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WATER AND ENERGY CYCLES

Taikan Oki1 and Pat J.-F. Yeh21Institute of Industrial Science, University of Tokyo,Tokyo, Japan2Department of Civil and Environmental Engineering,National University of Singapore, Singapore, Singapore

SynonymsGlobal water balance; Global water budget; Global watercycle

DefinitionThe global hydrological cycle can be described by thefollowing physical processes which form a continuum ofwater movement. Complex pathways include the passageof water from the gaseous envelope around the planetcalled the atmosphere through the bodies of water on thesurface of Earth such as the oceans, glaciers, and lakesand at the same time (or more slowly) passing throughthe soil and rock layers underground. Later, the water isreturned to the atmosphere. A fundamental characteristicof the hydrological cycle is that it has no beginning andit has no end.

IntroductionThe role of hydrological cycles in the Earth system, theamount of water on the Earth’s surface, and its distributionin various reserves are first introduced together with thewater cycles on the Earth. The concept of mean residencetime, water stored in various parts over the Earth’s surfaceas various phases, such as glacier, soil moisture, watervapor, and water flux among these reserves, such asprecipitation, evaporation, transpiration, and runoff, isbriefly explained with their roles in global climate system,and their quantitative estimates are presented. The zonally

E.G. Njoku (ed.), Encyclopedia of Remote Sensing, DOI 10.1007/978-0-387-36699© Springer Science+Business Media New York 2014

averaged net transport of freshwater and the role of riversin the global hydrological cycle are quantitatively shown.Finally, the measurements of certain fluxes and storages inthe global hydrological cycle by using satellite remotesensing are reviewed.

Earth system and waterThe Earth system is unique in that water exists in all threephases, that is, water vapor, liquid water, and solid ice,compared to the situations in other planets. The transportof water vapor is regarded as energy transport because ofits large amount of latent heat exchange duringphase change to liquid water (approximately 2.5 � 106 Jkg�1); therefore, water cycle is closely linked to energycycle. Even though the energy cycle on the Earth is an“open system” driven by solar radiation, the amount ofwater on the Earth does not change on shorter than geolog-ical timescales (Oki, 1999; Oki et al., 2004), and the watercycle itself is a “closed system.”

On the global scale, the hydrological cycles are associ-ated with atmospheric circulation, which is driven by theunequal heating of the Earth’s surface and atmosphere inlatitude (Peixoto and Oort, 1992).

Annual mean absorbed solar energy at the top of theatmosphere is maximum near equator with approximately300 W m�2, decreases suddenly at higher latitudes, and isapproximately 60 W m�2 at Arctic and Antarctic regions.Emitted terrestrial radiative energy from the Earth at thetop of the atmosphere is approximately 250 W m�2 for�20� north and south, gradually decreases at higherlatitudes, and is approximately 175 W m�2 at Arctic and150 W m�2 at Antarctic region. As a consequence, thenet annual energy balance is positive (absorbing) fortropical and subtropical regions in �30� north and southand negative in higher latitudes (Dingman, 2002).

If there are no atmospheric and oceanic circulation onthe Earth, temperature difference on the Earth should have

-9,

Water and Energy Cycles, Table 1 World water reserves (simplified from Table 9 of “World water balance and water resourcesof the Earth” by UNESCO Korzun (1978). The last column, mean residence time, is from Table 34 of the report)

Form of water Covering area (km2) Total volume (km3) Mean depth (m) Share (%) Mean residence time

World ocean 361,300,000 1,338,000,000 3,700 96.539 2,500 yearsGlaciers and permanent snow cover 16,227,500 24,064,100 1,463 1.736 1,600 yearsGroundwater 134,800,000 23,400,000 174 1.688 1,400 yearsGround ice in zones of permafrost strata 21,000,000 300,000 14 0.0216 10,000 yearsWater in lakes 2,058,700 176,400 85.7 0.0127 17 yearsSoil moisture 82,000,000 16,500 0.2 0.0012 1 yearsAtmospheric water 510,000,000 12,900 0.025 0.0009 8 daysMarsh water 2,682,600 11,470 4.28 0.0008 5 yearsWater in rivers 148,800,000 2,120 0.014 0.0002 16 daysBiological water 510,000,000 1,120 0.002 0.0001 A few hoursArtificial reservoirs 8,000 72 daysTotal water reserves 510,000,000 1,385,984,610 2,718 100.00

896 WATER AND ENERGY CYCLES

been more drastic; temperature in the equatorial zoneshould have been high enough that the outgoing terrestrialradiation balances the absorbed solar energy, and temper-ature in the polar regions, which should have been lowenough, as well. In reality, there are atmospheric andoceanic circulations that lessen this expected temperaturegradation in the absence of circulations.

Both atmosphere and ocean carry energy from theequatorial region toward both polar regions. In the caseof atmosphere, the energy transport consists of sensibleheat and latent heat fluxes (Masuda, 1988). The globalwater circulation is this latent heat transport itself, andwater plays an active role in the atmospheric circulation;it is not a passive compound of the atmosphere, but itaffects atmospheric circulation by both radiative transferand latent heat release of phase change.

Water reserves, fluxes, and residence timeThe total volume of water on the Earth is estimated asapproximately 1.4 � 1018 m3, and it corresponds toa mass of 1.4 � 1021 kg. Compared with the total massof the Earth (5.974 � 1024 kg), the mass of water consti-tutes only 0.02 % of the planet, but it is critical for the sur-vival of life on the Earth, and the Earth is called BluePlanet and Living Planet.

There are various forms of water on the Earth’s surface.Approximately 70 % of its surface is covered with saltwater, the oceans. Some of the remaining areas (conti-nents) are covered by freshwater (lakes and rivers), solidwater (ice and snow), and vegetation (which implies theexistence of water). Even though the water content of theatmosphere is comparatively small (approximately 0.3 %by mass and 0.5 % by volume), approximately 60 % ofthe Earth is always covered by clouds (Rossow et al.,1993). The Earth is the planet whose surface is dominatedby various phases of water.

Water on the Earth is stored in various reserves, andvarious water flows transport water from one to another.Water flow (mass or volume) per unit time is also calledwater flux.

The mean residence time in each reserve can be simplyestimated from the total storage volume in the reserve andthe mean flux rate to and from the reserve; there is evena distribution of flux rate coming in and going outfrom the storage (Chapman, 1972). The last column ofTable 1 presents the values of the global mean residencetime of water. Evidently, the water cycle on the Earthis a “stiff” differential system with the variabilityon many timescales, from a few weeks to thousandsof years.

Tmean ¼ Total storage volumeMean flux rate

(1)

The mean residence time is also important to consider

when water quality deterioration and restoration arediscussed, since the mean residence time can be an indexof how much water is turned over. Apparently, river wateror surface water is more vulnerable than groundwater to bepolluted; however, any measure to recover better waterquality works faster for river water than groundwater.Since the major interests of hydrologists have been theassessment of volume, inflow, outflow, and chemical andisotopic composition of water, the estimation of the meanresidence time of certain domains has been one of themajor targets of hydrology.

Existence of water on the earthTable 1 (simplified from a table in Korzun, 1978)introduces how much water is stored in which reserveson the Earth:

� The proportion in the ocean is large (96.5 %). Eventhough the classical hydrology has traditionallyexcluded ocean processes, the global hydrologicalcycle is never closed without including them. Theocean circulation carries huge amounts of energy andwater. The surface ocean currents are driven by surfacewind stress, and the atmosphere itself is sensitive to thesea surface temperature. Temperature and salinity

WATER AND ENERGY CYCLES 897

determine the density of ocean water, and both factorscontribute to the overturning and deep ocean generalcirculation.

� Other major reserves are solid water on the continent(glaciers and permanent snow cover) and groundwater.Glacier is the accumulation of ice of atmospheric origingenerally moving slowly on land over a long period.Glacier forms discriminative U-shaped valley over landand remains moraine when it retreats. If a glacier“flows” into an ocean, the terminated end of the glacieroften forms an iceberg. Glaciers react in comparativelylonger timescale against climatic change, and they alsoinduce isostatic responses of continental scaleupheavals or subsidence in even longer timescale. Eventhough it is predicted that the thermal expansion of oce-anic water dominates the anticipated sea level rise dueto global warming, glaciers over land are also a majorconcern as the cause of sea level rise associated withglobal warming.

� Groundwater is the subsurface water occupying the sat-urated zone. It contributes to runoff in its low-flowregime, between floods. Deep groundwater may alsoreflect the long-term climatological situation. Ground-water in Table 1 includes both gravitational andcapillary water. Gravitational water is the water in theunsaturated zone (vadose zone), which moves underthe influence of gravity. Capillary water is the waterfound in the soil above the water table by capillaryaction, a phenomenon associated with the surfacetension of water in soils acting as porous media.Groundwater in Antarctica (roughly estimated as2 � 106 km3) is excluded from Table 1.

� Soil moisture is the water being held above the watertable. It influences the energy balance at the land sur-face as a lack of available water suppresses evapotrans-piration and as it changes surface albedo. Soil moisturealso alters the fraction of precipitation partitioned intodirect runoff and percolation. The water accounted forin the runoff cannot be evaporated from the same place,but the water infiltrated into soil may be uptaken by thecapillary suction and evaporated again.

� The atmosphere carries water vapor, which influencesthe heat budget as latent heat. Condensation of waterreleases latent heat, heats up the atmosphere, andaffects the atmospheric general circulation. Liquidwater in the atmosphere is another result of condensa-tion. Clouds significantly change the radiation in theatmosphere and at the Earth’s surface. However, asa volume, liquid (and solid) water contained in theatmosphere is quite little, and most of the water in theatmosphere exists as water vapor. Precipitable wateris the total water vapor in the atmospheric column fromland surface to the top of the atmosphere.Water vapor isalso the major absorber in the atmosphere of both short-wave and longwave radiation.

� Water in rivers is very tiny as stored water all the timehowever, the recycling speed, which can be estimatedas the inverse of the mean residence time, of river water

(river discharge) is relatively high, and it is importantbecause most social applications ultimately depend onwater as a renewable and sustainable resource.

Overall, the amount of water stored transiently in a soillayer, in the atmosphere, and in river channels is relativelyminute, and the time spent through these subsystems isshort, but, of course, they play dominant roles in the globalhydrological cycle.

Water cycle on the earthThe water cycle plays many important roles in the climatesystem, and Figure 1, revised from Oki and Kanae (2006),schematically illustrates various flow paths of water(Oki, 1999). Values are taken from Table 1 and also calcu-lated from the precipitation estimates by Xie and Arkin(1996). Precipitable water, water vapor transport, and itsconvergence are estimated using ECMWF objectiveanalyses, obtained as 4 year mean from 1989 to 1992.The roles of these water fluxes in the global hydrologicalsystem are now briefly introduced:

� Precipitation is the water flux from atmosphere to landor ocean surface. It drives the hydrological cycle overland surface and changes surface salinity (and tempera-ture) over the ocean and affects its thermohaline circu-lation. Rainfall refers to the liquid phase ofprecipitation. Part of it is intercepted by canopy overvegetated areas, and the remaining part reaches theEarth’s surface as throughfall. Highly variable, inter-mittent, and concentrated behavior of precipitation intime and space domain compared to other major hydro-logical fluxes mentioned below makes the observationof this quantity and the aggregation of the process com-plex and difficult.

� Snow has special characteristics compared with rainfall.Snow may be accumulated, the albedo of snow is quitehigh (as high as clouds), and the surface temperaturewill not rise above 0�C until the completion of snow-melt. Consequently, the existence of snow changes thesurface energy and water budget enormously. A snowsurface typically reduces the aerodynamic roughness,so that it may also have a dynamical effect on theatmospheric circulation and hydrological cycle.

� Evaporation is the return flow ofwater from the surface tothe atmosphere and takes the latent heat flux from the sur-face. The amount of evaporation is determined by bothatmospheric and hydrological conditions. From the atmo-spheric point of view, the fraction of incoming solarenergy to the surface leading to latent and sensible heatflux is important. Wetness at the surface influences thisfraction because the ratio of actual evapotranspiration tothe potential evaporation is reduced due to drying stress.The stress is sometimes formulated as a resistance, andsuch a condition of evaporation is classified as hydrologydriven. If the land surface is wet enough compared to theavailable energy for evaporation, the condition is classi-fied as atmosphere driven.

Water and Energy Cycles, Figure 1 Global hydrological fluxes (103 km3/year) and storages (103 km3/year) with naturaland anthropogenic cycles are synthesized from various sources. Big vertical arrows show total annual precipitation andevapotranspiration over land and ocean (103 km3/year), which include annual precipitation and evapotranspiration in majorlandscapes (103 km3/year) presented by small vertical arrows; parentheses indicate area (106 km3/year). The direct groundwaterdischarge, which is estimated to be about 10 % of total river discharge globally, is included in river discharge (Revised fromOki and Kanae, 2006).

Water and Energy Cycles, Table 2 Annual freshwater transport from continents to each ocean (1015 kg/year) mean for 1985–1988.“Inner” indicates the runoff to the inner basin within Asia and Africa. � HH � Q· indicate the direct freshwater supply from theatmosphere to the ocean. N.P., S.P., N. At., and S. At. represent North Pacific, South Pacific, North Atlantic, and South Atlantic Ocean,respectively

N.P. S.P. N. At. S. At. Indian Arctic Inner Total

Asia 4.7 0.4 0.2 3.3 2.7 0.1 11.4Europe 1.7 0.0 0.7 2.4

From rivers Africa �0.2 0.9 �0.2 �0.4 0.1N. America 2.9 4.8 1.1 8.8S. America 0.5 0.4 5.7 8.3 14.9Australia 0.1 0.1 0.2Antarctica 1.0 0.1 0.8 1.9

From atmosphere Total 8.1 1.9 12.2 9.3 4.0 4.5 �0.3 39.7� HH � Q 9.9 �11.1 �12.7 �14.0 �14.0 2.2 �39.7Grand total 18.0 �9.2 �0.5 �4.7 �10.0 6.7 �0.3 0.0

898 WATER AND ENERGY CYCLES

� Transpiration is the evaporation of water throughstomata of leaves. It has two special characteristics dif-ferent from evaporation from soil surfaces. One is thatthe resistance of stomata is related not only to the dryness

of soil moisture but also to the physiological conditionsof the vegetation through the opening and closing of sto-mata. Another is that roots can transfer water fromdeeper soil than in the case of evaporation from bare soil.

Water and Energy Cycles, Figure 2 The annual freshwatertransport in the meridional (north–south) direction byatmosphere, ocean, and rivers (land) (Oki et al., 1995). Watervapor flux transport of 20 � 1012 m3/year corresponds toapproximately 1.6� 1015 W of latent heat transport. Shaded barsbehind the lines indicate the fraction of land at each latitudinalbelt.

WATER AND ENERGY CYCLES 899

Vegetation also modifies surface energy and water bal-ance by altering surface albedo and by intercepting pre-cipitation and evaporating this rainwater.

� Runoff at the hillslope scale is nonlinear and a complexprocess. Surface runoff could be generated when rainfallor snowmelt intensity exceeds the infiltration rate of thesoil or precipitation falls over saturated land surface. Sat-uration at land surface can be formed mostly by topo-graphic concentration mechanism along hillslopes.Infiltrated water in the upper part of the hillslope flowsdown the slope and discharges at the bottom of the hill-slope. Because of the highly variable heterogeneity oftopography, soil properties such as conductivity andporosity, and precipitation, basic equations such asRichards’ equation, which can express the runoff pro-cess fairly well at a point scale or hillslope scale, cannotbe directly applied to the macroscale because of itsnonlinearity.

Runoff returns water to the ocean which may havebeen transported inland in vapor phase by atmosphericadvection. The runoff into oceans is also important forthe freshwater balance and the salinity of the oceans.Rivers carry not only water mass but also sediments,chemicals, and various nutritional matters from conti-nents to seas. Without rivers, the global hydrologicalcycles on the Earth will never close.

The global water cycle unifies these componentsconsisting of the state variables (precipitablewater, soilmois-ture, etc.) and the fluxes (precipitation, evaporation, etc.).

Zonally averaged net transport of freshwaterThe meridional (north–south direction) distribution ofthe zonally averaged annual energy transports by theatmosphere and the oceans has been evaluated, eventhough there are quantitative problems in estimating suchvalues (Trenberth and Solomon, 1994). However, thecorresponding distribution of water transport has not oftenbeen studied although the cycles of energy and water areclosely related. Wijffels et al. (1992) used values of theconvergence of water vapor flux in the atmosphere (Q)from Bryan and Oort (1984) and discharge data fromBaumgartner and Reichel (1975) to estimate the freshwa-ter transport by oceans and atmosphere, but their resultsseem to have large uncertainties, and they did not presentthe freshwater transport by rivers.

The annual freshwater transport in the meridional(north–south) direction can be estimated from Q and riverdischarge with the geographical information such as thelocation of river mouths and basin boundaries (Oki et al.,1995). Results are introduced in the next section.

Rivers in global hydrological cycleThe freshwater supply to the ocean has an important effecton the thermohaline circulation because it changes thesalinity and thus the density. The impacts of freshwatersupply to ocean are enhanced in the case of large riverbasins because they concentrate freshwater from large

areas to their river mouths. It also controls the formationof sea ice and its temporal and spatial variations. Annualfreshwater transport by rivers and the atmosphere to eachocean is summarized in Table 2 based on the atmosphericwater balance (Oki, 1999). Some part of the water vaporflux convergence remains in the inland basins. There area few negative values in Table 2, suggesting that net fresh-water transport occurs from the ocean to the continents.This is physically impossible and is caused by errors inthe source data. Although detailed discussion of the valuesin Table 2 may not be meaningful, it is neverthelessinteresting that such an analysis does make at least quali-tative sense using the atmospheric water balance methodwith the geographical information on basin boundariesand the location of river mouths. In this analysis, it shouldbe noted that the total amount of freshwater transport intothe oceans from the surrounding continents has the sameorder of magnitude as the freshwater supply that comesdirectly from the atmosphere, expressed by Q.

The annual freshwater transport in the meridional direc-tion has also been estimated based on the atmosphericwater balance with the results shown in Figure 2. The esti-mates in Figure 2 are the net transport, that is, in the case ofoceans, it is the residual of northward and southwardfreshwater fluxes by all ocean currents globally, and it can-not be compared directly with individual ocean currentssuch as the Kuroshio and the Gulf Stream. It should benoted that the directions of river flows are mostly steadyunlike the ocean or atmospheric circulations and concen-trate the freshwater in one direction throughout the year.

900 WATER AND ENERGY CYCLES

Transports by the atmosphere and by the ocean havealmost the same absolute values at each latitude but withdifferent signs. The transport by rivers is about 10 % ofthese other fluxes globally (this may be an underestima-tion becauseQ tends to be smaller than the river dischargeobserved at a land surface). The negative (southward)peak by rivers at 30�S is mainly due to the Paraná Riverin South America, and the peaks at the equator and10 �N are due to rivers in South America, such as theMag-dalena and Orinoco. Large Russian rivers, such as the Ob,Yenisey, and Lena, carry freshwater toward the northbetween 50 �N and 70 �N.

These results suggest that the hydrological processesover land play nonnegligible roles in the climate system,not only by the exchange of energy and water at the landsurface but also through the transport of freshwater byrivers, which affects the water balance of the oceans andforms a part of the hydrological circulation on the Earthamong the atmosphere, continents, and oceans.

Remote sensing in global hydrological cycleOver the past 20 years, significant progress has been madetoward routine monitoring of certain fluxes and storages inthe global hydrological cycle by satellite remote sensing,while continued progress is anticipated from upcomingmissions. Table 3 gives an overview on the past, current,and planned future hydrological remote sensing

Water and Energy Cycles, Table 3 Summary of past, current, and

Satellite sensors/missions Hydrological

Geostationary Operational Environmental Satellite(GOES)

Precipitation

Special Sensor Microwave/Imager (SSM/I) Precipitation,Scanning Multichannel Microwave Radiometer(SMMR)

Snow water e

European Remote Sensing-1 (ERS-1) Radar Altimeterand SAR

Surface wate

TOPEX/Poseidon Surface wateEuropean Remote Sensing-2 (ERS-2) Radar Altimeterand SAR

Surface wate

Tropical Rainfall Measuring Mission (TRMM) PrecipitationAdvanced Microwave Sounding Unit (AMSU) PrecipitationModerate Resolution Imaging Spectroradiometer(MODIS)/Terra

Snow extent,

Geosat Follow-On Mission Surface wateAtmospheric Infrared Sounder (AIRS)/Aqua Water vaporAdvanced Microwave Scanning Radiometer – EOS(AMSR-E)/Aqua

Soil moisture

Moderate Resolution Imaging Spectroradiometer(MODIS)/Aqua

Snow extent,

ENVISAT Radar Altimeter-2 and ASAR Surface wateJason-1 Surface wateGravity Recovery and Climate Experiment (GRACE) Total water sSoil Moisture and Ocean Salinity (SMOS) Soil moistureSoil Moisture active Passive (SMAP) Soil moistureGlobal Precipitation Measurement (GPM) Precipitation

aNot directly measured; ancillary data required in the estimation

capabilities. Some of these important achievements inmeasuring global water cycle components are discussedbelow:

� Precipitation – The remote sensing precipitation obser-vation systems were originated about three decades ago(Griffith et al., 1978). Most rainfall products measuredfrom spaceborne platforms, for example, NEXRAD(Next-Generation Radar) and TRMM (TropicalRainfall Measuring Mission) Precipitation Radarrainfall measurements, are typically provided at largespace–time scales suitable for coarse-scale meteorolog-ical applications, such as climatologic analysis andwater balance studies. Satellite rainfall retrieval issubject to errors caused by various factors ranging frominfrequent sampling to the high complexity andvariability in the relationship of the measurement toprecipitation parameters. Therefore, advanced retrievalalgorithm and spatial downscaling techniques (e.g.,PERSSIAN, Precipitation Estimation from RemotelySensed Information Using Artificial Neural Networks;Sorooshian et al., 2000) are necessary to be applied tothe satellite rainfall data for the purpose of the studyof global water cycle. The planned Global PrecipitationMeasurement (GPM; http://gpm.gsfc.nasa.gov/), whichis an international satellite mission scheduled to launchin 2014, envisions a large constellation of passivemicrowave sensors to provide global rainfall productsat the temporal resolution of�3 h and spatial resolution

planned future hydrological satellite remote sensing capabilities

variables measured Time period of observation

1978–present

snow water equivalent, snow extent 1987–presentquivalent, snow extent 1978–1987

r height, soil moisture 1991–2000

r height 1993–2005r height, soil moisture 1996–present

1998–present1998–present

evapotranspirationa 2000–present

r height 2000–present2002–present

, snow water equivalent 2002–present

evapotranspirationa 2002–present

r height, soil moisture 2002–presentr height 2002–presenttorage, soil moisture,a groundwatera 2002–present

2009 - presentScheduled in 2014Scheduled in 2014

WATER AND ENERGY CYCLES 901

of �100 km2 (Hossain and Lettenmaier, 2006; Smithet al., 2007). The GPM mission will provide almostreal-time rainfall information at three hourly samplinginterval on a global basis, thus allowing hydrologists anopportunity to improve flood prediction capability formedium to large river basins, especially in the underde-veloped world where in situ precipitation gaugenetworks are sparse. Another current community-wiseagenda on the satellite precipitation missions is the“Program for Evaluation of High Resolution Precipita-tion Products (PEHRPP),” which is an effort led by theInternational Precipitation Working Group (IPWG) toevaluate the quality of currently available high-resolutionsatellite rainfall products (Ebert et al., 2007).

� Terrestrial water storage – Another contribution ofremote sensing technologies to understand the globalhydrological cycle has been the measurement of terres-trial water storage (TWS) and its components(Famiglietti, 2004). The major achievements in thisaspect include (1) soil moisture retrieval (Jacksonet al., 2002; Njoku et al., 2003) from the Soil Moistureand Ocean Salinity (SMOS; Pellarin et al., 2003) andHydrosphere Satellite Mission (Hydros; Entekhabiet al., 2004), (2) surface water height measurementusing the altimetry (Alsdorf and Lettenmaier, 2003;Alsdorf et al., 2007), and (3) integrated measurementof TWS from the Gravity Recovery and ClimateExperiment (GRACE) mission (Tapley et al., 2004),among others. TWS is a fundamental component ofthe global water cycle and an integrated measure of sur-face and subsurface water availability (i.e., the sum ofsoil moisture, groundwater, snow and ice, waters invegetation and biomass, and surface water in lakes,reservoirs, wetlands, and river channels snow). It hasgreat importance for the management of waterresources, agriculture, and ecosystem health. TWS con-trols the partitioning of precipitation into evaporationand runoff and the partitioning of net radiation intothe sensible and latent heat fluxes, with significantimplications for precipitation recycling, hydrologicalextremes (i.e., flood and drought), and land memoryprocesses (Shukla and Mintz, 1982; Eltahir and Bras,1996; Eltahir and Yeh, 1999; Koster et al., 2004), whileTWS change is a basic quantity in closing the terrestrialwater balance from local to regional and global scales(Ngo-Duc et al., 2005; Güntner et al., 2007; Yeh andFamiglietti, 2008). Despite its importance, its role inthe global hydrological cycle has received littleattention relative to other hydrological processes(Lettenmaier and Famiglietti, 2006), and there are noextensive networks currently in existence for monitor-ing TWS changes. Satellite observations of the Earth’stime-variable gravity field from the GRACE mission(Tapley et al., 2004) present a new opportunity toexplore the feasibility of monitoring TWS variationsfrom space. Short-term (monthly, seasonal, andinterannual) temporal variations in gravity on land arelargely due to corresponding changes in vertically

integrated terrestrial water storage (Wahr et al., 2004).This has allowed for the first time observations of vari-ations in total TWS at large river basins (Swenson et al.,2003; Chen et al., 2005; Seo et al., 2006; Winsemiuset al., 2006) to continental scales (Wahr et al., 2004;Ramillien et al., 2005; Schmidt et al., 2006; Kleeset al., 2007; Syed et al., 2008); for new approaches toremote estimation of discharge (Syed et al., 2005) andevapotranspiration (Rodell et al., 2004); of groundwa-ter variations (Rodell and Famiglietti, 2002; Yehet al., 2006) and snow water storage (Frappart et al.,2005); and for validation and improvement of theterrestrial water balance in the global land surfacehydrological models (Niu and Yang, 2006; Swensonand Milly, 2006).

� Water vapor –Another example of remote sensing mea-surements is the global water vapor distribution. Watervapor is one of the most important greenhouse gases;small amounts of water vapor in the form of cloudscan strongly affect both shortwave and longwave radia-tions. Buoyancy created by changes in the phase ofwater largely drives the vertical motion of the atmo-sphere. The Atmospheric Infrared Sounder (AIRS) onboard NASA’s Earth Observing System (EOS) Aquaspacecraft measures water vapor at�2 km vertical reso-lution with 10–15 % accuracy in clear sky conditions(Moustafa et al., 2006). Such information is availableonly in a small percentage of the globe because mostof the AIRS pixels are cloud contaminated. Success inapplying AIRS data has been achieved via selectivelychoosing cloud-free pixels. Additionally, surface lidarcan also provide useful information on water vapor,temperature, and winds in clear air below cloudsalthough they are rather expensive and delicate instru-ments of limited deployment and coverage. Global Posi-tioning System (GPS) also holds promise to be used asa water vapor sensor when combined with independenttemperature analyses.

SummaryThe global hydrological cycle consisting of oceans, watervapor in the atmosphere, and terrestrial water, is essentialto the Earth system. The cycle is closed by the exchangeof water and energy fluxes between these reservoirs.Although the amounts of water in the atmosphere and riverchannels are relatively small, their fluxes are large, andhence a critical role in society, which is dependent on wateras a renewable resource. The ultimate goal of the hydrolog-ical science is to enhance the understanding of global waterand energy cycles on various spatial and temporal scalesthrough monitoring and modeling, and the outcomesshould also be beneficial and accessible to other scientificdisciplines, the general public, and the decision makers.

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Cross-referencesCloud PropertiesIce Sheets and Ice VolumeRainfallTerrestrial SnowWater Vapor

WATER RESOURCES

Taikan Oki1 and Pat J.-F. Yeh21Institute of Industrial Science, University of Tokyo,Tokyo, Japan2Department of Civil and Environmental Engineering,National University of Singapore, Singapore, Singapore

SynonymsFreshwater resources; Runoff

DefinitionWater resources mainly correspond to freshwater whichcan be utilized by human beings for irrigation, industrialpurposes, and domestic uses. Even though the stocks ofwater in natural and artificial reservoirs are helpful toincrease the available water resources for human society,the flow of water should be mainly regarded as waterresources since water is a naturally circulating resourcethat is constantly recharged. Maximum (potential) avail-ability of renewable freshwater resources under given cli-matic condition has been traditionally regarded asprecipitation minus evapotranspiration, which corre-sponds to runoff. However, transpiration from soil mois-ture through crops is contributing to human society andthe flux is regarded as water resources as well. Therefore,evapotranspiration from soil moisture in croplands iscalled as green water nowadays, and conventional waterresources withdrawn from rivers, surface water, andgroundwater are called as blue water.

IntroductionAll organisms, including humans, require water for theirsurvival. Therefore, ensuring adequate and sufficientwater supplies is essential for human well-being. Storedwaters, such as deep groundwater and water in reservoirs,are often considered as water resources. However, they area part of hydrological cycles, and groundwater is also cir-culating even though its speed could be rather slow. Somegroundwater is called “fossil water,” which implies thatthe aquifer was recharged long term ago and that it willbe depleted if being overly exploited just as fossil fuels.From this point of view, flows of water should be consid-ered as water resources in the assessments, designing, andplanning of sustainable water usage.

Global water balance and water resourcesConventionally, available freshwater resources aredefined as annual runoff estimated by compiling observedriver discharge data or by using water balance equation(i.e., the residual of annual precipitation minus evapo-transpiration; see Baumgartner and Reichel, 1975;Korzun, 1978). Such an approach has and is continuingto provide information on the annual freshwater resourcesfor many countries. Theoretically, available annualfreshwater resources can be derived using annual

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precipitation (P) and evapotranspiration (E) measure-ments; however, the residual term annual runoff (R),namely, annual available freshwater resources, is rela-tively small compared to P and E. P and E are approxi-mately 1,100 and 1,200 mm/year, respectively, over theocean and 800 and 500 mm/year over the land (Oki,1999). Therefore, the estimation of R could contain certainamount of uncertainties due to even comparatively smallerrors in the estimation of P and E.

Atmospheric water balance computation using theinformation of water vapor flux convergence could bealternatively used to estimate global runoff distributionowing to the advent of four-dimensional data assimilation(4DDA) technique in atmospheric science (Oki et al.,1995). Even though the microwave remote sensing of pre-cipitable water (vertically integrated water vapor) andtemperature profiles is contributing to improve the qualityof 4DDA data, this approach is suitable for global over-view of the distribution of available freshwater resources(Trenberth et al., 2007).

Model-based estimation of availablefreshwater resourcesRelatively simple water balance models have been usedto estimate grid-based available freshwater resources inthe world (Alcamo et al., 2000; Vörösmarty et al.,2000). Later, land surface models (LSMs) were appliedfor the estimation of global water cycles (Oki et al.,2001; Dirmeyer et al., 2006) and for the global waterresources assessment by comparing the demand side forboth the twentieth century and the future (Shen et al.,2008). Some of these estimates on global water balancewere calibrated by multiplying an empirical factorinferred from available observed river discharge data.However, recent model developments are capable to esti-mate runoff with adequate accuracy without the need ofcalibration (Hanasaki et al., 2008a). Such estimates byusing numerical models require external atmosphericforcing data such as precipitation and temperature, andthe accuracy of estimated freshwater resources is highlydependent on the quality of the forcing data (Oki et al.,1999). Satellite remote sensing can provide reasonableestimates of the forcing data for the regions with low den-sity of in situ observations, particularly with regard to thequantities such as precipitation and downward shortwaveand longwave radiation. Additionally, 4DDA reanalysisproducts have been commonly used as the forcing datafor air temperature, humidity, and wind speed. In orderto assure the quality of the estimations, the results of landsurface simulations have been validated, typically, bycomparing with in situ discharge observations. However,recently, various satellite remote sensing observationshave been used to validate hydrological variables otherthan discharge such as inundated area (Prigent et al.,2007) and total terrestrial water storage (Kim et al.,2009). Also, some remote sensing variables, such as top-soil moisture (Reichle and Koster, 2005) and snow cover

(Zaitchik and Rodell, 2009), have been assimilated intomodeling system to reduce the simulation uncertainty.

Global distribution of availablefreshwater resourcesFigure 1 illustrates the global distribution of annual runoffestimated by land surface models from the multi-modelensemble simulations under the Global Soil Wetness Pro-ject (Dirmeyer et al., 1999). Annual runoff (Figure 1) canbe considered as the maximum available renewable fresh-water resources (RFWR) if waters from upstream cannotbe reused at downstream due to consumptive use or waterpollution (Oki et al., 2001). Runoff is accumulatedthrough river channels and realized as river discharge(Figure 1). River discharge can be considered as the poten-tially maximum available RFWR if all the water fromupstream can be used. Both runoff and river dischargeare concentrated in limited areas, and their amounts rangefrom nearly zero in desert areas to more than 2,000 mm/year in the tropics and greater than 200,000 m3/s of dis-charge on average near the river mouth of the Amazon(Oki and Kanae, 2006).

Some macroscale hydrological models recently devel-oped for water resources assessments have been equippedwith a reservoir operation scheme (e.g., Haddeland et al.,2006; Hanasaki et al., 2006) in order to simulate the “real”hydrological cycles and provide information on actuallyavailable freshwater resources. These water resourceshave been significantly influenced by anthropogenicactivities and modified from the “natural” hydrologicalcycles even on the global scale in “Anthropocene”(Crutzen, 2002).

Water resources for crop growthAn integrated water resources model can further be linkedto a crop growth sub-model designed for inferring thetiming and quantity of irrigation requirement and estimat-ing environmental flow (Hanasaki et al., 2008a). Such anapproach enables the assessment of the balances betweendemand and supply of water resources on a daily timescale. Using this approach (Hanasaki et al., 2008a),a gap in the sub-annual distribution of water availabilityand water use can be detected in the Sahel, the Asian mon-soon region, and southern Africa, where the conventionalwater scarcity indices such as the ratio of annual waterwithdrawal to water availability or the available annualwater resources per capita (Falkenmark and Rockström,2004) cannot properly detect the stringent balancebetween water demand and supply (Hanasaki et al.,2008b).

Moreover, macroscale numerical models can be associ-ated with a scheme tracing the origin of flow path as iftracing the isotopic ratio of water (Yoshimura et al.,2004). Such a water flow-tracing function, if incorporatedinto an integrated water resources model (e.g., Hanasakiet al., 2008a) with the consideration of multiple sourcesof water withdrawals including streamflow, medium-size

Water Resources, Figure 1 Global distribution of (a) mean annual runoff (mm/year), (b) mean annual discharge (million m3/year),and (c) water scarcity index Rws. Water stress is higher for regions with larger Rws (Oki and Kanae, 2006).

WATER RESOURCES 905

Water Resources, Figure 2 (a) The ratio of blue water to the total evapotranspiration during a cropping period from irrigatedcropland (the total of green and blue water). The ratios of (b) streamflow, (c) medium-size reservoirs, and (d) nonrenewablegroundwater withdrawals to blue water (Hanasaki et al., 2010).

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reservoirs, and nonrenewable groundwater in addition toprecipitation on the croplands, is able to trace the originof water used to produce the major crops (Hanasakiet al., 2010).

Figure 2a illustrates the ratio of blue water to the totalevapotranspiration during the cropping period in irrigatedcroplands. Here the blue (green) water is defined as theamount of water evapotranspiration originated from irri-gation (precipitation) (see Falkenmark and Rockström,2004). Figure 2a shows distinctive geographical distribu-tion of the pattern of the dependence on blue water. Totalannual blue water consumption is estimated approxi-mately as 1,500 km3/year, which is about 20 % of the totalconsumptive use of approximately 7,000 km3/year ofwater resources in croplands during the cropping period(Hanasaki et al., 2009). Further, the ratios of the sourceof blue water are shown for streamflow including theinfluence of large reservoirs, medium-size reservoirs,and nonrenewable groundwater in Figure 2b–d, respec-tively. Areas highly dependent on nonrenewable ground-water are detected in the Pakistan, Bangladesh, and inwestern part of India, north and western parts of China,some regions in the Arabian Peninsula, and the western

part of the United States and Mexico. Cumulativenonrenewable groundwater withdrawals estimated by themodel correspond fairly well with the country statisticsof total groundwater withdrawals, and such an integratedmodel has the ability to quantify global virtual water flow(Allan, 1998; Oki and Kanae, 2004) and “water footprint”(Hoekstra and Chapagain, 2007) through the major cropwater consumption (Hanasaki et al., 2009).

Remote sensing applications in water resourcesThe last two decades have witnessed significant achieve-ments toward routine monitoring of global hydrologiccycle components by using remote sensing techniques,while continued progress is anticipated from upcomingmissions. Among these space efforts, the following mis-sions are particularly relevant to water resourcesapplications:

1. Integrated measurement of terrestrial water storage(TWS) from the Gravity Recovery and ClimateExperiment (GRACE) mission (Tapley et al., 2004),launched in 2002

2. Soil moisture retrieval from the Soil Moisture andOcean Salinity (SMOS; Kerr et al., 2000), launched

WATER RESOURCES 907

by the European Space Agency (ESA) in 2009, and theSoil Moisture Active and Passive (SMAP) mission(Entekhabi et al., 2004), planned to launch by NASAin 2014

3. Surface water height measurement using the altimetryfrom the Surface Water Ocean Topography (SWOT)mission (Alsdorf and Lettenmaier, 2003), planned tolaunch by NASA in 2014

TWS is a fundamental component of in closing the ter-restrial water balance from local to regional and globalscales. As an integrated measure of surface and subsurfacewater availability, TWS bears significant implications forwater resources planning and management. Despite itsimportance, there are no extensive networks currently inexistence for monitoring TWS changes. Satellite observa-tions of Earth’s time-variable gravity field from theGRACE mission present a new opportunity to explorethe feasibility of monitoring TWS variations from space.Short-term (monthly, seasonal, and interannual) temporalvariations in gravity on land are largely due tocorresponding changes in vertically integrated terrestrialwater storage (Wahr et al., 2004). This has allowed forthe first time observations of variations in total TWS atlarge river basins (Swenson et al., 2003) to continentalscales (Wahr et al., 2004). Also, application using GRACEdata has been made in the estimation of discharge (Syedet al., 2005), evapotranspiration (Rodell et al., 2004),groundwater variations (Yeh et al., 2006), snow waterstorage (Frappart et al., 2005), surface water dynamics(Han et al., 2009), lateral redistribution of water storagethrough river networks (Kim et al., 2009), and validationand improvement of global land surface hydrologicalmodels (Niu and Yang, 2006).

The SMOS and upcoming SMAP missions are the firstdedicated satellite missions to measure surface soil mois-ture levels globally. Soil moisture is an important factorwhich interfaces water and energy exchanges betweenthe land surface/atmosphere and is the most importanthydrological quantity for agriculture. Water managementfor irrigation is a critical issue for global crop productionand food safety. Root zone soil moisture, which is stronglyrelated with transpiration (green water), will be more reli-ably estimated by merging SMOS and SMAP observa-tions with a land surface model in a data assimilationsystem, even though the enhanced technologies in thosesatellites are still limited to directly observe only topsoilmoisture. It will enable meteorology and food agenciesto forecast crop yield and enhance the capabilities of cropwater stress decision support systems, monitor global cli-mate change, detect droughts, and conduct flood forecast-ing and weather prediction.

The SWOT satellite mission and its wide-swath(20–120 km) altimetry technology for repeated elevationmeasurements can measure the water height variations ofthe global oceans and terrestrial surface waters accurately.For water resources applications, hydrological observa-tions of the temporal and spatial variations in water

volumes stored in all wetlands, lakes, and reservoirs areextremely important. However, because of coarse spatial(>100 km) and temporal (>1 month) resolutions, previ-ous researches using altimetry (e.g., Birkett et al., 2002)have been used in only limited conditions and objectives.By measuring water height and area variations in higherspatial (2 m � 10 m to 2 m � 60 m) and temporal (2weeks) resolutions which allow accurate estimation ofriver discharges and lake/wetland storages in remoteregions, SWOT will contribute to a fundamental under-standing of the terrestrial branch of the global water cycleand hence benefit global water resource planning andmanagement.

ConclusionsCurrent advancement of remote sensing technology hasnot proved to be well capable of observing global waterfluxes; thus, it is not an easy task to accurately estimateavailable freshwater resources based on remotely senseddata. However, remote sensing technique can help in pro-viding necessary information such as meteorological forc-ing data, land use and land cover, extent of surface waterbodies, and topography for the modeling estimation ofwater resources. Remote sensing of cropland coverage,planting date, and harvesting date, and detection of irri-gated areas are necessary for assessing the balancesbetween demand and supply of water resources in additionto the social information, such as population distribution,urban area, industrial water use, and domestic water use.Remote sensing of hydrological quantities, such as snowcover, soil moisture, and temporal change of total terres-trial water storage, can provide great values to constrainand validate hydrological model simulations and therebyassure accurate estimates of freshwater resources frommodel simulations of water storages and fluxes.

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Cross-referencesAgriculture and Remote SensingCrop StressEarth Radiation Budget, Top-of-Atmosphere RadiationIrrigation ManagementRainfallSurface WaterSnowfallSoil MoistureWater and Energy Cycles

WATER VAPOR

Eric FetzerJet Propulsion Laboratory, California Institute ofTechnology, Pasadena, CA, USA

SynonymsAtmospheric humidity; Atmospheric moisture

DefinitionWater vapor. Water in gaseous form, usually mixed withdry air in the Earth’s atmosphere.Water vapor mixing ratio. The ratio of density of watervapor to the density of air. Typical values range froma few grams/kilogram in the tropical lower troposphereto a few micrograms/kilogram around the tropopause.Relative humidity. The ratio of water vapor partial pressureto saturation vapor pressure. A commonmeasure of watervapor amount, but not conserved as an air parcel changestemperature. Mixing ratio is conserved.Saturation vapor pressure. Maximum partial pressure ofwater vapor adjacent to a plain surface of water or ice.Water vapor saturation vapor pressure follows theClausius-Clapeyron relation for water and varies approx-imately exponentially with temperature.Clausius-Clapeyron relation. Equation relating equilib-rium pressure of a gaseous substance adjacent to a solidor liquid surface. The Clausius-Clapeyron relation variesexponentially with temperature at about 7 %/K near

freezing. In terrestrial atmospheric sciences, this termgenerally refers to water vapor with respect to liquid wateror ice.Latent heat of vaporization.Heat required to convert a unitmass of liquid water to water vapor. The same heat isreleased whenwater vapor condenses to liquid, as happensin clouds. Analogous latent heat of fusion refers to conver-sion between ice and liquid. Water vapor latent heats arevery large: about 2.3 � 106 J/kg for conversion of liquidwater to vapor compared to dry air heat capacity of717 J/kg-K.See American Meteorological Society (2010) andWallaceand Hobbs (2006) for additional terms, including dewpoint, frost point, and specific humidity.

IntroductionWater vapor varies significantly throughout the atmo-sphere. While a trace species in the middle atmosphere,it is the third most abundant gas in the Earth’s lower tropo-sphere. It has three important roles in weather and climate.First, water vapor is the dominant greenhouse gas so it hasa significant effect on the planetary energy balance. Sec-ond, because the atmospheric capacity for water vaporvaries exponentially with temperature, its radiative effectsmay act to amplify any surface warming; water vaporfeedbacks are believed to roughly double anthropogenicwarming (Intergovernmental Panel on Climate Change,2007). Third, clouds and precipitation both begin as watervapor. Thus, water vapor acts indirectly on radiativelyimportant clouds, while its condensation releases latentheat and affects precipitation. A major challenge inweather forecasting is improved precipitation forecasts,and water vapor is the atmospheric source for precipita-tion. Latent heat release represents about half the warmingof the tropical atmosphere and makes a small but impor-tant contribution at higher latitudes, especially in stormsystems (Hartmann, 1994). The importance of water vaporhas made its observation a cornerstone of remote soundingtechniques for decades.

Influence on weatherThunderstorms and severe weather are significantlyenhanced by the presence of water vapor, especially atlow levels. Latent heat released by water vapor condens-ing onto cloud liquid droplets causes warming andreduced density, enhancing convective instability(Emanuel, 1994). This instability is most pronouncedwhere cooler, dry air overlies warm, moist air, as is com-mon in spring, summer, and fall in the AmericanMidwest;high convective instability is a major factor in the forma-tion of tornadoes. Latent heat release is also a major factorin organized tropical convective systems, includinghurricanes.

Radiative effects and climate feedbacksWater vapor is the dominant greenhouse gas in the atmo-sphere, with strongest absorption in the middle and upper

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troposphere (400–100 hPa pressure) where many of itsinfrared spectral lines become saturated (Liou, 1992).Water vapor is not well mixed (unlike other greenhousegases such as carbon dioxide or methane), so estimatingits radiative effects has required direct observations of itsdistribution. This has presented a challenge, because onlyrecently have high information content water vapor datasets become available from satellites (see below). Theglobal upper tropospheric water vapor record prior to2002 was based on regression models of local relativehumidity versus satellite-observed brightness tempera-tures in the 6.3 mm infrared band from broadband radiom-eters (Soden et al., 2005). Other early studies addressedupper tropospheric water vapor variability using balloon-borne sensors, but only in the early twenty-first centurydid those sensors become sensitive enough to detect thevery small water vapor amounts typical of the upper tropo-sphere (Voemel et al., 2007).

Water vapor is also an important factor in climate feed-backs. Atmospheric water vapor has an enormous sourceat the ocean surface, depends strongly on temperaturethrough the Clausius-Clapeyron relation, and acts asa greenhouse gas. Combined, these factors give watervapor a positive feedback (amplifying effect) on surfacewarming or cooling. Dessler et al. (2008) used satelliteobservations and El Nino-Southern Oscillation asa proxy for carbon dioxide-induced surface warming andexamined radiative forcing kernel functions from climatemodels. They showed that climate models’ water vaporfeedback in response to warming roughly doubled surfacewarming, consistent with the observed atmosphericresponse (see also Dessler and Sherwood, 2009). In fur-ther confirmation of water vapor response to surfacewarming, Santer et al. (2007) attributed an increase ina 20 year record of total water vapor as a response toanthropogenic warming.

Direct confirmation of upper tropospheric water vaporresponse to surface warming – and verification ofa positive water vapor feedback – remains a challenge(Boers and van Meijgaard, 2009; National ResearchCouncil, 2003). Also, the mechanisms whereby watervapor is mixed throughout the troposphere are not fullyunderstood (Gambacorta et al., 2008). The vertical distri-bution of water vapor is important in feedback processesbecause lower tropospheric water vapor strongly couplessurface changes to the atmosphere. Climate models arebased on deep convective parameterizations, which canlead to biased water vapor distributions in models (Pierceet al., 2006) along with height-dependent temperaturebiases (John and Soden, 2007). Because water vapor andtemperature are coupled by deep convection, these biasesare related and their radiative contributions partly cancel(National Research Council, 2003; Bony et al., 2006).John and Soden (2007), following Held and Soden(2006), show evidence that climate model feedbacks arerobust despite biases in mean fields.While the water vapor

feedback is well understood, parameterization of cloudphysics in climate models (including the couplingbetween clouds and water vapor) is a major source ofuncertainty in climate projection (Stephens, 2005;National Research Council, 2003).

Remote sensing of water vaporThe basis of all remote sensing or remote sounding sys-tems is an instrument, or set of instruments, to observeelectromagnetic radiation. (Remote sounding is the pro-cess of inferring vertical structure of temperature, clouds,and constituent gases like water vapor from observedspectral information.) In the case of water vapor, theobservations are typically in the 6.3 mm infrared band orthe 183 GHz microwave band. In addition to an instru-ment making observations, remote sensing systems usu-ally include a numerical post-processing system toretrieve vertical distributions of geophysical parametersfrom the calibrated observed radiances. Therefore, remotesensing “observations” of water vapor (and many otheratmospheric quantities) are in fact inferences about thestate of the atmosphere. This makes the attribution ofuncertainties an especially important and challengingcomponent of remote sounding.

History of satellite remote sensing of water vaporSatellite remote sensing of atmospheric water vaporextends back to the 1960s, when a variety of remote sens-ing instruments were launched into polar and geosynchro-nous orbits (Kidder and Vonder Haar, 1995). Many ofthese instruments included observations at wavelengthssensitive to water vapor. By the 1970s, basic methodolo-gies for observing water vapor from space wereestablished, and the United States began launching theTIROS Operational Vertical Sounder (TOVS) series ofsatellite instruments dedicated to tropospheric sounding.TOVS was first launched in 1978 and included the High-Resolution Infrared Sounder (HIRS) and the MicrowaveSounding Unit. The highest-quality long-term satellitewater vapor record is from Special Sensor MicrowaveImager (SSM/I) instrument, though only as total watervapor. Other improvements in water vapor include theincorporation of four microwave water vapor channelswith the Advanced Microwave Sounding Unit-B(AMSU-B) in 2000. AMSU-A, part of the AdvancedTOVS sounders, included only a single water vapor chan-nel. Technological advances have produced infraredinstruments with higher spectral resolution than theirmicrowave counterparts. This improved spectral resolu-tion leads to greater information about water vapor verticalstructure. The strength of microwave instruments is theirgreater coverage in the presence of clouds, though at thecost of vertical resolution. The Atmospheric InfraredSounder (AIRS) instrument, launched in 2002 by theUnited States National Aeronautics and Space Adminis-tration (NASA) on the Aqua spacecraft into a 1:30 equator

WATER VAPOR 911

crossing orbit, provides detailed information about watervapor (and a number of other quantities) from the surfaceto the upper troposphere with its 2000+ infrared channelsand 20 microwave channels in a co-boresited AMSU-Ainstrument (Chahine et al., 2006). A similar instrument,the Infrared Atmospheric Sounding Interferometer (IASI),launched in 2006 by the European Space Agency providessimilar information in a 9:30 local orbit on the first of threeMeteorological Operational satellites (Centre NationalD’Etude Spatiales, 2010). IASI observations are collo-cated with microwave observations from the MicrowaveHumidity Sounder (Polar Orbiting Environmental Satel-lite, 2010) for additional constraints on the water vapordistribution. Other modern satellite instruments observingwater vapor include theMicrowave Limb Sounder (MLS),a limb-viewing instrument with sensitivity from the uppertroposphere to the stratosphere and carried on the NASAAura platform. This is a follow-on to an MLS instrumenton the Upper Atmosphere Research Satellite (UARS) butwith sensitivity to both temperature and water vapor(UARS MLS could detect variations in relative humidityonly; see Fueglistaler for a review of instruments sensingthe upper troposphere and stratosphere). Aura carries theTropospheric Emission Spectrometer, with the uniqueability to sense the water vapor isotopologue HDO in thetroposphere (Worden et al., 2007).

Acknowledgment

This research was carried out at the Jet Propulsion Labora-tory, California Institute of Technology, under a contractwith the NASA.

BibliographyAmerican Meteorological Society, 2010. Online glossary. http://

amsglossary.allenpress.com/glossary/.Boers, R., and vanMeijgaard, E., 2009.What are the demands on an

observational program to detect trends in upper troposphericwater vapor anticipated in the 21st century? GeophysicalResearch Letters, doi:10.1029/2009GL040044.

Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S.,Dufresne, J.-L., Hall, A., Hallegatte, S., Holland, M. M.,Ingram, W., Randall, D. A., Soden, B. J., Tselioudis, G., andWebb, M. J., 2006. How well do we understand and evaluateclimate change feedback processes? Journal of Climate, 19,3445–3482.

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Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C.,Blaisdell, J., Chen, L., Divakarla, M., Fetzer, E. J., Goldberg, M.,Gautier, C., Granger, S., Hannon, S., Irion, F. W., Kakar, R.,Kalnay, E., Lambrigtsen, B. H., Lee, S.-Y., Le Marshall, J.,McMillan, W. W., McMillin, L., Olsen, E. T., Revercomb, H.,Rosenkranz, P., Smith, W. L., Staelin, D., Strow, L. L., Susskind, J.,Tobin, D., Wolf, W., and Zhou, L., 2006. The atmospheric infra-red sounder (AIRS): improving weather forecasting and provid-ing new data on greenhouse gases. Bulletin of the AmericanMeteorological Society, 87, 911–926.

Dessler, A. E., and Sherwood, S. C., 2009. A matter of humidity.Science, 323, 1020–1021.

Dessler, A. E., Zhang, Z., and Yang, P., 2008. Water-vapor climatefeedback inferred from climate fluctuations, 2003–2008.Geophysical Research Letters, doi:10.1029/2008GL035333.

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Fueglistaler, S., Dessler, A. E., Dunkerton, T. J., Folkins, I., Fu, Q.,and Mote, P. W., 2009. Tropical tropopause layer. Reviews ofGeophysics, doi:10.1029/2008RG000267.

Gambacorta, A., Barnet, C., Soden, B., and Strow, L., 2008. Anassessment of the tropical humidity-temperature covarianceusing AIRS. Geophysical Research Letters, doi:10.1029/2008GL033805.

Hartmann, D. L., 1994. Global Physical Climatology. San Diego:Academic.

Held, I. M., and Soden, B. J., 2006. Robust responses of thehydrological cycle to global warming. Journal of Climate, 19,5686–5699.

Intergovernmental Panel on Climate Change, 2007. Summary forpolicymakers. In Solomon, S., Qin, D., Manning, M., Chen, Z.,Marquis, M., Avery, K. B., Tignor, M., and Miler, H. L. (eds.),Climate Change 2007: The Physical Science Basis. Contributionof Working Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge, NY:Cambridge University Press.

John, V. O., and Soden, B. J., 2007. Temperature and humiditybiases in global climate models and their impact on climatefeedbacks. Geophysical Research Letters, doi:10.1029/2007GL030429.

Kidder, S. Q., and Vonder Haar, T. H., 1995. Satellite Meteorology:An Introduction. San Diego: Academic.

Liou, K. N., 1992. Radiation and Cloud Processes in the Atmo-sphere. New York: Oxford University Press.

National Research Council, 2003. Understanding Clime ChangeFeedbacks. Washington, DC: National Academies.

Pierce, D. W., Barnett, T. P., Fetzer, E. J., and Gleckler, P. J., 2006.Three-dimensional tropospheric water vapor in coupled climatemodels compared with observations from the AIRS satellitesystem. Geophysical Research Letters, doi:10.1029/2006GL027060.

Polar Orbiting Environmental Satellite, 2010. http://goespoes.gsfc.nasa.gov/poes/instruments/mhs.html.

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Soden, B. J., Jackson, D., Ramaswamy, V., Schwarzkopf, M. D.,and Huang, X., 2005. The radiative signature of upper tropo-spheric moistening. Science, 310, 841–844.

Stephens, G. L., 2005. Cloud feedbacks in the climate system:a critical review. Journal of Climate, 18, 237–273.

Voemel, H., David, D. E., and Smith, K., 2007. Accuracy of tropo-spheric and stratospheric water vapor measurements by the cryo-genic frost point hygrometer: instrumental details andobservations. Journal of Geophysical Research, doi:10.1029/2006JD007224.

Wallace, J. M., and Hobbs, P. V., 2006. Atmospheric Science: AnIntroductory Survey. San Diego: Academic.

Worden, J., Noone, D., and Bowman, K., 2007. Importance of rainevaporation and continental convection in the tropical watercycle. Nature, 445, 528–532.

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WEATHER PREDICTION

Peter BauerEuropean Centre for Medium-Range Weather Forecasts(ECMWF), Reading, UK

SynonymsWeather forecasting

DefinitionWeather. State of the atmosphere and its day-to-day varia-tion, mostly described by temperature, wind, cloudiness,and precipitation.Weather Prediction. Prediction of weather at a given timeand location using numerical models and observations.

Numerical atmospheric modelingNumerical models are used to simulate the evolution of allthose processes in the atmosphere and at surfaces thataffect the atmospheric state. Increasingly, these modelsadd complexity to the modeled components of atmosphereand surfaces that go beyond the basic formulation of mass,heat, and momentum transport. With increasing complex-ity of the models, a wider range of spatial and temporalprocess scales has to be accounted for and the diversityand nonlinearity of the modeled processes increases aswell (Kalnay, 2003).

In many applications, numerical models or modeloutput are combined, for example, by nesting smaller-scale models into larger-scale models; by coupling ofmodels for the atmosphere and land surfaces (see“Ocean-Atmosphere Water Flux and Evaporation”;“Land-Atmosphere Interactions, Evapotranspiration”),hydrology, waves, and oceans; and by coupling ofatmospheric dynamics with chemistry models.

Global numerical models usually employ a set of prim-itive equations to describe atmospheric dynamics underthe assumption of momentum conservation, heatexchange through thermodynamics, and mass conserva-tion (see “Atmospheric General Circulation Models”).Processes with scales well below the scales that thenumerical model can resolve are described by physicalparameterizations that approximate the effect of sub-gridprocesses on the resolved scales and vice versa. Amongthese are radiation processes (see “Radiation, Electromag-netic”), cloud condensation and convection, orographicdrag, turbulent diffusion, and most surface processes(Kalnay, 2003).

Since most processes exhibit nonlinear behavior andare discretized in space and time, the corresponding equa-tions are solved numerically. The choice of solutionmethod depends on the model type. Some global modelsare spectral models, where the horizontal dimension isdescribed by a set of waves and where the horizontal res-olution is proportional to the highest defined wave num-ber. Others solve the above equations on structured orunstructured horizontal grids. The vertical dimension is

usually defined by finite layers with varying choice ofcoordinate systems (e.g., sigma, eta, theta, hybridcoordinates; e.g., Warner, 2011).

At operational numerical weather prediction (NWP)centers, numerical models are run as part of the analysissystem that estimates the state of the (global) atmosphereat a given time as well as for producing the forecast overthe desired time range. The analyses are used to initializethe forecast model runs. These deterministic forecastsare often complemented by ensemble forecasts for whicha set of forecasts are produced from perturbed initial con-ditions, possibly using further stochastic input. The pertur-bations are supposed to represent the uncertainty of theinitial state estimate as well as the model, so that theensemble of forecasts provides an estimate of forecastuncertainty. To reduce computational cost, the ensembleforecasts are run at lower spatial resolutions than the deter-ministic model. With increasing forecast range (monthly/seasonal), ensemble-type modeling becomes more impor-tant because the nonlinear response to small differences inthe initial conditions and model errors become large andthe knowledge of ensemble forecast spread is crucial forinterpreting forecast skill.

Atmospheric data assimilationData assimilation systems (see “Data Assimilation”) inNWP provide the mathematical framework for performingatmospheric analyses that represent the best estimate ofthe state of the atmosphere at a certain time. The qualityof the forecast depends on both the accuracy of the numer-ical model and the accuracy of the initial state estimate.The initial state estimate is called the analysis and repre-sents an inversion problem. In general, this inversionproblem is underdetermined so that the analysis mustemploy information from a priori data (in NWP usuallyshort-range forecasts initialized with previous analyses)and observations using a mathematical framework tooptimally combine the two.

The complexity of the data assimilation system to beused depends on the application and the affordable compu-tational cost and can range from simple interpolation tech-niques to four-dimensional variational and ensembleKalman filter schemes or even nonlinear methods (Daley,1991; Ide et al., 1997; Rabier, 2005). Global analysissystems are solving the above inversion problem with statevector dimensions of the order of 108 (the product of thenumber of grid points, number of levels, and dimension ofstate vector) and observation vector dimensions of the orderof 107 (product of number of observation points, number oflevels, or satellite instrument channels/range gates).

Today, most global operational NWP centers are oper-ating 4D-Var (Lewis and Derber, 1985) data assimilationsystems that are capable of combining sparse andheterogeneously distributed data with a dynamical model.A computationally efficient derivative is the incremental4D-Var method that is based on the assumption that modelbehavior is nearly linear in the vicinity of a good

WEATHER PREDICTION 913

short-range forecast of the model state (first guess) so thatthe analysis is produced from incrementally updating thefirst-guess estimate (Bouttier and Courtier, 2002; Rawlinset al., 2007). The advantage of 4D-Var methods lies in thefact that (1) they produce meteorological fields that aredynamically consistent because the optimization isperformed over a time window through which the modelis integrated and (2) that computationally efficient adjointmodels can be used with incremental methods (Courtieret al., 1993). Systems like this are currently in use atECMWF, the UKMet Office, Météo-France, theMeteoro-logical Service of Canada (MSC), the Japan Meteorologi-cal Agency (JMA) (for the regional model), and in theUSA at the Naval Research Laboratory (NRL).

At regional scales, ensemble-based methods (Ander-sen, 2001; Houtekamer and Mitchell, 2001) becomeincreasingly implemented because they do not requirethe development of adjoint models and are capable ofexplicitly calculating analysis error statistics than can beused for estimating model errors and for initializingensembles of forecasts (Lorenc, 2003). Due to the neces-sity of running ensembles, the computational effort is con-siderable and currently not affordable at global scales withthe same horizontal resolutions as obtained with varia-tional assimilation methods.

However, increasingly hybrid systems composed ofhigh-resolution variational and low-resolution ensembledata assimilation are implemented in which the ensemblesystem provides background error statistics for the high-resolution analysis.

Satellite data in weather predictionSatellite data productsSatellite data can be assimilated as level 1 (e.g., calibratedand geo-located electromagnetic radiances) or level 2(e.g., derived geophysical products such as temperatureand humidity; see “Geophysical Retrieval, Overview”)data. The choice depends on various factors, most promi-nently on the amount of maintenance required in an oper-ational system. Level 2 product retrieval often employsa similar inversion framework as NWP data assimilationsystem to derive the desired parameters, namely, a prioriinformation from NWPmodels or climatologies, radiativetransfer, error, and bias models. Most level 2 products,however, employ different models and a priori constraintsthan used in NWPmodeling, and their error characteristicsare often not well defined or difficult to account for in dataassimilations systems (Joiner and Dee, 2000). Further,when retrieval algorithms or instrument characteristics(e.g., loss of channels, increase of noise, and instrumentrecalibration) change, the NWP data assimilation systemmust be tuned to the performance of the new product,which is often cumbersome in an operational framework.

On the other hand, level 1 satellite data assimilationrequires running radiative transfer models (see “RadiativeTransfer, Solution Techniques”; “Radiative Transfer,Theory”) that simulate observation-equivalent radiances

from the model state. For the most important applicationof satellite data in NWP, that is, the observation of temper-ature and moisture structures in the atmosphere, radiativetransfer models are very fast and accurate (Saunderset al., 1999; Han et al., 2006), and they allow the flexibleuse of radiometer channels as a function of situation-dependent sensitivity and potential channel corruption.They also greatly simplify observation error and biasestimation.

The same approach of level 1 data assimilation isincreasingly used for observations sensitive to clouds, pre-cipitation, aerosols, surface characteristics and trace gasesand observables from active sensors.

History of satellite data usageDerived wind vectors obtained from geostationary cloud(later also water vapor) feature tracking were among thefirst products used in the early days of satellite data assim-ilation. The associated cloud feature heights are retrievedfrom infrared window channels (Nieman et al., 1993),and the data produced good impact on analysis and fore-cast quality, mainly in the Southern Hemisphere where lit-tle conventional observations were available. With thelaunch of infrared and microwave sounders onboard polarorbiting satellites, more information on vertical temperaturestructures became available. At that time, the assimilationof retrieved geophysical products was preferred over radi-ances due to less efficient radiative transfer models, moresimple observation operators, and the uncertain impact ofsatellite data in general. Initial experiments with sounderdata produced even negative impact due to unknown biascharacteristics of the retrieved profiles and model error sta-tistics that were not tuned to deal with profile data obtainedfrom observations other than radiosondes.

An intermediate step between the assimilation ofretrieved products and radiance assimilation is based on1D-Var techniques. Here, a single-column (one-dimensional variational or 1D-Var) retrieval is performedwith radiance observations, and the retrieval product isassimilated globally by the corresponding global analysisscheme (optimum interpolation, 3D/4D-Var). The advan-tage of such an approach is that the constraints andassumptions used in the 1D-Var retrieval are very similarto those used in the global scheme because they areperformed within the same analysis system. One of theearliest protagonists of this approach were Eyre et al.(1993) in Europe, producing retrieved temperature pro-files from TOVS data that comprises observations fromthe HIRS, MSU, and SSU instruments. This approachhas greatly facilitated the promotion of satellite data usagein NWP and was later extended to the use of moisture-sensitive channels and instruments such as the AMSU-Band the SSM/I (Phalippou, 1996). Most of these systemswere later replaced by direct radiance assimilation,that is, excluding the intermediate retrieval step, for theabovementioned reasons (Andersson et al., 1994;Derber and Wu, 1998).

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The initial concerns over general satellite data impactwere mostly overcome by the time 4D-Var data assimila-tion systems were established, mainly because of theimproved treatment of spatial and temporal collocationbetween data andmodel simulations, better forecast modelerror formulations, and the improved interaction of tem-perature and moisture with model dynamics (Anderssonand Thépaut, 2008). In the 1990s and early years of thetwenty-first century, the dominant impact of satellite dataon numerical weather forecast skill was contributed byATOVS data that combines HIRS AMSU-A andAMSU-B observations. The first ATOVS sensor packagewas launched with NOAA-15 in 1998 and has been con-tinued until NOAA-19 (launched in 2009). For bothNOAA-18 and NOAA-19, the AMSU-B has beenreplaced with MHS. Since 2006, the same package is alsoavailable on the EPS series METOP.

The microwave sounder system is complemented by,so-called, microwave imagers (e.g., SSM/I, and the fol-low-on instrument SSMIS, AMSR-E onboard Aqua,TMI onboard TRMM, AMSR-2 onboard GCOM-W) thatcontribute information on sea-surface temperature, near-surface wind speed, integrated atmospheric moisture,clouds, and precipitation (see “Microwave Radiometers”).

A major step forward has been the development ofinfrared spectrometers (grating spectrometer AIRSonboard EOS Aqua in 2002, interferometer IASI onboardMETOP-A/B in 2006/2012 and CrIS onboard Suomi NPPin 2011) that provide unprecedented vertical resolutionand measurement accuracy by making available thou-sands of channels with very high spectral resolution cover-ing the 4–15 mm range. These instruments have producedsubstantial impact on NWP (Collard and McNally, 2009)and will complement microwave sounding radiometers forthe next 20 years. Due to their superior spectral resolution,these instruments are also crucial for atmospheric chemistryand air quality applications (Clerbaux et al., 2009).

Further, the utilization of information on atmospherictemperature and moisture structures obtained from thebending of rays of actively transmitted radio waves bythe GNSS has vastly increased over the past 5 years (Eyre,1994; Healy and Thépaut, 2006; see “Limb Sounding,Atmospheric”; “GPS, Occultation Systems”). Currently,the assimilation of GPS signals in occultation mode bydedicated receivers (onboard CHAMP, GRAS onboardMETOP, COSMIC Formosat-3 constellation) providesradiosonde-equivalent measurement accuracy, mainly inthe upper atmosphere where NWP models tend to exhibitsignificant errors.

Apart from temperature structures in the upper atmo-sphere, trace gas observations (mainly ozone) can providevaluable contributions to weather prediction since ozonehas a strong impact on radiative heating (see“Stratospheric Ozone”). Solar backscattered ultravioletinstruments (e.g., SBUV, OMI, and GOME-1/2) provide

the bulk of the observations when sunlight is available,while infrared spectrometers complement the observa-tions at nighttime.

Apart from clear-sky satellite data, efforts towardcloud-affected data assimilation have been successful inrecent years (Bauer et al. 2010; Geer et al. 2010; Baueret al. 2011. Bauer et al., 2006a, b). This was mainlyachieved by the greatly improved global model moistphysical parameterizations and the enhanced computa-tional capabilities that allow the operational employmentof multiple scattering radiative transfer models(Greenwald et al., 2002; Bauer et al., 2006c). The explicittreatment of clouds and precipitation in operational analy-sis systems is accompanied by a large set of uncertainties,for example, greater model nonlinearity, potentialdynamic instabilities, large and unknown error structures,as well as unknown model biases (Errico et al., 2007).

Atmospheric modeling requires accurate constraints ofenergy and water fluxes at the interface with land andocean surfaces. Over oceans, the interaction between windand waves is treated by wave models. Here, near-surfacewind observations from passive (microwave radiometers)or active (scatterometers; see “Radar, Scatterometers”)satellite data and direct observations of wave height fromaltimeters (see “Radar, Altimeters”) and directional wavespectra from synthetic aperture radars (see “Radar,Synthetic Aperture”) are part of the operational set ofassimilated data.

Over land surfaces, a recent development in NWP is theuse of microwave radiometer observations to constrainsoil moisture analysis (see “Soil Moisture”). This can beaccomplished by fixed aperture radiometer observationsat 6–10 GHz from TMI and AMSR-E (Reichle et al.,2007) and moderate resolution observations at 1.4 GHzfrom synthetic aperture imagery by SMOS (Merlin et al.,2006). Note that over land surfaces, RFI can impose seri-ous limitations on data quality and usefulness for NWP(see “Radio-Frequency Interference (RFI) in PassiveMicrowave Sensing”). Also scatterometer is used toextract information on soil properties.

Snow and sea-ice products, mostly obtained from pas-sive and active microwave instruments, provide importantinformation for NWP systems on how to define surfacealbedo and heat fluxes.

Figure 1 illustrates the dramatic increase of satellitedata diversity and volume since 1996 as well as a predic-tion until 2017 at ECMWF. The historic evolution of useddata does not necessarily reflect the sequential launch ofindividual satellites but rather the ability of NWP dataassimilation systems and computers to digest the availableinformation. Today, data from about 50 different instru-ments is used constraining geophysical parameters in theatmosphere and at both land and ocean surfaces. The gapbetween data volume contributed by conventional (i.e.,nonsatellite) and satellite data has largely widened. More

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than 40 million satellite observations are monitored perday, 95 % of which are assimilated directly as radianceswhile the remainder is composed of retrieved products.

Impact of satellite data on prediction skillData impact in NWP systems can be quantified in variousways and by assessing the impact on both analyses andforecasts. It is assumed that better analyses will providebetter initial conditions for forecasts. Analysis quality isusually quantified by the comparison of NWP modelfields to all available observations before and after theyhave been assimilated. A better system is expected to pro-duce a consistently better fit of the analysis to the

observations and should maintain this advantage up tothe next short-range forecast that has been initialized withthis analysis. Forecast skill can be quantified by compar-ing forecasts to both observations and analyses.

Figure 2 shows the evolution of the ECMWF forecastmodel skill over the period 1980-2010 for the 3, 5, and7 day forecasts over the Northern and Southern Hemi-spheres from the operational system (Figure 2a) and fromthe ECMWF reanalyses ERA-40 (gray, Uppala et al.,2005) and ERA-Interim (in colors, Uppala et al., 2008).Figure 2a illustrates the substantial increase of skill overthree decades and the strong reduction of the differencebetween skill over the Northern and Southern

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Hemispheres. While the former is the result of the com-bined evolution of model physics, data assimilation, andobserving systems, the latter is only explained by thecontribution of satellite data due to the sparseness ofconventional data over Southern Hemispheric oceans.Figure 2b provides another angle at disentangling the indi-vidual contributions to the time series of NWP forecastskill: The reanalyses have been produced with a modelversion that has been frozen in 2001 (ERA-40) and 2006(ERA-Interim), respectively. The small increase of skillover the ERA period is mostly due to the advancementof satellite observations, while the difference betweenERA-40 and ERA-Interim is mainly due to the improve-ment of model and data assimilation system between2001 and 2006. The larger gap of Northern and SouthernHemisphere forecast skill of ERA-40 compared toERA-Interim in the overlap period between 1989 and2001 illustrates the fact that the data assimilation systemof ERA-Interim exploits the same satellite observationsmore effectively than ERA-40.

If the impact of individual observing systems (satellitesor instruments) is evaluated, the most prominent assess-ment tool is the so-called Observing System Experiment(OSE), in which new data is added to an existing systemand the relative difference to a control system is evaluated.Similarly, individual data sources can be withdrawn froma full system to assess the impact of the withdrawn data.

More sophisticated methods involve the model opera-tors that are used in the data assimilation system. Basedon forecast error estimates from the difference betweenforecasts and verifying analysis, the model and observa-tion operator adjoints employed in 4D-Var can be usedto deduce the dependence of this forecast error on individ-ual observation types that were used in the initializinganalysis (Zhu and Gelaro, 2008). An alternative is theuse of ensemble-based analysis and forecasting tech-niques that evaluate forecast impact from ensemble spread(that represents model error) with or without specificobservation types. Finally, Observing System SimulationExperiments (OSSE) provide a framework for evaluatingthe potential impact of observations that do not yet existand therefore require an observation simulation from inde-pendent NWP models (Arnold and Dey, 1986; Tan et al.,2007).

A major OSE impact study was conducted in 2006–2007 to evaluate the impact of the satellite observing sys-tem in global NWP at ECMWF (Kelly and Thépaut,2007). The experiments demonstrated that (a) infraredspectrometers (AIRS, IASI) produce the largest impactper single instrument on geopotential height and tempera-ture forecast skill and (b) that the currently available con-stellation of 4–5microwave sounders (AMSU-A/B/MHS)produces a very similar relative impact compared to oneadvanced infrared sounder. The results are similar for theNorthern Hemisphere but with a smaller dynamic rangedue to the stronger constraint from denser conventionalobservations obtained over the continents. Further studies

suggest that, apart from infrared and microwave sounders,GPS radio-occultation and scatterometer data produce sig-nificant contributions to forecast accuracy since they aremost directly related to temperature and surface wind(pressure) with good global coverage.

In terms of atmospheric moisture, Kelly and Thépaut(2007) confirmed previous investigations showing thatSSM/I data has the strongest impact in the lower tropo-sphere over oceans complemented by AMSU-B data inthe mid and upper troposphere (Andersson et al., 2007).The impact of clear-sky microwave imager data is aboutas strong as that of cloud-affected data (Kelly et al., 2008).

Figure 3 shows a different measure of global forecastimpact from selected observation types that is obtainedfrom the 24 h forecast error sensitivity to the accumulatedsensitivity to all observation types (Cardinali, 2009). Themethodology is able to estimate observational impactwithout having to add/withdraw them but only applies toshort-range forecast impact estimation. In Figure 3a,the total impact of the most prominent satellite andconventional observations for a 4 month period(September–December 2008) is shown, while Figure 3bshows the impact per individual observation.

It is evident that infrared spectrometers and microwavesounders produce the strongest impact followed by radio-occultation observations. Figure 3b shows that surfaceobservations of pressure over the oceans from driftingbuoys but also all types of direct wind observations havea strong impact, which suggests the importance of accu-rate wind observations from satellites as expected fromADM/Aeolus in the future (see “Lidar Systems”).

It is important to note that the impact of individualobservations depends on the NWP system and the weightassigned to the observations in the analysis. It is thereforecrucial to evaluate the NWP model, the data assimilationsystem, and the entire set of used observations togetherto characterize the importance of existing satellite datafor NWP and to estimate the potential impact of futuresystems.

SummaryCurrent weather forecast skill is strongly driven by thesophistication of the physical processes represented innumerical models and advanced data assimilationschemes allowing vast amounts of data from conventionalsources and satellites to be used. Globally, more than 40million observations per day are used from about 50 dif-ferent satellite instruments to produce atmospheric ana-lyses with which the forecast models are initialized. Themost important instruments are passive radiometers thatmeasure infrared and microwave radiation emitted by thesurface-atmosphere system and that are mostly exploitedto derive information on temperature and moisture struc-tures. Increasingly, observations of clouds and rain, sur-face waves, land surface characteristics, and atmospherictrace gases are added. In parallel, numerical modelsbecome increasingly capable of representing more

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Weather Prediction, Figure 3 Relative contribution of different observing systems to 24 h forecast error reduction for September–December 2008 (Cardinali, 2009). Top panel shows contribution per observing system; bottom panel shows contribution persingle observation.

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complex physical and chemical processes at smallerscales. Ensemble analysis and forecasting systems allowthe estimation of analysis and forecasting uncertainties –a crucial information in forecasting highly nonlinear atmo-spheric phenomena. Future satellite observing systemswill develop toward more hyper-spectral instruments cov-ering wider spectral ranges with fine spectral resolution aswell as active instruments that sample vertical structuresand wind very accurately.

Abbreviations4D-Var–Four-Dimensional Variational AssimilationAIRS–Atmospheric Infrared SounderIASI–Infrared Atmospheric Sounding InterferometerATOVS–Advanced TIROS Operational Vertical SounderTOVS–TIROS Operational Vertical SounderHIRS–High-Resolution Infrared SounderAMSU-A–Advanced Microwave Sounding Unit AAMSU-B–Advanced Microwave Sounding Unit BMHS–Microwave Humidity SounderSSM/I–Special Sensor Microwave/ImagerSSMIS–Special Sensor Microwave Imager SounderTMI–TRMM Microwave ImagerTRMM–Tropical Rainfall Measuring MissionAMSR-E–Advanced Microwave Scanning Radiometer EMETOP–Meteorological Operational Polar satelliteEPS–EUMETSAT Polar SystemNOAA–National Oceanic and Atmospheric

AdministrationGNSS–Global Navigation Satellite SystemGRAS–GNSS Receiver for Atmospheric SoundingCHAMP–Challenging Minisatellite PayloadGPS–Global Positioning SystemNWP–Numerical Weather PredictionERA–ECMWF ReanalysisECMWF–European Center for Medium-Range

Weather ForecastsSMOS–Soil Moisture Ocean SalinityEOS–Earth Observing SystemCOSMIC–Constellation Observing System for

Meteorology, Ionosphere, and ClimateOSE–Observing System ExperimentOSSE–Observing System Simulation ExperimentSuomi NPP–Suomi National Polar-orbiting PartnershipCrIS–Cross-track Infrared SounderGCOM-W–Global Change Observation Mission - Water

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Cross-referencesAtmospheric General Circulation ModelsData AssimilationGeophysical Retrieval, OverviewGPS, Occultation SystemsLand-Atmosphere Interactions, EvapotranspirationLidar SystemsLimb Sounding, AtmosphericMicrowave RadiometersOcean-Atmosphere Water Flux and EvaporationRadar, AltimetersRadar, ScatterometersRadar, Synthetic ApertureRadiative Transfer, Solution TechniquesRadiation, ElectromagneticRadiative Transfer, TheoryRadio-Frequency Interference (RFI) in Passive MicrowaveSensingSoil MoistureStratospheric Ozone

WETLANDS

John MelackDepartment of Ecology, Evolution and Marine Biology,University of California, Santa Barbara, CA, USA

DefinitionsPassive microwave radiation is emitted from the Earth’sland, seas, and atmosphere at wavelengths generallybetween 0.15 and 30 cm or, if expressed as frequencies,between 1 and 200 GHz.Emissivity is the ratio of energy radiated by a material toenergy radiated by a blackbody at the same temperature.

IntroductionWetlands cover extensive areas worldwide (Lehner andDöll, 2004), have important ecological and biogeochemi-cal functions, and play critical roles in improving waterquality, mitigating floods, and providing habitat for fishand wildlife. For many wetlands, remote sensing is thepreferred approach to obtain a synoptic view of inundationand vegetative cover, and a suite of optical and microwavesensing systems and analysis algorithms are being appliedto wetlands (Sahagian and Melack, 1998; Melack, 2004).In the case of the large, temporally varying wetlands foundthroughout the world, a remote sensing system with fre-quent, near-global coverage and sensitivity to wetness isnecessary. These requirements are met by passive micro-wave sensors.

Passive microwave systems and analysesA global record of passive microwave radiation measuredfrom satellites is available from 1979 to the present. TheScanning Multichannel Microwave Radiometer (SMMR)was operated on board the Nimbus-7 satellite from 1979 to

1987, with global coverage every 6 days. The Special SensorMicrowave/Imager (SSM/I) replaced SMMR in 1987 andoperates today with 3 day global coverage from a satellitein the US Defense Meteorological Satellite Program. TheTropical Rainfall Measuring Mission (TRMM), launchedin 1997, included a microwave radiometer similar to thaton SSM/I but providing higher spatial resolution because itflies at lower altitude than the SSM/I. The Advanced Micro-wave Scanning Radiometer (AMSR), launched in 2002 onthe Aqua satellite, offers additional capabilities.

Measurements of passive microwave radiation areexpressed as brightness temperatures (�K) and arerecorded as vertical and horizontal polarizations at severalfrequencies (Choudhury, 1989). To reduce effects ofatmospheric water vapor and temperature on the measure-ments, the difference between the two polarizations,referred to as DT, is often used. However, surface rough-ness, exposed soil and rock, seasonal vegetation changes,and other atmospheric conditions can affect the DT.Prigent et al. (1998) have developed an approach to calcu-late microwave emissivities of land surfaces after removingthe contributions from the atmosphere, clouds, and rain andmodulation by surface temperatures by using ancillaryremotely sensed information andmeteorological reanalyses.In general, flooded regions have low microwave emissivi-ties and high polarization differences relative to non-floodedareas. Spatial resolutions of approximately 10–50 km limitthe application of the technique to large wetlands or toregions where the cumulative area of smaller wetlands com-prises a significant proportion of the landscape.

Calm water surfaces result in a strongly polarized emis-sion at 37 GHz (e.g., SMMRDTca. 60�K), although this isattenuated to varying degrees by overlying vegetation. Inthe absence of flooding, the dense vegetation and rela-tively level terrain typical of large wetlands presenta stable background of depolarized microwave emission(e.g., SMMR DT averaging ca. 4�K). Fluctuations in theextent of inundation can be quantified if the DT is raisedsufficiently above background. Inundation area can beestimated from the DT by mixing models that incorporatethe microwave emission characteristics of the major land-scape units (Sippel et al., 1994). The results have been val-idated against independent measures of flooding, such asriver-stage records in areas of floodplain where inundationis known to be controlled by a large river.

Passive microwave applicationsInundationA global monthly time series of inundation during the1990s was produced by Prigent et al. (2007) based ona combination of passive microwave surface emissivities,scatterometer responses, and visible and near-infraredreflectances for ca. 0.25� grid cells. The detection of inun-dation relied primarily on SSM/I data. In forested regionsit appears that the results do not indicate inundation ifstanding water occupies less than 10 % of the pixel.

922 WETLANDS

In comparison to inundation determined under low andhigh water levels at 100 m resolution with synthetic aper-ture radar for the central Amazon (Hess et al., 2003),Prigent et al.’s results do fairly well, underestimatinglow water area by 11 % and high water area by 30 %. Var-iations in wetland area match well with variations in waterlevel derived from TOPEX-Poseidon altimetry in theNiger, Ganges, Pantanal, and Amazon basins on a 4� grid.

In tropical areas, passive microwave remote sensingstudies have focused on analysis of the inundation patternsin seasonally flooded forests and savannas and on compar-ative analyses of hydrological patterns in the major wet-lands in South America (Hamilton et al., 2002).Observations with SMMR at the 37 GHz have been ana-lyzed to determine spatial and temporal patterns of inunda-tion on floodplains of the Amazon, Tocantins and Orinocobasins, and the Pantanal wetlands of South America.

Ecological and biogeochemical studiesInformation about inundation and wetland vegetation areessential for the understanding of carbon dynamics in theAmazon basin. By combining field measurements of car-bon dioxide concentrations in surface waters with passiveand active remote sensing of inundation, Richey et al.(2002) calculated the evasion (outgassing) of CO2 fromwater to the atmosphere in the central Amazon basin. Sim-ilarly, by combining measurements of methane emissionfrom a variety of habitats and sites with inundation andvegetation variations derived from microwave and opticalremote sensing analyses, Melack et al. (2004) estimatedmethane emissions from the Amazon basin.

Variations in the distribution and inundation of floodplainhabitats play a key role in the ecology and productionof many commercially important freshwater fish. Ina comparison of flooded areas estimated from a monthlyseries of passive microwave data (Sippel et al., 1998) withthe annual fish yield aggregated from the BrazilianAmazon,Melack et al. (2009) found significant relationships for smallspecies at lower trophic levels generally at short lag times(0–1 years), while those for large species at higher trophiclevels had considerably longer lag times (3–5 years).

SummaryThe principal advantages of the passive microwave obser-vations are their frequent global coverage and their abilityto reveal characteristics, such as inundation, of the landsurface beneath cloud cover and vegetation.

This encyclopedia includes no entries beginning with X, Y and Z.

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