For the northern region, rainfall is almost zero during the dry season, and extreme events contribute the most (20%) in May, followed by 19% in January (Figure 15b). In the coastal region, extreme events contribute strongly, with the largest contributions in the October–February period (Figure 15c). In this region, extreme events contribute, on average, 47% (43–53%) of the total in each month from October to February, with the highest (53%) in November. The inland region follows a similar pattern to the northern region (Figure 15d). For the highlands, rainfall occurs in almost all months, especially in March–May and July–August (Figure 15e). In this region, extreme rainfall events contribute the most (50% of the total) in February, followed by 49% in March. The rainfall characteristics in the southern region are different from in other regions, since rainfall occurs mainly in the March–May period (Figure 15f). In this region, extreme rainfall has the highest contribution (56%) in June. Hence, the contribution of extreme events to the total rainfall amount fluctuates from region to region, with the highest contribution (up to 56%) found in the southern region in June, while the largest contribution from the coastal region comes between October and February. Among the five climate regions, the coastal region is where extreme events comprise the largest fraction of the total rainfall.

4. Summary and Conclusions An analysis of the observational data for the 42-year period 1978–2019 has been carried out to understand the patterns of rainfall climatology and the frequency of extreme rainfall events over Saudi Arabia. The contribution of extreme rainfall events to the total amount of rainfall was also examined by analyzing the daily rainfall records in five climate regions of Saudi Arabia. The observational dataset shows that the highest amounts of rainfall occur during the wet season (October–May) and the lowest in the dry season (June–September). Climatologically, the greatest rainfall intensity occurs in the central and southwestern parts along the Red Sea coast. The northern, northwestern, and southeastern areas have a mean climatology that ranges from low to medium intensity. In general, the annual and wet seasonal rainfalls over Saudi Arabia reveal a decreasing trend during the study period. The highest rate of decrease in the annual (and wet-season) rainfall has 1 accelerated to between 10 to 20 mm decade− in the central and southwestern region, which is an alarming situation for the water resources, agricultural production, and socio-economic needs of the country. However, some stations in the eastern, northwestern, and southern parts show weak (not statistically significant) increasing trends. There are significant negative rainfall trends at annual and seasonal time scales over several stations for the entire study period (1978–2019). However, the trends in the dry season are small and insignificant. The contribution of extreme events to the total rainfall amount is important for application-oriented tasks. This analysis found that the 26 mm threshold is the appropriate country-averaged cut-off ≥ for defining extreme events. The contribution of extreme events to the total rainfall varies from month to month as well as from one region to another. The highest contribution (up to 56%) was found in the southern region in June, where the maximum contribution comes from coastal regions. Regionally, coastal regions contributed about 47% in each month from October to February, which is important for effective planning. For the country, extreme rainfall contributes the most (52% of the total) in November and the least (20% of the total) in July. Overall, extreme rainfall contributes between 8% (Arar) and 50% (Yenbo) of the total rainfall in Saudi Arabia. Further, it is found that there is an increasing trend in the frequency of extreme rainfall events, though this is statistically insignificant for many stations. This increasing trend in the frequency of extreme rainfall over Saudi Arabia may be associated with climate change over recent decades, which needs further investigation to understand the impact of climate change over the country. The analyses in this study have determined trends in extreme rainfall events which will be helpful for deciding appropriate mitigation measures, in particular for the water and agriculture sectors of the country. The identification of homogeneous and heterogeneous trends provides a basis for making predictions of these events. Future changes in the extreme rainfall trends in Saudi Arabia can be further explored using climate model simulations. Atmosphere 2020, 11, 964 24 of 26

Funding: This research received no external funding. Acknowledgments: The author thank the three anonymous reviewers for their valuable comments and suggestions. The author also acknowledges the Centre of Excellence for Climate Change Research (CECCR), King Abdulaziz University, for supporting this research work. The GAMEP is acknowledged for the observational dataset, and CHIRPS and GPCC are acknowledged through their website. The computations described in this paper were performed on the Aziz Supercomputer at King Abdulaziz University’s High-Performance Computing Center, Jeddah, Saudi Arabia. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Easterling, D.R.; Meehl, G.A.; Parmesan, C.; Changnon, S.A.; Karl, T.R.; Mearns, L.O. Climate Extremes: Observations, Modeling, and Impacts. Atmos. Sci. Rev. 2000, 289, 2068–2074. [CrossRef][PubMed] 2. Atif, R.M.; Almazroui, M.; Saeed, S.; Abid, M.A.; Islam, M.N.; Ismail, M. Extreme precipitation events over Saudi Arabia during the wet season and their associated teleconnections. Atmos. Res. 2020, 231, 104655. [CrossRef] 3. IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group to the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC); Cambridge University Press: Cambridge, UK, 2013. 4. Irizarry-Ortiz, M.M.; Obeysekera, J.; Park, J.; Trimble, P.; Barnes, J.; Park-Said, W.; Gadzinski, E. Historical trends in Florida temperature and rainfall. Hydrol. Process. 2013, 27, 2225–2246. [CrossRef] 5. IPCC. Climate Change 2007. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC); Cambridge University Press: Cambridge, UK, 2007. 6. Katz, R.W.; Brown, B. Extreme Events in a Changing Climate: Variability Is More Important Than Averages. Clim. Chang. 1992, 21, 289–302. [CrossRef] 7. Bell, J.E.; Brown, C.L.; Conlon, K.; Herring, S.; Kunkel, K.E.; Lawrimore, J.; Luber, G.; Schreck, C.; Smith, A.; Uejio, C. Changes in extreme events and the potential impacts on human health. J. Air Waste Manag. Assoc. 2018, 68, 265–287. [CrossRef][PubMed] 8. Almazroui, M.; Islam, M.N.; Athar, H.; Jones, P.D.; Rahman, M.A. Recent climate change in the Arabian Peninsula: Annual rainfall and temperature analysis of Saudi Arabia for 1978–2009. Int. J. Climatol. 2012, 32, 953–966. [CrossRef] 9. Saudi Arabia Population 1960–2019 Data. Available online: https://tradingeconomics.com/saudi-arabia/ population (accessed on 9 September 2020). 10. Sung, J.H.; Chung, E.S.; Lee, B.; Kim, Y. Meteorological hazard assessment based on trends and abrupt changes in rainfall characteristics on the Korean peninsula. Theor. Appl. Climatol. 2015, 127, 305–326. [CrossRef] 11. Bari, S.H.; Rahman, M.T.U.; Hoque, M.A.; Hussain, M.M. Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. Atmos. Res. 2016, 176, 148–158. [CrossRef] 12. Wang, X.-J.; Zhang, J.-Y.; Ali, M.; Shahid, S.; He, R.-M.; Xia, X.-H.; Jiang, Z. Impact of climate change on regional irrigation water demand in Baojixia irrigation district of China. Mitig. Adapt. Strateg. Glob. Chang. 2016, 21, 233–247. [CrossRef] 13. Goswami, B.N.; Venugopal, V.; Sengupta, D.; Madhusoodanan, M.S.; Xavier, P.K. Increasing trend of extreme rain events over India in a warming environment. Science 2006, 314, 1442–1444. [CrossRef] 14. Rajeevan, M.; Bhate, J.; Kale, K.D.; Lal, B. High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells. Curr. Sci. 2006, 91, 296–306. 15. Gao, X.; Peng, S.; Wang, W.; Xu, J.; Yang, S. Spatial and temporal distribution characteristics of reference evapotranspiration trends in Karst area: A case study in Guizhou Province, China. Meteorol. Atmos. Phys. 2016, 128, 677–688. [CrossRef] 16. Ongoma, V.; Chen, H. Temporal and spatial variability of temperature and rainfall over East Africa from 1951 to 2010. Meteorol. Atmos. Phys. 2017, 129, 131–144. [CrossRef] 17. Hag-elsaﬁ, S.; El-Tayib, M. Spatial and statistical analysis of rainfall in the Kingdom of Saudi Arabia from 1979 to 2008. Weather 2016, 71, 262–266. [CrossRef] 18. Nashwan, M.S.; Shahid, S.; Abd Rahim, N. Unidirectional trends in annual and seasonal climate and extremes in Egypt. Theor. Appl. Climatol. 2019, 136, 457–473. [CrossRef] 19. Nashwan, M.S.; Shahid, S. Spatial distribution of unidirectional trends in climate and weather extremes in Nile river basin. Theor. Appl. Climatol. 2019, 137, 1181–1199. [CrossRef] Atmosphere 2020, 11, 964 25 of 26

20. Chowdhury, S.; Al-Zahrani, M. Implications of Climate Change on Water Resources in Saudi Arabia. Arab. J. Sci. Eng. 2013, 38, 1959–1971. [CrossRef] 21. Almazroui, M.; Saeed, S. Contribution of extreme daily precipitation to total rainfall over the Arabian Peninsula. Atmos. Res. 2020, 231, 104672. [CrossRef] 22. El Kenawy,A.M.; McCabe, M.F.A multi-decadal assessment of the performance of gauge- and model-based rainfall products over Saudi Arabia: Climatology, anomalies and trends. Int. J. Climatol. 2016, 36, 656–674. [CrossRef] 23. Barlow, M.; Zaitchik, B.; Paz, S.; Black, E.; Evans, J.; Hoell, A. A review of drought in the Middle East and southwest Asia. J. Clim. 2016, 29, 8547–8574. [CrossRef] 24. Almazroui, M.; Islam, M.N. Coupled Model Inter-comparison Project Database to Calculate Drought Indices for Saudi Arabia: A Preliminary Assessment. Earth Syst. Environ. 2019.[CrossRef] 25. Myhre, G.; Alterskjær, K.; Stjern, C.W.; Hodnebrog, Ø.; Marelle, L.; Samset, B.H.; Sillmann, J.; Schaller, N.; Fischer, E.; Schulz, M.; et al. Frequency of extreme rainfall increases extensively with event rareness under global warming. Sci. Rep. 2019, 9, 1–10. [CrossRef][PubMed] 26. Martius, O.; Sodemann, H.; Joos, H.; Pfahl, S.; Winschall, A.; Croci-Maspoli, M.; Graf, M.; Madonna, E.; Mueller, B.; Schemm, S.; et al. The role of upper-level dynamics and surface processes for the Pakistan flood of July 2010. Q. J. R. Meteorol. Soc. 2013, 139, 1780–1797. [CrossRef] 27. Almazroui, M. The life cycle of extreme rainfall events over western Saudi Arabia simulated by a regional climate model: Case study of November 1996. Atmósfea 2012, 25, 23–41. 28. Almazroui, M.; Balkhair, K.S.; Islam, M.N.; ¸Sen,Z. Climate Change Impact on Monthly Precipitation Wet and Dry Spells in Arid Regions: Case Study over Wadi Al-Lith Basin. Adv. Meteorol. 2017.[CrossRef] 29. Tyrlis, E.; Lelieveld, J.; Steil, B. The summer circulation in the eastern Mediterranean and the Middle East: Influence of the South Asian monsoon. Clim Dyn. 2013, 40, 1103–1123. [CrossRef] 30. De Vries, A.J.; Feldstein, S.B.; Riemer, M.; Tyrlis, E.; Sprenger, M.; Baumgart, M.; Fnais, M.; Lelieveld, J. Dynamics of tropical–extratropical interactions and extreme rainfall events in Saudi Arabia in autumn, winter and spring. Q. J. R. Meteorol. Soc. 2016, 142, 1862–1880. [CrossRef] 31. Ehsan, M.A.; Nicolì, D.; Kucharski, F.; Almazroui, M.; Tippett, M.K.; Bellucci, A.; Ruggieri, P.; Kang, I.S. Atlantic Ocean influence on Middle East summer surface air temperature. NPJ Clim. Atmos. Sci. 2020, 3, 5. [CrossRef] 32. Nazemosadat, M.J.; Ghaedamini, H. On the relationships between the Madden–Julian oscillation and rainfall variability in southern Iran and the Arabian Peninsula: Atmospheric circulation analysis. J. clim. 2010, 23, 887–904. [CrossRef] 33. Kumar, V.V.; Jakob, C.; Protat, A.; Williams, C.R.; May, P.T. Mass-flux characteristics of tropical cumulus clouds from wind profiler observations at Darwin, Australia. J. Atmos. Sci. 2015, 72, 1837–1855. [CrossRef] 34. Hart, R.E.; Grumm, R.H. Using normalized climatological anomalies to rank synoptic-scale events objectively. Mon. Weather. Rev. 2001, 129, 2426–2442. [CrossRef] 35. Ehsan, M.A.; Tippett, M.K.; Almazroui, M.; Ismail, M.; Yousef, A.; Kucharski, F.; Omar, M.; Hussein, M.; Alkhalaf, A.A. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation. Clim. Dyn. 2017, 48, 3309–3324. [CrossRef] 36. Almazroui, M.; Islam, M.N.; Balkhair, K.S.; ¸Sen,Z.; Masood, A. Rainwater harvesting possibility under climate change: A basin-scale case study over western province of Saudi Arabia. Atmos. Res. 2017.[CrossRef] 37. Clarke, R.T. Rainfall Trend Analysis: Return Period. Encycl. Hydrol. Sci. 2006.[CrossRef] 38. Almazroui, M.; Islam, M.N.; Dambul, R.; Jones, P.D. Trends of temperature extremes in Saudi Arabia. Int. J. Climatol. 2014, 34, 808–826. [CrossRef] 39. Funk, C.C.; Peterson, P.J.; Landsfeld, M.F.; Pedreros, D.H.; Verdin, J.P.; Rowland, J.D.; Romero, B.E.; Husak, G.J.; Michaelsen, J.C.; Verdin, A.P. A quasi-global precipitation time series for drought monitoring. US Geol. Surv. Data Ser. 2014, 832, 4. [CrossRef] 40. Schneider, U.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Ziese, M.; Rudolf, B. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 2014, 115, 15–40. [CrossRef] 41. Almazroui, M.; Dambul, R.; Islam, M.N.; Jones, P.D. Principal components-based regionalization of the Saudi Arabian climate. Int. J. Climatol. 2015, 35, 2555–2573. [CrossRef] 42. Frei, C.; Schar, C. Detection probability of trends in rare event: Theory and application to heavy rainfall in the alpine region. J. Clim. 2000, 14, 1568–1584. [CrossRef] 43. Mann, H. Non-parametric tests against trend. Econometrica 1945, 13, 245–259. [CrossRef] Atmosphere 2020, 11, 964 26 of 26

44. Kendall, M. Rank Correlation Methods, 4th ed.; Charles Griﬃn: London, UK, 1975. 45. Sen, P.K. Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [CrossRef] 46. ¸Sen,Z. Flood Modeling, Prediction and Mitigation; Springer-Nature: Cham, Switzerland, 2018; 422p. [CrossRef] 47. Abid, M.A.; Almazroui, M.; Kucharski, F.; O’Brien, E.; Yousef, A.E. ENSO relationship to summer rainfall variability and its potential predictability over Arabian Peninsula region. NPJ Clim. Atmos. Sci. 2018, 1, 20171. [CrossRef] 48. De Vries, A.J.; Ouwersloot, H.G.; Feldstein, S.B.; Riemer, M.; El Kenawy, A.M.; McCabe, M.F.; Lelieveld, J. Identiﬁcation of tropical-extratropical interactions and extreme rainfall events in the Middle East based on potential vorticity and moisture transport. J. Geophy. Res: Atmos. 2018, 123, 861–881. 49. Almazroui, M.; Kamil, S.; Ammar, K.; Keay, K.; Alamoudi, A.O. Climatology of the 500-hPa Mediterranean storms associated with Saudi Arabia wet season precipitation. Clim. Dyn. 2016, 47, 3029–3042. [CrossRef] 50. Kamil, S.; Almazroui, M.; Kucharski, F.; Kang, I.S. Multidecadal changes in the relationship of storm frequency over euro-mediterranean region and ENSO during boreal winter. Earth Syst. Environ. 2017, 1.[CrossRef] 51. Ehsan, M.A.; Kucharski, F.; Almazroui, M.; Ismail, M.; Tippett, M.K. Potential predictability of Arabian Peninsula summer surface air temperature in the North American multimodel ensemble. Clim. Dyn. 2019, 53, 4249–4266. [CrossRef] 52. Tan, X.; Gan, T.Y.; Chen, S.; Horton, D.E.; Chen, X.; Liu, B.; Lin, K. Trends in persistent seasonal-scale atmospheric circulation patterns responsible for seasonal precipitation totals and occurrences of precipitation extremes over Canada. J. Clim. 2019, 32, 7105–7126. [CrossRef] 53. El Niño and La Niña Years and Intensities Updated through Jun-Jul-Aug 2020. Available online: https: //ggweather.com/enso/oni.htm (accessed on 9 September 2020). 54. El Kenawy, A.M.; McCabe, M.F.; Stenchikov, G.L.; Raj, J. Multi-decadal classification of synoptic weather types, observed trends and links to rainfall characteristics over Saudi Arabia. Front. Environ. Sci. 2014, 2, 37. [CrossRef]

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