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基于ArcGIS的水利大數(shù)據(jù)及應(yīng)用基于ArcGIS的水利大數(shù)據(jù)及應(yīng)用1團(tuán)隊(duì)簡(jiǎn)介水利大數(shù)據(jù)及其面臨的挑戰(zhàn)基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例主要內(nèi)容123團(tuán)隊(duì)簡(jiǎn)介水利大數(shù)據(jù)及其面臨的挑戰(zhàn)基于水利大數(shù)據(jù)的多災(zāi)害信息集2二、水利大數(shù)據(jù)及其面臨的挑戰(zhàn)二、水利大數(shù)據(jù)及其面臨的挑戰(zhàn)3水利工作關(guān)系到國(guó)計(jì)民生,尤其是我國(guó)水資源分布存在嚴(yán)重的時(shí)空分布不均特性,旱災(zāi)洪澇易發(fā)多發(fā)。水利行業(yè)在經(jīng)濟(jì)、生態(tài)、社會(huì)等方面都扮演著重要角色,對(duì)水利大數(shù)據(jù)的研究具有重要的現(xiàn)實(shí)意義和應(yīng)用價(jià)值。水利大數(shù)據(jù)是在大數(shù)據(jù)的理論指導(dǎo)及技術(shù)支撐下的水利科學(xué)和工程的重要實(shí)踐。水利工作及水利大數(shù)據(jù)的重要性水利工作關(guān)系到國(guó)計(jì)民生,尤其是我國(guó)水資源分布存在嚴(yán)重的時(shí)空4水利大數(shù)據(jù)水利大數(shù)據(jù)是指產(chǎn)生于各種水文監(jiān)測(cè)網(wǎng)絡(luò)、水利設(shè)施、用水單位和水利相關(guān)經(jīng)濟(jì)活動(dòng),并通過(guò)現(xiàn)代化信息技術(shù)高效傳輸、分布存儲(chǔ)于各地存儲(chǔ)系統(tǒng)、但又可以快速讀取集中于云端、實(shí)現(xiàn)深度數(shù)據(jù)挖掘并可視化的海量多源數(shù)據(jù)總和。ValueVelocityVolume海量快速價(jià)值Variety多樣Veracity真實(shí)水利大數(shù)據(jù)水利大數(shù)據(jù)ValueVelocityVolume快5交叉性,由于水利和其它領(lǐng)域具有交叉性,因此水利大數(shù)據(jù)和遙感大數(shù)據(jù)、氣象大數(shù)據(jù)、海洋大數(shù)據(jù)等交叉;時(shí)空分布性,需要依賴先進(jìn)大數(shù)據(jù)技術(shù)進(jìn)行處理分析,包括分布式大數(shù)據(jù)存儲(chǔ)框架、機(jī)器學(xué)習(xí)等數(shù)據(jù)挖掘方法;多元循環(huán)性,由水的多元循環(huán)決定的水利大數(shù)據(jù)在經(jīng)濟(jì)、社會(huì)、生態(tài)等領(lǐng)域的價(jià)值循環(huán)。水利大數(shù)據(jù)的外延交叉性,由于水利和其它領(lǐng)域具有交叉性,因此水利大數(shù)據(jù)和遙感6挑戰(zhàn)一:水利大數(shù)據(jù)的收集與集成水利大數(shù)據(jù)來(lái)源廣泛,不同的監(jiān)測(cè)平臺(tái)得到的數(shù)據(jù)具有不同的數(shù)據(jù)結(jié)構(gòu)、存儲(chǔ)系統(tǒng),非結(jié)構(gòu)化數(shù)據(jù)、半結(jié)構(gòu)化數(shù)據(jù)、結(jié)構(gòu)化數(shù)據(jù)并存;由于觀測(cè)條件的差異,數(shù)據(jù)可信度層次不齊,對(duì)數(shù)據(jù)清洗和質(zhì)量的確保提出了很高的要求;大數(shù)據(jù)的存儲(chǔ)與管理需要新型數(shù)據(jù)庫(kù)的支持,水利大數(shù)據(jù)的信息化還未與新型數(shù)據(jù)庫(kù)接軌。水利大數(shù)據(jù)面臨的挑戰(zhàn)挑戰(zhàn)一:水利大數(shù)據(jù)的收集與集成水利大數(shù)據(jù)面臨的挑戰(zhàn)7挑戰(zhàn)二:水利大數(shù)據(jù)的時(shí)空多維度分析水利大數(shù)據(jù)具有明顯的時(shí)空分布特性,時(shí)間、空間雙維度下的數(shù)據(jù)分析具有難度;水利大數(shù)據(jù)在其應(yīng)用領(lǐng)域講究實(shí)時(shí)性,比如洪水預(yù)報(bào)等,這對(duì)大數(shù)據(jù)的處理分析速度提出了高要求;水利大數(shù)據(jù)的深度挖掘有賴于引入先進(jìn)的人工智能算法,兩者的有效結(jié)合至關(guān)重要。水利大數(shù)據(jù)面臨的挑戰(zhàn)挑戰(zhàn)二:水利大數(shù)據(jù)的時(shí)空多維度分析水利大數(shù)據(jù)面臨的挑戰(zhàn)8挑戰(zhàn)三:水利大數(shù)據(jù)的共享與安全眾多水利數(shù)據(jù)掌握在政府機(jī)關(guān)部門,為非公開(kāi)數(shù)據(jù),形成數(shù)據(jù)孤島現(xiàn)象;水利數(shù)據(jù)是國(guó)家安全的重要組成部分,水利數(shù)據(jù)的共享與安全是一個(gè)值得探討的問(wèn)題。水利大數(shù)據(jù)面臨的挑戰(zhàn)挑戰(zhàn)三:水利大數(shù)據(jù)的共享與安全水利大數(shù)據(jù)面臨的挑戰(zhàn)9三、基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹三、基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹10基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹1、天、地、空、海,多基多源降水?dāng)?shù)據(jù)采集2、移動(dòng)眾包信息收集可視化云平臺(tái)mPing3、基于水利大數(shù)據(jù)的全球洪水泥石流災(zāi)害預(yù)測(cè)預(yù)報(bào)4、基于概率洪水風(fēng)險(xiǎn)預(yù)報(bào)EF55、城市洪水模型Urban

CREST介紹6、全球風(fēng)暴數(shù)據(jù)庫(kù)及CI-FLOW7、中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立8、基于ArcGIS的FFG介紹9、基于ArcGIS平臺(tái)開(kāi)發(fā)的ArcCREST介紹基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹1、天113小時(shí)臨近預(yù)報(bào)(250米/2.5分鐘)+36小時(shí)模型預(yù)報(bào)(1公里/小時(shí))1.天、地、空、海多基多源降水?dāng)?shù)據(jù)采集雙偏振雷達(dá)+衛(wèi)星+站點(diǎn)+模型3小時(shí)臨近預(yù)報(bào)1.天、地、空、海多基多源降水?dāng)?shù)據(jù)采集雙偏振12PERSIANN

全球衛(wèi)星產(chǎn)品(4km,

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JAM;5顆地球靜止衛(wèi)星(可見(jiàn)光紅外)以及4顆極軌衛(wèi)星(雷達(dá)和被動(dòng)微波)通過(guò)人工神經(jīng)網(wǎng)絡(luò)ANN/機(jī)器學(xué)習(xí)訓(xùn)練反演

HighQuality

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coefficientsTRMMAquaDMSPNOAAMETEOSATGOESTM1426深度學(xué)習(xí)方法研制全球衛(wèi)星產(chǎn)品研制青藏西南部IR云圖 相應(yīng)時(shí)段降水情況在深度學(xué)習(xí)中,我們可以將不同頻段的可見(jiàn)光、紅外、微波影像同時(shí)作為訓(xùn)練數(shù)據(jù)輸入模型,且不需要事先設(shè)定Feature,海量的遙感影像下,讓模型自己去尋找Feature。26深度學(xué)習(xí)方法研制全球衛(wèi)星產(chǎn)品研制青藏西南部IR云圖 相應(yīng)155-minute250mRainfall

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SLIDECoupledRoutingandExcessSTorage(CREST)Jointlydeveloped

byOU/NASARunoperationallyoverglobeDistributed,fullycoupledrunoffgenerationand

routingWangamnoddHelongetal.2011

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Application--CRESThasbeensetupatbothnationaland

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outputsAddressesserviceneedsinNWS;flashfloodingis#1weather-related

killer6/1112:30am-4am20deaths:LittleMissouriRiverCrestedfrom3ftto23.5ftwithin2

hoursIncludedataassimilationandprobabilistic

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etc)Urban Sewer/Pipeline Module included as a special InterflowProcess/reservoirHasbeentestedandimplementedinOklahomaCityandDallasMetropolitanatspatial

resolution5.城市洪水模型Urban CREST介紹AHigh-ResolutionUrbanCRESTFloodModelingandMapping

SystemForUrbanandBuilt-up

EnvironmentsTheNewFeaturesofuCRESTMod272010June14,OKCFlash

Flood101

km1ReturnPeriod

(years)2 10200+NoFloodingFloodingSevereFloodingUrban-CRESTFloodModelImplementedatOklahomaCity&Dallas

Metropolitan137

km2010June14,OKCFlashFlood1286.全球風(fēng)暴數(shù)據(jù)庫(kù)及CI-FLOWGlobalStorms(2000-2010)*Sellarsetal.(2013),ComputationalEarthScience:BigDataTransformedIntoInsight,EOSTrans.AGU,

94(32),2776.全球風(fēng)暴數(shù)據(jù)庫(kù)及CI-FLOWGlobalStor29Nov2011

BAMSTheCI-FLOWProject:ASystemforTotalWaterLevelPredictionFromTheSummitToThe

SeaCI-FLOWsummarypaperwithHurricaneIsabel,HurricaneEarl,&TropicalStormNicole

resultsVolume##Number#November

2011BAMSAmericanMeteorological

SocietyNov2011BAMSTheCI-FLOWProje30SuzanneVanCooten,…,YangHong,etal.,2011:

Theci-flowproject:asystemfortotalwaterlevelpredictionfromthesummittothesea.Bull.

Amer.Meteor.Soc.,92,1427–1442.已應(yīng)用到美國(guó)北卡羅來(lái)納州、墨西哥灣等易受颶風(fēng)和風(fēng)暴潮影響的海岸帶地區(qū)海洋風(fēng)暴潮與內(nèi)陸洪水監(jiān)測(cè)預(yù)警系統(tǒng)(CI-FLOW)SuzanneVanCooten,…,YangHo31CI-FLOWCoastalandInlandFloodingObservationand

WarningTrackingtheraindropsanddisastersfromtheSKYandtheSUMMITtothe

seaCI-FLOWCoastalandInlandFloo32CI-FLOW:HL-RDHM/SWAN/ADCIRCCoupled

ModelPrecipitationSig.Wave

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trackHydrologicModel:HL-RDHM,Vfloor

CRESTWaveModel:unstructured

SWANCI-FLOW:HL-RDHM/SWAN/ADCIRCC337.中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國(guó)的山洪預(yù)警系統(tǒng)量融合,驅(qū)動(dòng)CREST模型,模擬徑流分布與氣象局以及國(guó)家氣象中心合作開(kāi)發(fā)多源降水產(chǎn)品和地面臺(tái)站數(shù)據(jù)進(jìn)行雨地貌水動(dòng)力學(xué)模型模擬洪水淹沒(méi)情景的時(shí)空演進(jìn),實(shí)時(shí)動(dòng)態(tài)提取洪水淹沒(méi)范圍、水深分布和淹沒(méi)時(shí)間分布,實(shí)現(xiàn)對(duì)洪水的模擬7.中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國(guó)的山洪預(yù)警系統(tǒng)34洪水模擬的時(shí)間:199806280501001502002503000500010000150002000025000Date3/5/19975/8/19977/11/19979/13/199711/16/19971/19/19983/24/19985/27/19987/30/199810/2/199812/5/19982/7/19994/12/19996/15/19998/18/199910/21/199912/24/19992/26/20004/30/20007/3/20009/5/200011/8/20001/11/20013/16/20015/19/20017/22/20019/24/200111/27/20011/30/20024/4/20026/7/20028/10/200210/13/200212/16/20022/18/20034/23/20036/26/20038/29/200311/1/20031/4/20043/8/20045/11/20047/14/20049/16/200411/19/20041/22/20053/27/20055/30/20058/2/200510/5/200512/8/2005R_Obsin

(m^3/s)R(v2.1)in

(m^3/s)rain率定期驗(yàn)證期NSCE=0.897CC=0.947Bias=-1.57%20

年、10

年、5年、2年、1年

一遇洪水外州站CREST模型率定/模擬效果:氣象臺(tái)站數(shù)據(jù)驅(qū)動(dòng)7.中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立洪水模擬的時(shí)間:199806280501001502002535114114.5115115.5116116.51172525.52626.52727.52828.529114114.5115115.5116116.511725236iMAP

在嘉陵江流域的應(yīng)用結(jié)果7.中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立iMAP在嘉陵江流域的應(yīng)用結(jié)果7.中國(guó)區(qū)域多尺度洪水模擬及379.基于ArcGIS平臺(tái)開(kāi)發(fā)的ArcCREST介紹ArcCREST

UIPrecip

ThiessenEvap

ThiessenGeo

Data9.基于ArcGIS平臺(tái)開(kāi)發(fā)的ArcCREST介紹Arc38Usedforrainfallsites(Cell-baseddataneedsome

effort)Parametersdistributionneedmoreadvanced

methodBugsincode,theresultsarenot

correctGeoandHydrodatamanagementand

operationParametersdistribution

settingModelrunningandresults

showUsedforrainfallsites(Cell-39ArcCREST運(yùn)行結(jié)果分析ArcCRESTv1.0(Uncalib)ArcCRESTv1.0Nash-Sutliffe-0.415460.8121Bias

(%)-99.999915.25CC0.79630.8382300200100040050011325374961738597109121133145157169181193205217229241253265277289301313325337349361Discharge(m3)Time(24h)Discharge:ArcCRESTvs

GageCalibUnCalibActualR2=0.7025501001502002503000050100150200250300350ArcCRESTGageDischarge:ArcCRESTvs

GageR2=

0.7025ArcCRESTtendsto

overestimatedischargeUncalibratedresultsindicatenomodelsensitivityandunreliable

estimationsArcCREST運(yùn)行結(jié)果分析ArcCRESTArcCREST40FlashFloodGuidance:FFGistheamountofrainfallrequiredinagivenperiodoftimetoproducebankfullconditionsonsmallbasinsfromFlashFloodGuidance

1970toHydrologicFlashFloodGuidance201227-

4343-

5454-

6868-

8282-

9898-

115115-

139139-

192192-

3051hFFG(level1)

CMAunit:mm12-

278.基于ArcGIS平臺(tái)的FFG1hFFG(level1)CMAFFG(FlashFlood

Guidance)FlashFloodGuidance:FFGist41DistributedFFG(0.189°)inSouth

China采用ArcGIS插值模塊得到面臨界雨量分布單位:mmDistributedFFG(0.189°)inSout42FlashFloodPotential

Index(FFPI):DevelopedbyhydrologistGregSmith,CBRFC

(2003).Geographicalfeatures

playakeyroleinflash

floodingDevelopedasbackgroundinformationtobeincorporatedinto

productionofbettergriddedFlashFlood

GuidanceUsingtheFFPI,theroles

ofsoil,slope,vegetationandurbanizationcanbevisualized基于ArcGIS平臺(tái)的中國(guó)洪水風(fēng)險(xiǎn)潛在指標(biāo)FFPIFlashFloodPotentialIndex43基于ArcGIS的水利大數(shù)據(jù)及應(yīng)用課件44基于ArcGIS的水利大數(shù)據(jù)及應(yīng)用基于ArcGIS的水利大數(shù)據(jù)及應(yīng)用45團(tuán)隊(duì)簡(jiǎn)介水利大數(shù)據(jù)及其面臨的挑戰(zhàn)基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例主要內(nèi)容123團(tuán)隊(duì)簡(jiǎn)介水利大數(shù)據(jù)及其面臨的挑戰(zhàn)基于水利大數(shù)據(jù)的多災(zāi)害信息集46二、水利大數(shù)據(jù)及其面臨的挑戰(zhàn)二、水利大數(shù)據(jù)及其面臨的挑戰(zhàn)47水利工作關(guān)系到國(guó)計(jì)民生,尤其是我國(guó)水資源分布存在嚴(yán)重的時(shí)空分布不均特性,旱災(zāi)洪澇易發(fā)多發(fā)。水利行業(yè)在經(jīng)濟(jì)、生態(tài)、社會(huì)等方面都扮演著重要角色,對(duì)水利大數(shù)據(jù)的研究具有重要的現(xiàn)實(shí)意義和應(yīng)用價(jià)值。水利大數(shù)據(jù)是在大數(shù)據(jù)的理論指導(dǎo)及技術(shù)支撐下的水利科學(xué)和工程的重要實(shí)踐。水利工作及水利大數(shù)據(jù)的重要性水利工作關(guān)系到國(guó)計(jì)民生,尤其是我國(guó)水資源分布存在嚴(yán)重的時(shí)空48水利大數(shù)據(jù)水利大數(shù)據(jù)是指產(chǎn)生于各種水文監(jiān)測(cè)網(wǎng)絡(luò)、水利設(shè)施、用水單位和水利相關(guān)經(jīng)濟(jì)活動(dòng),并通過(guò)現(xiàn)代化信息技術(shù)高效傳輸、分布存儲(chǔ)于各地存儲(chǔ)系統(tǒng)、但又可以快速讀取集中于云端、實(shí)現(xiàn)深度數(shù)據(jù)挖掘并可視化的海量多源數(shù)據(jù)總和。ValueVelocityVolume海量快速價(jià)值Variety多樣Veracity真實(shí)水利大數(shù)據(jù)水利大數(shù)據(jù)ValueVelocityVolume快49交叉性,由于水利和其它領(lǐng)域具有交叉性,因此水利大數(shù)據(jù)和遙感大數(shù)據(jù)、氣象大數(shù)據(jù)、海洋大數(shù)據(jù)等交叉;時(shí)空分布性,需要依賴先進(jìn)大數(shù)據(jù)技術(shù)進(jìn)行處理分析,包括分布式大數(shù)據(jù)存儲(chǔ)框架、機(jī)器學(xué)習(xí)等數(shù)據(jù)挖掘方法;多元循環(huán)性,由水的多元循環(huán)決定的水利大數(shù)據(jù)在經(jīng)濟(jì)、社會(huì)、生態(tài)等領(lǐng)域的價(jià)值循環(huán)。水利大數(shù)據(jù)的外延交叉性,由于水利和其它領(lǐng)域具有交叉性,因此水利大數(shù)據(jù)和遙感50挑戰(zhàn)一:水利大數(shù)據(jù)的收集與集成水利大數(shù)據(jù)來(lái)源廣泛,不同的監(jiān)測(cè)平臺(tái)得到的數(shù)據(jù)具有不同的數(shù)據(jù)結(jié)構(gòu)、存儲(chǔ)系統(tǒng),非結(jié)構(gòu)化數(shù)據(jù)、半結(jié)構(gòu)化數(shù)據(jù)、結(jié)構(gòu)化數(shù)據(jù)并存;由于觀測(cè)條件的差異,數(shù)據(jù)可信度層次不齊,對(duì)數(shù)據(jù)清洗和質(zhì)量的確保提出了很高的要求;大數(shù)據(jù)的存儲(chǔ)與管理需要新型數(shù)據(jù)庫(kù)的支持,水利大數(shù)據(jù)的信息化還未與新型數(shù)據(jù)庫(kù)接軌。水利大數(shù)據(jù)面臨的挑戰(zhàn)挑戰(zhàn)一:水利大數(shù)據(jù)的收集與集成水利大數(shù)據(jù)面臨的挑戰(zhàn)51挑戰(zhàn)二:水利大數(shù)據(jù)的時(shí)空多維度分析水利大數(shù)據(jù)具有明顯的時(shí)空分布特性,時(shí)間、空間雙維度下的數(shù)據(jù)分析具有難度;水利大數(shù)據(jù)在其應(yīng)用領(lǐng)域講究實(shí)時(shí)性,比如洪水預(yù)報(bào)等,這對(duì)大數(shù)據(jù)的處理分析速度提出了高要求;水利大數(shù)據(jù)的深度挖掘有賴于引入先進(jìn)的人工智能算法,兩者的有效結(jié)合至關(guān)重要。水利大數(shù)據(jù)面臨的挑戰(zhàn)挑戰(zhàn)二:水利大數(shù)據(jù)的時(shí)空多維度分析水利大數(shù)據(jù)面臨的挑戰(zhàn)52挑戰(zhàn)三:水利大數(shù)據(jù)的共享與安全眾多水利數(shù)據(jù)掌握在政府機(jī)關(guān)部門,為非公開(kāi)數(shù)據(jù),形成數(shù)據(jù)孤島現(xiàn)象;水利數(shù)據(jù)是國(guó)家安全的重要組成部分,水利數(shù)據(jù)的共享與安全是一個(gè)值得探討的問(wèn)題。水利大數(shù)據(jù)面臨的挑戰(zhàn)挑戰(zhàn)三:水利大數(shù)據(jù)的共享與安全水利大數(shù)據(jù)面臨的挑戰(zhàn)53三、基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹三、基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹54基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹1、天、地、空、海,多基多源降水?dāng)?shù)據(jù)采集2、移動(dòng)眾包信息收集可視化云平臺(tái)mPing3、基于水利大數(shù)據(jù)的全球洪水泥石流災(zāi)害預(yù)測(cè)預(yù)報(bào)4、基于概率洪水風(fēng)險(xiǎn)預(yù)報(bào)EF55、城市洪水模型Urban

CREST介紹6、全球風(fēng)暴數(shù)據(jù)庫(kù)及CI-FLOW7、中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立8、基于ArcGIS的FFG介紹9、基于ArcGIS平臺(tái)開(kāi)發(fā)的ArcCREST介紹基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹基于水利大數(shù)據(jù)的多災(zāi)害信息集成與風(fēng)險(xiǎn)預(yù)警案例介紹1、天553小時(shí)臨近預(yù)報(bào)(250米/2.5分鐘)+36小時(shí)模型預(yù)報(bào)(1公里/小時(shí))1.天、地、空、海多基多源降水?dāng)?shù)據(jù)采集雙偏振雷達(dá)+衛(wèi)星+站點(diǎn)+模型3小時(shí)臨近預(yù)報(bào)1.天、地、空、海多基多源降水?dāng)?shù)據(jù)采集雙偏振56PERSIANN

全球衛(wèi)星產(chǎn)品(4km,

hourly)Hongetal.,2004,

JAM;5顆地球靜止衛(wèi)星(可見(jiàn)光紅外)以及4顆極軌衛(wèi)星(雷達(dá)和被動(dòng)微波)通過(guò)人工神經(jīng)網(wǎng)絡(luò)ANN/機(jī)器學(xué)習(xí)訓(xùn)練反演

HighQuality

衛(wèi)星降水產(chǎn)品MergeSatellites,ground(Radar&Gauge),andModel

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satellites(GOES8-10,GMS,MYSAT,MeteoSAT);allcalibratedby

TRMMPreci

Radar17+years(‘98-16’)ofdata;MostrequestedTRMMproductfrom

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et

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:(1700+

引用)2005

加入

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coefficientsInstant-aneousSSM/ITRMMAMSRAMSU3-hourlymerged

HQHourlyIR

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IRprecip3-hourly

multi-satellite

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coefficientsTRMMAquaDMSPNOAAMETEOSATGOESTM5826深度學(xué)習(xí)方法研制全球衛(wèi)星產(chǎn)品研制青藏西南部IR云圖 相應(yīng)時(shí)段降水情況在深度學(xué)習(xí)中,我們可以將不同頻段的可見(jiàn)光、紅外、微波影像同時(shí)作為訓(xùn)練數(shù)據(jù)輸入模型,且不需要事先設(shè)定Feature,海量的遙感影像下,讓模型自己去尋找Feature。26深度學(xué)習(xí)方法研制全球衛(wèi)星產(chǎn)品研制青藏西南部IR云圖 相應(yīng)595-minute250mRainfall

Dataover

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美國(guó)版災(zāi)害Crowdsourcing移動(dòng)平臺(tái)技術(shù)2.mPING美國(guó)版災(zāi)害Crowdsourcing移動(dòng)平612.移動(dòng)眾包信息收集可視化云平臺(tái)mPING–CrowdSourcingTooland

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reservoirLandslideModel

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andInundationSoilWater

ContentOther

variablesOccurrenceandLocationsof

landslidesRemoteSensing

basedPrecipitation

EstimatesTopographyLandcover/Land

Use3.基于水利大數(shù)據(jù)的全球水洪泥石流災(zāi)害預(yù)測(cè)預(yù)報(bào)NationalFlashLandslide

SystemEnsembleCoupledHydro-Lands643.

基于水利大數(shù)據(jù)的全球水洪泥石流災(zāi)害預(yù)測(cè)預(yù)報(bào)美國(guó)暴雨山洪泥石流災(zāi)害鏈業(yè)務(wù)化系統(tǒng)NFL:NMQ: NationalMosaicandMulti-SensorQPE

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HydrographsLANDSLIDE:SLope-Infiltration-Distributed

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ModelNMQRadarPrecipitationObservations250m/2.5

minFLASHDistributed

CRESTHydrologic

Models10-11June2010,AlbertPike

RecArea,

Arkansas250

mm150200Simulatedsurfacewater

flow20fatalitiesLANDSLIDELandslideHotspotModelsRed:

ObservationsPink:

PredictionsLandslide

prediction3.基于水利大數(shù)據(jù)的全球水洪泥石流災(zāi)害預(yù)測(cè)預(yù)報(bào)NMQ: N65IntegratedHydrologic-LandslideModeliCRESLIDE=CREST+

SLIDECoupledRoutingandExcessSTorage(CREST)Jointlydeveloped

byOU/NASARunoperationallyoverglobeDistributed,fullycoupledrunoffgenerationand

routingWangamnoddHelongetal.2011

HSJIntegratedHydrologic-LandslideModel:iCRESLIDEDevelopmentand

Application--CRESThasbeensetupatbothnationaland

basinscalesin

China;--iCRESLIDEshowsgreatcapabilityin

forecastingshallowlandslidesaroundthe

world;--Morefloodandlandslideeventdatais

needed.IntegratedHydrologic-Landslid66250m/5-minresolutionofQ2precipitationforcingandmodel

outputsAddressesserviceneedsinNWS;flashfloodingis#1weather-related

killer6/1112:30am-4am20deaths:LittleMissouriRiverCrestedfrom3ftto23.5ftwithin2

hoursIncludedataassimilationandprobabilistic

productsReadilyincorporatedual-polradarproducts(Q3)andstormscaleensemble

forecastsNFL:Real-time,directpredictionofflashfloodsa

realityPhotosource:National

Geographic250m/5-minresolutionofQ2pr67美國(guó)暴雨山洪泥石流災(zāi)害鏈耦合系統(tǒng)核心模型Physically-couplediCRESTSLIDE(SLopeInfiltration-Distributed

Equilibrium)020408010012000.460Radius

(m)PODFARCSIValidationwithinventory

dataRed:

ObservationsPink:

Predictions美國(guó)北卡州

梅肯縣Within18-m120-meterbuffer

zonePOD

>

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)16th

hrFSMapvs.

Time18th

hr21st

hr美國(guó)暴雨山洪泥石流災(zāi)害鏈耦合系統(tǒng)核心模型020408010068ForecastStreamflow

(2010)Recurrence

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(2015)State-Param

EstimationDREAM

(2010)Observed

StreamflowGroundwaterMODFLOWRoutingKinematicwave

(2014)Linearreservoir

(2010)4.基于概率洪水風(fēng)險(xiǎn)預(yù)報(bào)

EF5EnsembleFrameworkFor Flash Flood

ForecastingBestdistributedhydrologicSystem

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tempVIIRS?Surface

RunoffCREST

(2010)SAC-SMA

(2013)Hydrophobic

(2015)SnowmeltSNOW-17(2015)- 2m

TempCurrentVersionFutureAdditionForecastState-ParamEstimation69EF5:ProbabilityofFlashFloodForecast

(PFFF)基于概率洪水風(fēng)險(xiǎn)預(yù)報(bào)100%50%0%PFFF(RP=5yr

)EF5:ProbabilityofFlashFloo70TheNewFeaturesofuCRESTModel1-10MeterDEMandUrbanDrainage

SystemUrban Canopy and High Rise Building Impact on

the RainfallInterceptionEnhancedImpervious(pavement,roofetc.)andNon-impervioussurfaceinfiltrationandSurfaceProcesses(runoff,ET

etc)Urban Sewer/Pipeline Module included as a special InterflowProcess/reservoirHasbeentestedandimplementedinOklahomaCityandDallasMetropolitanatspatial

resolution5.城市洪水模型Urban CREST介紹AHigh-ResolutionUrbanCRESTFloodModelingandMapping

SystemForUrbanandBuilt-up

EnvironmentsTheNewFeaturesofuCRESTMod712010June14,OKCFlash

Flood101

km1ReturnPeriod

(years)2 10200+NoFloodingFloodingSevereFloodingUrban-CRESTFloodModelImplementedatOklahomaCity&Dallas

Metropolitan137

km2010June14,OKCFlashFlood1726.全球風(fēng)暴數(shù)據(jù)庫(kù)及CI-FLOWGlobalStorms(2000-2010)*Sellarsetal.(2013),ComputationalEarthScience:BigDataTransformedIntoInsight,EOSTrans.AGU,

94(32),2776.全球風(fēng)暴數(shù)據(jù)庫(kù)及CI-FLOWGlobalStor73Nov2011

BAMSTheCI-FLOWProject:ASystemforTotalWaterLevelPredictionFromTheSummitToThe

SeaCI-FLOWsummarypaperwithHurricaneIsabel,HurricaneEarl,&TropicalStormNicole

resultsVolume##Number#November

2011BAMSAmericanMeteorological

SocietyNov2011BAMSTheCI-FLOWProje74SuzanneVanCooten,…,YangHong,etal.,2011:

Theci-flowproject:asystemfortotalwaterlevelpredictionfromthesummittothesea.Bull.

Amer.Meteor.Soc.,92,1427–1442.已應(yīng)用到美國(guó)北卡羅來(lái)納州、墨西哥灣等易受颶風(fēng)和風(fēng)暴潮影響的海岸帶地區(qū)海洋風(fēng)暴潮與內(nèi)陸洪水監(jiān)測(cè)預(yù)警系統(tǒng)(CI-FLOW)SuzanneVanCooten,…,YangHo75CI-FLOWCoastalandInlandFloodingObservationand

WarningTrackingtheraindropsanddisastersfromtheSKYandtheSUMMITtothe

seaCI-FLOWCoastalandInlandFloo76CI-FLOW:HL-RDHM/SWAN/ADCIRCCoupled

ModelPrecipitationSig.Wave

HeightsTotalWater

LevelsRiver

BCsDischargeSurface

BCsPressureWind

ForcingSurface

BCsWave

ForcingHydrodynamicModel

(ADCIRC)HydrologicModelAtmosphericModelWave

ModelPrecipitationSource:QPE/QPFAtmosphericModel:NAMorNHC

trackHydrologicModel:HL-RDHM,Vfloor

CRESTWaveModel:unstructured

SWANCI-FLOW:HL-RDHM/SWAN/ADCIRCC777.中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國(guó)的山洪預(yù)警系統(tǒng)量融合,驅(qū)動(dòng)CREST模型,模擬徑流分布與氣象局以及國(guó)家氣象中心合作開(kāi)發(fā)多源降水產(chǎn)品和地面臺(tái)站數(shù)據(jù)進(jìn)行雨地貌水動(dòng)力學(xué)模型模擬洪水淹沒(méi)情景的時(shí)空演進(jìn),實(shí)時(shí)動(dòng)態(tài)提取洪水淹沒(méi)范圍、水深分布和淹沒(méi)時(shí)間分布,實(shí)現(xiàn)對(duì)洪水的模擬7.中國(guó)區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國(guó)的山洪預(yù)警系統(tǒng)78洪水模擬的時(shí)間:199806280501001502002503000500010000150002000025000Date3/5/19975/8/19977/11/19979/13/199711/16/19971/19/19983/24/19985/27/19987/30/199810/2/199812/5/19982/7/19994/12/19996/15/19998/18/199910/21/199912/24/19992/26/20004/30/20007/3/20009/5/200011/8/20001/11/20013/16/20015/19/20017/22/20019/24/200111/27/20011/30/20024/4/20026/7/20028/10/200210/13/200212/16/20022/18/20034/23/20036/26/20038/29/200311/1/20031/4/20043/8/20045/11/20047/14/20049/16/200411/19/20041/22/20053/2

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