Assessment of Rainfall Intensity Equations Enlisted …downloads.hindawi.com/journals/amete/2017/9496787.pdf6 AdvancesinMeteorology Number Station nameP10 (mm) Classification A Classification

Research ArticleAssessment of Rainfall Intensity EquationsEnlisted in the Egyptian Code for Designing PotableWater and Sewage Networks

Ayman G Awadallah12 Mostafa Magdy1 Ehab Helmy13 and Ehab Rashed3

1Resources and Environmental Department Dar Al Handasah Consultants (Shair and Partners) Cairo Egypt2Water Resources Engineering Civil Engineering Department Faculty of Engineering Fayoum University Fayoum Egypt3Sanitary and Environmental Engineering Public Works Department Faculty of Engineering Cairo University Giza Egypt

Correspondence should be addressed to Ayman G Awadallah aawadallahdarcairocom

Received 24 August 2016 Accepted 10 January 2017 Published 16 February 2017

Academic Editor Niko Wanders

Copyright copy 2017 Ayman G Awadallah et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The design of combined sewage system networks is based on the sanitary discharge (domestic industrial) and the runoff generatedby rainfallThe Egyptian code of practice for designing potable water and sewage networks gives two Intensity-Duration-Frequency(IDF) equations to calculate the intensity of rainfall to be applied to all cities of Egypt The purpose of this research is to study andassess the adequacy of the rainfall intensity equations suggested by the aforementioned Egyptian code This is carried out takinginto consideration the available rainfall ground station measurements and remote sensing Tropical Rainfall Measurement Mission(TRMM) satellite rainfall estimates This assessment leads to the following results For the Mediterranean coastal cities the codeof practice equations significantly underestimates the rainfall intensities for all storm durations which may lead to road networksdamage and accidents due to hydroplaning and road flooding On the contrary formany other southern cities along the Nile Valleythe code equations significantly overestimate the rainfall intensities which affects the economical aspect of the sewage networkConsequently the current research suggests new rainfall intensity equations instead of the code equations

1 Introduction

Althoughmost of Egypt is considered to be a hyperarid coun-try rainfall storms often cause serious accumulation of stormwater in arterial and main roads preventing quick drainageand causing major road accidents This leads occasionally tofatalities as it happened recently in Alexandria and Beheiracoastal governorates in 2015 and in Upper Egypt in Assiutin 1994 Hurghada in 1996 and Aswan in 2010 and hencecomes the necessity of introducing efficient drainagefloodprotection systems capable of preventinglimiting the loss ofproperty and lives

Thedesign of combined sewage networks (collecting bothsewage and storm waters) depends greatly on the rainfallintensity The Egyptian code for designing potable water andsewage networks [1] uses the rational method to estimatepeak discharges from storm events along with the following

equations to calculate the intensity of rainfall (119868) basedon the time of concentration (119879

119888) values in the case of

nonavailability of rainfall data

If 10 min lt 119879119888lt 20 min

there4 119868 =750

119879119888+ 10(mmhr)

If 20 min lt 119879119888lt 120 min

(1)

there4 119868 =1000

119879119888+ 20(mmhr) (2)

As commonly assumed in the rational method the stormduration is taken equal to the time of concentration (119879

119888) the

Egyptian code also states that the rainfall values are to bereduced by 30 in noncoastal cities

HindawiAdvances in MeteorologyVolume 2017 Article ID 9496787 10 pageshttpsdoiorg10115520179496787

2 Advances in Meteorology

Underestimation of rainfall intensity may lead to roadnetwork damage and accidents while overestimation of thisimportant design parameter can greatly affect the economicalaspect of the sewage network increasing the required quanti-ties of manholes catch basins pipe sizes and consequentlythe construction costs

2 Literature Review

The rainfall Intensity-Duration-Frequency (IDF) curvesreceived considerable attention in engineering hydrologyover the past decades Approaches based on statistical anal-ysis of data were developed for example Bell [2] and Chen[3] derived IDF formulae for USA Koutsoyiannis et al [4]proposed a new generalizing approach to the formulationof IDF curves using efficient parameterization Nhat et al[5] established IDF curves for the monsoon area of Vietnamwith two distinct procedures Raiford et al [6] updated theexisting IDF curves in their study region and obtained thesecurves at ungauged sites throughout the region using newlydeveloped rainfall frequency analysis techniques Awadallah[7] developed IDF curves for Jeddah Saudi Arabia com-paring several approaches ordinary and L-moment ratiodiagrams goodness-of-fit measures based on L-momentslog-log and the mean excess function plots and Akaike andBayesian Information Criteria Many forms of equations areused to describe the rainfall intensity duration relationshipsas follows

Talbot equation

119868 =119886

119889 + 119887 (3)

Bernard (or Montana) equation

119868 =119886

119889119890 (4)

Kimijima equation

119868 =119886

119889119890 + 119887 (5)

Sherman equation

119868 = 119886 (119889 + 119887)119890 (6)

where 119868 is the rainfall intensity (mmhr) 119889 is thestorm duration (min) and 119886 119887 and 119890 are parametersrelated to meteorological conditions

In Egypt Fahmi et al [8] developed generalized depth-duration-frequency equations in Sudr Region Sinai Penin-sula North East of Egypt Available short duration rainfallrecordswere analyzed for six stations all located inWadi Sudrcatchment Short duration ratios were derived and comparedwith regionalizedUSA and other international short durationratios Finally IDF relationships were developed and com-pared with equations from previously published studies inboth humid and arid climates

SCS type II 24 hrType I

Type IAType III

0010203040506070809

1

Dim

ensio

nles

s sto

rm d

epth

01 02 03 04 05 06 07 08 09 10Time (days)

Figure 1 SCS 24-hour hypothetical storm profiles

El-Sayed [9] constructed IDF curves for the whole SinaiPeninsula using rainfall frequency analysis techniques Inaddition a regional IDF formula was proposed to estimaterainfall intensity for various return periods and rainfalldurations at ungauged sites using the available rainfall dataThe Kimijima form of the IDF equation was used to describethe rainfall intensity duration relationshipThe parameters ofthis equation are determined based on the minimum RootMean Square Error (RMSE)

Additional to the choice of the IDF equation it is alsoessential to decide based on historical records of precipita-tion events the temporal distribution of the design stormDesign storms could be represented utilizing diverse distribu-tions One of the most widely used design hyetographs is theSoil Conservation Services (SCS) hypothetical storms profiles(see Figure 1)The SCS type II stormdistributionmdash suggestedby the SCS for use in the relatively arid states of the USA mdashis the most critical (conservative) design storm as it has thesteepest slope (highest rainfall intensity)That is the reason itis commonly used in theMiddle East region codes of practice

However some researchers have questioned the adequacy(and safety) of using the SCS type II storm profile in aridregions Among them Ahmed [10] analyzed available shortduration rainfall data for rainfall stations located in Kingdomof SaudiArabia and in Sinai (Egypt) in order to suggest designstorms representative of the recorded storms The proposeddesign storms were compared with published design stormsproposed by SCS The study concluded that most of rainfallstations of short duration rainfall ratios showedmore conser-vative patterns than the SCS type II storm profile

In the same line of thoughts a modified SCS dimension-less curve was proposed by Awadallah and Younan [11] toremedy unsafe peak discharge values resulting from utilizingthe SCS storm profiles The rationale of the modification isthat if the total storm duration is limited to a certain valuethe total SCS dimensionless storm ought to be confinedmoreor less in this storm duration

In the absence of ground stations subdaily rainfall dataone of the proposed approaches to determine the stormaverage durations is to make use of the satellite rainfallestimates One of the free wellsprings of satellite measured

Advances in Meteorology 3

Table 1 Characteristics of available rainfall gauging stations

Number Rainfall station Latitude Longitude Observation period Number of complete years of record Elevation ASL (m)Name deg (∘) min (1015840) deg (∘) min (1015840)

1 Salloum 31 32 25 11 1931ndash1990 60 42 Marsa Matruh 31 20 27 13 1905ndash2015 86 253 Alexandria 31 12 29 57 1957ndash2008 52 1784 Port Said 31 17 32 14 1901ndash2013 78 085 Cairo 30 5 31 17 1961ndash1990 30 3446 Beni Suef 29 12 30 1 1961ndash1990 29 3247 Minia 28 5 30 44 1961ndash2015 62 37158 Assiut (Airport) 27 3 31 1 1961ndash1997 37 2269 Sohag 26 34 31 42 1961ndash1998 38 616310 Luxor 25 40 32 42 1961ndash1990 30 832511 Aswan 23 58 32 47 1961ndash2001 41 1942312 Siwa 29 12 25 19 1920ndash1990 50 1513 Frafra 27 3 27 58 1961ndash1990 30 82214 Dakhla 25 29 29 0 1961ndash1990 30 1062115 Ismailia 30 35 32 14 1961ndash2001 33 115416 Suez 29 25 32 28 1961ndash2001 41 24817 Hurghada 27 9 33 43 1957ndash2010 52 84318 Kusseir 26 8 34 18 1961ndash2000 40 8719 Ras Binas 23 58 35 30 1961ndash2001 36 366

precipitation information is the data obtained from theTropical Rainfall Measurement Mission (TRMM) satelliteTRMM is the first spaceborne precipitation radar intended togive 3D-maps of storm structure The mission was launchedon November 28 1997 It provides systematic multiyearestimations of precipitation in the tropics as key inputs toweather and climate research The satellite observations aresupplemented by ground radar and rain gaugemeasurementsto frequently validate the satellite rain estimation techniquesUnfortunately no ground station in the Arab nations isutilized for the calibration procedure of the satellite TRMMdata

Few papers have investigated the use of TRMM datain IDF development because of the reduced capabilities ofTRMM data to reproduce extreme storms Endreny andImbeah [12] showed that it was essential to combine groundrainfall measurements and TRMM data for IDF generationin Ghana Awadallah et al [13] explored the joint use oflimited ground rainfall stations and TRMM data to developIntensity-Duration-Frequency (IDF) curvesHomogeneity ofthe means and variances were first checked for both types ofdata In this research TRMM data were also used to developratios between 24 hr rainfall depth and shorter durationdepths A G Awadallah and N A Awadallah [14] presentedthe use of three separate rainfall datasets maximum annualdaily data monthly data (both from ground gauging sta-tions) and TRMM data to develop robust IDF in Namibesouth of Angola TRMM data was used to derive relationsbetweenmaximummonthly andmaximumdaily rainfall andbetween subdaily and daily rainfall depths Awadallah andTabet [15] have also successfully mixed the information ofTRMM and available ground stations for maximum floodlevels estimation at high return periods

3 Study Area Data Collectionand Preparation

This current research focusses on Egypt and mainly itsmajor cities located in the Nile Valley and Delta along theMediterranean and Red Sea coasts and three major oases inthe western desert of Egypt Available rainfall information inthe study area is described in the following subsections

31 Rainfall Ground Stations Data The Egyptian roads codeof practice Vol7 [16] provides some rainfall records of max-imum daily precipitation (119875max daily) for 19 major Egyptiancities for the period from 1961 to 1990 For some rainfall sta-tions 119875max daily data are available for a wider period collectedfrom the Egyptian Meteorological Authority The locationelevation andnumber of years available for all ground rainfallstations used in this study are shown in Figure 2 and Table 1

32 Tropical Rainfall Measuring Mission Data (TRMM) Inthis study Version 7 TRMM Multisatellite PrecipitationAnalysis (TMPA) (3B42) research version 3-hour 025∘ times025∘ resolution data is used [17 18] Zulkafli et al [19] showedthat version 7 of TRMM data has less bias compared to otherTRMM versions TRMM data are used to get the 3-hourlyprecipitation depths (119875

3 hr) (mm) for each station location toenable us to derive the subdaily rainfall information namelythe ratio between 119875

3 hr and the 24-hr rainfall depth (11987524 hr)

1198753 hr data available from the TRMM are from 1998 to 2015

4 Methodology and ResultsThe applied research methodology can be summarized in thefollowing steps

(1) Frequency analysis of the annual maxima rainfallrecords was undertaken using various statistical


25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

30∘09984000998400998400

N25∘09984000998400998400

N

30∘09984000998400998400

N25∘09984000998400998400

N

Figure 2 Location of the available rainfall stations

distributions to estimate rainfall depths at high returnperiods The common statistical distributions testedin this study are the Gamma 3-parameter lognormalGumbel and Pearson type III distributionsThis classof statistical distributions was determined followingthe approach described in El Adlouni et al [20]using log-log and mean excess function plots Theapproach is implemented in HYFRAN (HydrologicalFrequency Analysis) software package (INRS 2008)Themethod of moments (MoM) was used for param-eter estimationTheMoM is the stipulated method inthe Egyptian code of practice for flood protection [21]

(2) Akaike Information Criterion (AIC) [22] andBayesian Information Criterion (BIC) [23] are usedto choose the best distributions to fit the data Bothcriteria are based on the deviation between the fitteddistribution and the empirical probability with apenalization which is a function of the number ofparameters of the distribution and the sample sizeThe distribution having the smallest BIC and AIC isthe one that best fits the data Table 2 recapitulates theselected statistical distribution for each station andthe obtained daily precipitation for various returnperiods (119875RT daily) (mm) usually needed for designingstormwater networks To get the 24-hr estimate(119875RT 24Hr) the daily estimate is multiplied by 113 [24]

(3) Starting from the 3-hourly TRMM precipitationdepth (119875

3 hr) for each station location the 6 hr pre-cipitation (119875

6 hr) is obtained by summing up twoconsecutive 119875

3 hr Precipitation depths at 9 hr (1198759 hr)

12 hr (11987512 hr) and 24 hr (119875

24 hr) are similarly obtainedFor each year the maximum precipitation depths119875max 3 hr 119875max 6 hr 119875max 9 hr 119875max 12 hr and 119875max 24 hrare extracted

(4) Frequency analysis is then performed on the annualmaxima for all durations to obtain rainfall depthvalues for return periods of 2 3 5 10 and 20years Ratios are derived between the subdaily rainfallvalues and the maximum 24-hr rainfall that is119875119909 hr11987524 hr at all previously mentioned return periods

and all previously mentioned 119909-hr durations Theratios averaged across all return periods are obtainedfor each duration These average ratios are comparedwith those embedded in the well-known temporaldistribution of SCS type IITable 3 shows the average TRMM ratios at 3 hrs6 hrs 12 hrs and 24 hrs for each rainfall stationas well as the percentage difference of 119875

119909 hr11987524 hrratios compared with the SCS type II ratios that isPerctratio = [(119875119909 hr11987524 hr)TRMM minus (119875119909 hr11987524 hr)SCS](119875119909 hr11987524 hr)SCS times 100 The rationale behind this is

that the TRMM rainfall values are not considered in


Table 2 Frequency analysis results for daily precipitation (119875RT daily) (mm)

Station name Selected frequency distribution Daily precipitation 119875RT daily at various return periods (mm)

2 yr 3 yr 5 yr 10 yr 20 yr

Alexandria 3-parameter lognormal 2620 3360 4280 5560 6900

Salloum Gamma 1890 2730 3660 4900 6100

Port Said Gamma 143 206 275 367 4560

Siwa Gamma 369 624 924 1360 1790

Minia Gamma 132 246 385 594 810

Assiut Gamma 0622 27 655 147 247

Sohag Gamma 021 090 220 497 841

Luxor Gamma 015 062 164 405 720

Marsa Matrouh Gamma 2470 3320 4220 5380 6480

Cairo Gamma 736 1090 1480 2010 2690

Hurghada Gamma 081 288 626 1270 2030

Ras Binas Gamma 182 622 1330 2650 4200

Frafra Gamma 025 097 218 455 738

Beni Suef Gamma 216 326 450 619 785

Aswan Gamma 009 042 106 247 424

Suez Gamma 469 859 1340 2050 2780

Ismailia Gamma 851 1230 1640 2190 2720

Kusseir Gamma 147 333 582 986 1420

Dakhla Pearson type III 0 00794 039 11 201

Table 3 Comparison of 1198753 hr11987524 hr ratios between SCS type II and TRMM

Station name TRMM ratios SCS ratios Percentage differenceMax 3 hr Max 6 hr Max 12 hr Max 3 hr Max 6 hr Max 12 hr Max 3 hr Max 6 hr Max 12 hr

Alexandria 061 076 094

060 071 084

167 704 1190Marsa Matrouh 074 089 097 2333 2535 1548Salloum 056 071 092 minus667 000 952Port Said 063 077 091 500 845 833Cairo 075 079 091 2500 1127 833Suez 070 080 090 1667 1268 714Ismailia 071 083 094 1833 1690 1190Hurghada 073 085 091 2167 1972 833Ras Binas 091 094 095 5167 3239 1310Kusseir 051 070 090 minus1500 minus141 714Siwa 064 077 090 667 845 714Farafra 085 093 093 4167 3099 1071Dakhla 069 073 091 1500 282 833Beni Suef 076 085 091 2667 1972 833Minia 065 078 085 833 986 119Assiut 057 075 092 minus500 563 952Sohag 065 075 090 833 563 714Luxor 056 070 084 minus667 minus141 000Aswan 072 085 097 2000 1972 1548


Number Station name P10 (mm)Classification A Classification B

A1 A2 A3 B1 B2 B31 Alexandria 5560 A1 B12 Marsa Matrouh 4180 A3 B13 Salloum 3850 A1 B14 Port Said 2880 A1 B15 Ras Binas 2650 A3 B26 Ismailia 2190 A2 B27 Suez 2050 A2 B28 Cairo 2010 A3 B39 Assiut 147 A1 B310 Hurghada 1270 A3 B211 Siwa 1240 A1 B312 Kusseir 986 A2 B213 Beni Suef 619 A3 B314 Minia 594 A1 B315 Sohag 497 A1 B316 Frafra 455 A3 B317 Luxor 405 A1 B318 Aswan 247 A3 B319 Dakhla 11 A2 B3

119912120783

119912120783

119912120783

119912120783

119912120783

119912120785

119912120785

119912120785

Figure 3 Modifications on station classification

their absolute values but relative to themselves (ieonly ratios of 119875

119909 hr11987524 hr are used) This approach ismore appropriate as the TRMM rainfall values arenot calibrated for Egypt and it was reported in manystudies that TRMM data tend to underestimate theextreme rainfall values

(5) Stations are grouped according to (a) the similarityof the TRMM 119875

3 hr11987524 hr ratio with the SCS type II1198753 hr11987524 hr ratio and (b) their geographic location To

apply the first grouping criterion the absolute value ofPerctratio could be used to classify the rainfall stationsinto three classes as follows

(i) Class A1 0 lt |Perctratio| lt 10(ii) Class A2 10 le |Perctratio| lt 20(iii) Class A3 |Perctratio| ge 20

Based on the geographic location and the knownrainfall patterns in Egypt rainfall stations could beclassified as follows

(i) Group B1 includes all stations lying north ofEgypt along the coast of the Mediterranean Sea

(ii) GroupB2 includes all stations lying east of Egyptalong the coast of the Red Sea and along the SuezCanal

(iii) Group B3 includes all stations lying along theNile Valley and in the Western Desert

(6) To further merge the two grouping criteria thefollowing is undertaken (Figure 3)

(i) Marsa Matrouh station would be better classi-fied as part of class A1 instead of class A3 to

be with the stations lying along the coast of theMediterranean Sea

(ii) Ismailia Suez and Kusseir stations would beclassified with class A3 instead of class A2because the max 24 hr precipitations (P) (mm)of Ismaelia Suez and Cairo stations are almostthe same

(iii) Farafra Beni Suef and Aswan stations couldbe classified with class A1 instead of class A3because the max 24 hr precipitations (P) (mm)of Farafra Beni Suef Minia Luxor and Sohagstations are almost the same

(iv) Dakhla Station could be classified with class A1instead of class A2 with the stations lying in theWestern Desert

(v) As such group A2 is emptied and only twogroups are left A1 and A3 which are nowtermed Group X and Group Y to avoid ambi-guity

(7) Short duration ratios (less than 3 hr) are obtained via(7) It is well known that ratios for durations from2 hours to 5 minutes are quasi-constant in differentclimates because of the similarity of convective stormspatterns This was first identified by Bell [2] and con-firmed for arid regions by FAO [25] and Awadallahand Younan [11] and references therein

Proposed ratiofor Duration 119863

=(119875119863hr1198753 hr)for SCS times (1198753 hr11987524 hr)for TRMM

(1198753 hr11987524 hr)for SCS

(7)

(8) First the intensity duration relationships are estab-lished in a ratio form relative to the 24 hr value The


Table 4 Relation between storm duration (min) and rainfallintensity ratios (Groups X and Y)

Time (min) Duration(min)

Rainfall intensity ratioGroup X Group Y

(10min lt duration lt20min)

6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325

IDF ratio equations are developed for two groups only(X and Y) This is carried out similar to the Egyptiancode of practice equations that is one relationship forstorm durations from 10min till 20min and anotherfrom 20min to 120min as shown in Table 4 andFigure 4To transform the IDF ratio relationships to the IDFabsolute values relationships the ratio relationshipsare multiplied by 119875RT 24Hr at the required returnperiod In fact 119875RT 24Hr is obtained by multiplying a113 adjustment factor by 119875RT daily of Table 2 The IDFrelationships are summarized by (8) and (9)For Group X (which contains Salloum MarsaMatrouh Alexandria Port Said Siwa Dakhla FrafraBeni Suef Minia Assiut Sohag Luxor and Aswan)

119868 =(25265119875RT 24Hr)

119879119888

0275810 min lt Duration lt 20 min

119868 =(10195119875RT 24Hr)

119879119888

07349


(8)

For Group Y (which contains Cairo Suez IsmailiaHurghada Kusseir and Ras Binas)

119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)

Group X ratiosGroup Y ratios

0

02

04

06

08

1

12

14

16

18

20 40 60 80 100 1200Storm duration (min)

Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r

Figure 4 Intensity ratio curve for groups X and Y

where 119868 is rainfall intensity (mmhr) 119875RT 24Hr is the24-hr rainfall at the required return period (mm)The proposed equations are used similar to anyIDF equations in the rational method that is first119875RT 24Hr is determined based on the available 24-hrrainfall and the storm duration could be assumedequal to the time of concentration 119879

119888(min) as per

the rational method assumption and consequentlythe rainfall intensity is determined as per (8) or (9)depending on the city For the cities not mentionedor where no available rainfall gauges are present it isrecommended to use the results of the nearest station(max 24 hrs rainfall at the required return period andthe corresponding proposed equation)

(9) For the 10-year return period the rainfall inten-sity values from the Egyptian code are comparedto those of the proposed equations and the ratio(119868code119868proposed) is calculatedThe results are presentedin Table 5

5 Verification of the Proposed Equations

Comparisons were carried out between the two code equa-tions the proposed equations and the actual IDF curves mdashprepared by independent sources and used in actual projectsin Cairo Alexandria and Hurghada mdash as shown in Table 6and Figures 5ndash7 The comparison shows that the equationsdeveloped by the current research are quasi identical to theIDF curves developed by independent sources

6 Conclusions and Recommendations

Two Intensity-Duration-Frequency (IDF) equations aregiven in the Egyptian code of practice for designing potablewater and sewage networks to be applied in all cities of Egyptfor the purpose of design of combined sewage networks Theobjective of this current research is to evaluate the suitability


Table 5 The percentage between the Egyptian codersquos equations and the proposed equations

Number Station (10min lt duration lt 20min) (20min lt duration lt 120min)1 Alexandria 4264 38582 Salloum 6158 55723 Port Said 646 5854 Siwa 13384 121105 Minia 27939 252806 Assiut 11290 102157 Sohag 33392 302148 Luxor 40978 370779 Frafra 36475 3300310 Beni Suef 26811 2425911 Aswan 67190 6079412 Marsa Matrouh 5672 513213 Dakhla 150873 13651114 Cairo 8966 848315 Hurghada 16506 1561516 Ras Binas 8270 748317 Suez 8552 773918 Ismailia 8006 724419 Kusseir 22227 20112

Table 6 Comparison of rainfall intensity 119868 (mmhr) for various storm durationsfor Cairo Alexandria and Hurghada for the 10-year returnperiod

Duration (min) Cairo 10 yr (mmhr) Alexandria 10 yr (mmhr) Hurghada 10 yr (mmhr)Actual IDF Proposed Eq Code Eq Actual IDF Proposed Eq Code Eq Actual IDF Proposed Eq Code Eq

10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


0

5

10

15

20

25

30

35

40

10 20 30 40 50 60 70 80 90 100 110 120

Inte

nsity

(mm

hr)

Duration (min)

Actual IDFPropose EqCode Eq

Figure 5 Comparison of rainfall intensity curve 119868 (mmhr) forCairo

Actual IDFProposed EqCode Eq

0102030405060708090

100

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)

Figure 6 Comparison of rainfall intensity curve 119868 (mmhr) forAlexandria


0

5

10

15

20

25

30

35

40

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)

Figure 7 Comparison of rainfall intensity curve 119868 (mmhr) forHurghada

and safety of application of these rainfall IDF equationsbased on the actual rainfall ground station measurementscollected from different cities in Egypt and remote sensingTropical Rainfall Measurement Mission (TRMM) satelliterainfall estimates Results indicated that the rainfall intensityequations suggested by the Egyptian code are not applicablein most cities Some rainfall stations produce records thatindicate higher values than the ones calculated by theaforementioned equations and this may lead indirectly toroad networks damage and accidents due to underestimationof the generated peak discharges Records from other rainfallstations yield values significantly less than the correspondingcalculated values by the same equations which can greatlyaffect the economical aspect of the sewage network as itmay increase the capital cost of the project The currentstudy proposes new general equations to be used Furtherstudies should check the short duration ratios obtained fromTRMMagainst ground stations short duration ratios if madeavailable

Competing Interests

The authors declare that they have no competing interests

References

[1] Ministry of Housing Utilities and the Urban DevelopmentEgyptian code of practice for the design and implementationof pipes used in potable water and sewage networks Code 102Volume 1 Cairo 2010

[2] F C Bell ldquoGeneralized rainfall-duration-frequency relation-shiprdquo Journal of Hydraulic Engineering vol 95 pp 311ndash327 1969

[3] C-L Chen ldquoRainfall intensity-duration-frequency formulasrdquoJournal of Hydraulic Engineering vol 109 no 12 pp 1603ndash16211983

[4] D Koutsoyiannis D Kozonis andAManetas ldquoAmathematicalframework for studying rainfall intensity-duration-frequencyrelationshipsrdquo Journal of Hydrology vol 206 no 1-2 pp 118ndash135 1998

[5] L Nhat Y Tachikawa and K Takara ldquoEstablishment ofintensity-duration-frequency curves for precipitation in themonsoon area of Vietnamrdquo Kyoto University Disaster Preven-tion Research Institute Annual Report 49 B 2006

[6] J P Raiford N M Aziz A A Khan and D N PowellldquoRainfall depth-duration-frequency relationships for SouthCarolina North Carolina and Georgiardquo American Journal ofEnvironmental Sciences vol 3 no 2 pp 78ndash84 2007

[7] A G Awadallah ldquoRegional intensity-duration-frequencycurves for Jeddah region Saudi Arabia using ordinary andL-moments approachesrdquo Journal of Flood Risk Managementvol 8 no 3 pp 195ndash207 2015

[8] A H Fahmi A G Awadallah A H Eman A Afify and MMottaleb ldquoGeneralized depth-duration-frequency relationshipin arid region a case study of Wadi Sudr Sinai Peninsulardquo inProceedings of the ASCE IV Middle East Regional Conferenceand IV International Symposium on Environmental HydrologyCairo Egypt 2005

[9] E A H El-Sayed ldquoGeneration of rainfall intensity durationfrequency curves for ungauged sitesrdquo Nile Basin Water Scienceamp Engineering Journal vol 4 no 1 pp 112ndash124 2011


[10] K A Ahmed Rainfall short time duration analysis in aridregions [MS thesis] Faculty of Engineering Cairo UniversityCairo Egypt 2011

[11] AGAwadallah andN S Younan ldquoConservative design rainfalldistribution for application in arid regions with sparse datardquoJournal of Arid Environments vol 79 pp 66ndash75 2012

[12] T A Endreny and N Imbeah ldquoGenerating robust rainfallintensity-duration-frequency estimates with short-record satel-lite datardquo Journal of Hydrology vol 371 no 1ndash4 pp 182ndash1912009

[13] A G Awadallah M ElGamal A ElMostafa and H ElBadryldquoDeveloping intensity-duration-frequency curves in scarce dataregion an approach using regional analysis and satellite datardquoEngineering vol 3 no 3 pp 215ndash226 2011

[14] A G Awadallah and N A Awadallah ldquoA Novel approach forthe joint use of rainfall monthly and daily ground station datawith TRMM data to generate IDF estimates in a poorly gaugedarid regionrdquoOpen Journal of Modern Hydrology vol 3 no 1 pp1ndash7 2013

[15] A G Awadallah and D Tabet ldquoEstimating flooding extentat high return period for ungauged braided systems usingremote sensing a case study of Cuvelai Basin Angolardquo NaturalHazards vol 77 no 1 pp 255ndash272 2015

[16] Ministry of Housing Utilities and the Urban DevelopmentEgyptian code of practice for urban and rural road works Code104 Volume 7 Cairo 2008

[17] G J Huffman R Adler D Bolvin and E Nelkin ldquoThe TRMMmulti-satellite precipitation analysis (TMPA)rdquo in Satellite Rain-fall Applications for Surface Hydrology M Gebremichael and FHossain Eds pp 3ndash22 Springer 2010

[18] G J Huffman and D T Bolvin TRMM and Other DataPrecipitation Data Set Documentation Global Change MasterDirectory NASA Washington DC USA 2013

[19] Z Zulkafli W Buytaert C Onof et al ldquoA comparative perfor-mance analysis of TRMM 3B42 (TMPA) versions 6 and 7 forhydrological applications over AndeanndashAmazon river basinsrdquoJournal of Hydrometeorology vol 15 no 2 pp 581ndash592 2014

[20] S El Adlouni B Bobee and T B M J Ouarda ldquoOn the tails ofextreme event distributions in hydrologyrdquo Journal of Hydrologyvol 355 no 1ndash4 pp 16ndash33 2008

[21] Ministry of Water Resources and Irrigation Egyptian code ofpractice for flood protection Cairo 2011

[22] H Akaike ldquoA new look at the statistical model identificationrdquoIEEE Transactions on Automatic Control vol 19 pp 716ndash7231974

[23] G Schwarz ldquoEstimating the dimension of a modelrdquoThe Annalsof Statistics vol 6 no 2 pp 461ndash464 1978

[24] D M Hershfield ldquoRainfall frequency atlas of the UnitedStates for durations from 30 minutes to 24 hours and returnperiods from 1 to 100 yearsrdquo Tech Rep 40 US Department ofCommerce Weather Bureau Washington DC USA 1961

[25] Food and Agriculture Organization (FAO) Arid Zone Hydrol-ogy FAO Irrigation and Drainage Paper No 37 FAO RomeItaly 1981

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of


EarthquakesJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering


GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of


OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


MineralogyInternational Journal of


MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in


Underestimation of rainfall intensity may lead to roadnetwork damage and accidents while overestimation of thisimportant design parameter can greatly affect the economicalaspect of the sewage network increasing the required quanti-ties of manholes catch basins pipe sizes and consequentlythe construction costs

2 Literature Review

The rainfall Intensity-Duration-Frequency (IDF) curvesreceived considerable attention in engineering hydrologyover the past decades Approaches based on statistical anal-ysis of data were developed for example Bell [2] and Chen[3] derived IDF formulae for USA Koutsoyiannis et al [4]proposed a new generalizing approach to the formulationof IDF curves using efficient parameterization Nhat et al[5] established IDF curves for the monsoon area of Vietnamwith two distinct procedures Raiford et al [6] updated theexisting IDF curves in their study region and obtained thesecurves at ungauged sites throughout the region using newlydeveloped rainfall frequency analysis techniques Awadallah[7] developed IDF curves for Jeddah Saudi Arabia com-paring several approaches ordinary and L-moment ratiodiagrams goodness-of-fit measures based on L-momentslog-log and the mean excess function plots and Akaike andBayesian Information Criteria Many forms of equations areused to describe the rainfall intensity duration relationshipsas follows

Talbot equation

119868 =119886

119889 + 119887 (3)

Bernard (or Montana) equation

119868 =119886

119889119890 (4)

Kimijima equation

119868 =119886

119889119890 + 119887 (5)

Sherman equation

119868 = 119886 (119889 + 119887)119890 (6)

where 119868 is the rainfall intensity (mmhr) 119889 is thestorm duration (min) and 119886 119887 and 119890 are parametersrelated to meteorological conditions

In Egypt Fahmi et al [8] developed generalized depth-duration-frequency equations in Sudr Region Sinai Penin-sula North East of Egypt Available short duration rainfallrecordswere analyzed for six stations all located inWadi Sudrcatchment Short duration ratios were derived and comparedwith regionalizedUSA and other international short durationratios Finally IDF relationships were developed and com-pared with equations from previously published studies inboth humid and arid climates

SCS type II 24 hrType I

Type IAType III

0010203040506070809

1

Dim

ensio

nles

s sto

rm d

epth

01 02 03 04 05 06 07 08 09 10Time (days)

Figure 1 SCS 24-hour hypothetical storm profiles

El-Sayed [9] constructed IDF curves for the whole SinaiPeninsula using rainfall frequency analysis techniques Inaddition a regional IDF formula was proposed to estimaterainfall intensity for various return periods and rainfalldurations at ungauged sites using the available rainfall dataThe Kimijima form of the IDF equation was used to describethe rainfall intensity duration relationshipThe parameters ofthis equation are determined based on the minimum RootMean Square Error (RMSE)

Additional to the choice of the IDF equation it is alsoessential to decide based on historical records of precipita-tion events the temporal distribution of the design stormDesign storms could be represented utilizing diverse distribu-tions One of the most widely used design hyetographs is theSoil Conservation Services (SCS) hypothetical storms profiles(see Figure 1)The SCS type II stormdistributionmdash suggestedby the SCS for use in the relatively arid states of the USA mdashis the most critical (conservative) design storm as it has thesteepest slope (highest rainfall intensity)That is the reason itis commonly used in theMiddle East region codes of practice

However some researchers have questioned the adequacy(and safety) of using the SCS type II storm profile in aridregions Among them Ahmed [10] analyzed available shortduration rainfall data for rainfall stations located in Kingdomof SaudiArabia and in Sinai (Egypt) in order to suggest designstorms representative of the recorded storms The proposeddesign storms were compared with published design stormsproposed by SCS The study concluded that most of rainfallstations of short duration rainfall ratios showedmore conser-vative patterns than the SCS type II storm profile

In the same line of thoughts a modified SCS dimension-less curve was proposed by Awadallah and Younan [11] toremedy unsafe peak discharge values resulting from utilizingthe SCS storm profiles The rationale of the modification isthat if the total storm duration is limited to a certain valuethe total SCS dimensionless storm ought to be confinedmoreor less in this storm duration

In the absence of ground stations subdaily rainfall dataone of the proposed approaches to determine the stormaverage durations is to make use of the satellite rainfallestimates One of the free wellsprings of satellite measured

















25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

30∘09984000998400998400

N25∘09984000998400998400

N

30∘09984000998400998400

N25∘09984000998400998400

N








12 hr (11987512 hr) and 24 hr (119875









2 yr 3 yr 5 yr 10 yr 20 yr


Salloum Gamma 1890 2730 3660 4900 6100

Port Said Gamma 143 206 275 367 4560

Siwa Gamma 369 624 924 1360 1790

Minia Gamma 132 246 385 594 810

Assiut Gamma 0622 27 655 147 247

Sohag Gamma 021 090 220 497 841

Luxor Gamma 015 062 164 405 720


Cairo Gamma 736 1090 1480 2010 2690

Hurghada Gamma 081 288 626 1270 2030

Ras Binas Gamma 182 622 1330 2650 4200

Frafra Gamma 025 097 218 455 738

Beni Suef Gamma 216 326 450 619 785

Aswan Gamma 009 042 106 247 424

Suez Gamma 469 859 1340 2050 2780

Ismailia Gamma 851 1230 1640 2190 2720

Kusseir Gamma 147 333 582 986 1420





060 071 084





119912120783

119912120783

119912120783

119912120783

119912120783

119912120785

119912120785

119912120785






















(1198753 hr11987524 hr)for SCS

(7)







6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325


119868 =(25265119875RT 24Hr)

119879119888


119868 =(10195119875RT 24Hr)

119879119888

07349


(8)


119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)


0

02

04

06

08

1

12

14

16

18


Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r



119888(min) as per












10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


0

5

10

15

20

25

30

35

40

10 20 30 40 50 60 70 80 90 100 110 120

Inte

nsity

(mm

hr)

Duration (min)




0102030405060708090

100

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



0

5

10

15

20

25

30

35

40

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



Competing Interests


References




































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

















25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

30∘09984000998400998400

N25∘09984000998400998400

N

30∘09984000998400998400

N25∘09984000998400998400

N








12 hr (11987512 hr) and 24 hr (119875









2 yr 3 yr 5 yr 10 yr 20 yr


Salloum Gamma 1890 2730 3660 4900 6100

Port Said Gamma 143 206 275 367 4560

Siwa Gamma 369 624 924 1360 1790

Minia Gamma 132 246 385 594 810

Assiut Gamma 0622 27 655 147 247

Sohag Gamma 021 090 220 497 841

Luxor Gamma 015 062 164 405 720


Cairo Gamma 736 1090 1480 2010 2690

Hurghada Gamma 081 288 626 1270 2030

Ras Binas Gamma 182 622 1330 2650 4200

Frafra Gamma 025 097 218 455 738

Beni Suef Gamma 216 326 450 619 785

Aswan Gamma 009 042 106 247 424

Suez Gamma 469 859 1340 2050 2780

Ismailia Gamma 851 1230 1640 2190 2720

Kusseir Gamma 147 333 582 986 1420





060 071 084





119912120783

119912120783

119912120783

119912120783

119912120783

119912120785

119912120785

119912120785






















(1198753 hr11987524 hr)for SCS

(7)







6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325


119868 =(25265119875RT 24Hr)

119879119888


119868 =(10195119875RT 24Hr)

119879119888

07349


(8)


119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)


0

02

04

06

08

1

12

14

16

18


Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r



119888(min) as per












10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


0

5

10

15

20

25

30

35

40

10 20 30 40 50 60 70 80 90 100 110 120

Inte

nsity

(mm

hr)

Duration (min)




0102030405060708090

100

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



0

5

10

15

20

25

30

35

40

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



Competing Interests


References




































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

25∘09984000998400998400E 30

∘09984000998400998400E 35

∘09984000998400998400E

30∘09984000998400998400

N25∘09984000998400998400

N

30∘09984000998400998400

N25∘09984000998400998400

N








12 hr (11987512 hr) and 24 hr (119875









2 yr 3 yr 5 yr 10 yr 20 yr


Salloum Gamma 1890 2730 3660 4900 6100

Port Said Gamma 143 206 275 367 4560

Siwa Gamma 369 624 924 1360 1790

Minia Gamma 132 246 385 594 810

Assiut Gamma 0622 27 655 147 247

Sohag Gamma 021 090 220 497 841

Luxor Gamma 015 062 164 405 720


Cairo Gamma 736 1090 1480 2010 2690

Hurghada Gamma 081 288 626 1270 2030

Ras Binas Gamma 182 622 1330 2650 4200

Frafra Gamma 025 097 218 455 738

Beni Suef Gamma 216 326 450 619 785

Aswan Gamma 009 042 106 247 424

Suez Gamma 469 859 1340 2050 2780

Ismailia Gamma 851 1230 1640 2190 2720

Kusseir Gamma 147 333 582 986 1420





060 071 084





119912120783

119912120783

119912120783

119912120783

119912120783

119912120785

119912120785

119912120785






















(1198753 hr11987524 hr)for SCS

(7)







6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325


119868 =(25265119875RT 24Hr)

119879119888


119868 =(10195119875RT 24Hr)

119879119888

07349


(8)


119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)


0

02

04

06

08

1

12

14

16

18


Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r



119888(min) as per












10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


0

5

10

15

20

25

30

35

40

10 20 30 40 50 60 70 80 90 100 110 120

Inte

nsity

(mm

hr)

Duration (min)




0102030405060708090

100

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



0

5

10

15

20

25

30

35

40

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



Competing Interests


References




































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




2 yr 3 yr 5 yr 10 yr 20 yr


Salloum Gamma 1890 2730 3660 4900 6100

Port Said Gamma 143 206 275 367 4560

Siwa Gamma 369 624 924 1360 1790

Minia Gamma 132 246 385 594 810

Assiut Gamma 0622 27 655 147 247

Sohag Gamma 021 090 220 497 841

Luxor Gamma 015 062 164 405 720


Cairo Gamma 736 1090 1480 2010 2690

Hurghada Gamma 081 288 626 1270 2030

Ras Binas Gamma 182 622 1330 2650 4200

Frafra Gamma 025 097 218 455 738

Beni Suef Gamma 216 326 450 619 785

Aswan Gamma 009 042 106 247 424

Suez Gamma 469 859 1340 2050 2780

Ismailia Gamma 851 1230 1640 2190 2720

Kusseir Gamma 147 333 582 986 1420





060 071 084





119912120783

119912120783

119912120783

119912120783

119912120783

119912120785

119912120785

119912120785






















(1198753 hr11987524 hr)for SCS

(7)







6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325


119868 =(25265119875RT 24Hr)

119879119888


119868 =(10195119875RT 24Hr)

119879119888

07349


(8)


119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)


0

02

04

06

08

1

12

14

16

18


Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r



119888(min) as per












10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


0

5

10

15

20

25

30

35

40

10 20 30 40 50 60 70 80 90 100 110 120

Inte

nsity

(mm

hr)

Duration (min)




0102030405060708090

100

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



0

5

10

15

20

25

30

35

40

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



Competing Interests


References




































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




119912120783

119912120783

119912120783

119912120783

119912120783

119912120785

119912120785

119912120785






















(1198753 hr11987524 hr)for SCS

(7)







6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325


119868 =(25265119875RT 24Hr)

119879119888


119868 =(10195119875RT 24Hr)

119879119888

07349


(8)


119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)


0

02

04

06

08

1

12

14

16

18


Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r



119888(min) as per












10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


0

5

10

15

20

25

30

35

40

10 20 30 40 50 60 70 80 90 100 110 120

Inte

nsity

(mm

hr)

Duration (min)




0102030405060708090

100

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



0

5

10

15

20

25

30

35

40

Inte

nsity

(mm

hr)

20 30 40 50 60 70 80 90 100 110 12010Duration (min)



Competing Interests


References




































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in






6 1529 165412 1295 140118 1150 1244


24 0993 107530 0848 091836 0742 080342 0663 071848 0600 064954 0548 059360 0506 054766 0471 050972 0441 047778 0416 045084 0392 042490 0372 040396 0354 0383102 0339 0367108 0325 0352114 0312 0337120 0300 0325


119868 =(25265119875RT 24Hr)

119879119888


119868 =(10195119875RT 24Hr)

119879119888

07349


(8)


119868 =(27207119875RT 24Hr)

119879119888


119868 =(11088119875RT 24Hr)

119879119888

07361


(9)


0

02

04

06

08

1

12

14

16

18


Rain

fall

inte

nsity

ratio

s wrt

PRT

_24H

r



119888(min) as per












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10 3362 3072 3000 9830 8442 3750 2645 2086 375012 2930 2727 8054 3409 1990 340915 26796 2766 2400 8301 7603 3000 1763 1879 300018 2639 2143 7254 2679 1792 267924 2292 1818 6300 2273 1557 227330 1924 1939 1600 5753 5330 2000 111 1317 200036 1692 1429 4650 1786 1149 178642 1507 129 4143 1613 1024 161348 1364 1176 3748 1471 926 147154 1249 1081 3432 1351 848 135160 1160 1154 1000 3641 3172 125 718 784 125066 1074 930 2953 1163 730 116372 1007 870 2767 1087 684 108778 948 816 2606 102 644 102084 897 769 2465 962 609 96290 852 727 2341 909 578 90996 811 690 2230 862 551 862102 775 656 2131 820 527 820108 743 625 2042 781 505 781114 713 597 1961 746 485 746120 754 687 571 2119 1887 714 465 466 714


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Geophysics







Journal of




Advances in











Geology Advances in


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Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in



























Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in










Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Documents

Assessment of Rainfall Intensity Equations Enlisted …downloads.hindawi.com/journals/amete/2017/9496787.pdf6 AdvancesinMeteorology Number Station nameP10 (mm) Classification A Classification