Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society

Multi-model ensemble hydrologic prediction using Bayesian model averaging

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights. © 2006 Elsevier Ltd. All rights reserved

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

A framework for building efficient environmental permitting processes

Despite its importance as a tool for protecting air and water quality, and for mitigating impacts to protected species and ecosystems, the environmental permitting process is widely recognized to be inefficient and marked by delays. This article draws on a literature review and interviews with permitting practitioners to identify factors that contribute to delayed permit decisions. The sociopolitical context, projects that are complex or use novel technology, a fragmented and bureaucratic regulatory regime, serial permit applications and reviews, and applicant and permitting agency knowledge and resources each contribute to permitting inefficiency when they foster uncertainty, increase transaction costs, and allow divergent interests to multiply, yet remain unresolved. We then use the interviews to consider the potential of a collaborative dialogue between permitting agencies and applicants to mitigate these challenges, and argue that collaboration is well positioned to lessen permitting inefficiency

Catchment Scale Simulations of Soil Moisture Dynamics Using an Equivalent Cross-Section based Hydrological Modelling Approach

Physically based distributed hydrological models are useful for simulating the spatial distribution of hydrologic fluxes across the catchment under various climate and land cover change scenarios. However, complexities associated with their implementation at large scales make their applications limited. Previously, an equivalent cross-section (ECS) based distributed hydrological modelling approach was developed for first order sub-basins to reduce the computational time/effort. Here, the ECS approach is modified for semi-distributed hydrological modelling at the catchment scale. The modelling approach is implemented for a 314 km2 McLaughlin catchment located in south-eastern New South Wales (NSW), Australia that consists of 822 first order sub-basins. A 26 year long streamflow record simulated using an ECS based modelling approach are compared against daily observed streamflow and four calibrated lumped conceptual hydrologic models, and found to be consistent. Further, the simulated actual evapotranspiration and soil moisture from the ECS approach are compared against the Australian Water Availability Project (AWAP) model simulations and results found to be consistent. In addition, the temporal dynamics of simulated soil moisture from the ECS approach is consistent with the satellite derived European Space Agency Climate Change Initiative (ESA CCI) surface soil moisture data. In the ECS based semi-distributed modelling, all parameters are derived from the actual topographic and physiographic information of the catchment and none of the parameters is calibrated. Therefore, this approach has the advantage of simulating streamflow in ungauged catchments compared to lumped conceptual models. The impact of spatially distributed climatic forcing and land cover on soil moisture is investigated across four landforms (upslope, midslope, footslope and alluvial-flats) and at various soil depths. Our results show increase of mean soil moisture in shallow layers of upslope toward alluvial-flats. However, mean soil moisture in deeper horizons remained almost constant across all landforms. The variability of daily soil moisture at surface soil layers is higher than the deeper soil layers for all landforms. Our results illustrated that disaggregation of a catchment to a series of ECS at the scale of first order sub-basins, captures dynamics of soil moisture and actual evapotranspiration across the landscape and results are consistent with the climatology, land cover type, topography and soil hydraulic properties. Further, the use of ECS approach in the McLaughlin catchment reduced the number of computational units by 40 times in comparison to 3-d grid based distributed modelling setup