Impact of time series data on calibration and prediction uncertainty for a deterministic hydrogeochemical model

Model calibration is fundamental in applications of deterministic process-based models. Uncertainty in model predictions depends much on the input data and observations available for model calibration. Here we explored how model predictions (forecasts) and their uncertainties vary with the length of time series data used in calibration. As an example we used the hydrogeochemical model MAGIC and data from Birkenes, a small catchment in southern Norway, to simulate future water chemistry under a scenario of reduced acid deposition. A Bayesian approach with a Markov Chain Monte Carlo (MCMC) technique was used to calibrate the model to different lengths of observed data (4–29 years) and to estimate the prediction uncertainty each calibration. The results show that the difference between modelled and observed water chemistry (calibration goodness of fit) in general decreases with increasing length of the time series used in calibration. However, there are considerable differences for different time series of the same length. The results also show that the uncertainties in predicted future acid neutralizing capacity were lowest (i.e. the distribution peak narrowest) when using the longest time series for calibration. As for calibration success, there were considerable differences between the future distributions (prediction uncertainty) for the different calibrations.     

Impacts of home shopping on vehicle operations and greenhouse gas emissions: multi-year regional study

As a shopping behavior trend, home shopping has become more popular. This shopping method seems to decrease shopping trips, yet more delivery trucks are required to be on the roads. In addition, even more personal trips may occur because saving time on shopping might allow more time for alternate activities. A study investigated the effects of home shopping on vehicle operations and greenhouse gas emissions. The purpose of that study was to identify the home-shopping impacts on transportation system, its net effects on traffic volume of the transportation network, its effects associated with environmental sustainability and then to provide some projections for future condition. Simulation results showed that home shopping will put additional burden on Newark transportation network, as identified through four measures of effectiveness (MOEs) which were travel time, delay, average speed and greenhouse gas (GHG) emissions.     

Understanding Recent Climate Change

Abstract:  The Earth's atmosphere has a natural greenhouse effect, without which the global mean surface temperature would be about 33 °C lower and life would not be possible. Human activities have increased atmospheric concentrations of carbon dioxide, methane, and other gases in trace amounts. This has enhanced the greenhouse effect, resulting in surface warming. Were it not for the partly offsetting effects of increased aerosol concentrations, the increase in global mean surface temperature over the past 100 years would be larger than observed. Continued surface warming through the 21st century is inevitable and will likely have widespread ecological impacts. The magnitude and rate of warming for the global average will be largely dictated by the strength and direction of climate feedbacks, thermal inertia of the oceans, the rate of greenhouse gas emissions, and aerosol concentrations. Because of regional expressions of climate feedbacks, changes in atmospheric circulation, and a suite of other factors, the magnitude and rate of warming and changes in other key climate elements, such as precipitation, will not be uniform across the planet. For example, due to loss of its floating sea-ice cover, the Arctic will warm the most .     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.     

The Future of Tropical Species on a Warmer Planet

Abstract: Modern global temperature and land cover and projected future temperatures suggest that tropical forest species will be particularly sensitive to global warming. Given a moderate greenhouse gas emissions scenario, fully 75% of the tropical forests present in 2000 will experience mean annual temperatures in 2100 that are greater than the highest mean annual temperature that supports closed‐canopy forest today. Temperature‐sensitive species might extend their ranges to cool refuges, defined here as areas where temperatures projected for 2100 match 1960s temperatures in the modern range. Distances to such cool refuges are greatest for equatorial species and are particularly large for key tropical forest areas including the Amazon and Congo River Basins, West Africa, and the upper elevations of many tropical mountains. In sum, tropical species are likely to be particularly sensitive to global warming because they are adapted to limited geographic and seasonal variation in temperature, already lived at or near the highest temperatures on Earth before global warming began, and are often isolated from cool refuges. To illustrate these three points, we examined the distributions and habitat associations of all extant mammal species. The distance to the nearest cool refuge exceeded 1000 km for more than 20% of the tropical and less than 4% of the extratropical species with small ranges. The biological impact of global warming is likely to be as severe in the tropics as at temperate and boreal latitudes.     

日本城市碳排放清单计算方法——以横滨市为例

日本是关注全球气候变化并作出行动最早的国家之一,根据其法律大中城市以上的行政区必须编制温室气体减排规划,包括排放清单、趋势预测、减排目标、措施及效果分析、方案实施与监督等。日本环境省为了帮助地方政府编制该规划,提供了城市层面的温室气体排放清单的编制方法指导。本文以横滨市为例,比较完整地介绍日本城市CO2排放清单编制基本方法,为我国城市碳排放清单编制提供参考。     

Estimation of Wastewater Treatment Objectives through Maximum Entropy

The paper examines how a public wastewater treatment plant balances objectives of cost minimization and pollution prevention. The parameters of the objective function and state equation in an optimal control model are estimated using maximum entropy and time series observations of water quality and expenditures for wastewater treatment. The estimation method does not require restrictions needed by other techniques used to estimate nonlinear, ill-posed problems. The parameter estimates indicate that the treatment plant emphasizes water quality enhancement over cost minimization. Results from the sensitivity analysis show that the plant favors conventional treatment over pollution prevention.     

Prediction of non-methane hydrocarbons in Kuwait using regression and Bayesian kriged Kalman model

This article describes the hierarchical Bayesian approach for predicting average hourly concentrations of ambient non-methane hydrocarbons (NMHC) in Kuwait where records of six monitor stations located in different sites are observed at successive time points. Our objective is to predict the concentration level of NMHC in unmonitored areas. Here an attempt is made for the prediction of unmeasured concentration of NMHC at two additional locations in Kuwait. We will implement a kriged Kalman filter (KKF) hierarchical Bayesian approach assuming a Gaussian random field, a technique that allows the pooling of data from different sites in order to predict the exposure of the NMHC in different regions of Kuwait. In order to increase the accuracy of the KKF we will use other statistical models such as imputation, regression, principal components, and time series analysis in our approach. We considered four different types of imputation techniques to address the missing data. At the primary level, the logarithmic field is modeled as a trend plus Gaussian stochastic residual model. The trend model depends on hourly meteorological predictors which are common to all sites. The residuals are then modeled using KKF, and the prediction equation is derived conditioned on adjoining hours. On this basis we developed a spatial predictive distribution for these residuals at unmonitored sites. By transforming the predicted residuals back to the original data scales, we can impute Kuwait’s hourly non-methane hydrocarbons field.     

Physiological and behavioral responses of temperate seahorses (Hippocampus guttulatus) to environmental warming

The aim of the present study was to evaluate, for the first time, the effect of environmental warming on the metabolic and behavioral ecology of a temperate seahorse, Hippocampus guttulatus. More specifically, we compared routine metabolic rates, thermal sensitivity, ventilation rates, food intake, and behavioral patterns at average spring temperature (18 °C), average summer temperature (26 °C), temperatures that they endure during summer heat wave events (28 °C), and in a near-future warming scenario (+2; 30 °C) in Sado estuary, Portugal. Both newborn juveniles and adults showed significant increases in metabolic rates with rising temperatures. However, newborns were more impacted by future warming via metabolic depression (i.e., heat-induced hipometabolism). In adult stages, ventilation rates also increased significantly with environmental warming, but food intake remained unchanged. Moreover, the frequency of swimming, foraging, swinging, and inactivity did not significantly change between the different thermal scenarios. Thus, we provide evidence that, while adult seahorses show great resilience to heat stress and are not expected to go through any physiological impairment and behavioral change with the projected near-future warming, the early stages display greater thermal sensitivity and may face greater metabolic challenges with potential cascading consequences for their growth and survival.     

Monitoring the growth of American beech affected by beech bark disease in Maine using the Kalman filter

The effect of beech bark disease on tree growth was tracked using paired resistant and susceptible American beech trees in two locations in Maine. Within each site, the paired trees were chosen in close proximity and with similar morphological characteristics (e.g. stem diameter and crown class) to minimize environment effects in subsequent analysis. A Kalman filter approach was employed to analyse the yearly time-dependent mean differences between paired susceptible and resistant tree-ring widths using simple structural time series models in state space form. On one site, under the influence of a moderate maritime climate, stand dynamics is hypothesized to account for the 34 year difference in onset of decline of trees in codominant, versus those in the intermediate crown classes. The harsher winter conditions associated with the second, more northerly site and known to limit the insect component of the disease complex, are hypothesized to be more of a factor in the close (six year) difference in decline onset between the two crown classes on this site. Some strengths and cautions in the Kalman filter approach are discussed in relation to the analysis of time-dependent trends in tree-ring series.     

Role of the natural and anthropogenic radiative forcings on global warming: evidence from cointegration–VECM analysis

Over the last few years there has been much debate about the hypothesis that anthropogenic emissions of CO₂ and other greenhouse gases increase global temperature permanently. By using recent advances in time series econometrics, this paper tries to answer the question on how human activity affects Earth’s surface temperatures. Bearing in mind this goal, we estimated the long-run cointegration relations between global temperatures and changes in radiative forcings by a set of perturbing factors. We found that the temperature response to a doubling in radiative forcing of anthropogenic greenhouse gases is + 2.94 °C [95 % CI: + 1.91, + 3.97], in perfect accordance with prior research, and that the orthogonalized cumulated effect over a 100 year time period, in response to a unit increase of size of one standard deviation in greenhouse gas radiative forcing, is + 3.86 °C [95 % CI: + 0.03, + 6.54]. Conversely, the amplitude of solar irradiance variability is hardly sufficient to explain observed variations in the Earth’s climate. Our results show that the combined effect of stochastic trends attributable to anthropogenic radiative forcing variations are driving the Earth’s climate system toward an ongoing phase of global warming, and that such long-run movement is unlikely to be transient.     

Estimating wildfire growth from noisy and incomplete incident data using a state space model

Wildfire behaviors are complex and are of interest to fire managers and scientists for a variety of reasons. Many of these important behaviors are directly measured from the cumulative burn area time series of individual wildfires; however, estimating cumulative burn area time series is challenging due to the magnitude of measurement errors and missing entries. To resolve this, we introduce two state space models for reconstructing wildfire burn area using repeated observations from multiple data sources that include different levels of measurement error and temporal gaps. The constant growth parameter model uses a few parameters and assumes a burn area time series that follows a logistic growth curve. The non-constant growth parameter model uses a time-varying logistic growth curve to produce detailed estimates of the burn area time series that permit sudden pauses and pulses of growth. We apply both reconstruction models to burn area data from 13 large wildfire incidents to compare the quality of the burn area time series reconstructions and computational requirements. The constant growth parameter model reconstructs burn area time series with minimal computational requirements, but inadequately fits observed data in most cases. The non-constant growth parameter model better describes burn area time series, but can also be highly computationally demanding. Sensitivity analyses suggest that in a typical application, the reconstructed cumulative burn area time series is fairly robust to minor changes in the prior distributions.     

Dynamic compartment approach for modelling regimes of carbon cycle functioning in bog ecosystems

Svirezhev's method of dynamic model design by a given “storage-flow” diagram [Svirezhev Y.M., 1997. On some general properties of trophic networks. Ecol. Model. 99, 7–17] is developed and used for investigating dynamic regimes of carbon cycle functioning in a typical boreal transitional bog ecosystem. Ecosystems are often represented by static “storage-flow” diagrams reflecting their structure and matter or energy transfer between components at fixed time moments. Using the data of such diagrams aggregated in ecological field studies one can construct a dynamic model of the ecosystem to predict its future behaviour and to estimate a response to external perturbations—natural and human. Stability of both current equilibrium and possible alternative steady states and more complicated attractors are studied under two types of parameter perturbation: CO₂ atmospheric concentration increase initiated by greenhouse effect, and change in the rate of carbon output from dead organic matter and litter which depends on the water table level and possible peat excavation. Calculation of bifurcation curves gives areas in the parameter space where stable functioning of carbon cycle is provided. Steady states can be interpreted as raised bog, meadow, forest and fen. CO₂ concentration increase leads the current state of transitional bog to loose stability with appearance of oscillatory dynamics and further evolution to the chaotic attractor. The model is rich by chaotic solutions serving as transition regimes between regular steady and periodic attractors. Another chaotic regime is formed from forest equilibrium and exists in the same area of phase space where current equilibrium is stable.     

Explanation of fluctuations in water use time series

This paper presents techniques for studying the influence of the climatic and other variables for the explanation of the water use with an example of time series in Gainesville, Florida. A statistical methodology is described for separating the different time scale components in time series of water use, namely, long term component, seasonal component, and short term component. We analyze each component separately and we prove that the temperature, precipitation, soil temperature, and relative humidity time series are the main climatic factors for the explanation of the long term, seasonal and short term component of the water use time series. Part of the residuals derived from the linear regression of the long term component of the water use can be explained by the unemployment rate. We also show that with the decomposition of the water use time series the explanation of the water use has been improved approximately two times. The explanation of the long term component of water use by the long term regional weather parameters can enable us to the long term regional prediction of the water resources availabilities. This methodology can be applied for studying the water use time series in other locations, as well.     

Impact of minimum winter temperatures on the population dynamics of Dendroctonus frontalis.

Predicting population dynamics is a fundamental problem in applied ecology. Temperature is a potential driver of short-term population dynamics, and temperature data are widely available, but we generally lack validated models to predict dynamics based upon temperatures. A generalized approach involves estimating the temperatures experienced by a population, characterizing the demographic consequences of physiological responses to temperature, and testing for predicted effects on abundance. We employed this approach to test whether minimum winter temperatures are a meaningful driver of pestilence from Dendroctonus frontalis (the southern pine beetle) across the southeastern United States. A distance-weighted interpolation model provided good, spatially explicit, predictions of minimum winter air temperatures (a putative driver of beetle survival). A Newtonian heat transfer model with empirical cooling constants indicated that beetles within host trees are buffered from the lowest air temperatures by approximately 1-4 degrees C (depending on tree diameter and duration of cold bout). The life stage structure of beetles in the most northerly outbreak in recent times (New Jersey) were dominated by prepupae, which were more cold tolerant (by >3 degrees C) than other life stages. Analyses of beetle abundance data from 1987 to 2005 showed that minimum winter air temperature only explained 1.5% of the variance in interannual growth rates of beetle populations, indicating that it is but a weak driver of population dynamics in the southeastern United States as a whole. However, average population growth rate matched theoretical predictions of a process-based model of winter mortality from low temperatures; apparently our knowledge of population effects from winter temperatures is satisfactory, and may help to predict dynamics of northern populations, even while adding little to population predictions in southern forests. Recent episodes of D. frontalis outbreaks in northern forests may have been allowed by a warming trend from 1960 to 2004 of 3.3 degrees C in minimum winter air temperatures in the southeastern United States. Studies that combine climatic analyses, physiological experiments, and spatially replicated time series of population abundance can improve population predictions, contribute to a synthesis of population and physiological ecology, and aid in assessing the ecological consequences of climatic trends.     

Temperature and industrial output: Firm-level evidence from China

We pair a firm-level panel of annual industrial output with a fine-scale daily weather data set, to estimate the responses of industrial output to temperature changes in China. We have four primary findings. First, industrial output is nonlinear in temperature changes. With seasonal average temperatures as temperature variables, output responds positively to higher spring temperatures and negatively to elevated summer temperatures. With temperature bins as temperature variables, output increases linearly with temperature up to 21–24 °C, and then declines sharply at higher temperatures. Second, lagged temperature changes exert large and significant impacts on current year’s output. Third, higher summer temperatures have larger detrimental effects on output in low-temperature regions than in high-temperature regions, which suggests that adaptation to warming may have been actively undertaken in high-temperature regions in China. Lastly, industrial output in China is projected to decrease by 3–36% by 2080 under the slowest warming scenario (B1) and by 12–46% under the most rapid warming scenario (A2) under the global climate models UKMO-HadCM3 and PCM.     

Validation of ecological state space models using the Laplace approximation

Many statistical models in ecology follow the state space paradigm. For such models, the important step of model validation rarely receives as much attention as estimation or hypothesis testing, perhaps due to lack of available algorithms and software. Model validation is often based on a naive adaptation of Pearson residuals, i.e. the difference between observations and posterior means, even if this approach is flawed. Here, we consider validation of state space models through one-step prediction errors, and discuss principles and practicalities arising when the model has been fitted with a tool for estimation in general mixed effects models. Implementing one-step predictions in the R package Template Model Builder, we demonstrate that it is possible to perform model validation with little effort, even if the ecological model is multivariate, has non-linear dynamics, and whether observations are continuous or discrete. With both simulated data, and a real data set related to geolocation of seals, we demonstrate both the potential and the limitations of the techniques. Our results fill a need for convenient methods for validating a state space model, or alternatively, rejecting it while indicating useful directions in which the model could be improved.     

Enhanced methane hydrate storage using sodium dodecyl sulfate and coal

A potential solution to reduce global warming is to store greenhouse gases. Greenhouse gas storage has been recently improved using several materials and techniques. However, the actual storage capacity is slow, limited, and costly. Here, we tested the use of an accelerating agent, sodium dodecyl sulfate, and coal to produce methane hydrate for methane storage. Experiments of storing methane gas in coal samples have been carried out under high pressures, 4 or 6 MPa, and low temperature of 273.15 K. Results reveal that sodium dodecyl sulfate improved the rate up to 58.26 cm³/min and the capacity up to 179.97 volume at standard temperature and pressure per unit volume of coal. This finding shows that sodium dodecyl sulfate is efficient to accelerate the formation of methane hydrate. We also found that storage capacity increased with pressure. We conclude that our method allows a gas storage capacity higher than any other medium materials reported previously.     

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment plants

The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia （NH3） concentrations： 1） data filtering using wavelet decomposition and reconstruction; 2） typical cycle identification using power spectrum density analysis; 3） fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1） for both of the two plants, the periodicity affects the flow rate and NH3 concentrations in different cycles （especially cycles longer than 1 day）; 2） when the flow rate and NH3 concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3） the periodicity influence is larger than rainfalls; 4） the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.     

大型火电厂热排放对环境气温增高的贡献检测

利用1960—2009年的年月平均气温观测资料,分析了被检站和参考站的年、季变化趋势特点,着重研究了大型火电厂的热排放对当地气温的影响程度和相对贡献比例。从增温趋势看,被检站和参考站年平均气温增温速率分别为0.33～0.48℃？10a和0.21～0.33℃？10a,年热排放增温率为0.11～0.20℃？10a。热排放引起的四季增温速率为春秋强,冬季次之,夏季最小。火电厂的热排放对当地气温增温的贡献显著,表现为夏季最大,春秋季次之,冬季最小,对年平均气温序列的增温贡献率为31%～49%。