Disaggregation Scheme Research Articles

Strategic Design and Analysis of a Relay Network in Truckload Transportation

The truckload industry faces a serious and chronic problem of high driver turnover rate—typically more than 100%—with staggering associated costs. Among the major causes of this problem are extended on-the-road times where drivers handle several truckload pickup and deliveries successively; nonregular schedules and get-home rates; and low utilization, i.e., less mileage/unit-time per driver, which leads to low pay. We consider the strategic design of a relay network that may potentially help to alleviate this problem by providing an efficient underlying network that facilitates an assignment of drivers to home bases (domiciles to which they stay close) and generation of more predictable schedules with continuity and higher get-home rates. In relay network design, we are interested in determining a number of relay point locations, assigning network nodes to these relay points (i.e., defining domiciles), and determining the actual route (from the origin to the destination) for each truckload on the network. In doing so, we explicitly consider driver tour lengths, load imbalance at relay points, and the percentage circuity constraints. We develop an efficient Benders' decomposition-based algorithm that is significantly enhanced via strengthened Benders' cuts, cut disaggregation schemes, heuristics for improved upper bounds, and surrogate constraints. Our approach provides the ability to solve large size instances within reasonable solution times and very small optimality gaps. We also examine the effects of changes in the problem parameters on the performance of solution algorithms. Furthermore, in our computational experiments, we provide an analysis of the conditions for which relay network presents most benefits as well as incorporation of direct shipments within relay network operations.

Effect of fragment-based perturbations on the disaggregation performance of a VLB streamflow generator

In order to adequately incorporate measures of reliability in water resources planning, synthetically generated streamflow sequences are often applied in addition to historic time series. In spite of decades of research on parametric stochastic generation methods, their practical use has not been prevalent and some practitioners have been applying much simpler non-parametric bootstrap methods. The traditional bootstrap however does not provide any flows other than those in the historic record and consequently obtains synthetic sequences of low variability. This considerably limits its value as an effective stochastic generator. The Variable Length Block (VLB) bootstrap was recently developed to get rid of this limitation and to enable a more realistic replication of multi-annual flow variability by using blocks of variable length. VLB replicates annual and monthly statistics reasonably but over-estimates the minimum monthly flows and also tends to over-estimate the skewness of monthly flows. VLB also has a tendency to under-estimate serial correlation towards the middle of the year and to over-estimate correlation closer to the end and the beginning of the year. This study presents an attempt to remove these deficiencies of the VLB disaggregation scheme. The disaggregation uses a weighted average of historic fragments (monthly flow/annual flow) obtained from historic matching years with perturbations superimposed to counter the smoothing effect of averaging. The perturbations are subjectively obtained from a triangular distribution and perturbations derived to specifically recover the monthly flow structure (after smoothing) are tried out in the new scheme. These perturbations are obtained as weighted difference of pairs of fragments from historic matching years. Disaggregation using these perturbations is tested on a 5-site stochastic data generation problem. Sixty-eight years of naturalized monthly flows are used and 101 synthetic sequences are generated simultaneously at the 5 sites.The fragment-based perturbations are found to result in gross under-estimation of the minimum monthly flows and to obtain matching performance of the triangular distribution-based perturbations in replicating the other statistics. The gross under-estimation of minimum flows is considered to be the result of inadequate matching of the historic years that uses a loose classification of annual flows based on mean annual runoff. Further work will therefore seek to improve the matching of historic years.

Asymptotic normality of the mixture density estimator in a disaggregation scheme

The paper concerns the asymptotic distribution of the mixture density estimator, proposed by Leipus et al. [Leipus, R., Oppenheim, G., Philippe, A., and Viano, M.-C. (2006), ‘Orthogonal Series Density Estimation in a Disaggregation Scheme’, Journal of Statistical Planning and Inference, 136, 2547–2571], in the aggregation/disaggregation problem of random parameter AR(1) process. We prove that, under mild conditions on the (semiparametric) form of the mixture density, the estimator is asymptotically normal. The proof is based on the limit theory for the quadratic form in linear random variables developed by Bhansali et al. [Bhansali, R.J., Giraitis, L., and Kokoszka, P.S. (2007), Approximations and Limit Theory for Quadratic Forms of Linear Processes’, Stochastic Processes and their Applications, 117, 71–95]. The moving average representation of the aggregated process is investigated. A simulation study illustrates the result.

Investigation of the performance of a simple rainfall disaggregation scheme using semi-distributed hydrological modelling of the Lee catchment, UK

A simple and practical spatial–temporal disaggregation scheme to convert observed daily rainfall to hourly data is presented, in which the observed sub-daily temporal profile available at one gauge is applied linearly to all sites over the catchment to reproduce the spatially varying daily totals. The performance of the methodology is evaluated using an event-based, semi-distributed, nonlinear hydrological rainfall–runoff model to test the suitability of the disaggregation scheme for UK conditions for catchment sizes of 80–1,000 km2. The joint procedure is tested on the Lee catchment, UK, for five events from a 12 year period of data from 16 rain gauges and 12 flow stations. The disaggregation scheme generally performs extremely well in reproducing the simulated flow for the natural catchments, although, as expected, performance deteriorates for localized convective rainfall. However, some reduction in performance occurs when the catchments are artificially urbanised.

Effect of Simulated Coal-Derived Gas Composition on H2S Poisoning Behavior Evaluated Using a Disaggregation Scheme

poisoning is an important issue for solid oxide fuel cells (SOFCs) operated with syngas. The effect of simulated coal-derived gas composition on poisoning behavior was evaluated using a disaggregation scheme where the influence of content was determined separately using a typical anode-supported SOFC operated with a mixture gas, while the effect of other compositions (CO, , and ) was investigated with simulated coal-derived gas having constant and CO flow rates balanced by a mixture gas (83% and 17% ). The results indicated that the extent of poisoning was not pertinent to content when the cell was tested galvanostatically with a current density of at using a mixture gas containing 10 ppm , and the poisoning impact can be completely removed by switching to sulfur-free gas. The CO, , and high water vapor content aggravated the poisoning effect, and the performance was almost irrecoverable when the cell was tested with a 35% mixture gas containing 12.5 ppm . However, the introduction of 10% and an increase in content from 3 to 10% in the mixture gas can promote the performance recoverability to a larger extent.

Hourly Analyses of Hydrological and Water Quality Simulations Using the ESWAT Model

Detailed analyses of hydrological and water quality variables are very important to study the dynamic processes in a river basin. In this study, we have further modified the Enhanced Soil and Water Assessment Tool (ESWAT) model by incorporating hourly evapotranspiration and overland flow routing modules. Results from comparison of the performances by two ESWAT versions indicate that the modified version performed better than the original model. The modified ESWAT model has reasonably reproduced observed time series runoff and most commonly collected water quality data. In addition, input data availability at required spatial and temporal resolutions is the major bottleneck in implementing many detailed hydrological models. In this paper, we have also developed a robust methodology to successfully disaggregate daily rainfall data into hourly datasets. Furthermore, we have assessed the implications of such daily rainfall disaggregation schemes on subsequent simulation of hydrological and water quality variables at river basin level. The outcomes suggest that the multivariate rainfall disaggregation scheme better reproduced observed rainfall and runoff data.

On the value of soil moisture measurements in vadose zone hydrology: A review

We explore and review the value of soil moisture measurements in vadose zone hydrology with a focus on the field and catchment scales. This review is motivated by the increasing ability to measure soil moisture with unprecedented spatial and temporal resolution across scales. We highlight and review the state of the art in using soil moisture measurements for (1) estimation of soil hydraulic properties, (2) quantification of water and energy fluxes, and (3) retrieval of spatial and temporal dynamics of soil moisture profiles. We argue for the urgent need to have access to field monitoring sites and databases that include detailed information about variability of hydrological fluxes and parameters, including their upscaled values. In addition, improved data assimilation methods are needed that fully exploit the information contained in soil moisture data. The development of novel upscaling methods for predicting effective moisture fluxes and disaggregation schemes toward integrating large‐scale soil moisture measurements in hydrological models will increase the value of soil moisture measurements. Finally, we recognize a need to develop strategies that combine hydrogeophysical measurement techniques with remote sensing methods.

On the Disaggregation of Passive Microwave Soil Moisture Data UsingA PrioriKnowledge of Temporally Persistent Soil Moisture Fields

Water and energy fluxes at the interface between the land surface and atmosphere are affected by the surface water content of the soil, which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite mission concepts as, for example, the soil moisture and ocean salinity (SMOS) mission, are dedicated to provide global soil moisture information with a temporal frequency of a few days to capture the high temporal dynamics of surface soil moisture. SMOS soil moisture products are expected to have geometric resolutions on the order of 40 km. Mesoscale flood forecasting or water balance models typically operate at much higher spatial resolutions on the order of 1 km. It seems therefore essential to develop appropriate disaggregation schemes to benefit from the high temporal frequency of the SMOS data for hydrological applications as well as, for example, local numerical weather prediction models that are operated at a resolution of a few kilometers. This paper investigates the potential of using prior information on spatially persistent soil moisture fields to disaggregate SMOS scale soil moisture products. The approach is based on a ten-year soil moisture climatology for a mesoscale hydrological catchment, situated in southern Germany, which was generated using a state-of-the-art land-surface process model. The performance of the disaggregation algorithm is verified by comparison of disaggregated soil moisture fields with another ten-year period. To investigate the potential of the suggested disaggregation method for SMOS soil moisture products, a ten-year synthetic brightness temperature data set is generated at the 1-km scale. soil moisture is then retrieved from the aggregated brightness temperature data at the SMOS type scale of 40 km and then disaggregated using the suggested approach. The results are compared against reference soil moisture at the 1-km scale. Uncertainties in the retrieval of the SMOS soil moisture products are explicitly considered, and the uncertainties of the disaggregated fields are quantified. The developed method shows a generally good performance for large parts of the test site, where soil moisture can be disaggregated with an accuracy that is better than the 4 vol.% benchmark of the SMOS mission. As the suggested method shows high sensitivity to biased soil moisture retrievals, uncertainties of the SMOS soil moisture products will directly reflect on the absolute accuracy of the disaggregated soil moisture fields, resulting in a much worse performance under noisy conditions. Nevertheless, the resulting soil moisture distributions show that it is feasible to derive relative soil moisture distributions in these cases.

Spatiotemporal Disaggregation of Remotely Sensed Precipitation for Ensemble Hydrologic Modeling and Data Assimilation

Abstract Historically, estimates of precipitation for hydrologic applications have largely been obtained using ground-based rain gauges despite the fact that they can contain significant measurement and sampling errors. Remotely sensed precipitation products provide the ability to overcome spatial coverage limitations, but the direct use of these products generally suffers from their relatively coarse spatial and temporal resolution and inherent retrieval errors. A simple ensemble-based disaggregation scheme is proposed as a general framework for using remotely sensed precipitation data in hydrologic applications. The scheme generates fine-scale precipitation realizations that are conditioned on large-scale precipitation measurements. The ensemble approach allows for uncertainty related to the complex error characteristics of the remotely sensed precipitation (undetected events, nonzero false alarm rate, etc.) to be taken into account. The methodology is applied through several synthetic experiments over the southern Great Plains using the Global Precipitation Climatology Project 1° daily (GPCP-1DD) product. The scheme is shown to reasonably capture the land-surface-forcing variability and propagate this uncertainty to the estimation of soil moisture and land surface flux fields at fine scales. The ensemble results outperform a case using sparse ground-based forcing. Additionally, the ensemble nature of the framework allows for simply merging the open-loop soil moisture estimation scheme with modern data assimilation techniques like the ensemble Kalman filter. Results show that estimation of the soil moisture and surface flux fields are further improved through the assimilation of coarse-scale microwave radiobrightness observations.

High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.

Influence of observed and RCM-simulated precipitation on the water discharge over the Sirba basin, Burkina Faso/Niger

The forcing of a hydrologic model (ABC) by both observed and simulated precipitation from a regional climate model (MAR) has been performed over the Sirba watershed (39,000 km2) located in the Sahelian region. Two aspects have been more specifically examined: the spatial and temporal representations of precipitation. The comparison between simulated and observed discharges—using observed rainfall datasets as forcing of the hydrologic model—has shown that the representation of daily precipitation (which is mainly convective in the Sahelian region) was not sufficiently accurate to correctly simulate the hydrologic response of the watershed. Since this response drives the soil water budget and consequently the amount of evaporation in forthcoming coupling experiments, it is thus necessary to develop more realistic infra-daily precipitation associated with convective events. A new temporal disaggregation scheme has been then developed. Considering observed as well as simulated precipitation fields, this method has significantly improved the simulated discharge at the catchment outlet. The major role played by the temporal component compared to spatial component of the precipitation has been then underlined. In addition, the present study shows the unsuitability of the simulated precipitation from the RCM to directly force a hydrologic model at infra daily timescale even if the cumulative amount and the main features of the precipitation seasonal cycle are well simulated.

Framework for Assessment of Relative Pollutant Loads in Streams with Limited Data

A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural networks through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in the southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.

Spatial-temporal rainfall modelling for flood risk estimation

Some recent developments in the stochastic modelling of single site and spatial rainfall are summarised. Alternative single site models based on Poisson cluster processes are introduced, fitting methods are discussed, and performance is compared for representative UK hourly data. The representation of sub-hourly rainfall is discussed, and results from a temporal disaggregation scheme are presented. Extension of the Poisson process methods to spatial-temporal rainfall, using radar data, is reported. Current methods assume spatial and temporal stationarity; work in progress seeks to relax these restrictions. Unlike radar data, long sequences of daily raingauge data are commonly available, and the use of generalized linear models (GLMs) (which can represent both temporal and spatial non-stationarity) to represent the spatial structure of daily rainfall based on raingauge data is illustrated for a network in the North of England. For flood simulation, disaggregation of daily rainfall is required. A relatively simple methodology is described, in which a single site Poisson process model provides hourly sequences, conditioned on the observed or GLM-simulated daily data. As a first step, complete spatial dependence is assumed. Results from the River Lee catchment, near London, are promising. A relatively comprehensive set of methodologies is thus provided for hydrological application.

Statistical performances of various deterministic and stochastic models for rainfall series disaggregation

This paper compares the performance of seven disaggregation models, based on various approaches and/or concepts, for the generation of 10-min time step rainfall series from hourly rainfall series. The so-called constant disaggregation model and a linear model based on the external temporal pattern of rainfall are first considered. The other models are stochastic: the first ones are based on a given probability density function applying for the 10-min rainfall amounts of the hour to disaggregate. This probability density function is either uniform or derived from the external temporal pattern of rainfall. The other stochastic models are scaling models using canonical or microcanonical multiplicative random cascades. The comparison of the models is based on their ability to reproduce some important statistical characteristics of the observed time series: variance, skewness coefficient, wet/dry properties of 10-min rainfall amounts; rainfall amounts quantiles for different return periods; autocorrelation of 10-min rainfall amounts. A continuous hydrological simulation is next applied to produce for each generated rainfall series a continuous discharge series used afterwards for a retention design. The ability of the different disaggregation models to produce rainfall time series resulting in the same retention design than the one obtained with the observed rainfall series is finally analysed. Deterministic models as well as simpler stochastic models have rather bad performances when compared to the others. Because it is non-conservative, the model based on a microcanonical random cascade performs also very poorly. It significantly overestimates all studied statistics. Models based on microcanonical random cascades achieve the best performance. They perform reasonably well for the reproduction of rainfall statistics and almost perfectly for the reproduction of runoff and storage design variables. Results finally highlight the interest of including in the disaggregation scheme information related to the external temporal pattern of rainfall.

Development of the ecohydrological model SWIM for regional impact studies and vulnerability assessment

AbstractIn this paper the ecohydrological model SWIM developed for regional impact assessment is presented, and examples of approaches to climate and land use change impact studies are described. SWIM is a continuous‐time semi‐distributed ecohydrological model, integrating hydrological processes, vegetation, nutrients (nitrogen and phosphorus) and sediment transport at the river basin scale. Its spatial disaggregation scheme has three levels: (1) basin, (2) sub‐basins and (3) hydrotopes within sub‐basins. The model was extensively tested and validated for hydrological processes, nitrogen dynamics, crop yield and erosion (mainly in mesoscale sub‐basins of the German part of the Elbe River basin). After appropriate validation in representative sub‐basins, the model can be applied at the regional scale for impact studies. Particular interest in the global change impact studies is given to effects of expected changes in climate and land use on hydrological processes and agro‐ecosystems, including water balance components, water quality and crop yield. This paper (a) introduces the reader to the class of process‐based ecohydrological catchment scale models, (b) introduces SWIM as one such model, and (c) presents two examples of impact studies performed with SWIM for the federal state of Brandenburg (Germany), which overlaps with the lowland part of the Elbe drainage area. The impact studies provide a better understanding of the complex interactions between climate, hydrological processes and vegetation, and improve our potential adaptation to the expected changes. Copyright © 2005 John Wiley & Sons, Ltd.

A Simple Method for Spatial Disaggregation of Radiometer Derived Soil Moisture using Higher Resolution Radar Observations

The SMEX02 experiments held in June–July 2002, at Iowa demonstrated the potential of the L band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving sub pixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at much finer scale. This paper presents a simple approach for disaggregation of coarser resolution radiometer estimates of soil moisture using higher resolution radar backscatter and vegetation water content measurements. The algorithm has been applied to coincident PALS radiometer and Airsar datasets of 400 m and 30 m spatial resolutions respectively acquired during the SMEX02 campaign. PALS radiometer estimates of soil moisture at a 400 m resolution have been disaggregated to 100 m resolution.

Orthogonal series density estimation in a disaggregation scheme

In this paper we consider long-memory processes obtained by aggregation of independent random parameter AR(1) processes. We propose an estimator of the density of the underlying random parameter. This estimator is based on the expansion of the density function on the basis of Gegenbauer polynomials. Rate of convergence to zero of the mean integrated square error (MISE) and of the uniform error are obtained. The results are illustrated by Monte-Carlo simulations.

Process parameterization and temporal scaling in surface runoff and erosion modelling

AbstractIt is common for runoff and erosion models to be run at coarse time steps (e.g. daily) owing to limited data availability. However, such models are unable to capture adequately the small‐scale surface runoff and erosion processes that are dominated by, for example, precipitation characteristics at time‐scales of minutes. This dilemma calls for the development of approaches that appropriately capture short time‐scale processes when running the models at daily scales. In this paper, we assess two scaling approaches that are simple and are intended to preserve prediction quality as time scales become coarser: (i) use of effective rates of precipitation and runoff, and (ii) use of a rainfall disaggregation scheme. Runoff and erosion are simulated using a six‐parameter surface runoff model and a one‐parameter erosion model. A downhill simplex optimization algorithm is applied for parameterization at 2 min and daily time steps, using measured 2 min and daily precipitation data respectively, against measured daily runoff and erosion. The 2 min simulation results are aggregated to daily values to make comparisons with the measured plot‐scale daily runoff and erosion data for 2 years (1997–98) from six sites in the Jhikhu Khola catchment in Nepal. The runoff and erosion simulation results calibrated from the 2 min precipitation data are good (E = 0·92 for runoff and 0·80 for erosion, all sites collectively), whereas other results, particularly the erosion simulation from the calibrated daily model, are very poor. These results are taken as the reference to compare with the two different scaling approaches. The effective rate approach is poor compared with the 2 min estimates but is a considerable improvement over daily erosion estimates. The rainfall disaggregation scheme is comparable to the 2 min estimates and produces better results than the effective rate approach. This study illustrates that rainfall scaling has significant scope for temporal scaling of hydrologic processes, particularly where continuous simulation is preferred. Copyright © 2004 John Wiley & Sons, Ltd.

Dissaggregation modelling of spring discharges

Disaggregation models are basically divided into three main groups: temporal, spatial and temporal-spatial. The focus of this paper is the application of temporal disaggregation models to disaggregate the seasonal flow in some large time intervals to sub-seasonal flows in some shorter time intervals. Two basic models are applied: the original model of Mejia and Rousselle and the corrected extended Lin model one-stage disaggregation. The flow totals from some karstic springs are used. Data for five springs in different areas of Bulgaria for the aims of the study are executed. The synthetic data generation for the chosen spring stations for a new realisation of thirty years is obtained. The multi-variate lag-one auto regressive model (AR(1) model) is applied for generation of the annual flow sequences. The Lin model single-site is performed for thirty years generation period. The Lin model is an improvement compared to the original extended model. The new Lin approach succeeds in the preservation of the additivity as well as the moments. Applying the Lin model one-stage disaggregation results in consistent model parameter estimates. As a second step in the research multi-site disaggregation schemes are also applied.

Representing Spatial Subgrid-Scale Precipitation Variability in a GCM

Abstract A method for spatial disaggregation of rainfall to be used within a GCM is presented and the climate sensitivity to its parameters is assessed. The method is based on the assumption of exponential distribution of rainfall rates that are a function of the area of the GCM grid square where precipitation is occurring. When the method is applied in offline experiments, decreasing the fractional area where precipitation occurs decreases canopy interception, and, therefore, the partition of surface evaporation between leaf water evaporation and canopy transpiration. The climate, simulated using the precipitation disaggregation scheme, shows considerable changes from the simulated control climate with overall warmer and drier tropical land and warmer high-latitude continental areas. Changes in the simulated climate can be explained by direct local land–atmosphere interaction processes enhanced by regional atmospheric feedback mechanisms and remote responses to changes in tropical heating linked to diffe...