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Data assimilation is increasingly being used to merge remotely sensed land surface variables such as soil moisture, snow, and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (1) parameter estimation to calibrate the land model to the climatology of the soil moisture observations and (2) scaling of the observations to the model's soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model's climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue. [ABSTRACT FROM AUTHOR]

This paper presents a method to estimate forest parameters and surface topography from NASA's Geosciences Laser Altimeter System (GLAS). Their potential use as observational inputs to models is demonstrated using a wind-risk model for the UK, ForestGALES. Relative heights above ground were used as biophysical parameter estimators. Top Height was estimated with R² = 0.73, RMSE = 4.5 m. Diameter at breast height estimates differed for conifer-dominated stands (R² = 0.72, RMSE = 0.07 m) and for stands containing mostly broadleaves (R² = 0.41, RMSE = 0.11 m). Ground elevation estimation produced R² = 0.99 7, RMSE = 2.2 m. These three parameters were applied to ForestGALES for stand-level assessment of wind-throw risk. Stability is sensitive to small differences in tree dimensions, and therefore vegetation parameters require greater accuracy than those currently retrievable from GLAS to more reliably determine risk of wind-throw. Future satellite lidar missions such as NASA's DESDynI sensor aim to produce improved vegetation parameter estimation plus greater spatial coverage which would offer more appropriate inputs for forestry models. [ABSTRACT FROM AUTHOR]

Statistically defensible methods are presented for developing geophysical detector sampling plans and analyzing data for munitions response sites where unexploded ordnance (UXO) may exist. Detection methods for identifying areas of elevated anomaly density from background density are shown. Additionally, methods are described which aid in the choice of transect pattern and spacing to assure with degree of confidence that a target area (TA) of specific size, shape, and anomaly density will be identified using the detection methods. Methods for evaluating the sensitivity of designs to variation in certain parameters are also discussed. Methods presented have been incorporated into the Visual Sample Plan (VSP) software (free at ) and demonstrated at multiple sites in the United States. Application examples from actual transect designs and surveys from the previous two years are demonstrated. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are simultaneously the largest cost center and greatest source of revenues for most hospitals. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. Longer than average surgery durations result in late starts not only for the next surgery in the schedule, but potentially for the rest of the surgeries in the day as well. Late starts also result in direct costs associated with overtime staffing when the last surgery of the day finishes later than the scheduled shift end time. In this article we describe a stochastic optimization model and some practical heuristics for computing OR schedules that hedge against the uncertainty in surgery durations. We focus on the simultaneous effects of sequencing surgeries and scheduling start times. We show that a simple sequencing rule based on surgery duration variance can be used to generate substantial reductions in total surgeon and OR team waiting, OR idling, and overtime costs. We illustrate this with results of a case study that uses real data to compare actual schedules at a particular hospital to those recommended by our model. [ABSTRACT FROM AUTHOR]

The fractional dispersion model for natural rivers, extended by including a first order reaction term, contains four parameters. In order to estimate these parameters a fractional Laplace transform-based method is developed in this paper. Based on 76 dye test data measured in natural streams, the new parameter estimation method shows that the fractional dispersion operator parameter F is the controlling parameter causing the non-Fickian dispersion and F does not take on an integer constant of 2 but instead varies in the range of 1.4–2.0. The adequacy of the fractional Laplace transform-based parameter estimation method is determined by computing dispersion characteristics of the extended fractional dispersion model and these characteristics are compared with those observed from 12 dye tests conducted on the US rivers, including Mississippi, Red, and Monocacy. The agreement between computed and observed dispersion characteristics is found to be good. When combined with the fractional Laplace transform-based parameter estimation method, the extended fractional dispersion model is capable of accurately simulating the non-Fickian dispersion process in natural streams. [ABSTRACT FROM AUTHOR]

We analyze the statistical properties of non-parametrically estimated functions in a functional-coefficient model if the data have a unit root. We show that the estimated function converges at a faster rate than under the stationary case. However, the estimator has a mixed normal distribution so that point-wise confidence intervals are calculated using the usual normal distribution theory rather than a Dickey–Fuller distribution. The results are used to show how one can discriminate between a unit root process and a non-linear functional-coefficient process. We illustrate the procedure using U.S. unemployment and interest rate data. [ABSTRACT FROM AUTHOR]

The heterogeneity of hydrogeologic properties at different scales may have different effects on flow and transport processes in a subsurface system. A model for the unsaturated zone of Yucca Mountain, Nevada, is developed to represent complex heterogeneity at two different scales: (1) layer scale corresponding to geologic layering and (2) local scale. The layer-scale hydrogeologic properties are obtained using inverse modeling, based on the available measurements collected from the Yucca Mountain site. Calibration results show a significant lateral and vertical variability in matrix and fracture properties. Hydrogeologic property distributions in a two-dimensional, vertical cross-section of the site are generated by combining the average layer-scale matrix and fracture properties with local-scale perturbations generated using a stochastic simulation method. The unsaturated water flow and conservative (nonsorbing) tracer transport through the cross-section are simulated for different sets of matrix and fracture property fields. Comparison of simulation results indicates that the local-scale heterogeneity of matrix and fracture properties has a considerable effect on unsaturated flow processes, leading to fast flow paths in fractures and the matrix. These paths shorten the travel time of a conservative tracer from the source (repository) horizon in the unsaturated zone to the water table for small fractions of total released tracer mass. As a result, the local-scale heterogeneity also has a noticeable effect on global tracer transport processes, characterized by an average breakthrough curve at the water table, especially at the early arrival time of tracer mass. However, the effect is not significant at the later time after 20% tracer mass reaches the water table. The simulation results also verify that matrix diffusion plays an important role in overall solute transport processes in the unsaturated zone at Yucca Mountain. [Copyright &y& Elsevier]

Presents an overview of stochastic simulation methodology as it is used in many waste-management applications. Description of the simulation procedure; Features of the Waste Isolation Pilot Plant in southeastern New Mexico; Cumulative distribution function of predicted groundwater.

The effects of different monetary rules on the rates of inflation and unemployment are studied by stochastic simulation of the Federal Reserve Board-MIT-Pennsylvania (FMP) Econometric Model and the St. Louis "Monetarist" Model. A number of heuristic and more formal statistical methods are used in evaluating the results, It is shown that simple feedback control rules--involving proportional and derivative controls--reduce the variability of the target variables relative to the rule in which the money supply is increased at a constant rate. The improvement is considerably greater in the St. Louis model than in the FMP model. [ABSTRACT FROM AUTHOR]

This article focuses on neoclassical theory of investment behavior. The authors pointed out in their "Investment Behavior and Neo-Classical Theory" that major, purportedly empirical findings of economists Dale W. Jorgenson and James A. Stephenson (J-S) depended critically upon their assumptions. The authors focused primary attention on what they now call their "maintained hypothesis," fled in part to the Cobb-Douglas production function, which constrains investment behavior to entail identically distributed lagged responses to changes in relative price and in output in accordance with long run elasticities of capital stock demand equal to unity. Utilizing a model which permitted separate, unconstrained estimation of price and output effects, the authors found that their own data for all manufacturing yielded results which contradicted their assumptions and conclusions. The authors pointed out that J-S raises some questions about the relevance of their model as an explanation of postwar investment in the United States.

Financial risk, access to capital, regulatory processes, and regional competition for limited water sources represent dominant concerns in the United States as well as the broader global water supply sector. This work introduces the WaterPaths simulation software: a generalizable, cloud-compatible, open-source exploratory modeling system designed to inform long-term regional investments in water infrastructure while simultaneously aiding regions to improve their short-term weekly management decisions, often made in response to droughts. Uniquely, WaterPaths has the capability to identify coordinated planning and management for groups of water utilities sharing water resources. WaterPaths' exploits dynamic and adaptive risk-of-failure (ROF) rules to trigger management and planning actions in temporally consistent pathways. The compact and efficient ROF-based representation of decision pathways allows WaterPaths to scale efficiently with the number of regional actors and their candidate actions. Lastly, as a platform for supporting decision making under deep uncertainty, WaterPaths accounts for a broad range of uncertainties including hydrological or climate extremes, permitting time, demand growth, effectiveness of water-use restrictions, construction costs, and financing uncertainties. To demonstrate the capabilities of WaterPaths, we introduce a new hypothetical water resources test case, the Sedento Valley. The Sedento Valley test case contains three resource-sharing water utilities that seek to regionally coordinate their policies for drought mitigation and infrastructure investment. The three utilities are challenged by a diverse set of deep uncertainties that encompass natural and human systems stressors. The Sedento Valley test case contributes a new opportunity for benchmarking decision-support tools and water-resources systems simulation software. • WaterPaths contributes a stochastic simulation to support infrastructure investment pathways. • Facilitating coordinated multi-utility regional planning and management. • State-aware dynamic and adaptive policy pathways for investment and management actions. • The simulation permits the flexible incorporation of broad categories of deep uncertainties. • The Sedento Valley hypothetical test case facilitates benchmarking decision support frameworks. [ABSTRACT FROM AUTHOR]

In this paper, the problem of distributed estimation in linear discrete-time large-scale stochastic systems is explored. The objective is to estimate the states as well as the unknown inputs of the system under partial observation of the system states by utilizing a number of estimators. The main assumption is that the estimator at each node of the system can exchange its estimated state with the neighboring estimators through communication links. With this consideration, a recursive distributed filter is introduced and its unbiasedness and minimum variance are examined. Furthermore, the necessary and sufficient conditions for the stability and convergence of the proposed distributed filter are investigated. Finally the performance on a numerical example is presented. [ABSTRACT FROM AUTHOR]

The article discusses the use of linear programming in U.S. industry. Three aspects of the application of linear programming are presented, including model building, mathematics, and computation. Many real-world industry cases have been related to finding the most economical way of transporting materials between various sources and destinations, and have been solved by special algorithms. In the mathematics of linear programming there are four main areas of research, including computational techniques that take advantage of special features of problems, methods of performing sensitivity analyses for the coefficients of the problem, methods that relate to dynamic problems, and stochastic techniques for handling uncertainty.