Investigating the Energy-Water Usage Efficiency of the Reuse of Treated Municipal Wastewater for Artificial Groundwater Recharge.

This project investigates the energy-water usage efficiency of large scale civil infrastructure projects involving the artificial recharge of subsurface groundwater aquifers via the reuse of treated municipal wastewater. A modeling framework is introduced which explores the various ways in which spatially heterogeneous variables such as topography, landuse, and subsurface infiltration capacity combine to determine the physical layout of proposed reuse system components and their associated process energy-water demands. This framework is applied to the planning and evaluation of the energy-water usage efficiency of hypothetical reuse systems in five case study regions within the State of California. Findings from these case study analyses suggest that, in certain geographic contexts, the water requirements attributable to the process energy consumption of a reuse system can exceed the volume of water that it is able to recover by as much as an order of magnitude.

STARE INTO THE FUTURE OF GEODATA INTEGRATIVE ANALYSIS.

Different kinds of observations feature different strengths, e.g. visible-infrared imagery for clouds and radar for precipitation, and when integrated better constrain scientific models and hypotheses. Even critical, fundamental operations such as cross-calibrations of related sensors operating on different platforms or orbits, e.g. spacecraft and aircraft, are integrative analyses. The great variety of Earth Science data types and the spatiotemporal irregularity of important low-level (ungridded) data has so far made their integration a customized, tedious process which scales in neither variety nor volume. Generic, higher-level (gridded) data products are easier to use, at the cost of being farther from the original observations and having to settle with grids, interpolation assumptions, and uncertainties that limit their applicability. The root cause of the difficulty in scalably bringing together diverse data is the current rectilinear geo-partitioning of Earth Science data into conventional arrays indexed using consecutive integer indices and then packaged into files. Such indices suffice for archival, search, and retrieval, but lack a common geospatial semantics, which is mitigated by adding on floating-point encoded longitude-latitude (lon-lat) information for registration. An alternative to floating-point lon-lat, the SpatioTemporal Adaptive Resolution Encoding (STARE) provides an encoding for geo-spatiotemporal location and neighborhood that transcends the use of files and native array indexing allowing diverse data to be organized on scalable, distributed computing and storage platforms.

Computational provenance in hydrologic science: a snow mapping example.

Computational provenance--a record of the antecedents and processing history of digital information--is key to properly documenting computer-based scientific research. To support investigations in hydrologic science, we produce the daily fractional snow-covered area from NASA's moderate-resolution imaging spectroradiometer (MODIS). From the MODIS reflectance data in seven wavelengths, we estimate the fraction of each 500 m pixel that snow covers. The daily products have data gaps and errors because of cloud cover and sensor viewing geometry, so we interpolate and smooth to produce our best estimate of the daily snow cover. To manage the data, we have developed the Earth System Science Server (ES3), a software environment for data-intensive Earth science, with unique capabilities for automatically and transparently capturing and managing the provenance of arbitrary computations. Transparent acquisition avoids the scientists having to express their computations in specific languages or schemas in order for provenance to be acquired and maintained. ES3 models provenance as relationships between processes and their input and output files. It is particularly suited to capturing the provenance of an evolving algorithm whose components span multiple languages and execution environments.