A new data set of soil mineralogy for dust-cycle modeling

The mineralogy of airborne dust affects the impact of dust particles on direct and indirect radiative forcing, on atmospheric chemistry and on biogeochemical cycling. It is determined partly by the mineralogy of the dust-source regions and partly by size-dependent fractionation during erosion and transport. Here we present a data set that characterizes the clay and silt-sized fractions of global soil units in terms of the abundance of 12 minerals that are important for dust–climate interactions: quartz, feldspars, illite, smectite, kaolinite, chlorite, vermiculite, mica, calcite, gypsum, hematite and goethite. The basic mineralogical information is derived from the literature, and is then expanded following explicit rules, in order to characterize as many soil units as possible. We present three alternative realizations of the mineralogical maps, taking the uncertainties in the mineralogical data into account. We examine the implications of the new database for calculations of the single scattering albedo of airborne dust and thus for dust radiative forcing.

Inverse modeling of the 14C bomb pulse in stalagmites to constrain the dynamics of soil carbon cycling at selected European cave sites

The decomposition of soil organic matter (SOM) is temperature dependent, but its response to a future warmer climate remains equivocal. Enhanced rates of decomposition of SOM under increased global temperatures might cause higher CO2 emissions to the atmosphere, and could therefore constitute a strong positive feedback. The magnitude of this feedback however remains poorly understood, primarily because of the difficulty in quantifying the temperature sensitivity of stored, recalcitrant carbon that comprises the bulk (>90%) of SOM in most soils. In this study we investigated the effects of climatic conditions on soil carbon dynamics using the attenuation of the 14C ‘bomb’ pulse as recorded in selected modern European speleothems. These new data were combined with published results to further examine soil carbon dynamics, and to explore the sensitivity of labile and recalcitrant organic matter decomposition to different climatic conditions. Temporal changes in 14C activity inferred from each speleothem was modelled using a three pool soil carbon inverse model (applying a Monte Carlo method) to constrain soil carbon turnover rates at each site. Speleothems from sites that are characterised by semi-arid conditions, sparse vegetation, thin soil cover and high mean annual air temperatures (MAATs), exhibit weak attenuation of atmospheric 14C ‘bomb’ peak (a low damping effect, D in the range: 55–77%) and low modelled mean respired carbon ages (MRCA), indicating that decomposition is dominated by young, recently fixed soil carbon. By contrast, humid and high MAAT sites that are characterised by a thick soil cover and dense, well developed vegetation, display the highest damping effect (D = c. 90%), and the highest MRCA values (in the range from 350 ± 126 years to 571 ± 128 years). This suggests that carbon incorporated into these stalagmites originates predominantly from decomposition of old, recalcitrant organic matter. SOM turnover rates cannot be ascribed to a single climate variable, e.g. (MAAT) but instead reflect a complex interplay of climate (e.g. MAAT and moisture budget) and vegetation development.

Analysis and modeling of Diacamma workers’ behavior

It is well-known that social insects such as ants show interesting collective behaviors. How do they organize such behaviors? To expand understanding of collective behaviors of social insects, we focused on ants, Diacamma, and analyzed the behavior of a few individuals. In an experimental set-up, ants are placed in hemisphere without a nest and food and the trajectory of ants is recorded. From this bottom-up approach, we found following characteristics: 1. Activity of individuals increases and decreases periodically. 2. Spontaneous meeting process is observed between two ants and meeting spot of two ants is localized in the experimental field.

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Modeling the photo-oxidation of dissolved organic matter by ultraviolet radiation in freshwater lakes: implications for mercury bioavailability

Uncertainties in projected ultraviolet (UV) radiation may lead to future increases in UV irradiation of freshwater lakes. Because dissolved organic carbon (DOC) is the main binding phase for mercury (Hg) in freshwater lakes, an increase in DOC photo-oxidation may affect Hg speciation and bioavailability. We quantified the effect of DOC concentration on the rate of abiotic DOC photo-oxidation for five lakes (DOC = 3.27–12.3 mg L−1) in Kejimkujik National Park, Canada. Samples were irradiated with UV-A or UV-B radiation over a 72-h period. UV-B radiation was found to be 2.36 times more efficient at photo-oxidizing DOC than UV-A, with energy-normalized rates of dissolved inorganic carbon (DIC) production ranging from 3.8 × 10−5 to 1.1 × 10−4 mg L−1 J−1 for UV-A, and from 6.0 × 10−5 to 3.1 × 10−4 mg L−1 J−1 for UV-B. Energy normalized rates of DIC production were positively correlated with DOC concentrations. Diffuse integrated attenuation coefficients were quantified in situ (UV-A Kd = 0.056–0.180 J cm−1; UV-B Kd = 0.015–0.165 J cm−1) and a quantitative depth-integrated model for yearly DIC photo-production in each lake was developed. The model predicts that, UV-A produces between 3.2 and 100 times more DIC (1521–2851 mg m−2 year−1) than UV-B radiation (29.17–746.7 mg m−2 year−1). Future increases in UV radiation may increase DIC production and increase Hg bioavailability in low DOC lakes to a greater extent than in high DOC lakes.

Greenhouse gas emission scenario modeling for cities using the PURGE model

Cities globally are in the midst of taking action to reduce greenhouse gas (GHG) emissions. After the vital step of emissions quantification, strategies must be developed to detail how emissions reductions targets will be achieved. The Pathways to Urban Reductions in Greenhouse Gas Emissions (PURGE) model allows the estimation of emissions from four pertinent urban sectors: electricity generation, buildings, private transportation, and waste. Additionally, the carbon storage from urban and regional forests is modeled. An emissions scenario is examined for a case study of the greater Toronto, Ontario, Canada, area using data on current technology stocks and government projections for stock change. The scenario presented suggests that even with some aggressive targets for technological adoption (especially in the transportation sector), it will be difficult to achieve the less ambitious 2050 emissions reduction goals of the Intergovernmental Panel on Climate Change. This is largely attributable to the long life of the building stock and limitations of current retrofit practices. Additionally, demand reduction (through transportation mode shifting and building occupant behavior) will be an important component of future emissions cuts.

Modeling bidding competitiveness and position performance in multi-attribute construction auctions

Currently, multi-attribute auctions are becoming widespread awarding mechanisms for contracts in construction, and in these auctions, criteria other than price are taken into account for ranking bidder proposals. Therefore, being the lowest-price bidder is no longer a guarantee of being awarded, thus increasing the importance of measuring any bidder’s performance when not only the first position (lowest price) matters. Modeling position performance allows a tender manager to calculate the probability curves related to the more likely positions to be occupied by any bidder who enters a competitive auction irrespective of the actual number of future participating bidders. This paper details a practical methodology based on simple statistical calculations for modeling the performance of a single bidder or a group of bidders, constituting a useful resource for analyzing one’s own success while benchmarking potential bidding competitors.

Simulating human and environmental exposure from hand-held knapsack pesticide application: Be-WetSpa-Pest, an integrative, spatially explicit modeling approach

This paper presents an integrative and spatially explicit modeling approach for analyzing human and environmental exposure from pesticide application of smallholders in the potato producing Andean region in Colombia. The modeling approach fulfills the following criteria: (i) it includes environmental and human compartments; (ii) it contains a behavioral decision-making model for estimating the effect of policies on pesticide flows to humans and the environment; (iii) it is spatially explicit; and (iv) it is modular and easily expandable to include additional modules, crops or technologies. The model was calibrated and validated for the Vereda La Hoya and was used to explore the effect of different policy measures in the region. The model has moderate data requirements and can be adapted relatively easy to other regions in developing countries with similar conditions.