The primary objective of this project was to complete development of
an arctic hydrologic model and to evaluate its usefulness in generating
information useful for a design tool in estimation of peak flow discharges.
The peak flow discharges studied were those generally analyzed and evaluated
in the design of facilities for stream crossings.The work upon which this report is based was supported by funds
(Project B-021 ALAS) provided by the United States Department of the
Interior, Office of Water Resources Research, as authorized by Water
Resources Research Act of 1964, Public Law 88-379, as amended

Carlson, Robert F.

Fox, Patricia

English

ScholarWorks@UA

Report No. IWR-55FLOOD FREQUENCY ESTIMATIONIN NDRTHERN SPARSE DATA REGIONSComp1et ion Repo rt.OWRR Project B-021 ALASRobert F. Carlson'Di rectorInstitute of Water ResourcesUni versity of AlaskaandPatricia FoxStatistical AnalystInstitute of Water ResourcesUniversity of AlaskaINSTITUTE OF WATER RESOURCES·Uni ve rs ity of AlaskaFairbanks, AlaskaJuly, 1974ACKNOVJLEDGEI1ENTSThe work upon which this report is based was supported by funds(Project B-021 ALAS; provided by the United States Department of theInterior, Office of Water Resources Research, as authorized by WaterResources Resealuh Act of 1964, Public Law 88-379, as amended.TABLE OF CONTENTSpageAC~10WLEDGEMENTS iiINTRODUCTION 1OBJECTIVES • 3RESEARCH RESULTS 5DECISION FLOW DIAGRM1 (illustration) 10DISSEI-HNATION OF RESEARCH. . • • • • 11r1ethods of Peak Flo!'1 Determination in Sparse DataRegi ons . • . . . • • • . . • • • • • • • • • • • 11Comparison of Current 1'1ethods for Peak F10\1 Determin-ation in Sparse Data Regions. 11A Northern Snol'lme lt Mode 1 12A Northern Snowmelt-Flood Frequency Model 13REFERENCES • • . • . . • • • •.• • • • • ·0 • • 15INT RODUCTI ONThe northern regi ons of the \·/orl d are presently a focal poi nt fori nternat ional i nte rest due to the trerr:endous resource development nowunderway. Hith Itenergy crisis lJ an everyday issue in the h'orld press,the 0; 1 and gas pi pel ines of the north offer important hope for sol utions.What we are no\'/ seeing is only the beginning of an explosion of resourcedevelopment ~ a development which \",ill be highly dependent on \'Jater resourceinformation.Flood regimes of northern regions are complicated by the extremeclimate and by ice formation. Construction of the many structures relatedto resource extraction ~ pi pel ines) roads, dams) reservoi rs, etc., makesit imperative that flood frequency information be available for designconsiderations. HOvJever, Alaska is characterized by a limited data base,which leads to questionable design flood estimates if standard estimationmethods are used.The necessary approach to flood frequency estimation in a regioncharacterized by a sparse data network and predominantly ice or snowmelt-caused floods, should have few parameters ltJhich require estimation. Thereshould be a strong physical basis for the method and it should be a techniquewhich depends on input with a high degree of area homogeneity. Thesecharacteristics will strengthen the validity of flood design estimates innorthern regi ons.Hhen devel opi ng a new techni que, such as new flood frequency desi gndetermination, it is important to examine older techniques. By evaluatingthese older techniques, several advantages can be incorporated in the newmethod. Determination of problem areas can lead to their solution in- thenew method. Older techniques can also be used for comparison with the newmethod or as a basis for further use of all o,r part of the older technique.Thi s report describes a se ri es of studi es di rected to\1a I'd the exami nat i onand development of better methods for flood frequency design in northernsparse data regions. The salient features of the overall work are describedin this paper. Details are reported in two technical reports and two journalpapers which are abstracted in a later section and are' listed below:r. t~ethods of peak-flow determination in sparse data regions (1974).Carlson, Fox and Shrader.II. A northern snowmelt model (1974), tlcDougall and Carlson.III. Comparison of current methods for peak flow determination insparse data regions (1974). Fox and Carlson.IV. A northern-snowmelt flood frequency model (1974). Carlson,McDougall and Fox.2•OBJECT! VESThe primary objective of this project was to complete development ofan arctic hydrologic model and to evaluate its usefulness in generatinginformation useful for a design tool in estimation of peak flow discharges.The peak flow discharges studied were those generally analyzed and evaluatedin the design of facilities for stream crossings. This overall objectivewas broken into four sub-objectives:1. Evaluation of existing methods of design which are used toprovi de peak f1 ow i nformati on.2. The selection of suitable test sites for testing the validityof the model.3. Furtller development of a northern hydrologi c model.4. Comparison of the model-generated information vlith informationgenerated by usual methods.RESEARCH RESULTSThe objecti ves 1i sted for thi s project were worked on concurrentlythroughout the research period. However, the order of development canbe traced as a series of steps.The first objective was satisfied by a preliminary investigationof the methods used in flood frequency determination. In Report I thehistorical development and current flood ~rediction methods are discussed.This phase of the project also encompassed an investigation of the physio-graphy and availability of data in Alaska. The results of this preliminaryinvestigation demonstrated the sparseness of the data in Alaska, identifiedhomogeneous physiographic regions and led to the selection of three methodsof flood frequency determination for further analysis. These three methods,Graphical, Log Pearson Type III, and Regional Multiple Regression, wereselected on the basis of:1. Applicability to a sparse data region2. Ease of calculation3. Superficial satisfaction of underlying assumptions4. General acceptance and/or previous use in Al askaDuring the initial phase of the project, \'Iork was also underway onthe second. obj~ctive. The Chena River was chosen as the site to be used forapplication of the model. The criteria used for selection of the test sitewere mostly related to the available hydrologic data. Since snowmelt andicemelt have been determined to be major factors in flooding in northernregions, one of the requi rements for the test site was the avail abil ity ofsnowcourse records in the basin. Flow gage records and a first class weatherstation within the basin were necessary requi rements. A suitable reCordlength (> 10 years) was desirable. Having restricted our consideration tothe basins along the pipeline corridor, the basin which satisfied theseneeds was the Chena River. The weather station and gage records at Fairbankswere used in the study, and snowcourse data was available.During this time, ten other basins along the pipeline corridor weresubsequently selected for consideration of the three methods chosen earlierfor further study. TIlese rivers were selected in order to compare resultsfor vari ous record lengths, regi ons, and bas in cha racteri sti cs . Un fortun-ately, the Arctic Coastal Plain had no rivers with sufficient record lengthto merit the comparisons. The ten rivers chosen, in addition to the ChenaRiver, were:1. Chandalar River2. Copper River3. Koyukuk Ri ver4. Sal cha Ri ver5. Gakona Ri ver6. Kl uti na Ri ve r7. Porcupine River8. Tazlina River9. Tonsina River10. Yukon RiverThe third objective was satisfied in two major steps. A snowmeltmodel, previously developed for arctic conditions, was generalized andrewritten to apply to northern envi ronments (Report II). This model,a modification of work done by Amarocho and Espildora (1966), requiresdaily \~eather input from a fi rst class weather station within the basinand initial conditions of the pack prior to the snowmelt period. Using6•heat flow calculations into and out of the snowpack derived from the inputand drive parameters, the model outputs the daily snowmelt and snowpackconditions for the enti re snowmelt period. The major assumptions of themodel are:1. Rainfall is insignificant during the snowmelt period.2. A weather station is available within the basin (or atleast nearby) \vhich records the needed dri ving vari ab1es.3. Continuous records are avail able for the snowmelt period.The snowmelt model was applied to the Chena River basin using dailyclimatological data from Fairbanks. The daily model output was comparedwith snow survey data for ten years. The predicted and observed snowpackconditions and snow depth compared favorabiy within elevation bands forthe basin.The second portion of this model, the flood frequency model, viasdeveloped from earlier work reported by Eagleson (1971,1972). It is ahybrid mode1in9 approach which used both stochastic and deterministicrationale. The snowmelt duration and intensity for "snowmelt storms"are fit to exponential distributions. A snO\vmelt storm is defined as thesnovlmelt which occurs from start to end of the snOl'lmelt period. The meltperiod may end due to a temperature drop or other physi cal input whichcauses retention or delay of the melting process. There are essentiallythree components: precipitation model, runoff model, and transformationmodel. The output from the snowmelt model for each elevation band is usedby the precipitation model for development of the statistical parametersfor intensity and duration. Three main assumptions are made in developmentof the relationships:1. Peak stream flows can be predicted using kinetic wave equations.7periods, T .e2. Direct runoff-producing area parameters and storm parametersare independent of each other, and storm parameters areindependent amongst themselves.3. The direct runoff-producing area is a band centered about thestream. Therefore, areally averaged storm snowmelt excessduration is greater than or equal to the combined time ofconcentrati on for the bas in and di rect stream runoff areas.Input required for the flood frequency model: snowmelt series, basindescriptors, and runoff-producing areas. The final prediction equationis of the formwhereA = area produci ng di rect runoff'X'S = marginal distribution parameter of point snowmelt intensity<t> = fraction of snowmelt occurring as runoffEl = average annual number of independent snowmelt events.The model produces estimates of flood magnitudes, Q , for given returnpSince the model has only been applied to one basin, its applicationrequi res further study. However, the sens iti vi ty ana lysis of its parametersand distributions (Report IV) does allow for easier application to anotherbasin. Some of the lesser developmental assumptions tend to limit modelapplications. For example, one such assumption is that the basin and thestorm patterns are homogeneous, yet, the flood frequency model does permitestimation of peak floods \'Jhen no stream gage records are available.The fourth objective 'lias accomplished by a statistically orientedcomparison of the four methods.of flood frequency determination. First8jjI,!itIIthe properties of the flood series were investigated, such as independenceand time correlation. Due to the small sample sizes, nonparametric anddistribution-free methods were chosen throughout the analysis. (A shortdescription of the statistical properties of the flood series for eachof the eleven basins is included in Report III). This all0\1ed considerationof the developmental assumptions implicit in each of the four methods.Flood series were then split for evaluation of the efficiency, consistency,and power of the methods in relation to each basin. The illustration.(p.10) demonstrates one of the decision trees developed during this sectionof the project. It is based on the availability of data for the analysis.This i.s a major deciding factor for sparse data regions. Further discussionof the degree of con fi dence and bi as of the esti mates can be found inReport III. Under certain basin and record conditions the simpler methodmay be the best estimator despite availability of data for more complexanalysis.Further studies concerning the implications of the degree of errorfor a given design flood estimate would give an even better understandingof which method to utilize for any given situation. It is also expectedthat ~Iith some refinements the model approach will provide very reliableestimates. As the results in Report III describe, it is essential thatthe applicability of the flood determination method be examined before itis used for a major design project. Blanket application of anyone method,for example Log Pearson Type III, will most often lead to poor estimatesdue to the variability between basins.9Decision Flow Diagram fqr Choosing Method for Determinationof Flood Frequency in Sparse Data RegionsUse Log Pearsontype illanalysisUse multipleNO regressiontechniqueYESUse snowmelt-flood frequencymodelNONONOUse graphicalflood frequencymethodYESYESDISSEMINATION OF RESEARCHT\'Io techni ca 1 reports and two journal reports were developed fromthis project:r~ethods of Peak Flow Determination in Sparse Data RegionsThi s report presents materi al prepared in the early stages of theproject and provides useful background information upon which the otherreports were based. Three issues must be understood when embarking ona flood frequency design task: the fundamental basis of the design method,actual execution of the task, and the· data envi ronment. The fi rst portionof this report presents a histOl'y and background of the many types offlood design methods presently available. The methods used by designagencies in Alaska are then reviewed. A presentation describing theAlaskan data and physical environment follows. The last section presentsa summary and overview of the report and its relation to the entire project.Three currently used methods are suggested for further study.[Carlson, Robert F., Patricia r~. Fox, and Stephen D. Shrader, 1974,Institute of Water Resources Report-IWR-52, Uni versity of Al aska,Fairbanks, Alaska, 37 pp.]Comparison of Current Methods for Peak Flow Determination in Sparse DataRegionsFour methods of flood frequency analysis are compared for use inAlaskan watersheds. The graphical frequency analysis, regional multipleregression technique~ and the Log Pearson Type III analysis are comparedfor several basins in Alaska with varying record lengths, basin areas-and physiographic regions. Consideration is given to the use of the -snowmelt-flood frequency model modifi ed for thi s project. The fourmethods are applied and compared for the Chena River basin. The methodsare evaluated on the basis of sensitivity, bias, consistency, power andconfidence estimations using split and enti re data records. A descriptionis also given of the statistical properties of the flood peak data whichis used for a review of the applicability of the assum~tions necessaryfor each method. Criteria for method selection are given on the basisof basin factors.[Fox, Patricia M. and Robert F. Carlson, 1974, Institute of~later Resources, University of AlaskJ, Fairbanks, Alaska.In press. ]A Northern Snovilnelt ModelSnowmelt is a dominant factor in the hydrologic system in Alaska.This report presents a model of the snowmelt process with calibration anddiscussion of applications. Derivation of the model stems from a descriptionof snowmelt physics in the fi rst section. The model is described, followedby a summary of its application to the Chena River basin and other morenorthern basins.The snowmelt model subdivides the snowpack into two layers. Dailycl imatol ogi cal parameters govern the heat transfer between snowpack andatmosphere. Once the heat fl ux emitted or recei ved by the sno\1pack hasbeen computed, the melting processes of each layer are considered. Computedparameters of the snm'lpack are depth, densi ty, water equi val ent,watercontent, temperature and thermal quality. Comparison of model predictionswith snmoJ courses in the Chena River Basin indicate the model performssat i sfactori 1y.12[McDougall ~ J. and Robert Carlson, 1974, Institute of Water ResourcesReport-IWR-54, University of Alaska, Fairbanks, Alaska.]A Northern SnO\'1melt-Flood Frequency ModelCoupl ing the snowmelt model developed earlier \'Jith a flood frequencymodel allo\'Js for determination of flood peaks in regions where snowmeltis the dominant flood source. The model presented in this paper is a hybridtype in that is is both stochastic and deterministic. It was patternedafter Eagleson (1971 and 1972)\'1ith the major modification from considerationof rainfall events to snowmelt events. The peak flm'J statisiticsarerelatea to the statistics of climate and to watershed parameters usingkinemati c \'1ave equati ons for homogenous catchments and storms. A completedevelopment of the model is presented so that differences from Eagleson! swork may be considered. This is followed by a calibration and sensitivityanalysis of the model parameters \'lith respect to the Chena River basin.[Carlson~ Robert, J. McDougall ~ and P. Fox~ Institute of Water'Resources, University of Alaska, Fai rbanks, Al aska. In press.J13REFERENCESAmorocho, J. and B. Espildora (1966). Mathematical simulation of thesnowmelt processes. Department of Water Science and Engineering,University of California, Davis. 165 pp.Eagleson, P.S. (1971). The stochastic kinematic wave. In: SystemsAppr>oach to HydPology . Proceeding of the first bilateral U.S.-Japan Seminar in hydrology. vlater Resources PUblication, Fort Collins,Colorado, pp. 202-225.Eagleson, P.S. (1972). Dynamics of flood frequency. fvater Resources Reseal'chVol. 8 (4), pp. 878-897.15

Flood Frequency Estimation in Northern Sparse Data Regions: Completion Report

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Flood Frequency Estimation in Northern Sparse Data Regions: Completion Report

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