A Review of Forest Gap Models

Forest gap models, initially conceived in 1969 as a special case of individual-tree based models, have become widely popular among forest ecologists for addressing a large number of applied research questions, including the impacts of global change on long-term dynamics of forest structure, biomass, and composition. However, they have been strongly criticized for a number of weaknesses inherent in the original model structure. In this paper, I review the fundamental assumptions underlying forest gap models, the structure of the parent model JABOWA, and examine these criticisms in the context of the many alternative formulations that have been developed over the past 30 years.Four assumptions originally underlie gap models: (1) The forest is abstracted as a composite of many small patches of land, where each can have a different age and successional stage; (2) patches are horizontally homogeneous, i.e., tree position within a patch is not considered; (3) the leaves of each tree are located in an indefinitely thin layer (disk) at the top of the stem; and (4) successional processes are described on each patch separately, i.e., there are no interactions between patches. These simplifications made it possible to consider mixed-species, mixed-age forests, which had been difficult previously mainly because of computing limitations.The structure of JABOWA is analysed in terms of the functional relationships used for formulating the processes of tree establishment, growth, and mortality. It is concluded that JABOWA contains a number of unrealistic assumptions that have not been questioned strongly to date. At the same time, some aspects of JABOWA that were criticized strongly in the past years are internally consistent given the objectives of this specific model.A wide variety of formulations for growth processes, establishment, and mortality factors have been developed in gap models over the past 30 years, and modern gap models include more robust parameterizations of environmental influences on tree growth and population dynamics as compared to JABOWA. Approaches taken in more recent models that led to the relaxation of one or several of the four basic assumptions are discussed. It is found that the original assumptions often have been replaced by alternatives; however, no systematic analysis of the behavioral effects of these conceptual changes has been attempted to date.The feasibility of including more physiological detail (instead of using relatively simple parameterizations) in forest gap models is discussed, and it is concluded that we often lack the data base to implement such approaches for more than a few commercially important tree species. Hence, it is important to find a compromise between using simplistic parameterizations and expanding gap models with physiology-based functions and parameters that are difficult to estimate. While the modeling of tree growth has received a lot of attention over the past years, much less effort has been spent on improving the formulations of tree establishment and mortality, although these processes are likely to be just as sensitive to global change as tree growth itself. Finally, model validation issues are discussed, and it is found that there is no single data source that can reliably be used for evaluating the behavior of forest gap models; instead, I propose a combination of sensitivity analyses, qualitative examinations of process formulations, and quantitative tests of gap models or selected submodels against various kinds of empirical data to evaluate the usefulness of these models for assessing their utility for predicting the impacts of global change on long-term forest dynamics.     

Landscape Roughness Parameters for Sherwood Forest – Validation of Aggregation Models

Various models for calculating the effective or area-averaged roughness length zoe have been tested for a partly forested area. Three types of model are considered: the tile approach for very large scales of inhomogeneity (> 20 km), drag models for very small scales (up to 1 km), and surface-layer methods such as the blending-height method for intermediate scales. Over partly forested areas, where both pressure effects and roughness sub-layer effects may become significant, small-scale models are expected be the most suitable type of model. The various model types were tested against new experimental data that were obtained over the partly forested Sherwood Forest area (UK). The best fit with the data was obtained with the blending-height method, rather than with the different small-scale models. This is remarkable as the surface-layer assumptions of the blending-height method were clearly violated: the calculated blending height was 7 m, as compared to the mean tree height of 20 m. Of the small-scale models, a sparse-canopy approach compared poorly with the experimental data. The drag models overestimated the area-averaged roughness to a lesser degree, but a major point of concern remains the choice of the model parameters. Therefore, suggestions are made for an improved choice of these parameters.     

A Comparative Analysis of the Structure and Behavior of Three Gap Models at Sites in Northeastern China

Three gap models, KOPIDE, NEWCOP, and ForClim, were compared with respect to their structure and behavior at four sites along an elevational gradient on Changbai Mt., northeastern China, under current climate and six climate change scenarios. This study intends to compare the three gap models under identical conditions, using a standardized simulation protocol. The three models were originally developed with different backgrounds and for different purposes. While they are relatively similar in the level of structural detail they include, they still differ in many respects regarding the assumptions that are made for representing specific ecological processes.The simulations showed that none of the three gap models provides satisfactory results in all situations; each gap model has strong and weak points in its behavior. While all models are fairly successful in simulating the composition of dominant species along the gradient under current climatic conditions, their projections under a set of hypothetical scenarios of climatic change diverge rather strongly. The analysis of these simulation results shows that several problem areas need to be addressed before any of the models can be used for a reliable impact assessment.Recommendations for improvements of the models are made, including the formulation of temperature and drought effects on tree establishment and tree growth, the size of the species pool, the appropriate choice of patch size and disturbance regimes, and allometric relationships. When aiming to use gap models under new environmental conditions, we propose to carefully reconsider their formulations based on our knowledge of the relevant processes in the region under concern, instead of using the models in an `as-is' mode.     

GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions

Accurate estimation of reference evapotranspiration (ET₀) becomes imperative for better managing the more and more limited agricultural water resources. This study examined the feasibility of developing generalized artificial neural network (GANN) models for ET₀ estimation using weather data from four locations representing different climatic patterns. Four GANN models with different combinations of meteorological variables as inputs were examined. The developed models were directly tested with climatic data from other four distinct stations. The results showed that the GANN model with five inputs, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed, performed the best, while that considering only maximum temperature and minimum temperature resulted in the lowest accuracy. All the GANN models exhibited high accuracy under both arid and humid conditions. The GANN models were also compared with multivariate linear regression (MLR) models and three conventional methods: Hargreaves, Priestley–Taylor, and Penman equations. All the GANN models showed better performance than the corresponding MLR models. Although Hargreaves and Priestley–Taylor equations performed slightly better than the GANN models considering the same inputs at arid and semiarid stations, they showed worse performance at humid and subhumid stations, and GANN models performed better on average. The results of this study demonstrated the great generalization potential of artificial neural techniques in ET₀ modeling.     

The use of general circulation models in climate impact analysis — A preliminary study of the impacts of a CO2- induced climatic change on West European agriculture

An investigation is made of the possible impacts of a climatic change (induced by a doubling of atmospheric carbon dioxide concentration) on the European agricultural sector. Two general circulation models have been used to develop climatic change scenarios for the European study area. From the scenarios, information was obtained concerning the possible behavior of temperature, precipitation, solar radiation, and relative humidity in the altered climatic state. This meteorological information was then employed in two separate crop-weather models - an empirical/statistical model (for winter wheat) and a simple simulation model (for biomass potential). This type of approach represents a considerable departure from that employed by previous large-scale climate impact studies. Both the seasonal and regional components of a possible climatic change are incorporated directly in the two crop-weather models. The results of this investigation demonstrate that a simple crop-weather simulation model may be more suitable for the purposes of agricultural impact analysis than the linear regression models frequently used in such studies. In order for such an impact analysis to be accepted as a valid scientific experiment, a full presentation of the underlying assumptions and uncertainties is essential.     

Rethinking barriers to adaptation: Mechanism-based explanation of impasses in the governance of an innovative adaptation measure

Many of the possible barriers in the governance of climate change adaptation have already been identified and catalogued in the academic literature. Thus far it has proven to be difficult to provide meaningful recommendations on how to deal with these barriers. In this paper we propose a different perspective, with different epistemological assumptions about cause and effect than most existing barrier studies, to analyze why adaptation is often challenging. Using the mechanismic framework, we study how the idea for an innovative “Water Plaza” was realized in the city of Rotterdam, the Netherlands. Mechanisms are understood as patterns of interaction between actors that bring about change in the governance process that lead to policy impasses. Our analysis reveals three mechanisms that explain the impasses in the first Water Plaza pilot project: the risk-innovation mechanism, the frame polarization mechanism, and the conflict infection mechanism. Only after several substantive changes in the project design, location choice, and process architecture was the project of Water Plaza's revitalized. We discuss how the short-sighted ideas about cause–effect relationships, reflected in the superficial identification of barriers, may prove to be counterproductive; if there is high uncertainty about the risks of an innovation, the solution of offering more certainty is not very helpful and could, as it happened in the case study, trigger other mechanisms, creating an even tighter deadlock. Our study also suggests that when adaptation is considered as something innovative, the chances will increase that the risk-innovation mechanism will occur. We conclude that unearthing mechanisms offers new opportunities and different types of strategic interventions in practice than most existing studies have offered.     

Observations on the use of analytical and numerical models for the description of transfer to porous surface vegetation such as lichen

Convective deposition of submicron-size aerosol to porous surface vegetation was studied by electrochemical simulation, under Reynolds and Schmidt similarity, to a rectangular array of closely-packed lichen and artificial wire roughness layers. Results, showing an approximate tenfold increase in deposition velocity over that of a flat plate placed at the same position, were compared with predictions made on the basis of various rough-surface transfer models, including those based on statistical eddy renewal, as well as with numerical solutions of the diffusion equation in statistically-renewed surface cavities. Most analytical models could be made to fit the observed data, at least for a limited range of flow velocities, but poorly known and poorly defined parameters limit their usefulness for predictive purposes; and their validity across a large variation in molecular diffusivity (or Schmidt number Sc) is generally not assured. Numerical models also depend on poorly substantiated physical assumptions but the effect of such assumptions on transfer can be calculated for a wider range of conditions than those permitting an analytical solution. This allows more direct feedback between model assumptions and calculated or observed transfer. Numerically calculated values for deposition velocity in air for Sc from 0.7 to 7000 and flow velocities from 0.2 to 5 m s^-1 are presented for different model assumptions, with values ranging from < 0.01 to > 1 cms^-1.     

Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters

This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.     

Bayesian multi-model projection of climate: bias assumptions and interannual variability

Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.     

Atmospheric and Terrestrial Water Balances of Labrador's Churchill River Basin,as Simulated by the North American Regional Climate Change Assessment Program

In an effort to understand the sources of uncertainty and the physical consistency of climate models from the North American Regional Climate Change Assessment Program (NARCCAP), an ensemble of general circulation models (GCMs) and regional climate models (RCMs) was used to explore climatological water balances for the Churchill River basin in Labrador, Canada. This study quantifies mean atmospheric and terrestrial water balance residuals, as well as their annual cycles. Mean annual atmospheric water balances had consistently higher residuals than the terrestrial water balances due, in part, to the influences of sampling of instantaneous variables and the interpolation of atmospheric data to published pressure levels. Atmospheric and terrestrial water balance residuals for each ensemble member were found to be consistent between base and future periods, implying that they are systemic and not climate dependent. With regard to the annual cycle, no pattern was found across time periods or ensemble members to indicate whether the monthly terrestrial or atmospheric root mean square residual was highest. Because of the interdependence of hydrological cycle components, the complexity of climate models and the variety of methods and processes used by different ensemble members, it was impossible to isolate all causes of the water balance residuals. That being said, the residuals created by interpolating a model's native vertical resolution onto NARCCAP's published pressure levels and the subsequent vertical interpolation were quantified and several other sources were explored. In general, residuals were found to be predominantly functions of the RCM choice (as opposed to the GCM choice) and their respective modelling processes, parameterization schemes, and post-processing.     

An analysis of the vertical structure equation in sigma coordinates

Abstract

An analysis of the vertical structure equation of sigma coordinate primitive equation models is given that brings together and extends the work of several authors. We derive the vertical structure equation, and obtain its solution for a two‐parameter family of vertical structure profiles that includes those of previous studies. For this family, it is shown that in the limiting case of an unbounded atmosphere the spectrum becomes partially continuous, rather than entirely discrete as in the bounded case. A criterion is obtained for the validity of the linearization used to derive the vertical structure equations, and it turns out that this criterion is satisfied by all but one of the previous studies. Asymptotic expansions are derived and used to explain two observations of Wiin‐Nielsen (1971a), viz. why the equivalent depths of the internal modes are relatively insensitive to the precise choice of lower boundary condition, and why one choice in particular leads to the elimination of the external mode; these asymptotic expansions also yield surprisingly accurate numerical values for the equivalent depths. Finally, the projection of atmospheric data onto modes found by direct numerical approximation of the vertical structure equation is shown, particularly for the least grave modes, to be very sensitive to resolution; consequently care must be exercised when interpreting the results of data projection studies that use this approach.     

Determining Effects of Area Burned and Fire Severity on Carbon Cycling and Emissions in Siberia

The Russian boreal forest contains about 25% of the global terrestrial biomass, and even a higher percentage of the carbon stored in litter and soils. Fire burns large areas annually, much of it in low-severity surface fires – but data on fire area and impacts or extent of varying fire severity are poor. Changes in land use, cover, and disturbance patterns such as those predicted by global climate change models, have the potential to greatly alter current fire regimes in boreal forests and to significantly impact global carbon budgets. The extent and global importance of fires in the boreal zone have often been greatly underestimated. For the 1998 fire season we estimate from remote sensing data that about 13.3 million ha burned in Siberia. This is about 5 times higher than estimates from the Russian Aerial Forest Protection Service (Avialesookhrana) for the same period. We estimate that fires in the Russian boreal forest in 1998 constituted some 14–20% of average annual global carbon emissions from forest fires. Average annual emissions from boreal zone forests may be equivalent to 23–39% of regional fossil fuel emissions in Canada and Russia, respectively. But the lack of accurate data and models introduces large potential errors into these estimates. Improved monitoring and understanding of the landscape extent and severity of fires and effects of fire on carbon storage, air chemistry, vegetation dynamics and structure, and forest health and productivity are essential to provide inputs into global and regional models of carbon cycling and atmospheric chemistry.     

第五代PSU/NCAR中尺度模式(MM5)介绍Ⅱ:第一猜测场的形成(DATAGRID模块)

:通过对DATAGRID模块的主要功能、基本原理及其结构的介绍 ,同时对模块的操作使用方法给予了说明。并对模块的主要控制参数进行了较为详尽的讨论 ,最后 ,举例说明了该模块中参数的正确选择对获取模式运行所需的网格点资料的重要性     

Modeling daily soil temperature using data-driven models and spatial distribution

The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.     

Application of stochastic simulation to climatic-change studies

Stochastic modelling provides a tool for exploring the full implications of the statistical behavior of historical records and can be used to predict the changing probabilities that events of various magnitudes will occur for different climatic change scenarios. Two simulation models are presented, one for daily air temperature, and the other for daily precipitation. The simulation procedures are: (1) extract salient parameter values from historical records; (2) simulate long sequences of data using the stochastic models, with or without a climatic change scenario as provided by a general circulation model; and (3) using the simulated data as inputs, derive the probability distributions of other variables based on known deterministic or probabilistic relationships between the input and the predicted variables.Given a doubling of carbon dioxide concentration in the atmosphere, the climatic models produce varying degrees of temperature and precipitation changes. Examples of application, including the derivation of snowfall and riverice data using simulated temperature and precipitation, illustrate that stochastic modelling offers a suitable approach to quantify the possible hydrologic impacts of climatic change.     

Firm relocation as adaptive response to climate change and weather extremes

Growing scientific evidence suggests that human-induced climate change will bring about large-scale environmental changes such as sea-level rise and coastal flooding, extreme weather events and agricultural disruptions. The speed and extent of these changes and the expected impacts on social and corresponding economic and industrial systems are now moving to the forefront of debates. In this paper, we argue that climate change will lead to significant disruptions to firms which might ultimately create the necessity of a geographical shift of firm and industrial activities away from regions highly affected by climate change. Such a shift might become necessary due to (1) direct disruptions through climate change impacts on firm operations, for instance through droughts, floods, or sea level rise, and due to (2) disruptions in a firm's supplier, buyer or resource base that lead to flow-on effects and adverse consequences for a firm. We propose a framework for integrating firm relocation decisions into firm adaptive responses to climate change. The framework consists of three assessment steps: the level of risk from climate change impacts at a firm's location, the feasibility of relocation, and associated costs and benefits. We apply the framework to two case examples. The first case of electricity distribution firms in Victoria/Australia illustrates how the relocation (undergrounding) of cables could decrease the vulnerability of distribution networks to bushfires and the risk of electricity-caused fires, but would require significant investments. The second case of firms in the Australian pastoral industry points to geographic diversification of pastoral land holdings as possible adaptation option, but also to constraints in form of availability of suitable properties, ties to local communities, and adverse impacts on biodiversity. Implications for adaptation research and practice are outlined.     

Nuclear energy response in the EMF27 study

The nuclear energy response for mitigating global climate change across 18 participating models of the EMF27 study is investigated. Diverse perspectives on the future role of nuclear power in the global energy system are evident in the broad range of nuclear power contributions from participating models of the study. In the Baseline scenario without climate policy, nuclear electricity generation and shares span 0–66 EJ/year and 0–25 % in 2100 for all models, with a median nuclear electricity generation of 39 EJ/year (1,389 GWe at 90 % capacity factor) and median share of 9 %. The role of nuclear energy increased under the climate policy scenarios. The median of nuclear energy use across all models doubled in the 450 ppm CO₂e scenario with a nuclear electricity generation of 67 EJ/year (2,352 GWe at 90 % capacity factor) and share of 17 % in 2100. The broad range of nuclear electricity generation (11–214 EJ/year) and shares (2–38 %) in 2100 of the 450 ppm CO₂e scenario reflect differences in the technology choice behavior, technology assumptions and competitiveness of low carbon technologies. Greater clarification of nuclear fuel cycle issues and risk factors associated with nuclear energy use are necessary for understanding the nuclear deployment constraints imposed in models and for improving the assessment of the nuclear energy potential in addressing climate change.