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6 Hydrology The global water cycle links all components of the Earth particularly for the southern hemisphere (see Chapter 3). system as water moves from the atmosphere to the land and TRMMâs ability to measure the height in the atmosphere ocean and through the biosphere and cryosphere. Water plays where precipitation is generated provides information on a central role in the climate system due to its importance in the vertical distribution of release of latent heat, which in the carbon cycle as water availability is a key factor for ter- turn improves our knowledge of atmospheric circulation restrial photosynthesis. Water is also essential to the energy and climate. Moreover, TRMM has demonstrated that the balance of the Earth because the rate of evaporation controls technology for reliable precipitation measurements from the latent heat flux. In addition, water is necessary to sustain space is now available and has provided the community with all life; and, consequently, furthering the understanding of important lessons to guide the design of the Global Precipita- global hydrology is of central importance to society. tion Mission (GPM; NRC 2007c). Through some remarkable technological accomplish- Before TRMM, rainfall estimates were obtained from ments, some important variables and processes associated ground-based sources (e.g., rain gauges and radar) and with the water cycle can be retrieved now from satellite from satellites with visible, infrared, and passive microwave observations: water vapor (see Chapters 3 and 5), precipita- sensors (e.g., Advanced Microwave Scanning Radiometer tion over oceans and land, snow, ice sheet mass and flow (see [AMSR] for the Earth Observing System [EOS], Advanced Chapter 7), continental groundwater storage, and sea surface Microwave Sounding Unit B, and Special Sensor Micro- temperature (see Chapter 8). The new perspective provided wave/Imager [SSM/I]). The scientific accomplishments of by satellite observationsârevealing high temporal and spa- TRMM are due primarily to two innovative aspects of the tial variabilityâhas transformed the global understanding mission: TRMMâs complementary suite of instruments and of hydrology more than any single observing platform could its orbital characteristics (NRC 2006). have done. At the same time, some important hydrologic TRMMâs instruments include a microwave imager, a measurements are not yet available from space, such as visible and infrared scanner, and a lightning imaging sensor snow water equivalent in mountainous areas, soil moisture, all on the same platform along with the first-ever precipita- and procedures to estimate evapotranspiration from remote tion radar in space. The suite of instruments on TRMM sensing. allows for intercalibration among the instruments as well as cross-calibration with sensors on other platforms. TRMMâs precipitation radar provides direct, fine-scale observations Precipitation estimates from the of precipitation and its vertical distribution. The satelliteâs Tropical Rainfall Measuring Mission 35âdegree inclination orbit and low altitude (402.5 km) In orbit since 1997, the Tropical Rainfall Measuring allows for sampling well beyond the tropics to 60Â° N/S, but Mission (TRMM) has transformed our ability to measure sampling in the tropics is more frequent. The orbit is not sun- the spatial and temporal variability of rainfall in the tropics, synchronous; therefore, in each month it acquires measure- especially over the oceans. Learning about precipitation over ments at all longitudes and all times of day. These advantages the oceans has catalyzed further understanding of air-sea augment the spatial and temporal views of standard polar- interaction, the role of runoff to the seas in ocean circula- orbiting environmental satellite trajectories. However, due to tion, and the vertical circulation of the oceans. In addition, TRMMâs narrow swath, data for any given storm or location it has led to great improvements in weather forecast skill, are available infrequently. The GPM proposes to overcome 50
HYDROLOGY 51 most of TRMMâs limitations and is central to ensuring the variability in the northern hemisphere, for example, leads availability of remotely sensed precipitation measurements to altered circulation patterns, suggesting implications for for climate research (NRC 2007a). climate predictability (Cohen and Entekhabi 1999). Through its technological innovations, TRMM has For four decades, satellite remote sensing instruments enabled the following scientific accomplishments for hydrol- have measured snow properties. These weekly measurements ogy and climate: establishing rainfall climatology, quan- represent one of the longest satellite-derived climate data tifying the diurnal cycle of precipitation and convective records, which now enables scientists to study long-term intensity, and profiling latent heating (NRC 2006). TRMM trends in seasonal snow cover (Frei and Robinson 1999). At data have also contributed to operational use: near-real- optical wavelengths, sensors such as the NOAA Advanced time TRMM-based multisatellite estimations of rainfall are Very High Resolution Radiometer (AVHRR) and the Landsat being used to detect floods in the United States and espe- Thematic Mapper (TM) have been used to produce maps of cially overseas where conventional information is lacking. snow cover at both continental and drainage-basin scales. The National Oceanic and Atmospheric Administrationâs In the EOS era, snow-cover products are available from the (NOAA) National Environmental Satellite Data and Infor- Moderate Resolution Imaging Spectroradiometer (MODIS), mation Service uses TRMM data as part of its Tropical the Multiangle Imaging Spectroradimeter (MISR), and the Rainfall Potential Program to estimate flood potential in Advanced Spaceborne Thermal Emission Reflection and hurricanes (Box 6.1, Figure 6.1). The National Aeronautics Radiometer (ASTER). Snow-water equivalent (the depth of and Space Administrationâs (NASA) TRMM-based Mul- liquid water that the snowpack would produce if it melted) tisatellite Precipitation Analysis is used globally to detect is regularly estimated at coarse spatial resolution from pas- floods and monitor rain for agricultural uses. The Naval sive microwave data, including SSM/R, SSM/I, and the Research Laboratory Monterey and the National Centers for EOS instrument AMSR-E in a time series that goes back to Environmental Prediction use TRMM data as a key part of 1978. However, at finer spatial resolution, necessary for the their multisatellite rain estimates. TRMM data are central to mountains, measuring snow-water equivalent is a difficult the success of these efforts because of their accuracy and the problem; and a proposed sensor for Snow and Cold Land significant sampling coverage by TRMM in the tropics. Processes (SCLP) is recommended as one of 17 high-priority The scientific accomplishments and operational advan- missions for launch before 2020 (NRC 2007a). tages of TRMM have spurred the development of the GPM KÃ¶nig et al. (2001) and Dozier and Painter (2004) have follow-on mission, scheduled for launch in 2013 (NRC reviewed developments in remote sensing of snow and ice. 2007a). GPM will consist of a core spacecraft with a dual- Among them is the use of snow-covered area from MODIS frequency precipitation radar and a multifrequency micro- in hydrologic analysis and modeling (Box 6.2, Figure 6.2). wave radiometric imager with high-frequency capabilities Through updates of a runoff model with measurements to serve as an orbiting âprecipitation physics laboratory.â In of snow cover, seasonal streamflow forecasts have been addition to the core spacecraft, GPM will include a constella- improved (McGuire et al. 2006). Unlike surface measure- tion of current and planned satellites with passive microwave ments, satellite observations are able to show the distribution radiometers. Together, the system will provide calibrated of snow over the topography, revealing that considerable global precipitation at 2- to 4-hr intervals. snow at higher elevations remains after all snow has disap- peared from the surface measurement stations. An additional property measured from MODIS is snow SEASONAL SNOW COVER albedo. In the current generation of climate and snow- Of the seasonal changes that occur on Earthâs land sur- melt models, snow albedo is typically either prescribed face, perhaps the most profound is the accumulation and melt or represented by empirical aging functions, when truly it of seasonal snow cover. Snow influences climate, weather, is a dynamic variable affected by grain growth and light- and the water balance. Snow cover has significant effects absorbing impurities. Newer analyses of snow cover are on energy and mass exchange between Earthâs surface and incorporating the seasonal evolution of both the snow cover atmosphere and is an important reservoir of fresh water. Its and its albedo. In the visible part of the spectrum, clean, high albedo changes the surface radiation balance; its low deep snow is bright and white, irrespective of the size of the thermal diffusivity insulates the ground; and it is a wet, cold grains. Beyond the visible wavelengths in the near infrared surface in the context of heat and moisture fluxes. Therefore, and shortwave infrared, however, snow is one of the most snow cover exerts a huge influence on the hydrologic cycle âcolorfulâ substances in nature. Newly fallen snow usu- during the winter and spring for much of Earthâs land area. ally has a fine grain size, but metamorphism and sintering Near many mountain ranges, the seasonal snow cover is the throughout the winter and spring increase the grain size, dominating source of runoff, filling rivers and recharging bond grains together, and reduce reflectance in wavelengths aquifers that more than a billion people depend on for their beyond about 0.8 Âµm (Warren 1982). This behavior of snow water resources (Barnett et al. 2005a). Snow affects large- is important to the snowpackâs energy balance because the scale atmospheric circulation. Early-season snow cover decrease in albedo often occurs during the spring when the
52 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS BOX 6.1 Improved Understanding of Hydrology and Climate from TRMM TRMM-based multisatellite data are being used as input into hydrologic models, including the Land Data Assimilation System, to better understand land-atmosphere interactions on scales of days to years (Rodell et al. 2004) and to study variations in river runoff (Fekete et al. 2004). These same data are being used to monitor crops in Central America and elsewhere and as input into river forecast models in South Asia and other locations. Analysis of TRMM precipitation radar data has been used to discover orographic precipitation processes and diurnal cycles of rainfall causing flash floods in headwater streams (Barros et al. 2004). TRMM observations led to the discovery of extremely tall convective towers within the vertical precipitation profiles of tropical cyclones. Kelley et al. (2004) reported that the chance of intensification increases when one or more of these âhot towersâ exist in the tropical cycloneâs eyewall (Figure 6.1). FIGURE 6.1â A cross-sectional view of Hurricane Katrina through the eye of the storm, as observed from TRMM. This image shows the horizontal distribution of rain intensity on August 28, 2005, when Katrina was a Category 3 hurricane with maximum sustained winds of 100 knots (115 mph). Rain rates in the central portion of the swath are from the TRMM precipitation radar, and the rain rates in the outer swath are from the TRMM microwave imager. The rain rates are overlaid on infrared data from the TRMM visible infrared scanner. Two isolated hot towers (in red) are visible: one in an outer rain band and the other in the northeastern part of the eyewall. The height of the eyewall tower is 16 km. Towers of this height near the core are often an indication of intensification as was true with Katrina, which became a Category 4 storm soon after this image was taken. SOURCE: NASA (2005).
HYDROLOGY 53 Box 6.2 High-Resolution Seasonal Snow Cover Data Improve Climate and Hydrology Models Because of the influence of seasonal snow cover on climate, weather, and water balance, it is a crucial quantity for climate and hydrology models. Furthermore, daily maps are necessary for hydrologic and climate models due to the dynamic nature of snow cover, which changes at a slower timescale than atmospheric phenomena but faster than other surface covers. The availability of daily global observations of this parameter was inconceivable prior to the satellite era. Nowadays, the global MODIS snow-cover product (Hall et al. 2002) is produced daily and as an 8-day composite at 500-m spatial resolution. For global climate models, daily snow cover is produced at 0.05Â° latitude-longitude grid cells (about 5.5 km in the north-south direction) along with monthly global composites. The composites are necessary because cloud cover and viewing geometry affect the daily images (Figure 6.2). a b FIGURE 6.2â MODIS image (left) and interpreted snow (white) and cloud (pink) cover over the Sierra Nevada, January 5, 2003. ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ http://modis-snow-ice.gsfc.nasa.gov/images.html. SOURCE: incoming solar radiation becomes greater as the solar eleva- albedo evolution, based on the movement of water molecules tion increases and the days get longer. in the snow to reduce the surface area of the grains in com- In the context of hydrologic models, this albedo decay a,b parison to their volume (Flanner and Zender 2006). 6-2 has spatial variability. Molotch et al. (2004) examined snow A recent development in mapping snow cover and ablation from a grid-based distributed snowmelt model, its albedo is âsubpixelâ analysis. Snow-covered area in using field data from extensive snow surveys during the melt mountainous terrain usually varies at a spatial scale finer season to initialize the model with a spatial distribution of than that of the ground instantaneous field of view of the snow-water equivalent and then to test the model with sub- remote sensing instrument. This spatial heterogeneity poses sequent surveys. Remotely sensed albedo typically differed a âmixed-pixelâ problem because the sensor may measure by 20 percent from albedo estimated using a common snow radiance reflected from snow, rock, soil, and vegetation. To age-based empirical relation applied uniformly across the use the snow characteristics in hydrologic models, snow domain. Snowpack models are just beginning to incorporate must be mapped at subpixel resolution in order to accurately
54 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS represent its spatial distribution; otherwise, systematic errors tion, and capable of acquiring imagery at only one angle, may result. For example, especially in drier years, much of SIR-A showed that in the dry Sahara Desert it could penetrate the snow cover is patchy at the lower elevations. An image as deeply as 3 m. These early images from the dunes and classification that identifies each pixel as either snow covered drift sand of the eastern Sahara showed previously unknown or not may miss much of this snow. buried valleys, geologic structures, and possible Stone Age Mapping of surface constituents at subpixel scale uses occupation sites (McCauley et al. 1982). Radar responses a technique called âspectral mixture analysis,â based on the from bedrock and gravel surfaces beneath wind-blown sand assumption that the radiance measured at the sensor is a several meters thick delineated sand- and alluvium-filled linear combination of radiances reflected from individual valleys, some nearly as wide as the Nile Valley and perhaps surfaces (Figure 6.3). Snow does not have a unique reflec- as old as middle Tertiary. The now-vanished major river sys- tance in each wavelength band, but given its physical char- tems that carved these large valleys probably accomplished acteristics such as grain size and amount and composition of most of the erosional stripping of this extraordinarily flat, impurities, a snow end member can be chosen that results in arid region. Stone Age artifacts associated with soils in the the lowest error in the solution of the simultaneous equations alluvium suggested areas that may have been sites of early (Painter et al. 2003). The information thereby derived is the human occupation. The presence of old drainage networks fractional snow-covered area for each pixel and the albedo beneath the sand (Figure 6.4) provided a geologic explana- of that snow. tion for the locations of many playas and present-day oases that have been centers of episodic human habitation. The success of the mission paved the way for a follow- dISCOVERY OF aNCIENT bURIED rIVER cHANNELS on, the SIR-B in 1984, which could collect data at more than In 1981 the first Shuttle Imaging Radar (SIR-A) was one angle by mechanically tilting its antenna, and then the launched on the space shuttle Columbia, assembled partly SIR-C (SIR-C/X-SAR) in April and October 1994. The syn- with spare parts from the 1978 Seasat synthetic aperture thetic aperture radar on board SIR-C was fully polarimetric, radar (SAR). With just a single frequency and one polariza- capable of both transmitting and collecting information at FIGURE 6.3â Fractional snow cover over the Sierra Nevada on April 1 (left) and May 1, 2005. Total snow-covered area is 23,100 km 2 in April and 14,900 km2 in May. SOURCE: J. Dozier, University of California, Santa Barbara.
HYDROLOGY 55 Wahr et al. (2004) use GRACE data to compare ground- water storage with a hydrologic model in the Mississippi and Amazon River basins and in the drainage flowing into the Bay of Bengal (Figure 6.5). When averaged over 1,000 km or more, the mass estimates inferred from the GRACE data clearly show annually varying changes in continental water storage, along with seasonal variability in the amount of water in the ocean. The amplitudes and phases of those signals are in general agreement with the hydrologic model. The inferred mass signals over the ocean are consistent with estimates of the water stored in the groundwater. Although the agreement degrades with decreasing averaging radius, the largest water storage signals are still clearly evident at averaging radii as short as 400 km. The globally averaged uncertainty in the amplitude of the annually varying mass signal recovered from these GRACE fields is 1.0 cm for a 1,000-km radius. Use of Satellite-Derived Elevation Data in Hydrology In February 2000, with the aid of a 60-m (200-ft) boom added to the SIR-C, the Shuttle Radar Topography Mission (SRTM) circled Earth for 10 days mapping 80 percent of the worldâs land area. The resulting high-resolution topographic map is the most accurate available and constitutes one of FIGURE 6.4â Image from Shuttle Imaging Radar-A (SIR-A) show- the major accomplishments of the nationâs space program. ing buried river channels in the Sahara Desert. SOURCE: http:// SRTM (Farr et al. 2007) provides a worldwide topographic www.jpl.nasa.gov/history/hires/1981/SIR-A_image.jpg. data set between 60Â° N and S latitudes with a consistent datum. Many areas otherwise lack topographic data, so these data enable spatial hydrologic modeling that would otherwise be impossible. Figure 6.6 shows an elevation and relief map of the whole African continent. vertical or horizontal polarizations. In addition, the antenna Since its launch, digital elevation models created from was electronically steerable and operated at three frequencies SRTM have been used in many applications, most notably (1.4, 5.6, and 10 GHz). The two flights allowed investigation tectonics, geomorphology, and hydrology. Because of their into the radarâs response to seasonal changes. The multipa- global consistency, SRTM data link continental hydrology rameter images were combined and enhanced to produce with the oceans. In hydrologic investigations, the first infor- some of the most spectacular radar images ever seen. mation in characterizing a problem is often the topography of a drainage basin. From the elevations, slopes and aspects can be estimated, which are essential for calculations of aNALYSIS OF gROUNDwATER FROM gRAVITY dATA solar and longwave radiation that can be used in spatially A pair of satellites launched in 2002 makes up NASAâs distributed energy balance models of snowmelt (Cline et al. Gravity Recovery and Climate Experiment (GRACE). The 1998), photosynthesis, and evapotranspiration (Anderson main source of variation in Earthâs gravity field is the move- et al. 2003). Analytical software packages use these slopes ment of water between its three main reservoirs: the ocean, and aspects as input parameters to delineate drainage basin ice sheets, and groundwater. Unlike most satellite remote boundaries, to characterize basins for their distribution of sensors, which measure electromagnetic radiation reflected slopes, and in routing water from precipitation or snowmelt or emitted from Earthâs surface and atmosphere, GRACE (Tarboton 1997). An additional hydrologic application of measures the distance between its two spacecraft, which SRTM data has been to measure water surface elevations changes in response to variations in Earthâs massâand directly (Alsdorf et al. 2007), which contributes to the therefore gravityâon the surface below them. The GRACE improvement of flood forecasting. measurement also senses mass change within the Earthâa Providing accurate flood forecasts from satellite observa- capability demonstrated by the measurement of seasonal tions is a high-priority mission with the potential to save lives change in continental aquifers. and property (NRC 2007a). This important societal challenge
56 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS cannot be answered adequately with the current global in situ networks designed to observe river discharge. Knowledge of soil moisture (and snow water stor- age, where relevant), surface water area, the elevation and slope of the water surface, and accurate hydrologic models is required to meet this chal- lenge. Although attempts to estimate soil moisture from the AMSR-E sensor have been made, they are only at the early experimental stage. Nevertheless, results have shown promises and the proposed Soil Moisture Active-Passive mission is central to making progress toward reliable flood hazard assess- ments (NRC 2007a). Despite the many accomplishments highlighted in this chapter, important challenges remain such as the GPM, soil moisture esti- mates, surface water and ocean topog- raphy (to improve estimates of water stored in lakes, reservoirs, wetlands, and rivers), and improved estimates of FIGURE 6.5â The mass variability within (a) the Mississippi River basin, (b) the Amazon snowpacks (NRC 2007a). River basin, and (c) a drainage system flowing into the Bay of Bengal, as inferred from the GRACE measurements (dots). Also shown are results inferred from a hydrologic model, as well as the best-fitting annual signal for both the GRACE values and the model predictions. Bottom panels show the optimal averaging kernels used to recover this mass variability. SOURCE: Wahr et al. (2004). Reprinted with permission from the American Geophysical Union, copyright 2004.
HYDROLOGY 57 FIGURE 6.6â Elevation and relief map of Africa from the Shuttle Radar Topography Mission. Color coding is directly related to topographic height, with brown and yellow at the lower elevations, rising through green, to white at the highest elevations. Blue areas on the map represent water within the mapped tiles, each of which includes shorelines or islands. SOURCE: NASA Jet Propulsion Laboratory.