Banjir: Perbezaan antara semakan

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Yosri (bincang | sumb.)
Tiada ringkasan suntingan
Yosri (bincang | sumb.)
Tiada ringkasan suntingan
Baris 96:
Model proses fizikal lembangan saliran yang lengkap adalah lebih rumit. Sungguhpun banyak proses difahami dengan baik pada satu titik atau kawasan kecil, yang lain tidak difahami dengan baik pada semua skala, dan proses interaksi di bawah keadaan cuaca yang normal atau ekstrim mungkin tidak diketahui. Model lumpur biasanya menggabungkan komponen proses permukaan tanah (untuk menganggarkan berapa banyak hujan atau salji cair sampai pada saluran) dengan satu siri model jangkauan. Sebagai contoh, model lembangan dapat mengira hidrograf aliran yang mungkin disebabkan oleh ribut 100 tahun, walaupun selang ulangan ribut jarang sama dengan banjir yang berkaitan. Model lumpur biasa digunakan dalam ramalan banjir dan amaran, serta dalam analisa kesan perubahan penggunaan tanah dan [[perubahan iklim]].
 
===Ramalan banjir ==
===Flood forecasting===
Menjangkakan banjir sebelum ia berlaku membolehkan langkah berjaga-jaga diambil dan orang ramai diberi peringatan<ref>{{cite web|url=http://www.environment-agency.gov.uk/homeandleisure/floods/58417.aspx |title=Flood Warnings|publisher=Environment Agency|date=2013-04-30|accessdate=2013-06-17}}</ref> supaya mereka dapat bersedia terlebih dahulu untuk keadaan banjir. Sebagai contoh, petani boleh mengeluarkan haiwan dari kawasan rendah dan perkhidmatan utiliti boleh meletakkan peruntukan kecemasan untuk perkhidmatan semula jika diperlukan. Perkhidmatan kecemasan juga boleh membuat peruntukan untuk mempunyai sumber yang cukup tersedia dari masa ke masa untuk bertindak balas terhadap kecemasan apabila berlaku. Orang ramai boleh mengosongkan kawasan untuk dibanjiri.
{{Main article|Flood forecasting|flood warning|}}
 
Untuk membuat ramalan banjir yang paling tepat untuk laluan air, yang terbaik adalah untuk mempunyai data sejarah siri masa panjang yang menghubungkan aliran sungai untuk mengukur peristiwa hujan yang lalu.ref>{{cite web|url=http://www.bom.gov.au/australia/flood |title=Australia rainfall and river conditions|publisher=Bom.gov.au|date= |accessdate=2013-06-17}}</ref> Menggabungkan maklumat sejarah ini dengan pengetahuan masa nyata mengenai kapasiti volumetrik bagi kawasan tadahan, seperti kapasiti lebihan bagi takungan, paras air tanah, dan tahap ketepuan kawasan akuifer juga diperlukan untuk menghasilkan ramalan banjir yang paling jitu.
Anticipating floods before they occur allows for precautions to be taken and people to be warned <ref>{{cite web|url=http://www.environment-agency.gov.uk/homeandleisure/floods/58417.aspx |title=Flood Warnings|publisher=Environment Agency|date=2013-04-30|accessdate=2013-06-17}}</ref> so that they can be prepared in advance for flooding conditions. For example, farmers can remove animals from low-lying areas and utility services can put in place emergency provisions to re-route services if needed. Emergency services can also make provisions to have enough resources available ahead of time to respond to emergencies as they occur. People can evacuate areas to be flooded.
 
Anggaran Radar bagi hujan dan teknik ramalan cuaca umum juga merupakan komponen ramalan banjir yang baik. Di kawasan yang mempunyai kualiti data yang baik, keamatan dan ketinggian banjir boleh diramalkan dengan ketepatan yang cukup baik dan masa yang mencukupi. Hasil ramalan banjir biasanya merupakan paras air yang dijangkakan maksimum dan kemungkinan masa ketibaannya di lokasi-lokasi utama di sepanjang laluan air,<ref name="Advanced Hydrologic Prediction System">{{cite web|url=http://water.weather.gov/ahps |title=Advanced Hydrologic Prediction System|accessdate=4 February 2013}}</ref> dan ia juga boleh membenarkan pengiraan kemungkinan tempoh pengembalian statistik banjir. Di kebanyakan negara maju, kawasan bandar yang berisiko banjir dilindungi daripada banjir 100 tahun - iaitu banjir yang mempunyai kebarangkalian sekitar 63% berlaku dalam mana-mana tempoh 100 tahun.
In order to make the most accurate flood forecasts for [[waterway]]s, it is best to have a long time-series of historical data that relates [[stream flow]]s to measured past rainfall events.<ref>{{cite web|url=http://www.bom.gov.au/australia/flood |title=Australia rainfall and river conditions|publisher=Bom.gov.au|date= |accessdate=2013-06-17}}</ref> Coupling this historical information with [[Real-time data|real-time knowledge]] about volumetric capacity in catchment areas, such as spare capacity in reservoirs, ground-water levels, and the degree of [[Phreatic zone|saturation]] of area [[aquifer]]s is also needed in order to make the most acrate flood forecasts.
 
Menurut Pusat Penemuan Ramalan Sungai Northeast (RFC) di Taunton, Massachusetts , Perkhidmatan Cuaca Cuaca Kebangsaan (NWS), satu peraturan untuk meramal banjir di kawasan bandar adalah yang memerlukan sekurang-kurangnya 1 inci (25 mm) hujan di sekitar tempoh masa sejam untuk memulakan air bertakung yang jelas di atas permukaan yang tidak dapat ditembusi. Banyak NWS RFC secara rutin mengeluarkan Panduan Banjir Kilat dan Panduan Kepala Air, yang menunjukkan jumlah hujan am yang perlu jatuh dalam masa yang singkat untuk menyebabkan banjir kilat atau banjir di lembangan air yang lebih besar.<ref name="Flash Flood Guidance">{{cite web|url=http://www.srh.noaa.gov/rfcshare/ffg.php |title=FFG|accessdate=29 January 2013}}</ref>
[[Weather radar|Radar]] estimates of rainfall and general [[weather forecasting]] techniques are also important components of good flood forecasting. In areas where good quality data is available, the intensity and height of a flood can be predicted with fairly good accuracy and plenty of lead time. The output of a flood forecast is typically a maximum expected water level and the likely time of its arrival at key locations along a waterway,<ref name="Advanced Hydrologic Prediction System">{{cite web|url=http://water.weather.gov/ahps |title=Advanced Hydrologic Prediction System|accessdate=4 February 2013}}</ref> and it also may allow for the computation of the likely statistical return period of a flood. In many developed countries, urban areas at risk of flooding are protected against a 100-year flood – that is a flood that has a probability of around 63% of occurring in any 100-year period of time.
 
InDi theAmerika United StatesSyarikat, anpendekatan integratedbersepadu approachuntuk topemodelan real-timekomputer hydrologichidrologi computermasa modellingnyata utilizes observedmenggunakan data fromyang thediamati [[U.S.dari Geological Survey]] (USGS),<ref name="USGS WaterWatch">{{cite web|url=http://waterwatch.usgs.gov/index.php?id=ww_current |title=WaterWatch|date=4 February 2013|accessdate=4 February 2013}}</ref>pelbagai variousrangkaian [[Weatherpemantauan spotting|cooperative observing networks]]kerjasama,<ref name="Community Collaborative Rain, Hail and Snow Network">{{cite web|url=http://www.cocorahs.org |title=Community Collaborative Rain, Hail and Snow Network|accessdate=4 February 2013}}</ref> various [[Automatedpelbagai airportpenderia weathercuaca station|automatedautomatik, weatherNOAA sensors]],Pusat thePenderiaan [[NOAA]]Jauh Hidrologik Operasi Kebangsaan ("National Operational Hydrologic Remote Sensing Center" - (NOHRSC),<ref name="National Operational Hydrologic Remote Sensing Center">{{cite web|url=http://www.nohrsc.noaa.gov |title=NOHRSC|date=2 May 2012|accessdate=4 February 2013}}</ref> variouspelbagai [[hydroelectric]]syarikat companieshidroelektrik, etc.dan combinedlain-lain withgabungan [[quantitativedengan precipitationramalan forecast]]shujan kuantitatif (QPF) ofjangkaan expectedhujan rainfalldan and/or snowatau meltsalji tocair generateuntuk dailymenjana orramalan as-neededhidrologi harian atau hydrologicyang forecastsdiperlukan.<ref name="Advanced Hydrologic Prediction System"/> The NWS alsojuga cooperatesbekerjasama withdengan [[EnvironmentAlam Canada]]Sekitar onKanada hydrologicmengenai forecastsramalan thathidrologi affectyang bothmemberi thekesan USAkepada andAmerika Canada,Syarikat likedan inKanada, theseperti areadi ofkawasan the [[Saint Lawrence Seaway]] .
According to the U.S. [[National Weather Service]] (NWS) Northeast River Forecast Center (RFC) in [[Taunton, Massachusetts]], a rule of thumb for flood forecasting in urban areas is that it takes at least {{convert|1|in|mm}} of rainfall in around an hour's time in order to start significant [[ponding]] of water on [[Impervious surface|impermeable surfaces]]. Many NWS RFCs routinely issue Flash Flood Guidance and Headwater Guidance, which indicate the general amount of rainfall that would need to fall in a short period of time in order to cause flash flooding or flooding on larger [[water basin]]s.<ref name="Flash Flood Guidance">{{cite web|url=http://www.srh.noaa.gov/rfcshare/ffg.php |title=FFG|accessdate=29 January 2013}}</ref>
 
Sistem Pemantauan Banjir Global, "GFMS," alat komputer yang memaparkan keadaan banjir di seluruh dunia, boleh didapati dalam talian. Pengguna mana-mana di dunia boleh menggunakan GFMS untuk menentukan bila banjir mungkin berlaku di kawasan mereka. GFMS menggunakan data presipitasi dari satelit mengamati bumi NASA dan satelit Pemangkasan Presipitasi Global , "GPM." Data hujan dari GPM digabungkan dengan model permukaan tanah yang menggabungkan penutup tumbuhan, jenis tanah, dan rupa bumi untuk menentukan berapa banyak air yang menyerap ke dalam tanah, dan berapa banyak air mengalir ke aliran sungai.
In the United States, an integrated approach to real-time hydrologic computer modelling utilizes observed data from the [[U.S. Geological Survey]] (USGS),<ref name="USGS WaterWatch">{{cite web|url=http://waterwatch.usgs.gov/index.php?id=ww_current |title=WaterWatch|date=4 February 2013|accessdate=4 February 2013}}</ref> various [[Weather spotting|cooperative observing networks]],<ref name="Community Collaborative Rain, Hail and Snow Network">{{cite web|url=http://www.cocorahs.org |title=Community Collaborative Rain, Hail and Snow Network|accessdate=4 February 2013}}</ref> various [[Automated airport weather station|automated weather sensors]], the [[NOAA]] National Operational Hydrologic Remote Sensing Center (NOHRSC),<ref name="National Operational Hydrologic Remote Sensing Center">{{cite web|url=http://www.nohrsc.noaa.gov |title=NOHRSC|date=2 May 2012|accessdate=4 February 2013}}</ref> various [[hydroelectric]] companies, etc. combined with [[quantitative precipitation forecast]]s (QPF) of expected rainfall and/or snow melt to generate daily or as-needed hydrologic forecasts.<ref name="Advanced Hydrologic Prediction System"/> The NWS also cooperates with [[Environment Canada]] on hydrologic forecasts that affect both the USA and Canada, like in the area of the [[Saint Lawrence Seaway]].
 
UsersPengguna canboleh viewmelihat statisticsstatistik foruntuk rainfallhujan, streamflowaliran sungai, waterkedalaman depthair, anddan floodingbanjir everysetiap 3 hoursjam, atpada eachsetiap 12titik [[kilometer]] gridpoint12 onkilometer apada globalpeta mapglobal. ForecastsRamalan foruntuk theseparameter parametersini areadalah 5 dayshari intoke themasa futuredepan. UsersPengguna canboleh zoomzum inmasuk tountuk seemelihat inundationpeta mapsbanjir (areaskawasan estimatedyang todianggarkan bedilitupi coveredoleh with waterair) indalam resolusi 1 kilometer resolution.<ref>{{Cite web|title=Predicting Floods|url=https://science.nasa.gov/science-news/science-at-nasa/2015/22jul_floods/ |website=science.nasa.gov|accessdate=2015-07-22}}</ref><ref>{{cite av media|url=https://www.youtube.com/watch?v=dfcr-4XmxNY&authuser=0 |title=ScienceCasts: Predicting Floods|date=21 July 2015|publisher=YouTube|accessdate=13 January 2016|via=YouTube}}</ref>
The Global Flood Monitoring System, "GFMS," a computer tool which maps flood conditions worldwide, is available [http://flood.umd.edu/ online]. Users anywhere in the world can use GFMS to determine when floods may occur in their area. GFMS uses precipitation data from [[NASA]]'s Earth observing satellites and the [[Global Precipitation Measurement satellite]], "GPM." Rainfall data from GPM is combined with a land surface model that incorporates vegetation cover, soil type, and terrain to determine how much water is soaking into the ground, and how much water is flowing into [[streamflow]].
 
Users can view statistics for rainfall, streamflow, water depth, and flooding every 3 hours, at each 12 [[kilometer]] gridpoint on a global map. Forecasts for these parameters are 5 days into the future. Users can zoom in to see inundation maps (areas estimated to be covered with water) in 1 kilometer resolution.<ref>{{Cite web|title=Predicting Floods|url=https://science.nasa.gov/science-news/science-at-nasa/2015/22jul_floods/ |website=science.nasa.gov|accessdate=2015-07-22}}</ref><ref>{{cite av media|url=https://www.youtube.com/watch?v=dfcr-4XmxNY&authuser=0 |title=ScienceCasts: Predicting Floods|date=21 July 2015|publisher=YouTube|accessdate=13 January 2016|via=YouTube}}</ref>
 
== Kejadian-kejadian banjir yang terkenal کجادين٢ بنجير يڠ ترکنل ==