H13A-0955
The WATERS Network Conceptual Design
The Water and Environmental Research Systems (WATERS) Network is a collaboration between the water- related Earth science and environmental engineering communities around a series of grand-challenge and strategic research questions. The vision of WATERS Network is to transform our ability to predict the quality, quantity and use of our nation's waters. The real transformative power of the WATERS Network lies in its ability to put sustained, spatially extensive, high-frequency information in the hands of researchers, information that will resolve how natural and engineered systems respond to perturbations. This knowledge then improves process understanding, and provides better predictive capabilities. In order to do this, the WATERS Network will create a national network of observatories equipped with multimedia sensors located across a range of different climatic and geographic regions and linked together by a common cyberinfrastructure. The network will incorporate existing and new environmental and socioeconomic data at various spatial and temporal scales. Data will include physical, chemical, and biological information to characterize surface water, ground water, land, socioeconomic and behavioral information to better frame human influences. Real-time data resources will be assimilated into an information system (cyberinfrastructure) that supports analytical tools and models, networking tools, and education and outreach services. The WATERS Network is an Environmental Observatory initiative of the U.S. National Science Foundation, developed in response to community planning over the past 10 years. It is being developed for the foundation's Engineering and Geosciences Directorates to jointly propose for funding consideration through the foundation's Major Research Equipment and Facilities Construction (MREFC) account. This presentation will summarize the current status of planning for the WATERS Network. http://www.watersnet.org/
H13A-0956
WATERS – Integrating Science and Education Through the Development of an Education & Outreach Program that Engages Scientists, Students and Citizens
The need to train students in hydrologic science and environmental engineering is well established. Likewise, the public requires a raised awareness of the seriousness of water quality and availability problems. The WATERS Network (WATer and Environmental Research Systems Network ) has the potential to significantly change the way students, researchers, citizens, policy makers and industry members learn about environmental problems and solutions regarding water quality, quantity and distribution. This potential can be met if the efforts of water scientists, computer scientists, and educators are integrated appropriately. Successful pilot projects have found that cyberinfrastructure for education and outreach needs to be developed in parallel with research related cyberinfrastructure. We propose further integration of research, education and outreach activities. Through the use of technology that connects students, faculty, researchers, policy makers and others, WATERS Network can provide learning opportunities and teaching efficiencies that can revolutionize environmental science and engineering education. However, there are a plethora of existing environmental science and engineering educational programs. In this environment, WATERS can make a greater impact through careful selection of activities that build upon its unique strengths, that have high potential for engaging the members, and that meet identified needs: (i) modernizing curricula and pedagogy (ii) integrating science and education, (iii) sustainable professional development, and (iv) training the next generation of interdisciplinary water and social scientists and environmental engineers. National and observatory-based education facilities would establish the physical infrastructure necessary to coordinate education and outreach activities. Each observatory would partner with local educators and citizens to develop activities congruent with the scientific mission of the observatory. An unprecedented opportunity exists for educational research of both formal and informal environmental science and engineering education in order to understand how the Network can be efficiently used to create effective technology-based learning environments for all participants. http://www.watersnet.org/
H13A-0957
A Vision for Advancing Hydrologic Science: CUAHSI's Next Five Years
The Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI) is entering the second phase of its existence and we will highlight aspects of the planned vision for the next five years. The vision is broad, and based on both community input and experience gained over the last six years. The required infrastructure is divided into four mutually supportive projects: informatics (Hydrologic Information System), instrumentation (Hydrologic Measurement Facility), multi-disciplinary synthesis, and field facilities (observatories). Additionally, the community has proposed the establishment of a formal education and outreach program, and the development of user support services for software that is ready for release to the community. These activities build the community's capacity to operate large-scale infrastructure as envisioned by the WATERS Network, as well as provide valuable services to the community with intrinsic scientific merit. Major new initiatives are the planning of a Community Hydrology Modeling Platform (CHyMP) to improve efficiency of model construction for hypothesis testing and observatory design, and execution of community field campaigns to test the generality of mechanisms through systematic data collection in different field environments. Biennial membership meetings to assess progress towards these goals have also been proposed. http://www.cuahsi.org
H13A-0958
CUAHSI Hydrologic Measurement Facility's Multiscale Evapotranspiration Node for the Hydrologic Sciences Community
The Consortium of Universities for the Advancement of Hydrologic Sciences Inc. (CUAHSI) established the Hydrologic Measurement Facility (HMF) to transform watershed-scale hydrologic research by facilitating access to advanced instrumentation and expertise that would not otherwise be available to individual investigators. Through community input, a robust and flexible framework was established to identify, obtain, provide and disseminate the first suite of instrumentation that intends to form the basis of a new measurement facility for hydrologic research. CUAHSI HMF proposed to acquire, for community use, a suite of evapotranspiration (ET) and vadose zone instruments that includes new and emerging technology applicable for a range of scales and science questions: 2 Eddy Covariance System, an Integrated Cavity Output Spectroscopy system (for 18O in air and water), a Large Aperture Scintillometer, and a wireless soil water sensor network. In addition to instrumentation or "beam time", expert technical assistance in deployment and operation of the instrumentation, and data reduction, quality control, and post-processing support will be fully supported by a team of scientists and technicians. Upon a successful review, the ET suite will be rapidly available to support individual PIs to pursue their specific science questions through a competitive process. Additional opportunities for graduate student and undergraduate research and inquiry-based education as well as K-12 education are a cornerstone of this project. http://www.cuahsi.org/hmf.html
H13A-0959
Emerging Technologies for Integrating Multi-Scale Observations of the Hydrologic Cycle
The results are presented of a recent National Research Council study on examining the potential for integrating spaceborne observations with complementary airborne and ground-based observations to gain holistic understanding of hydrologic and related biogeochemical and ecological processes and to help support water and related land-resource management. The study was motivated by the interrelated challenges of population growth, global climate change, and regional changes in land use and land management that will increasingly stress water resources around the world. Meeting these challenges will require significant improvement in our management of water resources, which in turn will require improvements in our capacity to understand and quantify the hydrologic cycle and its interactions with the natural and built environment. Recent and potential future technological innovations in sensors (in-situ, airborne, and space-borne) and sensor networks, cyber-infrastructure, data assimilation, modeling, and decision-support tools offer unprecedented opportunities to improve our capacity to observe, understand, and manage hydrologic systems. The committee investigated a number of aspects to turning this potential into a reality. These included development and field deployment of land-based chemical and biological sensors; the role of airborne remote sensing; interagency gaps between the steps of sensor development, demonstration, and operational deployment; the coordination of federal responsibilities for measurement, monitoring and modeling; and getting the new information to those who can use it. A variety of case studies were used to illustrate the needs and opportunities for new measurement capacity, including hydrologic monitoring in the Everglades, water quantity and quality in the Southern High Plains, malaria in Sub-Saharan Africa, hydroclimatic research in the Arctic, hydrologic extremes and water quality in the Neuse River watershed, and mountain hydrology in the western U.S. The committee was chaired by Kenneth W. Potter and vice-chaired by Eric F. Wood. Additional committee members were Roger C. Bales, Lawrence E. Band, Elfatih A.B. Eltahir, Anthony W. England, James S. Famiglietti, Konstantine P. Georgakakos, Dina L. Lopez, Daniel P. Loucks, Patricia A. Maurice, Leal A. Mertes, William K. Michener, and Bridget R. Scanlon. The study and its parent entity, the Committee on Hydrologic Science, were funded by NASA, NSF, USACE, NOAA, NRC, and EPA. http://dels.nas.edu/wstb
H13A-0960
Environmental Monitoring in a Box
Current data collection techniques are rather limited and make use of very expensive sensing stations, leading to a lack of appropriate environmental observations. We present SensorScope, a collaborative project between environmental and network researchers, that aims at providing an efficient and cheap out-of-the-box monitoring system. Sensorscope is based on a self-organized multi-hop wireless network, composed of a large number of solar powered sensing stations deployed over an area of interest. These sensing stations gather various information about their environment, such as air temperature and humidity, skin temperature, solar radiation, wind speed and direcction, precipitation, soil moisture, and soir watter content. SensorScope falls into the category of time-driven networks, as the stations intermittently transmit environmental data to a sink. The latter, in turn, is able to relay this information to a database server which makes all data publicly available in real-time by means of a Goggle Maps-based web interface. The main objective of the SensorScope project is to provide a low-cost and reliable WSN-based system for environmental monitoring to a wide community. It improves present data collection techniques with the latest technology, while exceeding the requirements of most environmental research. The Sensorscope system has already been sucessfully used in three different measurement campaigns: a glacier deployment at Plain Morte (Valais, Switzerland), a wetland monitoring program (Neuchatel, Switzerland), and an alpine deployment to study rock avalanches (Valais, Switzerland) http://sensorscope.epfl.ch
H13A-0961
Diurnal variability in riverine dissolved organic matter composition determined by in situ optical measurement in the San Joaquin River, California.
Rivers have been commonly perceived as homogeneous with respect to dissolved organic matter (DOM) concentration and composition, particularly under steady flow conditions over short time periods. Few studies have evaluated the impact of short term variability (<1 day) on DOM dynamics. Here we present results from a 2006 study in the San Joaquin River (California) where we evaluated the efficacy of using in situ optical measurements of absorption and fluorescence to elucidate changes in DOM composition. The in situ optical measurements used in this study clearly showed diurnal variations in DOM, which have previously been related to both composition and concentration, even though diurnal changes were not well reflected in bulk dissolved organic carbon (DOC) concentrations. An apparent asynchronous trend of DOM absorbance and chlorophyll-a in comparison to chromophoric dissolved organic matter (CDOM) fluorescence and spectral slope S290–350 suggests that no one specific CDOM spectrophotometric measurement explains absolutely DOM diurnal variation in this system; the measurement of multiple optical parameters is therefore recommended. The observed diurnal changes in DOM composition, measured by in situ optical instrumentation likely reflect both photochemical and biologically-mediated processes. The results of this study highlight that short-term variability in DOM composition may complicate trends for studies aiming to distinguish different DOM sources in riverine systems and emphasizes the importance of sampling specific study sites to be compared at the same time of day. The utilization of in situ optical technology allows short-term variability in DOM dynamics to be monitored and serves to increase our understanding of its processing and fundamental role in the aquatic environment.
H13A-0962
Southern Sierra Critical Zone Observatory: integrating water cycle and biogeochemical processes across the rain-snow transition
The Southern Sierra Critical Zone Observatory (CZO) is establishing a rain-snow transition research platform for research by investigators from multiple disciplines and a research program aimed at yielding general knowledge and tools for understanding the interactions between water, atmosphere, ecosystems and landforms in the critical zone. A primary, overarching goal is to understand how critical zone processes control fluxes and stores of water across the landscape, and how the water cycle modulates (bio)geochemical, biological, geomorphological and soil processes in the critical zone. Five science questions define and focus the core measurement and research program: i) how do coupled hydrologic and biogeochemical fluxes vary across the rain-snow transition, ii) what is the role of extreme hydrologic events in water and biogeochemical balances, iii) to what extent does vegetation modulate or actively control the primary subsurface fluxes of water and nutrients, iv) over what time and space scales, and during what seasons, are short-circuit pathways dominant in the critical zone, and v) how does the presence of a seasonal snowpack affect the subsurface, critical zone, soils, geomorphology, biogeochemistry and hydrology, and how will the system respond as climate warms and snowpacks recede. Some unique features of the Sierra Nevada system as compared to more mesic sites include: i) hydrophobic soils, ii) islands of fertility in soils, iii) dominant role of catastrophic events, e.g. fire, and iv) spatial decoupling of decomposition from root uptake in soil profile. The rationale for measurement design, including the value of high-frequency data will be illustrated, as will the strategy for providing community data and information, and educational programs. http://snri.ucmerced.edu/CZO
H13A-0963
The Sierra Nevada-San Joaquin Hydrologic Observatory (SNSJHO): A WATERS Network Test Bed
A mountain-to-valley virtual hydrologic observatory in Central California provides a focus for data and information in support of hydrologic research, a testbed for prototype measurement systems, and guidance for development of measurement and cyber infrastructure in an actual observatory. The multiple rivers and watersheds making up the 60,000 km2 greater San Joaquin drainage are physically disconnected by mountain-front dams that provide flood control, hydropower, seasonal water delivery and recreation. However, the mountain and valley portions are institutionally connected in multiple ways. For example, each year the winter snowpack and watershed conditions determine the magnitude of annual runoff. Errors in snowpack measurements and runoff forecasts have huge economic implications for valley water users. Second, valley flood control, water quality, irrigation demand and hydropower operations have a very strong interest in influencing mountain watershed management. The broader aim of the Sierra Nevada-San Joaquin Hydrologic Observatory is to build research infrastructure and promote research for improving the knowledge base for sound hydrologic management in the Sierra Nevada, San Joaquin Valley and across the Western U.S. In the Sierra Nevada the current focus is on developing spatially distributed instrument clusters that, when blended with remotely sensed data, will improve water balance closure from hillslope to watershed scales. Five instrument clusters at or just above the rain-snow transition are in place and under development. In the San Joaquin Valley, the focus is on sensor systems for observing fertilizer application rates in agriculture, groundwater-surface water exchanges in rivers, and flow and mixing in the confluence zones between the main stem San Joaquin and tributary Merced Rivers. A common digital library and analysis framework further links the mountain and valley portions of the virtual observatory (see https://eng.ucmerced.edu/dev00/snsjno).
H13A-0964
Headwaters of the Missouri and Columbia Rivers WATERS Test Bed site: Linking Time and Space of Snow Melt Runoff in the Crown of the Continent
This NSF WATERS Test Bed Site is focused on describing and understanding fundamental hydrologic processes to develop first-order models to link time/space scales of forcing/response in snow dominated watersheds. Our work places emphasis on fundamental processes to understand recent hydrologic trends in the context of historical and pre-historical natural variability. Our efforts include development of data and tools for formatting and archiving, statistical and time series analysis, assessment of snow cover and other hydrologic variables, and integration of remote sensing data which are suitable for mountain landscapes such as the northern Rockies. A second concentration of the Test Bed is to elucidate the links between snow cover, runoff and climate within several "un-altered" watersheds, nested in scale and spanning a steep climate gradient. A third focus is the comparison between the effects of direct human disturbance (water diversion, storage, land use, etc.) and larger- scale forcing (weather patterns, global warming and climate change) within the context of natural variability. Finally, we are building a complete digital watershed with climatologic and hydrologic data meshed with water and land use data in the basin-wide physiographic and ecologic context. Our aim is to develop a strong physical foundation for continuing the WATERS network concepts into the future and over the entire headwaters of the Columbia and Missouri Rivers.
H13A-0965
FerryMon: An Unattended Ferry-Based Observatory to Assess Human and Climatically- Induced Ecological Change in the Neuse River-Pamlico Sound System, North Carolina, USA
In North Carolina's Neuse River Estuary (NRE)-Pamlico Sound (PS) System, nitrogen (N)-driven eutrophication, water quality and habitat decline have prompted the State and US EPA to mandate watershed-based N load reductions, including a total maximum daily allowable N load (TMDL). Chlorophyll a (chl-a), the indicator of algal biomass, is the measure for the efficacy of N reductions, with "acceptable" values being <40 μg chl- a L-1. However, algal blooms are patchy in time and space, making exceedances of 40 μ g L-1 difficult to track. The North Carolina ferry-based water quality monitoring program, FerryMon (www.ferrymon.org) addresses this and other environmental monitoring needs in the NRE-PS. FerryMon uses NC DOT ferries to provide continuous, space-time intensive, accurate measurements of chl-a and other key water quality criteria, using sensors placed in a flow-through system and discrete sampling of nutrients, organics, diagnostic photopigment and molecular indicators of major algal groups in a near real-time manner. Complementing FerryMon are automated vertical profilers (AVPs), which produce chl-a and other water quality indicator depth profiles with very high time and vertical resolution. In-line spectral fluorometers (Algae Online Analyzers (AOAs)) will be installed starting in late 2007, providing rapid early warning detection and quantification of algal blooms. FerryMon permits spatial characterization of trends in water quality conditions over a range of relevant physical, chemical and biological time scales. This enhanced capability is timely, given a protracted period of increased tropical storm and hurricane activity that, in combination with anthropogenic nutrient enrichment, affects water quality in unpredictable, yet significant ways. FerryMon also serves as a data source for calibrating and verifying remotely sensed indicators of water quality (photopigments, turbidity), nutrient-productivity and hydrologic modeling. Data management and communication links allow FerryMon to integrate with complementary watershed, estuarine and coastal observational programs . FerryMon's technology is readily transferable to other estuarine, large lake and coastal ecosystems served by ferries and other "ships of opportunity". http://www.ferrymon.org
H13A-0966
Baltimore WATERS Test Bed -- Quantifying Groundwater in Urban Areas
The purpose of this project is to quantify the urban water cycle, with an emphasis on urban groundwater, using investigations at multiple spatial scales. The overall study focuses on the 171 sq km Gwynns Falls watershed, which spans an urban to rural gradient of land cover and is part of the Baltimore Ecosystem Study LTER. Within the Gwynns Falls, finer-scale studies focus on the 14.3 sq km Dead Run and its subwatersheds. A coarse-grid MODFLOW model has been set up to quantify groundwater flow magnitude and direction at the larger watershed scale. Existing wells in this urban area are sparse, but are being located through mining of USGS NWIS and local well data bases. Wet and dry season water level synoptics, stream seepage transects, and existing permeability data are being used in model calibration. In collaboration with CUAHSI HMF Geophysics, a regional-scale microgravity survey was conducted over the watershed in July 2007 and will be repeated in spring 2008. This will enable calculation of the change in groundwater levels for use in model calibration. At the smaller spatial scale (Dead Run catchment), three types of data have been collected to refine our understanding of the groundwater system. (1) Multiple bromide tracer tests were conducted along a 4 km reach of Dead Run under low-flow conditions to examine groundwater- surface water exchange as a function of land cover type and stream position in the watershed. The tests will be repeated under higher base flow conditions in early spring 2008. Tracer test data will be interpreted using the USGS OTIS model and results will be incorporated into the MODFLOW model. (2) Riparian zone geophysical surveys were carried out with support from CUAHSI HMF Geophysics to delineate depth to bedrock and the water table topography as a function of distance from the stream channel. Resistivity, ground penetrating radar, and seismic refraction surveys were run in ten transects across and around the stream channels. (3) A finer-scale microgravity survey was conducted over this area and will be repeated in spring. Efforts to quantify other components of the water cycle include: (1) deployment of an eddy covariance station for ET measurement; (2) mining flow metering records; (3) evaluation of long-term stream-flow data records; and (4) processing precipitation fields. The objective of the precipitation analysis is to obtain rainfall fields at a spatial scale of 1 sq km for the study area. Analyses are based on rain gage observations and radar reflectivity observations from the Sterling, Virginia WSR-88D radar. Radar rainfall analyses utilize the HydroNEXRAD system. Data is being managed using the CUAHSI HIS Observations Data Model housed on a HIS server. The dataset will be made accessible through web services and the Data Access System for Hydrology.
H13A-0967
Applicability of Raman Spectra Distributed Temperature Sensing in Hydrologic Observatories
The extension of Raman Spectra fiber optic distributed temperature sensing (DTS) to hydrologic and environmental applications is growing rapidly. These systems offer very high spatial (~1 m) and temporal (up to 0.1 Hz) resolution of temperature along fiber optic cables of up to 30 km. It is anticipate that DTS systems will become an integral part of many long term observatories as well as become standard for most watershed and groundwater studies. Data are presented both on the performance and applicability of several commercially available DTS systems and as well as a comparison of commercially available optical fiber appropriate for hydrologic applications. Comparative tests conducted for limnological studies in Lake Tahoe CA and in the H.J. Andrews Experimental Forest in Oregon show that the tested commercially available DTS systems perform very well for many hydrologic field applications, and that temperature resolution of as low as +/-0.05 oC are easily practical for cable runs of up to 1 km. Optical fiber cable comparisons indicate that capillary tube-sealed fiber will be needed for most deep (>50m) water installations, but that much lower cost fiber could be used for stream, soil and shallow lake monitoring. Key issues identified in these trials included the role of thermal loading on fiber optic connectors and lasers, the need for calibration sections to remove instrument drift.
H13A-0968
Swiss Experiment: a New Environmental Monitoring Platform in Alpine Environment
The emerging awareness of global change clearly shows the need for an innovative way of monitoring the environment in order to better characterize and understand the undergoing change. The Swiss Experiment (SwissEx) aims at being an important trigger for the creation of a new community involving the public, environmental and IT scientists and decision makers. The main SwissEx goal is to enhance our understanding of what environmental change means for alpine society from local to regional scales and identify key mechanisms involved in natural hazards, using new generation of wireless and inexpensive sensor network technology and associated models. To investigate the spatial and temporal variability of environmental variables and in particular to analyse the water budget of an alpine area, a massive amount of in-situ observations have been collected in a Swiss watershed (Dranse, Valais). This field deployment is composed of 40 Sensorscope stations that wirelessly measure air, soil and surface temperature, soil moisture, net radiation, precipitation, wind speed and direction, 3 disdrometers that collect information on the number, the size and the speed of raindrops, an optical fibre that can identify the river exchange with the groundwater and a scintillometer that can measures atmospheric turbulence and fluxes. This contribution presents preliminary results from this field campaign.
H13A-0969
TERENO – A new Network of Terrestrial Observatories for Environmental Research
In order to address the challenges of global change, interdisciplinary research in terrestrial environmental science is of great importance. Several environmental research networks have already been established in order to monitor, analyse and predict the impact of global change on different compartments and/or matter cycles of the environment. Typically these environmental research networks have focused on specific research questions, and compartments, such as CarboEurope, FLUXNET and ILTER. The infrastructure activity TERENO (Terrestrial Environmental Observatoria) aims the establishment of a network of terrestrial observatories, defined as a system consisting of the subsurface environment, the land surface including the biosphere, the lower atmosphere and the anthroposphere. Hydrological units will be used as the basic scaling units in a hierarchy of evolving scales and structures ranging from the local scale to the regional scale for multi-disciplinary process studies. Although terrestrial systems are extremely complex, the terrestrial component in most process-based climate and biosphere models is typically represented in a very conceptual and often rudimentary way. Remedying this deficiency is therefore one of the most important challenges in environmental and terrestrial research, and we suggest that terrestrial observatories could be an important step towards a new quality in environmental and terrestrial research. For the first phase three terrestrial observatories in Germany have been identified: the Lower Rhine Basin, the metropolitan area Leipzig-Halle, and the Northern pre-Alps including the long-term research stations Hoeglwald and Scheyern. The concept of TERENO is illustrated by the Lower Rhine Basin. A monitoring concept for the Rur catchment -the largest catchment in the observatory- will be described that is capable of measuring the spatial-temporal variability of the main hydrological processes and interactions as well as the varying residence times of the terrestrial water stores. More detailed measurements and characterisation of smaller, focal catchments will be embedded within progressively larger catchments, allowing the critical evaluation and development of hydrologic scaling strategies. Specific attention will be given to install novel wireless sensor technology and hydrogeophysical measurement techniques combined with remote sensing methods (e.g. precipitation radars and airborne remote sensing platforms). http://www.fz- juelich.de/icg/icg-4/index.php?index=768
H13A-0970
A High-Temporal and Spatial Resolution Soil Moisture and Soil Temperature Network In Iowa Using Wireless Links
Over the past year we have created an in-situ soil moisture and soil temperature network in a 200 acre agricultural plot at Ames, Iowa. This work is part of a collaborative effort between researchers at The University of Iowa, and Iowa State University. The purpose of the network is to provide high temporal and spatial resolution soil moisture and soil temperature data to validate remotely-sensed observations of the terrestrial water cycle. This is part of a larger effort by the authors and collaborators to improve the quantitative value of remotely-sensed observations of the water cycle. In addition to the soil moisture and soil temperature measurements, detailed precipitation data, and atmospheric data such as air temperature, humidity, pressure, wind direction and velocity, and solar radiation data are collected. The current soil moisture network consists of 10 Iowa and Iowa State stations, each equipped with seven pairs of soil moisture and soil temperature sensors. In the future, the network will be expanded to 15 stations. At each of the 10 station the sensors pairs are deployed at depths of 1.5, 4.5, 15, 30, and 60 cm to provide a vertical profile of soil moisture and soil temperature. Prior to installation we calibrated the soil temperature sensors to within 0.1 degree Celsius. The time-domain reflectometry soil moisture measurements are adjusted for local soil conditions. At each of the 10 stations, data are collected every 10 minutes. The data are transmitted wirelessly with low power radio links to a central location. The system started collecting data at the beginning of July, 2007. One of the challenges we faced is how to provide reliable solar power to the wireless nodes, since the current crop, corn, grows up to 3 m tall, and casts dense shadows. The corn also significantly attenuates the radios signals, and the radios fell far short of their advertized ranges. Consequently, we had to use high-gain antennas, and robust retransmit communication modes. We are in the process of establishing a live Internet connection to allow remote monitoring of the network, real-time retrieval and injection of the data into a relational database for publication on the Web.
H13A-0971
Remote Environmental Monitoring With a Wireless Sensor Network System
Wireless sensors have the potential to reveal dynamic environmental variables in remote landscapes at reduced long-term costs and offer a promising approach to revolutionize environmental monitoring. Better management of surface water in remote landscapes warrants close monitoring of moisture and temperature variability. This work describes field data demonstrating the functionality of a deployed wireless network system, consisting of various soil moisture sensors. Soil water potential sensors with an imbedded thermistor were deployed in a remote meadow along a topographic gradient with dense tree canopies in Wolverton Meadows in Sequoia National Park. The sensors responded to moisture and temperature variations and the wireless system met the goal of providing informative data on dynamic responses of soil moisture to rainfall and snowmelt. The deployed sensor system functioned well during harsh winter conditions at 7000 feet, requiring low power. The study highlights measurement accuracy limitations and presents an alternative, robust wireless Zigbee sensor network, using Crossbow motes. We demonstrate that deployment, implementation and long-term field monitoring in remote and challenging environments is possible with current technologies.
H13A-0972
Decision-Making Using Real-Time Observations for Environmental Sustainability; an integrated 802.11 sensor network
Meteorological conditions have important implications on human activities. They affect human comfort, productivity, and health, and contribute to material wear and tear. The University of California, San Diego (UCSD)'s proximity to the Pacific Ocean places it in a temperate microclimate which has unique advantages and disadvantages for campus water and energy use and air quality. In particular, the daily sea-breezes provide cool, moist, and salt-laden air to campus. For the Decision-Making Using Real-Time Observations for Environmental Sustainability (DEMROES) project a heterogeneous wireless network of monitoring stations is being set up across the UCSD campus and beyond. Conditions to be monitored include temperature, humidity, wind speed and direction, surface temperatures, solar radiation, particulate matter, CO, NO2, rainfall, and soil moisture. Stations are strategically placed on rooftops and lampposts across campus, as well as select off-campus locations and will transmit data over the UCSD 802.11 wireless network. In addition to rooftop and lamppost stations, mobile stations will be deployed via remotely controlled ground and air units, and stations affixed to campus shuttle busses. These mobile stations will allow for greater spatial resolution of the environmental conditions across campus and inter-sensor calibration. The hardware consists of meteorological, hydrological, and air quality sensors connected to (a) commercial Campbell datalogging systems with serial2IP modules and wireless bridges, and (b) sensor and 802.11 boards based on the dpac technology developed in-house. The measurements will serve campus facilities management with information to feed the energy management system (EMS) for building operation and energy conservation, and irrigation management. The technology developed for this project can be applied elsewhere thereby contributing to hydrologic and ecologic observatories. Through extensive student involvement a new generation of environmental scientists and engineers will be trained to work on the planning and execution of national observatories. http://maeresearch.ucsd.edu/kleissl/demroes
H13A-0973
GPS Precipitable Water Estimates Using Station Pressures Interpolated from the North American Regional Reanalysis (NARR) and Rapid Update Cycle (RUC)
Networks of fixed GPS receivers are in widespread use for seismic and tectonic investigations. If barometric pressures were available at the GPS sites, the GPS signals could also easily deconvolved to estimate zenith vertically integrated water vapor, or precipitable water (PW), at high temporal resolutions. PW increases markedly in storms and decreases to very low levels during dry periods, such as Santa Ana episodes, and is known to exhibit considerable variation over the complex coastal to interior landscapes of California. Unfortunately most sites, having been installed for solid earth applications, do not have co-located barometers. To solve this problem, we are using geographically interpolated barometric pressures and geopotential heights from the North American Regional Reanalysis (NARR) and the Rapid Update Cycle (RUC) to estimate station pressures at the GPS antennae. These estimates provide good enough approximations to the station pressure (~1 mB) to support PW estimates within 5% or better when compared with rawinsonde values. Interpolated station pressures have been evaluated by comparing them with measurements taken at Metar sites. The Scripps Orbit and Permanent Array Center (SOPAC) maintains an archive of GPS data, some from as early as 1990, although recent years are more complete. The archive provides approximately 500 GPS observations in the California and Nevada region every hour and provides a basis for producing GPS PW time series for use in climatological studies. This model-based approach for station pressure estimation allows existing (and past) geophysical GPS networks to be used as integrated water vapor sensor networks for only the cost of computing.
H13A-0974
Lessons Learned Using Radiotelemetry for Wildland Hydrologic Research
Over the past ten years, a 13-station network of hydrometeorological sensor platforms has been installed and operated within the Upper Lake Winona basin of the Ouachita Mountains of Arkansas for hydrologic research. During this time, the author has been forced to learn many lessons while implementing and using the radiotelemetry (RT) system associated with this network. RT is the use of radio transceivers to send data and commands between the user's computer and one or more field stations where data are being collected. Once online, an RT system can eliminate the time personnel spend in the field retrieving data from automated sensors, it can allow more frequent and automated data retrieval from field sites, and it can greatly improve the efficiency of manual sampling during storm-events by allowing field conditions (e.g., streamflow stage) to be determined before crews are deployed. However, use of an RT system also presents several challenges that are not well documented. Among these challenges are the additional equipment costs, the additional implementation time to bring an RT system online, and the concern over faster equipment obsolescence. Still other concerns are the potential for increased vandalism, site access control issues, and the additional technical knowledge required to use RT equipment. These lessons lead me to offer several recommendations to those considering using RT systems: 1. The value of RT use increases with the duration of the research. 2. Be very careful of site access and control issues. 3. Buy from equipment suppliers with strong technical support services. 4. Be aware that regular field visits will still be necessary. 5. Thoroughly document all equipment, configurations, specifications, and procedures used in the RT system.
H13A-0975
Hydrologic Observatory Data Telemetry Network in an Extreme Environment
A network of hydrological research data stations on the North Slope of Alaska using radio telemetry to gather data in "near real time" will be described. The network consists of approximately 25 research stations, 10 repeater stations, and 3 Internet-connected base stations (though data is also collected at repeater stations and research stations may also function as repeaters). With this operational network, radio link redundancy is sufficient to reach any research station from any base station. The data network is driven in "pull" mode using software running on computers in Fairbanks, and emphasis is placed on reliably collecting and storing data as found on the remote data loggers. Work is underway to deploy dynamic routing software on the controlling computers, at which point the network will be capable of automatically working around problems which may include icing on antennas, satellite sun outages, animal damage, and many others. http://www.uaf.edu/water/staff/irving/AGU-2007-poster/
H13A-0976
Integrating Sensor Data and Informatics to Improve Understanding of Hypoxia in the WATERS Network Testbed at Corpus Christi Bay, Texas
The goal of the WATERS Network Testbed in Corpus Christi Bay (Texas) is to better understand hypoxia by creating a prototype Environmental Information System (EIS) that links field data collection, real-time modeling techniques, and cyberinfrastructure. In this paper, we explore the connection between the bay's bottom-water hypoxia and wind mixing by integrating several field data sets within a machine-learning model and exploring the mechanisms leading to the model results using an independent data set. K-nearest neighbor machine learning models applied to several long-term data sets indicate that wind velocities are instrumental in forecasting hypoxic events. Additionally, statistical analysis suggests that the impacts of wind vary spatially throughout the bay. Forecasting algorithms can be employed to predict not only the expected value of dissolved oxygen levels throughout the bay, but also the probability of observing hypolimnetic hypoxia. Prior values of dissolved oxygen, salinity, wind direction, wind velocity, and water temperature have been shown to play a meaningful role in influencing the DO value twenty-four hours hence. Visualizing spatial maps of expected means and variances not only illustrate potentially hypoxia regions, but areas where future sampling would be most beneficial as well. We use a short-term field data set to explore the possible mechanisms controlling the observed statistical trends in long-term data sets. Field data taken from July 2006 document a specific hypoxic episode that follows a high wind event. Analyses of temporal changes in the vertical water column support the suspected connections between wind, salinity, and hypoxia, and suggest some possible mechanisms for this connection. It is suspected that wind controls the sinking of heavy, saline water into the bottom of Corpus Christi Bay from Laguna Madre, a nearby shallower bay. This isolation of dense water from surface oxygen replenishment may be critical in hypoxia development. Field data also suggest that subsequent water column mixing (following hypoxia formation) is controlled by the wind. The link between sophisticated statistical models and mechanistic analysis is used to support both analysis methods and independent hypotheses, and also to provide new insights. Many of the analyses conducted on the short-term data set are influenced by the results from our statistical models, and results from the mechanistic analysis has influenced some statistical analysis.
H13A-0977 INVITED
Towards a Dynamic Digital Observatory: Synthesizing Community Data and Model Development in the Susquehanna River Basin and Chesapeake Bay
Physically-based fully-distributed hydrologic models simulate hydrologic state variables spatiotemporally using information on forcing (climate) and landscape (topography, land use, hydrogeology) heterogeneities. Incorporating physical data layers in the hydrologic model requires intensive data development. Traditionally, GIS has been used for data management, data analysis and visualization; however, proprietary data structures, platform dependence, isolated data model and non-dynamic data-interaction with pluggable software components of existing GIS frameworks, makes it restrictive to perform sophisticated numerical modeling. In this effort we present a "tightly-coupled" GIS interface to Penn State Integrated Hydrologic Model (PIHM; www.pihm.psu.edu) called PIHMgis which is open source, platform independent and extensible. The tight coupling between GIS and the model is achieved by developing a shared data-model and hydrologic-model data structure. Domain discretization is fundamental to the approach and an unstructured triangular irregular network (e.g. Delaunay triangles) is generated with both geometric and parametric constraints. A local prismatic control volume is formed by vertical projection of the Delaunay triangles forming each layer of the model. Given a set of constraints (e.g. river network support, watershed boundary, altitude zones, ecological regions, hydraulic properties, climate zones, etc), an "optimal" mesh is generated. Time variant forcing for the model is typically derived from time series data available at points that are transferred onto a grid. Therefore, the modeling environment can use the Observations Database model developed by the Hydrologic Information Systems group of the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI). As part of a initial testbed series the database has been implemented in support for the Susquehanna and Chesapeake Bay watersheds and is now being populated by national (USGS-NWIS; EPA- STORET), regional (Chesapeake Information Management System, CIMS; National Air Deposition Program, NADP), and local (RTH-Net, Burd Run) datasets. The data can be searched side by side in a one-stop-querying- center, www.hydroseek.org , another application developed as part of the CUAHSI HIS effort. The ultimate goal is to populate the observations database with as many catalogues (i.e. collections of information on what data sources contain) as possible including the build out of the local data sources, i.e. the Susquehanna River Basin Hydrologic Observatory System (SRBHOS) time series server.
H13A-0978
Lessons Learned from the Deployment of a Hydrologic Science Observations Data Model
The CUAHSI Hydrologic Information System project is developing information technology infrastructure to support hydrologic science. The CUAHSI Observations Data Model (ODM) is a data model to store hydrologic observations data in a system designed to optimize data retrieval for integrated analysis of information collected by multiple investigators. The ODM v1, provides a distinct view into what information the community has determined is important to store, and what data views the community. As we began to work with ODM v1, we discovered the problem with the approach of tightly linking the community views of data to the database model. Design decisions for ODM v1 hindered the ability to utilize the datamodel as an aggregated information catalog need for the cyberinfrastructure. Different development groups had different approaches to populating the datamodel, and handling the complexity. The approaches varied from populating the ODM with a bare minimum of constraints to creating a fully constrained datamodel. This made the integration of different tools, difficult. In the end, we decided to utilize the fully populate model which ensure maximum compatibility with the data sources. Groups also discovered that while the data model central concept was optimized for data retrieval of individual observation. In practice, the concept of data series is better to manage data, yet there is no link between data series and data value in ODM v1. We are beginning to develop ODM v2 as a series of profiles. By utilizing profiles, we intend to make the core information model smaller, more manageable, and simpler to understand and populate. We intend to keep the community semantics, improve the linkages between data series and data values, and enhance data discovery for the CUAHSI cyberinfrastructure.
H13A-0979
Real-Time Bayesian Anomaly Detection in Streaming Environmental Data
Recent advances in sensor technology are facilitating the deployment of sensors into the environment that can produce measurements at high spatial and/or temporal resolutions. Not only can these data be used to better characterize systems for improved modeling, but they can also be used to improve understanding of the mechanisms of environmental processes. With large volumes of data arriving in near-real time, however, there is a need for automated anomaly detection to identify data that deviate from historical patterns. These anomalous data can be caused by sensor or data transmission errors or by infrequent system behaviors that may be of interest to the scientific or public safety communities. This study develops and evaluates two automated anomaly detection methods that employ Dynamic Bayesian Networks (DBNs). Dynamic Bayesian networks are Bayesian networks with network topology that evolves over time, adding new state variables to represent the system state at the current time. Filtering (e.g. Kalman filtering or Rao-Blackwellized particle filtering) can then be used to infer the expected value of unknown system states, as well as the likelihood that a particular sensor measurement is anomalous. Measurements with a high likelihood of being anomalous are classified as such. The methods developed in this study perform fast, incremental evaluation of data as it becomes available; scale to large quantities of data; and require no a priori information regarding process variables or types of anomalies that may be encountered. Furthermore, these methods can be extended to large networks of heterogeneous sensors and can consider several data streams at once, using all of the streams concurrently to perform coupled anomaly detection. This study investigates these methods' abilities to identify anomalies in eight meteorological data streams from Corpus Christi, Texas. The results indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle filtering outperform a DBN-based detector using Kalman filtering. These methods were successful at identifying data anomalies caused by two real events: a sensor failure and a large storm.
H13A-0980
Little Bear River Test-Bed: Tools for Environmental Observatory Design and Implementation
This project is examining a special case of a general problem important for environmental observatory design by developing a set of "smart" sensors connected to a central database. The sensors collect real-time, high frequency data of easily monitored variables (e.g. turbidity), and the control system uses that information with a Bayesian Network to initiate intermittent sampling of more difficult to measure constituents (e.g. phosphorus). The real-time values are related to the wet chemistry data and used as surrogates to quantify fluxes of interest. Specific objectives include the estimation of fluxes from surrogate data, the relation of fluxes to watershed attributes and management practices, and the development of two way linkages between the sensors, a central database, and models or data analysis software, all contributing to the overall goal of improving water quality. This presentation will report on the progress on this project. We have installed water quality and streamflow monitoring instrumentation at four locations, and have installed two weather stations. We have set up the CUAHSI Hydrologic Information System Server that includes an instance of the Observations Data Model relational database that stores the data, web services that provides programmatic data access and the Data Access System for Hydrology that provides a map based interface for data access. We have established a telemetry system to transmit data from the sensors to the server and have developed tools to ingest data streams into the observations database so that they are available in real time. We have also developed a software tool for screening and quality control of the data. Our present efforts are focused on developing the logic for adaptive monitoring to trigger the collection of chemistry sampling based on information from the continuous water quality and weather sensors. Preliminary analyses have revealed some interesting and as yet unexplained diurnal fluctuations in the turbidity data that we are collecting. http://water.usu.edu/littlebearriver/
H13A-0981
The Clear Creek Envirohydrologic Observatory: From Vision Toward Reality
As the vision of a fully-functional Clear Creek Envirohydrologic Observatory comes closer to reality, the opportunities for significant watershed science advances in the near future become more apparent. As a starting point to approaching this vision, we focused on creating a working example of cyberinfrastructure in the hydrologic and environmental sciences. The system will integrate a broad range of technologies and ideas: wired and wireless sensors, low power wireless communication, embedded microcontrollers, commodity cellular networks, the internet, unattended quality assurance, metadata, relational databases, machine-to-machine communication, interfaces to hydrologic and environmental models, feedback, and external inputs. Hardware: An accomplishment to date is "in-house" developed sensor networking electronics to compliment commercially available communications. The first of these networkable sensors are dielectric soil moisture probes that are arrayed and equipped with wireless connectivity for communications. Commercially available data logging and telemetry-enabled systems deployed at the Clear Creek testbed include a Campbell Scientific CR1000 datalogger, a Redwing 100 cellular modem, a YA Series yagi antenna, a NP12 rechargeable battery, and a BP SX20U solar panel. This networking equipment has been coupled with Hach DS5X water quality sondes, DTS-12 turbidity probes and MicroLAB nutrient analyzers. Software: Our existing data model is an Arc Hydro-based geodatabase customized with applications for extraction and population of the database with third party data. The following third party data are acquired automatically and in real time into the Arc Hydro customized database: 1) geophysical data: 10m DEM and soil grids, soils; 2) land use/land cover data; and 3) eco-hydrological: radar-based rainfall estimates, stream gage, streamlines, and water quality data. A new processing software for data analysis of Acoustic Doppler Current Profilers (ADCP) measurements has been finalized. The software package provides mean flow field and turbulence characteristics obtained by operating the ADCP at fixed points or using the moving-boat approach. Current Work: The current development work is focused on extracting and populating the Clear Creek database with in-situ measurements acquired and transmitted in real time with sensors deployed in the Clear Creek watershed.
H13A-0982
Near Real-Time Sensing of Clear Creek Water Quality
The transport of sediments, nutrients, and fecal bacteria from agricultural runoff through a watershed can have deleterious effects on receiving streams. It can impair aquatic ecosystems and cause excessive export of nutrients downstream, which can contribute to hypoxia. The ability to sense sediment and nutrient concentrations with high temporal resolution in near real-time could greatly improve our ability to understand processes which affect downstream water quality. Observations by sensors placed in streams can relay measurements to databases, and data mining can be used to glean information from streaming data for statistical and mathematical assimilation. Results from models can be used to provide advanced warning of harmful events and/or implement remedial measures. The goal of this research is to use the initial station of the Environmental Field Facility located in Clear Creek, Iowa to study processes and relationships which are essential to modeling water quality throughout the entire watershed. This station consists of several components including data loggers, telemetry hardware, and water quality sensors. Measurements collected at this field facility include conductivity, dissolved oxygen, pH, temperature, and turbidity. The measurements can be used as inputs to water quality models at the hillslope scale. This data will also provide estimates of other parameters that cannot be obtained in near real-time, and will improve our understanding of fundamental biogeochemical processes which dictate water quality in Clear Creek.
H13A-0983
A Data Management Framework for Real-Time Water Quality Monitoring
CSU East Bay operates two in-situ, near-real-time water quality monitoring stations in San Francisco Bay as a member of the Center for Integrative Coastal Ocean Observation, Research, and Education (CICORE) and the Central and Northern California Ocean Observing System (CeNCOOS). We have been operating stations at Dumbarton Pier and San Leandro Marina for the past two years. At each station, a sonde measures seven water quality parameters every six minutes. During the first year of operation, we retrieved data from the sondes every few weeks by visiting the sites and uploading data to a handheld logger. Last year we implemented a telemetry system utilizing a cellular CDMA modem to transfer data from the field to our data center on an hourly basis. Data from each station are initially stored in monthly files in native format. We import data from these files into a SQL database every hour. SQL is handled by Django, an open source web framework. Django provides a user- friendly web user interface (UI) to administer the data. We utilized parts of the Django UI for our database web- front, which allows users to access our database via the World Wide Web and perform basic queries. We also serve our data to other aggregating sites, including the central CICORE website and NOAA's National Data Buoy Center (NDBC). Since Django is written in Python, it allows us to integrate other Python modules into our software, such as the Matplot library for scientific graphics. We store our code in a Subversion repository, which keeps track of software revisions. Code is tested using Python's unittest and doctest modules within Django's testing facility, which warns us when our code modifications cause other parts of the software to break. During the past two years of data acquisition, we have incrementally updated our data model to accommodate changes in physical hardware, including equipment moves, instrument replacements, and sensor upgrades that affected data format. http://www.sci.csueastbay.edu/cicore
H13A-0984
Evolving the NCSA CyberCollaboratory for Distributed Environmental Observatory Networks
Since 2004, NCSA's Cybercollaboratory, which is built on top of the open source Liferay portal framework, has been evolving as part of NCSA's efforts to build national cyberinfrastructure to support collaborative research in environmental engineering and hydrological sciences and allow users to efficiently share contents (sensors, data, model, documents, etc.) in a context-sensitive way (e.g., providing different tools/data based on group affiliation and geospatial contexts). During this period, we provided the CyberCollaboratory to users in CLEANER (Collaborative Large-scale Engineering Analysis Network for Environmental Research, now WATer and Environmental Research Systems (WATERS) network) Project Office and several CLEANER /WATERS testbed projects. Preliminary statistics shows that one in four users (among over 400 registered users) provided contents with many other reading/accessing those contents (such as messages, documents, wikis). During the course of this use, and in evaluation by others including representatives from the CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science) community, we have received significant feedback on issues of usability and suitability to various communities involved in environmental observatories. Much of this feedback applies to collaborative portals in general and some reflect a comparison of portals with newer Web 2.0 style social -networking sites. For example, users working in multiple groups found it difficult to get an overview of all of their activities and found differences in group layouts to be confusing. Users also found the standard account creation and group management processes cumbersome compared to inviting people to be friends on social sites and wanted a better sense of presence and social networks within the portal. The fragmentation of group documents between local stores, the portal document repository and email, and issues of "lost updates" was another significant concern. This poster reviews the usability feedback, identifies key issues that hinder traditional portal-based collaboration environments, and presents design changes made to the Cybercollaboratory to address them. Feedback on the effectiveness of the new design from hydrologists and environmental researchers and preliminary results from a formal usability study will also be presented. http://ecid.ncsa.uiuc.edu/cybercollab
H13A-0985
Cyberinfrastructure for End-to-End Environmental Explorations
The design and implementation of a cyberinfrastructure for End-to-End Environmental Exploration (C4E4) is presented. The C4E4 framework addresses the need for an integrated data/computation platform for studying broad environmental impacts by combining heterogeneous data resources with state-of-the-art modeling and visualization tools. With Purdue being a TeraGrid Resource Provider, C4E4 builds on top of the Purdue TeraGrid data management system and Grid resources, and integrates them through a service-oriented workflow system. It allows researchers to construct environmental workflows for data discovery, access, transformation, modeling, and visualization. Using the C4E4 framework, we have implemented an end-to-end SWAT simulation and analysis workflow that connects our TeraGrid data and computation resources. It enables researchers to conduct comprehensive studies on the impact of land management practices in the St. Joseph watershed using data from various sources in hydrologic, atmospheric, agricultural, and other related disciplines.
H13A-0986
UNH Data Cooperative: A Cyber Infrastructure for Earth System Studies
Earth system scientists and managers have a continuously growing demand for a wide array of earth observations derived from various data sources including (a) modern satellite retrievals, (b) "in-situ" records, (c) various simulation outputs, and (d) assimilated data products combining model results with observational records. The sheer quantity of data, and formatting inconsistencies make it difficult for users to take full advantage of this important information resource. Thus the system could benefit from a thorough retooling of our current data processing procedures and infrastructure. Emerging technologies, like OPeNDAP and OGC map services, open standard data formats (NetCDF, HDF) data cataloging systems (NASA-Echo, Global Change Master Directory, etc.) are providing the basis for a new approach in data management and processing, where web- services are increasingly designed to serve computer-to-computer communications without human interactions and complex analysis can be carried out over distributed computer resources interconnected via cyber infrastructure. The UNH Earth System Data Collaborative is designed to utilize the aforementioned emerging web technologies to offer new means of access to earth system data. While the UNH Data Collaborative serves a wide array of data ranging from weather station data (Climate Portal) to ocean buoy records and ship tracks (Portsmouth Harbor Initiative) to land cover characteristics, etc. the underlaying data architecture shares common components for data mining and data dissemination via web-services. Perhaps the most unique element of the UNH Data Cooperative's IT infrastructure is its prototype modeling environment for regional ecosystem surveillance over the Northeast corridor, which allows the integration of complex earth system model components with the Cooperative's data services. While the complexity of the IT infrastructure to perform complex computations is continuously increasing, scientists are often forced to spend considerable amount of time to solve basic data management and preprocessing tasks and deal with low level computational design problems like parallelization of model codes. Our modeling infrastructure is designed to take care the bulk of the common tasks found in complex earth system models like I/O handling, computational domain and time management, parallel execution of the modeling tasks, etc. The modeling infrastructure allows scientists to focus on the numerical implementation of the physical processes on a single computational objects(typically grid cells) while the framework takes care of the preprocessing of input data, establishing of the data exchange between computation objects and the execution of the science code. In our presentation, we will discuss the key concepts of our modeling infrastructure. We will demonstrate integration of our modeling framework with data services offered by the UNH Earth System Data Collaborative via web interfaces. We will layout the road map to turn our prototype modeling environment into a truly community framework for wide range of earth system scientists and environmental managers.