基于ArcGIS的水利大數(shù)據(jù)及應(yīng)用課件

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團隊簡介水利大數(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)險預(yù)警案例主要內(nèi)容123團隊簡介水利大數(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)系到國計民生，尤其是我國水資源分布存在嚴(yán)重的時空分布不均特性，旱災(zāi)洪澇易發(fā)多發(fā)。水利行業(yè)在經(jīng)濟、生態(tài)、社會等方面都扮演著重要角色，對水利大數(shù)據(jù)的研究具有重要的現(xiàn)實意義和應(yīng)用價值。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)系到國計民生，尤其是我國水資源分布存在嚴(yán)重的時空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)測網(wǎng)絡(luò)、水利設(shè)施、用水單位和水利相關(guān)經(jīng)濟活動，并通過現(xiàn)代化信息技術(shù)高效傳輸、分布存儲于各地存儲系統(tǒng)、但又可以快速讀取集中于云端、實現(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海量快速價值Variety多樣Veracity真實水利大數(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)存儲框架、機器學(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、社會、生態(tài)等領(lǐng)域的價值循環(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)來源廣泛，不同的監(jiān)測平臺得到的數(shù)據(jù)具有不同的數(shù)據(jù)結(jié)構(gòu)、存儲系統(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；由于觀測條件的差異，數(shù)據(jù)可信度層次不齊，對數(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)的存儲與管理需要新型數(shù)據(jù)庫的支持，水利大數(shù)據(jù)的信息化還未與新型數(shù)據(jù)庫接軌。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)的時空多維度分析水利大數(shù)據(jù)具有明顯的時空分布特性，時間、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)域講究實時性，比如洪水預(yù)報等，這對大數(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)掌握在政府機關(guān)部門，為非公開數(shù)據(jù)，形成數(shù)據(jù)孤島現(xiàn)象；水利數(shù)據(jù)是國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值得探討的問題。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、移動眾包信息收集可視化云平臺mPing3、基于水利大數(shù)據(jù)的全球洪水泥石流災(zāi)害預(yù)測預(yù)報4、基于概率洪水風(fēng)險預(yù)報EF55、城市洪水模型Urban

CREST介紹6、全球風(fēng)暴數(shù)據(jù)庫及CI-FLOW7、中國區(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平臺開發(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)險預(yù)警案例介紹1、天113小時臨近預(yù)報（250米／2.5分鐘）＋36小時模型預(yù)報（1公里／小時）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＋模型3小時臨近預(yù)報1.天、地、空、海多基多源降水?dāng)?shù)據(jù)采集雙偏振12PERSIANN

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SWANCI-FLOW:HL-RDHM/SWAN/ADCIRCC337.中國區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國的山洪預(yù)警系統(tǒng)量融合，驅(qū)動CREST模型，模擬徑流分布與氣象局以及國家氣象中心合作開發(fā)多源降水產(chǎn)品和地面臺站數(shù)據(jù)進(jìn)行雨地貌水動力學(xué)模型模擬洪水淹沒情景的時空演進(jìn)，實時動態(tài)提取洪水淹沒范圍、水深分布和淹沒時間分布，實現(xiàn)對洪水的模擬7.中國區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國的山洪預(yù)警系統(tǒng)34洪水模擬的時間：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

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

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3051hFFG(level1)

CMAunit：mm12-

278.基于ArcGIS平臺的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平臺的中國洪水風(fēng)險潛在指標(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團隊簡介水利大數(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)險預(yù)警案例主要內(nèi)容123團隊簡介水利大數(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)系到國計民生，尤其是我國水資源分布存在嚴(yán)重的時空分布不均特性，旱災(zāi)洪澇易發(fā)多發(fā)。水利行業(yè)在經(jīng)濟、生態(tài)、社會等方面都扮演著重要角色，對水利大數(shù)據(jù)的研究具有重要的現(xiàn)實意義和應(yīng)用價值。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)系到國計民生，尤其是我國水資源分布存在嚴(yán)重的時空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)測網(wǎng)絡(luò)、水利設(shè)施、用水單位和水利相關(guān)經(jīng)濟活動，并通過現(xiàn)代化信息技術(shù)高效傳輸、分布存儲于各地存儲系統(tǒng)、但又可以快速讀取集中于云端、實現(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海量快速價值Variety多樣Veracity真實水利大數(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)存儲框架、機器學(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、社會、生態(tài)等領(lǐng)域的價值循環(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)來源廣泛，不同的監(jiān)測平臺得到的數(shù)據(jù)具有不同的數(shù)據(jù)結(jié)構(gòu)、存儲系統(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；由于觀測條件的差異，數(shù)據(jù)可信度層次不齊，對數(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)的存儲與管理需要新型數(shù)據(jù)庫的支持，水利大數(shù)據(jù)的信息化還未與新型數(shù)據(jù)庫接軌。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)的時空多維度分析水利大數(shù)據(jù)具有明顯的時空分布特性，時間、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)域講究實時性，比如洪水預(yù)報等，這對大數(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)掌握在政府機關(guān)部門，為非公開數(shù)據(jù)，形成數(shù)據(jù)孤島現(xiàn)象；水利數(shù)據(jù)是國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值得探討的問題。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、移動眾包信息收集可視化云平臺mPing3、基于水利大數(shù)據(jù)的全球洪水泥石流災(zāi)害預(yù)測預(yù)報4、基于概率洪水風(fēng)險預(yù)報EF55、城市洪水模型Urban

CREST介紹6、全球風(fēng)暴數(shù)據(jù)庫及CI-FLOW7、中國區(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平臺開發(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)險預(yù)警案例介紹1、天553小時臨近預(yù)報（250米／2.5分鐘）＋36小時模型預(yù)報（1公里／小時）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＋模型3小時臨近預(yù)報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（可見光紅外）以及4顆極軌衛(wèi)星（雷達(dá)和被動微波）通過人工神經(jīng)網(wǎng)絡(luò)ANN／機器學(xué)習(xí)訓(xùn)練反演

HighQuality

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

(NWP)PERSIANN全球衛(wèi)星產(chǎn)品(4km,hourly)Ho57TRMMAquaDMSPNOAAMETEOSAT(Europe)GOESGMS/MTSAT(Japan)TMPAuses4Polar-orbitalmicrowavesatellites(NOAA,DoD,NASA)and5Geo-IR

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

TRMMPreci

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

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et

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:（1700+

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加入

NASA：多衛(wèi)星聯(lián)合反演共性技術(shù)；(1700+引用)全球天地空標(biāo)準(zhǔn)產(chǎn)品系列：TMPA30-dayHQ

coefficientsInstant-aneousSSM/ITRMMAMSRAMSU3-hourlymerged

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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光、紅外、微波影像同時作為訓(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

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USA5-minute250m602.

mPING

美國版災(zāi)害Crowdsourcing移動平臺技術(shù)2.mPING美國版災(zāi)害Crowdsourcing移動平612.移動眾包信息收集可視化云平臺mPING–CrowdSourcingTooland

Data750,000+AppDownloadsSinceDec

20132.移動眾包信息收集可視化云平臺mPING–Crowd62硅谷SFIoT/BigDataWeather2.0

Service

Inc.硅谷SFIoT/BigDataWeather2.0S63EnsembleCoupledHydro-LandslideModeling

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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)測預(yù)報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)測預(yù)報美國暴雨山洪泥石流災(zāi)害鏈業(yè)務(wù)化系統(tǒng)NFL:NMQ： NationalMosaicandMulti-SensorQPE

(NMQ)FLASH： FloodedLocationsAndSimulated

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)測預(yù)報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美國北卡州

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(2010)Recurrence

Interval(2010)Inundation

(2015)State-Param

EstimationDREAM

(2010)Observed

StreamflowGroundwaterMODFLOWRoutingKinematicwave

(2014)Linearreservoir

(2010)4.基于概率洪水風(fēng)險預(yù)報

EF5EnsembleFrameworkFor Flash Flood

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yetPrecipForcingMRMSTMPA

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QPFEvapotranspirationFEWSNET

PETHRRR

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(2010)SAC-SMA

(2013)Hydrophobic

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

TempCurrentVersionFutureAdditionForecastState-ParamEstimation69EF5:ProbabilityofFlashFloodForecast

(PFFF)基于概率洪水風(fēng)險預(yù)報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)庫及CI-FLOWGlobalStorms(2000-2010)*Sellarsetal.(2013),ComputationalEarthScience:BigDataTransformedIntoInsight,EOSTrans.AGU,

94(32),2776.全球風(fēng)暴數(shù)據(jù)庫及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)用到美國北卡羅來納州、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

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trackHydrologicModel:HL-RDHM,Vfloor

CRESTWaveModel:unstructured

SWANCI-FLOW:HL-RDHM/SWAN/ADCIRCC777.中國區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國的山洪預(yù)警系統(tǒng)量融合，驅(qū)動CREST模型，模擬徑流分布與氣象局以及國家氣象中心合作開發(fā)多源降水產(chǎn)品和地面臺站數(shù)據(jù)進(jìn)行雨地貌水動力學(xué)模型模擬洪水淹沒情景的時空演進(jìn)，實時動態(tài)提取洪水淹沒范圍、水深分布和淹沒時間分布，實現(xiàn)對洪水的模擬7.中國區(qū)域多尺度洪水模擬及預(yù)警系統(tǒng)的建立中國的山洪預(yù)警系統(tǒng)78洪水模擬的時間：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