climate

Article Climatology for the Arid Region of the —Variability, Trends and Extremes

Platon Patlakas 1,2 , Christos Stathopoulos 1,2, Helena Flocas 1 , Nikolaos S. Bartsotas 1,2 and George Kallos 1,2,*

1 Department of Physics, National and Kapodistrian University of Athens, University Campus, Bldg. PHYS-V, 15784 Athens, Greece; [email protected] (P.P.); [email protected] (C.S.); efl[email protected] (H.F.); [email protected] (N.S.B.) 2 Weather & Marine Engineering Technologies P.C. (WeMET P.C.), 17456 Athens, Greece * Correspondence: [email protected]

Abstract: The Arabian Peninsula is a region characterized by diverse climatic conditions due to its location and geomorphological characteristics. Its precipitation patterns are characterized by very low annual amounts with great seasonal and spatial variability. Moreover, extreme events often lead to flooding and pose threat to human life and activities. Towards a better understanding of the spatiotemporal features of precipitation in the region, a thirty-year (1986-2015) climatic analysis has been prepared with the aid of the state-of-the-art numerical modeling system RAMS/ICLAMS. Its two-way interactive nesting capabilities, explicit cloud microphysical schemes with seven categories of hydrometeors and the ability to handle dust aerosols as predictive quantities are significant advantages over an area where dust is a dominant factor. An extended evaluation based on in situ   measurements and satellite records revealed a good model behavior. The analysis was performed in three main components; the mean climatic characteristics, the rainfall trends and the extreme cases. Citation: Patlakas, P.; Stathopoulos, The extremes are analyzed under the principles of the extreme value theory, focusing not only on C.; Flocas, H.; Bartsotas, N.S.; Kallos, G. Precipitation Climatology for the the duration but also on the intensity of the events. The annual and monthly rainfall patterns are Arid Region of the Arabian investigated and discussed. The spatial distribution of the precipitation trends revealed insignificant Peninsula—Variability, Trends and percentage differences in the examined period. Furthermore, it was demonstrated that the eastern Extremes. Climate 2021, 9, 103. part and the top half of the western Arabian Peninsula presented the lowest risk associated with https://doi.org/10.3390/cli9070103 extreme events. Apart from the pure scientific interest, the present study provides useful information for different sectors of society and economy, such as civil protection, constructions and reinsurance. Academic Editor: Mário Gonzalez Pereira Keywords: regional climatology; precipitation; rainfall; Arabian Peninsula; arid/semi-arid re- gion precipitation; dynamical downscaling; trend analysis; extremes; intensity duration frequency; Received: 5 May 2021 return periods Accepted: 16 June 2021 Published: 22 June 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in published maps and institutional affil- The climate of the Arabian Peninsula (AP) is affected by the Indian monsoon in the iations. south and the Mediterranean synoptic scale systems in the north. Furthermore, the complex landscape—consisting of highlands in the western and southwestern regions, vast arid and extra arid lands in the mainland, the in the west and the sand desert in the southeast—plays an important role in the formation of the regional climatic features. The topography in varies from low altitudes in the coastal areas (0 up to 100 m) Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. to high altitudes in the mountainous areas (more than 2000 m). This article is an open access article The precipitation regime of the AP is generally characterized by arid climatologi- distributed under the terms and cal characteristics. Limited and infrequent rainfall events occur mainly from October conditions of the Creative Commons through April [1], except for the southwestern region, where high precipitation amounts Attribution (CC BY) license (https:// are reported [2]. However, precipitation is not extensively analyzed due to its irregu- creativecommons.org/licenses/by/ larity in space and time and the sparseness and/or unavailability of long records of 4.0/). observational datasets.

Climate 2021, 9, 103. https://doi.org/10.3390/cli9070103 https://www.mdpi.com/journal/climate Climate 2021, 9, 103 2 of 25

Almazroui et al. [3,4] investigated the annual and seasonal distribution of precipitation along with trends, employing station data and gridded datasets for the period 1979–2009. Alsarmi and Washington [5] calculated trends of precipitation in AP using also station data. Barth and Steinkohl [6] investigated the role of synoptic scale systems on precipitation of AP and demonstrated the role of Mediterranean depressions, the Sudan low and convective systems. The climatological characteristics and mechanisms of rainfall in AP are reviewed by Hasanean and Almazroui [1]. Mahmoud et al. [7] evaluated the seasonal rainfall and rainfall intensity in Saudi Arabia as derived from the satellite IMERG products against station measurements. Extreme precipitation events commonly occur in AP throughout the whole year due to the deep convection triggered by tropical and extratropical forcing along with the orography [8]. These extreme events frequently cause floods with severe impacts on human life and economy. It is worth mentioning that the extreme daily precipitation events contribute to about 20–70% of the total precipitation over the AP [9]. Regional climatic patterns of temperature, relative humidity and wind in the AP were studied by Patlakas et al. [10] with the aid of a state-of-the art, limited area atmospheric modeling system. More precisely, the employment of a high resolution atmospheric model resolved the problem of limited or unavailable data in particular areas and provided an insight on complicated climatic features. The atmospheric model simulated the spa- tial and temporal variations of the above mentioned climatic parameters over AP with a more reliable and detailed behavior, when compared to previous results of regional climatic models. As a further attempt to extend the study of Patlakas et al. [10], this work aims to present a regional precipitation climatology for the AP, employing the regional atmospheric model RAMS/ICLAMS. The model has run for a 30-year period (1986–2015) with a spatial resolution of 9 km and a temporal resolution of 3 hours. The great advantages of adopting RAMS/ICLAMS for a study focusing on precipitation are its highly sophisticated physical parameterizations, including dust and its numerical schemes, allowing the depiction of local scale patterns that are not represented in the simulations of climatic models. More specifically, the objectives are: a) to investigate the spatial precipitation patterns in the AP on a monthly basis, aiming to better assess the spatial and temporal variation of precipitation b) to examine the precipitation extremes along with their characteristics, such as intensity, duration and return periods and c) to analyze the precipitation trends in the AP for a period of 30 years (1986–2015).

2. Materials and Methods 2.1. Intensity, Duration and Frequency of Rainfall Events The probability of the average rainfall intensity over given periods of time can be expressed under the principles of the Extreme Value Theory and depicted employing rainfall Intensity–Duration–Frequency (IDF) curves [11]. These are used to estimate the relationship between a precipitation event and its frequency in terms of return periods. Extreme cases derived from the IDF curves are adopted, among others, by hydrometeorol- ogists, engineers and civil protection agencies for the design of urban drainage systems and the assessment of regional flood vulnerabilities [12,13]. According to Koutsoyiannis et al. [14], a basic step towards their construction is the retrieval of extreme rainfall intensities for different durations through an annual maximum analysis. These annual maximum values (Annual Maxima—AM) are well represented by different theoretical distribution functions. Among the most common procedures is to fit the Generalized Extreme Value distribution (GEVd) to the created subset and retrieve the estimated fitted parameters for each selected duration. These parameters will be used for the estimation of the reoccurrence intervals and lead to the relation between the intensity, duration and frequency and subsequently to the creation of the IDF curves. In this study, the annual maximum rainfall rate values for the 30-year period and for each model grid were obtained. These maximum rain rate values were determined for Climate 2021, 9, 103 3 of 25

different accumulation periods: 3 h to 48 h with a 3-h interval. The AM are often well approached by the first type of the GEVd [15,16]. Therefore, the Gumbel distribution is selected since only two parameters are required [17]. The estimation of the parameters is performed based on the Maximum Likelihood (ML) method [18,19]. The scale and the location parameter (α and β, respectively) can be estimated through the simultaneous numerical solution of the following equations:

n n ∑ xt exp(−xt/a) 1  x  x − t=1 − a = 0 and − a·log[ exp − i ] − β = 0 (1) e n (− ) ∑ ∑t=1 exp xt/a n i=1 a

where x1, . . . , xn is a random sample and xe is the sample mean. The estimation of the design values/intensities (IT) for a preferred return period (T) is performed through the relation between the cumulative frequency (F) and T [16]:

F(IT) = 1 − (1/T) (2)

Substituting the cumulative distribution function (CDF) of Gumbel, transforms Equation (2) to the following:

IT = β − αln[−ln(1 − 1/T)] (3)

The procedure was followed for return periods set to 10, 25, 50, 75 and 100 years. More details and applications on IDF curves can be found in the following publications [20–24].

2.2. Data, Model Set up and Evaluation Metrics The data used for the analysis of the precipitation characteristics is the product of dynamical downscaling based on a regional atmospheric modeling system. The model used is the Integrated Community Limited Area Modeling System (ICLAMS) based on the Regional Atmospheric Modeling System (RAMSv6) [25,26]. RAMS/ICLAMS was devel- oped within the Atmospheric Modeling and Weather Forecasting Group (AM&WFG) of the University of Athens, Greece [10,27–31]. It has two-way interactive nesting capabilities, an explicit cloud microphysical scheme with seven categories of hydrometeors and the ability to handle dust aerosols as predictive quantities. Concentrations, size distributions and optical properties of all elements are computed online. Natural aerosols contribute to the calculation of the meteorological conditions through feedback mechanisms (direct, semi-direct and indirect effects). The aforementioned features render the use of the model as most appropriate, especially over an area where dust is a dominant factor. The simulation period covers the years between 1986 and 2015 (30 years). The model spatial resolution is 9 km and the corresponding timestep is 15 seconds. The domain extent (Figure1) has a significant buffer zone around the Arabian Peninsula so that the boundaries would not affect the study area. Vertically, it stretches up to 20 km with 30 levels, while the output is saved every 3 hours. ERA-Interim [32] data is employed for the regional model initial and boundary condi- tions. This dataset is a global atmospheric reanalysis product distributed by the European Centre for Medium-Range Weather Forecasts (ECMWF) beginning from 1979. A major advantage in the use of such products for climatological studies is the consistency, homo- geneity and robustness in terms of model outputs. For Sea Surface Temperature (SST), the high resolution 0.083-deg analyses from NCEP have been used [33]. The soil texture and properties within the boundaries of the Kingdom of Saudi Arabia are derived from a custom soil categorization dataset, based on a very high resolution soil database from Saudi Aramco. Soil data for the neighboring countries originate from the Food and Agriculture Organization of the United Nations (FAO). The geological categories were grouped into several soil textural categories based upon their common properties. Vegetation and land use were acquired by USGS following the Olson Climate 2021, 9, 103 4 of 25

Global Ecosystem categorization [34,35]. NDVI is based on the work of Defries and Climate 2021, 9, x FOR PEER REVIEW Townshend [36]. The horizontal resolution of the datasets is 30 arcsec (~9004 of 25 m). The vegetation together with the soil categorization and slope inclination were combined in ArcGIS in order to dictate the dust uptake areas in the brand new soil classification scheme.

Figure 1. The model domain employed for the study. The locations and the codenames of the stations Figure 1. The model domain employed for the study. The locations and the codenames of the sta- used for the evaluation study are depicted with blue. The locations selected for the application of the tions used for the evaluation study are depicted with blue. The locations selected for the applica- tion of the IntensityIntensity–Duration–Frequency–Duration–Frequency analysisanalysis areare markedmarked inin red.red. In order to perform a quantitative statistical evaluation of the model outputs, widely ERA-Interim [32] data is employed for the regional model initial and boundary applied error metrics were used for the daily accumulated precipitation fields. The Mean conditions. This dataset is a global atmospheric reanalysis product distributed by the Bias Error (MBE) or Bias is the result of the mean modeled minus the mean observed European Centre for Medium-Range Weather Forecasts (ECMWF) beginning from 1979. values, a first indicator of over/underestimation. Error variability was approached with the A major advantage in the use of such products for climatological studies is the con- statistical indicators of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), sistency, homogeneity and robustness in terms of model outputs. which are the mean of absolute errors and the square root of the mean of the square of For Sea Surface Temperature (SST), the high resolution 0.083-deg analyses from the errors, respectively. Moreover, the Pierson correlation coefficient as an indicator of NCEP havelinear been relationship used [33]. The and soil the texture standard and deviation properties of modeledwithin the versus boundaries observed of the values were Kingdom alsoof Saudi implemented Arabia are [37 derived]. from a custom soil categorization dataset, based on a very high resolution soil database from Saudi Aramco. Soil data for the neighboring countries 3.originate Results from and Discussionthe Food and Agriculture Organization of the United Nations (FAO). The3.1. geological Dataset Evaluation categories were grouped into several soil textural categories based upon their commonThe quality properties. of the model-derived Vegetation and temperature land use were and wind acquired speed by is USGS thoroughly fol- investi- lowing thegated Olson and Global commented Ecosystem in Patlakas categorization et al. [10 [34,35]], where. NDVI a good is based model on performance the work of is evident. Defries andThe Townshend additional evaluation[36]. The horizontal of this manuscript resolution focuses of the ondatasets precipitation. is 30 arcsec The (~900 model-derived m). The vegetationrainfall data together was evaluated with the againstsoil categorization corresponding and remote slope inclination sensing and were in situ co records.m- The bined in ArcGISprocedure in order consists to dictate of two the parts, dust one up quantitativetake areas in employing the brand severalnew soil statistical classifica- indices and tion scheme.one qualitative, which includes the comparison of the spatial distribution of modeled In orderprecipitation to perform against a quantitative remotesensing statistical estimates. evaluation of the model outputs, widely applied error metricsBeginning were with used the for second, the daily the accumulated used satellite precipitation precipitation fields. estimates The Mean stem from the Bias ErrorIntegrated (MBE) or Multi-satellitEBias is the result Retrievals of the mean for GPM modeled (IMERG). minus IMERG the mean utilizes observed a combination values, a firstof satellite indicator data of andover/underestimation. techniques. It integrates Error variability satellite products was approached in global with scale, such as the statistical indicators of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), which are the mean of absolute errors and the square root of the mean of the square of the errors, respectively. Moreover, the Pierson correlation coefficient as an in- dicator of linear relationship and the standard deviation of modeled versus observed values were also implemented [37].

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3. Results and Discussion 3.1. Dataset Evaluation The quality of the model-derived temperature and wind speed is thoroughly inves- tigated and commented in Patlakas et al. [10], where a good model performance is evi- dent. The additional evaluation of this manuscript focuses on precipitation. The mod- el-derived rainfall data was evaluated against corresponding remote sensing and in situ records. The procedure consists of two parts, one quantitative employing several statis- tical indices and one qualitative, which includes the comparison of the spatial distribu- tion of modeled precipitation against remote sensing estimates.

Climate 2021, 9, 103 Beginning with the second, the used satellite precipitation estimates stem from5 the of 25 Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG utilizes a combination of satellite data and techniques. It integrates satellite products in global scale, such as the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM)the Tropical retrievals Rainfall [38]. MeasuringVersion 6 of Mission the fina (TRMM)l product and that Global is used Precipitation in this study Measurement is an out- come(GPM) of retrievalsthe combination, [38]. Version filtering 6 of theand final interpolation product that of isall used available in this passive study is microwave an outcome precipitationof the combination, estimates, filtering microwave and interpolation-calibrated of infrared all available (IR) satellite passive microwave estimates and precipita- cali- brationtion estimates, from available microwave-calibrated precipitation gauges infrared [39] (IR). It is satellite provided estimates in a resolution and calibration of 0.1ᵒx0.1ᵒ, from everyavailable thirty precipitation minutes starting gauges from [39 ].2000. It is provided in a resolution of 0.1 × 0.1, every thirty minutesIn particular, starting fromthe comparison 2000. concerns the mean annual accumulated precipitation, illustratedIn particular, in Figure the 2 comparison(the mean monthly concerns accumulated the mean annual precipitation accumulated -IMERG precipitation,- can be foundillustrated in Appendix in Figure 2A) (the. Cumulatively, mean monthly the accumulated spatial distribution precipitation of rainfall -IMERG- between can be foundmod- eledin Appendix and remoteA). sensi Cumulatively,ng estimates the is spatial in an acceptable distribution agreement. of rainfall Both between modeled modeled and sa andt- elliteremote outputs sensing demonstrate estimates is the in anlow acceptable and moderate agreement. precipitation Both modeled fields andthat satellite dominate outputs the ndemonstrateorthwest and the southeast low and regions moderate of the precipitation Peninsula. fields Likewise, that dominatethe increased the northwestprecipitation and ratessoutheast emerging regions in the of thenorth Peninsula. due to the Likewise, cyclonic the activity increased of the precipitation Mediterranean rates Sea emerging and in thein east the northparts dueof the to Africa the cyclonic are evident activity in both of the products. Mediterranean Differences Sea andare noticed in the eastover parts the Redof theSea, Africa where are modeled evident precipitation in both products. presents Differences higher magnitudes. are noticed This over can the be Redassoc Sea,i- where modeled precipitation presents higher magnitudes. This can be associated with ated with the complex processes prevailing in the current area including the seasonal the complex processes prevailing in the current area including the seasonal variations variations and the intense convective activity. Furthermore, the absence of rain gauge and the intense convective activity. Furthermore, the absence of rain gauge data over data over ocean, which effectively adjusts the satellite precipitation estimates over land, ocean, which effectively adjusts the satellite precipitation estimates over land, might also might also have contributed towards a notable underestimation from IMERG over this have contributed towards a notable underestimation from IMERG over this area. Similar area. Similar conclusions are reached upon comparison of the mean monthly accumu- conclusions are reached upon comparison of the mean monthly accumulated precipitation lated precipitation (Figure 8 and Figure A1). (Figure 8 and Figure A1).

Figure 2. Mean annual accumulated precipitation of (a) the model output and (b) the IMERG product Figure 2. Mean annual accumulated precipitation of (a) the model output and (b) the IMERG for the years 2001–2015. product for the years 2001-2015. The NWP evaluation against station data was carried out with the use of different weights from the four nearest model grid points based on the distance from the station. This approach is considered as conventional for the provision of model outputs over a specific location and partly counterbalances the high spatiotemporal variability of precipitation. Precipitation measurements were employed from a network of meteorological sta-

tions operating in the peninsula. These are in accordance with the World Meteorological Organization (WMO) requirements, updated on daily basis [40] in 3-h intervals. 22 stations were used in the current study, primarily based on data availability and sufficient area coverage, for the whole 30-year period of the model simulations. At this point, it should be noted that the rain gauge data in the area are sparse and most of the precipitation events are of convective type, making the evaluation procedure rather difficult. The MBE results for the whole period fall within the range −0.2 and 0.2 mm, with the vast majority being restrained within the −0.1 and 0.1 mm margins (Figure3a). Concur- rently, the mean errors are almost equally distributed positively and negatively around zero. As a result, a concrete conclusion for a tendency of a systematic overestimation or Climate 2021, 9, 103 6 of 25

Climate 2021, 9, x FOR PEER REVIEW 7 of 26 underestimation in the model results cannot be extracted. Most of the RMSE values are traced between the 1 mm and 2 mm of rainfall.

Figure 3. (a) MBE versus RMSE between modeled and observed and (b) Taylor Figure 3. (a) MBE versusdiagram RMSE forbetween each station. modeled and observed precipitations and (b) Taylor dia- gram for each station. Additional statistical information is provided by the Taylor diagram (Figure3b), illus- A spatial examinationtrating theof the correlation modeled coefficient precipitation values estimates together is with provided the normalized in Figure standard deviations 4, illustrating the MBE,(the MAE, ratio RMSE between and modeled correlation standard results in deviation the locations and theof the observed stations one). Modeled and under consideration.measured Cumulatively, precipitation lower errors values can are be positively traced in correlated the North with mainland most of of the coefficient values the Peninsula and greaterfound in between the stations 0.4 and distributed 0.7. Overall, in the the West level and of Southwest. correlation This is rather is sufficient for the mainly due to the highermajority precipitation of the stations. amount Lows in degree these of regions. correlation It could coefficients, be also belowat- 0.5, is displayed tributed to the terrainfor elevation eight stations changes, alone. increasing These discrepancies from the shoreline can be attributedto the mainland. to spatiotemporal shifts of These topographicalmodeled characteristics versus observedtogether rainfallwith the patterns offshore as wellconvective as sub-grid activity scale in- phenomena. crease the uncertainty in theA spatial spatial examination and temporal of estimation the modeled of precipitationrainfall. estimates is provided in Figure4, illustrating the MBE, MAE, RMSE and correlation results in the locations of the stations under consideration. Cumulatively, lower errors can be traced in the North mainland of the Peninsula and greater in the stations distributed in the West and Southwest. This is mainly due to the higher precipitation amounts in these regions. It could be also attributed to the terrain elevation changes, increasing from the shoreline to the mainland. These topographical characteristics together with the offshore convective activity increase the uncertainty in the spatial and temporal estimation of rainfall.

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A spatial examination of the modeled precipitation estimates is provided in Figure 4, illustrating the MBE, MAE, RMSE and correlation results in the locations of the stations under consideration. Cumulatively, lower errors can be traced in the North mainland of the Peninsula and greater in the stations distributed in the West and Southwest. This is mainly due to the higher precipitation amounts in these regions. It could be also at- Climate 2021, 9, 103 7 of 25 tributed to the terrain elevation changes, increasing from the shoreline to the mainland. These topographical characteristics together with the offshore convective activity in- crease the uncertainty in the spatial and temporal estimation of rainfall.

FigureFigure 4. ( 4.a) (MBE,a) MBE, (b) (MAE,b) MAE, (c) (RMSEc) RMSE and and (d) ( dCorrelation) Correlation Coefficient Coefficient between between modeled modeled and and station station precipitation precipitation data. data.

ToTo furtherfurther assess assess the the performance performance of of the the simulated simulated precipitation precipitation fields, fields, an evaluationan evalua- analysistion analysis for each foryear each was year performed. was performed. This is presented This is presented through the through creation the of creation weighting of errorweighting plots forerror the plots MBE for and the MAE MBE statistical and MAE indicators statistical across indicators the whole across examination the whole period ex- (Figureamination5). The period range (Figure of inter-quantiles 5). The bounds range and of maximum/minimum inter-quantiles bounds values and around maxi- themum/minimum MBE and MAE values medians around is considerable the MBE an dued MAE to the medians extent ofis theconsiderable domain under due studyto the thatextent encloses of the areasdomain with under diverse study meteorological that encloses characteristics. areas with diverse Systematic meteorological errors, provided char- viaacteristics. the MBE, Systematic are centered errors, close provided to 0 mm, withvia the a small MBE, variation are centered of approximately close to 0 mm,±0.1 with mm. a Largersmall variation deviations of perapproximately year are present ±0.1 inmm. the Larger median deviations MAE values per withyear valuesare present remaining in the belowmedian 0.25 MAE mm values in most with of thevalues years. remaining below 0.25 mm in most of the years. Relevant weighting error plots have been constructed for an inter-annual evaluation analysis (Figure6). Most of the MBE values are close to zero, with a small underestimation found in January. Error ranges are wider from January to April and from October to Decem- ber. Similar results are also evident for the MAE values, with the largest errors presented in April. This is rather expected, considering that within these months, precipitation is more frequent and with higher rates, leading to spatial and temporal discrepancies. In order to understand the precipitation features and the climatic characteristics affect- ing them, the following chapters focus on: (i) a mean annual and monthly accumulated precipitation analysis to assess the seasonal changes in rainfall patterns, (ii) a trend analysis investigating the changes in the precipitation amount through this 30-year period and (iii) an intensity–duration–frequency analysis focusing on precipitation events that cause floods every year. Climate 2021, 9, 103 8 of 25 Climate 2021, 9, x FOR PEER REVIEW 8 of 25

Figure Figure5. (a) MBE 5. and(a) (b MBE) MAE and annual (b) MAEvariation annual between variation modeled between and station modeled precipitation and station data. precipi- Centraltation red marks data. display Central the redmedian, marks box display edges display the median, the range box between edges the display 25th and the 75 rangeth between percentilesthe and25th the and whiskers 75th percentiles point to theand minimum the whiskers and the point maximum to the values, minimum respectively. and the maximum values, respectively. Relevant weighting error plots have been constructed for an inter-annual evaluation analysis (Figure 6). Most of the MBE values are close to zero, with a small underestima- tion found in January. Error ranges are wider from January to April and from October to December. Similar results are also evident for the MAE values, with the largest errors presented in April. This is rather expected, considering that within these months, pre- cipitation is more frequent and with higher rates, leading to spatial and temporal dis- crepancies.

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FigureFigure 6. Inter- 6.annualInter-annual (a) MBE and (a) ( MBEb) MAE and variation (b) MAE between variation modeled between and station modeled precipitation and station pre- data. Centralcipitation red marks data. display Central the red median, marks box display edges display the median, the range box between edges the display 25th and the 75 rangeth be- percentilestween and the the25th whiskers and 75th point percentiles to the minimumand the and whiskers the maximum point to values, the minimum respectively and. the maximum values, respectively. In order to understand the precipitation features and the climatic characteristics af- fecting3.2. them, Mean the Annual following and Monthly chapters Accumulated focus on: (i Precipitation) a mean annual and monthly accumu- lated precipitationRainfall analysis in AP is mostlyto assess an the outcome seasonal of convectionchanges in rainfall processes. patterns, There are(ii) a intense trend local- analysisscale investigating events that the often changes lead toin floods.the precipitation The duration amount of precipitation through this is30 short,-year p typicallye- riod andlasting (iii) foran aintensity few hours.–duration This highly–frequency localized analysis nature focusing and the shorton precipitation duration of precipitationevents that causemakes floods its monitoring every year. a challenging procedure. On an annual basis, the mean accumulated precipitation distribution (Figure7) reveals that the southwest region of the AP receives 3.2. Meanthe Annual highest and amount Monthly of precipitationAccumulated Precipitation throughout the whole year due to the orographic Rainfallenhancement in AP ofis precipitationmostly an outcome and the of increased convection convective processes. activity. There On are the intense contrary, lo- there cal-scaleare events two dry that regions: often lead the northwestern to floods. The region duration (an extensionof precipitation of the Egyptianis short, typically dry zone) and lastingthe for southeastern a few hours. region, This highly over thelocalized Rub Al-Khali, nature and the world’sthe short largest duration sand of desert. precipita- tion makesPrecipitation its monitoring mostly a challenging occurs from procedure. October toOn April an annual (Figure basis,8). During the mean winter, accu- rainfall mulatedis mainlyprecipitation an outcome distribution of the (Figure Mediterranean 7) reveals that the and southwest frontal zones.region Secondaryof the AP low- receivespressure the highest systems amount that develop of precipitation under favorable throughout upper-level the whole conditions, year due involvingto the oro- upper- graphiclevel enhancement troughs and of jet precipitation interaction may and alsothe increased contribute. convective Furthermore, activity. cold airOn advection the con- from trary, thethere northeast, are two duedry toregions: the extension the northwestern of the Siberian region high, (an meets extension the warm of the humid Egyptian air coming dry zone)from and Sudan the oversoutheastern the southern region Red, over Sea throughthe Rub aAl corresponding-Khali, the world trough’s largest (Figure sand9a). The desert.latter tends to cause rainfall due to both instability and orography [41]. There are cases when weather systems move southward along the Red Sea trough and provide winter precipitation as far south as Mecca and sometimes as far as . In March and April, limited precipitation, often torrential, occurs. In , the highlands of Asir receive

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great amounts of rainfall affected by the monsoonal winds to support a steppe-like strip of land.

Figure 7 7.. Mean annual annual accumulated accumulated precipitation precipitation for for the the years years 1986 1986–2015–2015 (model (model output). output).

DuringPrecipitation March, mostly the desert occurs front from that October forms to during April (Figure the transitional 8). During period winter, of rainfall spring acrossis mainly southern an outcome Egypt and of the the Mediterranean northern Red Sea cyclones into central and frontal Saudi zones. Arabia Secondary results in thelow formation-pressure ofsystems a secondary that develop wave over under the favorable central Red upper Sea- (Figurelevel conditions,9b) associated involving with extensiveupper-level thunderstorms troughs and and jet intense interaction precipitation. may also In contribute. April, thunderstorms Furthermore, and cold heavy air rain ad- arevection associated from the with nor convectivetheast, due activity to the due extension to the passage of the Siberian of desert high, fronts meets and the the thermal warm troughhumid of air Sudan. coming from Sudan over the southern Red Sea through a corresponding troughIn summer,(Figure 9a) the. The Mediterranean latter tends to systems cause rainfall do not affectdue to the both region. instability The monsoon and orography south- westerlies[41]. There that are affectcases when the peninsula weather systems (Figure9 c)move are southward conditionally along unstable the Red and Sea provide trough moisture;and provide therefore, winter precipitation they can induce as far thunderstorms, south as Mecca and mainly sometimes in the southwesternas far as Yemen. part. In OctoberMarch and and April, November limited are precipitat the monthsion, when often the torrential, rainfall isoccurs. associated In summer, with secondary the highlands lows, generatedof Asir receive in the great examined amounts region of rainfall or Mediterranean, affected by the depressions monsoonal and winds fronts to thatsupport move a towardssteppe-like the strip central of land. AP (Figure 9d). TheDuring southwestern March, the partdesert of front the Arabian that forms Peninsula during isthe characterized transitional period by mild of steppespring climate in contrast to elsewhere, where a hot desert climate is met. This is attributed across southern Egypt and the northern Red Sea into central Saudi Arabia results in the to the fact that rainfall is relatively frequent, especially during winter and spring. The formation of a secondary wave over the central Red Sea (Figure 9b) associated with ex- precipitation climate of this part is influenced by the Red Sea, the topography of Arabian tensive thunderstorms and intense precipitation. In April, thunderstorms and heavy rain Peninsula and Eastern Africa and the Indian Ocean monsoon [41]. are associated with convective activity due to the passage of desert fronts and the thermal trough of Sudan. In summer, the Mediterranean systems do not affect the region. The monsoon southwesterlies that affect the peninsula (Figure 9c) are conditionally unstable and pro- vide moisture; therefore, they can induce thunderstorms, mainly in the southwestern part. October and November are the months when the rainfall is associated with sec- ondary lows, generated in the examined region or Mediterranean, depressions and fronts that move towards the central AP (Figure 9d).

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The southwestern part of the Arabian Peninsula is characterized by mild steppe climate in contrast to elsewhere, where a hot desert climate is met. This is attributed to Climate 2021, 9, 103 the fact that rainfall is relatively frequent, especially during winter and spring. The11 ofpr 25e- cipitation climate of this part is influenced by the Red Sea, the topography of Arabian Peninsula and Eastern Africa and the Indian Ocean monsoon [41].

FigureFigure 8 8.. MeanMean monthly monthly accu accumulatedmulated precipitation precipitation (modeled) (modeled) for (a) forJanuary, (a) January, (b) February, (b) February, (c) March,(c )(d March) April,,( Id May,) April, (f) June, ((ge)) July, May, (h (f)) August, June, (g) ( July,i) September, (h) August, (j) October, (i) September, (k) November (j) October, and (k ()l November) December and. (l) December.

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Figure 9. Mean monthly values of Sea Level Pressure for (a) January, (b) March, (c) July and (d) November. Figure 9. Mean monthly values of Sea Level Pressure for (a) January, (b) March, (c) July and (d) November. 3.3. Trend Analysis 3.3. TheTrend number Analysis of studies focusing on the estimation of rainfall trends in the AP em- ployingThe either number observational of studies orfocusing modeled on datasetsthe estimation is rather of limited rainfall [ 1trends,4,42,43 in]. the Therefore, AP em- theploying spatiotemporal either observational density and or themodeled extent datasets of this database is rather can limited be an [1,4,42,43] added value. Therefore, in un- derstandingthe spatiotemporal the recent density changes and inthe the extent precipitation of this database patterns can and be behavior. an added Tovalue this in end, un- thederstanding implementation the recent of both changes monthly in the and precipitation annual approaches patterns was and decided behavior. for To this this study. end, Thethe calculatedimplementation trends of are both based monthly on the and nonparametric annual approaches approach was by decided Sen (1968). for this The study. de- pictedThe calculated output was trends found are to based be statistical on the nonparametric significant, tested approach at a 0.05by Sen significance (1968). The level de- usingpicted the output Mann–Kendall’s was found totest be [44 statistical,45]. For significant, the sake of tested simplicity, at a the 0.05 results significan werece inte- level gratedusing tothea ten-yearMann–Kendallperiod’s and test normalized [44,45]. For by the the sake accumulated of simplicity precipitation, the results of were the same inte- referencegrated to period. a ten-year period and normalized by the accumulated precipitation of the same referenceThe spatial period. distribution of the accumulated precipitation trends on an annual basis presentsThe insignificant spatial distribution percentage of valuesthe accumulated in the time precipitati period 1986–2015on trends (Figure on an 10 annual). There basis is, however,presents ainsignificant decrease of aboutpercentage 0.5% pervalues decade in the observed time period over the 1986 eastern–2015 part(Figure of the 10 Empty). There Quarter.is, however Even, a ifdecrease this magnitude of about appears0.5% per to decade be trivial, observed it is essential over the as eastern it affects parta largeof the sandEmpty desert Quarter. located Even in the if this southern magnitude borders appears of Saudi to Arabia, be trivial, including it is essential also parts as it of affects , a Unitedlarge sand Arab desert Emirates located and Yemen.in the southern This comes borders in general of Saudi agreement Arabia, withincluding previous also studiesparts of thatOman, investigate United changesArab Emirates in precipitation and Yemen. observations This comes in AP.in general agreement with previ- ous studies that investigate changes in precipitation observations in AP.

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Hasanean and Almazroui [1] resulted in an estimated reduction in rainfall over the Hasanean and Almazroui [1] resulted in an estimated reduction in rainfall over the Arabian Peninsula for the period 1978–2009. Contrary to what it is observed here, the Arabian Peninsula for the period 1978–2009. Contrary to what it is observed here, the annual precipitation decreased over northern regions of the Peninsula and increased over annual precipitation decreased over northern regions of the Peninsula and increased over the south. The estimation, however, was performed employing datasets with a 10-year the south. The estimation, however, was performed employing datasets with a 10-year shift as compared to the dataset used here. At the same time, Almazroui et al. [4] found shift as compared to the dataset used here. At the same time, Almazroui et al. [4] found in- insignificant changes during the period 1979–1993 and a decrease over the period 1994– significant changes during the period 1979–1993 and a decrease over the period 1994–2009. 2009. Moreover, AlSarmi and Washington [5] reported an insignificant but yet a negative Moreover, AlSarmi and Washington [5] reported an insignificant but yet a negative trend intrend precipitation in precipitation in a study in a covering study covering the time the period time period 1986–2008. 1986–2008.

FigureFigure 10. 10.Annual Annual accumulated accumulated precipitation precipitation (modeled) (modeled) change change (percentage) (percentage) per per 10 10 years. years. Despite the fact that the inter-annual changes of the accumulated precipitation were Despite the fact that the inter-annual changes of the accumulated precipitation were found to be insignificant, the monthly changes are quite interesting (Figure 11). There are bothfound positive to be insignificant, and negative trendsthe monthly depending changes on the are month quite underinteresting investigation (Figure 11 with). There a range are ofboth approximately positive and 2% negative around zero.trends However, depending it should on the bemonth mentioned under thatinvestigation each percentage with a changerange of should approximately be studied 2% alongside around (and zero. compared However, to) it the should mean be monthly mentioned accumulated that each precipitationpercentage change discussed should earlier. be The studied reason alongside behind this (and suggestion compared is that to) thesometimes mean monthly consid- erableaccumulated percentage precipitation changes refer discussed to small earlier. amounts The of reason precipitation, behind rendering this suggestion them rather is that insignificantsometimes con fromsiderable a practical percentage point of view. changes refer to small amounts of precipitation, renderingA general them remark rather would insignificant be that from there a is practical a seasonal point shift of on view. the accumulated precipita- tion asA neutralgeneral to remark negative would trends, be that observed there is from a seasonal December shift to on April the accumulated and rather positive precip- trendsitation over as neutral the remaining to negative months. trends, In observed December, from a decreaseDecember of to about April 5 and mm/decade rather pos isi- visibletive trends in mid-North over the Peninsula.remaining Amonths. slightly In lower December decrease, a decrease is found inof Januaryabout 5 accompaniedmm/decade is withvisible a positive in mid-North trend ofPeninsula. about 5 mm/decadeA slightly lower in the decrease north of is Medina.found in LargerJanuary deviations accompa- withnied a with decrease a positive of more trend than of 10-15 abo mm/decadeut 5 mm/decade emerged in the from north February of Medina. to April Larger alongside devia- thetions borders with ofa decrease Saudi Arabia, of more Oman than and 10- Yemen15 mm/decade with the emerged highest values from February to be traced to overApril

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alongside the borders of Saudi Arabia, Oman and Yemen with the highest values to be traced over the northern parts of Yemen during April. A small increase in the order of the5-10 northern mm per parts decade of Yemen(maximum) during is April.the case A smallregarding increase the rest in the of orderthe months. of 5-10 The mm large per decade (maximum) is the case regarding the rest of the months. The large changes found changes found over sea are a result of the normalization process. They could be also over sea are a result of the normalization process. They could be also linked to reasons like linked to reasons like model bias and SST anomaly. Therefore, no special focus is dedi- model bias and SST anomaly. Therefore, no special focus is dedicated as the information cated as the information could be misleading. It is evident that the trends on an annual could be misleading. It is evident that the trends on an annual basis (Figure 10) are mostly basis (Figure 10) are mostly dominated by the winter/wet season changes (Oct–Apr, dominated by the winter/wet season changes (Oct–Apr, Figure 11). As stated earlier, the Figure 11). As stated earlier, the large percentage changes found during the dry season large percentage changes found during the dry season may result in small actual rainfall may result in small actual rainfall changes and a negligible impact. changes and a negligible impact.

FigureFigure 11 11.. MonthlyMonthly accumulated accumulated precipitation precipitation (modeled) (modeled) change change per per 10 years 10 years for ( fora) January, (a) January, (b) February, (b) February, (c) March, (c) March, (d) April, I May,(d) April, (f) June, (e) May, (g) July, (f) June, (h) August, (g) July, ( (ih) )September, August, (i )(j September,) October, ( (kj) October,November (k )and November (l) December. and (l ) December.

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Precipitation is an important factor for various activities in the region due to its scarcity. However, floods and/or flash floods tend to cause extensive problems on infrastructure and pose threat to human life as they often lead to casualties. In many cases, extreme rain rates associated with thermal convection are observed. During such intense rainfall events, runoff flows from the hills and mountains towards lower heights, causing flash floods in urban areas. This is also associated with the dryness of the soil and the absence of dense vegetation. Therefore, the study of the intensity and the duration of extreme precipitation events is more than valuable to a variety of sectors from engineers to civil protection agencies.

3.4. Extreme Value Analysis (Intensity, Duration and Frequency) The intensity related to the duration and probability of occurrence of rainfall events can provide useful information regarding the potential impact of extreme cases. Therefore, an analysis has been performed for specific locations. The sites (Figure1) were selected due to their importance based on population, economic and cultural characteristics and in order to be representative of the region’s various climatic patterns. Beginning with the locations in the eastern part of the AP, the average rainfall intensity for different durations and return periods, appears to be similar in terms of magnitude (Figure 12). A 3 h event with average rain rate of 4-5 mm/h is expected in Kuwait city, Burayadah, Manama, Doha, Dubai, Abu Dhabi, Muscat and Salalah every 8–10 years. Extreme events over these areas are rather convective with duration in the scale of minutes to an hour. Events with longer return periods could affect the same areas with 21–24 mm accumulated precipitation over 3-h duration and an average rain rate gradually falling for larger durations. Around 50 mm is expected over a 12-h period and a 1% probability of occurrence. Slightly higher rates are found in Riyadh with the 3-h accumulated precipita- tion ranging between 20 and 35 mm for a 10- and a 100-year return period, respectively. A quick reduction in rainfall intensity is observed in larger durations, resulting in similar patterns, as in the rest of the locations. This is rather expected due to the nature of the atmospheric systems. Climate 2021, 9, 103 16 of 25 Climate 2021, 9, x FOR PEER REVIEW 16 of 25

FigureFigure 12. 12.IDFIDF curves curves (modeled (modeled output) output) for forselected selected locations/cities locations/cities in the in east the eastpart part of the of Arabian the Arabian Peninsula Peninsula:: (a) Kuwait (a) Kuwait city, (b) Burayadah,city, (b) Burayadah, (c) Manama, (c )(d Manama,) Riyadh, ( d(e)) Riyadh, Doha, (f ()e )Dubai, Doha, ( (gf) Abu Dubai, Dhabi, (g) Abu (h) Dhabi,Muscat ( hand) Muscat (i) Salalah and. (i) Salalah.

TheThe western western part part of of the the AP AP is is characterized characterized by by a agreater greater variability variability (Figure (Figure 1 133).). It It is is highlyhighly affected affected by by the the Mediterranean Mediterranean lows lows in in the the n northorth and and the the monsoonal monsoonal activity activity in in the the south.south. An An additional additional characteristic characteristic is isthat that the the humidity humidity of of the the air air masses masses affecting affecting this this ppartart is is higher higher due due to to the the presence presence of of neighboring neighboring warm warm seas, seas, such such as as the the Mediterranean Mediterranean BasinBasin in in the the n north,orth, the the Red Red Sea Sea in in the the e eastast and and the the Sea Sea of of Aden Aden in in the the s south.outh. Therefore, Therefore, heavierheavier precipitation precipitation events events are are generally generally expected. expected. However, However, this this is is not not evident evident in in all all locationslocations as as different different rainfall rainfall patterns patterns are are observed. observed. Beginning Beginning with with the the more more arid arid areas, areas, Arar,Arar, Tabuk, Tabuk, Hail Hail and and Medina Medina are are characterized characterized by by lower lower values values compared compared to to the the eastern eastern side.side. Precipitation Precipitation is is rare rare and and extreme extreme events events are are less less likely likely to to take take place. place. Jeddah, Jeddah, Mecca Mecca andand Jazan Jazan have have quite quite comparable comparable behavior. behavior. This This can can be be attributed attributed to to their their similar similar climatic climatic characteristics. They are all nearshore locations, affected by the warm and humid air characteristics. They are all nearshore locations, affected by the warm and humid air masses originating from the Red Sea. The existence of Azir and Sarawat mountains on masses originating from the Red Sea. The existence of Azir and Sarawat mountains on the the east of the aforementioned cities often causes orographic lifting of the wind flow east of the aforementioned cities often causes orographic lifting of the wind flow leading to convection and extreme precipitation events. A total of 30 mm of accumulated precip-

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leading to convection and extreme precipitation events. A total of 30 mm of accumulated precipitation over a period of 3 h can be expected in Jeddah and even higher values in Mecca itationand Jazan. over a However, period of the 3 hr rain can rate be expected is quickly in reduced Jeddah and in longer even higher durations, values especially in Mecca in andMecca, Jazan. something However, that the further rain highlightsrate is quickly the short reduced duration in longer of precipitation durations, over especially the region. in Mecca, onsomething the other that hand further is characterized highlights the by short a wet duration climate. of Itprecipitation is located in overan altitudethe re- gion.of 2250 Sanaa m receivingon the other more hand than is charact 200 mm/yearerized by (Figures a wet climate.2 and7 ).It is Extreme located in events an altitude with ofa rainfall2250 metersintensity receiving of more more than than 22 200 mm/h mm/year and (Figures duration 2 and of about 7). Extreme 3 h are events expected with ata rainfalla 10-year intensityfrequency. of more Although than extreme22 mm/hr events and areduration more likelyof about to happen 3 hr are in expected this particular at a 10region,-year frequency. they have theAlthough same characteristic extreme events when are itmore comes likely to their to happen duration. in this Therefore, particular for region,a 10-year theyrecurrence have the same interval, characteristic around 65 when mm it of comes accumulated to their duration. precipitation Therefore, is expected for a 10within-year arecurrence period of 3interval, h and 130 around mm within 65 mm 12 h. of This accumulated means that precipitation quadrupling theis expected duration withinleads toa perioda doubling of 3 hrof and the 130 total mm amount within of12 precipitation.hr. This means Adenthat quadrupling is a sea side the location dura- tioncharacterized leads to a doubling by increased of the humidity total amount [10] with of precipitation. low amounts Aden of precipitation, is a sea side yet location higher charathancterized most of theby increased other locations humidity examined. [10] with low amounts of precipitation, yet higher than most of the other locations examined.

FigureFigure 13. 13. IDFIDF curves curves (modeled (modeled output) output) for forselected selected locations/cities locations/cities in the in west the westpart partof the of Arabian the Arabian Peninsula Peninsula:: (a) Arar, (a) Arar,(b) Tabuk,(b) Tabuk, (c) Hail, (c) ( Hail,d) Medina, (d) Medina, (e) Jeddah, (e) Jeddah, (f) Mecca, (f) Mecca, (g) Jaz (gan,) Jazan, (h) Sanaa (h) Sanaa and (i and) Aden (i) Aden..

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A better understanding of the behavior of extremes in the region requires the ex- amination of the spatial distribution of the relation between the intensity, the duration and the frequency of precipitation events. However, it is practically impossible to depict 3D information over a printed map. Therefore, in order to be able to have an overview of this behavior, the average intensity of a hypothetical event for selected durations and return periods was estimated. More specifically durations equal to 3, 6, 12 and 24 hours and return periods equal to 25, 50 and 100 years were selected (Figure 14, Figures A2 and A3). The general pattern of the spatial distributions regarding the average rainfall inten- sity for all durations and return periods follows that of the mean annual accumulated precipitation (Figure 7). As expected, the precipitation rate drastically falls for larger durations. In particular, for durations greater than 12 hours, it is practically an artificial Climate 2021, 9, 103 18 of 25 outcome of the averaging taking place during the application of the extreme value methodology described earlier, with an exception of the areas located southeast. This bottom half-coastal region in the east is the most affected by extreme events, something alreadyA expected better understanding from the discussion of the behavior on the intensity, of extremes duration in the and region frequency requires relations the exam- in inationMecca, ofJeddah, the spatial Jazan distribution and Sanaa. ofThis the zone relation receives between precipitation the intensity, with the intensities duration b ande- tweenthe frequency 10-20 mm/hr of precipitation and an average events. duration However, around it is 3 practically hr once every impossible 25 years. to depictSlightly 3D lowerinformation values but over with a printed a same map. pattern Therefore, are observed in order for to 6 be-hr able events. to have At higher an overview return of pe- this riodsbehavior,, intensification the average is expected. intensity ofHowever, a hypothetical an “upper event bound” for selected is observed durations as maximum and return valuesperiods tend was to converge. estimated. More specifically durations equal to 3, 6, 12 and 24 hours and return periods equal to 25, 50 and 100 years were selected (Figure 14, Figures A2 and A3).

FigureFigure 14 14.. AverageAverage rainfall rainfall intensity intensity (modeled) (modeled) for fordurations durations of 3, of 6, 3, 12 6, and 12 and 24 hours 24 hours (a–d (a, –resped, respec-c- tivelytively)) and and a 25 a 25-year-year return return per period.iod.

The general pattern of the spatial distributions regarding the average rainfall intensity for all durations and return periods follows that of the mean annual accumulated precipi- tation (Figure7). As expected, the precipitation rate drastically falls for larger durations. In particular, for durations greater than 12 hours, it is practically an artificial outcome of the averaging taking place during the application of the extreme value methodology described earlier, with an exception of the areas located southeast. This bottom half-coastal region in the east is the most affected by extreme events, something already expected from the discussion on the intensity, duration and frequency relations in Mecca, Jeddah, Jazan and Sanaa. This zone receives precipitation with intensities between 10-20 mm/h and an average duration around 3 h once every 25 years. Slightly lower values but with a same pattern are observed for 6-h events. At higher return periods, intensification is expected. However, an “upper bound” is observed as maximum values tend to converge. Climate 2021, 9, 103 19 of 25

4. Conclusions The Arabian Peninsula is one of the most important economic regions in the world and home to more than 80 million people. Its prevailing climatic characteristics belong to the desert climate in the Köppen classification, with high temperatures, low rainfall and strong dust presence. This leads to low levels of soil moisture and water supply, affect- ing agriculture and human activities in general. At the same time, intense precipitation events cause floods every year with severe damages and casualties. Therefore, an extensive analysis of the climatic precipitation features deemed necessary. The limited amount of in situ observations over large parts of the peninsula was a restrictive aspect in terms of a climate oriented analysis. In this study, this was confronted through the employment of a state-of-the-art atmospheric model. The model was used for the regional analysis covering the entire peninsula and provided atmospheric data for 30 years with a temporal resolution of 9 km and a spatial resolution of 3 hours. The atmospheric model perfor- mance in the representation of the rainfall patterns was thoroughly examined through a qualitative and quantitative analysis. In situ and satellite data were deployed and their juxtaposition with model outputs was indicative of a reliable model behavior. The mean accumulated precipitation spatial distribution and variations were efficiently captured both on an annual and a monthly basis. The output efficiency was also supported by the good statistical indices. The analysis was performed in three main components; the mean climatic characteris- tics, the rainfall trends and the extreme cases. Beginning with the spatial distribution of the mean annual accumulated precipitation, higher accumulation values were evident in the southwest due to the topographic characteristics. On the contrary, two dry regions emerge, the northwestern one as an extension of the Egyptian dry zone and the southeastern one over the Rub Al-Khali, the world’s largest sand desert. In the monthly analysis, lower precipitation was generally evident from October to April. During winter, rainfall was associated to the passage of low pressure systems originating from the Mediterranean Sea and the cold air being advected from the northeast. In the second scenario, air masses met the warm humid air over the southern Red Sea causing rainfall. In March and April, intense convective activity and heavy rain events might occur. During summer, the region is not affected by the Mediterranean systems. The monsoon southwesterlies often cause thunderstorms mainly in the southwestern part of the peninsula. Rainfall in October and November is associated with secondary lows generated over the Mediterranean. Another important issue to address when investigating the climatic characteristics of precipitation over a particular region is the potential changes on the total rainfall amount received. The spatial distribution of the accumulated precipitation trends revealed insignif- icant percentage differences in the time period of interest. A slight decrease, however, was observed over an extensive area covering the largest desert of the peninsula, across the Saudi Arabian borders. Regarding the monthly analysis, a seasonal shift on the accumu- lated precipitation was evident. Neutral to negative trends were observed from December to April and positive in the remaining months. In most cases, precipitation in the region occurs as an outcome of convection. It has a highly localized nature and a tendency to cause floods despite its short duration. The behavior of intense precipitation was examined through an extreme value analysis. The analysis took into consideration the intensity and the duration of a precipitation event simultaneously. The eastern parts of the peninsula presented the lowest risk associated with extreme events. Slightly higher rates, however, were met in Riyadh. The pattern in the western domain was more interesting. The top half experienced a similar pattern with the East. This was not the case, however, in the southwest, where a coastal zone affected by the warm Red Sea and the Sea of Aden, the mountains of Azir and Sarawat and the monsoonal activity faced the highest risk. Over this area, events with 3-h accumulated precipitation values over 50–60 mm were quite frequent in terms of return periods. These values were even higher in higher elevations. Climate 2021, 9, 103 20 of 25

The precipitation patterns and their variability often pose a global socioeconomic threat, let alone affecting the local population. This paper is an effort to outline the detailed climatic characteristics of precipitation in the Arabian Peninsula in space and time, which is especially important in a climate-changing environment. The outcome has an added value in terms of civil protection and industry applications such as constructions and reinsurance. However, a further examination of the behavior of additional contributing parameters will follow. This will provide a more sophisticated insight and a deeper understanding of the processes and their expected impact in the forthcoming years.

Author Contributions: Conceptualization, P.P., H.F. and G.K.; methodology, P.P.; software, P.P.; validation, C.S.; formal analysis, P.P.; investigation, P.P.; resources, G.K.; data curation, P.P.; writing— original draft preparation, P.P., H.F. and C.S.; writing—review and editing, N.S.B.; visualization, P.P.; supervision, G.K.; project administration, G.K.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by Saudi Aramco, Contract No: 6600033903 to WeMET P.C. Data Availability Statement: The data presented in this study is available on request from the corresponding author. Acknowledgments: We would like to acknowledge the National Center for Meteorology of Saudi Arabia and Mohammed Ahmed Alomari in particular, for the rain gauge data provided. Special thanks to the Saudi Aramco Employees Jumaan Al Qahtani, Ioannis Alexiou, Ayman for their fruitful collaboration. Nikolaos S. Bartsotas current affiliation is the National Observatory of Athens, Institute for Astronomy and Astrophysics, Space Applications and Remote Sensing—BEYOND Center of Earth Observation Research and Satellite Remote Sensing, Athens, Greece. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A Climate 2021, 9, x FOR PEER REVIEW 21 of 25

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Appendix A

FigureFigure A1. A1. MeanMean monthly monthly ac accumulatedcumulated precipitation precipitation (IMERG) (IMERG) for for (a ()a )January, January, ( (bb)) February, February, (c) March,March, ((dd)) April,April, (e(e)) May, May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November and (l) December. (f) June, (g) July, (h) August, (i) September, (j) October, (k) November and (l) December.

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Figure A2. Average rainfall intensity (modeled) for durations of 3, 6, 12 and 24 hours (a–d, respectively) and a 50-year Figure A2. Average rainfall intensity (modeled) for durations of 3, 6, 12 and 24 hours (a–d, respectively) and a 50-year return returnperiod period..

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Figure A3. Average rainfall intensity (modeled) for durations of 3, 6, 12 and 24 hours (a–d, respectively) and a 100-year return period.

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