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~ - Scientific Issues of Data Collection, Distribution, and Analysis Hydrologic processes are highly variable in space and time, and this variability exists at all scales, from centimeters to continental scales, from minutes to years. Data collection over such a range of scales is difficult and expensive, and so hydrologic models usually conceptualize processes based on simple, often homogeneous, approximations of nature. Hence a 2,000- km2 river basin is commonly modeled as a lumped system that responds as a point with average representative properties. Ground water flow is commonly treated as one-dimensional or two~imensional. Rainfall is expressed as a mean over large areas, and as depths over periods of a day. Snowmelt runoff volumes are forecast from averages of snow accumulation at a few index plots. These generalized conceptualizations reflect the normal dearth of data, which lack the temporal and spatial resolution to support more detailed modeling. This forced oversimplification is impeding both scientific understanding and management of resources. In the history of the hydrologic sciences as in other sciences, most of the significant advances have resulted from new measurements, yet to- day there is a schism between data collectors and analysts. The pio- neers of modern hydrology were active observers and measurers, yet now designing and executing data collection programs, as distinct from experiments carried out in a field setting with a specific research question in mind, are too often viewed as mundane or routine. It is therefore difficult for agencies and individuals to be doggedly persis- tent about the continuity of high-quality hydrologic data sets. In the excitement about glamorous scientific and social issues, the scientific community tends to allow data collection programs to erode. 214
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 215 Such programs provide the basis for understanding hydrologic systems and document changes in the regional and global environments. Modeling and data collection are not independent processes. Ide- ally, each drives and directs the other. Better models illuminate the type and quantity of data that are required to test hypotheses. Better data, in turn, permit the development of better and more complete models and new hypotheses. If we accept this synergism, the hydrologic sciences will be well situated for progress, which is needed because recent developments in spatial and temporal models and new data acquisition technology require a rethinking of many of the traditional hydrologic problems. We must, however, reemphasize the value and importance of observational and experimental skills. To address many of the issues described in Chapter 3, we need new observations of hydrologic phenomena. Some of the current uncertainties in our knowledge of the hydrologic cycle require better understanding of hydrologic processes, but progress in the hydrologic sciences will also depend on improved methods for collecting hydrologic data, more complete and better-organized archives of already-available information, and better mechanisms for distribution and exchange of data, particularly in developing countries and in the international arena. This chapter describes some requirements for and characteristics of hydrologic data, assesses the current hydrologic data base, and then discusses some opportunities to improve hydrologic data and their use. NEED FOR COLLECTION OF HYDROLOGIC DATA AND SAMPLES Hydrologic data are needed to measure fluxes and reser- voirs in the hydrologic cycle and to monitor hydrologic change over a variety of temporal and spatial scales. Historically most hydrologic data have been collected] to answer water resources questions rather than scientific ones. Hydrologists use information obtained in laboratories, such as soil particle size, solute concentrations, or electromagnetic spectra, but
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 217 most of their data must be obtained under field conditions. The reason is that, in addition to the elucidation of microscale processes, hydrologists are concerned with processes that have meaning only at the field scale or over long time scales, such as runoff and sediment yield from drainage basins or continental-scale drought. These re- quirements make hydrologic data collection complicated and expen- sive. Despite large financial investments, there remain important questions about hydrologic fluxes and reservoirs that are unlikely to be answered by the incremental growth of instrument networks. Technical and analytical innovations are necessary to overcome the paucity of useful hydrologic data now being collected and collated. To better characterize the hydrologic cycle requires data in several categories, and the choice of what to measure and where and when to measure influences what hydrologic questions we can investigate. 1. We require information about the fluxes and storage of water in its various phases as it moves through the components of the hydro- logic cycle. These include precipitation, snow accumulation and ab- lation, glacier flow and mass balance, discharge in rivers and streams, movement of ground water, and evapotranspiration. Also needed is information about the transport of solutes and sediments as well as the fluxes of energy that drive the hydrologic processes. 2. Hydrologic data are needed to monitor change, or lack of change, in the quantity and quality of water. The major effect of climatic variability on the humans, plants, and animals that inhabit the earth is felt through the hydrology. Similarly, changes in water chemistry cause concern among users of a water resource and can dramatically affect the fish and other biota that live in lakes and streams (Figure 4.1~. Thus we need baseline data, especially in tropical and semiarid areas. 3. In the traditional scientific sense, hydrologic data are needed to test hypotheses and models, and for exploration, to formulate new hypotheses. Hydrologic science can advance as a discipline only if measurement and theory evolve together. Sometimes the mechanisms that govern a complicated hydrologic process are known so poorly that precise data are needed simply to explore the phenomenon; then the next generation of measurements awaits conceptual developments that show which data are essential for testing ideas about how hydrologic phenomena occur. We know only what we measure, and we know what to measure only after some unifying conceptualization of the existing data has pointed the way. Finally, the measurement of hydrologic variables is a scientific endeavor itself. Future progress in hydrologic data collection should result from:
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218 o c 3 4> o - I 80 - ~ 40 . _ OPPORTUNITIES IN THE HYDROLOGIC SCIENCES ~ 25 ._ 10 , c~~~..o....~.~- ~cl ~ 0 ~ - 5 80 82 84 86 88 ~ 6 Yea r 50 40 ~ ~ ·_ ~ ~,.,~ e Go Lo,.,,. ,, o l 30 80 82 84 86 88 Yea r A: ^~? 0' . . . _ 80 82 84 86 88 Yes r 30 ._ ~ 25 - 0 E 25 o co to' ~ ~ ~ ~ D 80 82 84 86 88 Yea r ..'~'...f~N if o'/~' . . 80 82 84 86 88 Yea r 120 R·~ 0 - ~~ _ 95 cn ~.,,.aC, 4,. o,,.1 ._ ~0 ID A _ 70 . . . 80 82 84 86 88 Yes r 60 . 50 "~i 40 80 82 84 86 88 Yea r 15 . . . . 80 82 84 86 88 Y e a r - O 25 - In.,,. D . ~ ~ ~ ~—o _ 80 82 84 86 88 Yea r FIGURE 4.1 Changes in ionic concentrations in two streams in Shenandoah National Park, Virginia, 1980 to 1988. Units are microequivalents per liter for concentrations and centimeters per year for discharge. SOURCE: Reprinted, by permission, from Ryan et al. (1989). Copyright @) 1989 by the American Geophysical Union. · coordinated experiments where diverse efforts are pooled; · technological advances in such fields as remote sensing, instru- mentation, and information systems; · new forms of analysis such as isotope geochemistry, paleohydrology, and improved models of spatial and temporal processes; and · intensified efforts in design of monitoring networks and exami nation of data quality and compatibility. Need to Collect Data at Varying Scales Hycirologic processes operate over a range of temporal and spatial scales, and important questions exist at time scales from seconds to millennia and space scales from the molecular to the global.
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 219 Hydrologic processes operate over a continuum of space and time scales, from those of laboratory experiments to global transport of water and nutrients and from short-lived, transient phenomena to gradual secular variations. Some important questions studied in the laboratory or at scales of a field plot involve interactions between solutes and water or between water vapor, liquid water, and ice. For example: · The rate of elusion of chemical impurities from seasonal snow depends on interactions between solid, liquid, and vapor phases. · The cycling of hydrogen ions plays a critical role in determining the effects of acidic deposition on wild land and agricultural ecosys- tems. The largest components of hydrogen ion cycling are consumption by mineral weathering and production by plant roots. These components are difficult to estimate at field scales because we do not know enough about the kinetics of mineral dissolution reactions and biological release processes at these scales. Errors in estimating annual hydrogen ion consumption and production rates can be as large as the estimated annual input rate from acidic deposition. At the same time, our current knowledge of major fluxes of water in the hydrologic cycle involves large uncertainty. For example: · The mean annual discharge of the Amazon River is about 200,000 m3/s. Typical error estimates for the measurements are 8 to 12 per- cent, i.e., 16,000 to 24,000 m3/s, a rate slightly higher than the mean annual discharge of the Mississippi River. · Data show that sea level is rising slightly, but our investigations into the source of this rise, and the accuracies of our predictions of the future, are hampered because our measurements of the snowfall and iceberg calving from the Antarctic ice sheets do not tell us definitely whether the Antarctic ice volume is growing or shrinking. Thus the proportion of the water attributed to each of the sources causing this rise in sea level is not confirmed. How much comes from Antarctica and Greenland, from thermal expansion of the ocean waters, from shrinking alpine glaciers, and from depleted ground water reservoirs? Data are needed at a variety of scales, and the spatial and tempo- ral scales of available data restrict the questions that can be investigated. As is described in detail later in this chapter, the information is better for some hydrologic fluxes and reservoirs than for others. For most fluxes, however, a fundamental block to progress is our poor knowl- edge of how to interpolate between measurement points and how to extrapolate from few data points. For example:
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220 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES · Depths and water equivalences of a snowpack are measured at many snow courses in cold regions, but it is possible to use these data only as crude estimates of the water content of a regional or basin-wide snowpack. · Topographic influences on rainfall, evaporation, and soil moisture are poorly documented at scales varying from individual hillslopes to entire mountain ranges. An additional important issue in the sampling of hydrologic pro- cesses is the structure of the statistical fluctuation that the processes have at different scales of measurement. How do the mean and variance of annual rainfall change as a function of the area over which the estimation takes place? How do the mean and variance of evapo- transpiration depend on the time scale considered? What is the com- bined effect of time and space scales on the statistical properties of hydrologic variables? There is an urgent need to 1. quantitatively characterize the fluctuations of hydrologic vari- ables at different time and space scales; and 2. design data collection programs that will allow the study of theoretical constructs, described in Chapter 3, to structurally link the fluctuations at different scales. Need to Develop Accurate Hydrologic Data Bases to Improve Scientific Understanding Detection of hydrologic change requires a committed, international, long-term effort and requires also that the data meet rigorous standards for accuracy. Synergism between models and data is necessary to de- sign effective data collection efforts to answer scientific questions. Development of scientific theory in the absence of supporting facts does not lead to understanding and can result only in conjecture. The primary sources of facts for the hydrologic sciences are the mea-
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 221 surements that are made of hydrologic and ancillary variables. Most hydrologic data are collected by government agencies for a variety of purposes only one of which is the development of hydrologic un- derstanding. Historically, the major providers of funding for hydro- logic data collection have expected that the resulting data would be useful in setting water policies, developing water resources plans, designing water resources systems, operating the structures that make up such systems, and monitoring the management of water resources. Increased hydrologic understanding can and does contribute to im- proved information for these utilitarian purposes, but the design of hydrologic data networks seldom has as a primary objective the bet- terment of basic hydrologic understanding. Therefore, the data needs of the hydrologic scientist almost certainly will not be fully satisfied by the existing data networks that are supported primarily for operational and accounting purposes. The existing data networks should be viewed by hydrologic scien- tists as opportunities upon which they can build. To optimize these opportunities, it is first necessary to define the characteristics of the data sets that hydrologic scientists need. These characteristics include the variables to be measured and the locations, frequencies, durations, and accuracies of the measurements. They should be derived from knowledge about the hydrologic phenomena to be explored and from the hypotheses to be tested. Allocation of the resources available for data collection must seek complementarily between the scientific and operational data sets. However, the operational networks often change in character because of changing operational demands for data or because of budgetary pressures on the financial sponsors of the data networks. These changes most often are manifested as discontinuities in the time series of measurements, as shifts in the location of the measuring site, or as changes in the accuracy of the data. Thus a full measure of complementarily is an illusive objective but a worthy one that can be approached by adequate communication between research scientists and managers of data collection programs. Important hydrologic changes may be subtle or may be difficult to detect because of large interannual or inter-event variation, and spa- tial and temporal scales of available data restrict the questions that can be investigated. Some important processes are transient short- lived but repeated. Fluxes and reservoirs of water, energy, solutes, and sediment are monitored most intensively over those parts of the world that are humid-temperate, densely populated, and industrialized, but measurement networks are particularly sparse over the oceans and in regions that are subhumid, tropical, at high elevation, or lightly populated.
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222 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES Need to Collect Long-Term Hydrologic Data Long-term monitoring and the use of paleohydrologic records are fundamental to understanding the role of extreme events in hydrologic systems. The need for long-term measurements is becoming clearer in our investigations of environmental change, including hydrologic change. Some disciplines, such as paleontology and historical geology, have depended for their existence on the availability of data spanning long periods, from 100 million to 2 billion years. Other disciplines that have traditionally focused research over shorter time frames, such as the environmental sciences, now stress the critical importance of long- term records. Despite the increasingly recognized importance of data records of long duration, only a few dedicated research organizations have suc- cessfully maintained high-quality data collection efforts over periods of 50 to 200 years. Furthermore, these organizations have experienced difficulty in committing limited research monies year after year to an activity that is frequently termed "monitoring," often with pejorative overtones. Dams have been built in many areas of the world and the water has been allocated for power generation or irrigation based on only a few years of data, with the too frequent result that the anticipated volumes of water have been available only in years of above-normal runoff. But many scientific questions justify the collection of long- term data. Two general areas for which long-term hydrologic data are specifically needed are discussed briefly in the remainder of this section, but these examples are not meant to be exclusive or exhaus- tive. Understanding Hydrologic Behavior and Hydrologic Change Long-term data are required to understand the basic hydrologic behavior of natural landscape units. In most humid areas, we do not understand well enough the relationships between rainfall, evapo- transpiration, streamflow, and long-lived vegetation such as forests. Research efforts have typically focused on only a short segment dur- ing the life span of forest stands that may exceed a century. Moreover, in areas of low rainfall, where the occurrence of rain exhibits high
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 223 variability over time and space, understanding of such fundamental relationships is even less complete because sufficiently long data records are not yet available to separate the inherent spatial and temporal variability of the processes involved. We do not fully understand, for example, how evaporation and soil moisture are regulated in such situations. The need for long-term data is particularly acute for analyses that focus on hydrologic be- havior at the continental spatial scales and on long time scales. Detection of hydrologic change requires long-term data sets of greater quality and reliability than are normally needed in the investigations of processes. When we measure rainfall for such purposes as flood forecasting, modest changes in the techniques, such as movement or redesign of the gage, do not affect the usefulness of the data for telling us whether to expect a flood on the river. However, when we try to use the same data to identify a long-term trend that is superimposed on the natural year-to-year variability, movement or redesign of the gage may introduce artifacts into the data set, and these may be falsely identified as trends or may disguise hydrologic change. Identifying Extreme Events Identification and analysis of hydrologic extremes, such as floods or droughts, are needed to understand the functioning of human societies as well as natural and managed ecosystems. In most hydrologic processes the extreme events often have the greatest effects on both systems and humans. Because they are infrequent in occurrence, they are poorly represented in all but the longest hydrologic records; only a few data sets contain enough extreme events to allow a precise estimation of their return periods. Moreover, the dynamics of extreme events are hard to measure; stage versus discharge relationships for gaging stations are usually not calibrated for high stages, and scour- ing of the channel during such flows makes extrapolation of rating curves for lower stages prone to error. Flood frequencies and drought recurrences may be well defined for mid-range events, but the tails of the distributions are poorly quantified, both in temporal distribution and magnitude. A series of extreme events may represent just that, a combination of unlikely probabilities, or it can show a change in climate, whereby the events are no longer extreme but merely normal events within a new popu- lation. A good example is provided by analysis of the 1985-1986 drought in the southeastern United States. Estimation of the severity and interval of likely recurrence for this drought was made possible by
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224 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES :: W~ER~RESOUR(:ES~IAGEMENT~i~ i ~~ A A ~~ ~ A i ~~:~ ~~ ~~. H:u~nd~reds~of~bi I lilions~ot Eli den l~ars~ha~tei~b~et~:nikested :~s~~nce ~ lain i i I: ~~ ~ ter Or: ~ ~ ~~sta~ em: :~ ~~£~ hi. ~~arI~c ~~::~::~:n::r~l~:VAt~::~WAt~~:~::nro:~:ec~ts id: Jiiam~i.~:~i 1~—irate it ~~:~:i : ~: : : ~ ~ ~ ~ : : ~ ~: ~ ~ , ~: ~ ~ ~ : ~ ~ : ~ If:: : ~ ~ ~ If: : ~ ~ : : :-: i::: ~: ~ :~ i: ~ ~ ~ ~ ~ ~ : ~~: ~ ~ ~ ~ ~ ~ ~ po~r~1~0ns~ ~evees,~c~ ~a~nrte ~i~lmp~~; ~ gatio~ ~—~ i i ~ ~ ~ ~ ~ ~ ~ : ~ ~ ~ ~ ~ ~ ~ ~ : ~ ~ ~ w a ~ ~ ~ : t r : ~ a t m e R t : ~ ~ ~ i F , | ~ a n i t S ~ ~ ~ ~ i a ~ r e ~ ~ ~ e ~ p ~ S : . ~ ~ ~ ~ i i ~ ~ t ~ i b e s ~ ~ ~ : r e s e r ~ r ~ ~ ~ ~ ~ ~ ~ ~ :~ ~ ~ :~ ~ :~ ~ ~ i ~ i ~: ~ ~ ~ ~ ::~ h~ave:~been~:~sed,~: ~: are~: rn:ore::~:seinsitiv~e~ to:~ envimn n~n~i~esi~ :~a~ ~id:~t - . ~ ~i~ ain~n~ iiniveStme~ ~t:~i~n: n~eW~ n^e—: ~s ~ on~l~i~sr~al ~tti~ik~ tt~a~bast~i~ :~:~ ~i~:~:vea' s.:~ :~on~:haLs~: ~i~hNi~i~tfu~l aoi~t h~ikimianaa-~: ~:~ ~: In~ourwaterre~sources~ :~ ~ ~ :~ :~: ~: ~ ~ ~ ~ :: ~ ::: :::~:: :~::::::: :::::: :::: ~:~ ~:~ ~ ~i~:I~o~y~a~n~i~m~r~ta~nit~c~haHe~e~ ~ tacesi:~thls~nian~i~:l~ - : C~:I~S:~ffi~ ~: ~ ~ ~impro:ve~how~rs~: vast~nvest~; is~tak~;n~g ~:n1D~accaunt~eco-: i~ :: : ~:~ ::: :~:~: :~.:~::~ ~:~ :::~::~::~ ~:~:~.~:~:: ~:::~ ~::~ ~::~ :~::::~: ~:~::~ ::~::: ~::::~:~::~: ~:~::~:~ ::~::::~::~ :~::~:~:~ ~: ~: ~: ~ no~m~lc: ~env~l~mn~m~ental:~,~ ~a~n~d~sociial i valuies.~i~ l~creases~:~i~n :~a~n~nioal be~nefi~i~i~i~ ~ ~ ~ ~ ~ ~ ~i ~ ~ ~: i~ ~ ~ i~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ i ~ ~ ~ ~i~ ~ ~ ~ ~ ~ i~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ :~i i ~: ~ ~ ~i ~ i~ ~ ~ ::~ ~o~nly~a~i:~few~;percenti~ld ibe~i~wor~ ~ite~n~si~of~b~llil~=si~iof do4~Nia~i~a~coi~ld ~i ~ :: : :~:i::~: ~::~ ~:::~ ~ ~:~::::~ ~::~: ~ ::::~ ~:~:::~ ::~:~:~ ~:~:~:::~ ~:~:~:~ ~:~:~ ~:~:~::: ~:~ :~::: ~:~:~ ~:~:~::~::~:::~:::: ~:~ ~preiserve~e:nv~ir~onmenta~l val~ues~ ~that~i~re~i 3gno~red~ ~6ecades~iago.~i~ H~f-~ ~:~ ~ ~ ~ ~: . ~ ~ :~ ~ ~ ~ ~ ~ ~ ~ ~ ~ , ~ ~ ~ ca ~ Y'~ wate~r~p~ro~c~ ~ave~ ~een ~p a~n:~r,:ec ~::t~O~seirve~: oine~:set~pur~p oses -i:~ut:~i:~ ~::~ operat~d~to se~rw:~ano~er.:~O~n~e e~le~i~:i~s~i~Tenr e~ssee~l~ei:~. ~i ~Origi~:n~al Iy~developied~ ~r~i~:od ~c=oint~ ~ i~: in~p~ro~ites~mele~ir~ic~ :: ~ : ~ :: ::: ::: ~ ::: ~ : ::: ~ : :::: ::: :~ : ~ :~: :::: :: ~:: : ~:: ~ :: : ::~:: :: ::::: : :~: ~ : ~: : ::~ ::: ~ :: :: :: :::: : ~ :::::~: :::: ::::::::: : ::: ::: : ::::::: :: :::: :: ::::: ::::: ~:: :::: :::: :~ ~ : :~:::: ::~:~ : :~ ::: : ::~:: ~::: :~ :::: Dower ~:wit: 1~ ben:ef~i~ts~:~fa~r~:~ar~eater:~t ~:an ~It ~nise~resu ~n~:~t~om ~ll~di~:nt~.~ ~:~i~:~ Th~i~s :~:~:~ren~ce~b~ee~n~:w~h~i~:i~s:~i~ pl~:r~i~a~i - ~:~i~s~a,~u~W c~:~sug- ~ ~ ~ ~ ~ ~ ~ ~ · ~ ~ ~ ~ ~ ~ gests~a~ma~nage~ment~o~ortu~n~i - , ~e~3~il~a~ Y~s~l~n-~:ei ~m~uc l~:t se~p~ ~a~n~nI`~ ~ :: :::: :: ~ :~:::: ::~:: :: :: ~ :::: ~ :::: :: 1:::: ::::: :::~: ::: :~::: ::~:: ::: ::: ~:occurreC~::oe£aGes: ago.:: ~:~ ~:~ ~A~:~m~ajor~dieraT~w~ate:r:~:resources~ agen~cy~ t:h6~U .S. ~B~ureau~of ~Rec~larn:a ~::~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~, ~ ,, ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . :~ ~ ~ ~ ~ ~ ~ :: . .~ ~ ~ ~ ~ ~ ~ . ~ ~ ~ ~ t~iio~n'~:rec=~ogni:z~en~ ~th~iiS~i~ ch~a~l~lenge:~anO~mrm:al~ly~re2~lign~m~sssfon~iaMf~ ~ ~ ~ ~ ~ ~ ~ ~ ~ · ~ ~ ~ ~ ~ ~ ~ ~ ~ T~ ~ ~ ~ ~ ~ ~. ~ ~ ~ ~ ~ T~ ~ ~ ~ ~ ~ ~ ~ T~ ~ ~ ~ ::~: ~t;:o~m~ co~n~str:uct~ oa~ iaina :~towa~m~prove-~ ~mana~mein:it~a~n:~a~at~mn ~iot~i :i ::: : :: ~ : :: ~. ~ ~ ~ :: ~ ~ i: . ~ ~ ~ ~ . ~ ~ ~ ~ ~ ~ ~ ~ T~ ~ ~ ~ :~ ~ ~ ~ ~: ~ ~ _ ~ ~ts:~vast~ ~prior~:nv~es - ~em~i~t~ne~:~ il:7~mr~n:~states ~I~ne~n~atioi~n:~S~i~:a:rgest~ii~ :: ~ ~ ~: ~ ~ ~: ~ ~ ~: ~ ~ :: ~ :: ,: ~,~ ~ ~ .~ ~ ~ ~ ~ ~ ~ ,~ - ~: ~ . :~ ~ ~ ~ ~ ~wate:~:~resiolo~ll-c~eS~agelelcy~i~tne~:u~.~.i~i~Amy~i~rpS~or~ng~:J~n~eer~S;~:nais~i~nm~ir ~i~ :::~ ::::~:::: : :~::: :::: :::: :: ~ :::::::::: :::: : ::~::: ::::::~ : : ::~::: : :::~:: ::::~ :: :::~:: :::: ::~::::: :~:: ::::: ~::~: : ~: : ~:~ ~:::: :~:~ :~: ::::~::::: ~ ~ ::: ::: :1: :: ::: :1 :::: :~::: :::: ::::f: :~: ::::: ~: :: ::::~::: ::~: :: r:: :.:: :: :: ::f:. :::: :: .~ : :::: :~::: ::::: l :::::: ~:.: :~.:: .~:: :::~: ~:~:::: ::~. .:::::::::: :::: :::: ::~: :::: ~ ~ ~sucn ~a~:~to.-ma~l~:~s~p,~ou~t~: i~i~m~air.~y:~or~i~:l~ts~:lstr,ict :~an~i~a::lv~lsil~:o~n~rices~ ::~are~ :~fin~d~i~rg~new~n~on~strucitu~ral:~:s:ol~Won~s.~l~nagemebt~carifli~i:~Warise~among~:~ ~ ~ ~ ~ ~ ~ · ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~, ~ ~ . ~ ~ :: . ~ ~ ~ ~ . ~ :~:~ ~co~mnet~ ~u~se~s~ ~ wate~ ~ ~s~c.~i ~as~ ~lirr~lR~atIon~ ~m~uin~c~! Da]~ siU=,hm~~ ~-~i~ :~d~iropow~er~ ~::~:~,=~wer ~cool ing ~ wi~ifNe~prese~rvafion,~ fisl~i - ~:: q~.iW~i~:~ :~ control: ~'no.~ust;rla~l ~u~se~ ~:~:nav~f~gat:l~o~n,~ano recreat~:~.~ ~:~t~l:mm~:~ mana~g;em~ent-~:~:~ ~ ~ , ~ ~ , ~ :~ ::~:of :the :~n:at:ion'~s~ ~water:~ re~sourc~es~ :is:::~:e~ ~key~:~:to ::~reso.iiv~ing::~:se~cai~fli~c~ts.~::~:~:: :::: ::: :::::: ::~ :::: : :: :: ~::: :: :: :: :~::: : :: ~:~:: ~:::: : ::::: :~::::::: :: ~ :: :: :::~: : ::: :~::: ~ :::~::: ~:::::: ::~ ::: ~ ::~: ~ : ~ ~ ~ :::~ ~:i::::: :::: ~ : :~:~:~::~ :: :~: :~ ~better~ i~nform:atior.~:lis~the::i~key~to ~imp~£oved:i~:water ~imarlagem - ~:~ ~ ~ ~ ~:~ ~ j ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~: ~ ~ ~ :~ ~ ~ ~ ~ ~ ~ ~ ~ :~ T~he '.n~porta~nce ~ot water: re~soUrces~lnana~g~en'~ent ~was: ~.~::~t~g~h~l~lgh~ted~ ~by~ ~:~ : , :::~ : ::: , :: :: :~: :~ ~, ~, ::~ ~: ~ ~ :~ ~, , :~ :~ ~: ~: ~ :: ~ ~: ~ ~ ~ ~:~ ~: ~:~ ~ ~ the 1:98~8::droL~t~g~h~t. ~Print:~ano~ ~e~l~ectronic ~',neDia~oroug~t~; u~s~ ~cl~ear~ l~m~age~s~ of~: ~: ~ ~ , . ~ ~ ~ ~ ~ :: ~ the consequences o t ~d~rowght~ba~rges ~stranded~on~ sand~ba~:rs~ ~in~ th~e~t\A.isi-~ ~ ~ ::: ~ :~ :: ::: :~ ::: : :~ :: ~ : :::: ~ :: :::: :~ ~ :: ::::::: :::::: : :::: :::::::::: B~ ~ ~ ~ ~ ~ - ~ : ~ ~ . 1 :::~ ~:: ~ ~ ~ :~ ~ ~.: ~1 ~ 1 : ~ :: _~~ :: :: ~ : :~:~ : ::~: ::1:~ ~ :: sissippl Klver empty re~servo'~rs ~ano witnerea.~ corn~Tl~e~tG.s~.~ ~l~e~oro~t~grit~ ::i l I us:trated: d ramatic~al ly ~::~that:~th~e~ ~ avai~l~a:~l iW~l:~of ~water~:: i r~vo~Ives~:~r~i~sk :~a~:nd:~::~ t~hat th~e risk see~ns ~t o be~ increasing.~ ~ ~H~ow~ wil I in~creases in~ :atm~ospheric~ caroon~ U~ox~tUe~al~lu otner Ereenhou~se~aa~ses~chan~e:~ the cl~matet :~ W:~hat:~:~:~: ~ ettects w~ll I such ~ch~ar.ges~h~ave~ on~ou~r~wa~r~ ~r~eso~u~rc~es .li~ ~ bom~e ot the ~ntormat~on nee~nen~ to '~mp,rove:~:wa~ter ~reso~.~r~ces ~mana~ge-~ ~ ~ , . ~ ~ , ~ . ~ , . . ~ , ~ , ~ _ ~mer`.t ~:w' 1~.~ co~me~:~ tro~m~ ~ l~mprovec~ ~ ~a~pp~l~cat'~o:~.?.~o:J:~ex~::s~t~l~n~g~ ~tec~nn:ology~. ~ ~:~:tor: ~::: exa~mple~,~ ~new~d~ata: ~sy~stems~u~sing~ a~ut ~mated~:sur~ce~ :6bservat~ons:)~sa~te~l-:~: :~:Iite c:ommun:ications, :w:eather rada:r ~and:~satell:~ite::i:m~agery~re~beg~inning:: :: : : ~ :: :~ : : :: ,:: ::~ ~:: ~ : : :~ ~ ~: ; ::~:: :::: :: :: :~:::~ :::~: : :::: ::: :: :: ~: :~:: :::~ ::~:
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 265 other proxy studies could be applied. Sometimes they provide infor- mation where there are no instrumental records even today, for ex- ample, in coral reefs and ice cores. To execute the application requires interdisciplinary science. There must be expertise in the particular proxy area e.g., palynology, dendrochronology, sedimentology and expertise in hydrology itself to develop sensible and significant results. Although it has been used successfully in many areas of the world, there are now more opportunities than accomplishments in paleo- hydrology. Data Accessibility and Management Advances in the hydrologic sciences depend on how well investigators can integrate reliable, ~arge-scale, ~ong-term data sets. Storms, floods, and droughts are natural events that can be mea- sured just once, whereas laboratory experiments can be repeated. Instruments used in hydrology must be reliable and operated such that data captured are of known standards and precision. On rivers, measured stage data must be transformed to discharge. The stage-discharge relationships, commonly called rating curves, typically must be extrapolated to extreme stage values and may require adjustment as new "agings at the extremes become available. This adjustment may apply retrospectively for rating curves that have been used for many years, and so a data archive should store original stage mea- surements and rating curves separately, to allow this retrospective examination and adjustment. The data sets required to answer many of the open research ques- tions in hydrology will be complex. Inevitably, many scientists from a variety of disciplines and backgrounds will be involved in data collection and analysis, over a significant period of time. How can diverse investigators and investigations produce compatible data sets, assure their quality, and confidently assemble them for larger, indeed public, use and access? Creating effective data systems for assembling and distributing scientific data sets is not trivial and depends heavily on the personal efforts of active scientists. If the data systems are constructed within the scientific community by scientists themselves, rather than by independent data "experts," there will be many scientific opportunities as well as technical and political challenges.
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266 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES Data Management in the First ISLSCP Field Experiment The First ISLSCP Field Experiment has wrestled with these issues. FIFE was not designed exclusively as a hydrologic experiment, but it included collecting a comprehensive set of hydrologic and related data sets for a 15 x 15 km2 experimental site in central Kansas. The studies of some 30 cooperating principal investigators were actively supported by a data system built by participating staff scientists at NASA's Goddard Space Flight Center. The immediate goals of the data system were to capture and preserve the data and distribute them as rapidly as possible. After the conclusion of the field sampling, the system was converted into an open, widely available archive. The technical elements of data distribution were easily supported: magnetic media via mail for large volumes and an on-line access via electronic networks for browsing and routine data extraction. Equally important, however, was a user support staff. Technical and scientifically competent people were required to simplify access, prepare adequate documentation, and teach novice users about both the system and the data. Assessing data quality can be difficult enough for a single investi- gator working with his own data. The problem is compounded when the data are required quickly by cooperating investigators and distributed through a data system to people who may be unfamiliar with the technical details (or difficulty) of the measurement. The FIFE solution was to use the data system as the focus for a cooperative assessment of data quality. Data Storage and Access Optical disks and compact disks have become an attractive alternative to traditional magnetic tape or disk storage media, because they offer the capacity and security necessary for hydrologic archives, and because multiple copies of large archives can be made cheaply. For example, the entire daily stream gaging record of all gaging stations for one year is stored on optical disks for such countries as the United States and New Zealand. The need to publish expensive yearbooks of data disappears. Issues to Resolve The evolving requirements characteristic of active research demand a data system with real-time adaptability. This can be achieved by a scientifically involved data system team that puts a priority on service.
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 267 This is no small effort: it requires direction on a day-to-day basis by active scientists. In particular, for large projects, the role of a project information scientist must be recognized as critical and must be re- warded appropriately. In addition to personnel issues, there are several political aspects. Three legal categories of data can be defined: data that are acquired
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268 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES from public sources with no distribution restrictions, data that are collected by publicly funded principal investigators, and data that are acquired with public funds from private sources, corporations, or individuals with specific legal rights to restrict their distribution. The FIFE approach was to recognize a data collection and analysis phase in which data sets were exchanged and revised and their quality controlled, but during which general access was restricted. The experience suggests that the more direct control scientists have over the data system, the better it will serve science. In particular, direct control makes possible rapid adaptive responses to the unexpected opportu- nities that can develop in any experiment. A control data base can be a tool and focal point for cooperatively assembling and checking the data sets. Advances in computer technology make it possible to link such a data base electronically to field sites and investigator laboratories, where a common set of hardware and software tools can be inexpensively supported. These would allow each scientist to create his or her portion of the data base, in near real time. Challenges in Measuring Water Quality Public concern with pollution of water resources, as well as its effects on human health and the environment, is widespread and occasionally intense. Investigations of water quality must be designed accorcl- ing to sound scientific principles. In response to public concern, many studies are being conducted to monitor and assess the amount and distribution of pollutants entering the hydrologic cycle. If these studies are to be useful to understand the causes of observed conditions, and thus provide a foundation for cost-effective amelioration of water quality problems, they must address scientific principles as well as practical ones. Water Quality Monitoring and Assessment Data for water quality monitoring and assessment may be divided into three types:
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 269 · data collected to characterize ambient concentrations in lakes, rivers, and ground water; · data collected to monitor effluents; and · data collected to monitor water quality for a specific use. The remaining discussion focuses on ambient water quality data. However, managers of data collection programs for all three types of data need to become more aware of ways that data from individual programs can be made more useful for addressing issues that are beyond the immediate program objectives. These include: · the need to collect important ancillary data, to place the water quality data in the context of the natural and cultural setting; · the need to carefully document sample collection and labora- tory analysis procedures; and · the need to archive the raw data in easy-to-access computer files. Scientific Issues and Challenges Past experience shows that water quality data collected for utilitarian purposes are either difficult or impossible to use for scientific purposes. It is seldom appreciated that science-oriented designs not only contribute to advancing science but also significantly improve the process of attaining many practical goals. Water quality is threatened by thousands of potentially harmful substances. Developing effective evaluations of water quality for so many chemicals is an imposing challenge, requiring continued devel- opment of screening techniques and broad-spectrum analytical pro- cedures. We also need better ways to link contaminant selection to the physical-chemical properties of different substances, to the behavior of different substances in surface and ground water, sediments, and plant and animal tissues, to chemical usage estimates, and to the relative health and ecological risks associated with different pollutants. A related issue has been the failure of traditional monitoring pro- grams to identify emerging water-quality problems, possibly because of the lack of a significant link between these programs and scientific inquiry. For example, most water quality sampling in the United States has been targeted exclusively at substances for which regulations already exist, leading to a focus of effort on selected constituents— priority pollutants that occur infrequently, and often to a disregard for more important contaminants. Future data collection programs need to provide explicit flexibility to enable adjusting to changing environmental concerns and incorporating
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270 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES exploratory aspects into the design. Frequent interpretation of data also is required to identify emerging issues; the data should not be simply collected and archived for future analysis. The integration of biological measurements with physical and chemical measurements also can significantly strengthen the utility of a data collection pro- gram to help identify emerging problems. For example, biological properties may be more sensitive to water quality than are chemical or physical measurements. Too often chemical and biological mea- surements are considered competitive rather than complementary as- pects of water quality characterization. The design of water quality monitoring and assessment programs usually does not reflect consideration of the issue of scales. Yet the scale of focus will constrain the issues that can be addressed, for example, in providing information on non-point-source contamina- tion. Simple use of highly intensive area sampling will not produce significant results within the limits set by realistic funding and the human resources available. Instead, innovative designs must be developed that fully use the existing understanding of the physical, chemical, and biological processes that determine water quality. A major deficiency in environmental data collection programs has been the inadequate development of information useful for defining long-term trends in water quality. Part of the problem is simply that data collection programs are too easily abandoned when funding problems occur or in the excitement of responding to newer, more glamorous social or scientific issues. A greater commitment to continuity is needed. Moreover, a key challenge is to carefully balance long-term consistency with inevitable changes in hydrologic knowledge, the technology available for field and laboratory measurements, and the types of contaminants extant. To the extent possible, long-term pro- grams should rely on repetition of measurement, but they must also document carefully the criteria for site selection, the characteristics of sampled sites, and the methods of data collection and analysis. When changes in measurement techniques occur, the old and new techniques should be applied in tandem as long as is necessary to determine the relationships between them. Interrelationships among components of the hydrologic cycle must also be considered. Understanding of the connections among the atmosphere, surface water, and ground water needs to be incorporated into the design of environmental monitoring programs for these dif- ferent media. For example, atmospheric cycling can be critical to the transport of major and trace constituents of terrestrial waters. So in some circumstances, a basic understanding of atmospheric processes
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DATA COLLECTION, DISTRIBUTION, AND ANALYSIS 271 and appropriate atmospheric monitoring may lead to more effective collection of data describing water quality. Use of Biological Methods in Water Quality Analysis Biological information can complement chemical analysis to improve the measurement of water quality. Physical and chemical properties of water may vary rapidly, and intermittent or infrequent "grab" samples may give misleading indi- cations of prevailing water quality. The native biota may be better indicators of water quality and human effects because of their prolonged exposure, integrated response, and differing sensitivity to all the varying conditions of their environment. Indeed, organisms provide the only direct measure from which ecologically significant impacts can be deduced. All levels of biological organization molecular, cellular, tissue, organ, individual, population, and community have been used or proposed for use in water quality interpretation. The methods may or may not identify a particular cause of change, but a measurable biological response may help to identify physical or chemical tests that should be used in the search for a cause or causes. The first biological methods used in connection with water quality assessment were based on the observed presence or absence of species. Characteristic native species were used to demarcate zones of decreasing concentration downstream from a point of heavy organic loading. Particular species were thought to show the pollution condition in each zone. However, the supposed indicator species also occurred in unpolluted environments, and the zonation varied with the type and intensity of pollution and other hydrologic properties. Further work on human effects resulted in methods based on analysis of assemblages of species. The relative dominance of tolerant and intolerant species or of functional feeding groups in a biotic community is sensitive to water quality. These methods are successful when enough ecological knowledge exists about the species used, as is the case for most fish (although fish may be impractical to sample). They are less success- ful when the ecological requirements of the species are poorly known, as is usually true for algae and benthic invertebrates. In the absence of detailed information for the species of interest, effective ecological methods are available based on resemblance between biotic commu-
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272 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES nities in hydrologically similar streams, with and without human impacts. The selection of suitable reference streams is crucial to the success of this approach. The occurrence of one type of effect, sewage contamination, has traditionally been determined using as tracers microorganisms indigenous to the gut of humans and other warm-blooded animals. Bacterial density in laboratory cultures inoculated with water samples is inter- preted to show the degree of fecal contamination and the potential occurrence of associated human pathogens. Escherichia cold is replac- ing fecal coliform and fecal streptococcus in these tests as a more specific indicator of human effects. The sensitivity of organisms to target contaminants or the concen- tration of contaminants in living tissue can be used to detect the spatial distribution or biological availability of contaminants. The method samples native species or introduced, caged species. It is limited by differences in sensitivity or in uptake of contaminants among species, by lack of suitable widely distributed sentinel species in continental waters, and by effects of enclosure on caged organ- isms. Laboratory bioassays using sensitive organisms are performed to determine biological effects of specific environmental characteristics. Responses, usually from short-term tests, are measured as bioaccumulation or as changes in behavior or physiology. Although test conditions are standardized, thus far the results cannot be extrapolated to other test conditions or species. In particular, bioassay results do not directly provide adequate information about an effect on the long-term structure and functioning of ecosystems. Limitations of single-species bioassays have led to the use of laboratory or field-emplaced microcosms to determine the effects or the fate of contaminants. The sizes of such microcosms range from less than a liter to many cubic meters. Microcosms contain important components and exhibit important processes of natural ecosystems. They simplify environmental variability while exhibiting multispecies phenomena under controlled and replicable conditions. The results obtained from experimental microcosms are empirical analogues of whole-ecosystem functioning but require great care in broad extrapolation to the field. Methods based on levels of organization below the individual level are applied in the field or laboratory to detect, quantify, or determine possible human effects. Techniques based on enzymes, antibodies, tissue cultures, and gene probes are being used or actively developed. The degree of sensitivity and specificity possible with these methods suggests that their use in water quality analysis will increase. Clearly, biological data can supplement physical and chemical data
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D~ COLE DlS~lBU~^ ~~D APSIS 2~ to provide more holistic understanding of Me Coning and of the natural evolutionary bends of hydrologic system as emu as human effects on such systems To accomplish this in detain major advances are needed for determining the hydrologic implications of ecological results Also needed are improvements aimed at increasing the sen- si~iV, subdue ~ ~~ of biological methods Ed at decrease costs and analytical bme. To date, only for indicator bacteria have procedures been adequately st~dard~ed Ed ~ result made accessible in water quality data banks. Other biological data relevant to water quality assessment are scattered and are based on diverse methods of sampling and analyst. Standardized methods Would enhance the sciendRc value of biological ~krmabon by providing a refile baseline fir making Choral Ed spatial co~adso=. Proved cocoon of ecological results and their significance is also needed' in forms useful to other scientists and to the public. Biology can furnish uncommon insights for hydrologic science, in- ~ghts not achievable solely Tom a Edge of physics and chewy. For example, organisms are involved in the transport and cycling of elements in water and sediments. Organisms are targets of scientific Earl to preserve rare Ed Educed species. Pop~abo~ of orgasms are ~tendonaDy added by management programs and u~nhonaDy affected by natural and anthropogenic environmental effects These and other issues often require studies on large spatial and temporal scales. Saw sages Cold be ~o~ora~d Ho national Ed ~temabonal mater quality monitoring systems to provide the means to evaluate Ed Prove Completely developed but potendaNy valuable biological methods for understanding the organization and functioning of hy- drologic systems. SOURCES AND SUGGESTED READING Andre, ~ Cal ~ P. Goutorbe, and A. Perrier. 1986. HAPEX-~OBILHY: A hydrologic atmospheric experiment for me study of water budget and evaporation flux at the climatic scale. BulL Am. ~eteorol Sac. 67:138-144. Baumgarmer, A., and E. ReicheL 1975. The World Water Balance. Elsevier, Amsterdam, 179 pp. Bernabo, C. 1978. Proxy Data: Natured Records of Past Climates. NOAA Environmental Data Service ReporL U.S. Department of Commerce, Washington, D.C. Earth System Sciences Committee, NASA Advisory Council. 1988. Earn System ScF ence: A Closer View. National Aeronautics and Space Administration, Washing- ton, D.C. EOS Science Steering Committee. 1987. Earn Observing System. Vol. IL From Pattem to Processes: The Strategy of the Earth Observing System. National Aeronautics and Space Administration, Washington, D.C., 140 pp
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274 OPPORTUNITIES IN THE HYDROLOGIC SCIENCES Haeni, P. 1983. Sediment deposition in the Columbia and Lower Cowlitz rivers, Wash- ington-Oregon, caused by the May 18, 1980, eruption of Mount St. Helens. U.S. Geological Survey Circular 850-K, 21 pp. Kinter, J. L., and J. Shukla. 1990. The global hydrologic and energy cycles: Suggestions for studies in the pre-global energy and water cycle experiment (GEWEX) period. Bull. Am. Meteorol. Soc. 71(2):181-189. Krishnaswami, S., W. C. Graustein, J. F. Dowd, and K. K. Turekian. 1982. Radium, thorium and radioactive lead isotopes in groundwaters: Application to the in situ determination of adsorption-desorption rate constants and retardation factors. Water Resour. Res. 18(6):1663-1675. Robertson, W. D., and J. A. Cherry. 1989. Tritium as an indicator of recharge and dispersion in a ground water system in central Ontario. Water Resour. Res. 25:1097-1109. Ryan, P. F., G. M. Hornberger, B. J. Cosby, J. N. Galloway, J. R. Webb, and E. B. Rastetter. 1989. Changes in the chemical composition of stream water in two catchments in the Shenandoah National Park, Virginia, in response to atmo- spheric deposition of sulfur. Water Resour. Res. 25:2091-2099. Skinner, B. J., and S. C. Porter. 1989. Physical Geology. John Wiley & Sons, New York. Smith, R. A., and R. B. Alexander. 1986. Correlations between stream sulphate and regional SO2 emissions. Nature 322:722-724. Stockton, C. W., and G. J. Jacoby. 1976. Long-term surface water supply and streamflow trends in the upper Colorado River basin. Lake Powell Research Project Bulletin Number 18. University of California, Los Angeles. U.S. Committee for an International Geosphere-Biosphere Program. 1986. Global Change in the Geosphere-Biosphere: Initial Priorities for an IGBP. National Academy Press, Washington, D.C.
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