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by: Clifford Bednar


Clifford Bednar
GPA 3.53


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This 73 page Class Notes was uploaded by Clifford Bednar on Friday September 18, 2015. The Class Notes belongs to ABE 6254 at University of Florida taught by Staff in Fall. Since its upload, it has received 13 views. For similar materials see /class/206933/abe-6254-university-of-florida in Agriculture Education at University of Florida.




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Date Created: 09/18/15
ABE 6254 9112003 Precipitation in Modeling Rainfall as Model Input Form and detail largely determined by model output desired runoff volume requires daily rain peak rates require short duration amounts min hr erosion sediment and chemical transport require storm rainfall Amounts usually are from point reoords must be averaged overwatershed area Rainfall as Model Input Types of rainfall data required space one gage averaged to represent watershed multiple gages each assigned to a part of watershe hourly equal time increment breakpoint ABE 6254 9112003 RamfaH as Mode nput Types uvyawau da a vequwed 4mm mmu we x mums E s m m mmmm ng sxllgiy mum m Wm Ramau Energyvs mtensuy RamfaH as Mode nput Types uvyawau da a vequwed mm hum m duvmxan and quotequan m mammary2 new mm m mm m be cammw mnamwm mm mm be 5m as mmu ABE 6254 9112003 Snowfall Models Significant part of hydrology in many areas Important properties in modeling water equivalent density increases with time depth optical properties areal extent Snowfall Models Methods of division between rain and snow base on temperature of 0 C NRCS method uses 17 C at ground level since snow forms at heights combination of dry bulb and dewpoint temperature if only daily mean temperature is available assume fraction of precipitation is rain and rest snow Snowmelt Models Energy balance approach convection of sensible heat from air latent heat by condensation of moist air on pack heat 39om rain sensible and latent 39om rain 39eezing on pack conduction of heat 39om ground ABE 6254 9112003 Snowmelt Models Energy balance approach any heat raising pack temperature above 32 F is used to melt pack account for changing a bedo of pack with new show affects net radiation decreases over time Snowmelt Models Simplified approach degreeday accumulation because air temperature is easily measured Mimi Spatial variability comments regarding rainfall also apply to snowmelt varying cover depth as well as melt rates topo and slope orientation effects 9232003 ABE 6254 ntroduct on Evapuvauunhum suu mm Watev and mam nan uauun m man pveupnmmn sevapmmed mammpqs xmpanam my gammy antecedent mmsture Canter AMC Em Rx ABE 6254 9232003 Physics and physiology Energy driven primarily solar radiation s 7 a s R 7 l Energy balance vertical RquotALESX calcmzlmin Net Sensible Water Radiatlun A Va r LE a i ATMOSPHERE Physics and physiology Applied to plant canopy development of canopy interception LAI leaf area index SOI plant growth stage sensitivity to water stress humidity gradients wind effects Physics and physiology Soil evaporation energy shading mulch water availability if top 12quot is dry tends to insulate the soil transport humidity gradients temperature gradients ABE 6254 9232003 Physics and physiology Soil evaporation stages 39 Climate limiting energy concurrenmow of water limiting heat and water Landscape location in watershed ET Prediction 0 PET climate limiting approach for maximum ET from a full canopy of a well watered crop ET ux will not exceed available energ from both radiant and convection sources 0 AET estimates of ET speci c for crop soil location climate and time ET Prediction Methods Soil moisture bud et lysimeter or groundwater uctuations Energy budget PET 0025T 0078Rs Jensen amp Haise PET 040ms 50T 15 Turc Temperaturebased u kW Blaney Cridde ABE 6254 E tr ET Prediction Methods Aerodynamic profile measurements too complex for routine use Combination Penman 1963 39 with aerodynamic more accurate over a wide range of climatic condi ions Ai39iRnGAquotJV153 l4eaeed calcmzlday 9232003 Combination Method A slope ofvapor pressuretemperature curve y psychrometric constant 0386PL Where P average oarornetnc pressure L laterit heat of vaporization Rquot net radiation G soil heat ux W wind function 21O 0062 2 Wll id travel at neignt above ground as affected by crop eignt etc eaed saturated vapor pressure de cit ea sat irtern yo at rnean a ed sat yo at mean daily dew pt temp PET Selection criteria ET Prediction Methods Pan evaporation coef cient E pK Time scale transient daily weekly monthly season annual Data available sometimes data can be estimated ABE 6254 ET Pred ct on Se ectmn cmena meaavappwman a Use mm 5mm Nm smugquot 9232003 Mam Transpwranon amp gmwlh yexauunsmps mm aw Vdahanwps 7 cmP phenmagv mm m but an my grass Crop Canopy Curves ABE 6254 9232003 LeafArea ndex Crop Pheno ogy Root D stnbut on ABE 6254 9232003 Mo sture Stress Seasona Crop Coef c ents ABE 6254 9232003 80H Evaporatwon Auauabuuy Menevgy at sun su ace gnadmg hvp amcanapv Ah ny mm m tvanspun Watev uepenuem an sum Pvapemes 80H Evaporatwon Actua ET Ca m atmn mamas Jammy M um yummle ABE 6254 Adua ET Ca cu auon Methods mm 1972 7 evapmsl mn ana pmerma uansmmn as a mndmn mm 2 am sm wmer s hrmmv sm evavammn amasasas a mam amme Adua ET Ca cu auon Methods mm 19 amamansawanan sca cmmed as a mndmn MUN my amme v atevsuPPWm yams Adam tmnspwmmn 5 Emma as a mndmn amma a evmv ev m may mmem m sm 5 reached 9232003 ABE 6254 9232003 ABE 6254 9122003 In ltration In ltration Key component in runoffgenerating process Determines the flow path of water surface or subsurface Determines the amount and time distribution of rainfall excess available for ponding and runoff In ltration Three general cases rainfall rate lt saturated conductivity no runoffifsoil is deep and homogeneous rainfall rate gt saturated conductivity but lt in ltration capacity pending will occur if rain continues inde nitely rainfall rate gt infiltration capacity water is available for runoff ABE 6254 9122003 nmtrahon GenevaHyUeated as mmensmna smc yhue mm m PM W H H W datum at sum2 and 1 mm dav mv avd ABE 6254 9122003 nmtrahon amen n v mm missmsmmsmm a Mum cumbmmgywe ds m a an ax ww xmiyimmmm gangWmammm MW Capmanty F1 5mm 1 3 Ian Capmanty Bmaks and my wan ABE 6254 9122003 Hysteresis 5m 3 5 2 Arvin 39i E Iu JIJIJ mulling an FI u I I 1n 1 M3 A39 h39n39iwlxlun D mum I Fetter 1994 Hysteresis Drama i melbitlon Figurw 25 59mm mamas Corey 1994 Hysteresis ABE 6254 9122003 nmtratmn Vanab a as a as 33W a n sun v ahev mmsm a1 m and e ave maHeHhan m h and c nmtrahon Faduvs a emmg mmva un sm pmpemes hv muhc mummy K h espmmm mm mgmmg Mm pmpmmmw m m mmmmmm ABE 6254 9122003 nmtrahon Famuvs a edmg mmuanun mma my mmem Km mm WWW rm Mmquot hawemrmesvmmv m 015 s a mum mbe up nmtrahon Famuvs a edmg mmuanun R 3 K mum pr mm m mxw mummy w mu m 5mm seahng and emsmg rm Kamv ABE 6254 amp nmtrahon Famuvs a edmg mmuanun Lavered saus Imacluhau wnu mlwnvvmnmmulhmn 9122003 ABE 6254 9162003 In ltration In ltration Movement and entrapment of soil air Movement of air neglected by Richards eq air can be trapped by in ltrating water causes air pressure buil u i ance of wetting 39ont and reduction of in ltration rate may happen even in deep pro les some entrapment occurs even without air pressure buildup usually in larger pores In ltration Movement and entrapment of soil air many researchers have studied air movem 39 soil 2 phase flow system nonlinear partial differential equations are presented in te one xperimen aximum in ltration rate only 32 ofthat predicted from single phase owt eo macropores may act to offset this effect especially if open to surfac ABE 6254 9162003 nmtrahon Mode s Physwca yrbased Rmhams 2mm mm m humming mm mm mpmmauy mm mmm made s nmtrahon Mode s PhyswcaHyrbased thp mm m 957 r i 9 5 mm was mas M8 is regmanmexm m gtcK ABE 6254 9162003 Infiltration Models Physicallybased Green and Ampt model V FD Assumes sharp wetting front Y I wet soil I Applying Darcy s Law 9 e j 44KHU77SJ7LFLF 99 dry soil From continuity Fe9LFMLF Green and Ampt model Combining continuity and Darcy s Law V D I wet soil I H e 0 if shallow pending LBes J HF l39 KMS resultsm K 5 V I 9 9 I f 5 F dry sail Green and Ampt model Extended by Mein amp Larson for non ponded steady rainfall priorto ponding fR and FRA1 at time ofponding fRKS K MSf cumulative in ltration at time of ponding SIM R K 71 Fp ABE 6254 9162003 Green and Ampt model Time of ponding can be found for steady rainfall F rp R At times after ponding begins KMS s Green and Ampt model Has been applied to intermittent rainfall with good results provided time period between rainfall events is not large Extended for use in multilayered soils by several researchers Infiltration Models Empirical models Horton equation 1939 a a ltfi new get parameter values 39om experimental data his rationale for decrease over t39me a swelling ofcollolds and closing of Cracks 7 wasning orrine materlals into surface pores a rall l packing orl scil surface ABE 6254 nmtrahon Mode s Empmca mm s Hmnequamn 1939 9162003 nmtrahon Mode s Empmca mude s 4mm mm 1939 dr 7 1 74411 2 a ev megvmmn Iram 17quot xx Fmxm m a m Wneve v e ewrvaenHUessthan B ua men Next and mnemnmng sum9mm mam ave anthe 1 curve ms R lt 1 nmtrahon Mode s Empmca mudg s Hmnequamn 1939 ABE 6254 9162003 nmtrahon Mode s Empmca mudg s Hanan eqummn 1971 139 s a sum W e mmmmmmmm Wm SA 55 Gamma amquot M W sArmmoEm gsm use Ourmn mmquot nmtrahon Mode s Empmca mudg s s mam F 8C8 Method 3 0 0 Q 6 W d o h ABE 6254 9262003 UserFriendliness and New Tools UserFriendliness Interactive input approach part of model or separate preprocessor program questions and answers or multiple choice responses informative error responses that provide choices to resolve problems update an existing data input le with minimum effort UserFriendliness Interactive input approach complete check of all inputs default values Menudriven operation to prepare data run model plot output etc Graphic output screen or hard copy ABE 6254 9262003 New Tools Geographic Information Systems GIS spatial data structure set of relations to describe a geographical en ity computerized data base by cells land descriptor information rginia example computerized retrieval of this data facilitates model inpu New Tools Expert Systems use a model of expert reasoning and mimic human experts in solving problems depends on knowledge base heuristics rules of thumb production rules conditionconclusion pairs IFTHEN reasoning logic New Tools Expert Systems potential uses estimate model parameters provide data input restrict model options to those suitable for pa 39 blem interpret model results in context of problem using rulebased knowled study sensitivity of a decision to changes in 39n uts model parameters or user I ABE 6254 9262003 New Tools Decision Support Systems may combine GIS and expert systems concepts with models to aid in decision aking user interface to facilitate formulating information for planning or decisionmaking visual display of results from userselected choices ABE 6254 Erosion Processes Factors affecting Channel Processes Inflow from upstream areas ow and sediment are both important qS gt TE gt deposition occurs qS lt TE gt erosion occurs erosion may continue after upland sediment is cut off due to erosion of previously deposited sediment in channel Soil erodibility sediment size compaction etc Factors affecting Channel P ro ce sses Soil transportability aggregates or primary particles size Tillage Nonerodible layer tillage pan rock channel armoring by selective erosion Cover vegetation induces deposition 1072003 ABE 6254 1072003 Factors affecting Channel Processes Channel control raised field boundary or culvert slows ow and reduces capacity control below outlet grade may cause headcut and intense erosion 1 L quot Factors affecting Channel Processes Channel sidewall stability sloughing ow undercuts or moisture buildup may clean out or become stable depending on transport capacity Channel alignment intense erosion on outside of bends high shear stresses Governing Equations Continuity a a o q5 p5 y DMD 6X at H4 HP untributnns buildupurl ss sturagerate frumlateralinfluw Wim distanEE Wimmriuw depth neglects dispersion terms if assume quasisteady state 5DD ax ABE 6254 Governing Equations Relation between deposition and sediment load 06Tc 7 as 05Vs where am 151 orderdeposltlon coefficient 0 Va 1 where DE rill erosion detachment capacity rate Governing Equations Solved numerically since no general solution eXIsts Analytical solution is possible for some cases Variables ow depth from hydrologic analysis interrill delivery rate n39ll erosion detachment capacity transport capacity Modeling Approaches Empirical USLE and modi ed forms lumped in time and space based on 10000 plotyrs of data under natural rain and 1000 plotyrs under rain simulato A KLSC V schmeier and Smith lumps interrill and rill erosion regression equation with nonhomogeneous units 1072003 ABE 6254 Modeling Approaches Empirical USLE limitations not intended for single storm events for average annual soil loss doesn t estimate deposition doesn t estimate gully or channel erosion USLE x delivery ratio gives crude estimate of sediment yield not accurate on small areas Modeling Approaches Empirical Modi ed USLE estimates sediment yield from single storm events RW 905V Op 6 deposition segregates particle sizes due to fall velocities MUSLE doesn t account for this Physicallybased Models Advantages more accurately extrapolated n39ll and inten39ill processes separated more accurate for single storm events consider more complex areas consider deposition processes directly consider channel erosion and deposition 1072003 ABE 6254 1072003 Physicallybased Models nterri detachment D 00138Ki 296sin 0 79 056c Transport hydraulics for flow and drops T0 Aim rm 0 Ri Ils detachment capacity DC 31 710b Physicallybased Models Rills total ri erosion over Length x qm ay 7 8 smzKC2 when simplified Dm 057ch5 and TC asilqgt4 modified by expressions to account for various cover and management affects Physicallybased Models Channel transport same basic equations as for n39lls Impoundment processes tile outlet terraces sediment basins particles lost from impoundment volume in 3 ways out discharge pipe to bottom by in ltration to bottom by settling Overview of Models 0 CREAMSWT ACRUZOOO GLEAMS AGNPS 0 Opus 0 ANSWERSZOOO FHANTM SWAT2000 ADAPT WAM EAAMOD Content of Overview Overview of each model will include Brief model history Scope and objectives Summary of process components CREAMSWT Developed by USDA ARS for use by action agencies in 1980 and modified at UP for use in highwater table regions Assembled from currently available information by a large research team General exible model to evaluate relative BMP effects on water quality Intended to be simple yet remain physically descriptive CREAMSWT cont Continuous field scale daily time step original model can use either daily or bdaily Physicallybased with some empirical relationships Daily monthly and annual output summaries available Simulates runoff ET ercolation water table depth N P pes icide transport and management practices CREAMSWT cont UF modifications to original CREAMS modi ed 808 ON procedure ability to simulate water table in the root zone and below the root zone modi ed P algorithms to better represent low buffering capacity of atwoods soils GLEAMS Developed by USDAARS beginning in mid80s with enhancements to CREAMS to evaluate effects of management systems on agricultural chemical movement within and through the root zone Continuous field scale daily time step GLEAMS cont Modifications to CREAMS vertical ux of pesticides programs combined into one interactive program for hydrology erosion and emicals sediment particle characteristic calculation is changed improved nutrient cycling and transport Opus Developed by USDAARS in late 1980s in an effort to improve CREAMS Borrows some components from CREAMS EPIC SWRRB MUSLE and others Purpose is to study effects of weather and management inputs on water and pollutant movement in small watersheds Opus cont Continuous field scale daily time step with an option for detailed storm event simulation More detailed treatment of field topography and shape than GLEAMS Physicallybased with some empirical relationships Event daily monthly and annual output summaries are available Opuscont Simulates weather infiltration runoff ET subsurface drainage percolation watertable depth erosion crop growth agricultural management nutrient cycling and transport and pesticide fate FHANTM Modification of DRAINMOD developed at UF in early 90s to include runoff routing and P movement DRAINMOD developed at NC State in mid 70s for design and analysis of watertable management systems in high water table soils FHANTM cont Continuous field scale simulates water balance on hourly basis from hourly rainfall Physicallybased with simplifications Daily monthly and annual outputs Simulates infiltration runoff ET surface and subsurface drainage subirrigation watertable depth plant water stress and N amp P movement FHANTM cont Modifications to DRAINMOD N and P cycling routines from GLEAMS subsurface nutrient routing Surface runoff routing Simulate other management practices related to pollutant loading ADAPT Extension of GLEAMS developed at Ohio State in late 80s early 90s using algorithms from DRAINMOD to simulate profile drainage and subirrigation Additional options were added for ET infiltration snowmelt amp macropore flow Current version includes nutrient cycling and transport based on GLEAMS ADAPT cont Includes simulation of processes below the root zone where fluctuating water tables subsurface drainage and deep seepage may occur Continuous field scale daily time step for most processes Daily monthly and annual outputs are available EAAMOD Developed at UF in mid 90s to simulate P loadings from the organic soils and underlying marl rock of the Everglades Agricultural Area Emphasizes the effects of management practices in reducing P losses from fields with layered soils and a shallow water table EAAMOD cont Continuous field scale also a farm version available variable time step Uses grid cells with a finite difference numerical solution technique using a maximum one hour time step Simulates hydraulics of water ow water table depth vertical and lateral seepage and P cycling and transport EAAMOD cont Uses a V ndowsbased user input interface Detailed time series daily summary and full simulation period outputs are available in both tables and graphs Hem Seawe Mmva Subsunace Vwew AC RU2000 Developed in South Africa in the early 80s for hydrologic studies of water availability Components have been added to represent the complete hydrologic cycle Code recently completely rewritten in objectoriented Java ACRU2000 cont Multipurpose physical model with some empirical relationships Continuous watershed scale daily time step homogeneous cells of any shape Simulates surface runoff ET irrigation demand percolation crop yield sediment and N and P cycling and transport ACRU2000 cont Channel related processes include stream water routing base ow riparian zone effects irrigation water extractions and return ows and in stream dams Recent UF additions Simulate shallow water table and upliux Modi ed P adsorption and moisture response ions for nutrient transforma ions Multidirectional surface and subsurface ow AGNPS Developed jointly by USDAARS MPCA USDASCS and Minnesota SWCB in mid 80s to address quality of runoff from agricultural watersheds Goal was exible easytouse model with primary emphasis on sediment and nutrients Modified in late 90s by NRCS and ARS team to include continuous simulation AGN PS cont Continuous watershed scale daily time step homogeneous cells of any shape Simulates surface runoff ET percolation lateral groundwater movement sediment N P organic carbon and pesticide transport on daily basis Evaluates feedlots amp other point sources nonpoint BMPs amp resulting runoff quality AGN PS cont Simulates flow through drainage channel network and can output at node points within the watershed network Simulates stream channel hydraulics basic nutrient and pesticide transport sediment transport channel erosion and instream impoundments AGN PS cont Uses V ndowsbased input interface and provides easyreading interactivelygenerated output tables Newest version has an ArcView interface Eventbased and source accounting outputs over the simulation period ANSWERSZOOO Original version developed at Purdue University in early 80s for planning and evaluating strategies for controlling erosion and sediment movement in agricultural watersheds Modified for continuous simulation with N and P cycling and transport at Virginia Tech in early 90s ANSWERSZOOO cont Distributed parameter continuous watershed scale gridcell based cells 041 ha up to 3000 ha watershed daily time step 30 seconds during events Simulates infiltration runoff percolation crop growth ET sediment detachment and transport and N and P cycling and transport ANSWERSZOOO cont Uses an ArcViewbased user interface called Questions for inputs and graphical output Physicallybased simulates flow through some structural BMPs andthe drainage channel network and can output at cells within the watershed Developed as a planning tool for use on ungaged watersheds SWATZOOO Developed by the USDAARS in the early 90s as a merging of the SWRRB and ROTO mode s Borrows components from CREAMS GLEAMS EPIC MUSLE QUAL2E and SWMM models SWAT2000 cont Developed to predict the impact of land management practices on water sediment and agricultural chemicals in large complex watersheds Has been integrated into the US EPA s BASINS modeling framework Can use an ArcViewbased user interface called AVSWAT for inputs and ou u SWAT2000 cont Continuous watershed scale daily or subdaily time step subbasin cells of any shape that can be composed of multiple HRUs Simulates surface runoff ET percolation lateral groundwater movement plant growth sediment N P and pesticide cycling and transport SWAT2000 cont Simulates chlorophyll a CBOD and DO contributions to streams by surface runoff buildup and washoff from urban areas lnstream processes include hydraulic routing nutrient cycling and transport pesticide and sediment transport algal growth and CBODDOalgal interaction WAM A GIS based tool for determining the spatial influence of landuse and soil on hydrology and water quality at a watershed scale lnitially called WAM Watershed Assessment Model when it utilized ARCINFO grid coverages Sometimes called WAMView as it uses ArcViewshapefiles and grids WAM cont Utilizes embedded models such as GLEA S EAAMOD urban and wetland submodels Capable of assessing the use of Stormwater Treatment Areas STAs Reservoirassisted STAs Stormwater management BMPs Nonstructural BMPs WAM cont Simulates particulate and soluble P and N suspended solids BOD and is also capable of simulating culvert and weir crest elevation as a function of time Has been extensively used by water management agencies eg SFVVMD ABE 6254 1032003 Erosion Processes Erosion and Sedimentation Concepts Detachment dislodging of soil particles by erosive agents drop impact and flowing water exceed soil resistance to erosion Transportation entrainment and movement of sediment from original location by splash and ow Erosion and Sedimentation Concepts Deposition sedimentation release of particles from suspension temporary or permanent when load exceeds capacity ABE 6254 1032003 Major Problems of Erosion Reduces productivity of cropland Degrades water quality May carry adsorbed polluting chemicals Deposition reduces capacity of structures and requires costly removal canals streams reservoirs estuaries harbors etc Sources Exposed or disturbed areas Classified by type sheet stream channel landslide Sinks Areas of reduced transport capacity toe of concave slopes vegetative strips ood plains reservoirs Delivery ratio lt 1 due to sinks ABE 6254 1032003 Driving Forces Hydrology rainfall energy runoff patterns and rates channel ow overland ow drives sheet interrill and rill erosion upland er ion channel ow gully and stream channel erosion Driving Forces Modeling decision of rill or channel erosion is dependent on relative watershed size and sediment load rill erosion is dependent on interrill erosion if qS gt TE 2 deposition occurs if qS lt TE and ow exceeds soil resistance 2 erosion occurs Erosion Sedimentation Characteristics of small watersheds simple land pro le critical area typical farm field variable slope L and 3 cover and management vary ow collects and concentrates serious erosion ridges or vegetation on edges cause deposition may also be caused by culvert ow control ABE 5254 Bowen 7 Sed m entat on 42m m stmduva mamas arwalemi ls He am terrace mm mmquot m depth mm m m WWW cammexmgy h a m an mm ms was mm and mm mm wuym mm mm mam Mm ms emde w new Eros on Process nteract ons E Eros on Hymmugy Tupugvaphy gape englh steepness shape madmes energV ABE 6254 1032003 Factors affecting U pland Erosion Soil erodibility detachment by impact and ow may change overtime soil structure and organic matter affect erodibility Soil transportability particle size and aggregation speci c gravity 18 26 Factors affecting Upland Erosion Cover canopy mulch residue and dense close growing vegetation reduce impact and induce deposition Incorporated residue builds organic matter coarse material blocks rill development Factors affecting Upland Erosion Residual land use previous years use affect structure and root network Subsurface effects roots hold soil and increase in ltration Tillage detaches aggregates ABE 6254 1032003 Factors affecting Upland Erosion Roughness increases deposition and decreases transport until lled Tillage marks contour effects breaks through ridges cause n39ll erosion ABE 6254 9192003 Surface Runoff Hydrographs and Routing Lecture Overview De ne unit hydrographs and their utility in watershed modeling Methods of synthesizing hydrographs when eld data is unavailable Methods of routing surface runoff and channel ow Unit Hydrograph Runoff hydrograph that occurs for a unit depth of runoff over any specific time period A watershed has a different unit hydrograph for each possible storm duration Application of unit hydrograph to other runoff depths is found by multiplying runoff amount by unit hydrograph ordinates ABE 6254 9192003 um Hydrographs for Same Watershed AnzazAnzb Am Umt Hydrograph ksumpuuns mu 5 mm ummm mnstam 7 Ramersle s cansam vu ume maven mrmquot Hwyagyaph ve e s an thsme mam evmms a he hasn Cumpusne am Um Hydmgvaph ABE 6254 9192003 808 Synthetic Hydrograph f Raimaii mess L06TC 0 TP Au Tb 2677 167AQ q Tp Synthetic Composite Hydrograph 8 a 8 0 Discharge cu mlsec o 0 2 4 6810121416 Time hr Time of Concentration n77 4325 channel flow T 002L st 467 22nL T 0 overland flow 5 combined flow system 467 TC 002LC 773 3 ABE 6254 Runoff Routmg CummunyEq VOASAt LmeavR eseNuw KO P 7 940417970 4m nstamaneausmp MAMquot 9192003 cascaded Reservuws RunoffRouUng Reservuw R ummg 5 0 15m 0 in a estwnafe 07 ABE 6254 9192003 Runoff Routing Stream Routing variable sections stage vs storage changes wedge storage may be important SZI 902 II I Runoff Routing Stream Routing Muskingum method S pIism storage Wedge storage Runoff Routing Stream Routin Muskingum method Xis a weighting factor 0 X 05 K is a time constant quot wave travel time through stream reach ABE 6254 9192003 Runoff Routmg no 3 mm ABE 6254 1012003 Subsurface Flow Subsurface Flow Models Choice depends on processes to be simulated Base ow only Percolation redistribution and baseflow 2 Choioe depends on detail required Empirical Models Approach divide water into 1 or more storage zones or reservoirs based on physical layer thickness or flow regime amounts tracked by water balance or accounting equations 0 0H AteoAt ABE 5254 Empmca Modem Appmach spymgsmx a xrmupw 5 4212mm pavametev mm W degvaph mm sepamn ABE 5254 Modehng of Subsurface HOW Are We wanted m 1 2 m3 mmensmns7 The Rea Wo d 5 3D Approwmate as 27D ABE 5254 Approxwmate as 1D pseudo 27D Approxwmate PhyswcaHy Based Mode s Dupun Appmmmn sunaue We may swans Shine 5 man mmms be mama mm Dupmt Approwmahon 555254 Devw2d mm Davcy s Law Dupmt Approwmahon PhyswcaHyrbased Modem Pmcesses m Wage mmmmn mm W mm Wm 42mg Ween mm we WM ABE 6254 1012003 Physicallybased Models Processes subsurface drainage preferential ow paths 39actures solution cavities surface water ground water interactions streams discharge areas springs seeps etc Physicallybased Models Processes subsidence ground waterquality chemical reactions mass transport An Analytical Solution to the LaPlace Equation 0 T th 1962 Medium is homogenous Assumed the water table has a linear slope epth of the basin is onehalf the flow ength l Base of basin is an impermeable boundary Sides of basin are no flow boundaries 1012003 ABE 5254 n LaP ace Equauon mm 1952 To th s So uhon ABE 6254 Model Calibration and Testing 1 0142003 Calibration Process of adjusting model parameters toachev Calibration Select set of criteria for determining model accuracy provides a basis for adjusting process parameters goals minimize error differences between recorded output and simulated output keep parameter estimates consistent with watershed characteristics when possible ABE 6254 1 0142003 Calibration Evaluation approaches use subjective judgment of adequacy use quantitative criterion of accuracy eg 0A Flaw QWYI mlmleEd use multi objective function combining several measures Calibration Three approaches can be used trial and error adjustments based on pattern recognition and knowledge of processes automatic adjustments with programming internal in model check accuracy criterion adjust based on de ned procedure Calibration Three approaches can be used cont combination ofjudgment search to reach nearoptimal and systematic search to refine and nalize 555254 Mathemat ca Mode Concept Cahbrann cahmauun batten but mus1 5m da a m vemmauun epsndsanmade emvmc vhvsmuv ma emm m was uepe dsanv elevshed mm m w dry m m WWW m Cahbrann cahmanuns u same mudg m a Watevshed 4m peak awnaws mu may mmem mm vmume c 5 manenaandca hmes dmeren Pammetevs Cahbvatev a ues w my sensmvmy physwcamew data ABE 6254 Calibration Practical Approach 1 match to annual ow volume match to seasonal ow volumes match to weekly and daily volumes match hydrograph characteristics shape peak time to peak check for reasonable values of other major storages parameters and component outputs wa O1 1 0142003 Calibration Sources of error in simulating watershed respons bias in output due to incomplete or biased model structure random and systematic errors in input data eg rain used to represent conditions in time and space overwatershed random and systematic errors in measured output eg ow moisture content stage Testing Verification use of calibrated model to simulate data set not used in calibration eg 35 years immediately following calibration period examine goodness of fit criteria to determine if hydrologic estimates residual error achieved by cal brated model are acceptable ABE 6254 10142003 Testing Verification user needs to know expected error distribution of possible errors consequences of using erroneous information prospects for improving estimates Testing Sensitivity Analysis vary selected parameters individually through expected range of values and compare range of output values from each input variable sensitivity rate of change in one factor with respect to change in another factor S g where R is result P is parameter Testing Sensitivity Analysis relative sensitivity allows comparison across parameters and results of varying magnitudes


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