DAID-AO 553 WALKER tWILLIAM W) JR CONCORD MA F/9 13/2EMPIRICAL METHOODS FOR PREDICTING EUTROPHICATION IN IMPOUNDMENTS--ETC(U)NAY 81 W W WALKER DACW39-78-C-0053

UNCLASSIFIED WES-TR-E-81-9-1 N.

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_ Unclassified -

SECURITY CLASSIFICATION OF TIS PAGE 'IWho- 0.1. Krlto.d)

REPORT DOCUMENTATION PAGE BFRE COSTRLETINORM.- , RF.PaT NUMBER F2 OTACESSION No. 3. RECIPIENT'S CATALOG NUMBER

4. TITLE (And Sbtitt.) 5 . TYPE OF REPORT & PERIOD COVERED

EMPIICA PETODSFOR REDCTIG ETROPICAION Report 1 in a seriesI'MPOUNDMENTSb~ Report 1,~ PHASE I, DATA BASE _._PERFORMNG______REORTNUMBE

DEVELOPM.ENT 6 EFRIGOG EOTNME

*1 S. CONTRACT OR GRANT NUMBCR(..)

Williamn W. j6alker, Jrj Contract DACW39-78-0053'L'

Dr. Wllia W. alke, Jr i0. PROGRAM ELEMENT. PROJECT. TASKEnvionmetalEngieerAREA & WORK U NIT NUMBERS

1127 Lowell Road EWQOS Work Unit IEConcord, Mass. 01742

11. CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATV:

Office, Chief of Engineers, US.ArmyIWashington, D. C. 20314 I.-W V ROF PAGES

7lTMOIYORING AGENCY NAME &ADDESl different from Controlling fie 5 EUIY S o tiiwrrepd4U. S. Army Engineer Waterways Experiment Station UnclassifiedEnvironmental LaboratoryP. 0. Box 631, Vicksburg, M~iss. 39180 15.. bECL ASSIFPIC ATI ON/ DOWN GRADINGf SCHEDULE

.4 16. DISTRIBUTION STATEMENT (of thla Report)

.4 i Approved for public release; distribution unlimited.

17. DISTRIBUTION STATEMENT (of the obegract entretd In Block 20, It different from, Report)

IS. SUPPLEMENTARY NOTES

Available from National Technical Information Service, Springfield, Va. 22161.

IS. KEY WORDS (Continue on reverse alde It nececewy and Identity by block niontbet)

Data bases Models

Eutrophication Predictions

L Tlmpotindments

2G. A61TRACT rCwooftu amrverse ofd* if noc"Say od fadentify by block ntamber)

Simple formulations relating nutrient loadings and certain morphologiccharacteristics to trophic state have provided lake managers one means for

descibiq lke robemsandpredicting the potential impact of management deci-sion. Wilethe general utility of such models has been demonstrated fornatrallaksthe applicability of models for reservoirs has not received ade-

quate evaluation. - Evaluation and tk4 possible modification of existing models

1- . (Continued)

DO IM 1473 no ETOF IP Nfov eIs onsOLETE Unclassi fied

SECURITY CLASSIFICATION OF THIS PAGE (When, Dote Entered)

X__I

UnclassifiedSECURITY CLASSIFICATION OF THIS PAGE(Uha Data Eae,,wd)

20. ABSTRACT (Continued).

for predicting water quality and eutrophication potential,.is the objective ofEWQOS Work Unit IE.

"This report documents the establishment of a computerized data base con-taining water quality, hydrologic, and morphometric information for 299 reser-voirs operated by the U.-. Army Corps of Engineers (CE). Sources of informa-tion included STORET (including the National Eutrophication Survey data), 1---s.Department of Agriculture sedimentation survey data sheets, project designmemoranda and CE District and Division data files. Supplemental sources in-cluded maps, project brouchures, and reports. Programming for data manipulation

and analysis is in the PL/I and FORTRAN IV languages. BMDP and SAS programswere employed during preliminary analysis. The data base presently containsover 2.5 million water quality observations taken at 4451 stations located inor around 271 CE reservoirs.

Methods for estimating volume and area variations with elevation, requiredfor volume-averaging of water quality data and for calculating material load-ings,.)*ave been developed. Preliminary analyses have also been performed toassess the importance of spatial and temporal variability to the computation ofrepresentative water quality values. ,t

Appendix A contains data inventories for each project included in thedata base.

Unclassified

SECURITY CI.ASSIrICATION OF THIS PAGEen Data Entered)

-t -U

PREFACE

This report was prepared by Dr. William W. Walker, Jr., Environ-

mental Engineer, Concord, Mass., for the U. S. Army Engineer Waterways

Experiment Station (WES) under Contract DACW39-78-0053 dated 7 June 1978.

The study forms part of the Environmental and Water Quality Operational

Studies (EWQOS) Work Unit IE, Simplified Techniques for Predicting Reser-

voir Water Quality and Eutrophication Potential. The EWQOS Program is

sponsored by the Office, Chief of Engineers, and is assigned to the WES

under the purview of the Environmental Laboratory (EL).

The study was under the direct WES supervision of Dr. Robert H.

Kennedy and the general supervision of Mr. Donald L. Robey, Chief, Water

Quality Modeling Group; Dr. Rex L. Eley, Chief, Ecosystem Research and

Simulation Division; Dr. Jerry Mahloch, Program Manager, EWQOS; and

Dr. John Harrison, Chief, EL.

The Commanders and Directors of WES during this study were

COL John L. Cannon, CE, and COL Nelson P. Conover, CE. The Technical

Director was Mr. Fred R. Brown.

.14 This report should be cited as follows:

- Walker, W. W., Jr. 1981. "Empirical Methods

for Predicting Eutrophication in Impoundments;

Phase I: Data Base Development," Technical

Report E-81-9, prepared by William W.

Walker, Jr., Environmental Engineer, Concord,

Mass., for the U. S. Army Engineer Waterways

Experiment Station, CE, Vicksburg, Miss.

Aoes-on IPoP

NTIS C.AtTDTIC T-jUna, ;:

Di~tri*,,t, or/Avnil 1 4,.ity Codes

Avail and/or

fist spCaial

1L

4 ., l-

TABLE OF CONTENTS

Page

PREFACE ..... ................. .... .. .............. 1

LIST OF TABLES ............. ......................... 4

LIST OF FIGURES ............. ......................... 7

CONVERSION FACTORS, U. S. CUSTOMARY TO METRIC (SI)UNITS OF MEASUREMENT ........... ..................... 8

PART I: INTRODUCTION ........... ...................... 9

PART II: FACILITIES AND METHODS ..... ................ 12

PART III: DATA BASE STRUCTURE ...... ................. 14

PART IV: CODES - DATA BASE CODES ..... ................ . 16

PART V: LISTS - PROJECT LISTS ...... ................. 25

PART VI: WATS - WATERSHED CHRACTERISTICS ... ............ . 40

PART VII: RESER - RESERVOIR CHARACTERISTICS .. .......... 45

PART VIII: HYDRO -- HYDROLOGY FILES .... ............... . 50

PART IX: WQ - WATER QUALITY FILES .... ............... 59

Introduction .... .. ..................... 59STORET Data Acquisition and Processing .. .......... .. 59INFONET Data Acquisition and Processing .. .......... . 63Miscellaneous Data Acquisition and Processing ........ . 64WQ File Structures ....... .................... 64WQ Data Inventories ....... .................... 74

PART X: SED - SEDIMENTATION DATA ..... ................ . 83

PART XI: NES - EPA NATIONAL EUTROPHICATION SURVEY DATA ..... .. 87

PART XII: NUMERICAL CHARACTERIZATION OF RESERVOIRHYPSOGRAPHIC CURVES ...... ................. 96

Introduction ......... ....................... 96Approach .......... ......................... 96Curve-Fitting Schemes ....... ................... . 98Interpolation Schemes ....... ................... 102Conclusions .......... ........................ 104

2

hi PART XIII: VARIABILITY OF TROPHIC STATE INDICATORS

HIN RESERVOIRS ................... 107

Introduction ...................... 107Data Base ........................... 108

[ Case Studies of Spatial Relationships .. ........... 110Seasonal Relationships .................... 115

Variance Component Estimation .. ............... 117

Error Analysis..........................121Monitoring Implications. ................... 122Modelling Implications .................... 124i~~j ~Conclusions .. ............. ........... 127Implications for Dat: Reduction Strategies.. ........ 128

PART XIV: EVALUATION OF METHODS FOR ESTIMATING~, jPHOSPHORUS LOADINGS ................... 130

Introduction.........................130.4 Preliminary Analysis ..................... 130

Estimation Methods ...................... 136Tests Based Upon Simulated Data .. .............. 136

Tests Based Upon Real Data ................... 141Prior Error Estimation .................... 143

Conclusions .......................... 143

PART XV: CONCLUSIONS. .......................... 147

j PART XVI: RECOMMENDATIONS ..................... 149

REFERENCES...............................151

APPENDIX A: DATA INVENTORIES BY PROJECT AND DIVISION .. ....... Al

LIST OF TABLES

No. Page

1 District and Division Codes .... ............... ... 18

2 Data Source Codes ....... .................... 19

3 Station Type Codes ...... ................... 19

4 Pool and Outlet Codes ...... .................. . 20

5 Parameter Codes ....... ..................... . 21

6 Breakdown of Projects in the Central Project List

by District and Division ..... ................ 28

7 Record Format of the LISTS.CPL and LISTS.DPL Files . . 29

8 Listing of the LISTS.CPL File .... .............. . 30

9 Listing of the LISTS.DPL File .... .............. . 37

10 Record Format of the WATS.DAREAS File .. .......... . 42

11 Sources of Data for the WATS.DAREAS

File by Component ....... .................... 43

12 Inventory of Data in the WATS.DAREAS File by

CE Division ........ ....................... . 44

13 Record Format of the RESER.MORPHO File . ......... . 46

14 Inventory of Morphometric Data by CE Division ...... . 48

15 Listing of the RESER.COM File .... .............. . 49

16 Record Format of the HYDRO.KEY File .. ........... . 53

17 Record Format of the HYDRO.DAILY File .. .......... . 54

18 Record Format of the HYDRO.MONTHLY File .......... . 55

19 Record Format of the HYDRO.YEARLY File . ......... . 56

20 Record Format of the HYDRO.SUM File .. ........... . 57

21 Inventory of USGS Hydrologic Data by CE Division . . .. 58

22 Inventory of Water Quality Data by Source ......... . 60

4t

L .. ..... '

No. Page

23 Record Format of the WQ.KEY File ... ............ . 65

24 Record Format of the WQ.DESTAT File . .......... . 66

25 Record Format of the WQ.OBS File ... ............ . 68

26 Record Format of the WQ.SUM File ... ............ . 69

27 Sample Microfiche Water Quality Data Summary ...... ... 72

28 Inventory of Water Quality Data by StationType and CE Division ..... .................. . 75

29 Inventory of Water Quality Data by Component and

Station Type ........ ...................... 76

30 Inventory of Total Phosphorus, Chlorophyll-a, and

Secchi Data at Pool Stations by CE Divisions ...... . 78

31 Inventory of Eutrophication-Related Water QualityComponents by Station Type and Monitoring Agency . . . . 80

32 Sample Sedimentation Survey Sheet ........... 84

33 Record Format of the SED.RATES File . .......... . 86

34 Sample EPA/NES Compendium Printout .. ........... . 90

35 Record Format of the NES.SUM File .. ........... . 91

36 Summary of Impoundments in EPA National EutrophicationSurvey Compendium by Region, Trophic State, and

Impoundment Type ....... .................... 93

37 Summary of CE Impoundments in EPA National Eutrophica-tion Survey Compendium by CE Division and Trophic

State ........ ....................... .. 94

38 Summary of Lake/Reservoir Comparisons Derived fromEPA/NES Compendium ...... ................... . 95

39 Evaluation of the Power Function Model .. ......... . 100

40 Evaluation of Polynominal Functions .. .......... 103

41 Statistical Summary of EPA/NES Trophic Index Data

from 108 CE Projects ...... .................. . 109

5 I i

No. Page

42 Summary of ANOVA Results ..... ................ . 118

43 Regression and Corresponding Error Analyses ...... . 125

44 Fundamental Equations and Symbols .. ........... . 131

45 Preliminary Analysis of Flow/Total P ConcentrationRelationships ....... ..................... 132

46 Concentration/Flow Sensitivities by Componentand Station Type ....... .................... 135

47 Estimation Methods ...... ................... 137

48 Algorithm for Generation of Flow/ConcentrationTime Series ........ ...................... 138

49 Results of Method Testing Using Real Flow and

Concentration Data ....... ................... 142

50 Formula for Estimating the Varience of Loadings

Calculated Using Method 4 .... ............... . 144

Al Inventory of WATS.DAREAS File ... ............. . A2

A2 Inventory of RESER.MORPHO File ...... ............. A13

A3 Inventory of USGS Hydrologic Data .. ........... . A24

A4 Inventory of Water Quality Data by Station Type . . .. A34

A5 Inventory of Phosphorus, Chlorophyll-a, and SecchiData at Pool Stations ..... ................. . A45

6

LIST OF FIGURES

No___No. Page

1 Elements and Structure of the CE Reservoir Data Base . . . . 15

2 Regional Distribution of Reservoirs in Data Base ...... . 27

3 Sample Watershed Map ...... .................... . 41

4 Sample Water Quality Station Map .... .............. . 73

5 Regional Distribution of Lakes and Reservoirs Containedin the EPA National Eutrophication Survey Compendium . ... 92

6 Distributions of Volume and Area Slope Parameters ..... . 106

7 White River System ....... ..................... . ii

8 Sakakawea ......... ......................... 112

9 Old Hickory ......... ........................ 113

10 Barkley .......... .......................... 113

11 Monthly Variations in Trophic State Indices . ........ . 116

12 Variance and Covariance Components of Trophic StateIndices .......... .......................... 120

13 Variance Components of Trophic Index Means withinReservoirs ......... ......................... 123

14 Distributions of R2 Values at Tributary and DischargeStations .......... .......................... 133

15 Distributions of Regression Slopes at Tributary andDischarge Stations ....... ..................... 133

16 Bias in Loading Estimates as a Function of RegressionSlope and Estimation Method ..... ................ 140

17 Mean Squared Error in Loading Estimates as a Functionof Regression Slope and Estimation Method . ......... . 140

18 Observed and Estimated Mean Squared Error in LoadingEstimates ......... ......................... 145

7

CONVERSION FACTORS, U. S. CUSTOMARY TO METRIC (SI)UNITS OF MEASUREMENT

U. S. customary units of measurement used in this report can be con-

verted to metric (SI) units as follows:

Multiply By To Obtain

acres 4046.873 square metres

acre-feet 1233.482 cubic metres

cubic feet per second 0.02831685 cubic metres per second

j Fahrenheit degrees 5/9 Celsius degrees or Kelvins*

feet 0.3048 metres

inches 2.54 centimetres

miles (U. S. statute) 1.609344 kilometres

pounds (mass) per cubic foot 16.01846 kilograms per cubic metre

square miles 2.589988 square kilometres

tons (2000 lb, mass) 907.1847 kilograms

To obtain Celsius (C) temperature readings from Fahrenheit (F) read-ings, use the following formula: C = (5/9) (F - 32). To obtain Kelvin(K) readings, use: K = (5/9)(F - 32) + 273.15.

8

EMPIRICAL METHODS FOR PREDICTING

EUTROPHICATION IN IMPOUNDMENTS

PHASE I: DATA BASE DEVELOPMENT

PART I: INTRODUCTION

1. This report documents the development of a data base describing

certain water quality aspects of reservoirs operated by the U. S. Army

Corps of Engineers (CE). The data base includes information on project

location, morphometry, water quality, hydrology, and sedimentation. As

part of the !nvironmental and Water Quality operational Sutdies (EWQOS)

Program being conducted by the Office, Chief of Engineers, U. S. Army,

this work has been conducted to provide groundwork for assessing empiri-

cal approaches to describing and predicting reservoir trophic status.

2. One basic strategy employed in assembling this data base has

been to utilize existing, centralized sources of information first.

These have included nationwide data bases maintained by various federal

agencies, as well as a few sources of data tabulated at a regional level.

A framework has been designed and implemented for storing and accessing

this information, with flexibility for updating and general access, so as

to meet the specific objectives of this project. Having utilized cen-

tralized data sources to their fullest extent, data gaps have been identi-

fied and used to set priorities for locating and incorporating information

from relatively diffuse sources, such as specific project design memoranda

and other published or unpublished reports dealing with individual projects.

This stage-wise data-gathering procedure has been designed with efficiency

and cost-effectiveness in mind.

3. Another basic strategy which has been employed in compiling

water quality and hydrologic data has been to assemble individual obser-

vations in space and time (i.e., "raw data"), rather than average values.

This strategy accomplishes the following:

9

_0 &

a. It provides for the broadest possible range of futureuses of the data base.

b. It eliminates possible variations due to the use of dif-ferent averaging procedures.

C. It provides a basis for error analysis and assessment ofdata adequacy in future model testing.

These advantages must be weighed against the major disadvantage of the

approach--it involves management of a large amount of information. The

water quality file presently contains two million observations taken at

4451 stations located in or around 271* CE projects.

4. Management of these types and quantities of information entails

use of a consistent framework. one basic strategy has been to tag each

bit of information with district, project, and data source codes. While

the validity of the information at its original source cannot be sub-

stantiated, use of a systematic approach in building the data base insures

that the data are transferred and accessed properly. Keeping track of

original data sources provides a means of checking any piece of informa-

tion at its source and identifying discrepancies among multiple data

sources for the same value. The latter provides one indication of data

and source reliability. Another validity test involves checking

for internal consistency in a given set of values. For example, the

morphometric profiles have been tested by comparing reported volumes at

any elevation with the integral of reported areas with respect to depth.

The third validity test involves distribution of portions of the data

base to district offices for verification and editing. This entails

their cooperation and assumes that district-level sources of information

for specific projects are the most accurate. This approach has been

taken for upgrading the morphometric data file with reasonable success.

5. In its current state, the data base is a collection of infor-

mation in a well-defined framework. It is not a user-oriented system

designed for frequent interactive use. Such a system would require

*A total of 299 projects are included in the data base; no water qualitydata have been located for 28.

10

extensive software development geared to a specific computer system andto the intended uses of the information in various areas of reservoirmanagement. The scope of this project has been limited to compiling

the information, organizing it, and extracting portions that are directly

relevant to analysis of eutrophication problems in reservoirs. With

additional software development and systems programming, the data base

could be made accessible for more generalized purposes.

6. The complete data base consists of a collection of computer

files, reports, data forms, and maps. As discussed above, each piece

of information is referenced by CE district, project, and data sourcecodes. Part II of this report describes the facilities, methods, and

agency contacts used in this work. Part III describes the general

structure of the data base. Parts IV through XI document the sources

and approaches used in compiling each element and present data inven-

tories. Parts XII-XIV summarize results of specific analyses which lay

the groundwork for use of the data base in Phase II of this project.

These analyses cover the following topics: (XII) numerical characteri-

zation of reservoir hypsographic curves; (XIII) assessment of the

variability of trophic state indicators in reservoirs; and (XIV) testing

of methods for estimation of nutrient budgets. Conclusions and Recom-

mendations are given in Part XV and XVI, respectively. Appendix A

contains data inventories by project and district.

pI

PART II: FACILITIES AND METHODS

7. Compilation and manipulation of the various data files docu-

mented in this report have been done on an IBM 370-168 computer maintained

by the Information Processing Center of the Massachusetts Institute of

Technology (MIT). This facility has been used in a batch processing mode

(OS/VS1) and in an interactive mode through IBM's Conversational Monitor-

ing System (CMS). Three media have been used for data storage where

appropriate: (1) 9-track tapes (6250 bytes per inch); (2) 3350 disc

packs (OS and CMS); and (3) cards. Copies of the current versions of

all files have been transferred to tapes for secure storage and future

access.

8. While most of the information used to assemble the data base has

been read from tapes supplied by various agencies, some files (in par-

ticular, the project lists, morphometry, and sedimentation files) have

been assembled from tabulated data. In these cases, cards have been

used for data entry. Keypunching has been done and verified using con-

tract services offered by MIT.

9. Programming for data manipulation and analysis is in the PL/Iand FORTRAN IV ngges. The Biomedical Computer Program package

(BMDP) 1 and SAS 2 have also been used in preliminary data analyses. Plots

have been produced with a Calcomp line plotter.

10. Access to the Environmental Protection Agency (EPA) STORET3

system has been acquired through the cooperation of the Water Quality

Laboratory of the New England Division of the Corps of Engineers. The

staff of the Systems Analysis Branch of the EPA Region I Office in Boston

has been helpful in submitting STORET retrievals. The identification of

water quality and quantity monitoring stations has been done partially

using the services of the National Water Data Exchange of the U. S.

Geological Survey in Reston, Virginia. The Corvallis Environmental

Research Laboratory of the U. S. Environmental Protection Agency has

provided reports and data files from the National Eutrophication Survey

(NES)4 . Sedimentation survey sheets have been obtained through the

12

Sedimentation Laboratory of the U. S. Department of Agriculture and the

South Technical Service Center of the U. S. Soil Conservation Service

in Fort Worth, Texas.

11. Staff members of the Environmental Laboratory of the U. S.

Army Engineer Waterways Experiment Station (WES) have provided assistance

in extracting and coding morphometric and drainage area data from project

design memoranda and in coding water quality data complied outside of

STORET. The Ohio River Division (ORD) of the Corps of Engineers provided

tapes containing water quality data gathered by district monitoring

programs in that division.

4 13

I7

!I

PART III: DATA BASE STRUCTURE

12. Figure 1 depicts the organization of the data base intoeight major file groups:

CODES - Data Base CodesLISTS - Project ListsWATS - Watershed CharacteristicsRESER - Reservoir CharacteristicsHYDRO - Hydrology DataWQ - Water Quality Data

SED - Sedimentation DataNES - EPA National Eutrophication Survey Data

Each group contains a number of computer files, data forms, and/or maps.

File names are in two parts. The first refers to the major file group

and the second, to the specific file within that group. For example,

the water quality station key is given the name WQ.KEY. A lowercase

second name indicates that the element is a map or data form (not a

computer file).

13. U. S. customary units have been used most extensively in the

files. This has facilitated the transfer and verification of information,

since most of the original sources of morphometric, drainage arc4, hydro-

logic, and sedimentation data were also in U. S. customary units. One

exception to this convention is the EPA National Eutrophication Survey

Compendium4 file, which was supplied by the EPA in metric units.

14. The development, structure, and zontents of each file group

are discussed in the following sections. The sources and approaches

used in compiling the information are described. Each file is character-

ized with respect to format and content. Since most files are too large

for listing, data holdings are summarized in an inventory format, with

categories defined by file, variable, and CE division. Data inventories

by project and district are included in Appendix A. Record formats for

the files described in this report are defined as PL/I data structures.

14

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/ .~ 96

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* - On - ~15

PART IV: CODES - DATA BASE CODES

15. CODES consists of a group of files which define referencing

systems used in various portions of the data base. These include the

following:

CODES.DIS CE District CodesCODES. DIV CE Division CodesCODES.SOURCE Data Source CodesCODES.TYPE Station Type CodesCODES.POOL Pool CodesCODES.OUTLET Outlet CodesCODES.PARAM Parameter Codes

*IListings of these files are given in Tables 1 through 5.*16. District and division codes (Table 1) provide a numerical

indexing system for each of 36 districts and 10 divisions, respectively.

Districts are grouped within divisions. The New England Division is

unique in that it is not comprised of districts. In order to permit

referencing of all projects at the district level, district number one

has been defined to represent the New England Division.

17. Data source codes (Table 2) provide a referencing system for

nine data sources which are used frequently in the data base. Identi-

fying each data entry by source provides a basis for validation and

sorting out discrepancies among multiple data sources for a given project

and characteristic.

18. A total of nine station type codes have been defined for use

in the water quality and hydrology files (Table 3). These provide a

frame of reference for locating monitoring stations within a given

project. Broadly, these permit distinction among stations located on

upstream tributaries, within reservoir pools, and in or below reservoir

discharge streams. Within-pool stations are further classified as

upper-pool, mid-pool, or near-dam. Mid-pool is used as a default for

lake stations. The remaining two are used in cases where coordinates,

maps, and/or station location descriptions provide an adequate basis

for more refined classification. Secondary tributary codes (upstream

and downstream from impoundments) have been used only for some EPA

16

National Eutrophication Survey stations to aid in hydrologic budget

computations.

19. Pool and outlet codes (Table 4) are used in the morphometric

file. These provide systems for referencing various elevations to pool

allocations for specific uses, ranges of operating levels, and locations

and types of principal outlets. The systems were initially designed

at WES. Additional codes have been added as needed during subsequent

morphometric data compilation.

20. The parameter codes file (Table 5)* is used to reference

hydrologic and water quality data. The file contains 94 members, each

identified by a water quality parameter code, STORET code 3 , measurement

type, and units. The 5-digit STORET code is used in retrieving water

quality and hydrologic data from the STORET system. It is also used

to identify measurements in the hydrology files. In addition to the

89 basic water quality parameter codes included in the file, there are

11 redundant parameter codes, which have been used in retrieving water

quality data from STORET. Redundant codes result from multiple means

of expressing a given type of observation (e.g., temperature in degrees

C or degrees F or total phosphorus as P or as P04 ). Redundancies have

been eliminated in final data storage by applying appropriate conversion

factors in each case.

* Table 5 contains U. S. customary units of measurement. A table of

factors for converting U. S. customary units of measurement to metric

(SI) units is presented on page 8.

17J

Table 1

District and Division Codes

Code District Div code Division

01 New England 01 01 New England02 New York 02 02 North Atlantic

03 Philadelphia 02 03 South Atlantic04 Baltimore 02 04 Ohio River05 Norfolk 02 05 North Central

06 Wilmington 03 06 Lower Mississippi Valley07 Charleston 03 07 South West

h08 Savannah 03 08 Missouri River09 Jacksonville 03 09 North Pacific10 Mobile 03 10 South Pacific

11 Buffalo 05'112 Detroit 05

13 Chicago 0514 Rock Island 0515 St. Paul 05

16 Pittsburgh 0417 Huntington 0418 Louisville 0419 Nashville 0420 St. Louis 06

21 Memphis 0622 Vicksburg 0623 New Orleans 0624 Little Rock 07

V25 Tulsa 07

26 Fort Worth 0727 Galveston 0728 Albuquerque 0729 Kansas City 0830 Omaha 08

31 Walla Walla 0932 Seattle 09

33 Portland 09L34 Sacramento 10

35 San Francisco 1036 Los Angeles 10

18

Table 2

Data Source Codes

Code Data Source

00 Leidy and Jenkins01 EPA National Eutrophication Survey02 District or Division03 Sedimentation Survey Sheets04 Design Memoranda

05 USGS State Water Resources Data Reports06 USGS/WATSTORE File07 EPA STORET08 INFONET -Ohio River Division

Table 3

Station Type Codes

Code Station Type

01 Tributary

02 Pool03 Discharge

04 Pool (nr. dam)05 Pool (headwaters)06 Unused

07 Point source08 Sec. trib. (downstr.)09 Sec. trib. (upstr.)

19

Table 4

Pool and Outlet Codes

Code Pool Type

01 Flood control02 Conservation03 Water quality04 Minimum05 Summer

06 Winter07 Water supply08 Power09 Recreation10 Dead storage

11 Multiple use

12 Stream bed13 Top of dam14 Period of record minimum15 Period of record maximum

16 Normal17 Maximum power18 Minimum power19 Sediment20 Maximum regulated

Code Outlet Type

01 Intake02 Spillway crest03 Surface outlet04 Bottom of gated spillway

20

Table 5

Parameter Codes

STORET HYDRO WQ Component Units

00027 01 Code for agency collecting sample00028 02 Code for agency analyzing sample72025 03 Depth of pond or reservoir feet00068 04 Maximum sample depth feet72020 72020 05 Elevation ft > msl

00062 06 Elevation, reservoir surface ft72030 07 Elevation of reservoir pool ft > msl00054 00054 08 Reservoir storage acre-ft72033 09 Flow, average daily, spillway cfs

72034 10 Flow, instantaneous, spillway cfs

00061 11 Stream flow, instantaneous cfs

00060 00060 12 Stream flow, daily cfs00065 00065 13 Stream stage feet00010 00010 14 Temp deg-C

00011 *14 Temp deg-F00300 00300 15 02 Dissolved mg/l

00299 16 02 Dissolved, electrode mg/l00090 17 Oxidation reduction potential mv00094 18 Specific conductivity, field umhos/cm00095 00095 19 Specific conductivity, lab umhos/cm00400 00400 20 pH (field) su

00403 21 pH (lab) su00410 22 Alkalinity, total as CaCo3 mg/l00435 23 Acidity, total as CaCo3 mg/l00900 24 Hardness, total as CaCo3 mg/l00940 00940 25 Chloride mg/l

00945 00945 26 Sulfate total mg/l01045 27 Iron, total as Fe ug/l71885 *27 Iron total as Fe mmg/l01046 28 Iron, dissolved ug/l01055 29 Manganese, total as Mn ug/l71883 *29 Manganese total as Mn mmg/l

01056 30 Manganese, dissolved ug/l

(Continued)

* Redundant water quality parameter code.

(Sheet 1 of 4)

21

Table 5 (Continued)

STORET HYDRO WQ Component Units

00916 31 Calcium, total mg/l00915 32 Calcium, dissolved mg/l00927 33 Magnesium, total mg/l00925 34 Magnesium, dissolved mg/l00929 35 Sodium, total mg/l

00930 36 Sodium, dissolved mg/l00937 37 Potassium, total mg/l00935 38 Potassium, dissolved mg/l00070 00070 39 Turbidity jtu00074 40 Turbidity, transmissometer, percent

transmission percent

00076 41 Turbidity, Hach turbidometer ftu00078 42 Transparency, Secchi m00077 *42 Transparency, Secchi in

00031 43 Light, percent remaining at given depth percent00034 44 Depth at which 1 percent of surface

light rem ft00080 45 Color, true pt-co units00081 46 Color, apparent pt-co units00955 47 Silica, dissolved mg/l

00956 48 Silica, total mg/l00310 49 BOD5 mg/l00405 50 Carbon dioxide mg/l

00680 51 Carbon total organic mg/l00681 52 Carbon dissolved organic mg/i00685 53 Carbon total inorganic mg/i00691 54 Carbon, dissolved inorganic mg/l00665 55 Phosphorus, total as P mg/l00650 *55 Phosphate, total as P04 mg/i71886 *55 Phosphorus total as P04 mg/l

00666 56 Phosphorus, dissolved as P mg/l00669 57 Phosphorus total hydrolyzable as P mg/l00678 58 Phosphorus, hydrol + ortho, total,

autoanal mg/l00671 59 Phosphorus, dissolved ortho as P mg/l00660 *59 Phosphate, ortho as P04 mg/l70507 60 Phosphorus, inorganic total ortho as P mg/l

(Continued)

Redundant water quality parameter code.

(Sheet 2 of 4)

22

mp 'b'

Table 5 (Continued)

STORET HYDRO WQ Component Units

00600 61 Total N mg/l71887 *61 Nitrogen total as N03 mg/l00605 62 Organic N mg/l

00610 63 Ammonia N mg/l71845 *63 Ammonia, total as NH4 mg/i00625 64 Total Kjeldahl N mg/i00630 65 N02 + N03-N mg/i

00615 66 N02-N mg/i71855 *66 Nitrite total as N02 mg/l00613 67 Nitrite nitrogen dissolved as N mg/l71856 *67 Nitrite dissolved as N02 mg/l00620 68 N03-N mg/l71850 *68 Nitrate N as N03 mg/l00618 69 Nitrate nitrogen dissolved as N mg/i71851 *69 Nitrate N dissolved as N03 mg/l00500 70 Residue, total mg/i

00505 71 Residue, total volatile mg/l00515 72 Residue, total filtrable dried at

105 deg C mg/l00530 73 Residue total non-filtrable dried at

105 deg C mg/l80154 80154 74 Suspended sediment conc - evap at

110 deg C mg/i70300 70300 75 Residue total filtrable at 180 deg C mg/l

32209 76 Chlorophyll-A fluorometric, corrected ug/132217 77 Chlorophyll-A fluorometric, uncorrected ug/l32211 78 Chlorophyll-A trichromatic, corrected ug/l32210 79 Chlorophyll-A trichromatic, uncorrected ug/l32230 80 Chlorophyll-A mg/i

60050 81 Algae, total cells/ml00570 82 Biomass, plankton ml/i85209 83 Algal growth potential mg/l60990 84 Zooplankton, other ne/liter31616 85 Fecal coliform, memb filter, m-fc broth

44.5 deg no/100 ml

31673 86 Fecal streptococci, memb filter, kfagar, 35 deg no/100 ml

31679 87 Fecal strep mf m-ent no/100 ml

(Continued)

* Redundant water quality parameter code. (Sheet 3 of 4)

23

Table 5 (Concluded)

STORET HYDRO WQ Component Units

50051 88 Flow rate instantaneous mgd50053 89 Conduit flow - monthly mgd70301 70301 Dissolved solids - sum of constituents mg/l80155 80155 Sediment discharge tons/day70291 70291 Dissolved sulfate discharge tons/day70290 70290 Dissolved chloride discharge tons/day70302 70302 Dissolved solids discharge tons/day

2

I

(Sheet 4 of 4)

24L

PART V: LISTS - PROJECT LISTS

21. LISTS, the second major file group, defines the referencing

system used for CE projects in the data base. It consists of the fol-

lowing files:

LISTS.CPL - Central Project List

LISTS.DPL - Deleted Project List

4The development and contents of these files are discussed below.

22. The data base is built around a central list of 299 reservoirs

which have been identified from various sources and placed in the LISTS.CPL

file. The regional distribution of these projects is shown in Figure 2.

*Breakdowns by CE district and division are given in Table 6. The follow-

ing have been used as criteria for inclusion:

a.projects currently operated by the Corps of Engineers.

b. projects having seasonal or permanent pools.

The second criterion has been applied to eliminate locks and small run-

of-the-river impoundments with short hydraulic residence times and little

opportunity for inducing water quality changes, at least with respect to

* eutrophication.

23. The two primary sources of information used to develop an

initial project list include a tabulation of CE projects with surface

areas greater than 500 acres compiled by Leidy and Jenkins 5and a map

of CE water resource projects 6. Based upon CE Water Resource Development

Reports 7and information supplied by various CE district offices, the

* initial list has been screened to eliminate projects which are incom-

plete, not currently under CE control, and/or do not have appreciable

pools. A separate list of impoundments which have been eliminated has

been maintained for future reference (LISTS.DPL). Because it has not

been feasible within the scope of this project to compile and incorporate

data from detailed, project-specific reports, the current project list

may contain some impoundments which do not conform to the above criteria.

Similarly, some projects may have been missed. Inclusion and/or screening

25

of additional projects would be possible with more time devoted to com-

piling and examining detailed reports.

24. The record format used in the LISTS.CPL and LISTS.DPL files

is given in Table 7. Files are listed in Tables 8 and 9, respectively.

Each project has been assigned a unique, three-digit identification

code to facilitate referencing in the data base. The location of each

project is identified by CE division, district, state, county, latitude,

longitude, and hydrologic unit. Hydrologic unit maps compiled by the8

U. S. Geological Survey (USGS) have been used to provide basic location

data. Reservoirs lying on the boundaries of states, counties, and/or

hydrologic units have been referenced based upon dam location. State

and county codes refer to the standard federal coding system (FIPS)

3documented in the EPA's STORET user's manual. The latitudes and longi-

tudes of projects in which surface elevation monitoring stations have

been located refer to those stations, which occur most frequently at dam

sites. In other situations, coordinates have been approximated from

hydrologic unit maps and refer roughly to dam locations.

25. As shown in Table 1, the project list is cross-referenced to

three independent data bases:9

a. the EPA National Eutrophication Survey Working Papers

b. the U. S. Department of Agriculture (USDA) compilation ofreservoir sedimentation data1 0 .

5c. the CE project file compiled by Leidy and Jenkins

The cross-referencing system facilitates access to specific information

on projects contained in these sources.

26

z

4-)

0

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4-)

41

427

Table 6

Breakdown of Projects in the Central Project List by District and Division

TotalNumber Number

of ofCode District Projects Code Division Projects

01 New England* 22 01 New England 22

02 New York 303 Philadelphia 304 Baltimore 905 Norfolk 0 02 North Atlantic 15

06 Wilmington 307 Charleston 108 Savannah 209 Jacksonville 1

10 mobile 17 03 South Atlantic 2411 Buffalo 112 Detroit 013 Chicago 014 Rock island 2

415 St. Paul 13 05 North Central 16

16 Pittsburg 1417 Huntington 28

18 Louisville 15

19 Nashville 7 04 Ohio River 64

20 St. Louis 3

22 Vicksburg 7A'23 New Orleans 4 06 Lower Mississippi Valley 15

24 Little Rock 1025 Tulsa 35

26 Fort Worth 174

28 Albuquerque 4 07 South West. 66

29 Kansas City 1130 Omaha 20 08 Missouri River 31

31 Walla Walla 432 Seattle 633 Portland 17 09 North Pacific 27

34 Sacramento 1535 San Francisco 236 Los Angeles 2 10 South Pacific 19

Total 299 299

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PART VI: WATS - WATERSHED CHARACTERISTICS

26. WATS, the third major file group, contains information on

project watersheds. It consists of the following three elements:

WATS.maps - Watershed MapsWATS.POLYS - Watershed Polygon CoordinatesWATS.DAREAS - Drainage Area Characteristics

This information supplements the location descriptors contained in the

LISTS files. Each element is described below.

27. A set of watershed maps has been compiled from the USGS hydro-.18 9

logic unit maps , EPA National Eutrophication Survey Working Papers 9

11and a report on CE projects in the New England Division The maps are

labeled with descriptive data contained in the LISTS.CPL file and stored

in a loose-leaf notebook. Hydrologic unit maps, used most extensively,

are on a scale of 1:500,000. An example is given in Figure 3. In some

cases, projects have been built after map publication and only thp water-

sheds are shown.

28. WATS.POLYS contains the latitude/longitude coordinates of

polygons which contain projects and their watersheds. These coordinates

have been used to identify water quality monitoring stations in STORET

(see Part IX). Each record contains up to five coordinate pairs and is

referenced by district and project codes. Coordinates have been estimated

from watershed maps contained in EPA National Eutrophication Survey

reports 9 . The 108 projects which were sampled under that program are

represented in WATS.POLYS file.

29. WATS.DAREAS contains additional descriptive information on

project watersheds in the format given in Table 10. Each record is

referenced by district, project, and data source codes. Table 11 lists

the elements of the file along with corresponding data sources. Some

discrepancies among multiple data sources for the same project and

characteristic remain in the file, particularly in drainage areas andmean flows. These could be resolved through verification of the file

at the district level. An inventory of the file contents by division

is given in Table 12. Inventories by project and district are given in

Appendix A.

40

Figure 3

Sample Watershed Map

"RES: 194 POttE DE TERRE"DISTRICT. 29 KAN4SAS CITY "O*DIVISION: 8 MISSOURI RIVER*STATE: MO HYDROLOGIC UN4IT: 10290107*LATITUDE: 37.901 LONGITUDE: 93.318*MAJOR TRIBUTARY* POth! DE TERRE*SCALE: 1---10 MILES--- I

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44

PART VII: RESER - RESERVOIR CHARACTERISTICS

30. The fourth major file grouping, RESER, contains detailed

information on reservoir characteristics. It consists of the following

four elements:

RESER.MORPHO - Project MorphometryRESER.desc - Verbal Project DescriptionsRESER.broch - CE Recreational BrochuresRESER.COM - Comments

Each of these elements is described below.

31. The fluctuating pool levels characteristic of many reservoirs

necessitates the compilation of morphometric data which are referenced

to pool elevations. Volume and surface area variations with elevation

are required for estimation of volume-averaged water quality conditions,

given measuremnnts made at specific depths. Seasonal variations in

reservoir volume and discharge induce variations in mean depth and

hydraulic residence time which may, in turn, influence the response of

trophic state indicators to nutrient loading. Thus, detailed morphometric

information is an essential component of the data base for eutrophication

modelling and for other general uses.

32. The record format and contents of the RESER.MORPHO file are

described in Table 13. Each record is referenced by district, project,

elevation, and data source code. In addition to area, volume, length,

width, and shoreline length data, the file contains a series of pool and

outlet codes, as listed in Table 4. These codes provide supplementary

descriptive information on pool allocations for various uses, ranges of

operating levels, and locations and types of principal outlets.

33. The RESER.MORPHO file was initially based upon data extracted

from project design memoranda. The other principal data sources include:

(1) a report by Leidy and Jenkins 5; (2) USGS water resources data re-

ports, by state and year 3; (3) sedimentation survey sheets 2; and

(4) district and division offices. Information compiled from these

sources has been coded and sorted by project and elevation. Initial

screening was done to identify and correct, where possible, any

45

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obvious errors, such as decreasing area or volume with increasing eleva-

tion within a given project. There were sufficient inconsistencies among

the various data sources for many projects to warrant independent veri-

fication of the file. Accordingly, the file was distributed to the

districts through WES and additions and corrections were made based upon

district responses. Final editing was done to eliminate most of the

redundancies in the file due to multiple data sources for the same project

and elevation. A current data inventory by division is given in Table 14.

Inventories by district and project are given in Appendix A.

34. RESER.desc consists of a collection of verbal project descrip-

tions copied from USGS water resources data reports published annually13

by state . The descriptions are referenced by district and project

codes and assembled in a loose-leaf notebook. These descriptions summna-

rize hydrologic monitoring activities by the USGS, along with important

project characteristics and purposes. The file currently contains

entries for 260 out of 299 projects in the central project list.

35. RESER.broch is a collection of brochures published by CE dis-

trict and division offices as guides to recreation in specific projects.

These usually contain detailed project maps which are useful for locating

monitoring stations. Project purposes and characteristics are also sum-

marized. Each folder is referenced by district and project number and

stored in a hanging file. Currently, the file contains information from

eight districts: Pittsburg, Huntington, Louisville, Nashville, Vicksburg,

Tulsa, Forth Worth, and Sacramento.

36. The RESER.COM file contains miscellaneous descriptive infor-

mation on various projects. This file has been designed to hold data or

p comments which do not conform to other file formats. Each record is 80

characters long and is referenced by district and project codes. A

listing of the current version of this file is given Table 15.

47

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48

.4m

Table 15

Listing of the RESER.COM File

01157 Mad River dam now under control of State of Connecticut

01165 Connected to Hopkinton (01167) at high water

01167 Connected to Everett (01165) at high water

07232 Project transferred to Wilmington District (06)26363 Sediment survey refers to Old Waco

28408 No permanent pool

30214 2 Separate lakes (W. twin/E. twin) below elevation 1342

30333 Project never reached full pool

33298 Re-regulating dam for Green Peter (33299)

33305 Re-regulating dam for Detroit (33293)

34048 Sediment survey refers to Old Don Pedro

34051 Sediment survey refers to Old Exchequer

49

PART VIII: HYDRO - HYDROLOGY FILES

37. The fifth file group, HYDRO, contains detailed hydrologic

data, organized in the following files:

HYDRO.KEY - Station Key

HYDRO.DAILY - Daily ValuesHYDRO.MONTHLY - Monthly Summaries

HYDRO.YEARLY - Yearly Summaries

HYDRO.SUM - Grand Summaries

This information has been compiled to provide bases for nutrient budget

calculations, estimating pool hydraulic residence times (as influenced

by reservoir elevation and discharge), and depth-averaging of water

quality observations (as influenced by reservoir morphometry and pool

level). Because of the stringent water quality sampling requirements

for estimation of nutrient budgets, streamflow data required for such

calculations have been compiled only for those projects and years

sampled by the EPA National Eutrophication Survey. Attempts have been

made, however, to compile reservoir discharge and elevation/contents

data from all projects for 1965 to date using three data sources: USGS/

14 15WATSTORE , the EPA National Eutrophication Survey , and sedimentation

12survey sheets . The HYDRO.KEY file describes 1307 stations from all

three data sources in the format depicted in Table 16.

38. The first data source includes USGS stations monitoring

reservoir elevation/contents or streamflow at or below reservoir dis-

charge points. These stations have been identified using the Master16

Water Data Index of the USGS National Water Data Exchange and USGS

13water resources data reports by state and year . Daily values have

3been retrieved through STORET for the period from 1965 to the most

recent available as of February, 1980. Only those stations with daily

values entered in WATSTORE are included.

439. The EPA National Eutrophication Survey , which sampled 108 CE

projects, assembled a hydrologic data base compatible with its water

quality sampling network for use in nutrient budget computation3. It

includes streamflow estimates for upstream and downstream tributaries.

50

JAANII

For each station, flows are estimated on three time scales: daily (only

for the days on which water quality samples were taken), monthly (only

for the months in which water quality samples were taken), and normalized

monthly (normal flow for each month). This information has been retrie'ed15

from a tape provided by the EPA Corvallis laboratory . Monthly and

normalized monthly flows have been stored in the HYDRO.MONTHLY file.

A Because they only refer to water quality sampling dates, daily flows

have been stored along with the water quality data in the WQ.OBS file.

40. The third source of hydrologic data, sedimentation survey.1 12

sheets , has provided annual estimates of reservoir total inflow, mini-

mum elevation, and maximum elevation, typically for 10 water years in

* most of the 84 projects for which sedimentation survey sheets have been

located. This information has been stored in the HYDRO.YEARLY file.

41. The formats of the DAILY, MONTHLY, YEARLY, and SUM hydrology

files are listed in Tables 17 through 20, respectively. HYDRO.DAILY,

which contains data from USGS/WATSTORE stations only, is in the WATSTORE

format. It is linked to the project list through the sequence number

stored in the HYDRO.KEY file. The other hydrology files contain direct

references to districts and projects. In retrieving daily values, all

parameter codes recorded at each station were included. Thus, the daily

file, and the monthly, yearly, and grand summaries generated from it,

contain some water quality information monitored by the USGS on a daily

basis (e.g., temperature, conductivity, suspended solids). Parameter

codes and coverage are indicated in Table 5.

42. Monthly variations in reservoir discharge and elevation are

needed in order to provide bases for calculating pool hydraulic resi-

dence times and volume-averaged water quality conditions on a seasonal

basis. Table 21 presents an inventory of reservoir discharge, elevation,

and contents data monitored at USGS stations and contained in the

HYDRO.MONTHLY file. Table 21 is organized by division. Corresponding

inventories by reservoir and district are contained in Appendix A. Since

the monthly hydrologic summary has been qenerated from the daily values

file, these inventories also reflect daily data holdings.

51

43. The files contain reservoir discharge data for 245 out of the

299 projects in the central project list. Elevation and contents data

are included for 44 and 108 projects, respectively. Regional deficiencies

in elevation or contents data are particularly evident for the New England,

North Atlantic, South Atlantic, Ohio River, and Missouri River Divisions.

These deficiencies need to be corrected, probably using district-level

information sources, in order to provide a basis for model evaluations

under Phase II of this project.

52

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58

PART IX: WQ - WATER QUALITY FILES

Introduction

44, The sixth group of files, WQ, contains water quality infor-

mation for CE projects. It is organized as follows:

WQ.KEY - Station KeyWQ.DESTAT - Detailed Station DescriptionsWQ.OBS - Observations

WQ.SUM - Data Summary by Station and ParameterWQ.maps - Station Maps

Three sources of water quality data have been used: (1) EPA's STORET3 17

system ; (2) the INFONET system used by the Ohio River Division of the

Corps of Engineers; and (3) miscellaneous survey data for specific

projects. The numbers of stations and observations obtained from each

source are listed in Table 22. The use of these sources and resulting

file structures and contents are described in the following sections.

STORET Data Acquisition and Processing

45. As in Table 22, STORET is the primary source of water quality

information. The following sources have been used to identify station

codes and to associate them with specific CE projects:

a. the Master Water Data Index (MWDI) maintained by theNational Water Data Exchange of the USGS16 .

b. the USGS Catalogue of Information on Water Data1 8'1 9 .

c. a list of stations included in the National StreamQuality Accounting Network (NASQAN) maintained by theUSGS 20 .

9d. the EPA National Eutrophication Survey Working Papers 9

and

e. direct station identification retrievals from STORET

using a latitude/longitude search technique.

These sources are described below in the order used.

59

-'

Table 22H Inventory of Water Quality Data by Source

SampleAgency Stations Dates observations

STORET -EPA/NES 1,637 16,119 181,173

STORET - USGS 655 35,326 592,853

STORET - CE 470 34,890 322,337

*STORET - States 541 18,243 235,894

ISTORET - Other 357 8,078 154,266

INFONET - ORD 763 16,995 534,412

IMiscellaneous 28 170 2,259

*TOTAL 4,451 129,821 2,023,194

60

46. The Master Water Data Index (MWDI) documents water quality

and quantity monitoring activities by various local, state, and federal

agencies throughout the U. S. and contains information on site loca-

* tion, agency, dates, types of measurements, and data storage media.

It does not contain measurements, but serves as a means of locating

them. The MWDI registers all stations in the EPA STORET and USGS

WATSTORE systems, in addition to information on monitoring activities

by agencies which do not participate in other federal data banks.

L4 Thus, this file represents the most comprehensive one available for

identifying monitoring sites and locating data. An initial list of

monitoring stations associated with specific projects was derived by

applying a latitude/longitude search technique to two large station

files acquired on tape from the National Water Data Exchange. One

contained all stations in the 0. S. monitored by the Corps of Engineers,

and the other contained all lake or reservoir stations monitored by any

agency in the U. S. The station/project matching derived from this

search was verified manually by checking station location descriptions

and consulting maps, when needed.

47. The station listings derived from the MWDI did not contain

stations monitored by non-CE agencies on tributary streams. The second

'k and third sources listed above provided additional tributary stations20

operated primarily by the USGS. NASQAN stations are particularly

data-rich, having been operated by monthly frequencies since 1975 with

a broad water quality parameter coverage. Forty-nine such stations have

been located in or directly below the watersheds of projects in the

central project list.

48. The STORET station list also includes all stations operated

by the EPA National Eutrophication Survey in 108 CE projects. These

J stations include upstream tributaries, point sources, reservoir stations,

and reservoir discharge stations. Nutrient loading calculations for

these projects will provide a basis for the testing loading models in

Phase II.

61

49. Finally, a series of station identification retrievals were

done directly in STORET using a search technique based upon the latitude/

longitude polygons contained in the WATS.POLYS file. Because of cost

and time considerations, this technique was applied only to projects

which were in one of two categories: (1) sampled by the EPA National

Eutrophication Survey (since these will be the primary focus of Phase II

modelling efforts); or (2) without water quality data derived from

other station searching techniques. An extracting option available

in STORET was also used to identify only stations for which total phos-

phorus measurements were available.

50. Experience with these alternative station searching techniquesindicates that no one method is completely satisfactory. Each relies upon

the accuracy of the station characteristics and coordinates entered in

the STORET file, Polygon search techniques often retrieve extraneous

stations or miss relevant stations because of inaccurate latitude/longi-

tude entries in the STORET station file. Similarly, retrievals which

depend upon station types (e.g., "stream" vs. "lake") will miss stations

which have been inaccurately classified. For example, many stations

located in reservoir pools (based upon location descriptions and/or

coordinates) were classified as "stream" stations in STORET and MWDI,

The variety of methods employed to identify stations has helped to pro-

vide reasonable project coverage. All station/project matchings in the

final STORET station list have been checked manually with reference to

verbal station location descriptions and maps.

51. Preliminary STORET retrievals have been used to screen out

sites with little or no relevant information and to verify station codes.

Results have been obtained in the STORET Inventory format, which lists

station descriptions and statistical summaries of water quality components

monitored. Based upon these inventories, most stations with only one

sampling date have been eliminated from the station list.

52. Following the screening procedure, a second series of STORET

retrievals has been used to obtain copies of the data on tape for all

observations made after 1964. The most comprehensive retrieval format

62

available from STORET has been used. This format, termed "MORE=5",

provides complete station descriptions along with observations of up

to 50 different water quality variables at each station.

53. A total of 100 variables have been selected for inclusion in

the data base, as listed in Table 5. This necessitated two retrievals

for each station. The selection of variables is based upon the objec-

tives of the project and upon the results of the preliminary station

inventories, which gave initial indications of data availability as a

function of parameter code. The list contains some redundancies due

to multiple ways of expressing various types of measurements (e.g.,

temperature as degrees F or degrees C or phosphorus as P or P0 ). Con-4

version routines have been used to eliminate these redundancies in tape

processing.

54. In a final step, the STORET tapes have been processed to

genetate one file containing station descriptions (WQ.DESTAT) and another

containing water quality observations (WQ.OBS). This involved several

sort/merge steps to combine data from the individual STORET tapes in a

sequenced form. Overall, STORET has provided 1,486,523 observations at

3,660 stations.

INFONET Data Acquisition and Processing

55. The INFONET 1 7 system used by the Ohio River Division to manage

water quality data has been accessed as a second source of information.

Five tapes have been obtained from ORD, one containing station descrip-

tions and the other four containing water quality observations for each

of the four ORD districts (Pittsburgh, Huntington, Nashville, and Louis-

ville). Because the organization and formats of the INFONET tapes are

different from those obtained from STORET, a different set of programs

has been written and employed to extract the data and process it into a

form suitable for merging with output from the STORET tape processing.

56. A systematic procedure has been used in extracting data from

the ORD tapes. In the first step, stations of interest have been selected

63

from the ORD station tape based upon ORD project identification codes

and used to generate a station description file keyed to members of the

central project list. A list of primary and secondary ORD station codes

has been extracted from the station description tape. In the final step,

the station code and parameter code files have been used to extract

relevant observations from the ORD data tapes. This process has been

repeated for each district and the resulting files have been merged.

A total of 763 stations and 534,412 observations have been derived from

this data source.

Miscellaneous Data Acquisition and Processing

57. Water quality data acquired from STORET and INFONET have been

supplemented with miscellaneous data which has been manually coded and

entered directly into the water quality files. This has been done to

improve the regional coverage of the water quality data base. This

relatively time-consuming approach has been limited to two sources:21

(1) survey data obtained from Baltimore District for Almond, Whitney

Point, and Alvin R. Bush Reservoirs; and (2) survey data obtained from

the North Central Division2 2 for Eau Galle Reservoir and Lac Qui Parle.

58. Water quality data and station descriptions from these sources

have been coded at WES. Keypunching and verification have been done at

MIT. The resulting files containing 28 stations and 2259 observations

have been merged with data from STORET and INFONET in the formats des-

cribed below.

WQ File Structures

59. The water quality data base consists of four files (WQ.KEY,

WQ.DESTAT, WQ.OBS, WQ.SUM)and a set of station maps (WQ.maps). The

formats of the four files are given in Tables 23 through 26, respectively.

The structure and contents of each are discussed below.

64

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60. WQ.KEY (Table 23) contains station source and location des-

criptors and accounting information on the amount of data in the WQ.OBS

file, including the number of observations, number of sampling dates,

date range, and depth range. Station descriptors have been derived from

the WQ.DESTAT file, and accounting information from the WQ.OBS file.

Each station has been given a unique, 8-digit identifying code. The

first two digits represent CE district (Table 1) and the next three

represent CE project (Table 8). The last three contain a code which is

unique within each project. The following conventions have been used

in assigning the last three digits of the station code:

001 - 100 STORET stations retrieved in March of 1979101 - 200 STORET stations retrieved in March of 1980301 - 400 EPA National Eutrophication Survey stations501 - 600 INFONET stations (Ohio River Division)801 - 900 stations entered manually

Station numbers have been assigned sequentially within each category and

project, after sorting the stations by STORET agency and STORET station

codes. The station coding scheme permits sorting and analysis by

station, district, project, station, and/or data source. The WQ.KEY

file contains 4451 records (one per station), sorted by the 8-digit

station code.

61. WQ.DESTAT contains detailed information on station location

and data source. As shown in Table 24, it contains four record types.

The fourth type is repetitive and contains up to 15 lines of detailed

descriptive text on each station. WQ.DESTAT contains about 31,000

records, sorted by the first ten digits of each record (station code/

record sequence number).

62. WQ.OBS (Table 25) contains water quality observations. Each

record is identified by station, date, time, depth, and parameter code.

Standard STORET remark codes identify measurements which are less than or

greater than indicated values or duplicate values. The last part of

each record contains composite sample information. WQ.OBS, which con-

tains 2,023,194 records, sorted by the first 24 columns (station/date/

time/depth/parameter), is stored on tape (sequential access only).

70

63. WQ.SUM (Table 26) contains a water quality data summary by

station and parameter, derived from analysis of the WQ.OBS file. Statis-

tics include date range, depth range, value range, mean, standard devia-

tion, and value percentiles (25%, 50%, and 75%). Summary statistics are

derived from the first 1000 observations for each station/parameter combi-

nation. Each record represents one station/parameter combination and the

file contains about 75,000 records sorted by station and parameter codes.

64. To provide direct access to water quality station descriptions

and data summaries, the contents of the WQ.DESTAT and WQ.SUM file have

been produced in microfiche form. Frame format is illustrated in

Table 27. The heading of each frame contains the district, project,

station, and station type codes and names. Station descriptions are

entered from the WQ.DESTAT file. The data summary by parameter follows.

Frames are sorted by district, project, and station codes. A new fiche

card is begun with each district. Card labels indicate the district and

project described in the first frame. The last frame of each card

contains an index which lists the project, station, and associated

frame coordinates.

65. WQ.maps is a collection of station maps (one per project)

which have been produced on a Calcomp line plotter using information in

the WQ.KEY file. An example is given in Figure 4 which. can be compared

with the watershed map in Figure 3. Stations are located based upon

latitude/longitude coordinates. Different plot symbols are used to

identify station types. Only stations with more than 10 observations

are plotted. Adjustments in horizontal and vertical scales are made

for each map so that a linear distance scale is preserved and the map

fits within an 8.5 x 11 inch area. A scale factor in miles per inch is

derived from the LISTS.CPL file and plotted with a triangle. These maps

are useful for identifying station locations and for refining the station

type codes. They are subject, however, to errors in the station coordi-

nates derived from STORET or INFONET. Based upon the maps and station

71

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Figure 4

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m scale factors

(miles/inch)

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73

descriptions, some editing of obvious coding errors in station coordi-

nates has been possible.

WQ Data Inventories

66. Table 28 presents an inventory of water quality data by station

type and division. Corresponding inventories by project and district

are given in Appendix A. Overall, 271 out of 299 projects are represented

in the water quality files. Remaining regional deficiencies include

St. Paul District (6 out of 13 projects), Portland District (9 out of 17

projects), and Los Angeles District (0 out of 2 projects).

67. An inventory by station type and parameter code is given in

Table 29. As expected, temperature, pH, and oxygen are the most fre-

quently represented parameter codes in the file. No data were located

for one code (09-- average daily spillway flow).

68. Phosphorus, chlorophyll-a, and Secchi depth data are parti-

cularly relevant to assessing eutrophication problems and therefore to

Phase II modelling efforts. Table 30 presents an inventory of these

measurements made at pool stations (type codes 2, 4, or 5) by division.

Corresponding inventories by project and district are given in Appendix

A. Out of 299 projects in the central project list, total phosphorus

data have been located for 211, chlorophyll-a data for 132, and Secchi

depth data for 171. Some regional deficiencies in total phosphorus data

are evident particularly in the New England Division (12 out of 22

projects), North Atlantic Division (6 out of 15 projec's), North Central

Division (6 out of 16 projects), and North Pacifi- 1 ion (6 out of

27 projects). These are also reflected in the ._,.opi.. 1-d and Secchi

depth inventories. These inventories indicate that the data base will

not be sufficient to assess the trophic state of all CE projects with

appreciable pools. The complete coverage for roughly 130 projects

indicates, however, that the data base should be generally sufficient

for model testing purposes, the primary objective of Phase II. Regional

testing of models should also be possible, with a few exceptions (e.g.,

74

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the lack of chlorophyll-a or Secchi depth data for projects in New

England).

69. The EPA National Eutrophication Survey is a primary source

of data for testing nutrient loading models and relationships among

within-pool measures of trophic state. An inventory of data holdings

by agency, station type, and parameter code is given in Table 31. Two

agency groupings are used: "EPA", representing the National Eutrophi-

cation Survey; and "OTHER", representing all other agencies and monitoring

programs. Station types include tributary (type code 1), discharges

(type code 3), and pool (type codes 2, 4, and 5). The parameter list

includes a variety of nutrient, biological, chemical, and optical

characteristics pertinent to eutrophication analysis. Within each

category, the numbers of observations, sampling dates, stations, projects,

and districts are indicated, along with the total period of record in

station-months. Includina data from agencies other than the EPA/NES has

more than doubled the total numbers of observations of most parameters

and provided additional useful measurements for some projects, such as

algal cell numbers and volumes, turbidity, and suspended solids. At

pool stations, non-EPA agencies provide more chlorophyll-a observations

(2036 vs. 1499) concentrated in fewer projects (43 vs. 108). These and

other statistics in Table 31 reflect the relative intensities of ..-Iency

monitoring efforts -- many of the non-EPA programs are more intensive

and extensive temporally than the three-date, one-growing-season program

employed by the EPA/NES.

70. Based upon data inventories in Appendix A, some projects for

which non-EPA data monitoring of trophic state indicators has been

partic ly intense include the following:

06-372 John H. Kerr15-399 Eau Galle16-311 East Branch Clarion River18-093 Monroe18-120 Barren River19-340 J. Percy Priest26-359 Sam Rayburn

29-111 Pomona32-204 Kookanusa

79

Table 31

Inventory of Eutrophication-Related Water Quality Componentsby Station Type and Monitoring Agency

TYPE: TRIB OSCH ProtAGENCY: EPA OTHR ALL EPA OTHR ALL EPA 0THR ALL

_TOTAL P N08__9794_23496_3?290 4.50__12475__13925 6689 I4j5__j.1TOTAL P NOAT 9771 22370 32141 1428 11895 13323 1418 7889 9307TOTAL P NSTA 806 997 1803 116 416 532 479 737 1216TOTAL P NPRJ 109 I4. 6.... 108 211 237

10 8_19r6 711

TOTAL P NDIS 27 30 31 27 29 30 27 28 29TOTAL P MTHS 8469 35292 43761 1230 21050 22280 2628 18388 21016O THO-P NOS 9878 12198 22076 1456 5433 6889 6668 12851 19 19

ORTHO-P NDAT 9855 10386 20741 1434 5063 6497 1418 5931 7349ORTHO-P $TA 908_7 251534_ _ 15. !3_ _427 479 _7r lSSORTHO-P NPRJ 109 186 223 108 193 225 108 167 205ORTHO-P NOIS 27 29 31 27 28 30 27 26 29ORTHO-P MTHS _9 .Z8_.Q8 7 Q_88_529 1!F4 141R

NH3-N NOBS 10002 14010 24012 1464 6393 7857 6682 12284 18966NH3-N _NA 0979_13380 -23359__L447 6 4 __706 141 - -n7g f;NH3-N NSTA 808 829 1637 116 343 459 479 639 1118NH3-N NPRJ 109 197 221 108 198 226 108 169 202NH3-N NOIS 27 29__3 27. 28 30 27 5 2 9-NH3-N MTHS 8508 21659 30167 1231 11b33 12864 2628 14074 16702

TKN NOBS _970-11554215241461__325 786_5 7_5i_1CZ 3.__TKN NDAT 9953 11032 20985 1439 5211 6650 1391 4754 6145TKN NSTA 804 777 1581 116 276 392 470 525 995_TK N N P R. 10 9-l 75. 206_ _Q 1.73_21L- IC c 19TKN NDIS 27 29 31 27 26 30 26 24 28TKN MTHS 8505 18057 26562 1230 8830 10060 2599 10869 13468

N02+3-N NOBS 9999 12425 22424 1464 6000 7464 6682 11285 18067N02+3-N NOAT 9976 11907 21883 1442 5906 7348 1418 5302 6720

__NO2-3-NUN TA E07 -7Z__ 41_1548 _ tF___276 92_1 47Q -1 I ; 31NC2+3-N NPRJ 109 165 194 108 154 188 108 129 167N02+3-N NOIS 27 28 29 27 23 29 27 24 28402 . 7 i2577s I 11 65L 25 -7- C 5 8 1 1 - Z2 9 1-46.:

N03-N NOBS 5978 17079 23057 784 10629 11413 0 6563 65633NO 3-N _N 0L 0 aa E 133-2101 I a98 1_2 4 63 2 : 0.l AT:

N03-N NSTA 773 526 1299 111 274 385 0 409 409NO3-N NP.J 106 173 208 107 168 211 3 153 153

._N03-N .N LS 25o 2 R. 2 G 29 n qN03-N MTHS 4904 20780 25684 617 13582 14199 0 7588 7588

_CHL-A NO5 I 743 746 6 31R -. 140a -1r, 1-3CHL-A NOAT 3 728 731 6 313 319 1490 1129 2619CHL-A NSTA 1 74 75 2 30 32 482 147 629-C4L-A N PRL I ?Q 11 n I IQ 19 1log 4--1.3-CHL-A NDIS 1 14 15 1 12 12 27 13 28CHL-A MTHS 5 1189 1194 a 377 385 2660 3145 5805

ALGAE(#) NOBS 0 1058 1058 0 875 875 0 1076 1076ALGAE(#) NOAT 0 1053 1053 0 875 875 0 907 907ALGAE(.)LSTA 0 4A 48 a 22 1 2 0 i-1 113ALGAE(#) NPRJ 0 26 26 0 29 29 0 59 59ALGAE(#) NOIS 0 12 12 0 14 14 0 12 12ALGAE(# MTHS 0 1272 1272 0 1111 11,0 4 A - 4 A?

(Continued)

80

Table 31 (Concluded)

TYPE: TRIB OSCH POOLAGEN1CyI EPA OTHR ALL EPA OTHR ALL EPA OTHR ALL

_ALGAE(V)_NOBS 0 7 7 0 9 0 -9 _ 414._44_ALGAE(V) NOAT 0 7 7 0 0 0 0 430 430ALGAE(V) NSTA 0 1 1 0 0 0 0 40 40_ALGAE(VINPRJ 0 1 1 0 0 0 0 14 14ALGAE(V) NDIS 0 1 1 0 0 0 0 1 1ALGAE(V) MTHS 0 6 6 0 0 0 0 832 832

SECCHI NOSS 3 1181 1184 6 420 426 1487 5091 6578SECCHI NOAT 3 1152 1155 6 349 355 1478 4969 6447_SECCHI NSTA 1 172 173 2 35 37 481 406 977SECCH7 NPRj 1 42 43 1 28 29 108 121 171SECCHI NOIS 1 14 14 1 12 12 27 20 28SECCHI MTHS 5 2137 2142 8 597 605 2629 12314 14q43

TRANS(%) NOOS 5 40 45 24 12 36 6781 2556 9337_TRANS(%LNOAT 2 23 25 6 9 15 1419 368 1787TQANS(M) NSTA I 18 19 2 8 10 481 96 577TRANS(%) NPRJ 1 11 12 1 8 9 108 21 121TRANS(%L NO-IS 1 2 3 t 1 2 27 4 27T ANS(%) MTHS 3 6 9 8 10 18 2484 816 3300

_LIGHTtLNCBS 0 66 66 0 60 60 486 34o57 153LIGHT(%) NDAT 0 21 21 0 24 24 329 531 860LfGTr(%J NSTA 0 18 1 0 12 12 203 201 404

_LIGHT(%)__.NPRJ 0 10 10 0 12 12 52 42 9LLIGHT(%) NDIS 0 1 1 0 3 3 12 7 18LIGHT(%) MTHS 0 7 7 0 17 17 335 494 829

TUR elOT NOSS 0 24440 24440 0 14708 14708 0 22758 7228TUP1IDIT NOAT 0 23815 23815 0 14555 14555 0 10580 10580_TUPSIDIT NSTA 0 904 904 0 350 350 0 2 -62.._TURBIfT NPRJ 0 201 201 0 199 199 0 171 171TURBIDIT NOIS 0 30 30 0 28 28 0 25 25TURIOT MTHS 0 24945 24945 0 1290712907 0 19675 10675

SUSP SOL NOSS 0 12054 12054 0 5766 5766 0 10669 10669SUSP SOL NOAT 0 11470 11470 0 5672_.._672 0 .234SUSP SOL NSTA 0 686 686 0 264 264 0 4:9 429SUSP SOL NPRJ 0 168 168 0 169 169 0 116 116.SUSPSo0LN0S 0 29 29 0__26_26 0 21 2tSUSP SOL MTHS 0 18422 18422 0 8277 8277 0 8401 8401

-OXYGEN NOBS 8_42216_42224 n2580292_9234.2O2As4i€_.OXYGEN NOAT 3 33919 33922 6 25106 26112 1494 17465 18959OXYGEN NSTA 1 1062 1063 2 412 414 482 1051 1533-OXYGEN NPQJ 1 212 212 t_217 17 6Ig8 11 iA-OXYGEN NOI$ 1 30 30 1 29 29 27 29 29OXYGEN MTHS 5 40323 40328 8 22191 22199 2668 31533 34201

FLOW NOSS 7934 24977 32911 1447 15753 17200 0 1468 1468FLOW NOAT 7933 23692 31625 1447 15173 16620 0 1373 1373FLOW NSTA _ 90___ -5__ 3_.23 1.0AP :p.2 a a 376 0 al Q0FLOW NPRJ 106 156 185 106 175 210 0 35 35FLOW NDIS 27 30 31 27 25 30 0 17 17FLOW MTHS 6454 22028 28482 1174 14345 15519 0 107r 1075

83 I

Thus, it will not be necessary in Phase II to rely exclusively upon

data from the EPA/NES for assessing relationships among within-pool

measures of trophic state. The stringent water quality and flow sampling

program requirements required for estimation of nutrient budgets and

time limitations of this project suggest, however, that EPA/NES data be

used exclusively for evaluating nutrient loading models.

82

PART X: SED - SEDIMENTATION DATA

71. The seventh major file group describes sedimentation charac-

teristics of CE reservoirs. It contains the following elements:

SED.sheets - Sedimentation Survey Sheets* SED.RATES - Sedimentation Rate File

This information has been derived from a collection of sediment survey

data for U. S. reservoirs compiled in 1975 by the Agricultural Research

Service of the U. S. Department of Agriculture1 0 '12 A total of 84 CE

projects in the central project list were included in that compilation.

These are identified by the sedimentation survey key in the LISTS.CPL

file (Table 8).

72. SED.sheets consists of a collection of the most recent sedi-

mentation survey sheets contained in the appendix to the U. S. Department12

of Agriculture (USDA) compilation . These sheets contain detailed in-

formation on project location, morphometry, hydrology, as well as sedi-

mentation. An example is given in Table 32. Sheets have been assembled

in a loose-leaf notebook, identified, and arranged by district and proj-

ject code.

73. SED.RATES is a file containing the most recent estimates of

sedimentation rates for each of the 84 projects located in the USDA com-

pilation 10 . The format of this file is given in Table 33. Many of the

sedimentation rate medsurements antedate the water quality file. For 45

projects, the most recent survey data available were taken during or

before 1965. Rate estimates for some projects, however, will be useful

for testing relationships between sedimentation rate and nutrient trapping

efficiency during Phase II of this study.

83

Table 32

Sample Sedimentation Survey Sheet

RESEOR SDIMET U L *PART OfAGRICULTURE

DAT ESOI SM M EN CAT S= SOIL CONSERVATION SERVICE

93-34 Am 6.44 NA&E or RESERVOIR 46- 13bCAXA SOWE NO.

I. 1 OWNER Coru of Eznes sTREAM4NrhC~ida 13. srAT Oklahom13W I L MENEST P I . COUNTY tS 4

4- 53072W 3TWP. 19M RANES 0II CantonSTP. LA136 , 05 Lofta 98,36 IL Top OF oAMa ELVATION 1 A48.0 Q S PLWA RS2

I&. STORAM It. ELEVATION IL. OIINAL I& ORIGINAL IA. GROSS STORAGE. IS. OATZALLOCATION Tap oF POOL. SURFACE AREA, ACRE CAPACITY. ACRE.FE Lcne-fqv STORAGE 1114"N

IF ILOCOIO = 3.0 15.750_ 272,0 401,500 2/=5. hJLTIPuUuSE _____ _____25jul 47

&__POWER_--- T70 -- T-fl3T 129. zoo 1*5. DArt LO.

AL OP90 SEGA

.CONSERVATION ______ * 7 5 i .4 2.5 22.7'50 4 Jul '.8

17. LIPIETL OF RESER L wAiES AV, *IOT% or 0EsEifo'" 1. K.IISL TOTAL ORIIIAGE ARE 4/ I ,4S3 so. M4.1 2. MEAN ANNUAL POICCIPA"O" 19. 30 %CCES

~II . 5I EIETCNRSTN RA / 5,051 5.I2. EANWANNuAL suNore 58- vs %NcI'saL .vw- 300 IIILES'AV, WIor 40 "ILEs 24, "CAM ANNUA. RUNOFF 186 r00

21.MA. EEV 000 141N. ELEV. 15 75 [zs. ANNUAL TEMP RA'585 N"S16~p-.IL* O5' 25z i. rWE OF ]:K 4. Of ANMOS&I it AC su. AIT. lii .ITO

PON"C ACCL. $40wryt OR CONTOUR IN", AREA. AC EU I ACRE FtST )aC. 1, 0 Ac. OT

Jul 19 -- Rnge 44 13.50 401,500 21.15Iimay 195 5.83 5.83 pAng. Z 15, 750 390,800 2.101/Oct 195 6.42 12.23 Rang. 2 15.750 385,900 Z. 07

Sept 196 6.92 19.17 Range. 1 15.,700 383,200 '36

20. OATIK OF 1S. it tRIOO MNATER NFLOW. ACREJ;EE1. 3. AlArtit-E NFL, -0 ZAT!. AC..2'SURVEY PRECIPITATION R. MEAN ANNUAL a. "AR. ANNUAL. c. 1RI00 &mnLuft IA ANNUL L *0 OArf

N"b 1953 20.05 288,890 540.420 1,684,200 238890 1.684.200Oct 1959 19.51 162,760 422,930 1.044,900 222,780 12.729.100

SSept 1966 18.69 99,170 159,390 686,260 178.160 1,1,6

Z& *0 At OF 3? PERIOD CAPACMl LOSS. ACR-JETIL TOTAL SID OEPOSITS TO OArt. ACPE-SET114 SURVEY A. nwo roY.AL e, AV, ANNUM. Is. no so. w wA,~S TOTAL -o *ATe 6 w A04NUAL 'I' RE .- , .. 4

.Eay 1953 10,690 1,834 0.302 10.690 1.834 0.302Oct 1959 4,880 760 0.125 I15.570 1.271 0.209Sept 1966 2,660 364 0.063 I18.2Z30 951 0.156

,1/18.300 7/ 965 7/0.159

a Carl Opp at. AV. 3ISN*W. aSID. DP.7ONS PRSQ.II.-YR &I STORAGE LOSS. P.2 4. SID. INFLOW. so"S4U E . PE CU PT 1111100 L. TOTL, TO OATS &.AV ANN, 0. TOT TOOAT[I I. xCE1100 S. TOT 'OOATt

My 1953 70.9 466.4 466.4 0.457 2.66 7,224 7,212Oct 1959 56.2 65.3 255.8 0.317 3.88 1,795 5.138Sept 1966 56.1 76.4 190.6 0.237 4.54 3,447 4,199

(Continued)

84

Table 32 (Concluded)

25 O*15 43. DEPTH DESIGNATION RANGE IN FEET BELOW. AND ABOVE. CREST ELEVATIONsuRvEY 63-55155-451 5-40140-35 35-30 30-25 125-20 120-15 18 8-4r Icr55-+

PERCENT OF TOTAL SEDIMENT LOCATED NITHIN DEPTH DESIGNATION

ty 1.953 4.3 12.1 11.9 15.7 17.1 15.2 12.0 7.4 4.3 0.0 0.0 0.0Oct 1959 3.2 7.7 5.6 12.0 16.4 22.2 14.7 9.9 6.7 1.6 0.0 0.0Sept.1966 3.5 13.2 9.1 13.1 16.3 15.4 11.5 7.4 6.2 2.8 1.4 0.1

22. DArt Of REACH OESIGNATION PERCENT OF 7OTAL. ORIGINAL LENGTH OF RESERVO.SURVEY 0-L 1 10-20 1 -20-.3130-40 40.001 S0.0' I 5 90,100I 1, OS, -a0 l . s'-120F .121

PERCENT OF 7OTAL SEDIMENT ,.OCATED WITHIN IACH DESIGNATION

May 1953 2.5 6.4 10.7 19. 21.17.9 15.6J4.0 2.3 0.0 0.0 0.0 0.0 0 0.0*Oct 1959 2.3 2.2 4.7 17.q 27.M41.1 17.116 1 3.1 1.2 0.0 0.0 0.0 0.0 0.0

Sept 1966 4.3 5.7 7.7 18.1 22. 162 14.1 5. 3. 1.8 1.3 0.0 10.0 0.0 0.0

49. RJANGE IN RESERVOIR OPERATION*AT !" 0t0L19V. 59 !dO V INFLO% I AC FT N A. ER 'A. EUV --1-- , -FLOW.-c

T

1.947 (2, MCI Z 48 J 1957 16Z5.51 1596.32 422,930

194 1593 9,60 1959 1616.12 1,613.5 185211.949 .61,4.27 1,595.91 3760 15 540 11312 0,1L301950 16Z3.94 L60Z.35 532.L80 1960 1615.36 1613.79 145,230

.951 1628.05 1602.36 540.420 1961 1614.65 1613.79 135.3301952 1605.30 1598.65 81. 170 1962 1615.16 1613.12 "06.20

'953 1598.75 1588.66 -.9,530 1963 1614.21 1610.l 61.326.954 1596.75 1586.30 52,346 1964 1611.93 1600.86 20.513i955 1615.50 1585.66 22.3.790 1965 1616.13 1600.1 159,290

L956 I '613.27 1604.33 1 10,953 1966 16 15.52 1609.32 -20

_______________ LEVATIONPNA4 A*T kAA

tLEVA?.O,, .04 :~I , VA ~rV ~vAr IO AREA I .AAC17'V ,SLEV&TIOPO AC4 :A04CIr-

1580 0 3 1610 6.323 79,150i

*1585 242 256 1614 7,535 106.700L590 1,.89 3,987 1615 7,879 114,4001595 2,678 14,160 1620 9,497 157,8001596.5 3,318 18.430 1625 11.258 209,3001600 3,652 29,950 163 12,756 :69,800, i1603 ,411 42.100 1635 14 ,510 338,1001605 4.,873 51,390 1638 13,700 3 3 300

SRAIFK Rtr Closed. C ..

.0 c lo W Spillway crest is at elevation 1.613.3.

2/ Date of diversion.I/ Rservoir is being opetrated at "i ignat dll" pool elevation 1615.2 .i4 ch

temporarily provides amicipaL wacer supply for Oklahoma City.Area as determined by AWR commitee for April 1954 drainage area data publicat on.Excludes 4,642 sq. 21. of watershed not contributing to sediment; 1735 sq. at.above Fort Supply Dam and 25 sq. ad. surface area of Canton Lake.

j/ To provide a uniform presentation of data from all sedimentation resurveys ofCanton Lake, daca summaries for the 1953 and 1959 resurveys have been revised

to confom wuith present instructions.7/ Includes above crest deposits.

AAGENCY MAKING SURVEY U. S. Army Engineer District. Tulsa49. AGENCY SUPPLYING DATA U. S. ArMy Engineer District. Tulsa -O. DATE Oernbor 1970

85,

85 .

.. 61

10 0

ul .90c.O S* 1 j . ,

t* I S. C - C - Ui0 xIn L.L u*

*(4

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*IW W W4VWI86

PART XI: NES - EPA NATIONAL EUTROPHICATION SURVEY DATA

74. The eighth file group consists of data and reports obtained

4,9from the EPA National Eutrophication Survey 4

, exclusive of the water

quality, hydrologic, and drainage area data described in previous sec-

tions. It consists of the following elements:

NES.reports - EPA/NES Working Papers

NES.COMP - Compendium of Lake and Reservoir DataNES.SUM - Compendium Summary

This file group contains information on 108 CE projects as well as the

other 704 lakes and reservoirs which were sampled by the EPA/NES through-

out the U. S. Besides providing detailed information on point-source

nutrient loadings to CE projects needed for nutrient budget estimation

in Phase II, it provides a basis for comparisons of lakes and reservoirs

with respect to morphometric, hydrologic, and nutrient loading response

characteristics. Such comparisons, in turn, can provide means of inter-

preting the performance of lake nutrient loading models in reservoirs.

75. During 1972-75, the EPA conducted a systematic survey of 812

lakes and reservoirs in the U. S. The original objective of the National

Eutrophication Survey was to develop a data base for assessing the

impacts of point-source nutrient discharges on the trophic conditions9

of lakes and reservoirs. The NES produced a series of working papers

summarizing and interpreting the results for each impoundment or lake

monitored. Results have also been summarized in a compendium format and23

published in four volumes

76. NES.reports consists of the collection of working papers des-

cribing NES surveys and results in each of 108 CE projects. Each report

has been referenced to the data base by district and reservoir code.

Maps extracted from the reports have been included in the WATS.maps file.

77. NES.COMP is a computer file containing a nationwide summary

of NES data for 775 lakes and reservoirs, obtained from the Corvallis4

laboratory of the EPA . A sample printout of data from the file is

given in Table 34. Similar printouts have been produced for all CE

87

projects in the compendium file and arranged in a notebook by district

Iand project code. The compendium contains data for 106 out of 108 CE

projects sampled by the EPA/NES.

78. NES.SUM is a summary of the NES.COMP file, with a few modifi-

cations. The format of the summary file (Table 35) is more convenient

'4 for input to statistical analysis programs, since one record is used

for each lake or reservoir. A number of missing latitude and longitude

coordinates have been added, based upon estimates derived from USGS6

Hydrologic Unit Maps . Each lake or reservoir has also been referenced

by CE division to provide a basis for regional contrasts of lake and

reservoir characteristics.

79. A few simple analyses have been done to provide descriptions

of types and amounts of data contained in the NES.SUM file. The regional

distribution of lakes and reservoirs described in the file is depicted

in Figure 5. A breakdown of impoundment numbers as a function of trophic

state, impoundment type, and CE division is given in Table 36. The

numbers of CE projects are listed as a function of division and trophic

state in Table 37.

80. The results of preliminary lake/reservoir comparisons which

have been made using data from the NES compendium tape are summarized

in Table 38. Three groups of impoundments have been compared: natural

lakes (N=310); non-CE reservoirs (N=359); and CE reservoirs (N=106).

The stratification of thirty-six original and composite variables across1

these groups has been studied using the BMDP-77 computer program 1

Within-group means and Analysis of Variance (ANOVA) statistics are

listed in Table 38. Detailed results with histograms are presented in

a previous report2 4 .

81. While the scope of this report precludes detailed interpreta-

tions of the data, a few comments on these results seem appropriate:

a. At the 95% significance level, differences across groupsare evident in all cases except longitude, conductivity,phosphorus retention coefficient, nitrogen retentioncoefficient, and the second Vollenweider phosphorusloading statistic2 5.

88

b. Strongest differences across groups are apparent fordrainage area (F=77.5), drainage area/surface area(F=73.6), outflow rate (F=60.7), and drainage area/maximum depth (F=53.4).

c. Reservoirs have higher potential phosphorus concentra-tions, based upon phosphorus loading and the Vollenweiderstatistics, but lower observed phosphorus and chlorophyll-aconcentrations than natural lakes. This is an indicationthat the Vollenweider statistics may give biased predic-

tions in reservoirs (i.e., over-predict loading impact).

d. Reservoirs have less transparency, despite less chloro-phyll-a. This is probably an effect of mineral turbidityin reservoirs.

e. N/P loading ratios are somewhat lower for reservoirs thanfor natural lakes; the reverse is true, however, for

inorganic N/dissolved P ratios measured during the summerwithin the impoundments.

f. The composite statistic (Secchi depth/mean depth) isstrongly stratified across groups (F=44.8). Assumingthat the depth of the photic zone is roughly twice theSecchi depth, the means in Table 38 indicate that typi-cally 62% of the volume of lakes is available forphotosynthesis, compared with 30% in the case of CE

reservoirs. Reservoirs probably have greater shorelinedevelopment, however, and may be more influenced by photo-

synthetic processes occurring in littoral zones.

g. While a 4-degree difference is evident in the averagelatitude of CE reservoirs as compared with lakes, adetailed look at Figure 5 reveals that the distributionof NES lakes is bimodal, with clusters in the North(primarily Minnesota, Wisconsin, Michigan, and Maine)

and in the extreme South (Florida). The maximum reser-voir density occurs between these extremes.

Because of the strong regionality of the lake vs. reservoir distributions

evident in Figure 5, it is difficult to separate the effects of impound-

ment type from those of regior with analyses of the type described above.

Thus, in future, more complete analyses, it will be important to control

for regional differences by comparing subsets of lakes and reservoirs

within defined regions.

89

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94

Table 38

Summary of Lake/Reservoir Comparisons Derived from EPA/NES Compendium

WITHIN-GROUP mASS ANOVA RESULTS

(N-309) (N-360) (N-106)Nat. Non-CE CE Prob

C Variable Lakes Reesrv. Reserv. F (greater F)A Latitude (degrees N) 41.6 39.0 37.6 34.8 <.0001A Longitude (degrees W) 91.7 93.6 92.3 1.9 .16G Drainage Area (km2) 222. 1358. 3228. 77.5 <.0001G Surface Area (kim2) 5.6 8.6 34.5 44.4G Volume (km2m) 27.3 50.8 239. 36.4a Mean Depth (m) 4.5 5.7 6.9 10.4G Maxumum Depth (m) 10.7 15.8 19.8 18.8G Hydraulic Res. Time (yr) .74 .23 .37 23.5G Overflow Rate (m/yr) 6.5 25. 19. 40.9G Outflow Rate (km2m/yr) 47.9 236. 6s0. 60.7G Dr. Area/ Surf. Area 33. 156. 93. 73.6G Dr. Area/Volume(l/m) 6.6 26. 13. 39.5G Maximum Depth/Mean Depth 2.5 2.8 2.9 7.3 .0008G Relative Depth .40 .47 .30 8.6 .0002G Surf A/ Max Depth .53 .56 1.7 24.6 <.0001G Dr. Area/Max Depth 20. 86. 162. 53.4

A pH 8.06 7.74 7.63 24.1 <.0001G Alkalinity (mg/i) 87. 63. 65. 8.6 .0002G Conductivity (U?4os/cm) 253. 214. 208. 2.4 .0950G Total Phosphorus (mg/i) .054 .053 .039 3.6 .0268G Dissolved P (mg/1) .021 .018 .011 9.5 <.0001G Inorganic Nitrogen (mg/i).20 .26 .30 9.7 <.0001G Secchi Depth (m) 1.4 1.2 1.1 7.7 .0005G Chlorophyll-a (mg/i) 14. 10. 8.9 11.4 <.0001G Algal Assay Yld (mq/l) 2.5 2.2 1.3 4.0 .0194

G P Loading (g/a 2-yr) .87 2.9 1.7 29.1 <.0001A P Retention Coef. .36 .35 .40 0.7 .51A NPS Load Fraction - P .72 .80 .81 8.4 .0002G N Loading (g/m2 -yr) 18. 45. 28. 22.6 <.0001A N Retention Coef. .24 .20 .17 2.5 .081A NPS Load Fraction - N .87 .93 .96 18.7 <.0001

G N Load/P Load 20. 16. 16. 8.0 .0004G Inorg N/ Diss. P 9.1 14. 26. 29.4 <.0001G +Vollenweider Stat. 1 .31 .56 .42 15.2G +Vollenweider Stat. 2 .056 .067 .057 1.9 .1586

G Secchi D/ Mean Depth. .31 .20 .15 44.8 <.0001

* C = Code for Type of Mean (A = Arithmetic, G - Geometric)+ Statistics for Assessing Phosphorus Loading Impacts on Lake Eutrophication;

Stat.1 = LpQs' 5-(ref.25); Stat.2 a (LP/Qs)/(l + rT)- (ref.26)

95

PART XII: NUMERICAL CHARACTERIZATION OF RESERVOIR HYPSOGRAPHIC CURVES

Introduction

82. Estimates of reservoir volume and area variations with eleva-

tion are required for computing volume-averaged concentrations and pool

hydraulic residence times. This information is generally available in

the form of a table which lists area at and volume below specific eleva-

tions. Morphometry tables have been compiled for most of the 303 CE

projects in the data base. This paper describes numerical investigationsof these tables, specifically covering the relative accuracies of various

curve-fitting and interpolation techniques. Results will be used to

design methods for summarizing, storing, and applying morphometric

information in other phases of the project. Results are also relevant

to other aspects of reservoir management, specifically including the

estimation of reservoir volumes and sedimentation rates from range survey

or contour area data.

83. This study has been performed prior to completion of the

project morphometry file. A subset of data has been selected which

covers 147 projects, each with at least five listed elevations and with-

out obvious errors (such as decreasing areas or volumes with increasing

elevation). A total of 1285 elevation/area/volume data points have been

compiled for these 147 projects from various sources. Results of this

study are summarized below. Details can be found in a working paper26

submitted as a progress report under this contract

Approach

84. The objective is to design and evaluate methods for estimating

area and volume at any elevation, given information available for specific

elevations. Methods can generally be classified as curve-fitting or

interpolating. Accuracies of these methods can be assessed by comparing

observations and predictions of the following:

96

a. V(V) = volume estimated from volume/elevation points

b. AMV = area estimated from volume/elevation points

C. A(A = area estimated from area/elevation points

d. AV(A) = incremental volume estimated from area/elevationpoints

Statistics a and c are simple curve-fitting or interpolating problems.

Statistic b involves differentiation of the volume curve to estimate area:

A(V) = (1)

where

Z is the total depth, ft

Statistic d involves integration of the area curve between specific

depth limits (Z and Z ) to estimate incremental volume:1 2

22

85. En testing each method, a jackknife procedure 27has been used

to estimate the above statistics for internal elevation points (i.e.,

tests are not made for the top and bottom listed elevations in each

project, although these elevations are used in the fitting or inter-

polating process). In applying the jackknife, information from the

point being tested is not used in estimating the parameters of the pre-

dictive function. For instance, estimates of area or volume at the

second listed elevation are derived exclusively from information in the

first and third through last listed elevations.

86. Reported values and predictions are compared using the follow-

ing error statistic:

D = log~[2 (3)

where

Y = reported value (V, A, or AV)

Y = estimated value

D = error statistic

97

For errors of the magnitude studied here, the value of D approaches the

difference between the reported and predicted values, expressed as a

fraction of their average. In most cases, the D statistic seems to have

reasonably symmetric error distributions. For each of the four types

of area/volume comparisons, estimates of bias and standard error in D

have been derived for a variety of predictive methods, as described below.

SIi Curve-Fitting Schemes

87. A single-term power function has some useful properties for

fitting these types of curves and is the simplest of the methods evalu-

ated:

byc z (4)vba

= c z (5)a

Z = E- E (6)

where

cv, Ca, bv , ba = empirical parameters

z = total depth (ft)

E = elevation (ft, msl)

E 0 elevation at V=A=O (ft, msl)

Model parameters can be estimated using linear regression by transforming

V, A, and Z values to logarithms. The following equalities should hold

if the model is valid for a particular reservoir:

@V bvbVv=b c Zbv = A (7)

9z v v

**ca = bvc (8)

ba =b - 1 (9)a v

Equations (8) and (9) can be used to test the consistency of the volume

and area curves. The slopes of volume and area vs. total depth on a

98

I ..i

log/log plot shoulO differ by 1.0. Dividing equation (4) by equation (5)

yields the following:

bv baZbv =bvz (10)

a

If the basic model holds, the ratio of volume to area (or mean depth)

should be proportional to the total depth. Thus, the model can be tested

through regression analysis of the following equation:

)=CzZ (11)

A t-test can be applied to the mean and standard error b within each

project to determine whether the estimate is significantly different

from 1.0.

88. In testing the power function model, the parameters in equa-

tions (4), (5), and (11) have been estimated for each of 147 reservoirs.

The statistical distributions of the optimal slope parameters (by, ba,

b z ) are shown in Figure 6 on log-normal probability scales. Median

values are 2.97, 1.97, and 0.97, respectively. Thus, in the "typical"

reservoir, volume and area increase approximately as the cube and square

of total depth, respectively. This corresponds to the solid geometry

of an inverted cone or pyramid. About 10% of the projects have bv

values exceeding 4 and 4% have b values less than 2. The parameters[ v

b and b contain roughly the same information as the statistically-a v28defined "lake form" proposed by Hakanson

89. Based upon t-tests, the b parameter differs significantlyz(p=.05) from 1.0 in about 35% of the projects. This means that the

single-term power function model fits about two thirds of the projects.

In the remaining one third, the slope parameters by and b are inconsis-v atent and/or variable with depth.

90. Table 39 lists error statistics for this model. Estimates of

the first (O(V)) and third (A(A)) statistics are derived directly from

equations (4) and (5), respectively. Areas are estimated from volume

99.

Table 39

Evaluation of the Power Function Model

I!Gross* Median**

Statistic Standard Error Standard Error

V(V) .097 .062

i(v) .115 .081

A (A) .097 .062

Av(A) .177 .081

* estimated from mean-squared D value (equation (3))

** median of within-project mean-squared D values

100

curves according to equation (7). Changes in volume within various

strata are estimated from area curves according to:

d Z ba

AO(A) = c Z dZ (12)z/

22 11 (13)ba +1

Standard errors of D range from .097 (V(V)) to .177 (AV(A)). The higher

standard error of AV(A) could be related to the fact that it is an incre-

mental value which is more subject to measurement error on a percentage

basis than is total volume or area. A D standard error of .097 corres-

ponds roughly to a standard error of 10%, or to 95% confidence limits

of ± 20% in an estimated area or volume. The differences between the

gross standard error and the median, within-project standard error

reflects the skewness of the error distribution across projects, i.e.,

the model seems to fit some projects considerably better than others,

as indicated by the b distribution.

91. In order to improve upon the above model, variations in the

slope parameters b and b with depth must be accounted for. The sim-

plest way of doing this is to include higher-order terms in the regres-

sion equations:

mVA L c.Z*i (14)

i=O

mA* = f diZ*i (15)

i=O

where

ci, d.1 = empirical coefficients (i=0,m)

* = superscript denoting logl0 transformation

m = maximum degree of polynomial

The error distributions of these functions have been evaluated for

maximum degrees ranging from 1 to 5. For m=l, the scheme is equivalent

101

-I.

to the single-term power function model discussed above. For comparative

purposes, linear polynomial functions have also been tested:

m

mFd.z1 (17)

i=0

For each reservoir, the maximum degree of the polynomial has been limited

to the minimum of m and the number of elevations in the table minus two.

92. The error statistics in Table 40 indicate that logarithmic

polynomials are preferable to linear ones according to most criteria.

Log transformation tends to linearize the relationships and renders them

easier to fit with low-order polynomial terms. Some reduction in error

is achieved by including quadratic and cubic terms in depth. Addition

of fourth- and fifth-order terms, however, tends to increase estimation

error, presumably because higher-order polynomials can have local minima

and/or maxima between the fitted points. These are artifacts of the

fitting process which can cause large estimation errors between the

- I fitted points. Cubic polynomials apparently have about the best combina-

tion of flexibility and smoothness for these purposes and data densities.

Interpolation Schemes

93. Interpolation methods can be used as alternatives to the

curve-fitting techniques described above. Interpolation essentially

involves fitting different curves to different sections of each volume/area/elevation table. A variety of interpolation methods have been

26tested on the same data set used above . These involve different trans-

formations of the volume and area points, including inverse power func-

tions (first through fifth order) and logarithmic. Of the methods

tested, one involving logarithmic transformations of volume, area, and1< 26total depth points has been shown to have the lowest error statistics

The errors, however, are essentially equivalent to the error

102

Table 40

Evaluation of Polynomial Functions

Gross Standard ErrorMaximum

Transformation Degree V(V) A(V) A(A) AV(A)

logarithmic 1 .097 .115 .101 .182

2 .069 .083 .081 .166

3 .062 .067 .081 .164

4 .216 .292 .129 .173

5 1.198 1.280 .534 .491

linear 1 .371 .615 .200 .281

2 .249 .170 .134 .212

3 .134 .088 .106 .196

4 .150 .085 .111 .193

5 .221 .136 .147 .265

103

F characteristics of the best curve-fitting scheme (log/log cubic poly-

nomials). Results suggest that either of these methods probably

approaches the limits of data accuracy.

Conclusions

94. in one sense, numerical interpolation methods are preferableU to fitted curves because the former are more flexible and do not rely

strongly upon particular forms or curve shapes. interpolation requires

storage of and access to the entire table, however, as opposed to a few

parameters in the case of a fitted function. Fitted curves also provide

some smoothing of the entries in the table which may serve to filter

errors.

95. Based upon relative errors, storage requirements, and compu-

tational considerations, log/log cubic polynomials seem to be the best

alternative for summarizing hypsographic curves, given data of the

type examined here. The approximate equivalence of the V(V) and A(V)

error statistics indicates that storage of the parameters of the volume

curve alone would be adequate as a basis for estimating both area and

volume. Algebraic differentiation of the volume polynomial can be used

to estimate area at any given depth. Thus, both the area and volume

curves can be summarized by a total of four polynomial parameters

according to the following scheme:

Z= log 10(E- E 0 (18)

V= log (V) (19)10

tA* = l10 ()(20)V= c + c 1Z* + C 2 * +c 3 Z*

3 (21)

A =(c I+ 2c 2Z* + 3c 3Z*2 )V/(E -E )(22)

104

For cases in which data from only a few elevations are available and/or

I the quadratic or cubic terms do not add appreciably to accuracy (as

assessed via stepwise polynomial regression), first- or second-order

polynomials may be used.

96. Use of the method requires knowledge of the base elevation,

E . If not available, it can be estimated approximately by extrapolating0

a plot of V13vs. E to the horizontal axis. Any error in E 0estimated

in this way would be offset in subsequent estimation of the polynomial

1 coefficients.

* 1 97. The same functions can be used in very data-limited situations

I in which only estimates of mean depth, maximum depth, and surface area

:1 are available. Approximate parameter estimates in this case are given

by:

c2 = c = 0 (23)

1c, = Z a/Zma (24)

c = log 10(AZ )-an C1 log0(Z ma (25)

98. To insure that the fitted polynomial is not used outside of

the range of data availability, maximum and minimum elevations should

also be stored with the fitted coefficients. It will be necessary to

test the fitted coefficients to insure that the areas estimated through

differentiation (equation (22)) are positive and increasing with eleva-

tion throughout the application range.

105

Figure 6

Distributions of Volume and Area Slope Parameters

5.

3.

*volume~ -O

_,cr

.8 volume c -area >

.6

.4 - ---

1 2 5 10 20 50 80 90 95 98 99I PERCENTILE

106

PART XIII: VARIABILITY OF TROPHIC STATE INDICATORS IN RESERVOIRS

Introduction

99. The development and testing of empirical eutrophication models

for reservoirs requires averaging of water quality measurements over

spatial and temporal scales. If within-pool water quality variations

are not random with respect to date, station, or depth, then summary

- I statistics for a given reservoir will depend to some extent upon the

particular data-reduction method employed. The choice of reduction

method may, in turn, influence conclusions regarding the adequacy of

* existing models as well as the parameter estimates of any new models

* which may be developed.

100. There is no standard data reduction procedure which can be

used prior to model development, testing, or application. Methods have

included, for example: (1) taking the median or mean of all within-pool

observations 23; (2) sequential averaging over depths, stations, and29 30

dates ; (3) seasonal averaging within specific depth ranges ; and

* (4) various weighted-averaging schemes which reflect morphometric char-

acteristics. As compared with natural lakes, many reservoirs pose

* special data reduction problems due to extreme spatial and/or temporal

variations in conditions.

101. In this section, a subset of the current CE water quality

data base is analyzed in order to describe spatial and temporal varia-

tions in trophic state indicators within a group of reservoirs. The

analysis covers spatial relationships, seasonal relationships, variance

components, and error estimation. Implications of the results are dis-

cussed with respect to the design of monitoring programs and use of the

data in model development and evaluation. Details on many of the

procedures used and results discussed below can be found elsewhere 3 1 ,32

107

Data Base

9102. EPA National Eutrophication Survey (NES) data from 484

stations located within 108 CE projects have been used as a basis for

this analysis. The relatively uniform sampling program designs used by

the NES provide data which are suitable for statistical treatment. One

drawback, however, is that under this program, reservoirs were typically

sampled only three times during one growing season. In Phase II of this

project, there are plans to examine data from other agencies, which, in

many cases, are mre intensive and/or cover longer periods.

103. Surface total phosphorus, Secchi depth, and chlorophyll-a

values have been expressed in terms of Carlson's Trophic State Indices30

(Ip, IT and IB, respectively)

I = 4.2 + 33.2 logl 0 P (26)p 1

I = 60 - 33.2 logl0Z (27)

I = 30.6 + 22.6 logl0 B (28)

where

P = total phosphorus concentration (mg/m)

Z = Secchi depth (m)

5 3B = chlorophyll-a concentration (mg/m

The indices are calibrated such that the three versions are equivalent,

on the average, when applied to midsummer, epilimnetic data from northern,

natural lakes. Expression of measurements on the above scales tends to

reduce the skewness in the distributions of the variables and provides

benchmarks for assessing reservoir trophic state relationships in com-

parison to those typical of natural lakes. A statistical data summary is

given in Table 41.

108

&:L

Table 41

Statistical Summary of EPA/NES Trophic IndexData from 108 CE Projects

standardVariable n mean deviation minimum maximum

1 1421 55.8 13.1 24.1 99.0

IT 1493 58.9 12.7 25.4 112.9

IB 1505 48.2 10.3 8.0 81.6

10

109 I

Case Studies of Spatial Relationships

104. Spatial variations typical of a few reservoirs are depicted

in Figures 7-10. Mainstem stations are displayed in downstream order

within each reservoir (not to scale) and mean values are plotted for

each version of the Trophic State Index. These plots provide initial

perspectives on the types of spatial trends and relationships which can

be found in reservoirs and are important supplements to the more formal

statistical treatment of the data presented in subsequent sections. The

plots seem to illustrate a number of important controlling processes,

which are discussed below in relation to each reservoir.

105. Figure 7 contains data from the White River system on the

Arkansas/Missouri border. Four reservoirs are connected in series:

Beaver, Table Rock, Taneycomo, and Bull Shoals. With the exception of

Taneycomo, they are all deep reservoirs with hydraulic residence times

in excess of 250 days. Trophic State Index behavior in the first reser-

voir, Beaver, iz considerably different from that typical of the down-

stream impoundments. Concurrent reductions in I and IT most likely

reflect sedimentation and the three index curves do not converge untiJ

the dam. Once most of the sediment load has been removed in Beaver,

agreement among the index curves is good at most downstream stations.

Increases in Table Rock probably reflect the influence of a major point

source which accounts for more than 70% of the total phosphorus loading

to that reservoir. The relatively low values of IB in Taneycomo can be

explained by the direct influence of subsurface discharge from upstream

Table Rock Dam. Taneycomo has a short hydraulic residence time (7 days)

and surface water temperatures at the times when summer chlorophyll-a

samples were taken roughly matched temperatures at the 100-foot level

just above Table Rock Dam (-15°C). Taneycomo's short hydraulic residence

time is apparently insufficient to permit recovery of temperatures and

algal populations from those typical of the Table Rock hypolimnion.Decreases in all versions of the index are evident moving downstream in

Bull Shoals, and relatively stable conditions are reached over the last

four stations.

110

Figure 7 White River System

W HI TE R IV ER S YS TE M

BEAVER TABLE ROCK T.C. BULL SHOALS70(t=556 days) (t=330 days)(t=7 days) (t=269 days)

z

S60 P

S50

~40

0

DOWNSTREAM ORDER --

wif

Figure 8 Sakakawea

SAKAKAWEA (t=584 days)90

z s

80

c.70

c 60

Chl-

c5

00

30

DOWNSTREAM ORDER-.*

112

Figure 9 old Hickory

OLD HICKORY (t=11 days)70

P

60

"C

-40

k DOWNSTREAM ORDER-

Figure 10 Barkley

BARKLEY (t-16 days)

K ~60

CD)

Chl-a

40DOWNSTREAM ORDER--&

113

106. Lake Sakakawea (Figure 8), on the Missouri River in Montana,

shows TSI variations over 40 units, or sixteenfold differences in trans-

parency and total phosphorus. Upstream portions were classified by the

EPA/NES as "hyper-eutrophic" and stations near the dam as "oligotrophic".

Below the second station, the chlorophyll-a index follows but remains

roughly 5-10 index units below the phosphorus and transparency indices.

107. Old Hickory (Figure 9), on the Cumberland River in Tennessee,

shows relatively small downstream reductions in phosphorus and trans-

parency and a steady increase in chlorophyll-a. With a mean residence

time of 11 days and sediment removal by upstream reservoirs on the

Cumberland River, longitudinal variations in I and I are not as evidentP T

as in above panels. The gradual increase in IB might be a hydraulic

residence time effect, i.e., the time scale required for algal popula-

tions to "equilibrate" with available nutrient and light levels may be

appreciable in relation to time-of-travel within the pool.

108. Similar behavior is evident in Lake Barkley (Figure 10),

which is located further downstream on the Cumberland River and has a

residence time of 16 days. One important difference is that the phos-

phorus index remains consistently higher than the other versions. Both

ambient available nutrient concentrations and bioassays indicate, how-

ever, that algal populations in Lake Barkely were nitrogen-limited at

the times of sampling. Use of I p alone as a measure of nutrient avail-

ability may not be appropriate in this case.

109. The above case studies illustrate a number of factors which

can be important in assessing reservoir trophic state relationships:

sedimentation, upstream impoundment effects, hydraulic residence time

effects, and nitrogen limitation. Reservoir hydrodynamics partially

determine the transformations of these and other factors into spatial

variations in the trophic state indicators. Upstream/downstream varia-

tions contain information on rates and directions of controlling processes.

Graphing of spatial relationships and expression/analysis in terms of

distance (river mile) and/or time (time-of-travel) will aid in quanti-

tative data analysis and interpretation. Use of station means rather

114

than reservoir means seems to make more sense for model testing purposes.

Seasonal Relationships

110. Average seasonal variations in the index components are de-

picted in Figure 11. Station means have been computed and their effects

removed from the data prior to calculating the mean and standard error

for each month (March-November) and index component. Analyses of vari-

ance indicate that monthly effects are significant (p< .0001) for each

component and strongest in the case of chlorophyll-a. The seasonal

variations depicted in Figure 11 are characteristic of this collection

of reservoirs but not necessarily of each reservoir individually.

111. Average seasonal effects on phosphorus and transparency are

similar: both tend to be lowest during March and midsummer and highest

during April and November, possibly reflecting seasonal flow and turbid-

ity variations and influences of turnover periods. Monthly effects on

chlorophyll-a suggest a spring maximum (April-May), followed by a June

depression, a midsummer maximum, and lower values in November. Tempera-

ture and light effects may be responsible for the relatively low chloro-

phyll-a levels during March and November. The June depression may be

due to seasonal succession of algal species. A more detailed examination

of the data indicates that lower June chlorophyll-a levels are character-

istic of about half of the stations sampled in June, while the rest have

June levels more typical of May or July values. In testing seasonal30

aspects of TSI behavior, Carlson also noted a June depression in chloro-

phyll-a index relative to the phosphorus index in three natural lakes.

112. Differences among various versions of the index provide a

measure of "lake-like" behavior, since the index system is calibrated so

that IB' IT, and I values are equivalent, on the average, when applied

to midsummer, epilimnetic data from northern, natural lakes. Figure 11

indicates that the range of index means is generally lowest during mid-

summer (approaching 5 during August) and highest during March, June, and

November (approaching 15). Minor recalibration of the phosphorus and/or

115

Figure 11

Monthly Variations in Trophic State Indices

65

z60rLa~L

~50T=r

Ch--

00

3 5 7 9 11

MONTH

*mean ±2 standard errors

116

transparency index would bring I and IT into agreement for all seasons,

since the monthly effect curves in Figure 12 are roughly parallel. Since

seasonal chlorophyll-a behavior is fundamentally different, however,

recalibration alone would not eliminate biases (i.e., significant dif-

ferences between IB and I or between IB and IT) for all seasons.

113. Thus, seasonal factors must be considered in reducing the

data and in recalibrating/redesigning the index system for use in reser-

voirs. One approach would be to restrict averaging period to midsummer.

An alternative would be to include explicit seasonal effect terms in the

model or index system. These approaches will be investigated in future

model development work.

Variance Component Estimation

114. Two-way Analyses of Variance have been applied to each reser-

voir and index component to test for the significance of variations

associated with station and sampling date. Results are summarized in

Table 42. Significant (p, .1) differences among station means in I

IB' and IT were found in 46, 37, and 52 out of 105 projects, respectively.

Significant differences among date means were found in 62, 67, and 64

projects, respectively.

115. The following ANOVA model has been employed to derive pooled

estimates of the variance components of each version of the index:

hij r h + Shi + thj + ehij (29)

where

Yhij = index measurement in reservoir h at station i on date j

U = grand mean

rh = average effect of reservoir h on grand mean

Shi= effect of station i in reservoir h

t = effect of date j in reservoir h

ehij = random effect

117

Table 42

Summary of ANOVA Results

Station Effects

Date Effects Not Significant Significant"*

Phosphorus index---------------------------------------

INot Significant 32 11

Significant 27 35

-iChlorophyll-a Index ----------------------------------

Not Significant 33 5

Significant 35 32

Transparency Index -----------------------------------

Not Significant 27 14

Significant 26 38

*numiber of projects in category (total 105)

*significance defined at p <.10

118 -,. l

The relative magnitudes of the within- and between-reservoir components

are of special significance to monitoring and modelling efforts. With

relatively large within-reservoir components, it would be difficult to

obtain much accuracy in reservoir summary statistics (e.g., reservoir

mean) with limited data. This would reduce the explainable variance

(R2 ) of any model calibrated to the reduced data set, make it more dif-

ficult to distinguish among alternative model formulations, and increase

the error associated with model parameter estimates.

116. SAS procedures2 have been used to estimate the above variance

components for each index separately and to estimate analogous covariance

components for each pair of indices (IB/IT, I/Ip, IT/Ip). Results are

shown in Figure 12. The phosphorus and transparency index components

exhibit similar patterns: between-reservoir differences account for

60-66% of the total index variance, as compared with 29% in the case of

chlorophyll-a index. Between-reservoir variances indicate that differ-

ences in chlorophyll-a are considerably less marked than would be

predicted based upon differences in the phosphorus or transparency indices.

Conversely, there is greater temporal and random variance in chlorophyll-a

than in phosphorus or in transparency.

117. The covariance components on the right-hand side of Figure 12

provide some insights into relationships among the indices at different

averaging levels. Tne between-reservoir and between-station covariance

components are positive in all cases. Thus, the various versions of the

index correlate positively both among reservoirs and among stations

within reservoirs. Temporal components indicate a positive covariance

for phosphorus/transparency but slightly negative covariances for the

pairs involving chlorophyll-a. Thus, when temporal variations az a

given station are analyzed, one would expect, on the average, to find

a positive correlation only between the phosphorus and transparency

indices. This correlation might be attributed, for example, to turbidity

variations following seasonal or short-term (storm-event) flow variations.

Despite its positive covariance between reservoirs and between stations,

chlorophyll-a does not covary temporally with the other indices.

119

Figure 12

Variance and Covariance Components of Trophic State Indices

VARIANCE COVARIANCE' ~150 '''

*I RESERVOIR

* I 100 -

z 50\

o040 STATION

'-~ 20

= 40 'DATE

S20

40 RANDOM

20 I

0SP Zs Chl-a P- Z PChl-a Ch"-a Z

120

118. The EPA/NES data base includes measurements from one growing

season within any reservoir and does not permit testing for between-year

variance or covariance components. Thus, it is not possible with this

data set to test for year-to-year covariance between chlorophyll-a and

phosphorus or transparency. Distinguishing between seasonal and year-

*to-year variance components will be possible with an expanded data base

including data from other agencies and monitoring programs.

Error Analysis

119. The above analyses demonstrate that within-reservoir varia-

tions cannot be considered random with respect to dates or stations, the

primary parameters used in monitoring program design. This has impli-

cations for estimating the accuracy of reservoir or station mean values

calculated from the data set. If variations were random and serially

independent with respect to station and date, the following statistic

would be appropriate for estimating the variance of a reservoir mean:

Var(y) = Var(y) (30)

where

N = total number of observations within a reservoir for a

given index

Using two-stage sampling theory 3 3 , the following formula is more appro-

priate:2 2 2

s 0 t eVar(y) = + _ + - (31)

ns nt N

where2

a = mean squared deviation

n = number of stations sampleds

nt = number of dates sampled

The first term accounts for the influence of between-station varia-

tions, the second for between-date variations, and the third for random

variations. Note that equation (31) reduces to equation (30) when spatial

2 2 2and temporal variations are insignificant (o = 0= 0 and Var(y)= a

Because both spatial and temporal variations often exhibit patterns or

trends (see Figures 7-11), they cannot be considered serially independent.

Thus, equation (31) provides error variance estimates which are approximate

but useful for assessing the relative contributions of spatial and tem-

poral variance to the variance of reservoir means.

120. Based upon the total number of observations, stations, and

reservoirs in the NES data set, the "typical" reservoir survey program

covers 5 stations (n) on 3 dates (nt), for a total of 15 observations

(N). Using these values and the pooled variance cc.aponents depicted in

Figure 5, equations (30) and (31) have been applied to each version of the

index and results are displayed in Figure 13.

121. Considering the effects of spatial and temporal variance

components (equation (31)) increases mean error by about a factor of

three over estimates derived from equation (30). Most of the error vari-

ance is due to the temporal component, especially in the case of the

chlorophyll-a index. The error variances indicate that the EPA/NES

sampling strategy has provided estimates of reservoir geometric means

which are typically accurate (p< .05) to within factors of 1.6, 2.2, and

1.7 for surface phosphorus concentration, chlorophyll-a concentration,

and Secchi depth, respectively.

Monitoring Implications

122. Error anal1ses can be used to improve upon monitoring program

designs. For example, given the objective of collecting data to be used

in estimating a reservoir mean with minimum variance, the above results

suggest that an increase in samplinq dates would be more effective than

an increase in sampling stations, because the date effect term dominates

the error equation. Since the variance component estimates have been

pooled, these results apply to this collection of reservoirs as a whole

and not necessarily to each reservoir. The same approach could be

122

o

42-4

-

.JJ.P4

123

L,'VNNN~NN--N N'r' NNNNX

C) 4-)

applied using parameters estimated for each reservoir individually (n e,

nN, ar2 , cT 2). "Optimal" designs could be formulated based upon

the error analysis framework and upon functions which relate nt nt, and

- I N to monitoring cost. Given variance components estimated from prior

monitoring data, improvements in program design (changes in n , nt, and

N) for a given reservoir could be formulated to yield minimum error for

a fixed total cost or minimum cost for a fixed total error. The approach

could be expanded to include depth as a third sampling dimension. This

Li represents a logical application for the error analysis framework in a

monitoring context.

4 Modelling Implications

123. In evaluating models, differences between observations and

predictions can be attributed to three types of error: parameter error,

data error, and model error. The first reflects uncertainty in the modelj

coefficients, the second, errors in the predicted and/or predictor vari-

ables, and the third, influences of factors which are not considered in

the model structure. Analyses of the type conducted above can be used

* I to quantify potential data errors and separate them from the other corn-

ponents. This provides insights into the adequacy of a data base for use

in model testing. If, for example, the data error component dominates,I

it would be difficult to distinguish among alternative models or to

improve upon them without first improving the data base.

124. Table 43 summarizes results of regression analyses which4

have been done to summarize relationships among the three versions of

the index using station mean and reservoir mean values. Results further

demonstrate the need for recalibration of Carlson's index system in

applications to reservoirs. The sensitivity of the chlorophyll-a com-

ponent to phosphorus or transparency ranges from .30 to .37 and is in

all cases significantly different from 1.0, the value obtained when the

index system is calibrated to natural lakes. In contrast, the sensi-

tivity of I, to IT is .88 for stations means and .91 for reservoir means.

124

Table 43

Regression and Corresponding Error Analyses

* Total*" Sampling* Sampling ErrorModel r sb Error Error Total Error

Station Means (n=484)------------------------------------------------------

I = 28.7 + .35 Ip .55 .024 42.3 23.1 .55

I B = 30.0 + .31 1T .46 .027 47.6 22.7 .48

Ip = 4.0 + .88 1T .83 .027 47.6 25.9 .54

Reservoir Means (n=108)----------------------------------------------------

I = 28.0 + .37 'p .59 .048 32.5 16.3 .50B

IB = 31.0 + .30 1 .44 .059 41.0 16.0 .39

Ip = 2.0 + .91 IT .83 .059 41.0 22.1 .54

* Sampling Error dne to error in estimating mean index values withineach station or reservoir; sampling errors for station mean phosphorus,chlorophyll-a , and transparency indices are estimated at 14.3, 21.3,and 15.0, respectively; corresponding sampling errors for reservoir-means are 11.2, 14.8, and 13.1.

** Total Error = mean squared residual

* b standard error of regression slope

125 j,.N , *----------------------

* I While the correlation coefficients (and explained variance) are con-

siderably lower for predicting chlorophyll-a (.44-.59) than for predic-ting phosphorus from transparency (.83), the mean squared prediction

errors are similar.

i25. Part of the mean squared error for each regression model can

be attributed to sampling error in estimating the reservoir and station

mean values. The sampling error component of the mean squared error for

j each model is estimated from:

.1 2Var(y- b-x) Var(7y) + b Var (30) (32)

where,

y = predicted index

x =predictor index

b =slope of regression model

Results of error analyses have been used to estimate sampling errors in

station mean and reservoir mean values. Results indicate that roughly

_j half (39-54%) of the residual standard error can be attributed to sampling

error in the data values. The remaining variance presumably reflects

model error, or the effects of factors (e.g., season, sediment) which

are not accounted for in the model. The choice of averaging method

(stations vs. reservoirs) does not influence the model coefficient values

significantly. Mean squared errors are reduced when reservoir mean

values are used, partially resulting from a reduction in the sampling

error component. The standard errors of the regression slopes, however,

increase roughly twofold when reservoir mean values are used in place

of station means. Thus, using station means permits better definition

of model coefficients.

126. The intent of the regression analyses presented in Table 43

is to demonstrate the error analysis approach and identify influences

of data reduction method. The models suggest that chlorophyll/phosphorus

and chlorophyll/transparency relationships in these reservoirs are sig-

nificantly different from those which are typical of natural lakes.

126

The models are inadequate for predictive use, however, because a number

of important factors have not been considered, including season, nitrogen

limitation, hydraulic residence time, region, and external sediment

loading. These and other factors will be considered in developing a

Trophic State Index system applicable to reservoirs under Phase 11 of

this project.

Conclusions

127. Statistical models of index (or water quality) variations

within reservoirs have been shown to be complicated by the effects of

spatial and temporal variations and by non-randomness or serial depen-

dence in these variations. The above analyses have demonstrated how

some of these influences can be approximately treated and applied to

assess data adequacy for computing reservoir means and to improve upon

monitoring program designs. A more thorough statistical treatment

would require more intensive data sets and involve the construction of

more complex statistical models applied separately to each reservoir

using simulation techniques. This level of detail is not justified or

feasible within the context of this study.

128. The following general conclusions can be drawn from the

analyses of EPA/NES data in previous sections:

a. Spatial relationships among trophic state indicators areimportant in some reservoirs, especially when stationsare viewed in downstream order.

b. Analyses of Variance indicate that within-reservoir vari-ations are often non-random with respect to stationsand/or sampling dates within a given year. variationswith respect to date are typically stronger than varia-tions between stations, particularly in the case ofchlorophyll-a.

C. Some of the temporal variance within reservoirs andstations is fixed with respect to month or season.Seasonal effects on phosphorus are qualitatively similarto seasonal effects on transparency but differ fromseasonal effects on chlorophyll-a.

127

d. Between-reservoir variance in Carlson's chlorophyll-aindex is roughly one third of the between-reservoirvariances in the phosphorus and transparency indices.

e. For this data set, chlorophyll-a variance betweensampling dates within reservoirs is greater than itsvariance between reservoir means. The reverse is truefor phosphorus and transparency.

f. Covariance components indicate significant positive cor-relations among the three versions of the index systemwhen variations between reservoirs and between stationswithin reservoirs are considered. The chlorophyll-aindex does not correlate with either of the other indices,

however, when temporal variations (at a given station orwithin a given reservoir) are considered.

j. Because of non-randomness with respect to stations and

dates, the error variance of reservoir mean values foreach index are typically three times those estimated fromthe familiar formula for mean variance (G2/n).

h. Given the objective of estimating reservoir means withminimum variance, error analyses indicate that increasesin the number of sampling dates would be generally moreeffective than increases in the number of stations perreservoir for improvement in the EPA/NES sampling design.

i. Regression analyses indicate that chlorophyll-a levelsare significantly less sensitive to phosphorus or trans-parency, as compared with the natural lakes used indeveloping Carlson's index system. Future developmentof an index system for reservoirs should consider theeffects of season, region, nitrogen limitation, residencetime, and sediment loading.

j. Use of station means, as opposed to reservoir means, inrecalibrating the index system causes small increases instandard error of estimate but substantially reduces thestandard errors of model parameters.

Implications for Data Reduction Strategies

129. The conclusions in the previous section suggest that the fol-

lowing data-reduction/analytical strategies be used in testing and

developing models under Phase II of this project:

a. Since spatial variations and trends have been shown to beoften statistically identifiable and useful for interpreta-tion purposes, use of station means would seem to be more

128

desirable than use of reservoir means for model testing.Use of station means would also permit better definitionof model parameters.

b. It would be useful to develop a scheme for spatial orien-tation of stations within each reservoir with respect tomajor tributary (arm) and distance (river mile). Someaggregation of stations based upon proximity may befeasible within this framework.

c. Seasonal factors should be considered in averaging datawithin each station or station group. This would involve,for example, estimation of "spring", "summer", "fall", aswell as "growing season" and "annual" averages.

4d. For non-NES stations which are sampled during more thanone year, tests for significant year-to-year differencesshould be made and used to decide whether aggregation ofdata from different years is justifiable.

e. While the above analyses have been based exclusively uponsurface sampled for phosphorus, averaging with depthshould be considered, at least within the photic zone,for testing of relationships among phosphorus, chloro-phyll-a, and transparency.

f. To permit testing of loading response models, seasonalestimates of reservoir means could be derived by averagingacross stations. Due to longitudinal variations in manyreservoirs, however, near-dam or discharge conditionsshould also be used as bases for loading model evaluations.

129

................ .

PART XIV: EVALUATION OF METHODS FOR ESTIMATING PHOSPHORUS LOADINGS

Introduction

130. The estimation of reservoir nutrient budgets entails estima-

tion of the total mass of nutrients passing given sampling points over

a given period of time, typically at least one year. While continuous,

flow-weighted composite sampling for concentrations would provide the

best basis for deriving such estimates, usually only periodic grab-

samples are available for stream concentrations. These concentrations

must be integrated with flow records (typically continuous) in order to

estimate the desired loadings. The purpose of this section is to test

and compare alternative methods in order to provide some guidance for

k. future nutrient budget calculations on CE reservoirs.

Preliminary Analysis

131. The relationship between concentraton and flow influences

the appropriateness of a given loading calculation method at a given

station. A subset of flow and phosphorus concentration data from the

current CE data base has been analyzed in order to provide some perspec-34

tive on this relationship . The subset includes 86 tributary and 33

discharge stations, each with at least 25 total phosphorus/streamflow

pairs obtained from STORET. Table 44 describes the symbols and funda-

* mental equations used to characterize flow and concentration variations.

132. Results of the preliminary analysis are given in Table 45,I with data grouped by station type. The regression model relating thelogarithm of concentration to the logarithm of flow explains, on the

average, 12.3% of the variance in concentration at tributary stations

and 6.6% of the variance in concentration at discharge stations.

Figure 14 shows that the upper percentiles of the R 2distribution at

tributary sites are roughly twice those found at discharge sides.

130

Table 44

Fundamental Equations and Symbols

Loading Definition: L = Q C

w-x+Y

Flow/Concentration Model: C = a Qb

Y = log1 0a + b X

Distributions: Y:(mean= Py, std.dev.= a

X: (mean= 4X, std.dev.= a)

Symbols: L = Loading (mass/time)

Q Flow (volume/time)

C - Concentration (mass/volume)

W = log1 0 (L)

X log1 0 (Q)

Y log1 0 (C)

a,b = Regression model parameters

131

'4

Table 45

Preliminary Analysis of Flow/Total P Concentration Relationships

Station Type

Statistic Tributary Discharge

number of stations 86 33

fraction of variance explained 123 ± .151* .066 .078by regression model

residual variance .101 _ .075 .090 + .050

serial correlation of residuals .228 _ .205 .300 _ .276

conc/flow sensitivity (b) .124 - .250 .097 + .138

standard dev. of log 0(flow) .573 - .246 .600 + .303

standard dev. of log1 0 (conc) .323 - .133 .297 + .088

* mean + one standard deviation

1

132

Figure 14

'* Distributions of R2 Values at Tributary and Discharge Stations

.5

-'J . 5

(-TRi9IB7APY STATIONS

t .3

' A/

/" _ DISCHARGE STATIONS

LU

1 5 10 Z5 50 7s 90 95 99

PERCENTILE

Figure 15

Distribucions of Regression Slopes at Tributary and Discharg,- Stations

.66

.4 - TRIBUTARY STATIONS

A.2 'D ' ISCHARGE STATIONS

0-

o A -oLi -.2' " "

-.4 0,

.9 - IA , |

I S ill 2S s0 75 90 95 9

PERCENTILE

11

133

133. Both the mean and the standard deviation of the regression

slope (b) are larger in the case of tributary stations. The distribu-

tions of b are compared on a normal probability scale in Figure 15.

While the medians are nearly equal, the upper percentiles are higher

and the lower percentiles are lower in the case of tributary stations.

The wide distribution of b values across stations suggests that loading

* calculation methods must be capable of accounting for alternative flow/

concentration relationships.

134. These results indicate that concentration tends to be more

* flow-dependent at upstream tributary stations than at discharge stations.

* Reservoir pools may buffer variations associated with runoff events

-I(which would tend to produce high b values) or point source dischargesA

(which would tend to produce low b values). The fact that streamf low

is highly regulated at reservoir discharge points may also contribute

to weaker flow/concentration relationships. The serial correlation of

residuals (i.e., concentration, after the effect of flow is removed)

tends to be higher in the case of discharge stations (.30 vs. .23 on

the average). This suggests that seasonal factors or long-term trends

may have greater influences at discharge points.

135. Table 46 lists means regression slopes by station type and

component (dissolved P, total P-dissolved P, and total P, respectively).

This breakdown is based upon measurements of total P and dissolved P at

52 tributary and 17 discharge stations. For both station types, the

mean slopes of dissolved phosphorus with respect to flow are not signi-

ficantly different from zero. In the case of total P-dissolved P, how-

ever, the mean slope is .34 ± .05 at tributary stations and .11 ± .09

at discharge stations, although the latter is not significantly different

from zero. The influence of streamflow on the transport of particulate

j phosphorus probably accounts for these results. The dissolved component

tends to be more independent of flow than the particulate component.

The relative weakness of the dissolved phosphorus/flow relationship may

reflect a buffering effect of adsorption chemistry on stream phosphorus

levels and/or the fact that transport efficiency for dissolved phosphorus

134

Table 46

Concentration/Flow Sensitivities by Component and Station Type

'I

--------------- --------------- Component --------------

Station Number of Total P -

Type Stations Dissolved P Dissolved P Total P

Tributary 52 .341 - .050 .019 ± .034 .239 + .044

Discharge 17 .109 + .087 .036 - .101 .082 + .038

mean + one standard error of mean

1

13

-jL

- I is not velocity-dependent, as in the case of the particulate fraction.

Estimation Methods

*1 136. Four algorithms for estimating loadings have been tested:

a. average loading.

b. average concentration x average flow.

*C. flow-weighted average concentration x average flow.

d. regression estimate based upon log (concentration) vs.log (flow) relationship.

*These schemes are described in Table 47. A few other types of regression

models have also been evaluated, but none proved better than the model

used in Method 4 (see Table 47). Two approaches to evaluating these

methods have been taken: one involves subsampling from simulated flow

and concentration data and the other, subsampling from real flow and con-

centration data. These methods and results are described below.

Tests Based Upon Simulated Data

137. Table 48 describes an algorithm used to generate flow and

concentration time series using Monte-Carlo techniques. This method has

been used to produce five years of simulated daily-average flow and con-

centration data. Mean loadings have been calculated directly for each

year and compared with estimates based upon subsanipled taken at monthly

intervals and employing the calculation methods listed in Table 47

For each year, a total of thirty trials (sets of subsamples) have been

made, representing reqular sampling on day 1 to day 30 of each month,

respectively. Thus, error statistics are based upon a total of 150

trials for each method. Results have been found to be insensitive to

the number of trials. The simulation algorithm is not adequate for

evaluation of interpolation methods (in which the sequence of samples

is considered), since no serial correlation or seasonal factors areincluded. The parameters of the simulation model are typical of the

136

Table 47

Estimation Methods

Method I Average Loading

a- -

n

Method 2 Average Concentration x Average Flow

nir c

Method 3 Flow-weighted Average Concentration x Average Flow

Method 4 Regression Estimate

b+l

1: qi n i

where,

q-L flow on sample data i

aI concentration on sample date i

n - number of sample dates

b -slope of loq(c) vs. log(q) regression

Q -average flow over entire period, based upon continuous flow record

E - sum over all sample dates (i-1 to n)

137

iA'

*i Table 48

Algorithm for Generation of Flow/Concentration Time SeriesII11

Input Values:

Ix = mean of logarithm of flow = 0.

-P y = mean of logarithm of concentration = 0.

dx = standard deviation of logarithm of flow = .52

d = standard deviation of logarithm of conc= .26Y (random component)

b = slope of flow/concentration model = (-.8 .8)

Algorithm:

Xi = x + N(0,l)di x x

Yi =y + N(0,1)dy + (Xi- x)b

Symbol:

N(0,1) = normal random deviate with mean zero and standarddeviation one

138

data distributions depicted in Figure 15 and Table 45. A range of

flow/concentration sensitivities (b in Table 47) have been used (-.8 to

+.8).

138. The following error statistics have been used to compare

"observed" and estimated loadings for method and trial:

Bias. = 1 z - j T /,-V (33)M l jk

k=1

- where,

Bias. = bias associated with method j3

MSE. = mean squared error associated with method jJ

M = total number of trials = 150

Lk= actual loading for trial k (mass/time)

L = estimated loading for method j and trial k (mass/time)

For each method, these error statistics are plotted vs. the flow/concen-

tration sensitivity statistic in Figures 16 and 17, respectively.

139. only Method 1 is unbiased for all values of b. The MSE of

this method, however, is significantly higher than that of the other

methods at values of b greater than -.2. This method is best under

conditions where loading is relatively independent of flow (b- -l).

This might be the case, for instance, at a station which is located

below a major point source of nutrients, but not where storm-event or

non-point loads are significant.

140. The performance of Method 2, in which concentration and flow

are averaged independently, is a strong function of b, both with respect

to bias and variance. This method is unbiased only at b=0, but has

biases of +35% and -23% at b values of -.2 and +.2, respectively. Trends

in bias continue moving toward more negative or more positive b values.

The method has a sharp minimum in MSE at b=0. This scheme is apparently

best only when concentration and flow are truly independent, but gives

139

M00

)~ -04- ) -4

4

5/)

:1 (a-i 'dO

754 r.I~0 4 U) U)J

4) -42L

W4-)

Z 44

0 4- m v

4f-H m -2

Il-

0

04- E0 W

4- 0

C)Cw

-4 -H

U)flzjU

. 4 4 4 v/

~~140

r7_

substantial biases and MSE's for even weak tlow/concentration relation-

ships.

141. Method 3, which employs the flow-weighted average concentra-

tion, behaves qualitatively similar to Method 2, but is not as sensitive

to b values. Zero bias and minimum viriance are evident at b=O. This

method is analogous to a "ratio estimate" used in classical sampling35

theory142. Method 4 has less bias and variance than Method 3 for most

b values. Unlike the other methods, the regression model can adjust to

different types of flow/concentration relationships and estimation errors

are less sensitive to b values. Bias becomes more significant at high

values of b. At b=.6, for example, Method 4 underpredicts loading by an

average of 15%. The MSE at this b value is .13, which corresponds to a

standard error of + 36%. Thus, bias is less than one half of one stan-2

dard error and accounts for 17% of the MSE (.15 /.13) at b=.6. Additional

tests indicate that applying the regression model separately to each

daily flow in the year, a more tedious calculation, does not reduce the

bias or variance of Method 4 at any b value.

Tests Based Upon Real Data

143. Calculation methods have also been tested using the flow and

concentration data from the 119 stations analyzed above. Subsaples of

12 flow/concentration pairs have been taken at regular intervals from

each station. Loadings estimated for each subsample and method have

been compared with loadings estimated by applying Method 1 to all flow/

concentration pairs available for each station. A total of 508 subsamples

have been taken from 86 tributary stations and 190 subsamples from 33

discharge stations. Bias and MSE estimates are given in Table 49 for

each method and station type. These results are approximate because of

errors involved in estimating actual loading.

144. Generally, Methods 3 and 4 appear to perform better than

Methods 1 or 2 for both station types. The MSE of Method 3 equals that

141

Table 49

Results of Method Testing Using Real Flow and Concentration Data

Estimation Method**

Statistic Method 1 Method 2 Method 3 Method 4

- -- Tributary Stations (N=86,M=508)----------------------

Bias -.002 -.027 -.029 - .023

XSE .530 .297 .176 .176

. .-----Discharge Stations (N=33,M=190)---------------------

Bias .001 .068 -.017 -.000

MSE .272 .585 1108 .156

* -- number of stations, M f tumber of trials

** see Table 47

142

of Method 4 in tributary stations (.176), but is lower in discharge

stations (.108 vs. .156). When averaged over the range of b values in

the data set, none of the methods is appreciably biased.

Prior Error Estimation

145. To provide bases error analysis and assessment of data

adequacy, a means for estimating the variance of loading estimates de-

rived from a given set of flow/concentration data is needed. Table 50

presents a formula appropriate for use with Method 4 (or with Method 3

when b=0). This approximate formula, derived from expected value theory,

has two terms: one reflecting variance around the flow/concentration

regression model, and one reflecting variance in the estimate of the

slope parameter b.

146. Figure 18 compares the observed mean squared estimation

errors for Method 4 with error variances estimated from the formula in

Table 50 using simulated data. Reasonable agreement between observed

and estimated variances is apparent over the range of b values typically

encountered. Similar tests have been done using real flow and concen-

tration data. The formula in Table 50 overestimates the error mean

square by an average of 18% for tributary stations and underestimates

the error mean square by an average of less than 1% at discharge stations.

Conclusions

147. Preliminary data analysis has characterized the distributions

of flow/concentration relationships in tributary and discharge streams.

Methods 3 and 4 are generally better than Methods 1 or 2 for estimating

loadings, given the distribution of concentration/flow sensitivies en-

countered at various stations (see Figure 15). Method 3, which employs

the flow-weighted average concentration, is actually a special case of

Method 4, with b=0. In calculating loadings, it seems reasonable to use

a regression analysis to estimate the slope parameter b and to use

143

Ol

44

4-p 1 1

o 44

04

4

0.

41 4

41.

>I0 144

Figure 18

Observed and Estimated Mean Squared Error in loading Estimates

.20.

LUJ

I.. .4 . .

REGRESIONSTI'OAE~b

C145

'Cj

Method 4 unless the slope estimate is not significantly different from

zero, in which case Method 3 would be used. The formula in Table 50

can be used to derive approximate estimates of error associated with a

given mean loading calculated using Methods 3 or 4. Error variances

can be used, in turn, to characterize the accuracies of reservoir nutrient

loading and discharge estimates.

146

146

PART XV: CONCLUSIONS

148. The compilation, description, and preliminary analyses of

the data base described in previous sections lead to the following con-

clusions:

a. Using primarily centralized sources of information, ithas been possible to compile sufficient data on waterquality, hydrology, sedimentation, and other projectcharacteristics to permit testing of empirical eutrophi-cation models under Phase II of this study.

b. The hydrology files need to be augmented with monthlyelevation/contents data from several districts and

projects.

c. Information in the water quality files will not be ade-quate for assessing the trophic states of all 299 reser-voirs in the central project list. It should be adequate,however, for characterizing about 130 reservoirs and fortesting empirical models relating within-pool measuresof trophic state.

d. Use of data sources outside of the EPA/National Eutro-

phication Survey has generally more than doubled thetotal numbers of observations of various within-pooltrophic state indicators. Non-NES data are generallymore extensive temporally.

e. Comparisons derived from the EPA National EutrophicationSurvey compendium reveal significant differences betweenlakes and reservoirs in the means of most morphometric,hydrologic, and trophic state indicator variables. Com-pared with natural lakes, reservoirs have greater potentialnutrient enrichment problems, as gauged by Vollenweiderphosphorus loading models, but lower observed levels ofchlorophyll-a, on the average. The validity of existingloading models in reservoirs is in question due to lake/reservoir differences in morphometry, hydrodynamics,sedimentation, and region. Because of the relative geo-graphical distributions of lakes and reservoirs in theU. S., it is difficult to distinguish the effects of

region from those of impoundment type.

f. For the purposes of this project reservoir hypsographiccurves can be conveniently summarized using low-orderpolynomial equations relating the logarithm of totalvolume to the logarithm of total depth. Errors charac-teristic of this curve-fitting scheme are similar tothose characteristic of direct interpolation methods.

147

g. Within-reservoir variations of trophic state indicatorsgenerally cannot be considered random with respect tostation and date, the primary parameters used in monitor-ing program design. Spatial variations contain informa-tion on rates and directions of processes controlling

eutrophication within reservoirs. Variance componentanalyses indicate that phosphorus and transparency have

variance structures which are similar to each other, butfundamentally different from the variance structure of

chlorophyll-a. Implications of these variance structuresfor monitoring and modelling efforts have been discussed.

h. A method for estimating the mean and error variance ofnutrient loadings derived from continuous-flow and grab-sample-concentration measurements has been developed andtested using real and simulated flow/concentration timeseries. The method employs a regression model relatingconcentration to flow and has been shown to comparefavorably with alternative calculation methods withrespect to bias and variance of loading estimates undera range of conditions.

148

PART XVI: RECOM~MENDATIONS

149. The following additional data base development work is re-

*quired in order to permit testing of empirical eutrophication models

and should therefore be included in the scope of Phase II of this study:

a. compilation of monthly elevation/contents data from manydistricts and projects, as indicated by the inventoriesin Appendix A.

b. development of a scheme for sorting of water qualitystations in downstream order within reservoirs.

. P 150. While compiled for the specific purposes of this project, the

data base described in this report could be adapted for other, more

generalized uses. As discussed in the introduction to this report, the

current data base consists of a number of computer files, reports, maps,

and data forms organized in a consistent framework. It is not a system

designed for frequent interactive use. Given the current data base, the

development of such a system would involve the following:

*a. definition of objectives (desired scope of data base uses).

b. definition of operating environment (direct users, main-tainence personnel, and computer system).

c. development of appropriate software for various purposes(e.g., accessing, updating, editing, summarizing, dis-playing, analyzing).

d. compilation of any additional data required for intendeduses of the data base (e.g., inclusion of priority pollut-ants, development of a digital reservoir mapping capa-bility).

e.appropriate modifications in file designs.

f. establishment of channels and procedures for updating and

verification of information.

j.testing of the system in an operational environment.

h. documentation of the system.

i. orientation of potential users.

It is recommended that the Corps of Engineers consider expanding upon the

existing data base, as outlined above, in order to permit more generalized

use of the information which has been compiled under this project. The

149

final product would represent a valuable resource for reservoir design,

operation, and research at various organizational levels within the Corps

of Engineers.

-

-1-Ii--1

- ..

REFERENCES

- I1. Dixon, W. J. and M. B. Brown, ed., BMDP-77, Biomedical ComputerPrograms -P Series, University of California Press, Berkeley,1977.

2. Statistical Analyses Institute, SAS User's Guide, 1979 Edition.

SAS Institute Inc., Raleigh, North Carolina.

3. U. S. Environmental Protection Survey, Water Quality Control Inf or-mation System STORET, User Handbook, Office of Water and Hazardous

Materials, Washington, D.C., 1979.

4. U. S. Environmental Protection Agency, National EutrophicationSurvey, Computer Tape Containing NES Data Summary, obtained from

* the Corvallis Environmental Research Laboratory, Oreg., 1978.

5. Leidy, G. R. and R. M. Jenkins, The Development of Fishery Compart-ments and Population Rate Coefficients for Use in Reservoir Eco-

-* system Modeling, prepared for office, Chief of Engineers, U. S.* Army, WES-76-2, June 1977.

6. U. S. Army Corps of Engineers, "Corps of Engineers Civil WorksActivities" (map) , office of the Chief, June 1974.

7. U. S. Army Corps of Engineers, Water Resources Development for(state), miscellaneous states, published by Division offices, 1977.

8. U. S. Geological Survey, "Hydrologic Unit Maps", issued by state,prepared in cooperation with U. S. Water Resources Council, 1974.

9. U. S. Environmental Protection Agency, National EutrophicationSurvey, Working Papers, Corvallis Environmental Research Laboratory,Oreg., 1972-76.

10. Dendy, F. E. and W. A. Champion, "Sediment Deposition in U. S.Reservoirs: Summary of Data Reported through 1975", U. S. Depart-ment of Agriculture, Agricultural Research Service, MiscellaneousPublication No. 1362, 1977.

11. U. S. Army Corps of Engineers, New England Division, Project Maps,Flood Control Projects, revised to 30 September 1976, Waltham,Mass., 1976.

12. U. S. Department of Agriculture, "Reservoir Sedimentation Data Sum-mary Sheets through 1975:, supplement to reference (10), 1977.

13. U. S. Geological Survey, Water Resources Data (by state and year),Water Resources Division, 1977-79.

151

14. U. S. Geological Survey, WATSTORE User's Guide, National Water DataStorage and Retrieval System, Open File Report 75-426, Reston, Va.,1979.

15. U. S. Environmental Protection Agency, Data Tape Containing FlowData Compiled by the National Eutrophication Survey, CorvallisEnvironmental Research Laboratory, Oreg., 1979.

16. Perry, R. A. and C. J. Lewis, Definitions of Components of theMaster Water Data Index Maintained by the National Water Data Ex-change, U. S. Geological Survey, Open File Report 78-183, 1978.

17. U. S. Army Engineer Division, Ohio River, INFONET - LaboratoryMaster File System User's Manual, 1977.

18. U. S. Geological Survey, Catalog of Information on Water Data, 21volumes, Office of Water Data Coordination, 1974.

19. U. S. Geological Survey, Maps Showing Locations of Water Quality

Stations, supplement to reference (18), 1972.

20. Ficke, J. F. and R. 0. Hawkinson, "The National Stream QualityAccounting Network (NASQAN) - Some Questions and Answers", Geo-logical Survey Circular 79, Reston, Va., 1975.

21. U. S. Army Engineer District, Baltimore, Water Quality Data for

Almond, Whitney Point, and A. R. Bush Reservoirs, 1979.

22. U. S. Army Engineer Division, North Central, Water Quality Datafor Eau Gaulle Reservoir and Lac Qui Parle, 1979.

23. U. S. Environmental Protection Agency, National Eutrophication Sur-vey, Compendium of Lake and Reservoir Data, Working Papers 474 to477, Corvallis Environmental Research Laboratory, Oreg., 1975-78.

24. Walker, W. W., "Empirical Methods for Predicting Eutrophication in

Impoundments Phase I: Data Base Development", Interim Report sub-mitted to the U. S. Army Engineer Waterways Experiment Station,

Vicksburg, Miss., June 1979.

25. Vollenweider, R. A., "Advances in Defining Critical Loading Levelsfor Phosphorus in Lake Eutrophication", Mem. Inst. Ital, Idrobiol.,Vol 33, pp 53-83, 1976.

26. Walker, W. W., "Numerical Characterization of Reservoir Hypsio-graphic Curves", Working Paper No. 1, submitted to the U. S. ArmyEngineer Waterways Experiment Station, Vicksburg, Miss., December1979.

152

.........................................................

27. Mosteller, R. and J. M. Tukey, Data Analysis and Regression,-Addison-Wesley, N.Y., 1977.

28. Hakanson, L., "On Lake Form, Lake Volume, and Lake HypsiographicSurvey", Geografiska Annaler, Vol 59 (a), 1977, pp 1-29.

29. Lambou, V. W., L. R. Williams, S. C. Hern, R. W. Thomas, and J. D.Bliss, "Prediction of Phytoplankton Productivity in Lakes" in Ott,W. R., ed., Environmental Modellinq and Simulation, U. S. Environ-mental Protection Agency, Office of Research and Development andOffice of Planning and Management, 1976.

30. Carlson, R. E., "A Trophic State Index for Lakes", Limnology andOceanography, Vol 22, No. 2, pp 361-369, March 1977.

31. Walker, W. W., "Analysis of Water Quality Variations in Reservoirs -Implications for Design of Data Reduction Procedures", Working PaperNo. 3, submitted to the U. S. Army Engineer Waterways ExperimentStation, Vicksburg, Miss., March 1980.

32. Walker, W. W., "Analysis of Water Quality Variations in Reservoirs -Implications for Monitoring and Modelling Efforts", presented at theAmerican Society of Civil Engineers Symposium on Surface Water Im-

poundments, Minneapolis, Minn., June 1980.

33. Cochran, W. G., Sampling Techniques, 3rd ed., John Wiley & Sons,N.Y., 1977.

34. Walker, W. W., "Evaluation of Methods for Estimating PhosphorusLoadings from Grab-Sample Concentrations and Continuous-Flow Mea-surements", Working Paper No. 4, submitted to U. S. Army EngineerWaterways Experiment Station, Vicksburg, Miss., May 1980.

35. Snedecor, G. and W. Cochran, Statistical Methods, Iowa State Uni-versity Press, 6th ed., 1972.

15

153

APPENDIX A

'4 DATA INVENTORIES By PROJECT AND DIVISION

-44

Table Al

Inventory of WATS.DAREAS File

-'A2

I .. j0 0~..

M,- om N" N a Ino al qo ot0 OOOKOK zzz4~~Ik 0

i t If In I N9 ) "

Ol II OIW:IMcj 60

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I0 I0 1Im

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I 9 ,

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-'s - .-.M M v ~ m ~ w ,1

Ono MO nolonU Don(C M

-Z l - -9 - -' C

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1 1 a lo o ! 2a1 0 0 0 - 0 0 0 ,

110 I m om-M

a -01 I

1 00. - - - - - - - -1 -ma -~ . - VMVmI ~

a'Z To-Io =I

o a 4I

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a u la3,a I

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---- ---------- ----------- 0000 .0

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..... . . o a. . .. - -------N ~ o o - N N oooc oo o

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Table A2

Inventory of RESTER.MORPHO File

A13

------ -----a 1 1

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Table A3

Inventory of USGS Hydrologic Data

A24

3 0 0o,00~00000000000300.3 00 l 0000000'a000001 a @ 00 0

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Table A4

Inetr of Water Quality Data by Station Type

1A34

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r AD-AII 553 WALKER (WILLIAM W) JR CONCORD MA F/G 13/2

EMPIRICAL. METHODS FOR PREDICTING EUTROPHICATION IN IPOUNDNENTS--ETC(U)

NAY 81 W V WALKCER DAC3-78-C-0053

WiCLASSIFIED WES-TR-E-81-9-1 NL

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Table A5Inventory of Phosphorus, Chlorophyll-a, and

Secchi Data at Pool Stations

A45

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* I

In accordance with letter from DAEN-Ri),, DAEN-ASI dated22 July 1977, Subject: Facsimile Catalog Cards forLaboratory Technical Publications, a facsimile catalogcard in Library of Congress MARC format is reproducedbelow.

Walker, William W.

Empirical methods for predicting eutrophication inimpoundments : Report 1 : Phase I, data base development /by William W. Walker, Jr. (Environmental Laboratory,U.S. Army Engineer Waterways Experiment Station). --

Vicksburg, Miss. : The Station ; Springfield, Va.available from NTIS, [1981].

153, 55 p. : ill. ; 27 cm. -- (Technical report /U.S. Army Engineer Waterways Experiment Station ; E-81-9).

.0 Cover title."May 1981." Ci"Prepared for Office, Chief of Engineers, U.S. Army,

under Contract DACW 39-78-0053, EWQOS Work Unit IE.""Monitored by Environmental Laboratory, U.S. Army

Engineer Waterways Experiment Station."

Bibliography: p. 151-153.

1. Computer programs. 2. Eutrophication. 3. Mathematicalmodels. 4. Prediction theory. 5. Reservoirs.

AI

Walker, William W.Empirical methods for predicting eutrophication ... 1981.

(Card 2)

I. United States. Army. Corps of Engineers. Office of

the Chief of Engineers. II. U.S. Army Engineer Waterways

Experiment Station. Environmental Laboratory. III. TitleIV. Series: Technical report (U.S. Army Engineer Waterways

Experiment Station) E-81-9.TA7.W34 no.E-81-9

I D-Ai~l 553 EMPIRICAL METHODS FOR PREDICTING EUTROPHICATION IN 1II IMPOUNDMENTS REPORT i.. (U) WALKER (WILLIAM W) JRI CONCORD MR W W WALKER MAY 81 WES-TR-E-8-9-iI NLSSIFIED DAC39-?8-C-0e3FG 30MEu..NN 3 / 02N

136.

I 1.0 2 IL2

I~i.i

NATIONAL BUREAU OF STANDARDS-1963-A

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UPPLEMENTAt-

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INFORMATIN.

INFOM A ON "

DEPARTMENT OF THE ARMYWATERWAYS EXPERIMENT STATION. CORPS OF ENGINEERS

P.O BOX 631VICKSBURG. MISSISSIPPI 391W0

REPL.YTO(Y) ATTENTION OF

WESEV-1 11 March 1985

Errata Sheet

No.2

EMPIRICAL METHODS FOR PREDICTING

EUTROPHICATION IN IMPOUNDMENTS

Report 1

PHASE I: DATA BASE DEVELOPMENT

Technical Report E-81-9

May 1981

*Page 139, Equations 33 and 34: change (L k L.)k to (L -L).

MYORAULICS GEOTECNNICAL STRUCTURES ENVIRO0NMENTAL COASTAL ENGINEERINGLASORATORY LABORATORY LABOATORY LABORATORY RESEARCH CENTER

63. . . . . . .. . . . . . . .. . . . .

*FILMED

4-85-

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