Process-based Modeling Approach Research Articles

Modeling interrelationships between morphological evolution and grain-size trends in back-barrier tidal basins of the East Frisian Wadden Sea

In terms of grain size, surficial sediment distribution patterns in back-barrier tidal basins (e.g., the East Frisian Wadden Sea, Germany) often show a landward fining trend from the sea boundary to the mainland shore. In addition to the cross-shore patterns, there are lateral grain-size trends toward the watersheds of the basins and toward the watersheds of tidal flats bordered by tidal channels on either side. In the present study, interrelationships between morphological evolution and grain-size trends in the back-barrier tidal basins of the East Frisian Wadden Sea were simulated for a period of 60 years by a process-based forward modeling approach using the Delft3D system. The model outputs show that grain size displays a shoreward fining trend within the basin area, which is consistent with in situ observations; such a trend can be interpreted by the shoreward decrease in the cross-shore maximum velocity. Moreover, the model predicts lateral grain-size trends similar to those observed in the tidal basins: coarser sediment remains in the inlets and channels, while finer sediment settles at the tidal watersheds and on the tidal flats between channels. The spatial patterns of tidal flat sediment grain size within the tidal basins are thus related to the distance from the sea boundary and from the tidal channels. The modeling exercise also indicates that the development of the grain-size pattern observed in the East Frisian Wadden Sea is accomplished within a few decades, and that the time periods required to reach equilibrium are much shorter for grain size than for bed elevation. Evidently, spatial grain-size information can be used to assess sediment transport and morphological adaptation processes as, for example, attempted in sediment trend analysis procedures.

Predictive approaches to forest site productivity: recent trends, challenges and future perspectives

In the framework of sustainable forest management, measuring site quality and predicting site productivity remain a major forestry topic. Over the past years, it has been fostered by a number of site-growth modelling studies seeking to establish quantitative relationships between site index and explicit biophysical indicators. In addition, comparative modelling studies of site index and site productivity have highlighted limited accordance of their environmental determinism, questioning site index as a reliable indicator of site quality. Lastly, processbased modelling approaches have recently arisen as a means to predict site productivity over large regions. All theseinvestigationshavehoweverdrawnlimitedattentioninrecentsyntheses.Inthisreview,weprovideanoverview of the literature on these site-growth studies. Concepts and vocabulary related to site productivity are introduced. Taking regional studies as a baseline, we first highlight recent progress regarding the geographic areas encompassed, and the major role played by NFI programmes and spatialized environmental information. A trade-off between model accuracy and geographic extent is suggested, pointing out potential deficiencies in the modelling of site-growth associations, insufficient accuracy or resolution in climatic data, or uncontrolled factorsinsite-growthmodelsthatemergeonahigherspatialscale.Inappropriateuseofbiophysicalclassifications where environmental factors remain implicit is also emphasized. In a next step, wediscuss earlyand mostrecent indications on weaknesses of the site index concept when applied over large regions, including its differential response to climate relative to site productivity, regional variations in site index curves, site index dependence on standdensityandsubsequentlyonregionalsilviculturalpractices.Theroleofgeneticstructureoftreepopulations anditsintegrationintosite-growthstudiesisalsoreviewedanddiscussed.Theinterests,limitsandrecentadvances of process-based models as an alternative to evaluate site productivity are considered, as they may overcome some of the previous limitations. We last draw challenges and perspectives on the issue. We suggest that the accuracy ‐ and the need ‐ of site index as a founding concept of forestry science is questioned, by building direct productivity‐environment relationships based on NFI databases as a realistic option at hand. We formulate perspectives regarding the accuracy, resolution and enlargement of environmental indicators currently used, the inclusion of information on genetic structure of tree populations in the context of adaptation to future climate change, as well as the use of site productivity models in forest management.

Implementing contour bank farming practices into the J2000 model to improve hydrological and erosion modelling in semi-arid Western Cape Province of South Africa

Contour bank farming is a well-known agricultural management technique in areas which are characterised by intensive and erosive rainfalls. Contour banks are designed to reduce the flow velocity of overland flow and to intercept water before it concentrates in rills, thereby reducing the risk of soil erosion and land degradation. By their structure, contour banks noticeably impact surface runoff pattern both temporally and spatially. Also subsurface flow may be affected by contour banks. For example, if contour banks intersect the A- and B-horizon of the soil, it can cause significant infiltration of water into the C-horizon, which if saline, can generate saline interflow to downslope areas. Although these aspects have been highlighted in previous research efforts, the quantitative and qualitative impacts of contours on runoff generation and associated erosion dynamics or salinisation are rarely considered in process-based hydrological modelling approaches. In this study an approach was developed to improve distributed hydrological and erosion modelling by integrating contour banks in the delineation and routing of Hydrological Response Units. Applying the distributed and process-based hydrological model J2000 which was modified with a contour bank and erosion module it could be shown that the implementation of contour banks improved the model performance significantly.

Application of the Wind Erosion Prediction System in the AIRPACT Regional Air Quality Modeling Framework

<abstract> Abstract. Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM₁₀ emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM₁₀ emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM₁₀ emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM₁₀ concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM₁₀ concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively.

Coupled daily streamflow and water temperature modelling in large river basins

Abstract. Realistic estimates of daily streamflow and water temperature are required for effective management of water resources (e.g. for electricity and drinking water production) and freshwater ecosystems. Although hydrological and process-based water temperature modelling approaches have been successfully applied to small catchments and short time periods, much less work has been done at large spatial and temporal scales. We present a physically based modelling framework for daily river discharge and water temperature simulations applicable to large river systems on a global scale. Model performance was tested globally at 1/2 × 1/2° spatial resolution and a daily time step for the period 1971–2000. We made specific evaluations on large river basins situated in different hydro-climatic zones and characterized by different anthropogenic impacts. Effects of anthropogenic heat discharges on simulated water temperatures were incorporated by using global gridded thermoelectric water use datasets and representing thermal discharges as point sources into the heat advection equation. This resulted in a significant increase in the quality of the water temperature simulations for thermally polluted basins (Rhine, Meuse, Danube and Mississippi). Due to large reservoirs in the Columbia which affect streamflow and thermal regimes, a reservoir routing model was used. This resulted in a significant improvement in the performance of the river discharge and water temperature modelling. Overall, realistic estimates were obtained at daily time step for both river discharge (median normalized BIAS = 0.3; normalized RMSE = 1.2; r = 0.76) and water temperature (median BIAS = −0.3 °C; RMSE = 2.8 °C; r = 0.91) for the entire validation period, with similar performance during warm, dry periods. Simulated water temperatures are sensitive to headwater temperature, depending on resolution and flow velocity. A high sensitivity of water temperature to river discharge (thermal capacity) was found during warm, dry conditions. The modelling approach has potential to be used for risk analyses and studying impacts of climate change and other anthropogenic effects (e.g. thermal pollution, dams and reservoir regulation) on large rivers.

Weather-Driven Variation in Dengue Activity in Australia Examined Using a Process-Based Modeling Approach

The impact of weather variation on dengue transmission in Cairns, Australia, was determined by applying a process-based dengue simulation model (DENSiM) that incorporated local meteorologic, entomologic, and demographic data. Analysis showed that inter-annual weather variation is one of the significant determinants of dengue outbreak receptivity. Cross-correlation analyses showed that DENSiM simulated epidemics of similar relative magnitude and timing to those historically recorded in reported dengue cases in Cairns during 1991-2009, (r = 0.372, P < 0.01). The DENSiM model can now be used to study the potential impacts of future climate change on dengue transmission. Understanding the impact of climate variation on the geographic range, seasonality, and magnitude of dengue transmission will enhance development of adaptation strategies to minimize future disease burden in Australia.

Optimal preventive maintenance strategy using two repair crews having different efficiencies: a quasi renewal process based modelling approach

This paper considers randomly failing, single-unit equipment subject to a periodic preventive maintenance (PM) policy. In case of failure between successive perfect PM actions (renewals), imperfect repairs are performed following a decreasing quasi-renewal process. One of two different maintenance crews can perform the repairs. One team is more experienced, and consequently more efficient than the other, but more costly. A mathematical model is developed in order to determine the PM period, T, and the kth repair, during a PM period, after which the repair team should be changed, minimising the average total cost per time unit over an infinite time span. It is also proved that an optimal solution in terms of the PM period always exists for any given system lifetime distribution and any set of maintenance costs. Numerical examples are presented and the obtained results are discussed.

Analysis of Bed Load Equations and River Bed Level Variations Using Basin-Scale Process-Based Modelling Approach

Bed load transport is a key process in maintaining the dynamically stable channel geometry for restoring the form and function of river ecosystems. Bed load consists of relatively large sediment particles that are moved along the streambed by rolling, sliding or saltation. Currently, various empirical correlations are used to estimate bed load transport rates since no single procedure, whether theoretical or empirical, has yet to be universally accepted as completely satisfactory in this aspect. Bed load particles are primarily sourced from river bed materials or banks. The amount of bed load and its spatial distribution contributes significantly to river bed level changes. Hillslope sediment contribution, mostly available to the river in the form of suspended load, also plays an important role in river bed level changes. This study aims to analyse different bed load equations and the resultant computations of river bed level variations using a process-based sediment dynamic model. Analyses have revealed that different bed load equations were mainly deduced from the concept of relating bed shear stresses to their critical values which are highly factored by the slope gradient, water discharge and particle sizes. In this study, river bed level variations are calculated by estimating total surplus or deficit sediment loads (suspended loads and bed loads) in a channel section. This paper describes the application of different widely used bed load equations, and evaluation of their various parameters and relative performances for a case study area (Abukuma River Basin, Japan) using a basin-scale process-based modelling approach. Relative performances of river bed level simulations obtained by using different bed load equations are also presented. This paper elaborates on the modelling approaches for river bed load and bed level simulations. Although verifications were not done due to unavailability of field data for bed load, qualitative evaluations were conducted vis-a-vis field data on flow and suspended sediment loads as well as the bed loads presented in different past studies.

Projecting impacts of human disturbances to inform conservation planning and management in a dryland forest landscape

Systematic approaches to conservation planning and management require spatially explicit information on the dynamics of multiple disturbance processes, but progress in providing such information has been limited to date. Here we examine the use of a spatially explicit model of forest dynamics (LANDIS-II), incorporating a range of ecological processes, to examine the impacts of different types of disturbance on a dryland forest landscape in Central Chile. The model was parameterized using spatial data and results of a field survey, in which 21 native tree species were recorded, and one invasive exotic ( Acacia dealbata). Seven disturbance scenarios were simulated, with different combinations of fire, browsing and tree cutting. Model results indicated relatively little impact of disturbance on forest cover but substantial differences in forest structure, with relatively old-growth forest stands (>120 years old) being virtually eliminated from the landscape in scenarios with both browsing and cutting. Tree species richness tended to be lower in those scenarios without disturbance, highlighting potential trade-offs between forest structure and species richness. Spread of A. dealbata was projected only to occur in the presence of fire when combined with browsing and/or cutting. These interactive effects of different forms of disturbance illustrate the value of process-based modeling approaches for exploring the spatial dynamics of multiple disturbance processes, but highlight the difficulty of identifying an optimum disturbance regime that would enable conservation objectives to be achieved. It is suggested that process-based models should form part of an analytical ‘toolkit’ to support the practical implementation of systematic conservation planning approaches.

Diagnosing the equilibrium state of a coupled global biosphere-atmosphere model

[1] This study combines a conceptual modeling approach and a physical process–based modeling approach to diagnose and understand the equilibrium state(s) of coupled biosphere-atmosphere models using the NCAR CAM3-CLM3-DGVM model as an example, with a focus on the West Africa and South America regions. First, a conceptual model is parameterized according to results from offline simulations using CAM3-CLM3 and CLM3-DGVM. The resulting conceptual model is then used to predict the potential biosphere-atmosphere equilibrium state(s) for the coupled CAM3-CLM3-DGVM model. Finally, simulations using the coupled CAM3-CLM3-DGVM model itself are carried out to verify the results of the conceptual model. Only one equilibrium state is found in both the conceptual model and the physically based numerical model. The CLM3 model features excessively high soil evaporation and very low plant transpiration, which leads to high bare-ground ET and low sensitivity of ET to vegetation cover changes in the land model. Despite the high sensitivity of precipitation to evapotranspiration changes in the atmospheric model, the land model deficiency leads to a high amount of precipitation in the absence of vegetation cover and a low sensitivity of precipitation to vegetation changes in CAM3-CLM3, two conditions that are found to anchor a coupled biosphere-atmosphere model to a single equilibrium state. This statement also holds for the up-to-date version of the models using CLM4. This study provides an example for the importance of land surface processes in determining the potential equilibrium states of fully coupled biosphere-atmosphere models.

Modeling the occurrence of 15 coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process-based growth model and decision tree analysis

Question: Can we interpret how climatic variation limits photosynthesis and growth for one widely distributed species, and then relate these responses to model the geographic distributions of other species? Location: The forested region of the Pacific Northwest, United States and Canada. Methods: We first mapped monthly climatic data, averaged for the period 1950 to 1975 at 1 km resolution across the region. The recorded presence and absence of 15 native tree species were next mapped at 1 km resolution from data acquired on 22 771 field survey plots. To establish seasonal limits on photosynthesis and water use, a process-based growth model (3-PG, Physiological Processes to Predict Growth) was parameterized for Douglas-fir (Pseudotsuga menziesii), one of the most widely distributed species in the region. Automated decision tree analyses were used to predict the distribution of different species by creating a suite of rules associated with the relative constraints that soil drought, atmospheric humidity deficits, suboptimal and subfreezing temperatures would impose on the growth of Douglas-fir. Results: The 3-PG process-based modeling approach, combined with automated decision tree analyses, predicted presence and absence of 15 conifers on field survey plots with an average accuracy of 82±12%. Predictive models of current distribution for each species differed in the number of, order in, and physiological thresholds selected. A deficit in the soil water balance, followed by departures from optimum temperatures in the summer were the two most important variables selected in predicting species distributions. Conclusions: Although empirical models using different sampling techniques and statistical analyses may be more accurate in predicting current distribution of species, the hybrid approach presented in this paper provides a greater mechanistic understanding of the limits to growth and tree distributions. These attributes of process-based models make them particularly useful in designing mitigating strategies to projected changes in climate.

Characterizing the relationship between soil and growth of plantation teak (Tectona grandis L.f.)

A study was conducted on modelling the growth of Teak in relation to soil conditions using data from 52 permanent sample plots established in Teak plantations in Kerala, India. The plots were established during 2000-2001 and re-measured during 2004. Diameter increment was computed for all the plots. From each of the plots, soil samples were taken from pits at three depth layers viz., 0-20, 20-40, 40-60 cm and the soils were subjected to analysis for determination of particle size separates, bulk density (BD), particle density (PD), water holding capacity (WHC), soil pH, organic carbon (OC), exchange bases (EB), exchange acidity (EA), cation exchange capacity (CEC), base saturation (BS), Total N, available P, K, Na, Ca and Mg. Process-based modelling approach was tried to characterize the interrelation of tree growth vs soil properties. WHC, in the 20-40 cm depth level, turned out as the foremost soil variable significantly influencing tree growth. The adjusted R2 value for the diameter increment function was 0.55, a reasonable value to expect under uncontrolled conditions. However, this implies that a substantial part of the variation in growth happens on account of factors not included in the model. The results also indicated an almost linear decrease in diameter growth with increase in the soil WHC in 20-40 cm depth level, keeping other factors constant.

A dynamic process-based cost modeling approach to understand learning effects in manufacturing

Informed technology decision-making requires a structured understanding of cost evolution over time. A dynamic approach integrating learning curves and process-based cost modeling is introduced to examine learning in manufacturing. The approach is applied to the case of a hydroforming process, and quantifies the cost impacts of learning improvements in cycle time, downtime, and reject rates. A comparison with cases of automotive assembly and wire drawing illustrates that variation in learning is tied to the individual process cost structure. The results show aggregate cost evolution is strongly dependent on cost structure and that major cost elements may not align with major cost improvement-through-learning opportunities. The analyses can be used to focus intentional learning activities on primary learning operational drivers.

Landscape-scale control of carbon budget of Lake Simcoe: A process-based modelling approach

We present an application of the Integrated Catchments model for Carbon (INCA-C) to simulate dissolved organic carbon (DOC) dynamics in two main tributaries of Lake Simcoe. This is the first application of the INCA-C model to a large watershed with mixed agricultural, urban and forest land use. Understanding DOC dynamics in the Lake Simcoe watershed will aid in understanding the spatial and temporal patterns of organic and metal contaminant transport in the watershed which should, in turn, lead to improved watershed management. We were able to successfully simulate flows in the Beaver River and to capture the seasonal and inter-annual patterns in DOC concentration in the Beaver and White Creeks, two of Lake Simcoe's major tributaries. Sensitivity analysis showed the importance of hydrology and land use in controlling surface water DOC. The success of the model application presented here suggests that some of the same processes which control DOC in headwater watersheds are also operating in larger river basins.

Validation of plantation transpiration in south-eastern Australia estimated using the 3PG+ forest growth model

Forest plantations for wood production are an increasingly important land use in southern Australia, and there are potentially important hydrologic consequences of what is mostly a change in land use from agriculture to silviculture. An ability to predict, with some degree of accuracy, the impact of plantation expansion on surface water and groundwater resources is essential. A validated process-based modelling approach, integrating the many interacting environmental and management factors which may influence plantation growth and transpiration, can be used for this purpose. The 3PG forest growth model has been evaluated for a number of species from widely differing climate and site conditions. While growth predictions have been validated, little attention has been given to testing the accuracy of the transpiration predictions or the model's representation of the water balance. We enhanced the 3PG forest growth model (known as 3PG+) and then integrated it into the Catchment Analysis Tool (CAT), so that it now interfaces with a more detailed multi-layered, daily time step representation of the soil water balance. Simulated transpiration using 3PG+ in CAT was compared with field measurements in 30 plots (across 15 sites) representing 5 common plantation species ( Eucalyptus globulus, E. nitens, E. grandis, E. regnans and Pinus radiata) across ages 2–31 years. Mean daily plot transpiration during the measurement periods ranged between 0.4 and 4.2 mm day −1 (average 2.0 mm day −1). Simulated mean daily plot transpiration using 3PG+ in CAT for Eucalyptus was good (coefficient of efficiency = 0.80; R 2 = 0.81). While the model tended to slightly under-predict transpiration at higher measured rates (>3.5 mm day −1), predictions at monthly timescales had acceptable accuracy. The integration of 3PG+ into CAT resulted in an improvement in accuracy and applicability of CAT, and provides for the spatial application of 3PG+ across diverse and mixed land use catchments for investigation into carbon and water movement in forest systems.

Linking intra-seasonal variations in climate and tree-ring δ 13C: A functional modelling approach

Stable carbon isotopic composition (δ 13C) in tree rings is a widely recognized tool for climate reconstruction, and several works suggest that seasonal information can be extracted from intra-ring δ 13C variations. In this study, we explored the link between climate and intra-seasonal oak ring δ 13C using a process-based modelling approach. The ISOCASTANEA model was developed to compute the seasonal dynamics of tree-ring δ 13C for deciduous species from half-hourly climatic data by accounting for photosynthetic discrimination and carbon translocation and allocation at the tree scale and in tree rings. The model was applied from March 2005 to December 2007 in a 150-year-old deciduous oak forest. Canopy photosynthesis and stomatal conductance were calibrated using H 2O and CO 2 fluxes measured by the eddy flux technique, and simulated δ 13C values were compared to seasonal patterns of total organic matter δ 13C measured in tree rings for 2006 and 2007 at the same site. With the inclusion of carbon translocation and with regard to 13C enrichment of starch compared to soluble sugars, the model can reasonably simulate the intra-seasonal and inter-annual variability of tree-ring δ 13C using the same parameter values for 2006 and 2007. The amplitude of the seasonal carbon isotope pattern in tree rings was influenced by both photosynthetic and post-photosynthetic processes (starch enrichment and reserve use). The δ 13C variations in the early part of the ring, i.e., mainly in the earlywood, were related mostly to carbohydrate metabolism, although diluted information about environmental conditions during the previous year could also be found. The last part of the ring, consisting mainly of latewood, was found to be a good recorder of current-year environmental conditions, in particular relative humidity, at a fine temporal resolution when the growth rate was high. The sensitivity of the δ 13C in the early part of the ring to carbohydrate metabolism suggests that intra-ring δ 13C could be used to explore the relationship between tree decline or mortality and carbohydrate deficiency.

IQuantitator: a tool for protein expression inference using iTRAQ.

BackgroundIsobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.ResultsThis modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.ConclusioniQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.

Vadose zone attenuation of organic compounds at a crude oil spill site — Interactions between biogeochemical reactions and multicomponent gas transport

Contaminant attenuation processes in the vadose zone of a crude oil spill site near Bemidji, MN have been simulated with a reactive transport model that includes multicomponent gas transport, solute transport, and the most relevant biogeochemical reactions. Dissolution and volatilization of oil components, their aerobic and anaerobic degradation coupled with sequential electron acceptor consumption, ingress of atmospheric O 2, and the release of CH 4 and CO 2 from the smear zone generated by the floating oil were considered. The focus of the simulations was to assess the dynamics between biodegradation and gas transport processes in the vadose zone, to evaluate the rates and contributions of different electron accepting processes towards vadose zone natural attenuation, and to provide an estimate of the historical mass loss. Concentration distributions of reactive (O 2, CH 4, and CO 2) and non-reactive (Ar and N 2) gases served as key constraints for the model calibration. Simulation results confirm that as of 2007, the main degradation pathway can be attributed to methanogenic degradation of organic compounds in the smear zone and the vadose zone resulting in a contaminant plume dominated by high CH 4 concentrations. In accordance with field observations, zones of volatilization and CH 4 generation are correlated to slightly elevated total gas pressures and low partial pressures of N 2 and Ar, while zones of aerobic CH 4 oxidation are characterized by slightly reduced gas pressures and elevated concentrations of N 2 and Ar. Diffusion is the most significant transport mechanism for gases in the vadose zone; however, the simulations also indicate that, despite very small pressure gradients, advection contributes up to 15% towards the net flux of CH 4, and to a more limited extent to O 2 ingress. Model calibration strongly suggests that transfer of biogenically generated gases from the smear zone provides a major control on vadose zone gas distributions and vadose zone carbon balance. Overall, the model was successful in capturing the complex interactions between biogeochemical reactions and multicomponent gas transport processes. However, despite employing a process-based modeling approach, honoring observed parameter ranges, and generally obtaining good agreement between field observations and model simulations, accurate quantification of natural attenuation rates remains difficult. The modeling results are affected by uncertainties regarding gas phase saturations, tortuosities, and the magnitude of CH 4 and CO 2 flux from the smear zone. These findings highlight the need to better delineate gas fluxes at the model boundaries, which will help constrain contaminant degradation rates, and ultimately source zone longevity.

Modelling the drought impact on monoterpene fluxes from an evergreen Mediterranean forest canopy

In many ecosystems drought cycles are common during the growing season but their impact on volatile monoterpene emissions is unclear. Therefore, we aimed to develop and evaluate a process-based modelling approach to explore the explanatory power of likely mechanisms. The biochemically based isoprene and monoterpene emission model SIM-BIM2 has been modified and linked to a canopy model and a soil water balance model. Simulations are carried out for Quercus ilex forest sites and results are compared to measured soil water, photosynthesis, terpene-synthase activity, and monoterpene emission rates. Finally, the coupled model system is used to estimate the annual drought impact on photosynthesis and emission. The combined and adjusted vegetation model was able to simulate photosynthesis and monoterpene emission under dry and irrigated conditions with an R(2) of 0.74 and 0.52, respectively. We estimated an annual reduction of monoterpene emission of 67% for the extended and severe drought period in 2006 in the investigated Mediterranean ecosystem. It is concluded that process-based ecosystem models can provide a useful tool to investigate the involved mechanisms and to quantify the importance of specific environmental constraints.

A comparison of four process-based models and a statistical regression model to predict growth of Eucalyptus globulus plantations

In forest management and ecological research, consideration of the impacts and risks of climate change or management optimisation is complex. Computer models have long been applied as tools for these tasks. Process-based forest growth models claim to overcome the limitations of empirical statistical models, but the capacity of different process-based models and modelling approaches have rarely been compared directly. This study evaluates stepwise multiple regression models in comparison to four process-based modelling approaches (3-PG, 3-PG+, CABALA and Forest-DNDC) for greenfield predictions of Eucalyptus globulus plantation growth from 2 to 8 years after planting throughout southern Australia. The stepwise multiple regression models could not simulate plantation growth to a satisfactory level of precision over the entire simulation period, although 2 years after planting model efficiency was 0.46, greater than for any of the process-based models. The variables that were statistically important to predict early plantation growth were mean minimum temperature, stocking rate and the amount of applied N fertiliser. For plantations between 4 and 8 years of age, coefficients of model efficiency were between −0.55 and −0.99. Only process-based models provided the flexibility to realistically predict the impacts of the different growth conditions throughout of the simulation period. Amongst the process-based models, Forest-DNDC achieved the greatest model efficiency (0.28) at 2 years after planting and was the most consistent performing model (0.20–0.30), whereas CABALA achieved the greates model efficiency (0.70) after 8 years of plantation growth. Both 3-PG models performed best for 6-year-old plantations, but throughout the simulation period their predictive precision was strongly dependent on estimating site fertility. For 3-PG+, a statistical approach to estimate site fertility resulted in a model efficiency of 0.28 at 6 years after planting, whereas a subjective estimation of site fertility for 3-PG resulted in a model efficiency of 0.58. In general, the process-based models had difficulties in simulating very young plantations at less then 4 years after planting, plantations with high tree mortality rates and plantation response to extreme silvicultural management operations.