ALBERT

Description: Prediction Intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation (LUBE) method and extend it to a multi-objective framework. The proposed methods are demonstrated using a real-world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation (GLUE) approach.

Description: The past decade has seen significant progress in characterizing uncertainty in environmental systems models, through statistical treatment of incomplete knowledge regarding parameters, model structure, and observational data. Attention has now turned to the issue of model structural adequacy (MSA, a term we prefer over model structure “error”). In reviewing philosophical perspectives from the groundwater, unsaturated zone, terrestrial hydrometeorology, and surface water communities about how to model the terrestrial hydrosphere, we identify several areas where different subcommunities can learn from each other. In this paper, we (a) propose a consistent and systematic “unifying conceptual framework” consisting of five formal steps for comprehensive assessment of MSA; (b) discuss the need for a pluralistic definition of adequacy; (c) investigate how MSA has been addressed in the literature; and (d) identify four important issues that require detailed attention—structured model evaluation, diagnosis of epistemic cause, attention to appropriate model complexity, and a multihypothesis approach to inference. We believe that there exists tremendous scope to collectively improve the scientific fidelity of our models and that the proposed framework can help to overcome barriers to communication. By doing so, we can make better progress toward addressing the question “How can we use data to detect, characterize, and resolve model structural inadequacies?”

Description: Acute hypoxia induces apoptosis of pancreatic β-cell by activation of the unfolded protein response and upregulation of CHOP Cell Death and Disease 3, e322 (June 2012). doi:10.1038/cddis.2012.66 Authors: X Zheng, X Zheng, X Wang, Z Ma, V Gupta Sunkari, I Botusan, T Takeda, A Björklund, M Inoue, S-B Catrina, K Brismar, L Poellinger & T S Pereira

Description: In climate models, the land-atmosphere interactions are described numerically by land surface parameterization (LSP) schemes. The continuing improvement in realism in these schemes comes at the expense of the need to specify a large number of parameters that are either directly measured or estimated. Also, an emerging problem is whether the relationships used in LSPs are universal and globally applicable. One plausible approach to evaluate this is to first minimize uncertainty in model parameters by calibration. In this paper, we conduct a comprehensive analysis of some model diagnostics using a slightly modified version of the Simple Biosphere 3 model for a variety biomes located mainly in the Amazon. First, the degree of influence of each individual parameter in simulating surface fluxes is identified. Next, we estimate parameters using a multi-operator genetic algorithm applied in a multi-objective context, and evaluate simulations of energy and carbon fluxes against observations. Compared with the default parameter sets, these parameter estimates improve the partitioning of energy fluxes in forest and cropland sites, and provide better simulations of daytime increases in assimilation of net carbon during the dry season at forest sites. Finally, a detailed assessment of the parameter estimation problem was performed by accounting for the decomposition of the Mean Squared Error to the total model uncertainty. Analysis of the total prediction uncertainty reveals that the parameter adjustments significantly improve reproduction of the mean and variability of the flux time series at all sites, and generally remove seasonality of the errors, but do not improve dynamical properties. Our results demonstrate that error decomposition provides a meaningful and intuitive way to understand differences in model performance. To make further advancements in the knowledge of these models, we encourage the LSP community to adopt similar approaches in the future. Copyright © 2012 John Wiley & Sons, Ltd.

Description: Observing system simulation experiments were used to investigate ensemble Bayesian state-updating data assimilation of observations of leaf area index (LAI) and soil moisture (θ) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and θ observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Description: Methods to select parsimonious and hydrologically consistent model structures are useful for evaluating dominance of hydrologic processes and representativeness of data. While information criteria (appropriately constrained to obey underlying statistical assumptions) can provide a basis for evaluating appropriate model complexity, it is not sufficient to rely upon the principle of maximum likelihood (ML) alone. We suggest that one must also call upon a “principle of hydrologic consistency,” meaning that selected ML structures and parameter estimates must be constrained (as well as possible) to reproduce desired hydrological characteristics of the processes under investigation. This argument is demonstrated in the context of evaluating the suitability of candidate model structures for lumped water balance modeling across the continental United States, using data from 307 snow-free catchments. The models are constrained to satisfy several tests of hydrologic consistency, a flow space transformation is used to ensure better consistency with underlying statistical assumptions, and information criteria are used to evaluate model complexity relative to the data. The results clearly demonstrate that the principle of consistency provides a sensible basis for guiding selection of model structures and indicate strong spatial persistence of certain model structures across the continental United States. Further work to untangle reasons for model structure predominance can help to relate conceptual model structures to physical characteristics of the catchments, facilitating the task of prediction in ungaged basins.

Description: ABSTRACT The role of models in science is to facilitate predictions from hypotheses. Although the idea that models provide information is widely reported and has been used as the basis for model evaluation, benchmarking and updating strategies, this intuition has not been formally developed and current benchmarking strategies remain ad hoc at a fundamental level. Here we interpret what it means to say that a model provides information in the context of the formal inductive philosophy of science. We show how information theory can be used to measure the amount of information supplied by a model, and derive standard model benchmarking and evaluation activities in this context. We further demonstrate that, via a process of induction, dynamical models store information from hypotheses and observations about the systems that they represent, and that this stored information can be directly measured. This article is protected by copyright. All rights reserved.

Description: Spatially distributed models can potentially provide improved hydrologic predictions because of their ability to exploit spatially distributed data while providing estimates of hydrologic variables at interior catchment locations. However, attempts to estimate spatially distributed parameter fields via model calibration have been fraught with difficulty. This paper examines the factors that can influence the ability to infer spatial properties of a distributed model when the only information available for model evaluation is catchment streamflow response. In particular, we investigate the conditions under which spatial variability in parameters and rainfall cause sufficiently strong variations in the streamflow hydrographs to justify their representation in catchment models and whether such information can be detected via commonly used model performance measures. Our results show that spatial variability in parameter and precipitation fields can, indeed, have a detectable impact on the properties of the streamflow hydrograph but that this impact can be so greatly diminished by the damping and dispersive effects of routing that it is virtually nondetectable by conventional performance measures by the time the water reaches the catchment outlet. And although measures based on information theory may be able to detect subtle variations of this kind, the information may not ultimately be useful in the face of model structure and data errors. The only reasonable way forward therefore is to explore other kinds of catchment information (including multiple interior flow gauging locations) for use in estimation of spatially distributed parameter fields.

Description: Key Points Discovery can be advanced by taking a perspective based on Information Theory Much can be gained by focusing on the a priori role of Process Modeling System parameterization can result in information loss

Description: We assess the water balance of the Brazilian Cerrado based on remotely sensed estimates of precipitation (TRMM), evapotranspiration (MOD16), and terrestrial water storage (GRACE) for the period from 2003 to 2010. Uncertainties for each remotely sensed data set were computed, the budget closure was evaluated using measured discharge data for the three largest river basins in the Cerrado, and the Mann-Kendall test was used to evaluate temporal trends in the water balance components and measured river discharge. The results indicate an overestimation of discharge data, due mainly to the overestimation of rainfall by TRMM version 6. However, better results were obtained when the new release of TRMM 3B42 v7 was used instead. Our results suggest that there have been a) significant increases in average annual evapotranspiration over the entire Cerrado of 51 ± 15 mm yr -1 , b) terrestrial water storage increases of 11 ± 6 mm yr -1 in the northeast region of the Brazilian Cerrado, and c) runoff decreases of 72 ± 11 mm yr -1 in isolated spots and in the western part of the State of Mato Grosso. Although complete water budget closure from remote sensing remains a significant challenge due to uncertainties in the data, it provides a useful way to evaluate trends in major water balance components over large regions, identify dry periods, and assess changes in water balance due to land cover and land use change.