Abstract:
DepthDurationFrequency(DDF)relationshipsar ecurrentlyconstructedbase donatsite
frequenc yanalysisofrainfalldat aseparatelyfordifferentdurations.Theserelationshipsar e
notaccurat eandreliablesincetheydependonassumptionssuc ha sdistributionselection
foreac hduration;theyrequirealarg enumberofparameters,experienceintensive
equationsandregionalizationisalsoverypoorandcoarse.Inthisstudy,scalingproperties
ofextremerainfalldepthserie sar eexaminedtoestablishscalingbehaviorofstatistical
momentsandquantileestimatesoverdifferentdurations.Theannualextremeserie sof
precipitationmaximaforstormdurationrangingfromO.Sh rto24hrobserveda tnetworkof
raingauge ssitedi nOromiaregionalstat ewereanalyzedusinganapproachbasedon
moments.Theanalysisinvestigatedth estatisticalpropertieso frainfallextremesand
detectedthatth estatisticsofth erainfallextremesfollowsapowerlawrelationwithits
iduration.Moreover,thevariationsofth edistributionparameterswithdurationsofannual
• jmaximumrainfalldepthserie swereexploredandfoundthatth elogEVl ,EV landlogistic
!distributionparametersexhibitapowerlawrelationshipwithdurations.Followingth e
analysis,scal einvarianceofextremerainfalldepthserie sisinvestigatedanddissipative
'i(multiplescaling)natureofextremerainfalldepthserie sisconsidered,thu sintroducinga
!generaldistributionfree frameworktodevelopDepthDurationfrequency(DDF)model.A
^DepthDurationFrequency(DDF)modelwithgriddedse tofparametersisdevelopedfor
estimationofpointrainfallfrequenciesforarang eofdurationforan ylocationi nOromia
nationalandregionalstate .ADDFmodelwasfittedtoserie sofannualmaximaandits
parametersweredeterminedbyaleas tsquare smethodandthes eparameterswere
interpolatedandmappedona1kmgrid.Themodelallowsforaparsimoniousandefficient
parameterizationofDDFrelationships,anditsperformanceisshowntoimprovethe
reliabilityandrobustnes so fdesignstormpredictionsa scomparedwiththos eachievableby
interpolatingthequantilepredictionsofextremerainfalldataforspecificdurations.
Moreover,designrainfallestimatesfoundfromthescalingDDFmodelar ecomparableto
estimate sobtainedfromtraditionaltechniques ;however,th escale dapproachwasmore
efficientandgivesmorereliableestimatecomparedwithth eobservedrainfalldeptha tall
stations.
