A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.     

Visualisation of the complexity of EUSES

The interdependencies of parameters applied in the models of EUSES are visualised in a directed connectivity graph. The parameters (inputs, defaults, state variables, outputs) are represented by boxes (nodes) and their relations by lines (edges). The visualisation, on the one hand, clarifies the complexity of the models in EUSES and, on the other hand, creates an overview and transparency. The parameters’ relations to each other can be recognised faster, and the models can be better understood. The complexity was quantified by the number (variety), kind (substance parameter, physico-chemical parameter, concentration, other parameters), and depth (dimension) of the parameter and the number of relations (connectivity). The variety of EUSES (without the modelsSimple Treat andSimple Box whose interior structure is not documented and without the effect and risk characterisation) amounts to 466, the connectivity to 961, and the maximal dimension is 21.     

高锰酸钾预氧化后地表水中三氯甲烷生成量的预测模型研究

在饮用水处理中,适当地采用高锰钾预氧化可以降低水中的三氯甲烷生成量。本文研究了影响三氯甲烷生成量的因素,如反应时间,反应温度等,并以这些影响因素为参数通过一元线性回归等数学方法,建立了各影响因素下三氯甲烷生成量折了模型和综合预测模型。     

玉溪市未来人口预测三种模型的分析

人口问题是一个国家或地区可持续发展的前提。人口规模是否合理,对一个国家或地区未来的经济、社会和生态环境可持续发展影响深远。运用马尔萨斯人口模型、Logistic增长模型和线性函数三种理论模型,利用《玉溪市统计年鉴》统计数据对玉溪市2010—2020年的人口发展规模做出预测,预测结果显示3种模型均取得了较好的模拟效果。Logistic增长模型的预测值最小,故采用其预测值作为预测结果。     

Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

Source-contribution assessment of ambient NO₂ concentration was performed at Pantnagar, India through simulation of two urban mathematical dispersive models namely Gaussian Finite Line Source Model (GFLSM) and Industrial Source Complex Model (ISCST-3) and model performances were evaluated. Principal approaches were development of comprehensive emission inventory, monitoring of traffic density and regional air quality and conclusively simulation of urban dispersive models. Initially, 18 industries were found responsible for emission of 39.11 kg/h of NO₂ through 43 elevated stacks. Further, vehicular emission potential in terms of NO₂ was computed as 7.1 kg/h. Air quality monitoring delineates an annual average NO₂ concentration of 32.6 μg/m³. Finally, GFLSM and ISCST-3 were simulated in conjunction with developed emission inventories and existing meteorological conditions. Models simulation indicated that contribution of NO₂ from industrial and vehicular source was in a range of 45-70% and 9-39%, respectively. Further, statistical analysis revealed satisfactory model performance with an aggregate accuracy of 61.9%.     

Assessing exposure to allied ground troops in the Vietnam War: A quantitative evaluation of the Stellman Exposure Opportunity Index model

The Exposure Opportunity Index (EOI) is a proximity-based model developed to estimate relative exposure of ground troops in Vietnam to aerially applied herbicides. We conducted a detailed quantitative evaluation of the EOI model by using actual herbicide spray missions isolated in time and space. EOI scores were calculated for each of 36 hypothetical receptor location points associated with each spray mission for 30 herbicide missions for two time periods – day of herbicide application and day 2–3 post-application. Our analysis found an enormous range of EOI predictions with 500–1000-fold differences across missions directly under the flight path. This quantitative examination of the EOI suggests that extensive testing of the model’s code is warranted. Researchers undertaking development of a proximity-based exposure model for epidemiologic studies of either Vietnam veterans or the Vietnamese population should conduct a thorough and realistic analysis of how precise and accurate the model results are likely to be and then assess whether the model results provide a useful basis for their planned epidemiologic studies.     

Development and Comparison of Multiple Regression Models to Predict Bankfull Channel Dimensions for Use in Hydrologic Models

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross‐sectional area to drainage area (DA) without limiting their use to well‐gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation (PCP) and temperature (TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables (DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference.     

A new approach to natural capital sustainable development

The three-dimension (3D) ecological footprint makes the analysis of the relationships between the demand and supply of natural capital more credible by importing footprint depth and footprint size. This article used China’s regions as the object to analyze the high-level sustainability of the natural capital from the view of “ecology – efficiency – fairness” multidimensional framework. Research showed that China’s ecological footprint has risen while bio-capacity per capita has descended in recent 20 years. This paper also discusses the spatial distribution of China’s natural capital ecological sustainability, efficiency sustainability and fairness sustainability. Finally, it builds multi-criteria evaluation (MCE) models to get multidimensional sustainability framework taking ecological sustainability, efficiency sustainability, and fairness sustainability into consideration.     

Optimizing Bankfull Discharge and Hydraulic Geometry Relations for Streams in New York State1

Mulvihill, Christiane I. and Barry P. Baldigo, 2012. Optimizing Bankfull Discharge and Hydraulic Geometry Relations for Streams in New York State. Journal of the American Water Resources Association (JAWRA) 48(3): 449-463. DOI: 10.1111/j.1752-1688.2011.00623.x Abstract: This study analyzes how various data stratification schemes can be used to optimize the accuracy and utility of regional hydraulic geometry (HG) models of bankfull discharge, width, depth, and cross-sectional area for streams in New York. Topographic surveys and discharge records from 281 cross sections at 82 gaging stations with drainage areas of 0.52-396 square miles were used to create log-log regressions of region-based relations between bankfull HG metrics and drainage area. The success with which regional models distinguished unique bankfull discharge and HG patterns was assessed by comparing each regional model to those for all other regions and a pooled statewide model. Gages were also stratified (grouped) by mean annual runoff (MAR), Rosgen stream type, and water-surface slope to test if these models were better predictors of HG to drainage area relations. Bankfull discharge models for Regions 4 and 7 were outside the 95% confidence interval bands of the statewide model, and bankfull width, depth, and cross-sectional area models for Region 3 differed significantly (p < 0.05) from those of other regions. This study found that statewide relations between drainage area and HG were strongest when data were stratified by hydrologic region, but that co-variable models could yield more accurate HG estimates in some local regional curve applications.