2012 AGU Chapman Conference on Remote Sensing of the ...

Numerous challenges remain, however, in integrating thesedata and providing them at scales appropriate for managingwater resources in a dynamic climate. Continuous in siturecords extend for more than 130 years in some locations ofthe U.S. These systematic records can never be duplicatedand are an invaluable source for understanding changes inthe environment. Moreover, archived hydroclimate data mayprove useful for purposes unimagined during networkimplementation, e.g., streamflow data initially collected forwater development in the U.S. West now provide the longestsystematic record of climate variability and change.Development of water budgets in the natural and engineeredenvironment remains a challenging problem, and must beinformed by data from remotely-sensing, in situ sensors, andmodels. The U.S. Geological Survey Water Census initiativehas, for example, a goal to develop water budgets for all HUC12 (hydrologic unit code) watersheds in the U.S. A piloteffort for water budget development recently was completedin the U.S. portion of the Great Lakes watershed, anddemonstrates the challenges of developing water budgets atthis scale. One challenge in water budget development isthat many important processes, e.g., evapotranspiration,cannot be directly measured. Process understanding requiresdata at the scale of the process in order develop models thatcan be used to predict effects of changes on water budgets.Data collected using in situ sensors play a critical role inprocess understanding and extension of this understandingto predictive models. Aspects of the engineered waterenvironment are not measured and must be estimatedthrough statistical sampling, models, and basinwide data,available only through remote sensing. Water quality is animportant aspect of water availability. In order to accuratelypredict the quality of water leaving a particular reservoir (i.e.,unsaturated zone, etc.), it is necessary to understand andquantify the water movement through that reservoir. This,again, requires process understanding that is dependent onthoughtfully collected in situ data and models to extrapolateto meaningful spatial scales. Although recent progress hasbeen made, there is tremendous need and opportunity fornew sensors for measuring key water-quality parameters. Thetransition of new data types and modeling results to watermanagement requires a deliberate effort. Whereas, forexample, a point rainfall amount is readily understood bywater managers, integrated, multi-sensor products that offermuch more information than a single measure currently arerarely appreciated or used in operational activities. Newsensors, more sophisticated models, and increasinglyresolved remotely-sensed data are exciting scientificchallenges, but this new information also must be madereadily available for water management and archived in aconsistent and accessible manner for future analyses.Barnes, MalloryDetection of Spatial and Temporal Cloud CoverPatterns over Hawai’i Using Observations fromTerra and Aqua MODISBarnes, Mallory 1 ; Miura, Tomoaki 1 ; Giambelluca, Thomas 2 ;Chen, Qi 21. NREM, University of Hawaii - Manoa, Honolulu, HI, USA2. Geography, University of Hawaii - Manoa, Honolulu, HI,USAAn understanding of patterns in cloud cover is essentialto analyzing and understanding atmospheric and hydrologicprocesses, particularly evapotranspiration. To date, there hasnot yet been a comprehensive analysis of spatial andtemporal patterns in cloud cover over the Hawaiian Islandsbased on high resolution cloud observations. The MODISinstruments aboard the Aqua and Terra satellites have thepotential to supply the necessary high resolutionobservations. The MODIS instrument has a spatialresolution of 250m in bands 1-2, 500m in bands 3-7, and1000m in bands 8-36 and acquires data continuously,providing global coverage every 1-2 days. The objectives ofthis study were to generate high resolution cloud cover datafrom the Terra MODIS satellite sensors and to evaluate theirability to detect the spatial patterns of cloudiness and theirdiurnal changes over the Hawaiian Islands. The TerraMODIS cloud mask product (MOD35) was obtained for themonth of January for three years (2001 – 2003). MOD35provides cloudiness data at 1km resolution twice per day(once in the morning and once at night). Monthly statisticsincluding mean cloud cover probability at each of the twooverpass times were generated by processing the dailyMOD35 cloudiness time series. The derived monthlystatistics were analyzed for diurnal changes in total amountand spatial patterns of cloudiness. Our results indicated thatthere were consistent differences in the diurnal cycle betweenwindward and leeward sides of the main Hawaiian Islands.In general, the windward sides of the islands were cloudier atnight than during the day. The leeward sides of the islands,on the other hand, were generally less cloudy at night thanduring the day. On Hawai’i, the windward (Hilo) side of theisland was cloudier at nighttime than during the day. Theleeward (Kona) side, in contrast, was less cloudy at nighttimethan during the day. The pattern was similar on Oahu,where the windward sides of the island were cloudier atnight but the leeward sides and central portion of the islandwere less cloudy at night than during the day. Kauai andMolokai demonstrated the pattern as well, with thewindward side of both islands more cloudy at night and theleeward sides less cloudy at night. Overall, there was morecontrast between cloudiness on windward and leeward sidesat nighttime than during the day. These results appeared tocorrespond with existing knowledge of the diurnal variationin precipitation. We conclude that MODIS 1 km daily cloudmask data can provide spatial patterns of cloud cover overthe Hawaiian Islands in detail. We plan to extend ouranalysis to include the Aqua MODIS cloud product(MYD35), process the full MODIS record (10+ years) for36