Large-scale statistical analysis of NLDAS variables and hydrologic web applications

The Land Data Assimilation System (LDAS) is a model developed by the National Aeronautics and Space Administration (NASA) for the purpose of quantifying the heat and water fluxes between the atmosphere and the land-surface hydrology. LDAS has two forms: National (NLDAS) and Global (GLDAS). The NLDAS grid is 1/8° with hourly and monthly estimates since 1979. The LDAS model output provides a comprehensive time-space dataset. A statistical analysis is necessary to obtain descriptive information, understand seasonal patterns, spatial distribution, and frequency distribution of the model output. The current conditions can be compared to those in the past by using statistical distributions for each variable unique to each time interval and spatial grid point. This dissertation objectives are: (1) perform a statistical analysis on the time series of NLDAS variables and model their spatial-temporal probability distributions, (2) improve data exposure through the comparison of current values with the past using web applications, and (3) evaluate the framework for access to NLDAS data. The methodology presented consists of: (1) the estimation of the NLDAS cumulative distribution functions (CDFs) on a daily and a monthly time step and development of the probability models for five variables: precipitation, runoff, soil moisture, evapotranspiration, and temperature. (2) The creation of dynamic websites displaying the maps, time series, and latest values in the NLDAS model and its relation with the historic distributions. And (3) the implementation of time-indexed and spaced-index data access procedures. The methodology is implemented using the latest technologies in high-performance computing (HPC), cloud storage and deployment, and Geographic Information Systems (GIS) that allow performing this analysis on a large dataset (NLDAS) on a national scale, using the United States as a study case. A statistical analysis of the NLDAS model output and the comparison of current values with the historic distribution provides a thorough insight of the ranges, extremes, and seasonal variation of the hydrologic variables. The exposure of large scientific datasets such as NLDAS though the use of standards and web applications can enhance its use in hydrologic sciences and engineering.Civil, Architectural, and Environmental Engineerin