North Slope Decision Support System nsdss.ine.uaf.edu Presented at the DataNet Federation Consortium’s DataGrid Sessions, April 2013 Presented by Jack Hampson/Stephen Bourne, Atkins Bill Schnabel, Amy Tidwell, Univ. Alaska Kelly Brumbelow, Texas A&M Stephen Bourne, Leslie Gowdish, Atkins Research sponsored by National Energy Technologies Lab, U.S. Dept. of Energy Subjects NSDSS Overview Research Topics addressed in NSDSS Water Resources Artificial Intelligence: The Unacceptable Time Series NSDSS Overview • Decision Support System used in ice road planning on the North Slope of Alaska • Research Grant from US. Dept. of Energy. 2008-2012. • Includes Web App and Cyberinfrastructure • Cyberinfrastructure includes multiple ODM-based databases • Cyberinfrastructure includes catalog service, similar in concepts to HIS Central • Includes Model Sharing of Ice Road Plans, Water Budget Models, Lake Dissolved Oxygen Models. NSDSS Web App • MS Silverlight App • Workbench User Experience paradigm. Map is the work surface. Widgets provide functionality and float over map. • Home Bar provides access to search, data publishing, ice road planning, environmental analysis widgets. NSDSS Web App: Search and Data Exploration Type in data you want. Semantic Mediation handles not getting the exact right name (Rain = Precip = P = R = Rainfall =…) Search Area is current map extent. Search results are presented in the Data Explorer Widget. Results are: • Field data (from ODM databases) • Gridded data (from NetCDF databases) Clicking on a search result item highlights the sites on the map with the data you are looking for. Click the site to select, then click Chart data in the widget. The data is presented in the chart. Data can be adjusted in terms of time step and statistic. Data can be downloaded. NSDSS Web App: A Slope-wide Lakes Database Click on any lake on the North Slope and you get information on its name, size, fish species, and models that have been generated for it. NSDSS Web App: Data Publishing Site Panel: • View Existing Sites. • Select Site. • Add Site. Variable Panel: • View Existing Variables. • Which Variables the selected site has. • Add new variables Data Panel: • Paste in data from Excel. • Chart data to verify correctness • Commit Edits to upload data to ODM. NSDSS Web App: Lake Water Budgets and DO Models Select Lake of Interest • Select any lake on the landscape • Interested in Lake Water Budget to understand impact of removing water for ice road construction Tool suggests data to use (see discussion on the unacceptable time series) Water Budget Model is built through an interactive tree workflow. • Four steps. • As you click on items in the tree, the controls for entering data are presented in the right panel. • Here, the Step 2. Enter Inputs has been clicked. The grid shows the GCM data that must be collected to do the forecast of water budget terms (Rain, Temp, etc.). You can chart the GCM data when it is collected (see right). • Needs Rain, Snow, Temp, Net Radiation • Requires GCM data for forecast • Historic data from Sites for GCM downscaling. • Uses cyberinfrastructure and catalog service to search for data. NSDSS Web App: Ice Road Planning Top Routes • Top ten routes • Need to consider multiple routes because there are multiple criteria. Best route for one criterion might not be best for all. Ice Road Routing • Specify Start and End Point and which lakes to use. • Algorithm based on behavior of ants in finding food. • Finds best routes from start to end points based on duration of construction, construction cost, and road travel time. • Avoids sensitive vegetation, steep grade, endangered and at-risk species habitats. Ice Road Construction and Usage Schedule • Estimates start and end of Tundra season (ie. when cold enough to build ice roads) based on historic and GCM-based forecast of temperature. • Using multiple historic years and GCM forecast to estimate uncertainty in Tundra Seasons start and close. NSDSS Technology Research Topics • Subjects researched: • Maturing Water Resources Search: • Semantic mediation allows us to type “Rain” and get all data related to rain in the search area, whether it’s called Rain or Rainfall, or R, or P, or whatever. • What if we can type, “Polar Bear dens,” or “Lakes with Water Budget Models”? Can we extend search to understand a broader range of intent? • On-the-fly Unit Conversion • Data Fuzzing • What if you don’t want to say exactly where endangered species are for fear of poaching, but you want to confirm they are present in the general area? • Became an exemplar case in the NSF Data Conservancy Project. • The Undiscovered Cyberinfrastructure: • What if the client doesn’t know what databases are out there? • What if the web services methods are different at each database – ie. not standardized? • Can the central catalog not only communicate to the client which database to go to, but how to collect the data from the database? • Developed methods to generalize catalog communications, to inform the client of the name of web method and the parameters that must be presented in the call to get the data required. • Net CDF • Can a standardized NetCDF web service and related file-based database be created? • How shall it extract and provide data to the user? • What about publishing NetCDF data – model results, etc? NSDSS Technology Research Topics (cont) • Subjects researched: • Flattening the Model-building Curve: The Model-Any-Lake workflow. • How can we make it much easier to create models for hydrologic features • Is it possible to build a tool where all the lakes in the landscape are presented, and you can build a water budget, or water quality model for any lake you choose without the need for data collection and processing? • Can all of the needed data be present? Can you quality control the data right within the tool before using it? • Model Publishing and Sharing • Models saved as xml blobs in database • Models presented in Web App through map. Select a lake, see its models. Use models in later IRP analysis. Makes way for hydrologists doing the modeling, and planners doing the planning. • All IRP, Water Budget, and DO models saved in the same cyberinfrastructure • Models carry instructions on which data to pull from CI as input • Models carry result data and inter-processing data within xml. • Using GCMs for forecasting • Can GCM data be integrated into the modeling exercise and used in ensemble forecasting? • Introduced on-the-fly GCM downscaling to ensure GCM data contains local climate signature. • Works within the “model-any-lake” workflow. • Water Resources Artificial Intelligence NSDSS Technology Research Topics (cont) • Subjects researched: • Water Resources Artificial Intelligence • Cyberinfrastructure brings data, lots of data! • Need to start developing tools that “shake the cyber trees” and deliver the best data for the modeling processing we intend to do. • See the next few slides for a presentation on the Unacceptable Time Series. The Unacceptable Time Series Water Resources Artificial Intelligence Suggesting Input Data Models of many kinds require time series input. With time series standardization, tools can be developed to seek out time series that match the intent of the modeling exercise from multiple databases in the cyberinfrastructure – “Shaking the cyber trees”. Challenge is to add in human quality control at the right times in the process. Example – North Slope Lake Water Budgets From the North Slope Decision Support System Project – Dept. of Energy Grant. Tool allows you to select any lake on the North Slope and create a water budget model for it using field observations data and data from GCMs. Tool suggests best sites by searching an area around the lake for Precip, Temp, and Net Radiation. Sites are sought within a 100km radius of the target lake. Rainfall, Temp, Net Radiation, and Snow Depth Time Series Discrimination Often, more than one time series is found that will provide data. Which one is better? Algorithm balances distance from lake with time span of available data using an Index. Index = aDistance + bTimeSpan The highest index time series is suggested. Distance to lake and time span of data is considered in selecting a time series Deeper Time Series Investigation Once time series are specified, the next step is to collect the data. Once downloaded, data quality is assessed. Often, there are gaps in the data, some that make it impossible to use the data. To assess the data, the tool provides a “Check Completeness” option. In this case, the net radiation data only has observations from April to December each year. The 12-month climatology needed for the water budget can’t be made. Note that this type of quality control can only be done if the tools knows the intent of the model. The model needs observations in all 12 months of the year, and the QC routine has to check for this. Without knowledge of intent, QC has to be more general, and modeling opportunities may be lost. The Do Not Use List If a time series is deemed unusable, users can elect to find another site. At this point, the model is added to the “Do Not Use List,” which is specific to the model. Then, on suggesting another site, the tool knows to avoid the unacceptable time series.