16 research outputs found
Synthesis, Crystal Structure and Spectroscopic Studies of 2-{(E)-[2-Hydroxyphenyl)imino]methyl} Phenol Schiff Base Molecule
Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective
Evaluation of fiber-reinforced and cement-stabilized rammed-earth composite under cyclic loading
Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications
Influence of errors in radar rainfall estimates on hydrological modeling prediction uncertainty
Experimental investigation of non-stabilized and cement-stabilized rammed earth reinforcement by Waste Tire Textile Fibers (WTTFs)
Antitumor Effect of IP-10 by Using Two Different Approaches: Live Delivery System and Gene Therapy
Reproducibility of soil moisture ensembles when representing soil parameter uncertainty using a Latin Hypercube-based approach with correlation control
[1] Representation of model input uncertainty is critical in ensemble-based data assimilation. Monte Carlo sampling of model inputs produces uncertainty in the hydrologic state through the model dynamics. Small Monte Carlo ensemble sizes are desirable because of model complexity and dimensionality but potentially lead to sampling errors and correspondingly poor representation of probabilistic structure of the hydrologic state. We compare two techniques to sample soil hydraulic and thermal properties (SHTPs): (1) Latin Hypercube (LH) based sampling with correlation control and (2) random sampling from SHTP marginal distributions. A hydrology model is used to project SHTP uncertainty onto the soil moisture state for given forcings. For statistical comparison, we generate 20 ensembles for 7 ensemble sizes. Variance in ensemble moment estimates decreases with increasing ensemble size. The LH-based approach yields less variance in the estimate of ensemble moments at all ensemble sizes, an advantage greatest with small ensembles. Implications for hydrologic uncertainty assessment, data assimilation, and parameter estimation are discussed.United States. National Aeronautics and Space Administration (NASA grant NNG05GA17G)Massachusetts Institute of Technology (Martin Family Society of Fellows for Sustainability fellowship)United States. Army Research Office (grant W911NF-04-1-0119