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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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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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