Hydrological Models for Water-Resources System Design and Operation
WORLD METEOROLOGICAL ORGANIZATION OPERATIONAL HYDROLOGY REPORT No. 34
HYDROLOGICAL MODELS FOR WATER-RESOURCES SYSTEM DESIGN AND OPERATION
by A. Becker and P. Serban
WMO-No.740
SECRETARIAT OF THE WORLD METEOROLOGICAL ORGANIZATION - GENEVA - SWITZERLAND 1990 "'-
© 1990, \Yo.r1d Meleo~91iigical Organization ISBN: 92'63-10740-4
NOTE
The designations employed and the presentation-of material in this publication do not imply the expression of any opinion whatsover on the part of the Secreta~t of the World meteorological9rganization cpnceming the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. CONTENTS
FOREWORD v
SUMMARY (English, French, Russian, Spanish) vi
1. INTRODUCTION 1
2. MAiN FIELD OF APPLICATION OF HYDROLOGICAL MODELS 2 2.1 Main categories ofmodel uses 2 2.2 Real-time uses 2 2.3 Predictiou, planning and design 2
3. CLASSIFICATION OF HYDROLOGICAL MODELS 7 3.1 Classification principle 7 3.2 Classification in terms of the type of system and hydrological process or variable...... 9 3.3 Degree ofcausality 11 3.4 Time and space discretization in hydrological modelling 12
4. FUNDAMENTALS IN DETERMINISTIC HYDROLOGICAL MODELLING :...... 16 4.1 Basic structure and requirements ofdeterministic simulation models 16 4.2 Time and space discretization and their dependence on the purpose of model application andinput data availability 17 4.3 . Main processes to be considered in modelling the land phase ofthe hydrological cycle 18 4.4 Special aspects and problems in sub-process modelling ;...... 20 4.5 Groundwater flow models 22 4.6 Flow routeing models for rivers and channels 22 4.7 Composition ofintegrated hydrological models for river basins 24 4.8 Assessing human influences on the natural hydrological regime by means ofmathematical models 26 4.9 Particularities ofreal-time forecasting procedures 29
5. EVALUATION OF THE AVAILABILITY OF HYDROLOGICAL MODELS 33 5.1 Criteria and sources used for evaluating model availability 33 5.2 Hydrological model availability of hydrological models 34 5.3 General evaluation of the availability ofhydrological models 34 5.4 Procedures for model calibration and testing : ;...... 36 5.5 Procedures for real-time forecasting and control ,...... 37 5.6 Procedures for planning and design :...... 37 5.7 Summarizing remarlcs ;...... 38
6. RECENT TRENDS AND DEMANDS IN THE FIELD OFHYDROLOGICAL MODELLING 38 6.1 Developments in computer technology 38 6.2 General problems in software transfer 39 6.3 Existing barriers in hydrological modelling ,...... 39 6.4 The scale problem in hydrology and the need for physically based macroscale hydrological land-surface models 40 6.5 Water-quality modelling :...... 42 6.6 Other aspects 42
7. CONCLUSIONS 43
8. SELECTED REFERENCES 43
iii CONTENTS
ANNEXES...... 47
1 Standardized fonnat for HOMS - component descriptions 48 2 Questionnaire on the characteristics of operational hydrological models 49 3 Models available as HOMS components (12 pages) ~...... 51 4 List of models - RA VI (Europe) - survey (6 pages) 63 5 List of models - RA I (Africa) - survey (1 page) 69 6 List of models - RA II (Asia) - survey (3 pages) 70 7 List of models - RA III (South America) - survey (5 pages) 73 8 Surface water flow and supply model evaluation , ,.... 78 9 Surface water-quality model evaluation 79 10 Groundwater model evaluation...... 80
iv FOREWORD
The Commission for Hydrology (CHy) at its seventh session in 1984 appointed Dr A. Becker as Rapporteur on Hydrological Models and requested him to prepare a report on the availability and use of hydrological models in water resources system design and operation, in particular in·relation to the development of the Hydrological Operational Multipurpose Subprogramme (HOMS). A fIrst draft of this report was presented in 1987.
Simultaneously, Dr P. Serban, the Rapporteur on Operational Hydrological Models of the Working Group on Hydrology of Regional Association VI (Europe), was asked to analyse the replies to a questionnaire on the characteristics of operational hydrological models circulated in the European region, and to present the results of this analysis in a technical report. This report, "Operational Hydrological Models Used in the Region", was issued in 1986.
Recognizing the value of combining these two reports into a comprehensive publication, the two rapporteurs kindly accepted to undertake this task with the assistance of the Secretariat. It is with great pleasure that I express WMO's gratitude to Dr Becker and to Dr Serban for the time and effort they have devoted to the preparation of this valuable publication.
G.O.P.Obasi Secretary-General
v SUMMARY·
The mathematical representation of hydrological processes has a long history, bnt it is only within the last two decades that hydrological models have become snfficiently comprehensive and widely available for them to be accepted as operational tools.
Such models are now used for a variety of purposes, each purpose setting its own requirements. The result has been the development of a whole range of deterministic models exhibiting differences in basic structure, the processes they simulate and the manner in which this simulation is carried out. The principle features identified relate to time and space discretization and the modelling of:
Precipitation and evapotranspiration; Canopy interception and infiltration; Soil water and sub-surface flow formation; Groundwater storage and outflow; Overland and channel flow.
These differences are used as the basis for a comprehensive classification system covering both surface water and groundwater models.
Particular attention is paid to the characteristics ofmodels for use in real-time forecasting.
The great majority of hydrological models are used only within the context of research studies, but many have been successfully introduced into operational practice. An extensive international survey of such operational models is presented, cross-referenced to the classification system introduced earlier.
The field of hydrological modelling is one of great diversity and development. A review ofrecent trends and demands is therefore presented. highlighting developments in computer technology and water-quality and macroscale models, and the barriers to further progress.
The conclusions point to the number of well-developed models that are in operational use while, at the same time, noting the serious gaps in technology that still exist. Hydrological modelling is seen as being a strong growth area in the decades to come.
vi La representation mathematique des processus hydrologiques a une longue hislOire, mais ce· n'est qu'au cours des deux demieres decennies que les modeles hydrologiques sont devenus suffisamment clairs et largement disponibles pour atre consideres comme moyens operationnels.
De tels modeles sont actuellement utilises dans des buts differents, chacun d'eux defmissant ses propres besoins. Par consequent, lOute une serie de modeles deterministes s'est developp6e, soulignantles differences de structure de base, les processus qu'i1s simulent etla maniere dont cette simulation est produite.
Les particularites relevees ont traitala differenciation dans Ie temps et dans l'espace etala modelisation:
• de Ia precipitation et de l'evapotranspiration; • de l'interception et de l'infiltration de la couvertore nuageuse; • de Ia formation d'ecoulement d'eau pres de la surface et de l'ecoulement souterrain; de l'emmagasinement d'eau souterraine et du debit sortant; du ruissellement et de I'ecoulement dans des canaux.
Ces differences constituentla base d'un systeme de classification comprehensible englobant ala fois les modeles d'eau de surface et d'eau souterraine.
Uue attention lOute particuliere est portee aux caracteristiques des modeles a utiliser pour la prevision en temps reel.
La majorite des modeles hydrologiques sont uniquement utilises dans les recherches, mais beaucoup ont ere introduits dans les pratiques en matiere d'hydrologie operationnelle. Une longue etude est presentee avec des renvois au systeme de classification introduit au prealable.
I.e domaine de la modelisation hydrologique est d'une grande diversire et est en train de se developper. Une revue des tendances recentes et de la demande est done presentee, soulignantles progres de la technique informatique, des modeIes pour la qualit6 de l'eau et des modeIes a grande echelle, et les obstacles ralentissant ces progreso
La conclusion indique Ie nombre de modeles bien developpes qui sont operationnels lOut en notant les lacunes importantes qui subsistent encore dans les techniques. La modelisation hydrologique est consideree comme etant un domaine dans lequel de grands progres seront faits au cours des prochaines decennies.
vii PE3IOME
M'lTeMaTII'le ThICHe MO,l(eJlH ceiiqac HClIOJlb3ylOTCH )(JIH MHOrnx l.\eJIeii. ICaJKJlaH - H3 ICOTOpLIX npe)('bHBJlHeT CIIOH TpeOollaHHH Pe3YJlbTaTOM CfaJIO pa3BHTHe l.\eJIOrO pH)(a ,l(eTepMHHHCfCICHX MO,l(eJIeii. pa3JIHqaIOIllHXCH CTpyICTypoii. npol.\eccaMH ICOTOpLle OUK BOCnpoH3I1O.IlBT. H cnoroOoM HX MO,l(eJIHPOBllJIHH. K OClIOBHLIM XapaKrepHCfHICaM· OTHOCHTCH BpeMellHaH H lIPOCTpallCfOOllllllJl ,/\IICKpeTlI3aUHH H MO,l(eJIHpoBaUKe: o OCa,lUWB H aBall0TJlauCllHPaI.\HH; o 33,llep:llCallHH oca)(KOB paCfHTeJIbHLIM nOICpoBOM H HH4>HJlbTpalIHH; o BJlarK H no_ H '!>OpMHpoBaHHH nO,l(llOOOpXlIOCfllOro nOTOKa; o IGlKOllJleHHH rpYllTOBLIX 80,1( H OTTOKa; o nOOOpxlIOCfllOro H PYCllOBOro CfOICa. HacTOHIllHe OTJlHqHH HCnOJlb3YlOTCH B ICaqecTBe OCIIOBLI )(JIH pa311OCTOpOlllleii lCJIaccH4>HICal\HOHlIOii CHCTeMLI. OXBaTLlBaIOIlleii MO,l(eJIH K3K nooopXllOCTlILIX. TalC H rpYHTOBLIX BO,I(. OcoOoe BlIHMallHe y,l(eJlHeTCH XapaKTepHCTHlCaM MO,l(eJIeii )(JIH HcnOJlb30BalIHH B nporH03HpoBallHH B pea1IbHOM MaCIIITaoo BpeMeHH. IioJlbIIIHHCfBO rlf.llpoJlorHqOCKHX MO,l(eJIeii HCnOJlb3YeTCH TOJlblCO B XO,l(e HCCJIe)(OBaTeJIb C4>epa rlf.llpoJlOrnqecKoro MO,l(eJIHpoBaIIHH HBIlHeTCH pa3HOCTOpoHIIeii H paaBHHalOIlleiiCH. llooTOMY B I1yOJlHK3.I.\HH H3J1araeTCH oOwp HOBLIX TpellAOB H nOTpeOHOCfeii, OCBelllaIOTCH I1OC1le,I(HHe ,l(OCfH:lKeHHH H ICOMHblOrepuoii TeXJlOJlOrHH, MO,l(eJIHX KaqecTBa BO,I(ll H MaKpoMaCIIITaOHLIX MO,I(eJIHX, olicy:lK)(alOTCH npellHTCTHHH na nyTH IC II/UIbooiiIIIeMY nporpeccy. B BLIBO)(aX OTMeqaeTCH, 'l'O pH)( XOPOiliO paapa60TallllLlX MO,l(eJIeii HClIOJlb3YeTCH B onepaTHHHoii np3KTHKe, B TO :lICe BpeMH B reXHOJlOrHH BCe ellle HMelOTCH cepLe3Hlle npoOJleMll. rlf.llpoJlornqecxoe MO,l(eJIHpoBllJlHe paccMaTpHBaeTCH IGl oOJlaCfb HIITellCHBlIOro pa3BHTHH B OJlH:lKaiiIIIeM ,l(eCHTHJleTHH. viii RESUMEN La representaci6n matematica de los procesos hidrol6gicos tiene una larga historia pero es unicamente en las dos ultimas decadas que los modelos hidrol6gicos han lIegado a ser 10 suficientemente integrales y ampliamente disponibles para ser aceptados como instrumentos de tipo operativo. Tales modelos se usan ahora para una variedad de prop6sitos, cada uno de los cuales tiene sus propios requisitos. EI resultado ha sido el desarrollo de toda una gama de modelos determinfsticos que presentan diferencias en su estructura basica, en los procesos que simuhm y en la manera en que esta simulaci6n se lIeva a cabo. Las principales caracterfsticas identificadas tienen relaci6n con Ia discretizaci6n en el tiempo y el espacio y Ia modelizaci6n de: Precipitacion y evapotranspiraci6n; Intercepci6n por las copas de los arboles e infiltraci6n; Formaci6n de agua superficial y de flujo subsuperficial; • Almacenamiento y descarga de agua subterranea; • Escurrimiento superficial y en los drenajes naturales. Estas diferencias son utilizadas como base para un sistema de clasificaci6n integral que abarca modelos para agua superficial y para agua subterranea. Se presta especial atencion a las caracterfsticas de los modelos para usa en prevision entiempo real. La gran mayorfa de los modelos hidrol6gicos se usan en el contexto de estudios de investigaci6n pero muchos han sido introducidos con exito en la practica operativa. Se presenta una amplia encuesta internacional sobre tales modelos operativos y se comparan con el sistema de c1asificaci6n presentado anteriormente. EI sector de los modelos hidrol6gicos es de gran diversidad y desarrollo. Se presenta por consiguiente una revisi6n de tendencias y demandas recientes, haciendo hincapie en el desarrollo de la tecnologfa de computadoras, modelos para calidad del agua y de macroescala y en los obst3culos para el desarrollo futuro. Las conclusiones se refieren al numero de modelos bien desarrollados que esllln en uso operativo notando, aI mismo tiempo. las serias Iagunas queaUn existen en Ia teenologfa. Los,modelos hidrol6gicos se yen como un sector de fuerte crecimiento en las d6cadas futuras. ix HYDROLOGICAL MODELS FOR WATER·RESOURCE SYSTEM DESIGN AND OPERATION 1. INTRODUCTION (b) The increasing need for investigating the effects of different human influences and impacts on the hydro During the last two decades research in the field of logical regime and on water quality. Reference is made hydrology has been increasingly concerned with the here in particular to land-use changes, climatic variabil development of mathematical models and with their ity and climate changes, and intensified water and application for various purposes such as hydrological land-use practices; forecasting, data extrapolation in time and space, and the (c) The widespread andrapid growth in the availability and prediction and assessment of the effects of human influence use of computer power, in particular of microcomput on the natural hydrological regime. This development is ers, not only by research institutions, water authorities clearly indicated by the increasing number of publications, and Hydrological Services, but also by individuals (the symposia and other events devoted to hydrological "revolution" in computer technology); modelling. However, the more hydrological models were (d) The availability of remotely sensed data on important developed, the more it became obvious that a gap hydrological land surface-related parameters and char existed between theory and practice, that is, between the acteristics, such as temperature, soil moisture, models and their practical application. WMO therefore precipitation, evapotranspiration, snow storage and initiated and organized a series of intercomparison albedo. projects, namely: Some of these developments and the resulting requirements (a) Intercomparison of conceptual models used in opera in hydrological modelling will be considered in later tional hydrological forecasting (WMO, 1975); chapters of this report, the general aims of which have been defmedas: (b) Intercomparison of models of snowmelt runoff (WMO, 1985); and To characterize briefly existing hydrological models in terms of their general function, character, type (c) Simulated real-time intercomparison of hydrological and their availability for the solution of practical models (WMO, 1987). problems; It is also relevant to note that WMO's Hydrological • To point out those fields in hydrology and Operational Multipurpose Subprogramme (HOMS) was water-resource systems management where models initiated in order to facilitate the transfer of hydrological are available for application and other fields where technology, and a number of mathematical models are they are still missing or do not fulfil the requirements; included as components of HOMS. The first edition of the HOMS Reference Manual was published in 1981, the To define criteria for selecting models for specific second in 1988 (WMO, 1988b); supplements are distributed applications. every year. One significant fact should, however, be pointed out All these projects, along with analogous efforts of early in this introductory chapter: No model can stand by other international organizations, in particular Unesco and itself in solving real world problems. Two components are lAHS, improved the possibilities of applying hydrological always required as a prerequisite, the model and adequate models for practical purposes. Nevertheless, the present input data and information. It is evident that models will situation is still characterized on the one hand by "inflation" fail (Le. give inadequate or unreliable results) if the model of mathematical models in hydrology, while on the other inputs do not fulfil basic requirements regarding data relia hand the models and methods needed for the solution of bility, accuracy and representativity, at least to the degree important problems, as indicated under (a) and (b) below, required for solution of the given problem. This is essen are still missing or are unsatisfactory. It can be said that, in tial not only in model applications but also in model general, the development of resources still lags behind the calibration, and it often affects substantially the selection development of needs. and structuring of a model. Important influences on developments in hydrological Thus, in addition to a suitable model, adequate and modelling are: reliable input data and information are also essential. In (a) The demand for more comprehensive and more many cases their collection and appropriate supply for reliable information on hydrological processes and model application need more effort and are more important characteristics and on the availability of water for deriving the desired information and results than the resources (quantity and quality) in space and time on particular structure and level of complexity of the model. the basis of different space and time scalcs (micro-, This aspect will be considered in several sections later in meso-, macroscale; real-time, short-term, long-term); the report. I HYDROLOOICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION .. " - .. 2. MAIN FIELDS OF APPLICATION OF Tasks I, 2 and 3 are listed in hierarchical order. HYDROLOGICAL MODELS Activities under I are always necessary even when no 2.1 Main categories ofmodel nse forecasting and control computations will follow. Forecasting 2 requires activities mentioned under I as Mathematical models have found increasing practical prerequisites, and control activities 3 require 1 and 2. The application in two main categories of problem in role which models play for tasks 1 and 2 in forecasting real opcrational hydrology and water-resource system planning time hydrological systems is illustrated in Figure 1 (Nemec, and management: 1986). This clearly illustrates the observation made at the (a) Real-time forecasting and control; end of the Introduction that input data availability is always (b) Prediction, planning and design. a most important prerequisite for any model application. The demand for applying mathematical models clearly A third category might be considered which would include increases from tasks 1 to tasks 2 and 3. Obviously, activities research problems but may be more generally described as: under 3 are only required and possible in systems where the (c) Other problems. ongoing processes and state conditions can be controlled by This category concerns the use of hydrological models, in existing hydraulic structures. Well-known cases ofsuch appli particular for improving our understanding ofprocesses and cations are flood control; water transport planning and control; our modelling capabilities, and for the estimation of model optimum operation of reservoirs, in particular for hydroelec parameters and similar tasks. tric power production, low-flow augmentation and water The characteristic difference between the first two supply; water pollution control and irrigation system control. categories results primarily from the time horizon under Accordingly, two categories can be distinguished in real-time consideration. Real-time problems are always related to applications ofmathematical models: the present situation, to given existing system structures and to (a) Forecasting without control (FO in Figure 2); and initial conditions, and they are concerned with forecasts (WMO, 1988a) for a few hours up to two days (short-term); (b) Forecasting with control computations (PC in Figure 2). two to ten days (medium-term); months oryears (long-term). Reservoir control illustrates the importance of real Planning and design studies are principally long term, time control computations, because this activity nearly that is, they need long-term predictions of the hydrological always leads to a conflict of interests between different conditions and water-resource availability over time water users. On the one hand is the requirement to keep horizons of 10 to 100 years. They also have to take into reservoirs as empty as possible to ensure a high degree of account the long-term variations of the hydrological flood protection, while on the other hand is the need to keep processes in their stochastic character, as well as different them filled so as to maintain an adequate water supply for or changing system structures and conditions in the users (e.g. power production) during low-flow periods. planning alternatives considered. It has been found that efficient real-time control of 2.2 Real-time uses reservoirs can often result in a remarkable reduction in the amount of water released during water shortages; this can In real-time monitoring and control of hydrological and increase the volume of water retained in storage by an water-resource systems the following general tasks are average of about 20 per cent (Serban, 1986; NERC., 1975). undertaken (Figure I): An efficient real-time forecasting and control system, 1. Collection, primary processing and storage of real-time including all the aforementioned activities (1, 2, 3), can also data, observations and other information on the state eliminate the need for additional investment in a higher conditions and the expected (forecasted) changes in input of reservoir storage capacity ofthe same order. the hydrological systems ofinterest (water stages, discharges, 2.3 Prediction, planning and design water quality, precipitation, snow data, temperature, etc.). Any water-resource project, whether it serves for water 2. Real-time forecasting of hydrological variables (short· supply, flood or environmental protection, energy production, term, medium-term and long-term), in particular river irrigation, navigation or for other purposes, requires some discharges, water levels, water storages, water quality and ice degree of detailed investigation. A general comprehensive conditions. overview ofthe different stages in water-resource system and 3. Real-time control of state conditions and processes in project planning is given in Figure 3 (after Haimes et al., hydrological and water-resource systems, e.g. for 1987). It illustrates well the complexity and the necessary Storage in and release from reservoirs, lakes, ponds, sub-activities in this process, as well as the demand for math reservoir systems and other surface water systems; ematical model applications (the related sub-blocks in Figure 3 are marked by double-lined frames). Water transfers within river basins or from one basin As a result of such factors as the multiple uses of water, to another; the increasing influence of human activities on water Water withdrawals for different purposes; resources, and the complex interrelations and interactions Waste-water treatment and release. to ensure water between the two, the planning process and water management supply for different users, flood and environmental in general are becoming increasingly complex. Moreover, protection and other demands of the society. while the demand for information used to be restricted to a 2 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND~ OPERATION Requiflements (a) Activities Real Time ])ato Collection (b) Data Transmission (c) Primary Data PfIOcessing and Filing ---, I (d) I Historical and Analysis of the Evaluation of Basin Data Actual state Results, ]Jato Files r- Conditions COflflection etc. 1"\ I' (e) , FOflecast f- Opeflafional FOflecast Pflocedufle (Real Time) Evaluation Faflecasting - (Stflucfufle, and 4- Madel Files Including Updating. etc.) Computeflized Contflol . Updating (F) Dissemination of FOflecasts) Wamings, Contflol Insffluctions, Repoflts etc. Figure 1- General structure and components of operational hydrological real-time forecasting systems (developed from Nemec, 1986) 3 ~ Real- time FOflec(Jsfing P"ediction J Planning Qthe.fl Purposes ~ and Contflol - and IJesign § ~ L~ t""' is: 8 ~ ~ Extrapolation P"ediction in ~ £oflecasting .. £o"ecasting Research} Process of Qbser ved View of l:.and .... (wlfhQut Contf'Ob and Contf'Ol Studies etc. ; I I cond(onf Use Changes etc. , FO' I PO I"PLI IDP I ~ ~ I --. I ~ I Sho"t':" Medium Long Cont"ol Rules Structural ! ~ teflm teflm " tef'm Derivation Planning o PC PD ~ ~ Figure 2 - Main pU!pOses ofusing hydrological models HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION stage 1 : statement of need Plan inltiatl on and / prellmlnary contraintsl Goals and planning ~ objecti ves Preliminary formulation ------Stage 2 : Data collection: Data - hydrological collectibn Data - economic and sources: - environmental processI ng ex I sti ng I- - sociological Data and new - structural I---- processing - legal - insti tutionai - organisational ------ Stage 3: Generati on of Formulation a I ternati ves and screening of project alterna Screening model tives formulation and ,"ode I component r----.... / sel ecti on Interaction with: I Methods: - engineers. - - hydrologists. opti mati on - - mulliobjective other 'scl entists analysis - decision makers publl c - hierarchial - analysis - other agencies - etc. Negotiations ~ and conflict -----. resolution Screened set no j of alternatives satisfactor ? yes Selected project End of al ternatives prefeasibillty study G (nex~ page) Figure 3, Part 1 - Stages in the water resources planning process 5 HYDROLOOICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND' OPERATION ~ Poli tical process r abort ----"-- --" -- -- -_. -_.- .__._------_._- -- stage 4: Development Detai 1ed. formulation of final of projects study results lmpact Model building and Risk analysis model analysis assessment (as in Stage 3) Costs, Design parameters Operation benefits, for structures rUles, & risks operation models - End of Selection of feasibility project plans study -_._------ Political process (fund allocation) ------._------.. __._-" -- ---"---- -_.- Stage s· Design Construction Operation Figure 3, Part 2 - Stages in the water resources planning process 6 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION limited number oflocalities and parameters which character Water supply for different users, user groups or ized the hydrological phenomena and regime, numerous categories of user (see Figure 4, centre right part), in elements and parameters are now demanded even for smaller particular for industrial, energy and agricultural rivers and land surface units. Furthermore, many rapid and production, including irrigation; significant changes are taking place in the environment over Waste water treatment and release; large areas as a result ofhydraulic structures, the extensive use ofwater resources, development offorest areas (including de Low flow control; and reforestation), the expansion of agricultural cultivation Storm-water management; over previously bare or forested lands, the development of drainage and irrigation areas, increased use of chemicals in Flood control and protection; agriculture, and the growth ofindustrial complexes and urban Environmental control and protection; ization. These activities are expected to continue in the future. Itcan be stated that human activities have reached a level such Transportation and communication. that the resulting environmental impacts often dominate the This planning process has to be based on the results of natural phenomena. Under these circumstances, significant the "first phase predictions". It concerns in particular the modifications ofthe hydrological cycle components are liable construction of dams, weirs, levees, bridges, channels, to OCCllI. water pipelines, water treatment plants, installations for All these developments and their effects have to be taken water distribution, irrigation, artificial groundwater into account in mostplanning and design investigations which recharge and erosion control, and, increasingly, agricultural, consequently become more comprehensive and complicated forest and water management practices. and which increasingly require the application of mathemati The derivation of efficient strategies and rules cal models for deriving "optimum" or efficient planning and for water-resource use, allocation, management and design alternatives and long-term water-resource management protection for any given or planned system structure (pC in strategies (Haimes et al., 1987). Figure4 was prepared to illus Figure 2) is always an important sub-task in planning trate this developing demand. It gives an overview of some water-resource systems. importantrecent developments in the world, their influence on In all cases the degree of complexity of an inves the availability ofwater resources (in quantity and quality) and tigation, and consequently the use of mathematical models, on the most important interactions (Becker, 1988). is strongly dependent on: In considering these interactions and interrelations, two phases ofthe planning process may be distinguished, from the (a) The specific conditions of the water-resource system hydrological point of view. concerned (structure of the surface and groundwater resource systems, number and location of water uses First Phase and control structures, etc.); Long-term prediction of the hydrological parameters required (b) The number ofinvestigated planning alternatives (struc in the planning process (in the form ofcontinuous time series; tural alternatives, scenarios for system management, characteristic parameters of extreme events, such as floods or input developments, land-use changes, etc.); droughts, with a given occurrence probability; etc.): (c) The available input data and information. (a) For the given climate and hydrological conditions, i.e. The large variety of requirements, resulting in an by extrapolating the available hydrological iuformation increased demand for more powerful models and more flexi in time (for the planning period) and space (for ble programme systems, has already been mentioned. ungauged sites, sub-basins, etc.) while preserving the statistical characteristics of the observation period (assumption ofstationarity in climate and hydrological 3. CLASSIFICATION OF HYDROLOGICAL regime; blockPO in Figure 2); MODELS (b) For expected or planned changes in land use (PL in 3.1 Classification principle Figure 2); For various reasons it is useful or even necessary to classify (c) For expected or predicted climate changes and for hydrological models. This is helpful, for example, in selecting extreme conditions or events (for example as observed a model appropriate to the application and in assessing model in neighbouring areas, generated or expected; PL in availability. From the point of view ofmodel users, a classi Figure 2); fication according to the following problem-related and (d) For any other "scenario" ofinterest. user-oriented criteria is the most appropriate: (a) Purpose of model application; Second Phase (b) Type of system to be modelled; Planning and design ofhydraulic structures or systems ofsuch structures (PD in Figure 2) which have to serve for one or a (c) Hydrological process or related variable (criterion, number ofthe following purposes: component) to be considered; 7 General} - GfowfrigPopufation and Increasing Civilization Develop- - Increasing Urbanisation and Industrialization ments - Intensified Agricultural Production --r Decreasing Availability of• Increasing Water D-emandano the Water Resources and Requirements for a High Stability ;r: Increasing Danger of Unde of Water Supply and for a ~ Conse- / sired Conditions Sufficient Protection quences for the expressed by: expressed by: ~ Hydro a<: logical Increased Frequency Increasing Waste Increasing Water Demand: Increasing Demand 8 Regime of Critical Low Flow Load of Rivers and Larger Quantities, High for Environmental ~ and for and Flood Flow Other Water Bodies Quality, High Reliability and Flood Protection ~ Water I ~ Resour caused by: especially: ces Increasing De 00 mand for Waste ~ ,- Increased overland - Point sources of Water Releases flow from urbanised pollution (indus ~---, 1i I for: I and deforested areas trial, domestic etc.) '" namely: with increased I ~ t - Municipal -Municipal ,- Accelerated direct danger of emergen -Sufficient water ~ I purposes quality from an flow from drained cies ~ areas I -Industrial ecological point - Areal sources of I 1- Industry of view and for ~ o r - Extended water pollution, e.g. I -Uve stock the multipurpose HI I withdrawls fields treated I 1- Irrigation use of water I with fe rti Iizers, I -Thermal ~ I insecticides etc. 1 1- Energy -Flood protection I I production -Others for human settle : - Atmospheric fallout I ments, fields etc. I 1- Traffic etc. L L L ··=-:::r ------~ Figme 4 - General developments in the world and their effects on the availability and qualitity of water-resources HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION (d) Degree ofcausality of the process; Procedures for preparing and presenting the output in a user-oriented format. (e) Required time and space discretization. In many cases models form the necessary core, or at Of these criteria, three require more detailed examination least an important component, of a procedure. Procedures and general acceptance, namely (a), (d) and (e). An are required as a "frame" for running models for specific attempt is therefore made to clarify the situation on the applications, and often they determine the "availability" or basis of our improved understanding of the ongoing "non-availability" of a model. Therefore, although the processes and demands for model applications. The models are the primary subject of this report, procedures suggested categories under these three criteria are also have to be taken into consideration. explained in Tables I and 4 and Figures 2 and 5. It is now clear that the classification identifiers The purposes of model application (criterion (a» have explained in Table I and Figure 2 denote primary already been discussed in the previous chapter. The procedures which should be directly useable for operational classification presented in Table I and represented in applications in the field of hydrology and water resources, Figure 2 is therefore recommended for nse. in particular for real-time forecasting (FO, FC) or for TABLE t prediction, planning and design (pO, PL, PD, PC). Scheme for classifying hydrological models In terms of the pnrpose of their application 3.2 Classification in terms of the type of system and (to be read in conjtulction with Figure 2) hydrological process or variable The classification in terms of these two criteria causes no Number Purpose ofapplication Jcientifier* major problems. It has been submitted for the purpose of this report and in view of the information that has been I. Real-time uses F compiled on the availability of hydrological models. l.t Forecasting - without considering FO Table 2 defines those water-resource systems and their control aspects important elementary sub-systems which are ofprimary inter est in operational hydrology. A distinction is made between 1.2 Forecasting - considering control FC elementary systems (1) and complex or coupled systems (2). aspects TABLE 2 2. Prediction, planning and design P Types of hydrological system, as used in this report in the clas 2.1 Prediction - without considering land PO sincatlon of hydrological models use and climate changes Type ofsystem to be modelled Identifier 2.2 Prediction - considering land-use PL Number changes l. Elementary systems: 2.3 Planning and design (of hydraulic PD l.l Hydrotopes (elementary "unifonn" uni HU structures, etc.) areas, plots) 2.4 Derivation of efficient or "optimum" PC 1.2 Non-unifonn small or medium-sized SA control strategies, rules, etc. land surface areas (combinations of 3. Other purposes (research, model OP some hydrotopes) calibration, etc.) 1.3 Aquifers (groundwater systems) AQ *These identifiers are used in the model availability evaluation 1.4 River reaches or channel reaches RR procedure described later in this report. 1.5 Reservoirs or lakes RL In connection with this classification, it is important to explain the difference between a model and a procedure: 2. Complex (coupled) systems: While a model describes a definite process or system 2.1 Surface water systems consisting of CS (independent of the specific purpose of the model several river reaches, eventually with application), a procedure is understood to be a combination lakes, reservoirs, etc. (seqnence) of sub-procedures including a model or some 2.2 River basins or other larger land CB models as sub-routines, for instance: surface units Procedures which handle a set of given input data With regard to the coupled systems, it should be noted and transform it into the form required by the model that under CS (complex surface water systems) only the (input sub-routines); surface water system itself, consisting of rivers, channels, Computational procedures based on a hydrological lakes, reservoirs, etc., is understood to be modelled while model or a combination of models and approaches the "feeding" land surface parts of the river basin, aquifers, (including, for example, an updating procedure for etc. are not explicitly considered. Their outputs are treated real-time forecasting); as given boundary conditions of the surface water system 9 Coupled Deteflministic - Stochastic Models flIT ...... Detef!ministic Models Stochastic MOdels S I.... :: Degflee g of ~ Causaltly Fundamental·· Conceptual Black- Box PflObabilistic lime Seflies ~ b.ov,s (I-Iydl'O(fyn) Models Models Models 6eneflGtion 1'1. ~ i I ::; 'lJL ' lJB SP ST ig Q Distributed- Models I Lumped Models '"~ I [L Spatial ~ Discfle- jnL 1- Applied Identifier) > ~ tization 6f'id Based Semidist"i - "§/rJtisticaI " e.g. lJL No ~ Scheme (Eiementar>y buted(Lorgep lJistribution i Dist,,;bution ~ Unit Areas) Sub- o!'eOS) (Imp. PerotTI) ~ I6 IS LS I LO Figure 5 - Classification of hydrological models in terms of purposes of application, degree of causality and appplied spatial discretization HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION (CS); however, they form an important part of CB (river (DC) Conceptual models reflecting these laws in a basin) models for those areas. simplified approximate manner and involving in The classification in terms of hydrological process or general a certain degree of empiricism (grey- box related variables is presented in Table 3. While in general models); very wide fields are defined (e.g. water quality (6), soil moisture and evapotranspiration (1», a sub-division of (DB) Black-box models which do not explicitly take into category 3 (river discharge and water level) into two sub account the governing laws but only the cause categories (time steps greater than one day or smaller effect relation of system inputs to outputs, in a than/equal to one day) seemed to be appropriate for this very general and purely empirical manner. report. This indicates already that, within or in accordance The other main group of hydrological models which with the general frames defined by the main types of primarily do not consider the principle of causality are the hydrological systems and processes or variables as listed in stochastic models (S in Figure 5). One sub-category of Tables 2 and 3, any required alternative or sub these models-the so-called probabilistic models (SP)-are classification can be introduced. generally represented by probability distribution functions TABLB3 of the hydrological variables of interest such as, for example, maximum and minimum discharges (flood peak Hydrological processes or related variables to be considered in flows, low flows, etc.) and water levels or storage volumes. the classification ofhydrological models They are often described in terms of parameters such as Number Hydrological variables to Identifier averages, standard deviations and co-efficients of skewness. be considered A fundamental assumption which forms the basis for these models is that no causal relation should exist between the I. Soil moisture. evapotranspiration ES different elements of the considered process (variable). (including other related variables) The second sub-category of stochastic models is the 9:J time series generation models (ST) which can be used for 2. Groundwater storage, level, discharge extrapolating in time a sequence of recorded variables or events while preserving their statistical parameters. The 3. River discharge and water level: well-known ARIMA model belongs to this category. 3.1 - in small time steps I(S 1 day QF It becomes clear from the above that stochastic models 3.2 - in larger time steps (> 1 day) QM are usually related to a definite hydrological variable (process) at a given observation station (e.g. gauge), and 4. Water temperature, ice conditions and TW only in an "integrated", more general manner to one of the other related variables systems indicated in section 3.2. Therefore, the type-of system criterion cannot always be applied in connection 5. Sediment yield and related variables QS with stochastic models in the evaluation procedure used later in this report except where there is a clear relation to a 6. Water quality criteria WQ definite system. Conversely, deterministic models are clearly related to definite systems, as listed in Table 2, and 3.3 Degree ofcausality thus to their inputs, state conditions and outputs. It should be mentioned that hydrological processes Causality is expressed in the form of cause-effect relations. always include deterministic and stochastic elements They are best reflected in deterministic models (D in Figure 5) (coupled deterministic-stochastic models at the top of which relate given dependent variables y (effects, outputs or Figure 5). This is true not only for prediction, planning and dependent state variables ofa considered system) to a set of design models where at least the "prediction part" often independent variables x (causes, inputs orother state variables needs a stochastic model component as a decisive element, ofthe system such as initial and boundary conditions): but also for real-time forecasting models, where the y=f~~ (1) updating procedure and all system input forecasting where a are coefficients or parameters describing the procedures (precipitation, snowmelt, etc.) represent or system behaviour. require a stochastic model component. There is a large variety of deterministic models. They Another well-known fact is that, in a deterministic are different in their basic structure, physical "soundness", model of a complex hydrological system, we are often dimensionality, etc., depending on the purpose of the unable to take into account all variables and factors modelling system and the process to be modelled. Aspects (parameters) which influence the output. Therefore, in of dimensionality, space and time discretization will be model applications, a certain error Ey (n) will occur which considered in the next chapter. Here the three main decreases as the number n of independent variables and categories ofdeterministic models are introduced (Figure 5): parameters is increased up to a definite number n, of these variables, as illustrated in Figure 6. This error, which can (DL) Models based on the fundamcntallaws of physics be described by a stochastic model, includes two typcs of (in particular hydro- and thermodynamics), error: the model error itself and the measurement error chemistry, biology, etc. (white-box models); (represented as EM in Figure 6). 11 HYDROLOGICAL MODELS FOR WAlER-RESOURCE SYSTEM DESIGN AND OPERATION Ey 3.4 Time and space discretization in hydrological modelling The appropriate time step in a modelling task is mostly random detennined by the purpose of the model application. For zane· flood studies, erosion studies and a number of water quality studies, time steps of one hour to one day are normally tada required, while for other purposes longer time steps up to in futl./f>e one month can often be accepted. Some recently developed N:"'';-- models are designed in such a way that different time steps can be applied. This aspect is essential in the selection of an appropriate model for a specific application. Similarly essential but more difficult in modelling is the a 1 z 3 n space discretization, sometimes characterized as "topological numbeJ' of vapjables modelling". There are two basic categories of space considefled discretization (Figure 5 and Table 4): distributed models (I) and lumped models (L). In discussing these types of Figure 6 - Modelling error as a function of the number of discretization and related sub-types, we will use river basins or elements considered land surface areas as a first example (CB and SA in Table 2). TABLE 4 Categories ofspace discretization in hydrological modelling oflarge areas, river basins, etc. Type ofmodel Identifier Input, output, Characteristics, stale variables parameters* 1. Distributed models considering I - elemenlaI}' unit areas (grid based) IG u(x,y,z,t) k(x,y,') - larger sub-areas of approximately similar IS Uij(l) k i• j behaviour (semi-distributed) 2. Lwnped models L - black·box or conceptu~ LO Uj (I) kj - considering a statistical spatial distribution of LS uj (A/A, I) kj (A/A) important parameters *Some parameters may be a function of time. i-number ofhydrological sub-areas; j - number oflayers (levels).considered; Ap - part of the total catchment area A. If the understanding of the process and the measuring According to Table 4 distributed models in their basic technique can be improved (in the future) then this error form take into account the spatial variability of model EN = Ey (n,) will be reduced to EN =Ey (nf). The error inputs, outputs, state variables and parameters in a detailed reduction (N -> N) as indicated in Figure 6 would then be form (IG), for instance by sub-dividing a land surface area composed of two parts, arising from: into elementary unit areas detennined either by a detailed regular grid (as in Figure 7, pan c) or otherwise. In many (a) The reduction of the measurement error (M -> M'); cases, especially in microscale considerations, the grid (b) The increase of n up to Ilf. areas are chosen small enough to ensure the validity of the governing fundamental physical and other laws. This Accordingly, the role of the stochastic sub-model for the means, in terms of the scale definition presented in model error function will decrease. Figure 18, that they should not be greater than abont I km2• In summary, it can be said that stochastic models fonn The application of distributed models for river basins a substantial part of each prediction, planning and design or other land surface areas is required in view of the study. In real-time applications their role increases signifi following facts: cantly with increasing lead time of the forecast. In The spatial variability of precipitation and other short-term forecasting they represent an element of meteorological factors important for hydrological secondary importance which is included in the updating modelling; procedure, while in medium- and long-term forecasting they reach rather quickly the same level of significance as The non-uniformity in space of watershed in planning and design studies. characteristics (topography, vegetation, soils, etc.); 12 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION Legend: ~ ./ , r-... Watef' SUI' faces /'1-'" IA ~ ::\.. LA ([[/JJ) Uf'ban Al'eas I IA . r +- \ !'.. "'I'. - ~ I\. (-A -,,) FOf'esf II h( 1'- 1'.. c::=> Ofnef' /I " Rivef's II r-.... - l' " Wofef'sned 1.../IJ iii: _.... I"- V V':: l-" 't r?"f')., I.... ""-- "'" ,,'-' VI Il It I:f ~ 1\ "" 1\ ...... 1'--= h:. IK t'-1:\.\ 00;;;; ..... Figure 7 - Representation of different space discretization schemes in river-basin modelling: (a) Lumped (4 sub-systems) (b) Semi-distributed (e) Distributed (grid-based) 13 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION COMPUTATION COMPUTATION POINT REACH INPUT GAUGE OUTPUT GAUGE TRIBUTARY FLOOD PLAIN BOUNDARY COMPUTATION POINT FLOOD PLAIN BOUNDARY nooo PLAIN BOUNDARY TWO·DIMf,NSIONAL Figure 8 - Space discretization schemes in river modelling: (a) One-dimensional distributed (b) Two-dimensional semi-distributed (c) One- and two-dimensional distributed 14 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION The spatial differences and non-linearities of the In this connection it should finally be pointed out that mass and energy transfer processes taking place in a a sub-division of a river basin into sub-basins according to watershed; Figure 7, part a, must not be misinterpreted as a semi distributed model. It is a combination of lumped models The non-uniformly distributed influences of human adapted to the sub-basins concerned. activities on the components of the hydrological In the case of surface water systems (RR, RL and CS cycle and on their interaction. in Table 2) the discretization procedure is slightly modified Lumped models (L), as the other extreme, do not take and is principally related to the dimensionality of the model into account the areal distribution of the above-mentioned which may be one-, two- or three-dimensional. characteristics, and are therefore much simpler and are For simple flow routeing through river reaches without easier to handle. Despite or even because of this simplicity, extended flood plains, reservoirs, lakes, etc. a one they are most widely applied for a number of purposes, in dimensional description is generaIly sufficient. An particular in simple design studies and in real-time overview of available models of this type is given in forecasting of discharges. Table 7 (see section 4.6 below). In applying these models, Between the two extremes there are compromise the river system is sub-divided into sub-reaches according solutions which try to overcome the limitations of the to the existing gauging stations and also to the position of lumped models (La) and to avoid the computational major confluences of tributaries with the main river. demands and large amount of input data and parameters Whereas with a conceptual or black-box model these required for grid-based distributed models (IG). A very reaches-are each modelled as a single model element, the early attempt in this direction was the introduction of application of a distributed hydraulic model offers the "statistical distribution functions" for essential parameters possibility of sub-dividing the river reach into shorter (sub-category LS in Table 4). Examples are the use of a "computation reaches" in accordance with Figure 8, part a. linear distribution of the soil capacity for infiltration and Whenever extended flood plains exist, it is more evapotranspiration within a river basin (the so-called appropriate to apply a two·dimensional distributed or a "source-area" method used by Crawford and Linsley semi-distributed model. However, even here a fine grid (\966)) or a linear distribution of the soil storage capacity representation as shown in Figure 8, part c is only for capillary water in the rooted soil layer as introduced by necessary in very detailed and specific investigations, e.g. Becker in 1975 (see Becker and Nemec, 1987). in planning and design of hydraulic structures. In many A further step involves sub-dividing the land surface or other cases it is also possible to apply the much simpler but river basin area into larger sub-areas (zones) of approximately often equally efficient solution of a semi-distributed equal inputs and hydrological behaviour (Figure 7, part b). conceptual model based on a "quasi-two-dimensional" This sub-category (IS) of distributed models is denoted in description of the river reach. The river channel itself is Table 4 as semi-distributed, after Refsgaard (Knudsen et aI., then represented as a separate primary conceptual model 1986). At present this category ofmodel is being increasingly and secondary conceptual sub-models arc introduced for accepted and applied because it avoids a number ofthe prob the inundation plains; these are routeing models with lems associated with the application at larger scales of different parameters for the river channel and the storage distributed grid-based models. These problems have been elements for polders, etc. These secondary sub-models are pointed out in recent publications and relate, for example, to activated only after a certain threshold discharge- the an appropriate assessment offeedbacks, areal variabilities and inundation discharge~is exceeded. Such a semi spatial integration features within larger areas (Dooge, 1985; distributed model is illustrated in Figure 8 part b. Specific Becker and Nemec, 1987). This category of model is also forms of these are the so-called multi-linear models (Table better able to work in the general situation with respect to the 7 and section 4.6). availability ofmodel input data. It is necessary to state that relations exist between the The following criteria should be taken into account model types explained in section 3.3 and the space when a river basin or land surface area is sub-divided into distribution schemes mentioned before. These relations are sub-areas (category IS): represented in Table 5. They must be taken into account in selecting a model and its appropriate space discretization (a) The areal distribution of the most important scheme. meteorological inputs to hydrological systems, in Finally, some general aspects should be listed which particular precipitation and potential evapo influence the space discretization in both river basins and transpiration (taking into account the available surface water systems: measuring system and observation data); (a) The purpose of the model application and the (b) The areal distribution of land characteristics which required accuracy of the model outputs; significantly determine the general hydrological conditions and regime such as (b) The amount and quality of available input infor - Topography; mation and data; - Soil and land use; (c) The specific requirements and constraints to be - Hydrogeology; fulfilled for applying a selected mathematical model - Others. (stability and convergence of the solution, etc.); 15 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERXrION TABLE 5 Relation between model types and spatial discretization schemes River basin spatial discretization: Rivers, lakes, reservoirs [ -- - Model type I -- --_.- - distributed semi-- lumped one- two- three-- I distributed dimensional dimensional dimensional f [ Hydrodynamic fundamental laws + (+) . + , + + I. Conceptual (+) + + + + . Black-box . . + + . . I.c -t = po::::iblc '- = not possible ,(+)::: possible under certain conditions (d) The t.ype and control effect of existing or planned and momentum or energy) which are. in modified forms, hydraulic structures, land-use and land-management valid for any system under consideration. The coefficients practices. in these equations (the "model parameters"), or at least most of them, characterize the "nature" or the specific Some explanations which may help in finding the behaviour of rhe prototype system under study. They are appropriate type of model and discretization scheme are dependent on relevant prototype characteristics, such as given in the next chapters; especially in Tables 6 to 8. topography and orher factors (Figure 9). , After a model has beel) calibrated, that is, all 't. ',FUNDAtl'IENTALS IN DETERWNISTIC parameters have been determined (often on the basis of a HYDROLOGICAL MODELLING relatively small number of data collected from a real A 1 hydrological system), it is possible to compute "effects" ~ Basic structure and requirements of deterministic .. (system outputs) for longer and; for orher periods (than the simulation models calibration period) for which rhe "causes" (system inputs) In the previous chapter hydrological models were classified are given. These may be observed, forecasted or generated, in accordance with the full spectrum of required models for instance by means of a stochastic input time series and the purposes of rheir application. The main aim of this generation model. The "stochasticity" of rhe process is here chapter is to supply 'some more specific information on included through the system inpilts. while rhe system model basic approaches in deterministic modelling of hydrological itself is generally considered to Ile deterministic. proc{~sses and systems with specific reference to the most If the system model is "physically based", then rhe fundamental hydrological processes: water storage and model parameters are identical with or related to the discharge. These variables characterize the quantitative respective prototype characteristics (e.g. storage capacitics, avaiiabiiity of water resources in river basins and in their roughness coefficients, transmissivities). Physically based ~ relevant sub-systems. Infmmation on them is required in models provide a number of important advantages and are almost any hydrolojlical or water-resource system rherefore increasingly in demand. Important advantages are investigation and they are, rherefore, ofgeneral interest. (Klemes, 1985): I 'rhe principal purpose of using deterministic (a) These models are geographically and climatically hydrological models is to-simlilat~ ("imitate") the behaviour transferable. Geographical differences can be taken of the hydrological system nnder study, that is, to compute into account by adjusting model parameters and system outputs from given inputs (boundary conditions) climatic differences or climate change by system and initial state conditions (Figure 9). The system output input data modification; I might be a set of time-dependent output or state variables Q(t), ;such as runoff or discl1arge. real evapotranspiration, (b) The parameters of rhe models can be derived from water level, water storage or soil moisture. These assessable (measured or estimated) catchment hydr(l)logical characteristics may be related to a drainage characteristics, even for ungauged and hydro basin or its sub-systems, such as aquifers or surface water logically insufficiently explored river basins and land bouies. The sysiem input P(t) is a set of climatological surface areas; I factors (precipitation, potential evapotranspiration or related meteorological factors) and, for some sub-systems (c) Observed, planned or predicted land use or other (rivers, aquifers...) water inflow. changes of the system behaviour and conditions, in 1~he model equations used in computations represent particular those caused: by human impacts and specific expressions or solutions of the governing influences, can be taken into account in rhe planning fnnnnmental physical or other laws, in particular the phase by changing the respective model parameters COntiilUity and dynamic equations (conservation of mass (for assessment, prediction.or planning purposes). i 16 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION CIimatological Physical Laws Hydrological and Hydflological State Vaf'iables FactOI'S (Initial Model Stfluctufle (State Condi- a:nd BoundoflY tions and Conditions) Outputs) System Model Inputs Outputs (D etel'ministic) I i I i Af1(]- I ISimu- - I I Iysis I Ilotion Model Pal'Ometel's I I l ~ ~ I Stochastic Notufle of Stochastic Model the System POflOmete-"-; Model Pl'ototype Cha flOctef'istics Figure 9 - Schematic representation of the role and character of determinisic hydrological models In addition to the requirement for physically based 4.2 Time and space discretization and their hydro-logical models, the following general requirements dependence on the purpose of model application should always be taken into account: and input data availability The model should reproduce the process as What structure a hydrological model should have, in accurately as possible; particular which time and space discretization scheme should be used, is strongly dependent on the purpose and It should be understandable and "attractive" for the objective of the model and on the availability of input data. user in practical applications; Following the rules outlined in the previous section, and It should have a modular structure so that sub-models recognizing the need to design a model as complicated as (components) can be easily exchanged according to necessary but at the same time as simple as possible, some the specific demand, purpose of use and availability general recommendations are given in Table 6 for choosing of sub-models; appropriate time and space discretization schemes for well known cases ofriver basin modelling (Becker, 1988). The amount of input data and information required It can be seen in Table 6 that in six of the eight listed for model calibration and application should be as cases lumped or semi-distributed models are considered as smalI as possible. sufficient; the lumped models are used primarily in the simpler Considering the fact that some of these requirements cases or for "quick solutions". In the remaining two cases a are contradictory, it is always necessary to find an optimum semi-distributed model (using a zoning approach), and espe balance between the demand for a very detailed and cially in case 8, a distributed approach (considering comprehensive model (in order to reach a certain degree of elementary unit grid areas) is required. Naturally, the accuracy in simulating the prototype behaviour) and the distributed approach can also be applied in other cases where desire to maintain model simplicity. the required input information and data are available. Within the last decade remarkable progress has been It should be pointed out here that input data achieved in the field of deterministic modelling of availability in model application and calibration hydrological systems. A variety of successfully applied substantially influences the selection and structuring of a models are available which are well reflected in the model, including the time and space discretization to be different surveys and inquiries of WMO, the results of used. Consider, for instance, a real-time rainfall-runoff which will be presented in Chapter 5 of this report. forecasting system for which only daily totals of rainfall 17 TABLE 6 ApprQp-riai:e time .ann sp:lI~tnli~ffi.tr.attar.-trn"iwrinl:o;:llllrmrteliiug-trr ...ellltlm:r-I{f"p1ft"poses ami ohjectives of modpl applications (Bpcker,-198S>------ No. Purp,!~e-'E~ji!EljV.£ Q[lnQdel applicatJ-Ons Time- step HoriWnla1 distribution ~I --+-I_,_DT_,. , --I,In uu- -.... ,.' 11. IReal-time analysis ~d short-tenn forecasting ana cOlltrol, including 11 to () hM~ (1..11 ImtIa--.n(J1il; ILumped Qr semi-disLrihmcd - H flood fortcIl3ung and ~nr:.t~l -.- _- I~p tQ ~ day) _- I(2:!lni.'1g l.'1t!l h!"ge!" sub-.!!!"eE!s). I ~I ~:::le:li;,::~~7~~:~!~~~C;::1:::c::O'\::!:~ :'::::00 of I:;:~d~ ,I ' ,. tlxtrapolahon ot given discharge senes (prediction) I day. at as 2. aoove I 4. computation of design floods As }. aoove I 5 ILarger-scale planning of wareNllsource mm and managemelll including _! __~~_l~~_~_O_~a~~-, __~_s~i~l~ . 1 j' planning of new structures, de~~l~p~~~t-of-~~ntr~1 ~i;;-t~gi;. etc. --- - 1 day --, ,'------1 rDevelopment of flood-control strategies for teservoirs and reservoir IAs I. above - -- - L ,J I: systems IAssessment and prediction of effects of large-scale land~u!le chafige~~ -- -~~--5.- ~~~e Semi-distributed, possibly ------tclimate cha..t1ge~ 18 Atmospheric) Meteoro - it(§) £vap9/f'Qn- o Precipitation P logical I spirC1fion ER (Rain, Snow) Inputs(p,EP..J-W-____. .J. .-.---.-- ! rt~--1® Snow Accumulation (Snow Wafefl Storage) @ Snowmelt ISurface . ; Compo- , 1-----1 @ IntercWJiion and Surface MOistening WO I nents , . ~@ SOil Water Supply POI'-'-I;';--~' , RJ I~ Channel I;;: FIRST \, r-@SOiISUrfaCeStorage(DepreSSionStorOge··.):ristorageandr.::-. ~ LE.vEL I, d Overland Routing oJ:]; ~ HODEL . {ff) InfIltration F Flow RO B. r I" Qsm ~ 1® Soil Moisture Recharge ws,' Dischaflge Q '0- I Unsatu- ,..... (Capillary water) , ,.-.---- rated Zone I 6flavify Wate!' Sto!'age SH and Flow, I .' I '" InclUding Macropo!'e Flow 01 1nterflow I '" . '-- I® 'RH ' ~ Capi/~ar.v ~® Legend: _I -t Rise -. Pe".colotiqn P6. ---l I I t---. I 6roundwoter m Upper Horizons I Systems ® ~ (Shallow) Peflched) S6 9 Base Flow (StOf'(Jges) i Satuf'Qted i ~ 7 Zone : ~® !Jeep Peflcolation RG ~ I Fluxes ~ SECOND l J(9) 6roundwater in Deeper Horizons , (Flows) LEVEL I~ (Large Scale AqUifers etc.) I® Delayed ~ MODEL Base Flow R6 I ! • • ' ._. z Figure 10 - Components and sub-processes of the land phase of the hydrological cycle This sequence clearly reflects the vertical structure ofthe it can be said that in the past, river basin modelling very natural system (see Figure lOy-alIa- anyl1Iolt 20 HYDROLOOICAL MODELS FOR WATER-RE,';OURCE SYSTEM DESIGN AND OPERATION Further rainfall overflows and snpplies the soil surface as soil layers (Dunne et al.• 1975). This dependence can be inpnt PO. In subsequent dry periods, the interception used for estimating the time variation of the source areas, storage evaporates at a rate equal to potential evapo AS, (see, for example, Becker, 1988) which could provide a transpiration. Often WOMAX is taken as a constant better physical basis and be more suitable for areal lumped parameter (e.g. 5 mm), for the sake of simplicity. extrapolation in the larger-scale modelling of infiltration In more detailed models WOMAX is introduced as a time excess than the classical infiltration equations, even if the varying parameter, in distributed models as a space variable latter are modified for longer time intervals and areas. according to the state of the vegetation in the prototype The other important sub-process to be considered here system; sometimes it appears even in a more detailed is evapotranspiration. The estimation of areal evapo version (e.g. Rutter et aI., 1975). However, this simple transpiration is very difficult because three different and model, even in its improved form, can only be considered complex factors are involved: as satisfactory when computation time increments of one The controlling atmospheric factors (radiation, day or less are used. For larger time intervals, empirical or temperature, wind, etc.); other relations are applied. More critical, and also more difficult, is the modelling The type and state of the vegetation cover; of excess infiltration, overland flow formation and The soil moisture distribution. unsaturated flow and matter transport in the ground «d) and (e) in Figure 10). All the classical physically based Each of these is variable in time and space (see, for models and relations for computing these processes (e.g. example, Brutsaert, 1982). It is therefore easy to Nielsen et al., 1986; Van Gnuchten and Jury, 1987) as well understand why no satisfactory methods and models for as field observations during overland flow-producing estimating areal evapotranspiration have as yet been rainfall events indicate that developed. This was well reflected in the presentations and discussions in a workshop on the subject, during the XIX (a) Rainfall excess is strongly time variant with rainfall General Assembly of the ruGG in Vaucouver, Canada in intensity; this would require, in the majority of August 1987. The results of this workshop may be rainfall events, computations in time intervals of one summarized briefly as follows (Black et al., 1987): hour or even less (e.g. 10 min.); (a) At present the following methods and models (b) Rainfall excess is highly variable in space and depends (relations) are more or less widely applied for on rainfall intensity, soil and vegetation conditions, practical purposes: topography, etc. and their areal distribution. - Estimation methods based on measurements and From these facts, it can be concluded that to correctly estimates of potential evapotranspiration (e.g. apply the classical infiltration models would require working Penman, Priestly-Taylor equation); with time intervals (DD of 10 minutes up to a maximum of one hour. Accordingly, onecan hardly claim that unsaturated - Combination equations with resistenee expressions flow and infIltration excess computation models are available (e.g. Penman-Monteith); for operational application with larger-scale lumped or semi - The Bouchet-Morton complementary relationship; distributed models where time intervals of3, 6, 12, 24 hours or even larger are usually applied. With such time intervals, most - Energy balance methods (including those using of the infiltration sub-models currently used for practical the Bowen ratio); purposes lose their physical significance and become gener - Planetary boundary-layer methods (using ally nothing more than "stochastic" models or empirical boundary-layer profile of gradient data); relations, the parameters of which are "adjusted" (somehow calibrated) or estimated (from experience). (b) Unfortunately, most of these methods cannot Quite different ways of thinking and approaches are be applied directly for larger-scale estimations, and required if we are to model on a physical basis larger-scale only the first two equations offer the opportunity to unsaturated flow and infiltration or excess rainfall. This think predict (at least to a certain degree) the effects of ing has to take into account our improved understanding ofthe expected land-use changes and other factors on areal ongoing processes influenced by the heterogeneity of soils, evapotranspiration; macropores, etc. (Kirkby et al.• 1978; Yeh et al. 1985; Beven (c) The large-scale oriented models of the planetary and Clarke, 1986). It may start from a knowledge of source boun-dary layer are still in the research phase, particu areas (AS) ofinfiltration excess in river basins which, after an larly in view of the given time and space variability of initial filling period during heavy or long-lasting rainfall, important processes, the incomplete mixing and the become saturated up to the surface producing direct overland like. flow ata rate equal to that ofthe falling rain (PO): Considering this unsatisfactory situation, WMO has initiated RO ~ PO.AS (2) s a project, "lntercomparison ofmethods and models for estimating After an initial filling these source areas (AS) often area evapotranspiration", whose aims are to better understand the grow dynamically with increasing moisture supply, that is, practical applicability ofthe existing estimatiou teeImiques and to with increasing total soil water storage volume in the upper promote ongoing research activities (Becker, 1987). 21 4.5 Groundwater flow modcis - FOr the Hnear reservoir (m = 1) a very simple and useful Groundwater"flow In aquifers is very-oCten-descrIbed -by- ----sclut«m of-eqH~Ong (§.)-aRd-(~) C~"1 be derived (wit.l:l p == equations obtained by combining Darcy's-iaw;as a dynamic Pm - constant dUrIng a computat1Oo tIme step from! - At to t): equation with the contim.q,ty-equatwn; adapted to the specific q(t) ~ c.q(-t-At) + (I-e).Pm (1) hydrogeological conditions of the system being investigated. eniploy~-fooo~ recession 'Iconvoluilonii The most frequently of the combined equa tenn te.rm _ tions for two-dimensional flow modeHing in acquifers are:- where c = exp (-Al/k). (8) For steady flow: - It should be pointed oui, however, that this simplified dW dW - approach can oniy be appiied if the overali-storage and total -(K.-)a +-(Ky-)+q=Oa (3) dX ax ay ay ------discharge alone are of LYIterest, but not the internal groundw&= For unsteady flow: tyr flow and m_atter transport process"" within the differ<)nt aquifers of the basin. For groundwater systems with clearly A ,Am A Am. :'lA> ~(KxV-) + ~(K,V-) + q =8, = (4) ditterenllime delays (in dillerenl levels, orin tIat ormountain ax ax-oy iJy at OiiS sub-areas of the river basin), different models with different narameters k and c = dk) .hc1nld he annlied_ whcrcK . = -hydraulic conductivity; 7:" ------...--, ------rr----- 4> = groundwater head~_ .._ 4.'---F-IGw..routeing-nwdclti fffi" -l'rnrn-and1:hsRncI3 ------ S, = specific storage coefficient; q discharge (sources and sinks); A compiete account of unsteady fluid flow in three dimen x,y :::::: horizontal cO-OIdinai.es. sions is provided by t.l-Ie equation ofmass conti.1"lUity a.qd t.l:le In its e.arly days. grollndwa~er-f!Dw ffiQdeH:!!!g was Nayi~r.-_S.t()kes XQrc",c!l'()!l'"nW!lL<)_q!!!!!i.Qn§__ (G~be-"L!!n(t _ based mainly on analog m.odel technigues. The introduction Bradshaw, 1977). For most flow-routeing cases the full and wide use of high-capacity computers caused th" rapid equations are more complex- than iiiwarhiilled-bY development of numencaI modds with a wider scope for requin~ments and data availability; a one-dimensional, application. Grcund\vtHer-f!cw -madels-l)eeame more gU\dualJy--yari~d_fulw_v.crsimLi'lJher£.foffLapplied.._Eirllt_ widely operational during the periodJ.21i6-1968. derived by Saint-Venant, the resulting equations are often At present there is a wide variety of operational quiitedIn-ilform·sIniilar toCungeei"l: (1980)· models, from the one-layer two-dimensional"niod"ls-up-tii- Continuity equation: tI;e multi-layer models and three-dimensional models; the range 3I~ covers monoph3~models.-and-polyphase-models- ...... (9) to handle fresh water-salt water Qr water-air systems (water movement in the unsaturated zone), migration-models (for approaching the subjects of matter trMspOrt in groundwater and groundwater pollution) and heat-transport- mooels-lo--oo-- applied to the study of systems under. significallt theJmaL influences. Accordingly, it may be said that groundwater-flow modelling has become a domain of disu--ibuted modelling, with grid-hasecl (as in Figure 7 parLe) _(lLfjJ\it~_eJemenL_ representations of the aquifer or river basin under study. For llIore specific detailssee, for example, Bredehoeft el al. where Q = llfscharge; (i982) or Engeien Mti Jones (1986).- A == cross-sectional area offlow; Nevertheless, in a- namber of -larger~seale-river--basffi..- h =- water deptlL(water sutface elevation); oriented investigations _wilIJ_.JnWgruted. .JiYJ:L lr = friction slope; basin models, it has been found acceptable to approximate 10 = bOttom siope; the outflow behaviour of aH groundwater systems of the -. ---. -·x -. "'- -dtslllllre·along-tlre-riVer-channel;· basin (with time delays focdischarges ·within-a cerlain -----t--=--time.;------range) by a sLl1g1e, ratlter sLrnple-a..'ld (}t."'aSi-one.-dimep.skmal g = gravit;lti9nal aq:e)eratiol); __ q = lateral inflow. storage element, namely, a linear or nonlinear reservoir. ----_..- ...._-~ ... _.. ._. _._.... _..._._. __ . In this case the basic equations are: The friction slope lr is calculated from a resistance Continuity equation: dS/dl = p - q (5) formula such as lbe Darcy-Weisbach equation - Dynamic equation: S = k.qm- --- -. ------(6)"- 2 ------_._-~- _._._-_._. _...-_.. -_. f,=..JlL (11) where S = storage (total storage volume); . 8.¢A 2 p = input (total groundwater recharge per time unit); where d =depth and j =the Darcy-Weisbach resistance q =: output (loml grfil1~dw-a[et:-Gi~.fiMge-{}f·- ccefficiefit··-~·-·--·-· .-- ..- --- ...-.... -- .... -- .-.-..... -.-- --. -.-.. --- ....-.------base flow); In mILabgye._llil!llll!on_(.91 ~i\\1rJ:SS~s _J;Q!lS~ryJlli9)Ut(. m = exponent. mass while equation (10) relates rate of change of 22 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION momentum to applied forces. Derivations of the equations model. This is the most general model, within the are presented by Cunge el al. (1980), among others. assumtions noted above, allowing for backwater effects and Assumptions implicit in the equations include the the upstream propagation of waves. It is the most efficient following (Bathurst, 1988): and versatile of the flow-routeing models, but is also the 1. Wave depths vary gradually, meaning that the pressure most complex. distribution along a vertical should be hydrostatic, or that Hydraulic models generally require numerical vertical accelerations should be small: There should be no solution. Summaries of some of the techniques involved evidence ofrapidly varied flow such as hydraulic jumps. together with more detailed reviews of the characteristics of flow-routeing models are given, for example, by Cunge el 2. Longitudinal changes in cross-sectional shape, channel al. (1980), Fread (1985) and Dooge (1986). These alignment and frictional properties are continuous. techniques require high-capacity computers and large 3. Friction losses in unsteady flow are not significantly amounts of calibration data referring to riverbed and flood different from those in steady flow. plain characteristics, in partie-lar river cross-sectional profiles, roughness coefficients, etc. They are essential for 4. Variation in the distribution of velocity across the a number ofapplications as mentioned in Table 7. channel does not significantly affect wave movement. Another way of approaching flow routeing has been 5. Wave movement can be considered to be two introduced by hydrologists who describe the routeing dimensional, with the effects of lateral differences in process by simpler mathematical relations which result in surface elevations at cross-sections being negligible. more robust models known as hydrological or conceptual models. These models are based on a special form of 6. Average bed slope is small enough so that IJ '" sin IJ '" equation (9) which is valid for a longer river reach: tan IJ and cos IJ '" 1, where IJ = angle between the channel bed and the horizontal. dSldl = Q[ - Qo (14) where S is the total storage volume in the reach and QI is 7. Fluid density is constant. the inflow to and Qo the outflow from the reach. The 8. Channel geometry does not change with time. momentum equation (10) is replaced by a more-or-Iess empirical relation between S and QI and/or Qo: These assumptions are generally satisfied in most instances of flood ronteing, although assumptions 3, 7 and 8 may require Muskingum model introduced by McCarthy in 1935 careful consideration when sediment transport is significant. (Cunge, 1969): The Saint-Venant equations form the basis ofmost chan S = k.[x.Q[ + (l-x).QoJ (15) nel-flow models, the so-called hydrodynamic or hydranlic with x taking values from 0 to I; models. In their full form they are nonlinear and there is no known general analytical solution. In order to facilitate solu Kalinin-Miljukov model (1958): cascade of n linear tion, therefore, various simplifications have been introduced, reservoirs with: in which terms in equation (10) are neglected. (16) Solutions involving equation (9) and parts of equation (10) are known as simplified hydraulic ronteing methods. "Lag and route" model consisting of a linear reser-voir According to equation (10), they include the kinematic coupled with a pure translation element (SSARR, 1972). wave model: More attractive than these "empirical" models is the (12) solution of the diffusion wave equation (13) or the full and the diffusion wave model Saint-Venant equation (10) derived under certain assumptions (Dirac im-pulse as input, or linearization; see, It - 10 + ak = 0 (13) for example, Dooge, 1986). ax However, the application ofall these models is limited to The kinematic model should be applied only where the systems behaving, at least approximately, in a linear manner. depth-discharge rating curve is single valned and where They fail in rivers with inundation plains which, backwater effects are insignificant. The diffusion model in the case ofoverbank flow, behave in a strongly non-linear allows for the attenuation of a flood wave and for manner. For simulating such events and conditions, multi backwater effects, but should otherwise be limited to slowly linear models have proved to be very useful and efficient. or moderately rising flood waves in channels of generally They describe nonlinear systems by means of two or more uniform or quasi-uniform geometry. different linear models operating in parallel. The inflow to the Some authors have shown that the initial terms river reach is first distributed into sub-inflows to different IlQ a Q2 F:-and - (-» linear sub-models. The computed outflows of these are then al ax A recombined in order to get the total outflow of the reach of the momentum eqnation (10) are, in numerous practical (Figure 11). The first version of such a model, consisting of cases, two orders of magnitude smaller than the bottom two linear models in parallel with amplitude distribution ofthe slope 10 (cf. Bathurst, 1988). input, was introduced in 1969 by Becker and Glos (the so Solutions involving the full Saint-Venant equations called "threshold model"). For more details see Becker and are known as the complete hydraulic or dynamic wave Kundzewicz (1987). 23 TABLE 7 Gen erar- cn.ar.H~ierjs.ii['~ Hod fi{'lm'"{lf~pptt(";1ffi)1I1)f1))Rf·6iu®:fs:iQlhll:stY~mfii)"W-fil-ut~mlrfiR\(f~,lS-fQl' graduHiiy varied j]QWS M~dcl-~l ~pecific model~ Major fields of J!dvantages Special requirments category I application andproblems Hvdraulic (hvdro- Full Saint-Venant Water leyeLand_f1ow Mo.nadequale..-______Larae amounL';:, quality Id;namic) ~~els Iequations (dynamic Icomputation in any Idescription of gradually data on ~hannel - ~ I Iwith distributed IWaVe mood) Itype ~f river or canal, Ivaried Slteamfl6W :ih any Iand flood plain goo- I Iparameters - even-those with - Itype of river, even for - Imelry, roughness, etc. -I IDiffusion wave model signiHeant eae!E\ - \.,. ~. . ICG·o··n-"d·gl·u~ogn,8flhem High~ca~acity computers - I I ~ effeGL'l (suboriLisal I-Iflow) - I - 1reqoired - - I Musi p In several applications to rivers with overbank flow model, in combination with a simple computation (see e.g. Becker and KundzewiCz, 1987) it has been-proved procedure for estimating "effective iainfaU'~(direcnurtoff) that multilinear models are- ncarly-as-sim-ple -as--lineul- l1'l·input-ro·the-unit-hydrogtaph applicntion-;'" .... -.._- ..... models and therefore as-accurate as hydrodYflamic models, Later, simple mnceptual models. were introduc.ed as at least in cases where the constraints outlined in Table 7 sub-system models. in particular those based on a series of for their applicatIon are-fulfilled. They iepresei>t an storage reservoirs or tanks with a certain storage capacity important alternative to hydralIiic rnodels-:------and outflow characieristicS\e.g; accordin& io equation (6); generally called Nash--eascade models). Specific 4.7 Composition of itiIegrated hydrologkalinodiilS--foT arran)(emet1J~_!if_~I1C!1. sl1\J:;mQdels_ xe_~!!lt(K(j!!. _(I..n!!!!l\JeL rhel' basins of.. well-known conceptual river-basin models of "tank·cascade The composition ofa hydrological model for a laild surrac" type". -Th-esemcluile-llie-Stanford Watershed Model, area is dependent on the-appHed space-ilis-tribut"ion represented in Figure 12 ~Crawford and Liils1ey; 19661, -L~e principle. In the cal)e of lumped modelling, the required SSARR roodeL(1972)~ thdLBV_ (Retgstrllin, 19.76),_the_. sub-process models can be arranged simply in a ca.;:cading IRMB (BuHol and Dupriez, 1976), the Sacramento model manner in the sequence listed ifl section 4.3 and represeflted (Burnash and Ferral, 1980), the Tank-model (Sugawara in Figure iO (see sections 4.4 to 4:6). ---rne- ou1JlUfTIlomr ec ai., 1984) and Olhers. sub-system is input to the subsequent--sys-te-m--(or-vice- The most important performance characteristics of versa). Areal variabilities are neglected or approximately these models afld the limitatiQns on their use are briefly taken into account by statistical distribution principlcs (LS outlincd in Tablc 8 (lowcr part). A morc cxtensive in Table 4). Feedbacks are similarly treated. discussion of these 'limitations, in particular the problems This composition approach has-beeI1app1iedln-a-large- associated--wit:h-the-jiLime-llTId-~'Pace-extrapo1auonlt-of-the- numbe·r of rive·r-basin mode-Is, be-g-inning-f.fom--th-€--fir-st• mode! fur--app!icatiQll-IO-l!oobs=e4-comlliilJ!'l~,-bas-bee-l1. applications of the Ilnit-hydrograph, a lumped black-box preseflted by Klemes (1985). This clearly underlines the 24 HYDROLOOICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION r------,IineOfl submode/s XN(f) I h (t) I I I N I Algoflithm I N I fOfl 00000 I N x(t)=Z xn(tJI distf'ibuting + YN(t) y(t) a:£ Y (t) n.1 i the Xn (t) I h (t) I Yn(t) I n=1 n extef'no/ I n I -I- I input -I- (t) i 00000 y, signal X(t) I I x7 ft) I h (t) I IL I 7 I -.JI Figure 11 - Concept of a multilinear model need for models which have a more correct physical basis, explanation of symbols is given in Table 9 and in Figure 10. the parameters of which are identical or clearly related to The composition ofa river-basin model which takes this measnrable prototype characteristics, as already mentioned sub-zoning into account would result in a semi-distributed in section 4.1. model as schematically represented in Figure 15. It can Because of these requirements and in view of the clearly be seen that different vertical "cascades" (sequences) limitations of lumped models, the development of are arranged in parallel (sub-areas AG, AH, AN, AIMP, AW) distributed and semi-distributed river-basin models and are treated separately, though with due consideration for was initiated. The SHE-modelling system jointly developed existing horizontal interactions. by the Institute of Hydrology (UK), the Danish Hydraulics Other important criteria for sub-zoning are: Institute and SOGREAH (France) offers one version of a (a) Significant differences in meteorological inputs (e.g. me detailed distributed modelling system for river basins and dcpendence ofprecipitation and evapotranspiration on other areas of interest (Abbott et al., 1986). Its general elevation); strueture is schematically represented in Figure 13. It is based on a refined space discretization of the catchment and (b) Different land uses (forests, agricultural fields, on the numerical integration equations for momentum and pasture, etc.); mass conservation describing the physical processes in the (c) Soil and vegetation types; catchment. Accordingly, it fulfils all requirements of a distributed physically based model and involves all the (d) Morphological parameters. advantages and problems explained for this model category An example of the sub-division of a river basin in the upper part of Table 8. A quite similar model was according to (b) is shown in Figure 7. developed by Kutchment et al. (1983). The CEQUEAU Existing examples of semi-distributed river-basin model also belongs to this category (Girard et al., 1981). models are specific version of the TANK model represented Taking into account the enormous efforts required and in Figure 16 (Sugawara et al., 1984), the EGMO model problems involved in adapting and applying well-founded (Figure 15; Becker and Pfiitzner, 1986) and the WATBAL distributed models to a real river basin, the semi-distributed model (Knudsen et al. , 1986). modelling approach using larger sub-areas of similar Fortunately, for a number of tasks (problem solutions), hydrological behaviour as modelling units bccomes an some ofthe sub-processes listed in Figure 10 can be neglected attractive proposition. When considering the sub-zoning, it or simulated in a simplified manner. This conccrns, for is necessary to define first the different types of sub-area to instance be separately modelled. One version of an appropriate differentiation scheme is presented in Table 9 (Becker and Groundwater flow, in the case of flood studies; Pfiitzner, 1986). This considers important areal differences Infiltration and direct flow formation in low flow in the hydrological regime, with particular concern for and/or groundwater flow investigations, etc. evapotranspiration and direct runoff formation. The application of this classification to the river basin Thus, according to Table 6, the structure and composition cross-sectional reprcsentation presented in Figure 14 results in of a river-basin model is strongly dependent on the purpose me sub-zoning indicated in the upper part ofthat figure. The and objcctive of its application. 25 TABLE 8- Gener~l characteristics and fi~ld_s_ot application of hydrological models for river basins and other land surface areas Model category I ---t'tainj'ie1ds-o[appiicaIion - -Advantages Special requirements andproblems I~istribut~d, .gri~" I~e~iled investi~~ti~n .~f hYdr~ I~p~licatiO~ ~f fun~ament"al ~a~s of -En~~OU-~" ~tfori"in ~Q(fel-developme~tI oased ana pnyslcally glCaI processes, IrtclUomg erosH.ln. nyaro- ano mennouynam1cs. eu;;. I I I I:nu uperauun ". . _. _ I based (IG} Im,"" """'port, .,,"" q",lhy i,,_ 1:::~:,cabllity to g.ugeri "' u"g.uged ILarge amoum of requ:lrca mpUl oata - I I Iheir real areal dinribution ' or are-as _ (basin clu!:r ISemi-distrihuted As above, hUl for larget-scale Usc of larger sub-areas as elementary Limited p()s~ibililies of applying Iphysically-based or investigations Iconceptual (IS) IReal-time hydrological forecasting ' ;;;:::::i~yn;::."ge~_~rung~~cd fl :~;~~~~nnf~·~:_o:~~:ro-~nd -.- -1:_ 1- IJdIlIDIl or areas uenvatton 0 .Ileverou. parameters vy __ I - -~• ..::.. 1 __I .:~~" -~r ...... -ien:li-zatio... ::~ ~::a:;:: :,:.::~:a:~:res·~:U:i~:~:.C~ I ::;:::;; with, II nnt .-jr- t-= ------_ characteristi£L.....~_~_. ~__ 11~~abl~..f2L !I!I!i!U:-.!cal~ i!1"y!l'5!!Ki!.ti5.)D! _ I IRelativelv easy to_understand and use~ friendly in application ------I-:'~~~Ptab]e" amount of input data for I I mod~l cilibrati~~ and Qperntion ~n - I I -"-siriaU" computers - - -- ILu~~e~ c.onceptual I~~t~a~~lation of.tune senc~ a~d ~l Eas: to ~~,derstand and to bpe~le, eVen I;e; limited, :a"~e ~f application Of macK. !JOx. - I~~:Kdi;c:~:~Xlln~e l'remcl1ons 01 un SlIllill CUlllpu~ellr, \Otll~ ~~ug~u uasllIsj ." I(LS,LD) IReal-time hY~ro!ngi£atfme~asting_ ~iii~~OUn[ reqll~re~~!~[d_a~:~ ~:;::;~ :~-a::~:~~s When larger rivet basins are considered, it is necessary out carefui scientific soppoti and pennanent conitoi will iead to use a combination of a mode! for the river or surf'ace to !argeragriculturID yields only i'lidally. !!.-will-later le.HrI to water system itself (CS in T,!Ne 2). ~.i!h _riy~r:l)a.si!'-'I1 26 HYDROLOGICAL MODELS FOR WATER-RESOURCE-SYSTEM DESIGN AND OPERATION INPUT (Confinuous Rainfall, Evapaflofion, Radiation I 7l!mpeflatufJe I Cloud Covefll Windl Tide, lJiveflSions, etc.) 1 ------1 r------Precipitation I I ather dam I I I Snow I --JSnow lreltf-- -- stOf'Oge I !t I Evapo- I -jStOI'Oge transpirotiOn Inteflception I I I Impervious I ~ area I I I I Infiltration Overland flowf- I I I I ~ ! upper I Calibflation I IStoragel zone I using many 5011 I parameters Uppefl zone defining ~ rInterflOW{ I I depletion I catchment ...... charocte- I Lowefl I ristics I H5torage l--- zone l Groundwater> I I • I I IPercolation I '- I I Channel / Groundwatefl I I storage inflow I / + I Inactive Re servoirI-- Channel f--- I Diveflsio/7l 5toroge operotion r- routing '- I t I. StfleGmf70w I I L------~------_~ t OUTPUT (Continaus Streamflow) Velocity) Stage Figure 12 - The Standford watershed model IV 27 -rI tess -pel'l77tJobie I Solupoled flow model (reclrJnguloP3rid rO'. aedrQclsL 1- dimims/onol unsofupoted zone - model fa/' eaclJ gr7d element Figu:n~_ 13 - Structural scheme of ~uiver basin with indication_ofimportant sub-system TABLE 9 Sub-areas ufa river basin ",lIh slguificantly dllTerent hydrological regimes I: Open watersurfaces _ rAW .t Potenl~a_l Eqiiaiio 'totafpreclpliallon"(lOO%j " 1- Impervious areas (rocks, I AIM P I Only during rainfall, i.e,-near-fN"earlowfpreclpft'"ation (near 100%) _.~. II..... Pfj8ved:, etc;)...... _. _.. ... __ . _ ilt areas wllh~ - d"p"- gfOiliidwater ~ ~A.S mois~l;e If shalluw gruuuuw 28 HYDROLOOICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION Surface area A of tile drainage basin AH, AII'fP AHz--,f--- AW---;f--AN-- AIiADD ER AS ~ ~ ------ '»f!dracl< Sedimentary permeable layers Figure 14 - River basin cross-section with indication of sub-areas of different hydrological regime Typical activities of the aforementioned types are listed in and 4.7, are also useable in principle for real-time fore Table 10 and grouped according to (a) and (h). The casting. They compute system states and outputs from hydrological processes which are affected to a greater or given inputs and initial conditions, as shown in Figure 9. lesser extent are listed on the right of the table. As no model can perfectly represent the real system, Taking account of activities of types (a) requires a simulation errors occur in any model application. In real-time distributed or semi-distributed physically based model ofthe forecasting applications, considerable efforts are made to respective land surface area. The parameters of Ibis model minimize these errors using so-called updating procedures must be identical wilb or clearly related to the characteristics which take into account the last observed values of the fore ofIbe prototype system (see Tables 4 and 8). Itis then possi cast variable (e.g. discharges or water stages) or, more strictly, ble to adjust the relevant model parameters according to the the differences between the "simulated" and last observed expected land-use change (e.g. root deplb, infiliiation capacity, values of the forecast variable (see e.g. Anderson and Burt, moisture storage capacities, etc.) and to predict the conse 1985). Thus, a real-time forecasting model is generally a quences on the hydrological processes and on the water combination of two components (Figure 17): resources with given system inputs as observed or predicted. A simulation model; Thesepredictions were inlroduced in section 2.3 as activity (h) (pL in Figure 2) in Ibe fIrst phase ofthe planning process. An updating procedure. Activities (h) in Table 10 togelber represent one of the Updating procedures are continuously applied in real main subjects to be investigated in Ibe second phase of the time forecasting. They do not involve the periodic planning process (see section 2.3). They are of interest not recalibration of a simulation model, which is necessary only in the slructural planning (PD), but also in the derivation only after important events (e.g. major floods) or system of efficient management siiategies and control rules for changes (e.g. due to human influences). How updating hydranlic slIuctures (pC in Figure 2). In all cases, it must be generally affects a forecast hydrograph is illusiiated in the possible to make specific slIuctural modifications of the lower part ofFigure 17 (Serban, 1988). modelled water-resource systems in line with the relevant Considering the different possible origins of simu- slructura! planning, and this has to be considered in Ibe choice lation errors: and development ofa model. This also ensures that different management or coniiol siiategies can be investigated. Errors in the input data; 4,9 Particnlarities ofreal-time forecasting procedures Errors due to imperfect model slructure, limited number ofcalibration data, changing system conditions; Models which simulate a definite process, for example flow routeing or river-basin models as described in sections 4.6 Rating curve errors 29 Evapo~ran.plratlon J ..,.".. --.l C" T AW Sh..llQv (J~91!JHt I!I!pwl"- V~t~H' ------water J.r.... -YTo-ua - -Surtao-Ii FigUre 15 - Compositions ofa semi-diStributed river-basm mouel on lhe basIS of a zonmg conception (Model- EGMO-afte1'-Becker--and-Pfdtzmtri-1-981) ------ a) 0) Figure 16 - "Zoned" TANK model (after SugaWaIRet aI., 1984) as a special version ofa serni~distributedriver·basinmodel a) Sub-areas with different water storage behaviour . - . b) ReiatedTankmOdeY(t6iIei:iJ~ ~ -... .__ - _ _ _. 30 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION ThBLE 10 Automated procedures are reproduceable and fuJIy Different types of human activity which influence Objective. This point is sometimes used to justify their use hydrological processes and water resources in preference to manual procedures. The manual-interactive procedures are based on the Basic Human activities Processes practical experience of the forecaster, and so include a type influenced certain amount of subjectivism. In applying manual procedures, interactive programmes are required which AGRICULTIJRE (a) allow for a real "man-machine" dialogue. Irrigation ET/WS/SG Some models use both automated and manual Drainage RO/WS/SG procedures. In these cases the automated adjustment Soil erosion coorol RO/QS/WS schemes are used only as instruments for assisting the user Agrophytotechnology RO/QS/WS/ to adjust the model to the observed conditions. After ET applying the automated procedures, the forecaster reviews Agrochernistry the model state and compares it to the data measured over pollution, WS/SG the basin. If there are errors the user subjectively changes Phytopathology } certain variables and runs the automated procedure again. SYLVlCULTIJRE Input variables to be updated are precipitation Afforestation ET/RO/QS (rainfall, snowmelt), air temperature and the snow-covered Deforestation ET/RO/QS area. In some cases snowmelt is updated as an intermediate Torrent alleviation Q/QS variable (snowmelt model output being a rainfall-runoff URBANlZATION ANa model input). INDUS1RALlZATION Most procedures which update input or state variables are iterative and of the "trial-and-error" type, as with many Urbanization All processes models it is difficult to solve the reverse problem: to (b) Groundwater use SG estimate model input when model output and parameters Surface water use Q are given. Sewage discharge (pollution) Q/(wG/SG) The most important stages in this type of procedure, RlVERENGlNEERlNG which are carried out at each forecasting step j, are: WORKS, CONSTRUCTION OF (a) Computation of the error ej (difference between the Reservoirs Q/QS measured and simulated system output at time}); Dykes Q • (b) Comparison ofej with the allowable threshold error; Diversions Q Note: ET ¥ evapotranSpmtbOn (c) Adjustment ofthe selected input variable(s) taking into RO - overland flow account the defined adjustment increment for each vari WS - processes in the unsalUrated zone SG - processes in the saturated zone able and the maximum uumber of increments ofchange QS - erosion-deposition-transport allowed at any computation step; Q - channel flow three different sets of variables are taken as the snbject of . (d) Re-running of the model with the adjusted input updating: va(iables. (a) Inpnt variables (in particular raiufall and/or snowmelt); State variables to be adjusted are: waler equivalent of the snpw cover and levels of water storage at the surface, in (b) State variables (Waler storages); the unsaturated an<~. saturated zones and in the riverbed. (c) Output variables (discharges or related variables). One justification for'updating state variables is the fact that errors in model inputs are primarily accumulated as errors Certain models make use of updatiug procedures for in the water storages 'of the simulation model, which then both iuput aud output variables, whereas others use the directly cause the errors in the output variables. updating of both the state and oUlput variables. As a rule, The amount of water stored is often updated by means model parameters are not updated. because iu· most·models aLi Kalman filter. In applying the linear Kalman filter, the they are not independent of each other and the modification folloWing is required (Gelb, 1974): of one parameter would call for the modification of other parameters. Manipulation of parameters and studies of the A description of the system dynamics by means of a relation between them should preferably be carried out system oflinear equations of the form during calibration and not on a continuing basis during forecasting operations. (17) The updating procedures can be classified according to A definition of the measurement equation relating the their means of application as follows: measurcments which are generally carried out on the I Automated procedures; system output to the considered state variables: II Manual-interactive procedures. (18) 31 where X = vector of the state variables, describing The V and W errors are considered as independent and -----normiiijy-dEtributed~ -the system evofiiifoii;------m ------II - a COTItroi vec:torcuntairring the input Ttre-use'ottf'1tf1h"'e-1K~a1h,nn"atn11f'i1i1ltotee>,c-lhle"'Ih-1''''s-1imllHt'fh",ehc"o"'''lstiidtlet'c,'-'altition ¥aciables; _ _ of the most significant error sources (input and output !/> transition matrix; variables, non-optimal parameter values) and it is on this I = inp~iadj;;-stffietit matrix; -account that the discharges -can be computed logether with W =- modelling errorvector; -their confidence-llmm: ------.-- .-- z measurement vector; Output -variables to -be updated -are discharges~ H ;:: measurement s~lection matrix.: _water-levels, fJood volume, I)ydrograph sh~pe orrelated v!,,"i V ~ measurement error vector. abIes. The discharge.updating procedures most widely used The matrices !/>' Qand H defi'-=n"'inOCg~th=e~cIh~ara=c~te=n~sZtlc~s~o=f -in practice are based on auto--regressive models that simulate u'1e modelled system call be COfisumt or variable Jil time. ·Lie errors between t....i6 computed and measured hylli---ographs. I Q /.,\ (upstream)------!Q."1 m_ m mumu_ nun_ 0-1" nF, /'1 I ''jiMULATiON" IQ5 - r. UPOATii'fG f QF ----PRoa:ouRc--I--~ '9---1 i-10D£L Ni ... - 1-IL..--__--.-__---1 t I •. u u --- t - - • I I • ~ -----J-----m~ 'oll ------_m--4 ------ .j" r ~J_ .. _ fI .... ''''-'~J-- -- , ~::: 1:~j(:/Q5 I _ /f' L/ ! J -- /e~L[-- 1 -/;"'--r-+---- l -' >/ I ,__ L_:_- _ I J I ------l_----...L._-----_-_--J-l-:.~_ ~...... ,;.,...... - --r ---. f _ u.d._ j J+/ time p raimbil-·····-·············-----· ... J iorecasting momeni M OoLite! me!eO!ologdal fm;!nrs QF forecas!e.d hydrograph QS ,imulatoo hydrograph e simulation- - error- QM measured hydrograph £ forcecasting error Figure 17 - Block scheme of a forecasting procedure and of the principle effect ofupdating 32 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OFERATION Some models use an efficient procedure for discharge as components of the Hydrological Operational updating based on a recursive relation of the type Multipurpose Sub-programme (HOMS); QFj+! = k,QMj +!'J.Q (19) (b) The results of different surveys (enquiries) of the WMO Regional Associations; where QFj +! = forecasted discharge at momentj+I; QMj = measured discharge at momentj; (c) Other specific publications. k, = recession coefficient varying as a function ofdischarge; HOMS was approved in May 1979 by the Eighth !'J.Q = inflow due to rainfall and water released by WMO Congress and initiated as a means of facilitating the snowmelt at time stepsj andj+l. transfer of available hydrological technology operationally used in the fields of network design; hydrological Forecast updating procedures are generally efficient over observations, coUection, processing and storage of data; lead times smaller than the system memory. and hydrological modelling. Updating procedures ofthe "trial-and-error" type for the As one of the first activities within this sub input variables are efficient both on the rising and on the programme, a standardized format was designed for use in falling limb of the hydrograph and they yield the best results compiling brief descriptions of the components made for snowmelt floods. available through HOMS by the various institutions and Updating procedures for the state variables based on a organizations concerned (see Annex 1). Each component Kalman filter imply model linearization and its rewriting in can be requested by any other institution or organization in state space form. The programming is difficult and, in addi the world which is interested in using it. Thus any model tion, the memory occupied by the program is large and the or procedure included in HOMS can be considered, by program takes quite a long time to run. This procedure is opti definition, to be available for practical application. All mum from a mathematical standpoint and yields satisfactory descriptions of HOMS components received by the WMO results with short lead times. Its efficiency, however, is still Secretariat and approved by the competent bodies of the open to discussion for longer lead times and with respect to the WMO Commission for Hydrology are included in the flood crest, because the hydrological conditions may differ HOMS Reference Manual. greatly from those at the conditions at the time offorecasting. The first edition of the Manual was published in 1981 The updating procedure based on a Kalman filter is (WMO, 1981) and the second edition was issued in 1988 advantageous inasmuch as it allows the determination of the (WMO, 1988b). It is in the form of a loose-leaf collection confidence limits of the forecast discharges, which are very which can easily be supplemented, "updated" or otherwise useful in assessing the uncertainty in forecasts. revised. For the present report, all descriptions available in Updating procedures applied to model output variables sections J, K and L of the Manual in July 1988 were and based on autoregressive models are easily programmed analysed and classified according to the criteria defmed in and applied with a short computation time. Their efficiency Tables I, 2, 3 and 4 in Chapter 2. The result, which is depends on the degree of error persistence between the presented in Annex 3 and summarized in Tables 11 and 12, measured and compUled hydrographs. Accordingly, updating is analysed in the next chapter. procedures based on the autoregressive model yield very good The series of enquiries mentioned above under (b) was results along the falling limb of the hydrograph. initiated by Regional Association VI (Europe) at its eighth Unfortunately, in the vicinity of the flood crest, error persis session (Rome, 1982). A questionnaire on the charac tence is least and errors may show a tendency to oscillate. teristics of operational hydrological models used in RA VI was prepared; a copy is reproduced as Annex 2. It sought 5. EVALUATION OF THE AVAILABILITY OF the following information: HYDROLOGICAL MODELS Name of model, author, institution, country, place 5.1 Criteria and sources nsed for evaluating model ofapplication; availability Model objective, algorithm and outcomes; An enormous number of different models exist within the model categories considered in the previous chapter. Most Modelled processes, including human influences; of these were and are the subject ofresearch and have been Required data for calibration and application; applied only in a research mode. Others have been successfully introduced into practical applications and are Data acquisition, transmission and processing. now used routinely for practical purposes. The primary The 63 replies to the questionnaire were carefuUy emphasis of this report is on the latter models. Therefore, analysed and the results of the analysis presented in a the practical use and applicability of a model is taken as the comprehensive report by Serban (1986). Important parts of primary criteria for evaluating its availability. Three main that report are included in the present report. The sources of information were used as a basis for the summarized outcome of his analysis is attached as Annex 4 evaluation in this respect: and will be commented upon in the next chapter. (a) The HOMS Reference Manual (WMO, 1988b), Similar enquiries were also initiated by other WMO sections J, K and L of which include hydrological Regional Associations, the summarized results of which are models and procedures which are generally available presented in Annexes 5 to 7. These results and the other 33 publications referred to above under (c) will also be (b) RA - from replies to the inquiries in Regional discussed in the next chapter. ------A:ssodau-orrs-f (Africa), H (A~h:,), HI (Sout11 America) amI vI (Iiurol"'). 5.2 HydrologicaL!!!.illlel JlYi\i!ability according to The infonnation presented in columns 15 to 17 ofThble different surveys and sources _11 on models. for va.riables ..other than discharges and w.ater As already mentioned, the results ofthe analysis ofsectio.nsJ", levels is more or less accidental and not representative from a K and L ofthe HOMS Reference Manual and of the replies to -gcneral point ofvlew'-Itlsonlysupplleifbecause ofits avail· O,e enquiries in Regional Associations I (Airica),n (Asia), III -ability from the SOurces of infQtmatioirconsidered. The (South America) w."1d VI (Europe) ur~ presented in -..'\nnexes 3 -identifiers included with the numbers h"!_rQl~m!!s 15 to 17 to 7, and in summarized form in Tables 11 and 12. _ ,relate to me purpose ofapplication Of the re''Pective model. In Annexes 3 to 7 the following information is In addition 10 the information presented in Table 11, presented for each model or procedure: ' 'some information on the availability" of groundwater models WaS derived and is reflected in Tublc 12. A.lthough 1. Country oforigin of the mOdel; this. information cannot be considered as complete~ it 2. Name of the model; indicates that groundwater models are available for 3. Model index (composed of the official ISO different practical purposes, in accordance with Annex 10. abbreviation of the countrys-naiffe-ailli -a cunni-fig -,- In'pnncipie";-buth- tabie,,'are lmif·expianatory:-Ris number); clear [hat conceptual lumpe-d models are best reptesente.d A and widely applied for the soll!tio of pra~tical problems}n ~. Purpose of the mode! application chanu;t-erized by t.l:!e ll identifiers given i!! Table I and Figure 2; hydrology, for real·time forecasting as well as for predICtiOn;' plannInj(aiTII -ae8lgn-j;urPOiies;' H'l"wever; 5. Type of system mo 34 TABLE 11 Availability of hydrological models for surface water bodies and systems, surface areas and river basins Variables: Swfac~ Waler lel1els ordischarges Q DIMrs Purpose ofapplication ~ Forecasting Predif;tion (P) Motkl type Space discretization 15 Type of Evalua- Without With Observed With land- PlaMing and FuNi.nu",tol COfll:eptual Blackbo:% Distributed Semi· i.ump,d Temper- Erosion, Water system tion control control conditiOIl$ use changes design laws dirtribwed Q/UT' sediment qua/lily source i FO Fe PO.OP PI. <10 PD >10 OL DC OB IG IS L 1W QS WQ I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ~ SmaIl """ HOMS 13 6 3 1 1 2 18 8 5 21 1 RA 3 ~ SA 23 11 7 I 30 11 1 39 (PL) ~ River reaches HOMS 7 1 8 8 15 9 2 13 2 11 3 RA RR 16 1 3 6 8 13 3 8 16 PO w '"" Reservoirs. HOMS 1 5 6 3 9 3 2 7 1 1 1 Ia RA lakesRL: 2 2 1 1 7 1 7 (PO) (PO) (FC) ~ Surface water HOMS 1 4 5 5 4 5 2 2 1 '" systems CS RA 6 2 1 2 6 3 5 pd ~ Large river HOMS 8 4 2 3 6 1 20 2 10 12 1 2 RA basins CB 8 16 2 1 4 31 3 28 (PO) (PO) ~ ~ ~ ~ ~ TABLE 12 real systems to be modelled. This means that they are not AvaiiabilHyuf gruumlwater lIIudefsHl:ClJrding to iiieH(f~fS------dirf:.ctly-trarrs"ferrabJ"0;-t)rrthe oilier ii~IIdl fll'cJgram packages Reference Manuai and resuits- or-enquiries Iii d9 exist in a generalized form (e g in Haimes et 0' i 1987).. RAs-!,JI,-!!!,!lIId-VI of WMO Tbey are, ho.wever, oot generally available, for example as HOMS components. Rainfaif.runoff models and the related software caiJ be Source Groundwater .I~ - classified as well-d3V613pOO-ooooraing-to-the above-mentioned .. ml_ · UN Technic~1 Report (1986). Tbis conc«rns, however..only LevelEtorflge I ~uatlly I those models which are constructed from classical conceptual FO · sub-models,-and other simplified sub-process models' (for P~±H - infiltration, moisture accounting, streai11flow routeing, etc.) as I-HO- MS--II---+-7-1 4 I · characterized ill se.ctions 4.2 to 4.5 7 4.7 and in Table 8. Semi... ,-R,-As_.J..-....:....t ...... L..~9_I 3 II. I distributed or distributed physically based river basin and (a) Elementary mathematical sub·models and basic other hydrological land smface models, in particular those that relations such as regression relations, empirical aHow for an investigation ofeffects and influences ofhuman ielatioils; convolution equations, diffeiCntial or activities on the hydrological regime and on water quality, are difference equations, ap]lJ'Slxim.ale anal}'lical ~t.iJJ jnJhc_r~~wch :mg..qey.el.Ql1m~n.t~tag~. Thi~j~. (1jS!;\!~$~(\ solutions, equation solvers, etc.; in sections 4.7, 4.8 and 6.5. In principal, while they are not yet widely available or (b) Elementary hydrological land surface sub-systcms used, physically based meso.. dJ,d macroscalc models for land models, for example for interception, infiltration, xmface..lmitcar.e.. r.equjted...wiJ.bin. ..tb.e .World .Climate rainfall excess, soH moisture accounting, Programme (WCP) in connection with Atmospheric Global evapotranspiratioR-,- sheet flow, -snowmelt and-others; Circulation Models (AGCM) ana for other pmFoses'- 'fhis'ls (el Rivef~feach an-ct-channet-reach models, which include: discussed in Chapier 6. streamflow-routeing models. .(.eoncept!laL linear and ---F-ul'lhel'-reEearGl!-l!!!dOOw!epmenl·work-ig.aJg(l.nwJgl! in non·linear models, hydrauliclll_odels with distributed Jhe_fiel(1.0fC()1J)l1 e Also available are (b)-elementary hydrological i~nd HowCVel 1 the--carrbrated-nrodels are not directly useable for surface sub-system mod0~s-. -Specifi{;[email protected] investigating the effe-cLot: climate_or latId-use change.s on problems in modelling some of these suh"proc6sscs arc UIC hl'drolog!c~,- regi~I~. ~n~wate~res.oure_es" an 36 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION (a) Intercomparison of conceptual models used Still under development are forecasting procedures for in operational hydrological forecasting (WMO, 1975); water quality parameters, sediment yield and ice conditions in surface waters, although a number of procedures already (b) Intercomparison of models of snowmelt runoff exist and are applied for operational purposes. (WMO, 1985); (c) Simulated real-time intercomparison of hydrological 5.6 Procedures for planning and design models (WMO 1987). A situation similar to that for real-time forecasting and In connection with those projects, generalized techniques control is found in the planning and design of water for model testing have been developed and criteria character structures and water-resource systems. This situation is izing their performance have been introduced and applied. adequately reflected in Tables 11 and Annexes 8-10, and These can be recommended for general use. also by some recent United Nations and Unesco publications (United Nations, 1986; Haimes et al. 1987; 5.5 Procedures for real·time forecasting and control Zuidema et al., 1987; Lowing et al.. 1987). According to these publications and to generally available information, it The progress achieved in this important field of model can be said that adequate procedures for operational use do application is well documented in Thble 11 and in Annexes exist for nearly all the following sub-categories of 3 to 9. This concerns nearly all sub-categories of real-time procedure for planning and design: forecasting procedures as listed below: (a) Basic purposes of planning and design, such as (a) Stage and flow forecasting in rivers using hydrological generation of hydrologic time series, opti-mization, data as boundary conditions, i.e. upstream, downstream economic evaluation, etc.; and intermediate inflows, outflows, stages, etc.; (b) Long-term prediction of available water resources; (b) Basin runoff (discharge) forecast from hydrome teorological input data (including urban runoff); (c) Planning of watershed management taking into account effects on water balance components, the (c) Seasonal flow forecasting, including low-flow hydrological regime and water quality; forecasting; (tl) Estimation of flood parameters (peak, volume, (tl) Forecasting of soil moisture conditions (including exceedance probability, design flood, etc.); water demands for irrigation); (e) Flood control planning, flood plain management and (e) Forecasting ofgroundwater levels and flows; urban storm-water management; lfJ Forecasting of freezing and break-up of ice in rivers, lfJ Planning and design ofirrigation and drainage systems; lakes, reservoirs, etc.; (g) Determination of design parameters and management (g) Forecasting ofsediment yield; strategies for reservoirs, in particular multi-purpose (h) Forecasting ofwater quality; reservoirs; (I) Realtime control ofhydrological processes and water (h) Deriving ofstructural developments and man-agement resource use and management (quantity and quality). strategies for complex and large-scale water-resource systems, in particular for river basins with reservoirs. According to the state of model availability as Some of these procedures and techniques can be characterized in sections 5.2 and 5.3, procedures for considered as "well developed", in particular for water forecasting water stage and discharge «a) to (c) above) are supply networks and sewer design, urban storm-water the most advanced and are in routine use in many countries. management and floold control planning «e) and (tl) This is confirmed by reports recently published on above), planning and design of irrigation aud drainage discharge forecasting from precipitation (Zhidikov and Romanov, 1989). A number of routinely used procedures systems (lfJ), reservoirs«g» and water-resource use and management in river basins also exist for soil moisture accounting and forecasting. «h». However, this is not reflected in HaMS. Further development work is required in two directions: A characteristic and essential feature of most procedures for real-time forecasting, in particular short The involvement of new, more comprehensive and complex models with improved physical bases, as term forecasting, is their updating capability. This was characterized in sections 4.7 and 4.8; considered in section 4.9 (see also Anderson and Burt, 1985). As stated there, some models used in real-time the development and introduction into practical forecasting have an updating capability inherent in their application of computerized interactive decision structure, because the observed data of the forecast variable support systems, expert systems, etc. that combine, in are used as input variables in the model (e.g. difference a user-friendly manner, data-base management, equation-type models, ARMA models and others). Other simulation and optimization techniques, heuristic models use a separate procedure for updating the forecasts qualitative approaches and possibly also artificial (e.g. a Kalman filter). intelligence methods (Loucks and Fcdra, 1987). 37 5.7 Summarizing remarks promoted the development, use and hence the availability .. _. -___. ------;------of-rcmthem1illcm-mooels-in--hydroluift find wilter-resOurces The most slgOlfJcant p~ogress m the field of hydrologIcal planning ann management It bas reslllte 38 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION (d) To solving other complex problems (Loncks and All the above conditions and constraints can seriously Fedra, 1987). limit the availability of models, even if these models are well established and tested. They therefore have to be It is clear that further developments in computer considered in the evaluation of model availability. technology are still in progress and that new software solutions and packages will take advantage of the increased 6.3 Existing barriers in hydrological modelling possibilities. There are barriers which currently hamper developments in 6.2 General prohlems in software transfer hydrological modelling and which are likely to remain in Applications software is at first always of the research type, areas of importance for future hydrological research. These developed for a particular problem or project, often are excellently summarized by Kundzewicz, Afouda and inadequately documented and not generalized for various Szolgay (1987): applications. Nevertheless, the software is often given I. One factor that makes hydrological modelling difficult is away free of charge or at minimal cost. This can easily lead the high level ofnoise and uncertainty in hydrological systems to frustration for the user becanse the implementation is as compared with other dynamic systems. Hydrological generally difficult, time-consuming and not without danger. systems, being ofnatural origin, are irregular and non-homoge Basic assumptions underlying each model or program may nous. The signal-to-noise ratio in hydrology is significantly be misunderstood, or "local specifications" not transferable. lower than in most technical systems. However, many signals To detect such specifications in somebody else's source are often neglected and regarded as noise in order to focus code and to replace them by others is often very hard or attention on "pet" hydrodynamic signals within the concept of impossible, in particular if one has only an executable the hydrological cycle as a big hydraulic machine. program version and little documentation. In such cases it is often necessary, or at least very 2. A characteristic property ofhydrological systems is the helpful, if the original developer of the model or a very limited possibility of performing active experiments. The experienced user is able to assist in the model transfer to chance to design active experiments hardly exists. the specific location of interest, and if that person Typically, geo-physical, including hydrological, experi participates in the model adaptation and calibration. ments are performed by nature and the scientist can merely In other cases well-developed programs are acquired passively observe them. After having observed different by "software houses", promoted and then sold under events, those appropriate for further study (e.g. those most different brand names and in slightly different versions. suitable for model identification) can be evaluated. The advantage of these programs is that they are more 3. The passage from the descriptive to the causal stage, likely to be fully operational and can be backed up by the seen as the mark of reaching maturity, has not been gener supplier. There is definitely no guarantee, however, that the ally achieved as yet in hydrology. As no solid causal programs are really "good", and that the underlying models foundations have been developed, many methods have sufficiently represent reality (United Nations, 1986). been introduced from other disciplines and used to redefine This means thatin the evaluation ofmodel availability it hydrological problems rather than to solve them. One can is always necessary to consider the status ofthe model and of expect more significant efforts to be located in the areas its program documentation and support, Le. to distinguish where descriptive hydrological methods are not yet super between "well-developed" and "research" software. seded by causal reasoning. For example, the empirical Other problems in software transfer and general regionalization equations obtained via multidimensional application which may reduce model availability are regression analysis that are still used cannot be regarded as (Loucks and Fedra, 1987): a satisfactory representation of hydrological processes. The increasing dependence of users on available 4. The choice of a probability density function (p.d.f.) for problem-solving software packages in the fields of describing hydrological variables is still affected by subjective hydrology, water resources and environmental reasoning. None of the existing p.d.f.s result from genetic management, in connection with the often insuffi (physical) onsiderations, and it is a case of matching abstract cientlevel of the users' understanding of the theory formulae with existing hydrological data. This is particularly behind the program and model, so that erroneous apparent in the analysis ofrare events. As the existing lengths results cannot be recognized by the user when they ofrecords are rarely satisfactory from the statistical viewpoint, appear and the necessary corrections made; conclusions concerning extremely rare events remain largely The persisting difficulties in transferring application uncertain. Depending on the choice of the p.d.f. used for software from one computer system to another due to extrapolation, different values of a I OOO-year flood may be different hardware configurations, operational obtained and this can have important economic consequences. systems, graphics software, etc.; Itseems desirable to strive towards the development ofp.dJ.s which take into account the physics of the process (e.g. via The existing variety of magnetic storage media in the physically sound stochastic differential equations). form of at least four different sizes of floppy disc, incompatible tape and disk drives, multiple formats 5. There are at present two inherent limitations to the used on such media, etc. development of causal models in the hydrological sciences. 39 On the one hand, the available data may not suffIce for the lor example, lor small sites, elementary unit areas or plots, CGrtst'uctkm -of cause-effect rc1atiooships-;--whereas--an-the------hydrGtopes--or--hydrolergieaHy "homogenooils'l sub-areas. other, known _theoretical causal laws maylleJosufficient for ~arger areas and scales their application becomes construction of an cxact model becanse of the complexity questionable and critical. The reasons were extensively of feedbacks and interrelations in natural systems. Progress discussed in-a number of pUblications. The follow-ing in causal modelling cannoi-be based 011 IIIore manipulatiun -selected quotations characwrize the problem: with our pre-sent Umite-d hydro-logical knowle.dge. ThL<;Lhas- obviously been the case in the history of mathematical Fiering (1982): modelling in many areas of hydrology. one of the assumptions frequently made is thal our understanding of the microsome elements and proceSses 6. The identification and verification barriers are still very (in t.~e hydrologic cycle) can, with minor modificatiaTIs. apparent. This is particularly trne for the interface between be extrapOlated in principle to thc undcrstanding oUhe point and areal hydrology dUe-to the stron-g spadai macroscale environment, thus enabling reliable variability ill nat!l!'al rharacteristics. predictions to be made by linking the solutions to form 7. Another important ballicr is the separation between a causal chain. Unfofttifiately. it seldom happefi~ ih~ probabilistic .and d.eterministic approaches, A pure. way_ Sooner or later, at some scale or characteristiC deterministic relationship penaining to conceplS ralher lhan __ ~illl"l1s1lJll,-rnl'(;hlll\i~~c _"XJJIll'!at!oll brea]{s__cl0\\IIIlllll1_Ls to evenls can be realized only und-ir -strictly- controiied necessarily replaced by unverified causal hypotheses or conditions. Althongh Ihis was recognized by hydroiogislS statistical representations of the processes. more t,lmn two decades agol scientific methodology of this McNaughtOn and jarvis (1983): complex type is still f"Lfrom being well shaped and tested. -- -There a,e--m;,--gelrerai -laws--o-f-ptanrre-spollse -co- It is not yet certain what the most effective combination of deterministic terms and uncertainlYeIenuiiiiliis.- --- -environment. PIa.'ll form and -function- are -both -highly- variable. Our basic knowledge consists of a collection 8. Another impediment to progress in hydrological of empirical examples, all different but with some modeHing relates to scale and cohcepliiilTIzatioi!. This is -commonTreii 40 Classifications Space Scale Atmo Hydrolo Geographi Di Area Allocation of Important Activity gical cal spheric stance (km Z ) Areas in Hydrology (m) ~ ~Earth 1:l I T II Global Surface Required ~ Water Balance 0 T------707 -----: Develop- of the Earth. ~ Il:: U ments g Regional of Continents « (Continents'~706 I ... ~ u 1Mio - ----J and Regi ons ~ ..L ~ Regions) 'T' 1 '"r Basin Water Balance ~ 5 10000 laJ ---- -1-10 __ and Process Simulation ~ 10... 1 Sub-regional ~. in Large Basins. Areas Figure 18 • Time and space scale in hydrological modelling H\'DROLOOleMo MeBE"'l FOR WATER-REsolJReE SYSTEM DESIGN AND OPERATION conditions for which they have been calibrated (by using A separate report would be necessary to cOver the full avaiiabie streamfiow records, ete.J;-nrnt'~forexllffipl 42 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION effective and comprehensive use of remotely sensed data. Bathurst, 1. C., 1988: Flow processes and data II should be mentioned that remote sensing can help to provision for channel flow models. In: Modelling overcome many of the problems concerning the availability geomorphological systems. M. G. Anderson (Ed.), of input data for models which are discussed in this report, John Wiley and Sons Ltd. particularHy those referred to near the end of the Becker, A. and Pfiitzner, B., 1986: Identification and Inlroduction. Therefore, the availability ofremotely sensed modelling of river flow reductions caused by data and development of models to use such data are evapolranspiration losses from shallow groundwater areas. matters ofconsiderable importance. 2nd Scientific Assembly of the IAHS (Symposium S2, This list of problems and demands in hydrological Budapest, July 1986). lARS Publication No. 156. modelling could easily be continued, especially in light of the barriers explained in section 6.3 (Kundzewicz Becker, A., 1987: Project on the intercomparison ofmethods et al.. 1987). and models for estimating areal evapolranspiration-Project description. WMO, Geneva. 7. CONCLUSIONS Becker, A. and Kundzewicz, Z., 1987: Nonlinear flood routing with multilinear models. Water Resources This report has given a general overview of the avail Research, Vol. 23, No.6, pp. 1043-1048. ability of hydrological models for the solution of practical and research problems in the field of hydrology and Becker, A. and Nemec, J., 1987: Macroscale hydrologic models water resources. in support to climate research. In: The influence of climate A number of well-developed models and related change and climatic variability on the hydrologic regime and computer programs are available and in operational use for water resources. Proceedings of the Vancouver Symposimn, different real-time forecasting and conlrOl pUIJloses as well August 1987. lARS Publication No. 168. as for planning and design, including long-term water Becker, A., 1988: Deterministic modelling in hydrology. resource management. Lecture notes. Nineteenth International Postgraduate Nevertheless, some serious gaps exist in the speclrum Unesco Course on Hydrology. V1TUKl, Budapest. ofrequired models. These concern, in particular, Beven, K. J. and Clarke, R. T., 1986: On the variation of Larger-scale hydrological land surface models; infillration into a homogeneous soil malrix containing a population of macropores. Water Resources Research, Models of the soil-plant-atmosphere continuum Vol. 22, No.3, pp. 383-386. for estimating areal evapolranspiration dependent on the conlrolling meteorological, vegetation, soil and Bergstrom, S., 1976:' Development and application of a other parameters; conceptual runoff model for Scandinavian catchments. SMHl,RHC7. Water-quality and all kinds ofecological models; Black, T. A. et al., 1987: Estimation of areal evapo Models capable of directly processing remotely transpiration. Proceedings of the Vancouver Symposium, sensed data and information, in particular radar and August 1987. IAHS Publication No. 177. satellite data for real-time forecasting and other purposes. Bredehoeft, J. D. et al., 1982: Groundwater models. Studies and reports in hydrology No. 34. Unesco, Paris. An entire set of technical reports would be required to cover the whole field of hydrological modelling, describing Brutsaert, W., 1982: Evaporation into the atmosphere. D. the various types of model in existence and the demands Reidel Publishing Company, Dordrecht, Boston, London. and directions in recent and future modelling. This report Bultot, F. and Dupriez, G. L., 1976: Conceptual hydro has only been able to give an overview and to outline some logical model for an average-sized catchment area. Journal important aspects and future demands in hydrological of Hydrology, 29, pp. 251-292. modelling. Hydrological modelling will clearly be a slrong growth area in the decades to come. Bumash, R. J. C. and Ferral, R. L., 1980: Conceptualiztion of the Sacramento Model. RA II-RA V Roving Seminar on 8. SELECTED REFERENCES Mathematical Models Used for Hydrological Forecasting. WMO H/S-46/DOC. 7, WMO, Geneva. Abbott, M. B. et al.. 1986: An inlroduction to the European Hydrological Model "SHE". Journal of Hydrology 87, Cebeci, T. and Bradshaw, P., 1977: Momentum transfer in 1986, H. 1/2, pp. 45-134. boundary layers. McGraw-Hill, New York, 391 pp. Abriola, L. M., 1987: Modeling contaminant Iransport in Crawford, N. H. and Linsley, R. K., 1966: Digital simulation the subsurface: An interdisciplinary challenge. Reviews of in hydrology: Stanford watershed modellV. Technical report Geophysics, Vol. 25, No.2, pp. 125-134. AGU, US No. 39. Dept. ofCivil Eng., Stanford University. National Report 1983-1986. Cunge, J. A., 1969: On the subject of a flood propagation Anderson, M.E. and Burt, T.P., 1985: Hydrological computation method (Muskingum method). Journal Forecasting. John Wiley and Sons, Chichester and New York. Hydrology Research 7, No.2. 43 HYDROr;OGICALMODL~F(JRWXTER;;:RESOURCE-SYSTEMTIESIGN-AND-OPERAT10N- Cunge, J. A., Holly,!. M. and Verwey, A., 1980: Practical Kirkby, M. J. el al., 1978: Hillslope hydrology. Wiley- llSpects ofcomputational river hydiiliJlii£PiiiilriiJ;'Uiiioon.- ..-.. lilTersci"jjci,-;-Ni,-wTor1i~'-'· Dickinson, R" 1985: Status of the formulation of the Klemes, V" 1985: Sensitivity of water-resource systems to land-surface hydrologICal processes in GCMS. In: RepOrt climate variatIons, WCP Report No. 98, WMO, Geneva: of the First Session of·the-JSt Seientific Steering-Group on Knudsen, J.:Thomsen, A. and Refsgaard, J. C., 1986: Land Surface Processes and Cljmate. Geneya,.:U,k.,'i. .WATBAL - a senri'distribDled;physicaiiyb3l>'OO hydrological January 1985). WCP Report No. 96, WMOrrD-No. 43, WMO, Gcncva. . . -modelling system. Nordic Hydrology, Vol.!?, No. 415. - . . -Kraijenhof, D.A. and Ivfull, l.R. (Eds), 1986: River flow Dooge, J. C. L, 1985: Hydrological modelling and the _modelling and forecasting. n. Reidel PublIshing Company. parametric formulation ofhydrologIcal processes on a large Dortrecht. scale. In: Report of the First Session of the JSe- Scientific Steering Group on Land Surface Processes and Climate. Kuittinen. R.. 1990: Application of remote sensing to Geueva, 21-25 January 1985). WCP Report No. 96, hydrology. Operational Hydrology Report (in preparation) WMO(Tb-No.43, WMO, Geneva. WMO, Geneva. Dooge, J. C. 1., 1986: Theory offlood routing. In: Kraijenhoff, Kundzewicz, Z. W., Afouda, A. and Szolgay, J., 1987; D. A. and Moll, J. R. (Eds.), RiveiflowIniiaeTlIng'aiidroie= fiiliitlieiiiiificar'moaellIil-g:' Iii:" Byorolog'}'" 2000- 'reii'M!: casting. D. Reidel Publishing Compaiiy, Dorttecht. IAHS publication (in press). Dunne, T~, Moore, T. R. and Taylor, C. N., 1975: Recognition KUlChmMt, L. S.; Demidov, V. N. and MOIoviiov, J. G~, and prediction af runoff producing zones 198i3-:·· ·(j)(}l>M"l'OBlH"H'-1'8~llilro"'GTOl' 44 HYDROLOGICAL MODELS FOR WATER-RESOURCE SYSTEM DESIGN AND OPERATION SSARR, 1972: Streamflow synthesis and reservoir WMO, 1985: Intercomparison of models of snowmelt regulation. US Army Engineers Division, North Pacific, runoff. Operational Hydrology Report No. 23, WMO-No. Portland, Oregon. 646, WMO, Geneva. Sugawara, M. et al.• 1984: TANK model with snow WMO, 1987: Real-time imercomparison of hydrological component. Research notes of the national research center models. Report of the Vancouver workshop (1987), for disaster prevention, No. 65, Japan. Technical report to CHy No. 23, WMO/TD No. 255, WMO, Geneva. United Nations, 1986: Application ofcomputer technology for water resources development and management in developing WMO, 1988a: Technical Regulations, WMo-No. 49, countries. Technical Report. United Nations, New York. WMO, Geneva. US OTA, 1982: Use of models for water resources WMO , 1988b: Hydrological Operational Multipurpose management, planning and policy. US Congress, Office of Subprogramme (HOMS) Reference Manual, 2nd Edition. Technology Assessment. US Government Printing Office, WMO, Geneva. (First edition: 1981). Washington, D.C. Yeh, T.-C. J., Gelhar, L. W. and Goutjals, A. 1., 1985: Van Gnuchten, M. T. and Jury, W. A., 1987: Progress in Stochastic analysis of unsaturated flow in heterogeneous unsaturated flow and transport modelling. Reviews of soils. Water Resources Research, 21(4), pp. 377-472. Geophysics, Vol. 25, No.2, pp. 135-140. AGU, US Zhidikov, A. P. and Mukhin, V. M., 1989: Methods of National Report 1983-1986. long-range forecasting of low flows and droughts. Wallis, R. Jr. ,1987: Hydrology-The computer revolution Technical report to CHy No. 27, WMO/TD-No. 320, continues. Reviews of Geophysics, Vol. 25, No.2, pp. 101 WMO, Geneva. 105. AGU, US National Report 1983-1986. Zuidema, F. C. et al., 1987: Manual on drainage in WMO, 1975: Intercomparison of conceptual models used urbanized areas. Vol. I-Planning and design of drainage in operational hydrological forecasting, Operational systems. Studies and reports in hydrology No. 43. Unesco, Hydrology Report No.7, WMO-No. 429, WMO, Geneva. Paris. 45 ANNEXES 47 Annex I SiandanitzettfOllllal of a IIOl\JIS COlJlpOilent desCiiption .- . __ .- - - WORLD METEOROLOGICAL ORGANIZATION HOMS-COMPONENT ...... (Indic-ator) (. : ) (Date of issue) ...... (Name of the component) I. Purpose anctobjectives ___ __ 2. DescripfiOiC - -- .- -_. ,_ Input 4. Output 5. Operational requirements-andl'estrictions 6. Form ofpresentation Opcratkmal experience - ---- 8. Originator and tcchnical support 9. Availability 10. Conditio'!s on use (First entered: (date)- Last.update: - (Total length: maximum 2 pages) 48 ANNEX 2 Annex 2 W 0 R L D M E·T E 0 R 0 LOG I CAL o RGANIZAT ION ======-====== REGIONAL ASSOCIATION VI (EUROPE) Date ------QUESTIONNAIRE on the characteristics of operational hydrological models 1. Country 2. Name of model, author, institution where he/she works 3. Model objective 4. Model algorithm 4.1 Modelled processes 4.2 Components of Manis inflence 49 _l-IYDROLOGlCALMODELS-PDR-WAIERRESOURCE SYSffiM_DESIGN_ AND_DP-E..llAUDN Annex 2. p. 2 5. Required data ------5.1 Model calibration 5.2 M¢C.el 6. Data aCqUisition ~~ ~~~ _ 7. Dat_a tx.ansmi.ssion__;;:_;;.__======_=~;;.,,;= -_--_--_--_--_-_-__-_--_-_-_-_--_-_- _ 8. Data processing ------ 9. Place of model_application a_nd its outcomes _ Hl'. Do you intend tc) y,,~t__t.Jl~_!"C' ll~ References ------~ 50 Annex 3, p. I Models available as HOMS - components Model classification criteria(+} Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3456 7 B Water supply streamflow and storage forecasts. AUl FO QM DB Australia Operational flood forecasting program suite AU2 FO SA QF DB Storage capacity of a reservoir for low-flow Belgitun regulation BEl PD RL QM DC LO Storage capacity of a flood-control reservoir BE2 PD RL QM DC LO Culvert hydraulic model CAl OP RR QF ,. z '-" Hydrologic and hydraulic procedure for flood ~ plain delineation CA2 PD cs QF - v.> One-dimensional hydro-dynamic modelling CA3 PD CS QF DL IG Simple reservoir routing CA4 PD RL QF DC LO Canada Hydro configuration modelling system (HCMS) CA5 PD CS QM DC LO Digital aquifer model CA6 PO AQ SG DL IG Noncompliance analysis prog~am CA7 OP WQ Water quality simulation model CAB PO RR WQ DL IG (+) Defined in Tables 1 to 4 and Figures 2 and 5. Anne:H: 1. p. 2 ------Mod~clal~siHc,lti~~riteria------Siup'pl.i!=l~ Mqdel ----- TYE;;of------DE~gre:e(:;!--Spclq:e --- __CCow~~ ~~~the~ie] ~Lde~__~~~~~,--Variab~CaLu.sa.lit~disf~retizati~ :r 2 ' 3 4 5 "5 1 Il , , A_fO_~~_~_~_~_p_~~_"'_l QJ~ G __w_a_t,_er_sh_ed_m_O_d_e_l_f_O_,'_f_l,_OO_d CN_l ILI_± .DC _--li_-t1_ "~ o Method~ f~r computJ.hg des 'go flc,ods CN2: IPO A Ql1 DC I L iCh~,.rla "~ -D-eS-'-i9-O-\-f-l-~od :eS1t-i-ma-:-t-io-,-"-u-,-n-o-~-J-h-l-s-t-o-r-i-c-al-'----l--i--:~------:------'-b----~ --1- flolod ~t~ ir~ frequ!enc:r- analysis , !CN3: ~O -: Qf SF - I i 8 I: I'----'------:--~------c------I III I: ~ I' I ______--L---I' I ' , 0' I .Math.eJn41ti(~a.l 'model ~or two dJ,mens~Qf1al' ::: i I ~ s"'lioi~y distribuhpo '0 "st~aries iCN4: PO RR I I'Q ' Dr., ,1Gili; ~ ~ i MODRIE!moLl 'for olratioo ~,------!COl-----;-----S:T-E-~-.---E--,---w i I c.l ~ o I: 0 I : I I '" "'" {:ol?l'\Pi.a I ---:------f------+-----'----~------___+__----- I ----- , B oP S ,_, ,_C_Alf_rn_R<_l_,_m_o_d_e_l : :_'_' :__,__C_O_2 __: A__: '__'__'_'__'__ '"G1 -t-__ '_-H ~ ~ivi~.r i l~O I~G ~ : :PC2:Hyd['octyna.mical nlooeU ',0 I' csl! I j , RR i QJr PL i r.l i : I !--+-f:: I 1 1: i~ i ' I --~:----i------tJ , I I: ' : : I I NO~IN ca~Ca("ie hyd~'ol~gical I'rrpd~l CS~ l~O R~! m nonlinear I : Olr 0, lOCo ,,, LO I ; ~ I ' ". " I I' : :. . I . I r'~e"'hoslo'Jiakia i -'-,--~--'----:-,----:--:------+--,----:------~: "., ...." . -, ... I I:' :' ',' "i : 0 : ~ MUF:SYS 03 multipL1'rpo':ae unsteady floW! 'i : I : I ~~ ~imti,llation ,sy'stem : i' CS~ I I~C: CB i QI~ JDC IS I ~ I. I I ii, I m ~' i ,----r-- : 0 : i ----;---- I re~ler\(oir opera~iOl:l ~lD Q~~ kS2 i mCld,el: CS4: I i RL; DC LO I: i +---;:--, :--i-----;------,--- I~AMS1.llFF G,ener,al p'!Jrpose fIdod !forecal~ti~g mOdt1ling ,system : : I DI Model classification criteria Supplier Model Type of Degree of Space ICOWltry) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 5 6 7 B Denmark SYSTEM general purpose river and estuary (continued) hydrodynamic model DK3 PD RR QF DL IG Deterministic rainfall-runoff model for basins of 100 to 1000 sq. krn. FRl FO SA QF DC LO France Rainfall-runoff model for medium-sized urban basins FR2 PO SA QF DC LO Short method for cost/benefit analysis of protective measures against flooding FR3 OP BASTER long-term mean value of basin water > balance DDl PO SA ET DC LO Z U> W ~ w GDR EGMO hydrological land surface and river basin model system DD2 PL CB QF DC IS CAMOS/EGMO for operating hydrological land surface and river models DD3 OP CB Q DC IS Flooding forecasting by graphical correlation 001 FO SA QF DB LO Hungary Flood peak forecasting by a grapho-analytic technique 002 FO RR QF DB LO Flood peak forecasting by a multiple linear regression model 003 FO RR QF DB LO Jumex 3. p. 4 ------~===~delc1l~ss:.fic:ationcriteria==== :;uppl~EII" ~iode] Type of Degree of Spa,ce _[CounttR ~~~~~ldel ~nde~~~~~ste,~~,iah~~:ausili~discr~tizrti,on 1 2 3 4 5 6 7 B' Fl'~ltirl~~di'=~~=~lel-~~~~-lRR-'-~--~--C LO -rt--- ,I, " I I ' ~ Re.::ur~uvE~ fOI"E~caslt~ng n've'I" flow US1.Ui:J a' I ,",o I HIte,r I HU5 :'FO RR QF DC LO t"" Kalma\, i 0' 0: ~,-,-,--:----,---,--,-,-,~i------t---,~-- (9: . Re"al-time; ,adaptive::. hyc;llroJLogirca~ predil::ticm 1 ~6 liFO AA : ~~F 'DC ,LO ' i F:: I, ' " II I, \ I ~i ---;-~i~-'----!------~i I i ~------,------i , ~! sele~til:ln t"'1 Ri'tTerj stcitioln for forecasting : ffi17 I :OE' i -ll' I , I ' ~->---'-~---;----_t----';; I I "" ! " !' I, ~ ion as,: thl,;!' 1.IlVetlSe task of !" i "" i ~ fOL"ec~st~ng ~~F ~c d 'i J, ! HUB OE? RR ! LO! f;j i: ' ~__~__: ~L------_------:- I :" I ~ : . , I I' I '"~ ~ Deter~inJ:ng surfac''e wlilteI" resources fqr' I I semi-cu:-id ,and arid! bal;:im; wi.toout Pati31 HU9 PO SA QM DB LO § llwlgary ______: .~_'__~ I --;-__--+-- , [;1 (continued) '" De:,s:igI,l of storage reservoir Iby :stO:Cha:s:ti~ Ii: ~ si~nulc~ti(ln, , ,:, : HL!110 i PD:, RL! QM ST ~ --;------:------:--:--'----.------;--~------;~+ g De<~ign of re,servoi:rs ~J~ cllll ~xt~nsion pf i! ; ! '" Mo~an" 6 theory HUl11 I PD i RL '(~M S'T ~ i '' ;>, i : i i ----r------;~-----' ~ De.~igI1 of cOI-olpera~inj;r re!sle~vo~rs in :S1erles ~12 I ,PD i RL ! QM S'T' d , , :. :I '., I .---c----'----+---I---~ §'" I . : ~': I : : Analy:~is lof reservpir' OpElratio~ in the: case; : i '!, : ~ ofi random drafts : :, ' ~13 : : PD ! RL : QM ST ,---!-~----i-_'_-__ ,>------+--- -I i ' Del:ermination of h.~dri:l..uliC Jonductivity ~y i te~t pumping' with ?bS~rvclt,i~n ~ell.s I Illh~ lOP IIQ DL IG , --,----':---i-- ',~ '!' , Annex 3, p. 5 Model classification criteria Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 5 6 7 B Hungary Computation of drawdown of vertical and (contipued) horizontal partially penetrating wells InJl5 OP AQ SG DL IG Ireland The linear perturbation model lEI FO SA QF DB LO Seasonal forecast of inflow to a lake ILl PO SA QM DB LO Forecasting inflows to a lake IL2 FO SA QM DB LO Groundwater levels forecast IL3 FO IIQ SQ DL IG Peak discharge frequency in an arid region PO IL4 - QF SP :> Z u, u, ~ Runoff model for cultivated soils IL5 PO SII QF DC LO w Israel Aquifer simulation system IL6 PO AQ SG DL IG Multi-aquifer simulation system IL7 PO IIQ SG DL IG Computation of sea water-fresh water interface ILB PO IIQ WQ DL IG Groundwater interface model IL9 PO IIQ WQ DL IG Groundwater salinity model ILlO PO IIQ WQ DL IG Evaluation of water resources in a country ILl! PO CB SG;QM DC LO Anne}; 3. p., 6 ------~~classific.~ti(~:rit.er~~------SupplielC' MI)de], ----- TYl~;t""""------Iijeg,rE;ec~--Spiice --- __(C~~~ ~lmeofthe~jel ~~l:, ~~~~~I__Variab~~llit~disc,retiz~~ 1 2 3 4 5 '-L ;_, '_B _ , CLSX' constrained linear sysbam 4axtimdeld model ITl FO 'B QF OIl lroO' I , , , I _-+-__, -+-__+_ ~--- , I, ' ' , , I MISP real',-time f;tre~amflow fo:('ec<1~st.ing model IT;~ I FO lB' QF DlI ,LO '" II ' I ~ -,-----'------ii, ,1:-- o Sen;,i-conc(ept&al watiershed model: i IT:1 • ·:PO "IA I QF ]:.O! " i , I, ': I I' I,"f: ,I,,, ! ~ I, -:-----'-,-'!--,------:- 'i ' ! ----:------11 IT~---L----(~~~-- :~ Charmer nFtw()rk co!put'ation i_,_!__: ! : Dr. ~ !Itally !:'; :',":,' i I : I 1: MUltiVf1ri~te streamflow genel!:'at$r :f:or sho:rt i :: I," , I, a ,I anell long-!tertn c~rcl~cit'y I: ::IT~i ~O -' IQiM S1~ '" i ! i ': : ': ' -L -+-___+_ %. @ ,I 'IDItOSlb s~mulation :model fori fi.~w in a i I I! '" '-"0\ costal aq1Jilfl~r ITUi PO J~Q SiG I Dr. m :m g ------,------,-,--,,---,---,- g '"t? r;j !::tW::::", I'· .':: I: -:: i -::----:-::----:t:-- i ;;:: -----'--~':::,t tJ --+--- ' ,_-'-'--'-' , ,' i I Ii: I I '" \Tanik model Ii, JPl I FO . C:S: QF DC I IS : ~ , ': " ' r : I' Japan I" ':. I' :" :;; :t' tJ i~O££,-:~cullati,On !bY the --:bra~e f'unctiO~ JP2 ,PO i S~A: Q:~ DC :--LO 'll I', ' ,:: I iil NOrOl-a-y-,--If----;..'A-S;i-~-s-t,.m ~or us~ '" caJ.ib,ati,on ,::' Of" T,----i------'----'-'--'--'-y' ~ hy~ol,ogi"al model' :' NO!. rO' I C~: QIP DC 1=--+---0 , I I: " ; ,: :I ': . Philip1?in~s, Mi~-cj:lmputer balseq fl'pod foyec~stilng ,sys eml PH~(JFll) WO ClB QIP DC IS. .'--'-., Annex 3, p. 7 Model classification criteria Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 S 6 7 B HBV conceptual watershed model SEI FO SA QF DC LO A method to forecast the spring flood volume SE2 FO SA QM DB LO Sweden --- Two dimensional many layer hydrodynamic model SE3 PD RL QF DL IG Two dimensional hydrodynamic estuary model SE4 PO RR QF;WQ DL IG Model to forecast rainfall floods SUI FO SA QF DC LO Model for the calculation of snow-melt and rainfall runoff SU2 FO SA QF DC LO > Z '-" ill --J >< Method for short-te~ forecasts of discharges w in mountain rivers SU3 FO SA QF DC IS USSR Short-term forecasts of spring inflow to reservoirs on plainland rivers SU4 FO SA QF DC LO Method of unsteady flow calculation in braided river beds SUS Fe RR QF DL IG Operational calculation of the water storage in rivers from water level data SU6 PO RR QF DC LO Computation of reservoir sedimentation SU7 PD RL QF DC LO Alme:c 3,. p. 8 ------,------,Mode~l classific,~tic~~rilteria------Country MI)deJl ,----- TYJ?eC;f------n;egn~e(~--space--,- _~m~) ~me,~the~~ ~deJ~__ ,PUrpo~sten\__.Va,r~ab~~usc~Ht:ydiscretizatic~ ------,------,------,---I 2' 3 4 5,'6 7 8 UK Inllow_-storage-()ut.flo~1 (ISOJ fuu,'1ction models GEl FO I" i QF DC ,to ------1-- I' f I I ::: , ' ,SRi" s,\owmelt-rwloff model US l : Fa QF IX: I IS ~----'------'--- -L__ "I" I" I, I NWSFRSj-SA?-SMA 8ac~amento sO,p ~isltuI'e I I"-f ! 'acc~in9 modell : US~ ro (~\ QF;E3 De LIS \ , , ,,' , I' I: r--'----~:-'------l--- -,------,------Il:: I, : ': I I ------TT' § NWSRF~-SNpW-17 fmo,,~ acc'Wn'Ula!i:io~l ar;ld :! ~ I ~ abllati'on inod~~l' I US:~ FO SA I DC LO I' : I I "" I " I, ---~----,-----~--~--,------~ , i ' ,: ::,' I I :SS~RR ~tr~amflOl'r s~~t~esis al~d ~eselrvadr i ! , ~ 'regulation,: :; :!US~~ Fe CB i 'Q,F DC: La ~ Ii: i: :' I I' , --.--~, '"'lO ------, ~ NWSRFS-MCIEl3 lnanual :calibr;atit)n I?roc;Jral1ll US~ I OF;ES LS OP CB I DC: _ ---,--_:_--_:_~ ! ,--L- _ ~ NWS,RFS-STi~T-1lME statistied ~UIlW)Iari - meab I 'd '~Sl:: ha'l~ge::;, : : US" 'Op' I:'~ i aJ.:-'I'd'y I,,; III I ': 0'" '" I_-:-__------;------:--!--'----.------;--~--~:_---~ ~ United IRes:our,c:e inforrmaLtidn .and .anaiys.is llsing g:rid i I : ! I" , ,I . , ~ States icel.;! data bank Ii' US7 I OP i-+- ': -t------:----' :- ) ---y------:~-___n;- g ITechnilQlue:s for e~stj;matiLon of! pr6batlle rnax'imum I :: I I ~ Ipr~CiPitatitOIl: US~ ; I i lOP !' I I @ -'-~------~--~-~'--~------T- '" • :: I ' : i' i ~ EAD: ex:!?ecic.ed annual flood dwg¢ ccmputation, US9: OP : : ~ ; , ' ; . ,: I " , DAMiCAL daiDagE~ rElac)'t stage·-damage cc,lculat~on US~O' ,'DP , , , : ~!,;! , .L_-:-i-I---+-, , , , ------ lImlex 3, p. 10 ------~delclasdic:atioocriter~------;;;UppliE~r' ~Iodel -----TYpeOf------Dte9r~~--~laCe--- _(Co1~~ ~~e~~~~~ ~nde~~~~~'ste~~k~:aus~di~~ltizatibn ___1 2 ._~ ~-__4 5 .-.-6-- L B_ , NWI~RFS,-IJ\,G:/K . lag and lie routing US23 I PO QF DC .+0 _---J- I I L-t= , ------i -- ~ I DWOPElI~ dYnaIII~~?erclticlna:l~del ~~-L~_ __~~ ~~ i~ _ , I I I § ST:~P • I .', I I jacu.:water and: fI()odHay ana:lyses ! US25 I PC RR I OF ' DL, "IS ! "g h~drLli=-grabhi"s•• pacH~i-----!",~---;----7--~;__--I + Dl-.------~SI . HGI' US26 I I § ' -----r-:-·-----.------;------:--I--~--'--_:__--r-----____;_------,. T ... ". i 1-:------~ ~ .. I":tD~ i~t.riOr, dr..i",.,ge fld:Odrou.tin!~j US~ ':~ .. ~--\~-. nr~'--.--'-lt°i ~ !;l : DYlIMOq d~namic rat~ngcu,v. !mc~el , i USZB OP i ~!F i ___L-l-.- . ------L- T: _ 0\ ! " , '"~ ~~F "" SHP stream hYdriaUl.iiCS lI?ac:k.age U8:,29 I PD IRR ! 01:" IG I ! ~ Un.ited. ----.-__r__ I. -,- StclteS' , , &l (cc~ntinue~~ IHE.C':-2 'water sur:Eacp. pl':-ofil,esl U8,'30 I PD; IRR i QF DI~ IG '"~ , i ' : i ! i ! !' I -.--- i I-r-- ~ p:rog~am, US~l iWSP2 computer : i i i PD : JRR i QF 01" 'IG ~ ------I~:--_t_~--'--'-. -'--~I-.-----~,--+ i5 \--'. I, , z ISWCfULAAT stalge-aiS?haI:ge r1el:at.ibn i:l.t c::uhi'E!:rt's US~2 I PO i! ! ,,. , 'I ++'I ~ l-~:------:------1 :: -l------;---- . I: ,I , " : I' ,I ~ Iwa~er..:,sur.face plrof::.le COlIiiput:ati!:>n IROdel US~3 I POi , l~ ; QiF "ot':' IG I ! ~ --,--.--'-----;' , ! i ---:------:----=-r ~ Re~ervoir te",pe:,at~re :stratificrti,m ~~ .PD, : I~~~----~:_---:----+_. : I, , I Storm storag1e, l~re~tmernt, overflow, runo~f ': I , model ,. i' .. ,US'S I PL : CB: Q'F;i'lQ DC IS ___'__;__: ; -+-'__~--.------:------.--~--:--i~+ , , , " I, . Annex 3, p. 11 Model classification criteria Supplier Model TyPe of Degree of Space (Country) Name of the mode~ index Purpose system Variable Causality discretization 1 2 3456 7 6 WQRRS water guality for river-reservoir systems US36 PO CS QF;WQ OL IG HEC-6 scour and deposition in rivers and reservoirs US37 PO CS QS OL IG United States HYDUR hydropower analysis using streamflow (c.ontinued) duration procedures US36 OP RL HEC-3 reservoir system analysis for conser-vation US39 PO CS QM DC IS HEC-S simulation of flood control and conservation systems US40 PO CS QM DC IS >- '"~ ~ Yugoslavia Estimation of the unit hydrograph and w correction of net rainfall time distribution YUl OP SA QF OB LO Mekong One-dimensional mathematical model of deltaic HOMS river system (Mekong delta model) MHllFRl PO RR QF OL IG focal point Program for preliminary economic evaluation of a hydropower project (HLHEAD) MH2 PO CS QM DC La Application of hydrologic models for river forecasting MH3(US) FC CB QF DC La Upper Nile Model result analysis by the methods of the HOMS WHO model intercornparison lJNl OP focal point Annex 3, ::? 12. ------.===M,odelc:~lificati~nc:ritda==== Supplier Model Type of Degree of Spaoe _~)~tm ~m~~~lode_l ind~__~~~~~V2ric~__~sal~~~·etization ------,------1 2 " 3 l, 5 6 7 'B, Sac.r~unenbo use~ model :iIIooifiedl f'or in the. .Il I Up-pet NV,e Be.'in pro:ject: UN2WS2, I pel i CB QF,ES DC 'I LS , ,------~----- , I I ~ UJ>per Nile i CI,l.3J1el rQutin9 M~lskingLlm;-C:unge method UlNJCGB) i PO RR QF IX I I JLO ~ HelMS,'I --j- I ,"-,------,------I, ~ fqcal point: .:' . ' i i I 1 I (~ontinued)l Cha~~l :sol~ti;)n !rou,tin9 -: implicit of full ! I, I: II ! ~ • '~rn.:lt"nn:g I I U'1'J4 I PO RRi, QF DL IG ,I I Lalke jro~ing uSin~ a monthly t~mel in1:l,er~al ! Ufl5 : PC RL; QM ~\lC to; i I I 'I !' ' ~------i --r- I!' I i '- i ,: ------i i'" ! ! Cornpl{ter; program for :evalua~io~ 0:: rE!giqnal iii~ : ; f],ood. ri~t ':: : MoJOl PO -I QF SP - I, I WMIQ 1 I ' I I I . " i?3 Secretariat ------,----'--'----'--JT: [)J'" multipul~po~e 8~.mu]ation MlTSIM river bas.:L(1 I II : ! i I ~ meldel' Wl102 Pll', CB I ~ DC LO: g .I ~ II 'I -.-- ,.! j-iI- ~ ..; I ~ d 135: (5: z' >:z: t1' 0': ",m: ~. o'~ z' Annex 4, p. 1 List of models - RA VI (Europe) Model classification criteria(+) Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 5 6 7 8 River level forecasting model on the Danube at Linz ATl FO RR QF DB LO Austria River level forecasting model on the Danube at Vienna ATl FO RR QF DC LO EKW-EG forecasting model for flood waves AT3 FO SA QF DB LO ODK-AG forecasting model AT4 FO RR QF DC LO Belgiwn Conceptual hydrological model for an > averaged-sized catchment area BEl PL SA DC LO z 0'\ QF;QS w ~ x FSBGIMl forecasting model for the maximum <: monthly levels BGI FO - Q DL LO Bulgaria Forecasting model for the average monthly, seasonal and annual discharge BG2 FO - Q ST Two-stage adaptive estimator and predictor of the runoff of medium and large scale watersheds CSI FC CB QF DC LO Czechoslovakia Real-time estimation and short-term forecasting of tributaries of the cascade of reservoirs CS2 Fe CB QF DC LO (+) Defined in Tables 1 to 4 and Figures 2 and 5. ",,"ex 4. p .• ------~==M~lClassif'icatio:ncrit,eria ==== Countqr M.od8~ T¥pe of . . - - D!:lg~e'e o:f Spiace, _I:ma~______Name of t.he model index PJ:!.EEose~~§!L-Yari~e-~usalit,~disGretizi'ation 1 ~~-.------.-.. ~--~--~-~---2----.-B-~~ l.l-~'F NIIM SS , floc>dwave• forecesting moe.el DU I FC I Cll QF DC ; 'tf, ------I I~_I t ------l---• _ i ' I I ' ~ D~2: ;;tl; NliIM ,La"'faJ 1 - runof f ".IOdE 1 PO S1\ Qr DC I f'O 0- ;-~-----:------.__r.-.--,--:~,--i- E Denmark I " : ! I Cl: i ' n: W!:'TBAL hlYd~Olc?g'ical ;>:;. rai;nfalll - ~~~lel D~3 II : ~_ CB__:__ QF____ I DC SD! ,I E:' I: ;: : i:' i T 0' S~E ll;lode~l T E1.).ropean hydrologic:a:l s)ys~'em, --~. --' --, -- -- d ': , I 1 '(4 Fli ! I: , i : ""OJ, ~: "',q I ,; , 'L . ",: FinlatLd SpriL~g flo~d #OIreca::;ti4g ~lode~l ' Flh FO S,,"! QF DC i :LO ~i , I' I" 01, !. : : I ! ----r----- I ---.--. "" J1;: , . F.il Snow ae<:umtlla!:ion and "ell. in,1 meldel DD'l FQ SA QF . DC.. 'i['~i "',0: C. ------_._------~----,-_!_-----;---~------; ;;tl: , I .: I iii &Ii .~ OkORS (,odE! 1 f or thE! o~timumflcjod icontrol of rE~se~:'voi[' ~;Y'steIlll::; i DE:3 FQ RL QF DC La ~ : .f-,--.---",-",,:-, Annex 4, p. 3 Model classification criteria Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 S 6 7 B NIDDA model for flood wave simulation in large watersheds DE4 PL CB QF DC La FGMOD model for runoff computation (floods and low flow) DES FC CB QF DC LO Germany, Federal Water quality model DE6 FC RL WQ DL DE Republic of (continued) 81M-MOD IV model for simulating water quality characteristics DE7 PO WQ D Computation model for groundwater flow DEB PO AQ SG DL DE :>- '"U> ~ Water management model for groundwater PO AQ DL DE DE9 SG .".. Runoff extrapolation model starting from precipitation FRI PL SA QF DC La UREP 13 rainfall-runoff model for rural and urban areas FR2 PL SA QF DC La France Distributed model for surface runoff and groundwater flow simulation FR3 PL SA QF DC DE MOD LAC simulation model for surface runoff in a watershed with reservoirs FR4 PD CS QF DC DE SAPHARI hydrological forecasting model FRS FO RR QF SI La Ar~e:ll: 4;, p. 4 ------.------.------.------~~dE~:l~slsHicatic~~riteria------Sup!?l i er !1od~! 1 ------1!yp~~:--·-----____riS~~r--~latCe,--- ~~t~ ~I~~themodel--,~------jl~~~--1~~~~--~ri.ab]~~lus~:litLdisere,ti~at,ion ____l -;L------.---. '~------~I---_~f__.---·--6; 1 .-- 8_.~ Models fO·LI flbw :forl=!ca::iting ar:.d simulation FI,6 i,~_+ I ·_Q_P S_F' L-- ~ $ __ ~ : . i M~del £1I::>r hYd~ological ~ and climatollog:lca1l •a d\ata processing PI17 i ~O QF' SF I 1- b i . a ~ ~ _P7\_o_o_~_r_" u_t_+t-n-·~-'-i~m_'-od-e-l- ----.-~;-, --i-~-.-~~F~~~8~--;-: _~~~. ~R~R-' -i--~~Q~r-._ -_~~~£~~·~~~'~.~~·~~·-*-E-'-i -rl __ .:-, _i_:_ ~ -i_p_d_. o ~ ~ P~9 •~ M\!SIi; m"dei f0r flo" r6utllng, ,': : PD RR : QlF L DE I ~ ii' : I !' Ii't , o : : i ,: ~,----!------i------i C~UE: I Erane ! model ifOf assessi*g the elffiects of i ! -~ (cont nu~d) hydraul:lc ~tn~ctl,,1reSI oll;l flood rquting ~lO PCI RR I QF IDL DE I ' , I I •~ ~ ~! RE!SeJ~VO:Lr ll~anagrement mc?del FRill IPD RL Qf IDC La c: ~ I ~__~, Q• : I I· •~ GARDENI1'A model f:or di!:;chalrgEI ,aJi,d piesornetrtc : : •~ lE~vej. fqrec~asti:n{]l.\' ~: simul.ation and FRJ.2 pO ; : $1\ ! QF IDe lW. , ~ I . :' . i ' . ,, . , a ,~-.,..-,-----T--.------.-.---,--':IT: 1 , ., , . , i , I; Simu~at];on t}-~e a! model fan g' roundwa, ..t,e, r table • I . . . FR13 i 'i I'D i . I\Q : SIl PL i Itt)Ei z ! ----,..:-, ,~ ..--L-__...~ .--__. _.__.-,__ .. : ~ ; I 'I .: i : I o .~t::lt:,~at~c ; I ':' ; I R,lan2lg.ement mc]del of ldw £!lows in ~, o a r~~er:1 , FRJ.1· I Fe . CB : QF ,. PC' ., ' WI! •m ------.--:------;----:--,----~', •~~ I . j j -T------.--... .--; ~, ----r-- z DI!,cM fo~ecastinq rnod;el ifor: streaf:npw routin~ HllJ. i );'0· RR i QF DC I..O Hungary ,~ FVi1SMOV njodel for the si,~ul!ation iof ~loodwave in, I"ive~s w'ith Illovable ;bed . H1J~ PO! RI< Qf DL DE .'-----~. -~-'--r--.-- Annex 4, p. 5 Model classification criteria Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 5 6 7 a Ireland Hybrid model for flow forecasting on large catchments lEI Fa SA QF DB La Regional model for the estimation of Israel statistical runoff parameters ILl PO - QF SP Bayesian calibration of models IL2 OP PREDIS model for runoff simulation NLI PL SA QF DL DE NETFLOW model for streamflow routing NL2 PD RR QF DL DE > Netherlands Z ~ -..J GROVERPLA model for groundwater flow ifi simulation in a vertical plane NL3 PO AQ SG DL DE ....~ GROMULA model for groundwater movement simulation within a multi-layer aquifer NL4 PO AQ SG DL DE GCHJ forecasting model for floods in mountain regions PLI Fa CB QF DC La ISOP forecasting model for floods in mountain regions PL2 Fa SA QF DL La Poland MONICA and NONS forecasting models for the runoff and discharge hydrograph PL3 Fa CB QF DC La DOWI model for level and discharge forecasting on the lower Vistula PL4 Fa RR QF DL DE An,pex 4. p. 6 ------.------.------~bden~sificatio~riteria------SUPl?lill::r 1'10dEI! ------~I~!3'~------li.8gree ·'Qf--SPace--,- _(Co1Jnt~ NaJ~~helmo~ .------_~,ME~~~Ios~~~~riabl~~~lithdis~retiz'ation 1 ;~' , 3 4, 5 ,6' 7 8 ------~-.------,------.---,--- , ' The OMEGA rrode1 for I fla)w fiimulatior1 ar-ld i I I forec:as':J.nq I, PTI ~O S QF DC ~O I 4- ;i ~I I ' -~--,-----~------:--- ~ Portu9al ------,---- (:; xsumc ::..~aiI1fa:Ll ~~ runoff mode!! P'I'2 i pt, S QF 1DC I l ..... r- , I I"" _,------i--~' . • : ":; 1 8 , I, '" : I 'I ' I' , I ,: : I I, ---L---=tI :I ~ ~SIST 1~un9ff! SiIllU]2ltiqn mode!!: !' ; PW,3' ! P[), C:e I Q'F OC :L~,! , I: :' ': I:I Ii' ,. , , '_i_---.,.-+-+------:-----~---.--.~---+.-----i----.--,-i------. .------., , , ' """ I I' Ii1Ju moo)el ! EO\~ doodwa~e f'on:c.a~:ti.rlg' ' : 1<'11 : fd SA : Qf Illc [.0 ~ -.-+,-----+---;----:----'---7----... -7------.. '+-'--.--'-- '"~ +-----.I~2 :+,-'Eo~ 1<9~ Romanta model i flooc\wate f'orEic•• ,!ti.ng ; fO SA .! Qf Illc i La ~ ~--.--r------. ~ ! :' i ---1---.----+-----:--.--- 0\ , 00 VJ[DR.!I,. m~)del for 1:1oodwcive fOI'e'Ccl~tj,ng and ~ , 00' 0: oI~eration ()f thle reser-yoir 1«:13 FC , CB ! Qf :DS co ______:-, I '---i---.,..-.) '"g : l : ' , I i' i #or~ca::;tingr n\ode~l ~or , •~ Sweden HBV di."ch~rg~ • hj'C!rograph ' 1 ' S$:. FO , :SA i Qf DC La ~ E ---+---+J-----,-.----. , ,-·-'--:--'~-t-i o . i :' : --;----+--'-----'--'--. ~ srlo~elt • SwitzElr!.:md The *RM :,forecasti,ng Imoqel ,fOt' I . \ ! "", I \ 6 , nmo~f : ; C~l FO z i SA I Qf 1lC! ID~S: ~ , , " ------+:i+-'-----~' .~. i----;------·---.-.--:-- . , 6 i': ,,,, :, I a USSR : FJ.oo4wa~e for~caslting modell u:s ing riadar • : deLta'; I SUI Fe. i ,CIl i Qf DC: 11-"0' ~• '1' ' ~ ------;~~ ,~+---'--~-,-.--!------:-- - 5z YU90s1av:,a: NCln-lin~ar adalptive 'moqel 'fot' l~ve~ and c1is~haI'ge 'foreca,sti:ng , WI FO', l --:---+-- ,---;"---L---H~·~-'--, '.,...... ,------~' Annex 5 List of models - RA I- Survey (Africa) Model classification criteria(+) Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 456 7 8 Kenya Nigeria Upper Nile '" HOMS '" focal point (+) Defined in Tables I to 4 and Figures 2 and 5 * Index of the original supplier Annex 6 List of Itode]s - Rli. II - Survey' (,~sia) ------==== MDdel classification c:dtleri,,:l. (+ ). === SuppliEir ;Model Type of Delgre,e Q:E Spl:lCe _fCO\mb~ ~U~mod~ ~nde~~~~~~~~rilable_Ca1~~~JiS(~reUzati()n __1 '----',~ 2 3 I 4 ~-----.!:----7-----\L--- I I II ; L Physically ba~ed ,Mus:kingurt'l model IN! I po! RR QF DC i I!",O ,I' I! I I ~----- ~ N~n-line'iar Ica~c,a~e mod~l INt i Fa CB I QF I DC I J...o I : i : I I'"" I ~ : !,: , -r-!: ----r------l:------'-~:- R~al",time filoqd f'ore'ca~ting ba"~d on "Ri'ver mL\ltiaqu.ifer intdra9tion ' 11/11 po~ AQ SG DL DE , , -i------'--I.....---;--j. , , (+) Defined ij Ta,bleis tOI 4 and Figux:!es 2 and !S. ' , " I Anlrl.ex 6 ~ .1?" 3 ------.------.------~Dde,~lasI6if~tiol~rihd~~------SuppliE!r Moqell -"""""""'--Tvpe(;f------' De~~rer~ oJE-----· Sp,ace ,--- _(CQt:lntr~ Nam~UhLmod§L 2nde'K__2~Dse_s~...!!!....-~rillbl,~Causalit1'L.....!:Iis(~re1:izati('~ - __L ,_~ L----~--~---~---.. __~-- I_l_,-- Pakistan CL,s model ITlO i ~O fA QIi' DB , ~o (continuE,dl I I 1 II i I :t , ' ------~I 1 I ------,- Qatar H1iWMD7 groundwater program QAl iIi'O AQ sa DL , rPE ~ ,: - ,'I ' - I --:-----,..---,---, ,\ I i" I I: . " II I '=lI I ~ :foI'!eca,st~ng V}J~ ~ St:reamflow ynoqel I I, FOI CB I QF DC I:::J-L0I ! ;: I: I I III I: I ---;-----i,---c'C--! ~-'------:----;~----r----4- I I 8 ------~ Ioma·s-Fier1.iing; mo'del ' II US\\' I PO' - i Q - - I I _~; ~----:------\----:-~ ,l ': -- ____!._____, '-'- '_ ' 2 Vietna:ro : : I; : Ii: , I I ~ S~RR mode 11 ! I : : US~~ Ii'C CB! QE' -tDC; LO F.l ;, i! I ~~------.------_+_------.-:------J I ' I ! ~,'" N IlK;~* '_[,~ IJAM_m_O_d_e'_l : :-'------i-- 'I FO --:---_SA_-----i--_QF ,__DC_,_,__ ~ ; : i [rl TANK model I JP:7* I FO I CB : QF DC DS \ I I \ \ ;;i'" ---~-':__l__:- ~ i i.; I .; . t:l t:~e oririn~l ;: * Index: of :S:UPJi?JilE!L ! : I ~ : ' ~ ~ I' !, I I I·I ! Iil I, ~ ~ Annex 7, p. 1 List of models - RA III - Survey (South America) Model classification criteria(+l Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable causality discretization I Z 3 4 5 6 7 8 Flood forecasting model US' Fe CB QF DC LO DAMBRK usn' PD CS QF DL DE Snowmelt-runoff model USI' FO SA OF DC DS HEC-5 model US40' PD CS QM DC DS HEC-4 model USZZ' PO - QM ST ,. Z -.J HEC-Z model US30' PD RR QF DL DE ill '" ~ -.J Argentina HEC-I model USI6' PD CB QF DC LO Sacramento model USZ' FO CB QF,ES DC LS Soil moisture model ARI PO AQ SQ;ES DL DE HIDRO model hydrodynamic BR'. FO RR QF DL DE Regression model i'.RZ FO RR QF DB LO Kalman model VE' FO SA QF DB LO (+) Defined in Tables 1 to 4 and Figures 2 and 5. , Index of the original supplier. Annex 7 ~ p. 2 --.-.------.------.------====~Oielclassificationcdteda==_= SU:l?pl ier Moclel Type of Degree of Spac,e ---.!.Q~~EX)' Nlame of thla mode1 incLex Pur~~.§.Y!3~I_'~~----.9ausali~~§.£Eetizat:ion ___1 ~--L------~-'-----L---5-~-6------L 8_.-,- ImlilYM'~de~: , ' (:A* __L_I ±B _----=~__~~_~:Iw__ '''' Argentina lrmblO mollel': (:A* ! PO A OF DC i I LO ~ (continued)1 I ' i II S I I.: I I I I , 9r'XSWM;~ mbde~; ¢A* !: FC eB; QF '~'c "LO! , ., I, : '! I I I i --v--c:Bi-,~- CAS'IOR mabel: ----; :,. i llRl'.; DC----++-i ; ; ;, : i."::, !' ~ I' -;------r---~~~--r_~---_,__----- i' ------i ~ ~AIRA "Dde~ ~R2: i ;..:. ! FC cia i ,iF ' DC'" L' ~ : I! : ~__' i_.------'------'-----~ I' ."~ ~IL):K mOd~l i ; !! i ~"R* .po SA: ~!F Ie ['1 i ------,---.-r--', " " I I: '" ,M"ultiple regres;sio:rl mPde,l l~O SA QF [IB L ~ llR3 i I :_------_.----_:_----~--~. M :I I' I :" I ... \reni teli ChDW model us*1 i 1'0: SA! QF DB Ld: .; , ·m" , i . -+--+- :i: b :t;:del I i 'Ld i .":m i ' 13R4~__I~--F~: \~ r~~-~ :5 E'razil .'" I : . .",> "RE1" ITI(Odel llR5: 1'0 ! 11R i QF DC:-ti-i :t' i " I I '0 .--L-i ~----I------'------:>0 i I '"", 'm ; ~'ci ,~ MS.RCE !""del . llR6 i Iu.i QF DC Ld I H ,:-.-~~ I -t--. ~~ ,I SACM mOde] 13RZ! i DC LI , . , 11'0 1m QF , , . --;--,----.:----;--' -~-r--_+_;---:-'-ii-,--~ .' ! m.~NA ~del JBRB' , PO i ! ST Q'X , : ': , .f-: . '-:----:----;~' , . • I I ,SIMUU!;DIN model " : IBR9,: p,D IlL QM IX:: L: ------' ,------,----~------'-----W. , I Annex 7, p. 3 Model classification criteria Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable causality discretization 1 2 3 4 5 6 7 B RAFA-l BRIO FO SA QF DB LO Time series generation model BRll PO QM ST Sistema TARTARUS BRl2 FO CB QF DC LO RVD model BRl3 PD CS QF DC LO Sistema preva 2 BRl4 PO QF SP VOLESP model BRl5 FO RR QF DC LO ;>- -.J Z V> iii CMEIA model BRl6 PD CB QF DC LO -.J" Brazil SMAP model BRl7 PO SA QF DC LO (continued) MOSH model BRIB FO CB QF DC LO SARR model US4" FC CB QF DC LO STANFORD model US" FO CB QF DC DL Decision model BR19 FC CS QF DC LO OPERA mOdel BR20 FC RL QF DC LO " Index of the original supplier. l!~n:ne!x 'J, 1:'. 4 ----.--.~~---.------.----.--.------.----'-'-'-Moc~cla~ssHic~oocri~~~-'----- Supl'lie. Model ----- TY!;;ec;r------tleg':ee~~:paqe,-· _--!.Q2Y!:!m_I ...... ,~!!ID!t....Q.f~!.JB!:Ide:l__,,...... ,...,_.__. _.,...... ,...... - im~_~~~~~l-,·va~·ial:~_,____£:au~:ali1Y,.. , d~scr·etiz~t iOIl: I ' 2 ::1 45:6 7 B ------:--r-BII~~m~~d~el~]~;_,_'~~~~~~~~~~~~~'~~~~~~~~13~R~2L~~-!-ll'~C~I~~t--(l-F------D-C---i-tl-'~~. :~ Brazil ! t I' I : . (con.tin.uedl 'SIMMQE, model, I~R· : PO B (IF DC '~ LCI ~ , I I 9 --j--;"-,' f-----'---r--~---~-----:_r:------,-. ~ROIlD mO,\~1 ~R2i: ~ : ; ,', • fO SII i (IF +K:"" LC: i ~ t ~ !'" , ~ischa , I "1: ,CQI,IVOq model ': .NL·: ~ ,FO i SII : QF IlC L ~ z _-f--f--., . ; -:--;------~ Co](.mbia ,coss~ mode: 1 US.,: I ::oc i eB: !.F DC , L' : i 'i : ,----.;-' ',...... -+--'----,-':---~ I ' : ,I : : 'CIlI~rrjll mode,l C02.! PO ,SII: QF JlC L, , ,~--,--~, '~!, --'------,------,; i ' • Inde,~t ofr the nrigincll s,:upplielr. : . i Annex 7, p. 5 Model classification criteria Supplier Model Type of Degree of Space (Country) Name of the model index Purpose system Variable Causality discretization 1 2 3 4 5 6 7 B DOBLE model NL" PD CB QF;QM DC LO ----~_._-_ ... ~_. Colombia (continued) UNIMORF US" PO SA QF DC LO REMANSO NL" PO RR QF DC La Flood forecasting model ECl Fe RR QF DC LO Ecuador PRES model EC2 FO SA QF DC LO OPEDIA model PYI Fe CS QF DC La ;t> 2 OJ '" Paraguay DMI model PY2 FO RR QF DB LO >< '" HYMO-lO CA" PO SA QF DC LO Hydrodynamic model PY4 Fe CS QF DL DE Cuenca model PY5 FC CS QF DC La Terra-baygorria model PY6 FC CS FQ DC LO Venezuela Sacramento model US2" FO CB QF;CS DC LS " Index of the original supplier. 1-1 YDR01.O'..JU;AL--M(JlJ.J1b~-FUl{-W~l~.liR-RliS{)Ul{{_~-SY:S'l~HM--DL~illN-AND-(''P-E-RATION-- Annex 8 -S!!rf.ace-waterJ1ow...and.-s"'IjIP~Ppl"J1-'J1mllooodl 0"'''011 ratIna W~r ~~!I~mty~ - - - 1. Flood for,emtl", cWid contral a. Flood piicill far c:hannwl CWld bridge d..iUi't II b. F!c=d h,;;;i"",,,,~ f:: :=_ ... :..::' =~:*= end ~~!!'!!!'~~ C c;. SlmultQflIWI flood hydrographl f. II...... '1'81 l)'Irlm ...Ift' an" o~"atlon • C cI .. Flood cI.pth "'etPPlng for f!~ pi'!:!!!" I~d-."... pl~"I"Q C •• Effletl of land \,11-. en dowmfraani flows for ups.r.cm lond-"I. p(~nlftl C f. _FIIXld iI.aka m.er dl2ll fallurb few ,mera'na' arel:lorecln... olannina 0 liII. Soli ~olltur. candltlonl for Imel dralnae,. d~..lgn' . ~ C 2. Drouvht -and -low-flOw rlvtn" Q. ~ow river "OWl fGf' effatreorn UMI C f~~!!!!u!"f . b~ TImina af drcuah. ",.,Me. f....Imatlna cumulative .c:anomlc Imoac:t a c. Soli ~iI~ ~ttl~ fg prMlpltgtl~"~ppB"Y!M C 3. 51....",1_ rogulatlon ------o~-Runofr~l_ fot m.,mu~--Obta-inGbI.--)t'f.iif------.------. ------j,.------(fns!!4d~ !eijt!ll,.\Ri) by !!JMff tiM!!!! ~ (ltI!I!th ~ ~ )'.~) f'? ~~!' !I~i~ a c. Srmulti:llMOUl NneH YCIIlulMI In r.glonol lireama for l'eli/Ilondl wafer iUppJy pl..mk", C ~. !!'l=~= f!~ ~~~: ~o; l~'::"J' !'!~!' f!~ f~-!!I!.M!M!~ -'!!h._w~pYt__ ~t~!@!. ------.------C-- FI'" ..... Wlldllf. b. Whilin.y.....Iml", 01 1_ II.... I... Iilh III.cycl. matching . B -e-. -itming of c:irqh, ...,.n:.. for ..timannv mtnimvnr nalllNGi,- or lok.- lmls d. Flaw velocltr.. wIthin ItreClfti fat ..tlmotlng Iff.en on fl'" _pecl.. ____.__ ._._..t!!~ -!J.!w ,~~, f!~ f!if.._!WUl.lft.lng_~!m_ct!!~!!I~MJg_~,~_ b. Timl", 01 flow ...,.,.... fa' mwdd"!!wlth recreation plriada Ill. iWnuH ,ImI' ~iw11h-hr UJn~-VJlq-YRn)-for---..timotin,-t~-fmpuiir- of fluctuation. In 10k. I.wl, -II Navlsatl., .------ti;-low--iiw-,-lT.... ,. ntwmlnlnt war-rwG)I' _.._Ity C; ~e HIgh ,!~, fI~ f~ .'.m!"'~J"9 "~~mliOft !",,.rf..,..,-:e_ C c. FormClt'... af aur#ocll Ie. fat a:ltennll'ilng navitotlon Interflrence D ------cr. --Timlnsrtil-ftaw ~C.-fOf ftTtmatino--~he-"f1fii"-'V5".,CitIni- ..,_Ity B Ii. Runolf .Imo pa._ (within ...d ...... y....) Ie< dotlgnlng ,...... fl_ __ tegulQtlC1n1 B c. 51mult..,"", ftlnoff vol","" In regla10l Itre~ for ..-;Ional ilneratlng -i}'i-- ,1"","",, ------Water UMII ". Domestic watlt' suppl)' •• T1mTng .,-vi...... Ie<-diffvo;y iYI....-••Tvn ------ b~ Water priuumI thraughaut dallvery -)/'Item fOf' delivery •.,.tfm design c. Volume of 1oIlI' for slzlno suppl)' facllltl.. d. R.tvffi flGW orwliiffiWi fw don~l...-wiiii,---u.. CGH=ctlcn I;;~rn; ------~o_ TJIfIJ.,._ol ~tM' ~_.f__ d.dl.,~_..yt.bDlJI..dlltCle., _ b. Volumll ,. 1oIlI' 101 dZIft!IL!Pf'I)' '~-:!Ilt!'" l:. Rljtvtn flow yaluma fa; dtainCI!J1j iy1imn-dal~fj ? Other oH:tte:m lAeI ! 8~ WtlPf ~, .ff'clllt~ _. _~ __ .fff8~Lci_l"c;rlt~~ ~@ffI~'."l;Y on I'8t\,1m flcrws &tr JlYdluatiog CCltlMtYatlon mlGlUrtll c Ratl", Key, A Madolllng .nll. phyolc.r pr..... at tho curren' II-.(-th...... d"'-ii-gooa Iii. -Iii ·iuPiilYlng ih.-n•••••nnfonri..filii. a Infom'lllt'an .hIM ~~ «td 900d~ _ C Modelling _ ",*",alo lab I... _. _n. o lntiiirmiith~i b.---nrwn unlOi'lifactury 'Ciid--~Cin-.------ F The supplied lnfannatlan II geillll"ally uma.ltfaclay_ Sayre., Cine. of rlchnalogy AlllllrMnt, 1982 78 ANl\TEX 9 Annex 9 Surfare water qualit)' model evaluation IV II III Ccmpuret, Ovatell le ... el No c:cmpytet, CcmFlU~t, CcmFlutet, complex of modellIng not eomgle-. not eomQlex comgjex ocetationai lophiltication Nonpaint loutce pollutlcn cnd lQ"ld ule Urbat rvndf: 4 Sautee/generatian•••••••••••••••••••••••••••••• c C 8 8 TtQtllpotf to teceiying wat.t•••••••••••••••••••• A A A TtOr1lpotf in tec.iYing wat.t•••••..•••••.••••••• C 8 8 Impac:b gn c.neficlol vie••••.• , •••••••.••••••• C C C C Control aptlon,/cOitl •••••••••••••••••••••••••• 8 8 8 8 etlXian and .edlmentorlanl 4 Sourc./genet.tIOl . A c c c TtQ"llpGtf to NCelyin; wOf·er ••••••••••••••••••••• C c Trat"port In tecel",lnv wot.r••••••••••••••••••••• 8 c Impoctl on beneficial u . B Control optlon./COIb ••••••••••••••••••••••••••• ·A Sallntty: 9 Source/len"atlon•••••••••••••••••••••••••••••• A A A TtCl'l'porr t. tecel",ln; water••••••••••••••••••••• A A A TrcntflOl1' In tec.i",l"g water••••••••••••••••••••• A A A Impocl'l on beneficial use ••••••••••••••••••••••• a C C Control optfonti/COltl ••••••••••••••••••••••••••• A Oth., agrlculNtol ",,lnoff: 3 S""rc./,.netotlon•••••••••••••••••••••••••••••• c c B B TtCl"lIJlWf to recelying wate' , ••• a A A Ttcnl~ In recel",I"I woter••••••••••••••••••••• c B B Impcx:1'I an beneficial ""e ••••••••••••••••••••••• c c C C Airborne poll",tc.1Rt 6 SCl.ltce/genetatlon•••••••••••••••••••••••• , ••••• A A A A Trcrupotf '0 receiving wote'••••••••••••••••••••• A A a B Ttm'llport In rec.lving water••••••••••.•••••••••• C C c Impacts on beneficial ule•••••••••••••••••••••• : C C c Cgnlrol .100000/cOltl . A A A A Wat.r ~u.1I (olh.r than nanpaint laurclS CI'1d land vie) care II oear oru 7 Source/gen.rotlon•••••••••••••••••••••••••••••• A A A A Tronspet! to tecelving water••••••••••••••••••••• A A A A TrCl'upatt in raeeivinv wo..r••••••••••••••••••••• A A A A Impacl'l on beneflclol ""'•••••••••••••••••••••••• C C C C Cantr04 oatICll"lI/cc.n ••••••••••••••••••••••••••• a a a n,ennol pollutions 9 Source/Q41Mratlon•••••••••••••••••••••••••••••• A A A A Trcn.~ to teeeivin. worer••••••••••••••••••••• }o. A A A Transport In ,..eelvinv water••••••••••••••••••••• a A A A Impcctl CIn beneficial u . c C C C Ccnrroi oprioru!cClln ••••••••••••••••••••••••••• A A A A Toxic materlolt: Source/generation•••••••••••••••••••••••••••••• c c c c TrcnlpGl't to ,.eelvln; water••••••••••••••••••••• c c Trmlport In reeeivinv water••••••••••••••••••••• c c Impact on anefleiol ""'e•••••••••••••••••••••••• c c Ccntrol optlon./eClln •••••••••••••••••••••••.••• c c Drinking wate, quality: 2 Scura•••••••••••••••••••••••, •••••••••••••••• A c TrwcthMnt••••••••••••••••••••••••••••••••••••• A c Impacrs on beneficial Ule ••••••••••••••••••••••• C Water qua.llty Impoctl en aquatic life •••••••••••••••• 8 B B 3 K.y: A Reliable, credible modelling may be readily vsed fet mOlt proble.,.. of thil 11.Ibiuuo. Some modell may C4 luitable for "9Ulatlon atd design. a SQI'MI 01 C, but lOTIe models may be Vleiul for pla"lning and r.lared ClUrpoIel, and luitable for dUlIrming ~latl.,. .ffects. C Moc:Ielllni is poIIlble. Credibility end rellobllity of ""ults il low due to wecic:nene. in Ine dota cOle. M.ocIelilng of thl. type i. not u.vally performed. Overall le~l 01 modelll"9 lophlltlcation: o No model, ovalloble. 10 Rourln. use 01 model, of all typea. 79 !-]YJ)JU1COGICAL MODELS FOR WATER RESOURCE SYSTEM DES1GN AND OPERATION Annex 10 Ground waler model evaluation ___ Sit____ Model 'i_ l..~_Q.1 Recitlnal Tronlpgn Transport Transpart ___ I -Ti&ii~t w!OJ -r;~.;p;lrt wi wi. ~, ../9 Flow onlv r.actlons r.ac.rlcns Flow onlv redctlon. r.actlons Flaw enl)' r.actio'll un un un .., 'et I.f multi IQf lat laL jat I.f .., SQt I.' I.' sgt I.' .., lQt 'Qt I.' .tat Flow condltlonl P F P fluid P F PP F P P F P P P F P F P F luu.. _Qucr1tiJv-avail='. luDall••.• _ " A B • Qu_ontlty-conjunctl~ _uu:_~_.... . a R A B B B Quallty-occldeftral petraleum_ prQClUcn." "- -- .. c ------~ ---€----€ Qy,allty-accldanlg;! IndUt·~ttll'Ulalll _ ch.mlcal •••••••••_•••••••• B CCC R B c c Quallty-~rlcultutal p••ticld.. !!!"!d h!!~~!~!'!:!~;;;; .•.•• .--. ••• B C C <=- --..ft------a----e-----€------.---- Quality"c:arlculhJr. lalt buildup J CC B _ ('" Quality"wClita dliPOial ICI1dfl/h B C CC R B c c Quality..,eawar., intTVIlon •••• BB C C B c c - law= .... ;vhlii'" ~...-iiltil.io.;ii1iid iii t."t. Calumn. mod.1 type. ald I~QI. oi appllcQtlan., ••g., rfl. sixth calumn applies to a lita..tcQI. problam In which ~lIurClftr m~m9n' i. dltKrlbfld" bY-Q--tro,ifigr-fmOd.rwlHiOi.it chemical r&oetlotlt undar ,eturel.d flow CQ'ldltlon In rrlXtunrd media. Application Ical.t Sit. mad.l. d.alil'lQ with Q"'~ l~tI than g 1."", tqYQf1I ",I!",. L.==! ~!; d:Gllnv with m'&-;;a--;raa-t~-~kal- Ci"-f~w-iq~~ iidlw- bl;jt-l~ii than -g-f..-w- tnoui"cnd -lquurw -mrl..-. Ileqlonal - Ol"ac. grtater ~ha"l a it... thoul4ma ~l,leiNI 1Il!!_". Abtnvi."ftr wI with. wi. wlthou'. ... ;:tur:t=d -;~d wat:rflow--cCli'ldltIOl'ti;;- unlClt loH1lGturated flow condltrona. p porous media. F froctIJred 01 lolutlan cavity m.dla. Elilrl..: A 0 !.,J;=!!! pr~!d!...e t=c! hQ'#!hg Q k!;n- d~n:· of f:;!lvb-illty md ~~~blntt g-i''''.,,--ivfflel.Fir agrg. - B• ...lIoela conc.pt\.lQl t~ capabla of Ina"t... t_rm (a faw yaan) pndlctlon with a moc!aral'e loval of cr&e1ibl1lty giv_n IUfflC::i.nt e1ata. C• UlIriui cl:I"Ic.pnJlli raai -tot "'lpini- tn_ -nycraiQ9lir-'ynfnn-lze compffcahid- -Iiyw-olcijic'-aia- qualHy--ai:iici: --_. ------R w iI.vdii1 i~1'jj 1. iil.!l in ih. r...earcn nega. no model exl.fl. AlmK • l!M:!d.! !ype !"!~t ~I!~~!. -to l;;IJ~ ='~c. 80