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Integrating Multiscale Observations of U.S. Waters 2 Sensing from the Molecular to the Global Scale: New Opportunities and Challenges The hydrologic sciences are based on data, and much of that data has to be collected using measurements of the real world, from remote locations in Africa or the Arctic to sewage systems to national forests during hunting season. Each data point results from some form of environmental sensing. Thus, the hydrological community has focused tremendous effort, in collaboration with scientists and engineers from a host of disciplines, on developing cost-effective, high-quality, reliable sensing devices from simple staff gages to complex satellite technologies coupled with geographic information systems (GIS). In the case studies provided in Chapter 4, examples are given of how a wide variety of sensing technologies and approaches can be used not only for research but also for applications such as water management and human health, on which societies depend. Rather than provide a lengthy review of the development and deployment of sensor technologies established in the hydrological sciences, this chapter focuses primarily on new opportunities and their attendant challenges for sensing of hydrologic and related parameters. The intent is to review current and anticipated capabilities for sensing from the molecular to the regional and global scales, in part to allow the reader to better understand the state-of-the-art and in part to indicate where integrative development needs to be fostered so that the potential of new sensing and information technologies can be realized. Taken as a whole, this chapter should provide a convincing argument that the hydrological sciences are poised for a major advance brought about by the convergence of new sensors and sensing approaches from the molecular/nano-to the global scale. Because all of these new or potential technologies require interdisciplinary cooperation to achieve their full potential, it is crucial that researchers in the hydrological community communicate to those in other disciplines (e.g., electrical engineering, chemical engineering, computer science, nanotechnology) the tremendous needs and challenges in water, and their important role in addressing them.
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Integrating Multiscale Observations of U.S. Waters This chapter is divided into four main sections. First, current and emerging sensor networking technologies are described in detail, with a focus on embedded sensor networks, which will provide a platform or ground-truthing for many of the other methods. Second, recent and emerging biogeochemical sensor technologies, many of which can be integrated into emerging in-situ embedded sensor networks to broaden the parameters measured at low cost, are described. Third, current capabilities and potential for Earth observations from airborne platforms are discussed. Finally, current capabilities and potential for Earth observations from spaceborne platforms are reviewed. Although the chapter is organized in this manner, the different technologies tend to work best when they are well integrated across scales, using information from one scale to help refine strategies at another scale and purposely focusing efforts for data collection at different scales on specific locations of interest, with appropriate time and density of sampling. Temperature is a key example. It has numerous applications, such as industrial water management, aquatic habitat mapping, and crop evapotranspiration estimates. It is also a quantity that can be measured using in-situ, airborne, and spaceborne platforms. IN-SITU SENSOR AND SENSOR NETWORKING TECHNOLOGIES Hydrologic science has used networks of physical and, in many cases, chemical sensors for decades. Examples include the U.S. Geological Survey (USGS) stream gaging network, the Natural Resources Conservation Service (NRCS) snow telemetry network and more recently their soil moisture Soil Climate Analysis Network (SCAN), and the National Oceanic and Atmospheric Administration (NOAA) cooperative weather station network. Besides these operational networks, there is a wide range of state, local, and research networks that include state departments of transportation that monitor weather impacts on highways, state environmental and agricultural services, and research networks such as the Ameriflux network for monitoring carbon and water fluxes. The hydrologic research community has just begun to take advantage of recent developments in sensor technologies, wireless communications, and cyberinfrastructure to develop increasingly sophisticated sensor networks allowing for sampling at greater spatiotemporal resolution and for more comprehensive ‘sensor-to-scientist’ operation (e.g., Barrenetxea, 2006; Cayan et al., 2003; Hanson et al., 2003; Hamilton et al., 2007; Harmon et al., 2007; Seders et al., 2007). State-of-the-art sensing capabilities for environmental observatories reflect the co-evolution of sensors (NSF, 2005), communication technologies (Porter et al., 2005), and cyberinfrastructure (Estrin et al., 2003). There are both promising opportunities and major challenges associated with effectively linking space-based and ground-based environmental observations. This section contains (1) a
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Integrating Multiscale Observations of U.S. Waters description of a variety of sensor network configurations that are emerging, (2) a description of the emerging sensor and communication technologies that lie at the heart of these observational systems, and (3) identification of some of the challenges to implementing these new networking approaches and technologies in actual integrated field observatories. Sensor Network Configurations Beyond the traditional sensor network, an emerging technology that is becoming increasingly important is the “embedded sensor network” (ESN; e.g., Seders et al., 2007), also referred to with a slightly different acronym as “embedded network sensing” (ENS; e.g., Harmon et al., 2007). As used in this report, an ESN is made up of spatially distributed sensor-containing platforms or pods, connected to and often controlled by computers, used to measure conditions at different locations, such as air temperature, solar radiation, relative humidity, or water properties. Ideally, these devices are small and inexpensive, so that they can be produced and deployed at greater density than allowed by more traditional and expensive devices, potentially in large numbers for some applications. In order to be field deployable at low cost, their energy requirements, memory, computational speed, and bandwidth (due at least in part to Federal Communications Commission (FCC) regulations) are constrained. The additional term “sensor web” (Delin et al., 2005) is used specifically to imply a very dense network of numerous sensors forming a high-resolution mesh; here the single term ESN is used for ease of discussion. In an ESN (Figure 2-1 and Table 2-1), there is computer-based dialog amongst the various sensor pods and with a gateway computer. Hence, the network embeds a computational intelligence in the environment that allows adaptive monitoring as well as potential control of a local environment. One of the primary advantages of an ESN over a group of individual, unconnected sensors is the ability for the network itself to make decisions based on environmental indicators, such as to change sampling strategy to minimize energy requirements and extend sensor lifetime (e.g., Seders et al., 2007; Ruggaber et al., 2007). Such a network also permits real-time sensor-to-scientist operation, such as sensor calibration, tools for data mining, modeling capabilities, and data visualization (Hamilton et al., 2007). For example, sensor networks may be programmed to switch from low-level hourly or daily sampling to more frequent sampling when one pod or a group of pods senses a potential hydrologic event, such as the start of an algal bloom or a change in stream stage in response to a storm event. This embedded computational intelligence is essential for applications in remote locations where researchers need to minimize human field effort, minimize energy requirements, and maximize sensor lifetime.
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Integrating Multiscale Observations of U.S. Waters FIGURE 2-1 A schematic example of an embedded sensor network (ESN) in a lake environment. The shaded area indicates flexible networking connections between the pods with each other and the gateway. SOURCE: Seders et al. (2007). © 2007 by Mary Ann Liebert, Inc, publishers. TABLE 2-1 Embedded Sensor Network (ESN)a Embedded Networked Sensing Embed numerous distributed devices to monitor and interact with physical world Network devices to coordinate and perform higher-level tasks Tightly coupled to physical world Control system with, small form factor, wireless pods Exploit collaborative sensing take action Exploit spatially and temporally; dense, in-situ, sensing and actuation a Or ENS or sensor web. SOURCE: Adapted from http://research.cens.ucla.edu and http://sensorwaresystems.com.
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Integrating Multiscale Observations of U.S. Waters In a typical ESN, each pod is equipped not only with sensors but also with a radio transceiver, a small microcontroller that communicates with the pods, and an energy source—usually a battery and where feasible a solar cell; although the sensor pods may also be hard-wired for some applications. The sensing devices in an ESN are ideally small, robust, and inexpensive, so that they can be produced and deployed in large numbers. In order to be field deployable at low cost, device energy requirements, memory, computational speed, and bandwidth (due at least in part to FCC regulations) are typically constrained. A key desirable feature of ESNs is that they have the ability to self-organize in order to cope with changes. Data from the sensors are usually aggregated and analyzed, either by a computer within the network or outside it. That is, the pods in an ad-hoc wireless sensor network are effectively self-organizing and hence do not require detailed, pre-programmed knowledge of network topology in order to function. Such networks will also be robust to a certain degree of network modification and will continue to function as new nodes join the network or existing nodes fail or move to new physical locations. The ESN, with its unique global information-sharing protocol, forms a sophisticated sensing tapestry that can be draped over an environment. This approach allows for various complex behaviors and operations, such as real-time identification of anomalous or unexpected events, mapping vector fields from measured scalar values and interpreting them locally, and single-pod detection of critical events, which then triggers changes in the global behavior of the sensor network (Delin et al., 2005). Note that a pod in an ESN is merely a physical platform for a sensor and thus can be orbital or terrestrial, fixed or mobile, and often has real-time accessibility via the Internet. Pod-to-pod communication is both omni-directional and bi-directional where each pod sends out collected data to other pods in the network. As a result, on-the-fly data fusion, such as false positive identification and plume tracking, can occur within the ESN, and the system subsequently reacts as a coordinated, collective whole to the incoming data stream. An example of how an ESN can be used for real-time management and control of combined sewage outflow is provided in Box 2-1. It should be noted that ESN accomplishments have been modest to date and that their potential significance is unresolved. However, their upside potential is so high that it will be important to attempt to resolve the many challenges that will present themselves for field deployment. In summary, an ESN is a network of small, sensor nodes or pods communicating among themselves using radio communication, and deployed in large scale (from tens to thousands, creating a sensor “web” at high density) to sense the physical, chemical, and or biological world. Unique characteristics of an ESN are as listed below (Maurice and Harmon, 2007, and other manuscripts in Environmental Engineering Science special edition, March 2007). This list describes the optimum or ideal ESN that the scientific community can strive for, but it is
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Integrating Multiscale Observations of U.S. Waters BOX 2-1 An Embedded Sensor Network for Control of Combined Sewage Overflow Events Many wastewater systems in the United States and abroad contain at least some components that were built to accept both sanitary wastewater and stormwater runoff, in order to minimize original construction costs. The problem with such systems is that during times of high flow, following storms or rapid snowmelt, the combined flow can become too great for sewage treatment facilities, and is then diverted directly into lakes and rivers, resulting in a combined sewage overflow (CSO) event. Following passage of the Clean Water Act in the early 1970s, municipalities were faced with a mandate to prevent such CSO events for the sake of the ecosystem and human health. Although many cities have invested millions to billions of dollars in updating their sewage systems, each year in the United States, CSO events still result in the release of hundreds of billions of gallons of untreated wastewater into lakes and rivers. In order to help municipalities to minimize CSO events, low-cost, embedded sensor networks are being developed specifically to address the CSO problem. Recently, Ruggaber et al. (2007) developed and deployed an embedded sensor network in South Bend, Indiana (USA). This system was designed to decrease the frequency and severity of CSO events by maximizing the existing storage capacity already present in the city’s combined sewer system. The embedded sensor network uses data gathered from a distributed network of sensors, many of which use wireless technologies, to provide decentralized, distributed, real-time control of the combined sewer system’s storage capacity using automated valves called Smart Valves (Ruggaber et al., 2007). By decentralizing the decisionmaking and control, each section of the sewage system is able to maximize its efficiency, providing a finer control mesh that minimizes flooding. The embedded sensor network controls the storage of stormwater runoff in a large retention basin using level data from sensors within the basin and at the combined sewage system outfall, which is several miles away. Before the embedded sensor network was in place, the basin often was ineffective, resulting in CSO events following even relatively small storms. After emplacement of the network, the basin has been able to store all of the water that enters during almost all storm events, preventing a CSO event into the local St. Joseph River. Once the threat of a CSO event decreases, the sensor network system automatically releases the water stored in the combined sewage system to prepare for the next storm. The real-time measurements within the combined sewage system can also be used to convert the existing CSO planning models into real-time control and operations models; to improve land management; to develop a real-time CSO public notification plan; to expedite maintenance; and to revise and improve the city’s
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Integrating Multiscale Observations of U.S. Waters overall CSO strategy (Ruggaber et al., 2007). This is thus an example of how an embedded sensor network can be integrated with municipal operations and public services to provide substantial environmental benefits at relatively low cost and with much faster and easier deployment than traditional technologies. Ultimately, individual municipal embedded sensor networks can be combined into a national network to provide protection to streams and lakes on a regional to national scale. likely that not all qualities will be attainable for all applications, given the many challenges of real-world environmental sensing. Small-scale sensor nodes Limited power requirements that they can harvest or store Ability to use in harsh environmental conditions Self-recognition of node failures Adaptive monitoring, actuated sample collection Potential real-time control of a local environment Optimal management of power consumption related to sensor excitation and wireless data transmission Potential mobility of nodes Dynamic network topology Self-recognition of communication failures Heterogeneity of nodes Large-scale deployment Unattended operation Web-based data and interaction (e.g., visualization) The Need for New Sensor Probe Technologies Fortunately, in concert with development of ESN technologies, sensors themselves are becoming increasingly smaller, more robust, and less expensive. In particular, physical sensors such as those that measure air and water temperature, water pressure, radiation, relative humidity, and wind speed and direction have evolved over decades and are now mass-produced and routinely packaged together in small instruments along with power and communication devices (Delin, 2002; Szewczyk et al., 2004; Vernon et al., 2003; Woodhouse and Hansen, 2003; Yao et al., 2003). These small and often inexpensive instruments often can be left in the field unattended for longer periods of time than traditional instruments, although problems of fouling, drift, etc. still need to be addressed. However, current physical sensor technology does not provide three-dimensional parametric physical information for air, water, soil, and groundwater over spatial scales ranging from the
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Integrating Multiscale Observations of U.S. Waters micro-scale (e.g., pore volume) to the kilometer-scale, and integrating a web of sensors measuring at different scales (e.g., web pods with kilometer-scale satellite measurements) is poorly developed. In contrast to hydrometeorological variables, sensor development for many important chemical and biological measurements is relatively immature (Estrin et al., 2003; NSF, 2005). For example, sensors that measure nutrients in soil and water (e.g., phosphorus, nitrate) remain relatively expensive and are subject to rapid fouling or degradation. Chemical sensors are needed to measure a wide range of elements and molecules for inorganic, organic, and biochemical molecules in all environmental media (atmosphere, soils, sediments, groundwater, and fresh and marine waters). In particular, reliable means for detecting toxins and determining the presence and amount of nitrogen and phosphorus forms are critically important. Biological sensor technologies in general are the least mature, but investments such as those described in the section below should result in tremendous scientific advances. Biological sensors can provide key information on the function and structure/composition of biologically influenced ecosystems in real time (NSF, 2005). Development of a wide range of field-robust chemical and biological sensors is one of the greatest challenges facing widespread deployment of sensor networks in the hydrologic sciences. The characteristics needed by sensors within ESNs provide challenging design criteria: They need to be cheap enough to be widely disseminated, small, reliable, and robust. Specifically, they should have low power consumption (and/or use of renewable energy); be robust to temperature fluctuations; have low maintenance requirements; remain free of measure drift over the lifetime of deployment (or be able to self-calibrate or be capable of remote calibration); be resistant to deteriorating accuracy of measurements attributable to organic interaction with sensors (i.e., biofouling); and have environmentally benign components and operation. The sensor network itself has many requirements; it needs algorithms to help detect individual faulty sensors and to flag data accordingly, flexibility of platform location, intelligent and synergistic operation, methods to maintain data and physical security, and an ability to query sensors and possibly recalibrate them remotely. Thus, users have a long list of desirable sensor features, and such add-ons will tend to drive costs up. NEW AND EMERGING BIOGEOCHEMICAL SENSOR APPROACHES AND TECHNOLOGIES Over the past decades, a variety of new approaches have been developed for understanding and quantifying hydrobiogeochemical processes in the field. Some of these approaches have been well established and widely adopted; others are just emerging and may or may not live up to their initial promise to expand our observational abilities.
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Integrating Multiscale Observations of U.S. Waters Biogeochemical field sampling began as a labor-intensive activity, requiring researchers to go to individual sites in the field and collect samples for field and laboratory experiments. In addition to being labor intensive, this sampling tended to be biased towards daytime sampling in good weather, that is, the summer field season. The development of automatic sampling devices such as the ISCO sampler, which could collect water samples over time in stored bottles for later retrieval, has proved helpful in expanding many studies, but this has numerous limitations, especially for parameters that need to be measured immediately. Data-sonde devices can be used for sampling surface and groundwaters and can be equipped with a variety of probes from temperature and dissolved oxygen to chlorophyll and nitrate. Although they have the ability to collect data continuously, they are expensive to equip and operate and the probes are subject to drift and fouling, which limit their usefulness. Nevertheless, together with automatic samplers, they have led to significant advances in hydrobiogeochemical understanding. A variety of sensors and field analytical devices relying on optical, ultraviolet, and infrared measurements have become available (McDonagh et al., 2008); connected to a range of sampling devices, they can be used to make multiple measurements, and can be integrated into projects depending upon cost and robustness requirements (e.g., temperature fluctuations). Reviewing all of the new biogeochemical sensor technologies would be well beyond the scope of this report. Hence, in the sections below, the focus is on several examples of emerging technologies that may prove useful in hydrobiogeochemistry. First, a series of microsensor applications is presented followed by a discussion of molecular- to nanoscale sensors. Then applications of in-situ microcosms are introduced, followed by robotic samplers for biogeochemical monitoring. Later in this report (Chapter 4), some potential applications of the more developed of these technologies and approaches to existing hydrologic observatories are identified. As global observation systems are implemented, it will be essential to integrate the wide variety of different sensing approaches, in order to take advantage of the increased scientific capabilities and understanding. Microsensors Microsensors are miniaturized devices, micrometer to millimeter in size. They measure physical and chemical quantities such as pressure, acceleration, temperature, speed, and chemical or gas concentration. Typically they convert such physical or chemical quantities into an electrical signal, which, when calibrated, can be used as a proxy for the quantity itself. Below, three examples of microsensor applications to environmental problems are presented: (1) oxygen and pH sensors applied to acid-mine drainage, (2) nitrate sensors for biogeochemical
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Integrating Multiscale Observations of U.S. Waters chemical applications, and (3) “lab-on-a-chip” designs for detecting pathogens in wastewater. Microsensor Arrays for Biofilm Activity Associated with Acid-Mine Drainage Mining activities expose rock surfaces to contact with air and water. Any sulfides present in such mine tailings, waste rock piles, or underground cavities can be oxidized under these conditions. Such oxidation reactions can lead to highly acidic conditions and elevated concentrations of dissolved metals such as iron, manganese, lead, cadmium, mercury, zinc, and copper. This highly acidic, metal-rich water can pollute streams and rivers and is a major environmental problem throughout the world, from the coal regions of Pennsylvania to the silver mines of Bolivia. Biogeochemical processes play an important role in controlling metal dynamics within acid-mine impacted streams. The reaction rates of acid-mine drainage reactions are accelerated many orders of magnitude by metal- (e.g., iron) and sulfur-oxidizing bacteria. As in other bacterial processes, bacteria adhere to surfaces in aqueous environments and excrete a slimy substance or biofilm that attach them to mineral surfaces. Recently, Haack and Warren (2003) used microelectrodes to probe in situ the dissolved oxygen and pH profiles within biofilms in an acid-mine drainage stream associated with a nickel mine in Ontario, Canada (Figure 2-2). Diel profiling with the microelectrodes demonstrated that biofilm oxygen and pH gradients varied both spatially and temporally, demonstrating that the biofilms are highly dynamic biogeochemical environments. By combining this in-situ microelectrode field analysis with sampling of metals and microorganisms, the authors demonstrated that hydrous metal oxide (HMO) minerals within the biofilm exert important influences on the concentrations of metals such as Ni, Co, and Cr, and that the HMOs themselves are effected by seasonal and diel processes. Combination of such high-resolution mechanistic information with more traditional field sampling and remote sensing can help to understand better the physical, chemical, and biological behavior of the acid-mine drainage sources, and to develop more-focused monitoring and remediation strategies. Some of the remote sensing methods that could be combined with these new measurements include airborne thermal infrared and the airborne visible and infrared imaging spectrometer (AVIRIS). As the acid-mine drainage reactions are exothermic and generate warm water, airborne thermal infrared (TIR) can be applied to identify hidden sources of acid-mine drainage (Sams and Veloski, 2003) discharging into streams. The content of alkalinity-generating rocks within watersheds affected by acid-mine drainage determines the capacity of the streams to neutralize the acid. It is possible to identify and determine the areas covered by carbonate minerals such as calcite within the watershed using AVIRIS (Dalton et al., 2004).
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Integrating Multiscale Observations of U.S. Waters FIGURE 2-2 A microelectrode array probing pH and dissolved oxygen gradients in a biofilm associated with acid-mine drainage, Ontario, Canada. SOURCE: Reprinted, with permission, from L. Warren, McMaster University, Canada. Scaleable Nitrate Microsensors in the Form of a Plant Root Microfabrication processes are enabling the creation of mass producible sensors in form factors more attuned to the environmental media in which they are deployed. For example, flexible, miniature, and inexpensive nitrate sensors are being fabricated by electropolymerizing pyrrole onto carbon fiber substrates, using nitrate as a dopant (Bendikov et al., 2005). The resulting sensor (length 1 cm or less; diameter 7 to 10 um; Figure 2-3) is a size and shape that is ideal for deployment in the pore space of soils and sediments. In addition, these highly selective electrodes require no excitation voltage and can quantify nitrate concentrations to just below 10−4 M (3 ppm). In-situ tests have revealed that prototypical microsensors of this type are short-lived in the environment, losing their sensitivity in a span of hours to days (depending on conditions), but can be reconditioned and redeployed within 12 hours. Research is underway on modified fabrication techniques and materials (Hatchett and Josowicz, 2008) as well as microfluidic strategies for sample pretreatment and may soon produce more robust microsensors for long-term deployments.
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Integrating Multiscale Observations of U.S. Waters integration strategies, its current limitations must be well understood in order to focus research priorities. Here three critical areas for research—rather than an exhaustive list—are described as examples of the significant challenges that lie ahead for remote sensing of hydrologic and related parameters. These areas include the impact of heterogeneity on enhanced retrieval algorithms, enhanced antenna engineering, and the role of geodetic satellites in hydrology. The focus here is limited to liquid water and snow. Although the dynamics and mass balances of glaciers and ice sheets are well monitored by satellite (e.g., Rignot and Thomas, 2002; Velicogna and Wahr, 2006a,b), a full assessment of the role of cryospheric remote sensing in integrated hydrologic observations is beyond the scope of this particular study. Microwave emissions from the land surface have a strong sensitivity to several hydrologic variables, including soil and snow water content, as well as other land-surface characteristics such as vegetation water content, soil texture, and surface roughness (e.g., Jackson et al. 1999). As such, the development of inversion algorithms for the production of minimally biased estimates of the variable of interest, e.g., soil moisture, from passive and active microwave measurements, has proven challenging. Stated more simply, the variable of interest (such as soil moisture) is not directly observed by the sensor (a problem endemic to many types of environmental sensors) and has to be indirectly estimated using ancillary data. The inversion problem is further complicated by the need to account in algorithm development for the tremendous heterogeneity of land surface within a sensor footprint. Hence, there is a pressing need for focused efforts to develop enhanced algorithms that account for the scaling of relevant heterogeneous variables within satellite footprints to best understand the relationship between observables and derived hydrological products. The integration of different types of data—traditional measurements such as grab samples, in-situ sensor network data, and airborne data—will help in the development, verification, and validation of these algorithms. The problems posed by land-surface heterogeneity underscore the need for terrestrial observatories for hydrologic and related environmental sciences; it is the integration of in-situ aircraft and satellite observations that will ultimately yield significant advances in understanding Earth processes, while also advancing individual sub-disciplines. Considerable engineering challenges also pose obstacles to advancing space-based hydrological observation. For example, remote sensing of the moisture content of surface soils has been most successful using passive microwave instruments. The capability currently exists to retrieve surface (0-2 cm) soil moisture estimates from the space-based Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instrument at frequencies of about 6 GHz (C band) (Njoku et al., 2003). However, previous research has shown that frequencies in the 1 to 3 GHz (L band) range are better suited for soil moisture retrievals because the microwave emissions emanate from within a deeper soil layer (0-5 cm) (e.g., Jackson et al.,
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Integrating Multiscale Observations of U.S. Waters 1995, 1999). Moreover, since biosphere-atmosphere moisture exchanges are influenced by the soil moisture within the entire vegetation root zone, the ability to remotely monitor soil water within the deeper soil profile would be a major advance in Earth observation science. The inherent spatial resolution of AMSR-E soil moisture data is 60 km, yet higher resolution (e.g., 10 km) is optimal for hydrometeorological application. The spatial resolution of microwave data is controlled by antenna size: wider antennas yield higher spatial resolutions. For example, a spaceborne L-band passive microwave instrument would require a 6-meter wide antenna to deliver soil moisture at 40-km spatial resolution. While land-based systems such as ground-penetrating radar offer promise for monitoring deeper soil water content, and airborne L-band passive microwave instruments have proven soil moisture mapping capabilities (Wang et al., 1989; Jackson et al., 1995, 1999, 2002), innovative sensor design and antenna technologies are required before the success of ground and aircraft systems can be achieved in space. A third avenue for pursuing major advances in hydrologic remote sensing is space geodetic remote sensing. Water cycle observations derived from satellite altimetry (e.g., TOPEX/Jason) and from space-based measurements of Earth’s time variable gravity field (e.g., the Gravity Recovery and Climate Experiment, GRACE) provide opportunities to observe terrestrial hydrology in new and informative ways (Alsdorf and Lettenmaier, 2003; Famiglietti, 2004; Lettenmaier and Famiglietti, 2006). Birkett (1995, 1998), Birkett et al. (2002), and Alsdorf et al. (2001) have demonstrated that satellite altimetry can successfully monitor surface-water heights from rivers, lakes, and floodplains. Satellite altimetry missions such as TOPEX/Poseidon and Jason, which are optimized for ocean applications, have great promise for monitoring the elevation and storage changes of inland water bodies, but unless future altimetry missions (Alsdorf et al., 2007) give greater consideration to continental freshwater targets, their utility in hydrology will be limited. The GRACE mission (Tapley et al., 2004) is now providing estimates of total (combined snow, surface water, soil moisture, and groundwater) water storage variations from the large basin to continental scales and providing new insights into the hydroclimatology of terrestrial water storage. When combined with ancillary data, GRACE can also be used to estimate evapotranspiration (Rodell et al., 2004; Ramillien et al., 2005; Swenson and Wahr, 2006) and discharge (Syed et al., 2005) for large (200,000 km2) river basin systems. While interferometric synthetic aperture radar (InSAR) has been shown to successfully monitor ground deformation resulting from groundwater recharge and discharge (Bawden et al., 2001), the GRACE mission may provide the only viable means for quantifying groundwater storage changes using remote sensing (Rodell et al., 2006; Yeh et al., 2006). An InSAR mission has been proposed by the Decadal Survey (NRC, 2007) for applications such as glacier velocity and surface elevation. With such new opportunities for hydrologic obser-
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Integrating Multiscale Observations of U.S. Waters vation comes the challenge to the hydrologic community to embrace these non-traditional data types and to incorporate them into modeling and analysis studies. The need for such methods is highlighted in Chapter 4, for example in “Impacts of Agriculture on Water Resources: Tradeoffs between Water Quantity and Quality in the Southern High Plains,” and “Hydrological Observations Networks for Multidisciplinary Analysis: Water and Malaria in Sub-Saharan Africa,” where there is a great need for integrative data at large spatial scales or in remote locations. Beyond the three challenges describe here, several other factors impede progress towards integrating remote observations with in-situ and aircraft data. For example, some critical stocks, fluxes, and properties of the terrestrial water cycle remain poorly monitored. Inadequate spatial and temporal sampling rates, deficiencies in retrieval algorithms, technological obstacles, or simple lack of a dedicated mission, all contribute to gaps in a comprehensive, satellite-based hydrology observing system. For example, while liquid precipitation is well monitored from space, the Tropical Rainfall Monitoring Mission (TRMM) does not provide higher-latitude coverage. Remote sensing of snowfall and snow depth are critical research areas, in particular due to the rugged conditions and inaccessibility of high-altitude basins. Chapter 4’s “Mountain Hydrology in the Western United States,” elaborates on the pressing need for these measurements. Remote sensing of evapotranspiration remains problematic owing to its reliance on several other variables (e.g., surface radiation and meteorology), many of which are not well measured from space. Satellite monitoring of water quality is an important research frontier. All of the research areas described above are certain to advance with development of embedded sensor networks, including new sensors, and integration in environmental observatories. It is crucial, however, for researchers working on different systems and scales to keep abreast of advances in related disciplines and to work collaboratively, so that the full potential of spaceborne observations of hydrological sciences can be reached. In spite of the limitations identified here, great progress has been made towards routine monitoring of certain water fluxes and storages, while continued progress is anticipated from current and upcoming missions. Table 2-3 gives an overview of current and near-future hydrologic remote sensing capabilities. For example, TRMM has been successful in providing precipitation observations that, when combined with other sensor data and model output, form the backbone of the widely-used Global Precipitation Climatology Project (GPCP) products (Huffman et al., 1997). Snow extent, because of its high albedo, has traditionally been well observed by sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) (Hall et al., 2002), while snow water equivalent products, e.g., from AMSR-E (e.g., Kelly et al., 2004), are beginning to emerge. AMSR-E now
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Integrating Multiscale Observations of U.S. Waters TABLE 2-3 Overview of Current and near-future Hydrologic Satellite Remote Sensing Capabilities Hydrologic Variable Satellite Sensor Spatial Resolutiona Time Period of Observation Snow extent Scanning Multichannel Microwave Radiometer (SMMR) 27 km × 18 km to 148 km × 95 km 1978-1987 Special Sensor Microwave/Imager (SSM/I) 15 km × 13 km to 69 km × 43 km 1987-present Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 500 m × 500 m 2/2000-present Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua 500 m × 500 m 7/2002-present Snow water equivalent Scanning Multichannel Microwave Radiometer (SMMR) 27 km × 18 km to 148 km × 95 km 1978-1987 Special Sensor Microwave/Imager (SSM/I) 15 km × 13 km to 69 km × 43 km 1987-present Advanced Microwave Scanning Radiometer – E (AMSR-E) 6 km × 4 km to 75 km × 43 km 5/2002-present Surface-water height European Remote Sensing-1 (ERS-1) Radar Altimeter Every 320 m along track 1991-2000 TOPEX/Poseidon Every 580 m along track 1993-2005 European Remote Sensing-2 (ERS-2) Radar Altimeter Every 320 m along track 1996-present ENVISAT Radar Altimeter-2 Every 320 m along track 2002-present Jason-1 Every 290 m along track 2002-present Geosat Follow-on Every ~ 600 m along track 2000-present Soil moisture European Remote Sensing (ERS)-1 SAR Variable, ~ 20 m × 15,8 m 1991-2000
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Integrating Multiscale Observations of U.S. Waters ERS-2 SARSatellite Sensor Variable, ~ 20 m × 15,8 m 1996-present ENVISAT ASAR 150 m × 150 m 2002-present Advanced Microwave Scanning Radiometer-E (AMSR-E) 51 km × 29 km 5/2002-present Groundwaterb Gravity Recovery and Climate Experiment (GRACE) 450 km × 450 km 3/2002-present Total water storage Gravity Recovery and Climate Experiment (GRACE) 450 km × 450 km 3/2002-present Precipitation Geostationary Operational Environmental Satellite (GOES) 4 km × 4 km or 8 km × 8 km 1978-present Special Sensor Microwave/Imager (SSM/I) 25 km × 25 km 1987-present Tropical Rainfall Measuring Mission (TRMM/TMI) 0.25° × 0.25° 1998-present Advanced Microwave Sounding Unit (AMSU) 4 km × 4 km 16 km × 16 km or 48 km × 48 km 1998-present 1998-present Evapotranspirationa Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 500 m × 500 m 2/2000-present Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua 500 m × 500 m 7/2002-present Gravity Recovery and Climate Experiment (GRACE) 450 km × 450 km 3/2002-present Streamflowa From surface-water height data above Gravity Recovery and Climate Experiment (GRACE) 450 km × 450 km 3/2002-present anative resolution; multiple entries imply frequency dependence. bnot measured directly; ancillary hydrological data required to derive variable.
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Integrating Multiscale Observations of U.S. Waters provides estimates of surface soil moisture (Njoku et al., 2003), and GRACE produces estimates of monthly variations in total water storage from the large river basin to continental scales (e.g., Syed et al., 2008). Surface-water heights can be monitored from satellite altimeters for several inland water bodies and large rivers, from which discharge estimates can be derived (Alsdorf et al., 2007). The following three examples demonstrate the role that satellite remote sensing can play as a key element in any strategy to integrate diverse measurement types across space-time scales to provide best-available information to decisionmakers and researchers. The focus is on new areas for hydrological remote sensing in order to highlight the types of research, understanding, and products that the satellite information will enable. Remote sensing and groundwater storage variations. Groundwater accounts for roughly 20 percent of global freshwater consumption. However, accurate monitoring of groundwater storage variations, including recharge and discharge, is a difficult task owing to the sparse nature of groundwater well measurements and the dearth of in-situ soil moisture sensors. Several authors have demonstrated the strong correlation between InSAR observations of land deformation with aquifer compaction (Galloway et al., 1998; Hoffman et al., 2001), with seasonal variations in groundwater storage (Watson et al., 2002), and with groundwater pumping and recharge (Bawden et al., 2001; Lu and Danskin, 2001). More recently, Rodell et al. (2006) and Yeh et al. (2006) have shown that observations of surface and unsaturated water mass can be removed from GRACE estimates of total water variations (in the High Plains aquifer, in the Mississippi basin, and in Illinois) to isolate the groundwater storage change signal. However, these latter studies highlight the need for greatly enhanced soil moisture and surface-water storage observations to produce minimally biased groundwater storage change estimates. Hence, a blueprint for a groundwater observing system in a large aquifer system such as the High Plains would include an embedded sensor network of groundwater monitoring wells, in-situ soil moisture sensors, gravimeters, airborne and remotely sensed soil moisture, and GRACE data, assimilated into a spatially distributed hydrological or groundwater model. The observing system would provide aquifer-average and spatial patterns of groundwater recharge, water table variations, and discharge, which would be available in near-real-time to the user community, using cyberinfrastructure technologies. Without the combination of in-situ sensing, remote sensing constraints provided by GRACE, and the aircraft-satellite-sensed soil moisture, a viable observing system would not be possible. Remote sensing and the three-dimensional distribution of terrestrial waters. Given the importance of fresh water to the human population, it is surprising to note there is no comprehensive, large-area, freshwater observing system that can describe the lateral and vertical distribution of surface, soil, and groundwater
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Integrating Multiscale Observations of U.S. Waters across and through the landscape. The groundwater observing system outlined above can be extended to form a terrestrial waters observing system by the addition of snow and surface-water monitoring. An array of advanced sensors for snow depth (see Chapter 4, Mountain Hydrology in the Western United States for a detailed discussion of the challenges for advancing snow remote sensing) and surface-water storage could be deployed with the soil moisture network for in-situ tracking of surface mass variations. As above, airborne and satellite soil moisture remote sensing would provide critical boundary conditions. A hydrology-specific satellite altimetry mission is an essential component in this framework, for without it there is no way to routinely monitor seasonal variations in surface-water storage. Again, a data assimilating model could serve as the integrator for the various data types, and its output would include maps of snow, surface, soil, and groundwater storage. The National Operational Hydrologic Remote Sensing Center’s (NOHRSC) effort to integrate airborne and ground-based snow survey data with a weather-forecast model to estimate snow water equivalent provides an example of how this can be accomplished (http://www.nohrsc.nws.gov). A three-dimensional characterization of terrestrial water distribution would be an invaluable contribution to water management and hydrologic research. Moreover, it would form a first step towards characterizing the three-dimensional circulation of surface and subsurface waters, which is critical for understanding water and contaminant flowpaths. Remote sensing and water quality along coastal margins. Over half of the U.S. population resides in coastal counties, where most rapid growth rates in the country are occurring (Crossett et al., 2004). Unfortunately, degradation of land, air, and water resources is a consequence of the continued urbanization occurring in these regions. Remote sensing of terrestrial and coastal water quality along land-ocean margins is an important frontier in satellite monitoring of environmental quality. In fact, it is now well documented that the quality of terrestrial freshwater inputs into the coastal zone is a critical determinant of beach water quality (Ahn et al., 2005). If freshwater remote sensing methods can be enhanced, they could be combined with satellite observations of precipitation, soil moisture, river heights, coastal ocean water quality, and existing sensor web technologies for in-situ monitoring in both land and coastal waters. The satellite data could serve as pods in the sensor web, triggering intensive sampling in regions where precipitation is actively falling or anticipated, or as regional soil moisture or river heights increase and contaminants are mobilized, or with the spreading of a freshwater or coastal plume. In Southern California for example, where the beach water quality sampling takes a full day, results are often obsolete by their time of release (Jeong et al., 2006). Consequently, millions of beachgoers are either put at risk from late warnings, or are inconvenienced by unnecessary closures. A land-
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Integrating Multiscale Observations of U.S. Waters ocean margin water-quality observing system would integrate sensor web data from river, groundwater, and wastewater systems along with airborne and satellite fresh and coastal ocean water quality. A coupled, regional land-ocean model would assimilate these data and provide real-time forecasts of beach water quality, greatly enhancing capabilities for timely beach closure. As in the examples above, the synoptic view provided by remote sensing would be essential for integrating sensor web data into a viable scheme for real-time prediction along large areas of densely populated coastline. The Southern California Coastal Observing System (SCCOS); (http://www.sccoos.org) provides a good example of how such a system would function, but primarily for the ocean. The Real-Time Coastal Observation Network (ReCON; Ruberg et al., 2007) offers a second example for the North American Great Lakes, but as with the SCCOS, for the water body only, without significant monitoring and modeling of the contributing watersheds. Conversely, the South Florida Water Management Case Study describes a comprehensive, advanced terrestrial surface-and groundwater monitoring system that could be readily linked to an ocean observing system like SCCOS or ReCON. Remote sensing of surface-water heights, inundation extent, and groundwater storage changes could be integrated into existing surface- and groundwater monitoring and modeling activities in South Florida, and coupled to a coastal observing framework. Such an integrated observing system would not only greatly improve inland and coastal water-quality monitoring and prediction, but would enhance decisionmaking information streams with important implications for balancing economic development and societal demands for wetland restoration in the Everglades region. SENSOR MAINTENANCE It should be clear from the discussions in this chapter that a major issue is the cost and maintenance of sensors at all scales. This is not only true for sophisticated chemical sensors; even off-the-shelf technology that senses fairly basic parameters (e.g., turbidity, chlorophyll, dissolved oxygen, oxidation-reduction potential, and temperature) can pose this challenge. Maintenance problems can easily consume any perceived cost savings of technologically advanced sensors, and few entities may be interested in providing financial support for ongoing operations and maintenance of a network. Operation and maintenance costs may even be a critical and understated problem in the National Science Foundation (NSF) Environmental Observatory programs, and NASA and NOAA satellite programs have not been immune to the problem either. Therefore, observations programs at any scale may wish to carefully consider these costs at an early stage of project planning.
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Integrating Multiscale Observations of U.S. Waters COMMUNITY INVOLVEMENT For certain types of environmental pollutants such as lead and arsenic, as well as pathogens and diseases such as malaria, communities can become important components of sensor networks, empowering individuals to assist scientists with monitoring and control. For example, in the Chapter 4 case study on “Water and Malaria in Sub-Saharan Africa,” community members are being trained to be active participants in a study of monsoons and malaria in the semiarid Sahel zone of Africa. This group is studying the environmental determinants of malaria outbreak using observations, and numerical simulation, including hydrologic modeling. Direct community involvement has also proved effective in measuring lead contamination in the inner-city and arsenic contamination in India and Asia, using test kits. In the United States, especially in the eastern states, citizens have formed numerous watershed groups. Their purpose is to help to maintain the environmental health of the streams or to work to recover the health of impacted streams. One activity that is common in these groups is the environmental monitoring of the rivers. Often they are supported and advised by nongovernment and government organizations. For example, the Northern Virginia Soil and Water Conservation District provides training and equipment to volunteers in the assessment of ecological conditions in streams based on the presence and abundance of benthic macroinvertebrates. Volunteers also take chemical measurements such as pH, total dissolved solids, temperature, discharge, nitrate/nitrite, and turbidity. Experienced monitors, often including members of the scientific community, train volunteers to implement appropriate methodologies for aquatic monitoring. Attention to such detail is important because some methodologies (e.g., for sampling benthic macroinvertebrates) are region specific. Training volunteers makes it possible to study the streams more frequently and to have more monitoring stations. These types of activities can be a mechanism leading to more comprehensive studies and conscious involvement of the community in environmental issues. A long-standing citizen-based observation program is the Cooperative Observer Program of the National Weather Service (NWS). This is a network of over 11,000 volunteers, established in 1890, to make weather observations and establish and record climate conditions in the United States, with a traditional emphasis on agriculture. Today the network is increasingly used to support meteorological and hydrological forecasts and warnings and to verify forecasts. Making good use of active community involvement in sensing requires careful training and validation of results, as well as detailed calibration of test kits against traditional laboratory-based analysis. Nevertheless, such community involvement expands scientific databases when integrated with other traditional and developing methods in the hydrologic and related sciences.
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Integrating Multiscale Observations of U.S. Waters SUMMARY This chapter summarizes current and emerging sensor and sensor networking technologies that are being developed for measuring hydrological and environmental processes. The research and development activities related to embedded sensor networks, biogeochemical sensor technologies, and at larger scales sensors designed for airborne and space platforms, offer significant opportunities to advance our understanding of critical hydrological and environmental processes through improved observations at different scales. The committee heard a number of presentations from federal agency scientists, academics, and private industry. These presentations ranged from descriptions of current measurement approaches (e.g., NWS snow measurement techniques in the Sierra Nevada that have not changed appreciably for 100 years) to applications of sensors networks (e.g., the real-time control storm runoff). From these presentations and related material reviewed by the committee, emerged Figure 2-10, which describes the participants and summarizes the steps FIGURE 2-10 A summary of the steps involved in advancing from experimental development of a sensor through its operational deployment. The role of different, major activities and participants are shown as contributing to the process at different stages, and the federal agencies that would have major interest in these stages are also shown. USDA = U.S. Department of Agriculture.
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Integrating Multiscale Observations of U.S. Waters in going from sensor development to operational deployment. There are many players involved in each step, and Figure 2-10 attempts to list them, and much on-going activity. Nonetheless, the process is far from smooth or seamless. As examples, current operational sensor networks generally have no plans to utilize new sensor and sensor networking technologies that would integrate various types of measurements to produce improved operational observations. At the same time, most university centers working on sensor development have no plans or resources to test their sensors within larger integrated field demonstrations that incorporate a variety of sensors, inter-connected through embedded networks and cyberinfrastructure for their potential delivery to data users. From Figure 2-10, the committee sees Balkanization of the sensor development process, and subsequent gaps in agency programs, including potentially new roles for NSF as the nation’s primary research funding agency. Specific recommendations are offered in Chapter 5 for addressing these gaps.