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Mô hình hoá mưa - dòng chảy ( Phần cơ sở - Nxb ĐH Quốc Gia Hà Nội ) - Phụ lục B

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Mô hình hoá mưa - dòng chảy ( Phần cơ sở - Nxb ĐH Quốc Gia Hà Nội ) - Phụ lục B

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Giải thích các thuật ngữ Actual evapotranspiration (Bốc thoát hơi thực): Cường độ bốc hơi từ bề mặt hoặc lớp phủ thực vật vào khí quyển dưới điều kiện khí tượng thịnh hành và có sẵn nước (mục 3.3, hộp 3.1) Aerodynamic resistance (Sức cản khí động lực): Thông số tỷ lệ cho dòng nhiệt thấy được và tiềm tàng trong phương trình Penman - Monteith (mục 3.3, hộp 3.1) Areisotropic (Dị hướng): Tính từ mô tả cho môi trường rỗng, trong đó độ dẫn thuỷ lực là thực sự lớn hơn trong hướng dòng chảy chắc chắn (cũng xem isotropic)...

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Nội dung Text: Mô hình hoá mưa - dòng chảy ( Phần cơ sở - Nxb ĐH Quốc Gia Hà Nội ) - Phụ lục B

  1. Phô lôc B Gi¶i thÝch c¸c thuËt ng÷ Actual evapotranspiration (Bèc tho¸t h¬i thùc): C­êng ®é bèc h¬i tõ bÒ mÆt hoÆc líp phñ thùc vËt vµo khÝ quyÓn d­íi ®iÒu kiÖn khÝ t­îng thÞnh hµnh vµ cã s½n n­íc (môc 3.3, hép 3.1) Aerodynamic resistance (Søc c¶n khÝ ®éng lùc): Th«ng sè tû lÖ cho dßng nhiÖt thÊy ®­îc vµ tiÒm tµng trong ph­¬ng tr×nh Penman - Monteith (môc 3.3, hép 3.1) Areisotropic (DÞ h­íng): TÝnh tõ m« t¶ cho m«i tr­êng rçng, trong ®ã ®é dÉn thuû lùc lµ thùc sù lín h¬n trong h­íng dßng ch¶y ch¾c ch¾n (còng xem isotropic) (hép 5.1) Ateendent condition (§iÒu kiÖn tr­íc): Tr¹ng th¸i ­ít cña l­u vùc tr­íc mét sù kiÖn hoÆc mét thêi kú m« pháng (môc 1.4) Aquiclude (Líp c¸ch n­íc): Líp ®Êt ®¸ hoÆc kh«ng thÊm n­íc (môc 5.11) Atmospheric demant (Nhu cÇu khÝ quyÓn): C­êng ®é bèc tho¸t h¬i tiÒm n¨ng cho ®iÒu kiÖn khÝ quyÓn xem xÐt nh­ nhiÖt ®é, ®é Èm, tèc ®é giã mµ kh«ng cã giíi h¹n v× sù s½n cã cña n­íc (môc 3.3) Autocorrelater errors (Sai sè tù t­¬ng quan): Chuçi thêi gian cña sè d­ m« h×nh kh«ng ®éc lËp ë mçi b­íc thêi gian, nghÜa lµ biÓu thÞ t­¬ng quan thèng kª ë mét hay nhiÒu b­íc thêi gian riªng biÖt (xem Heteroseelastic) Automatic optimization (Tèi ­u ho¸ tù ®éng): HiÖu chØnh c¸c th«ng sè m« h×nh b»ng mét thuËt to¸n m¸y tÝnh ®Ó cùc ®¹i hoÆc cùc tiÓu ho¸ gi¸ trÞ hµm môc tiªu (môc 7.1) Base flow (Dßng ch¶y c¬ së): PhÇn cña thuû ®å dßng ch¶y sÏ tiÕp tôc nÕu kh«ng cã m­a tiÕp theo. §«i khi lÊy t­¬ng ®­¬ng tæng dßng ch¶y s¸t mÆt ®ãng gãp vµo dßng ch¶y s«ng, nh­ng c¸c ®o ®¹c dÊu vÕt m«i tr­êng cho r»ng ®©y kh«ng ph¶i lµ thuËt ng÷ tèt v× dßng ch¶y s¸t mÆt cã thÓ lµ l­îng ®ãng gãp ­u thÕ ®Õn thuû ®å tõ nhiÒu trËn m­a (môc 2.2) Baseflow separation (Ph©n t¸ch dßng ch¶y c¬ së): Mét thñ tôc kÕt hîp víi thuû ®å ®¬n vÞ ®Ó ph©n t¸ch thuû ®å thµnh dßng ch¶y do m­a vµ dßng ch¶y c¬ së. NhiÒu ph­¬ng ph¸p kh¸c nhau s½n cã, hÇu hÕt kh«ng cã c¬ së ch¾c ch¾n (môc 2.2) Basic function (C¸c hµm c¬ b¶n): C¸c hµm néi suy sö dông biÓu thÞ cho sù thay ®æi cña biÕn dù b¸o bªn trong mçi phÇn tö cña phÐp gi¶i phÇn tö h÷u h¹n (hép 5.3) Bayes equation (Ph­¬ng tr×nh Bayes): Ph­¬ng tr×nh ®Ó tÝnh to¸n x¸c suÊt sau 325
  2. khi nhËn ®­îc mét x¸c suÊt tr­íc vµ mét hµm h÷u hiÖu. §­îc dïng trong ph­¬ng ph¸p GLUE ®Ó tÝnh to¸n träng sè h÷u hiÖu m« h×nh sau tõ träng sè chñ quan tr­íc ®ã vµ mét ®é ®o h÷u hiÖu cho ®¸nh gi¸ m« h×nh (môc 7.7 vµ hép 7.2) Behavioural Simulation (M« pháng hµnh vi): Mét m« pháng ®­a ®Õn sù t¸i t¹o chÊp nhËn ®­îc cña bÊt kú quan tr¾c s½n cã cho ®¸nh gi¸ m« h×nh. M« pháng kh«ng chÊp nhËn ®­îc lµ kh«ng cã hµnh vi (môc 7.2.1; 7.7) Big leaf model (M« h×nh l¸ c©y lín): BiÓu thÞ cña líp phñ thùc vËt trong dù b¸o bèc tho¸t h¬i nÕu nã lµ mét bÒ mÆt ®ång nhÊt (hép 3.1) Black box model (M« h×nh hép ®en): Mét m« h×nh liªn hÖ chØ ®Çu vµo vµ ®Çu ra dù b¸o b»ng mét hµm hoÆc c¸c hµm to¸n häc mµ kh«ng cã mét cè g¾ng nµo ®Ó m« t¶ qu¸ tr×nh ®iÒu khiÓn ph¶n øng bªn trong hÖ thèng (môc 1.1; 4.1) Blind validation (KiÓm chøng mï): §¸nh gi¸ m« h×nh b»ng gi¸ trÞ th«ng sè ®· ­íc l­îng tr­íc khi cã bÊt cø ®Çu ra nµo (môc 5.4) Boundary condition (§iÒu kiÖn biªn): Sù rµng buéc vµ gi¸ trÞ c¸c biÕn yªu cÇu ®Ó ch¹y m« h×nh cho mét khu vùc vµ mét thêi kú cô thÓ. Cã thÓ bao gåm c¸c biÕn ®Çu vµo nh­ m­a vµ nhiÖt ®é, hoÆc c¸c rµng buéc nh­ x¸c ®Þnh ®Çu n­íc cè ®Þnh (®iÒu kiÖn biªn Dirichlet), biªn kh«ng thÊm (®iÒu kiÖn biªn Neumann), hoÆc c­êng suÊt dßng x¸c ®Þnh (®iÒu kiÖn biªn Cauchy) (môc 1.3; hép 5.1) Calibration (HiÖu chØnh): Qu¸ tr×nh hiÖu chØnh gi¸ trÞ cña th«ng sè ®Ó thu ®­îc sù phï hîp tèt h¬n gi÷a c¸c biÕn quan tr¾c vµ dù b¸o. Cã thÓ lµm b»ng tay hoÆc dïng thuËt to¸n hiÖu chØnh tù ®éng (môc 1.8; ch­¬ng 7) Canopy resistance (Søc c¶n líp phñ): Søc c¶n ¶nh h­ëng ®Õn sù vËn chuyÓn h¬i n­íc tõ khÝ khæng cña l¸ c©y vµo khÝ quyÓn (môc 3.3) Capillary potential (TiÒm n¨ng mao dÉn): ¸p suÊt liªn quan ®Õn ¸p suÊt khÝ quyÓn ë ®ã n­íc trong ®Êt ®­îc gi÷ trong c¸c kh«ng gian trèng cña ®Êt. Trong ®Êt kh«ng b·o hoµ, tiÒm n¨ng mao dÉn lÊy gi¸ trÞ ©m t­¬ng ®­¬ng ¸p suÊt cña h¹t n­íc xuyªn qua bÒ mÆt cong n­íc- khÝ trong lç hæng cña ®Êt. Celerity or wave speed (§é nhanh hay tèc ®é sãng): Tèc ®é mµ nhiÔu lo¹n ¸p suÊt lan truyÒn qua khu vùc dßng ch¶y. PhÇn quan träng cña thµnh phÇn n­íc cò lín cña dßng ch¶y do m­a trong nhiÒu l­u vùc. Cã thÓ rÊt kh¸c nhau víi c¸c qu¸ tr×nh kh¸c nhau vµ l­u vùc Èm ­ít (môc 1.5; 5.5; hép 5.7) Complementary approach (TiÕp cËn phô): Mét ph­¬ng ph¸p dù b¸o c­êng ®é bèc h¬i thùc dùa trªn ý kiÕn cho r»ng c­êng ®é bèc h¬i thùc (vµ do ®ã ®é Èm xung quanh) lín h¬n th× ®é ®o bèc h¬i tõ bÒ mÆt tù do hoÆc thïng ®o bèc h¬i sÏ nhá h¬n (môc 3.3) Conceptual model (M« h×nh nhËn thøc-quan niÖm): M« h×nh thuû v¨n x¸c ®Þnh trong d¹ng c¸c ph­¬ng tr×nh to¸n häc. §¬n gi¶n ho¸ cña m« h×nh gi¸c quan (môc 1.3) Contributing area (DiÖn tÝch ®ãng gãp): Mét thuËt ng÷ trong sù ®a d¹ng cña c¸c con ®­êng trong thuû v¨n. HÇu hÕt ®Òu liªn quan ®Õn phÇn cña l­u vùc ®ãng gãp dßng ch¶y mÆt hay s¸t mÆt do m­a cho thuû ®å (môc 1.4) 326
  3. Data assismilation (§ång nhÊt sè liÖu): Qu¸ tr×nh sö dông sè liÖu quan tr¾c ®Ó cËp nhËt dù b¸o m« h×nh (xem Real-time forecasting and updating) (môc 5.6) Degree- day method (Ph­¬ng ph¸p ®é-ngµy): Ph­¬ng ph¸p dù b¸o tuyÕt tan nh­ lµ tû lÖ víi ®é chªnh lÖch gi÷a nhiÖt ®é trung b×nh ngµy vµ gi¸ trÞ ng­ìng (môc 3.4) Depression storage (L­îng tr÷ h¹ thÊp): N­íc v­ît qu¸ kh¶ n¨ng thÊm cña ®Êt duy tr× trong c¸c lç hæng bÒ mÆt tr­íc khi x¶y ra dßng ch¶y trµn xu«i dèc cã ý nghÜa. Cã thÓ thÊm muén h¬n vµo ®Êt sau khi m­a kÕt thóc (môc 1.4). Deterministic model (M« h×nh tÊt ®Þnh): M« h×nh víi mét bé ®iÒu kiÖn biªn ban ®Çu sÏ cho duy nhÊt mét ®Çu ra hoÆc mét dù b¸o (môc 1.7) Diffusivity (KhuÕch t¸n): S¶n phÈm cña ®é dÉn thuû lùc kh«ng b·o hßa vµ gradient cña ®­êng cong liªn hÖ tiÒm n¨ng mao dÉn víi l­îng Èm ®Êt (môc 5.1.1, hép 5.1) Distributed model (M« h×nh ph©n bè): M« h×nh mµ gi¸ trÞ dù b¸o cña biÕn tr¹ng th¸i kh¸c nhau trong kh«ng gian (th­êng lµ c¶ thêi gian) (môc 1.7) Dotty plots (§å thÞ ®iÓm): Mét c¸ch biÓu thÞ kÕt qu¶ cña m« pháng Monte-Carlo trong ®ã mét hµm môc tiªu tõ mçi m« pháng ®­îc vÏ ®èi chiÕu víi gi¸ trÞ chän ngÉu nhiªn cña mçi th«ng sè. Do ®ã ®å thÞ ®iÓm biÓu thÞ phÐp chiÕu cña c¸c ®iÓm mÉu trªn bÒ mÆt ph¶n øng vµo trong trôc th«ng sè ®¬n (xem Objective function, Response surface) (môc 1.8; 7.7) Double mass curve (§­êng cong khèi kÐp): §å thÞ cña thÓ tÝch luü tÝch liªn kÕt víi hai tr¹m ®o (m­a hoÆc l­u l­îng) (môc 3.2) Dynamic contributing area (DiÖn tÝch ®ãng gãp ®éng lùc): DiÖn tÝch t¹o dßng ch¶y mÆt cã khuynh h­íng më réng suèt trËn m­a (môc 1.4) Eddy correlation method (Ph­¬ng ph¸p t­¬ng quan xo¸y): Kü thuËt ®o bèc tho¸t h¬i thùc vµ dßng nhiÖt thÊy ®­îc b»ng tÝch luü sù dao ®éng nhanh cña ®é Èm vµ nhiÖt ®é, kÕt hîp víi xo¸y rèi trong líp biªn thÊp h¬n (môc 3.3.3) Effective rainfall (M­a hiÖu qu¶): Mét phÇn cña ®Çu vµo m­a r¬i ®Õn l­u vùc, t­¬ng ®­¬ng víi phÇn dßng ch¶y do m­a cña thuû ®å (nh­ng còng l­u ý r»ng dßng ch¶y do m­a cã thÓ kh«ng ph¶i lµ tÊt c¶ l­îng n­íc m­a) (môc 1.3; 2.2) Effective storage capacity (Kh¶ n¨ng tr÷ hiÖu qu¶): HiÖu sè gi÷a ®é Èm ®Êt hiÖn thêi trong ®Êt kh«ng thÊm trªn mùc n­íc ngÇm vµ b·o hoµ (môc 1.5) Ephemeral stream (Dßng phï du-t¹m thêi): Dßng th­êng bÞ kh« gi÷a c¸c thêi kú m­a (môc 1.4) Equifimality (T­¬ng ®­¬ng): Kh¸i niÖm cho r»ng cã thÓ cã nhiÒu m« h×nh cña l­u vùc lµ t­¬ng thÝch chÊp nhËn ®­îc víi c¸c quan tr¾c s½n cã (môc 1.8; 7.7; 7.9) ESMA model (M« h×nh ESMA): xem Explicit soil moisture accouting model Evaluation (§¸nh gi¸): xem Validation Explicit solution (PhÐp gi¶i hiÖn): TÝnh to¸n ®éc lËp cña biÕn dù b¸o ë b­íc thêi gian nµy khi cho gi¸ trÞ cña biÕn ë b­íc thêi gian tr­íc (xem Implicit solution) 327
  4. Explicit soil moisture accouting model (hoÆc ESMA: ®«i khi gäi lµ m« h×nh quan niÖm)(M« h×nh gi¶i thÝch ®é Èm ®Êt hiÖn): M« h×nh thuû v¨n t¹o nªn d·y c¸c phÇn tö l­îng tr÷ víi c¸c ph­¬ng tr×nh ®¬n gi¶n ®Ó ®iÒu khiÓn sù chuyÓn ®æi gi÷a c¸c phÇn tö. HÇu hÕt ¸p dông cho m« h×nh tËp trung, nh­ng mét sè m« h×nh sö dông thµnh phÇn ESMA ®Ó biÓu thÞ cho ®¬n vÞ ph¶n øng thuû v¨n ph©n bè (môc 2.4). Field capacity (L­îng tr÷ n­íc thùc ®Þa): BiÕn x¸c ®Þnh kh«ng chÝnh x¸c th­êng biÓu thÞ nh­ l­îng n­íc cña ®Êt khi nã cho phÐp tho¸t n­íc tõ b·o hoµ ®Õn khi sù tho¸t n­íc nhanh ngõng l¹i (xem Soil moisture deficit) (hép 6.2) Finite difference (Sai ph©n h÷u h¹n): BiÓu hiÖn gÇn ®óng cña vi ph©n kh«ng gian hoÆc thêi gian trong d¹ng cña c¸c biÕn, ph©n chia bëi c¸c kho¶ng gi¸n ®o¹n trong kh«ng gian vµ thêi gian (hép 5.3) Finite-element method (Ph­¬ng ph¸p phÇn tö h÷u h¹n): BiÓu thÞ gÇn ®óng cña vi ph©n thêi gian vµ kh«ng gian trong d¹ng cña tÝch ph©n cña hµm néi suy ®¬n gi¶n chøa c¸c biÕn x¸c ®Þnh ë nót cña sù gi¸n ®o¹n kh«ng ®Òu cña miÒn dßng ch¶y vµo c¸c phÇn tö (hép 5.8) Fuzzy logic (Logic mê): HÖ thèng c¸c quy t¾c l«gic chøa c¸c biÕn liªn kÕt víi ®é ®o mê liªn tôc (th«ng th­êng trong kho¶ng 0 ®Õn 1) thay cho ®é nhÞ ph©n (®óng/sai, 0 hoÆc 1) cña l«gic truyÒn thèng. Quy t¾c lµ s½n cã cho c¸c to¸n tö nh­ céng hoÆc nh©n cña ®é ®o mê vµ cho nhãm c¸c biÕn nhãm trong tËp hîp mê. Quy t¾c ®ã cã thÓ sö dông ®Ó ph¶n chiÕu c¸c kiÕn thøc kh«ng ®Çy ®ñ vÒ c¸c biÕn sÏ ph¶n øng nh­ thÕ nµo trong c¸c hoµn c¶nh kh¸c nhau (môc 1.7; 5.2.2) Gain (Lîi Ých): Mét hÖ sè ¸p dông cho mét hµm chuyÓn ®æi tõ thang ®é vµo ®Õn thang ®é ra trong ph©n tÝch hÖ thèng tuyÕn tÝnh, cã thÓ lµm thÝch nghi trong dù b¸o thêi gian thùc (hép 8.1) Geomorphological unit hydrograph (§­êng ®¬n vÞ ®Þa m¹o): §­êng ®¬n vÞ rót ra tõ quan hÖ cÊu tróc cña ®Þa m¹o l­u vùc, ®Æc biÖt cÊu tróc nh¸nh cña m¹ng s«ng (môc 2.3; 4.7; 2) Global optinium (Tèi ­u toµn côc): Mét bé gi¸ trÞ th«ng sè ®­a ®Õn sù phï hîp tèt nhÊt cã thÓ cho mét tËp hîp quan tr¾c (môc 1.8, 7.2) Head (§Çu n­íc): BiÓu thøc cña ¸p suÊt nh­ lµ nguån n¨ng l­îng trªn mét ®¬n vÞ träng l­îng th­êng sö dông trong thuû v¨n thuû lùc,v× nã cã ®¬n vÞ ®é dµi ( PhÇn 5.11). Heteroscedasitic error (Sai sè hçn hîp): Chuçi thêi gian cña sè d­ m« h×nh thÓ hiÖn sù thay ®æi ph­¬ng sai trªn mét thêi kú m« pháng (xem Autocorrelated error) ( môc 7.3, hép 7.1) Hortonian model (M« h×nh Horton): S¶n sinh dßng ch¶y bëi c¬ chÕ m­a v­ît thÊm. §­îc ®Æt tªn Robert E.Horton (xem Partial area model) (môc 1.4) Hydrological responee unit (§¬n vÞ ph¶n øng thuû v¨n): Mét phÇn mÆt ®Êt x¸c ®Þnh trong d¹ng c¸c ®Æc tr­ng cña ®Êt, thùc vËt vµ ®Þa h×nh cña nã (môc 1.7, 2.3, 3.8, 6.1 6.3) Hysteresis (TrÔ): ThuËt ng÷ ®Ó chØ ra r»ng quan hÖ gi÷a l­îng n­íc trong ®Êt vµ 328
  5. tiÒm n¨ng mao dÉn hoÆc ®é dÉn thuû lùc lµ kh¸c nhau khi ®Êt ®ang ­ít so víi khi ®Êt ®ang kh« (môc 5.1.1; hép 5.1) Implicit solution (PhÐp gi¶i Èn): Gi¶i ®ång thêi c¸c biÕn dù b¸o ë mét b­íc thêi gian sau khi cho gi¸ trÞ c¸c b­íc thêi gian tr­íc, th­êng dïng phÐp lÆp (xem Explicit solution)(môc 5.1.1; hép 5.3) Imcommensurate (V« ­íc): Sö dông ®Ó ph¶n ¸nh biÕn hoÆc th«ng sè víi cïng mét tªn nh­ng cã l­îng kh¸c nhau v× sù biÕn ®æi cña quy m« (môc 1.8) Infiltration capacity (Kh¶ n¨ng thÊm): C­êng ®é giíi h¹n ë ®ã mÆt ®Êt cã thÓ hÊp thô m­a, nã phô thuéc vµo c¸c nh©n tè nh­ l­îng Èm tr­íc, thÓ tÝch n­íc thÊm, sù cã mÆt cña c¸c lç hæng to hoÆc líp vá bÒ mÆt (môc 1.4; hép 5.2) Infiltration excess runoff (Dßng ch¶y v­ît thÊm): Dßng ch¶y t¹o thµnh do c­êng ®é m­a v­ît qu¸ kh¶ n¨ng thÊm cña bÒ mÆt ®Êt. Cã thÓ dïng ë quy m« c¸c ®iÓm côc bé trong l­u vùc (khi dßng ch¶y mÆt cã thÓ thÊm xu«i dèc tiÕp theo) hoÆc ë quy m« l­u vùc ®Ó thÓ hiÖn r»ng mét phÇn cña thuû ®å m­a t¹o thµnh bëi c¬ chÕ m­a v­ît thÊm (môc 1.4). Initial condition (§iÒu kiÖn ban ®Çu): Gi¸ trÞ cña biÕn l­îng tr÷ hoÆc ¸p suÊt yªu cÇu ®Ó ban ®Çu ho¸ mét m« h×nh ë lóc b¾t ®Çu mét thêi kú m« pháng (môc 5.1) Interception (Gi÷ l¹i): M­a ®­îc gi÷ l¹i trong líp phñ thùc vËt, sau ®ã bèc h¬i ng­îc trë l¹i khÝ quyÓn (môc 3.3.2; hép 3.2) Inverse method (Ph­¬ng ph¸p nghÞch): HiÖu chØnh m« h×nh b»ng c¸ch hiÖu chØnh th«ng sè ®Ó gi¶m sù kh¸c nhau gi÷a c¸c biÕn quan tr¾c vµ dù b¸o (môc 5.1.1) Isotropic (§ång h­íng): TÝnh tõ m« t¶ cho m«i tr­êng rçng trong ®ã ®é dÉn thuû lùc lµ nh­ nhau trong tÊt c¶ c¸c h­íng dßng ch¶y (xem Anisotropic) (hép 5.1) Land surface parametrization (Th«ng sè ho¸ mÆt ®Êt): M« h×nh thuû v¨n dïng ®Ó tÝnh dßng n­íc vµ n¨ng l­îng tõ mÆt ®Êt ®Õn khÝ quyÓn trong m« h×nh hoµn l­u khÝ quyÓn (môc 2.4) Lead time (Thêi gian dù kiÕn): Thêi gian yªu cÇu cho dù b¸o ®i tr­íc thêi ®iÓm hiÖn thêi trong dù b¸o thêi gian thùc (môc 8.1) Learning set (Bé luyÖn): Bé sè liÖu quan tr¾c sö dông ®Ó hiÖu chØnh trong m« h×nh m¹ng thÇn kinh (môc 4.3) Likelihoot measure (§é h÷u hiÖu-§é ®o ®óng ®¾n-): §é ®o ®Þnh l­îng cña sù chÊp nhËn ®­îc cña mét m« h×nh hoÆc bé th«ng sè riªng trong t¸i t¹o l¹i ph¶n øng thuû v¨n ®· ®­îc m« h×nh ho¸ (môc 7.7; hép 7.1; 7.2) Linearity (TuyÕn tÝnh): M« h×nh lµ tuyÕn tÝnh nÕu ®Çu ra tû lÖ trùc tiÕp víi ®Çu vµo (môc 2.2; hép 2.1; 4.1) Linear storate (L­îng tr÷ tuyÕn tÝnh): Thµnh phÇn m« h×nh trong ®ã ®Çu ra tû lÖ trùc tiÕp víi gi¸ trÞ l­îng tr÷ hiÖn thêi. Khèi c¬ b¶n cña m« h×nh hµm chuyÓn ®æi tuyÕn tÝnh chung vµ hå chøa bËc thang Nash (môc 2.3; hép 1.4) Local optinium (Tèi ­u côc bé): §Ønh côc bé trong bÒ mÆt ph¶n øng th«ng sè ë ®ã mét bé th«ng sè nhËn ®­îc phï hîp víi quan tr¾c h¬n tÊt c¶ c¸c bé xung quanh nã, 329
  6. nh­ng kh«ng tèt nh­ tèi ­u toµn côc (môc 7.2) Lumped model (M« h×nh tËp trung): M« h×nh coi toµn bé l­u vùc nh­ mét ®¬n vÞ tÝnh to¸n ®¬n vµ dù b¸o chØ nh÷ng gi¸ trÞ trung b×nh trªn toµn l­u vùc (môc 1.5, 1.7) Macropores (Lç hæng to): Lç hæng lín trong ®Êt cã thÓ thµnh ®­êng ®i quan träng cho sù thÊm hoÆc ph©n phèi l¹i cña n­íc b»ng c¸ch ®i qua khu«n ®Êt nh­ dßng ­u tiªn. Cã thÓ do ®Êt bÞ nøt vµ h×nh thµnh c¸i hom giá, kªnh rÔ vµ hang ®éng vËt (môc 1.4) Monte - Carlo simulation (M« pháng Monte - Carlo): M« pháng liªn quan ®Õn ch¹y nhiÒu lÇn mét m« h×nh sö dông bé th«ng sè hoÆc ®iÒu kiÖn biªn chän ngÉu nhiªn kh¸c nhau (môc 7.5; 7.6; 7.7) Network width function (Hµm ®é réng m¹ng): §å thÞ sè ®o¹n s«ng trong m¹ng s«ng ë c¸c kho¶ng c¸ch tÝnh tõ cöa ra l­u vùc. Cã thÓ dïng nh­ c¬ së cho c¶ thuËt to¸n diÔn to¸n tuyÕn tÝnh vµ phi tuyÕn (môc 4.3; 4.7.1) Nomogram (To¸n ®å): Mét ph­¬ng ph¸p kinh nghiÖm cho ­íc l­îng dßng ch¶y b»ng mét d·y ®å thÞ (môc 2.1) Nonlinear (Phi tuyÕn): M« h×nh lµ phi tuyÕn nÕu ®Çu ra kh«ng tû lÖ trùc tiÕp víi ®Çu vµo nh­ng cã thÓ kh¸c nhau víi c­êng ®é hoÆc thÓ tÝch cña ®Çu vµo hoÆc víi ®iÒu kiÖn tr­íc (hép 2.1) Nonparametric method (Ph­¬ng ph¸p kh«ng th«ng sè): Mét ph­¬ng ph¸p ­íc l­îng c¸c ph©n bè mµ kh«ng cã bÊt kú gi¶ thiÕt nµo vÒ d¹ng to¸n häc cña ph©n bè (môc 7.2.1) Nonstationarity (Kh«ng dõng): M« h×nh trong ®ã c¸c th«ng sè thay ®æi theo thêi gian (hép 2.1) Objective functions (Hµm môc tiªu): §é ®o cña viÖc m« pháng phï hîp tèt víi c¸c quan tr¾c s½n cã (môc 1.8; 7.3; hép 7.1) Optimization (Tèi ­u ho¸): Qu¸ tr×nh t×m bé th«ng sè ®­a ®Õn sù phï hîp tèt nhÊt cña m« h×nh víi sè liÖu cã s½n. Cã thÓ lµm b»ng tay hoÆc b»ng thuËt to¸n tèi ­u ho¸ (môc 1.8; 7.4) Overland flow (Dßng ch¶y trµn): Dßng ch¶y xu«i dèc cña n­íc trªn mÆt ®Êt khi v­ît kh¶ n¨ng thÊm hay kh¶ n¨ng tr÷ chç tròng cña bÒ mÆt (môc 1.4) Parameter (Th«ng sè): H»ng sè cÇn x¸c ®Þnh tr­íc khi ch¹y m« pháng m« h×nh (môc 1.5; 1.8) Parameter space (Kh«ng gian th«ng sè): Kh«ng gian x¸c ®Þnh bëi ph¹m vi c¸c th«ng sè m« h×nh cã thÓ víi mçi chiÒu cho mçi th«ng sè (môc 1.8; 7.2) Parsimony (Chi li): Kh¸i niÖm ®«i khi biÕt nh­ dao c¹o Occam mµ mét m« h×nh kh«ng phøc t¹p h¬n cÇn thiÕt ®Ó dù b¸o c¸c quan tr¾c ®ñ chÝnh x¸c (hép 4.1) Partial area model (M« h×nh diÖn tÝch riªng phÇn): S¶n sinh dßng ch¶y (bëi c¬ chÕ v­ît thÊm) chØ trªn mét phÇn cña s­ên dèc (diÖn tÝch riªng phÇn) trong l­u vùc (môc 1.4) 330
  7. Pedo transfer function (Hµm chuyÓn ®æi thæ nh­ìng): Hµm dù b¸o c¸c th«ng sè thuû lùc ®Êt tõ c¸c kiÕn thøc cña kÕt cÊu ®Êt vµ c¸c biÕn kh¸c dÔ ®o ®¹c h¬n (môc 3.8; 5.1.1; hép 5.5) Perceptual model (M« h×nh gi¸c quan): M« t¶ ®Þnh tÝnh cña qu¸ tr×nh ®iÒu khiÓn ph¶n øng thuû v¨n cña mét vïng (môc 1.3; 1.4) Phreetophytes : Lo¹i c©y mµ dÔ cña nã bßn rót n­íc tõ mùc n­íc ngÇm (môc 1.4) Potential evapotranspiration (Bèc tho¸t h¬i tiÒm n¨ng): C­êng ®é bèc tho¸t h¬i tõ bÒ mÆt hoÆc líp phñ thùc vËt kh«ng h¹n chÕ vÒ l­îng n­íc s½n cã (xem Atmospheric demand) (môc 3.3; hép 3.1) Preferential flow (Dßng ch¶y ­u tiªn): Sù tËp trung côc bé dßng ch¶y trong ®Êt cã thÓ lµ ¶nh h­ëng cña c¸c lç hæng lín, sù biÕn ®æi côc bé trong thuéc tÝnh thuû lùc hoÆc ®µu nhän bÒ mÆt ­ít chuyÓn ®éng vµo profile ®Êt. Cã thÓ t¹o ra sù thÊm nhanh vµ s©u cña n­íc b»ng c¸ch ®i qua nhiÒu khu«n ®Êt (môc 1.4) Principle of superposition (Nguyªn t¾c xÕp chång): Thªm vµo ph¶n øng cña m« h×nh tuyÕn tÝnh ®Ó t¹o nªn mét ph¶n øng tæng céng (môc 2.2; hép 2.1) Procedural model (M« h×nh thñ tôc): M« h×nh biÓu thÞ nh­ ch­¬ng tr×nh m¸y tÝnh. Cã thÓ lµ phÐp gi¶i chÝnh x¸c hay gÇn ®óng cña ph­¬ng tr×nh x¸c ®Þnh m« h×nh quan niÖm cña hÖ thèng (môc 1.3) Raster digital elevation model (M« h×nh ®é cao sè ho¸ Raster): TËp hîp l­íi cña gi¸ trÞ cao tr×nh t¹i c¸c kh«ng gian ®Òu (môc 3.7) Rational method (Ph­¬ng ph¸p tû lÖ): Ph­¬ng ph¸p kinh nghiÖm sö dông lÇn ®Çu trong thÕ kû 19 cho dù b¸o l­u l­îng ®Ønh dùa trªn diÖn tÝch l­u vùc vµ ®é ®o m­a trung b×nh (môc 2.1) Real time forceasting and updating (Dù b¸o thêi gian thùc vµ cËp nhËt): Dù b¸o dßng ch¶y thùc hiÖn suèt mét trËn m­a, th­êng ®Ó dù b¸o kh¶ n¨ng cña lò lôt víi sù cËp nhËt thÝch øng cña th«ng sè m« h×nh dùa trªn sai sè gi÷a c¸c biÕn quan tr¾c vµ dù b¸o (xem Lead time) (môc 4.8; 8.4; hép 8.1) Reliability analysis (Ph©n tÝch ®é tin cËy): §¸nh gi¸ tÝnh bÊt ®Þnh trong dù b¸o m« h×nh b¾t nguån tõ tÝnh bÊt ®Þnh trong c¸c gi¸ trÞ th«ng sè, th­êng b»ng gi¶ thiÕt h×nh d¹ng ch¾c ch¾n cho mÆt ph¶n øng (xem Response surface) (môc 7.1; 7.5) Responce surface (BÒ mÆt ph¶n øng): BÒ mÆt x¸c ®Þnh bëi gi¸ trÞ biÕn ®æi cña hµm môc tiªu v× nã thay ®æi víi sù biÕn ®æi gi¸ trÞ th«ng sè. Cã thÓ cho nh­ lµ bÒ mÆt "låi" vµ "lâm" trong kh«ng gian nhiÒu chiÒu x¸c ®Þnh bëi c¸c th«ng sè, ë ®ã “låi” thÓ hiÖn sù phï hîp tèt víi quan tr¾c, cßn "lâm" thÓ hiÖn sù phï hîp tåi víi quan tr¾c (xem parameter space) (môc 1.8; 7.2) Riparian area (DiÖn tÝch ven s«ng): PhÇn l­u vùc kÕ cËn dßng s«ng vµ th­êng lµ nguån quan träng nhÊt cña dßng ch¶y mÆt vµ s¸t mÆt (môc 1.4) Runoff (Dßng ch¶y): (xem Overland flow, Storm runoff, Surface runoff, Subsurface stormflow) Runoff coefficient (HÖ sè dßng ch¶y): Tû lÖ cña l­îng m­a xuÊt hiÖn trong 331
  8. thuû ®å dßng ch¶y do m­a. Gi¸ trÞ sÏ phô thuéc vµo thµnh phÇn dßng ch¶y m­a cña thuû ®å ®­îc x¸c ®Þnh nh­ thÕ nµo? (môc 2.2) Runoff routing (DiÔn to¸n dßng ch¶y): ChuyÓn ®éng dßng ch¶y mÆt, s¸t mÆt do m­a ®Õn ®iÓm quan t©m, th­êng lµ cöa ra cña l­u vùc, quan t©m tíi tèc ®é dßng ch¶y mÆt, s¸t mÆt vµ s«ng (môc 1.6; 4.4; 5.5; 5.6; 6.1) Saturation excess runoff (Dßng ch¶y v­ît b·o hoµ): Dßng ch¶y t¹o ra bëi m­a vµo trong ®Êt b·o hoµ, thËm chÝ khi c­êng ®é m­a cã thÓ kh«ng v­ît c­êng ®é thÊm th«ng th­êng cña ®Êt. Cã thÓ dïng c¶ ë quy m« ®iÓm côc bé bªn trong l­u vùc (khi dßng ch¶y mÆt cã thÓ thÊm tiÕp xu«i dèc) hoÆc ë quy m« l­u vùc ®Ó thÓ hiÖn phÇn cña thuû ®å m­a t¹o bëi c¬ chÕ v­ît b·o hoµ (môc 1.4) Similar media (Ph­¬ng tiÖn t­¬ng tù): Ph­¬ng ph¸p thu phãng cña ®Æc tr­ng ®é Èm ®Êt cña ®Êt kh«ng ®ång nhÊt b»ng gi¶ thiÕt vÒ cÊu tróc cña ph­¬ng tiÖn (vÝ dô h×nh häc cña khu«n ®Êt lµ gièng nhau, chØ kh¸c nhau ë thang ®é dµi cña khu«n mÉu kh¸c nhau) (môc 5.4) Slope - area method (Ph­¬ng ph¸p diÖn tÝch-®é dèc): Ph­¬ng ph¸p ®o l­u l­îng ®Ønh sau trËn lò khi dïng mét ph­¬ng tr×nh dßng ch¶y ®Òu b»ng c¸ch ­íc l­îng diÖn tÝch mÆt c¾t ngang, ®é dèc mÆt n­íc vµ hÖ sè nh¸m t¹i mét vÞ trÝ (môc 3.2) Snow course (TuyÕn kh¶o s¸t): §­êng c¾t ngang ë ®ã tiÕn hµnh ®o ®¹c ®Òu ®Æn c­êng ®é vµ ®é s©u tuyÕt (môc 3.1) Soil moistur characteristic (§Æc tr­ng ®é Èm ®Êt): §­êng cong hoÆc hµm sè liªn hÖ ®é Èm ®Êt víi ®é dÉn thuû lùc kh«ng b·o hoµ vµ tiÒm n¨ng mao dÉn (môc 5.1.1; hép 5.2) Soil moistur deficit (§é hôt Èm ®Êt): BiÕn tr¹ng th¸i dïng trong nhiÒu m« h×nh thuû v¨n nh­ mét biÓu thøc cña l­îng tr÷ n­íc trong ®Êt. SMD b»ng 0 khi ®Êt ë kh¶ n¨ng thùc ®Þa vµ lín h¬n khi ®Êt kh«. Nã th­êng biÓu thÞ b»ng ®¬n vÞ ®é s©u cña n­íc (môc 1.4; 3.1) Specific moisture capacity (Kh¶ n¨ng ®é Èm riªng): Gradient cña ®­êng cong liªn hÖ ®é Èm ®Êt kh«ng b·o hoµ víi tiÒm n¨ng mao dÉn (môc 5.1.1; hép 5.2) State variable (BiÕn tr¹ng th¸i): BiÕn trong m« h×nh lµ mét phÇn cña phÐp gi¶i ph­¬ng tr×nh m« h×nh vµ thay ®æi suèt thêi gian m« pháng nh­ng kh«ng lµ mét dßng hoÆc sù trao ®æi cña khèi. Cã thÓ bao gåm biÕn l­îng tr÷ vµ ¸p suÊt, phô thuéc vµo ®Þnh nghÜa m« h×nh (môc 5.8) Stemflow (Dßng th©n c©y): M­a xuyªn vµo ®Êt qua c¸c nh¸nh c©y (môc 1.4, hép 3.2). Stochastic (NgÉu nhiªn): M« h×nh lµ ngÉu nhiªn nÕu cho mét bé ®iÒu kiÖn biªn vµ ban ®Çu, cã thÓ cã mét kho¶ng cña ®Çu ra, th­êng víi mçi ®Çu ra liªn hÖ víi mét x¸c suÊt ®· ­íc l­îng (môc 1.7). Storm profile (Tr¾c diÖn m­a): Chuçi c­êng ®é m­a trong suèt trËn m­a (môc 3.1) Storm runoff (Dßng ch¶y do m­a): Cã nhiÒu ®Þnh nghÜa m©u thuÉn nhau vÒ dßng ch¶y m­a. ë ®©y lµ phÇn cña thuû ®å s«ng do m­a v­ît qu¸ vµ ë bªn trªn mét l­u l­îng ®· xÈy ra mµ kh«ng cã m­a vµ cã thÓ bao gåm c¶ qu¸ tr×nh dßng ch¶y mÆt, s¸t mÆt, c¶ ®ãng gãp cña n­íc m­a vµ n­íc cò (môc 1.4; 1.5; 1.6) Streamline (§­êng dßng): Mét ®­êng song song víi h­íng dßng ch¶y (xem 332
  9. Stream tube)(môc 3.7) Stream tube (èng dßng): PhÇn cña khu vùc dßng ch¶y ®ãng kÝn gi÷a hai ®­êng dßng x¸c ®Þnh (môc 3.7) Sublimation (Th¨ng hoa): Tæn thÊt trùc tiÕp n­íc tõ khèi tuyÕt vµo kh«ng khÝ do bèc h¬i (môc 3.1). Subsurface stormflow (Dßng ch¶y m­a s¸t mÆt): §ãng gãp vµo thuû ®å s«ng bëi qu¸ tr×nh dßng s¸t mÆt duy nhÊt (môc 1.4). Surface runoff (Dßng ch¶y mÆt): §ãng gãp vµo thuû ®å s«ng tõ dßng ch¶y trµn (môc 1.4) Tessenlation (Kh¶m): Gi¸n ®o¹n ho¸ kh«ng gian thµnh l­íi kh«ng gian hoÆc m¹ng phÇn tö (môc 3.7) Throughfall (Xuyªn): M­a r¬i ®i vµo ®Êt trùc tiÕp hay gi¸n tiÕp tõ l¸ c©y (môc 1.4; hép 3.2) Through flow (Dßng ch¶y xuyªn): Th­êng dïng cho dßng ch¶y s¸t mÆt xu«i dèc gÇn bÒ mÆt dèc trong tr¾c diÖn ®Êt (môc 1.4) Time compression assumption (Gi¶ thiÕt nÐn thêi gian): Xö lý l­îng n­íc thÊm suèt trËn m­a v× nÕu nã ®· thÊm ë kh¶ n¨ng thÊm cña ®Êt ®Ó tÝnh to¸n mét thêi gian tÝch ®äng t­¬ng ®­¬ng (hép 5.2) Time to ponding (Thêi gian tÝch ®äng): Thêi gian lÊy trong suèt trËn m­a ®Ó lµm cho bÒ mÆt ®Êt thµnh b·o hoµ (hép 5.2) Transfer function (Hµm chuyÓn ®æi): BiÓu thÞ ®Çu ra tõ hÖ thèng do mét ®¬n vÞ ®Çu vµo (môc 3.7) Triangular irregular network (M¹ng tam gi¸c kh«ng ®Òu): Mét c¸ch biÓu thÞ ®Þa h×nh b»ng m¹ng c¸c tam gi¸c gi÷a c¸c ®iÓm cao tr×nh ®· biÕt (môc 3.7) Uniform flow (Dßng ch¶y ®Òu): Dßng ch¶y kªnh hë hoÆc dßng ch¶y trµn trong ®ã ®é dèc bÒ mÆt b»ng ®é dèc ®¸y ®Ó tæn thÊt n¨ng l­îng do øng suÊt tiÕp ma s¸t, ®­îc tÝnh chÝnh x¸c bëi phÇn n¨ng l­îng tiÒm n¨ng thu ®­îc nh­ n­íc chuyÓn ®éng theo däc s­ên dèc (môc 5.2.2; hép 5.6) Unit hydrograph (§­êng ®¬n vÞ): Ph¶n øng dßng ch¶y m­a tõ mét ®¬n vÞ l­îng m­a hiÖu qu¶ (môc 2.2; 2.3; 4.8) Validation (KiÓm chøng): Qu¸ tr×nh ®¸nh gi¸ m« h×nh ®Ó kh¼ng ®Þnh r»ng chóng lµ ®¹i biÓu chÊp nhËn ®­îc cña hÖ thèng. C¸c nhµ khoa häc cã mét vµi ý kiÕn víi kh¸i niÖm kiÓm chøng (môc 1.8) vµ tèt h¬n lµ sö dông "®¸nh gi¸" hoÆc "kh¼ng ®Þnh" thay cho kiÓm chøng (nã cã gèc Latinh lµ ®é ®o møc ®é thËt cña m« h×nh (môc 1.8; 5.3; 10.5) Vector digital elevation model (M« h×nh cao tr×nh sè vecto): Mét tËp hîp ®iÓm cao tr×nh kh«ng gian kh«ng ®Òu b»ng ®Þnh nghÜa c¸c ®­êng ®ång møc cao tr×nh (môc 3.7) Wave speed (Tèc ®é sãng): Xem Celerity 333
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