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.

Modeling study of the aspect ratio influence on urban canopy energy fluxes with a modified wall-canyon energy budget scheme

The influence of the aspect ratio (building height/street canyon width) and the mean building height of cities on local energy fluxes and temperatures is studied by means of an Urban Canopy Model (UCM) coupled with a one-dimensional second-order turbulence closure model. The UCM presented is similar to the Town Energy Balance (TEB) model in most of its features but differs in a few important aspects. In particular, the street canyon walls are treated separately which leads to a different budget of radiation within the street canyon walls. The UCM has been calibrated using observations of incoming global and diffuse solar radiation, incoming long-wave radiation and air temperature at a site in So Paulo, Brazil. Sensitivity studies with various aspect ratios have been performed to assess their impact on urban temperatures and energy fluxes at the top of the canopy layer. In these simulations, it is assumed that the anthropogenic heat flux and latent heat fluxes are negligible. Results show that the simulated net radiation and sensible heat fluxes at the top of the canopy decrease and the stored heat increases as the aspect ratio increases. The simulated air temperature follows the behavior of the sensible heat flux. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling hourly and daily fractions of UV, PAR and NIR to global solar radiation under various sky conditions at Botucatu, Brazil

In this analysis, using available hourly and daily radiometric data performed at Botucatu, Brazil, several empirical models relating ultraviolet (UV), photosynthetically active (PAR) and near infrared (NIR) solar global components with solar global radiation (G) are established. These models are developed and discussed through clearness index K(T) (ratio of the global-to-extraterrestrial solar radiation). Results obtained reveal that the proposed empirical models predict hourly and daily values accurately. Finally. the overall analysis carried Out demonstrates that the sky conditions are more important in developing correlation models between the UV component and the global solar radiation. The linear regression models derived to estimate PAR and NIR components may be obtained without sky condition considerations within a maximum variation of 8%. In the case of UV, not taking into consideration the sky condition may cause a discrepancy of up to 18% for hourly values and 15% for daily values. (C) 2008 Elsevier Ltd. All rights reserved.

Impact of biomass burning aerosols on precipitation in the Amazon: A modeling case study

A study of the potential role of aerosols in modifying clouds and precipitation is presented using a numerical atmospheric model. Measurements of cloud condensation nuclei (CCN) and cloud size distribution properties taken in the southwestern Amazon region during the transition from dry to wet seasons were used as guidelines to define the microphysical parameters for the simulations. Numerical simulations were carried out using the Brazilian Development on Regional Atmospheric Modeling System, and the results presented considerable sensitivity to changes in these parameters. High CCN concentrations, typical of polluted days, were found to result in increases or decreases in total precipitation, depending on the level of pollution used as a reference, showing a complexity that parallels the aerosol-precipitation interaction. Our results show that on the grids evaluated, higher CCN concentrations reduced low-to-moderate rainfall rates and increased high rainfall rates. The principal consequence of the increased pollution was a change from a warm to a cold rain process, which affected the maximum and overall mean accumulated precipitation. Under polluted conditions, cloud cover diminished, allowing greater amounts of solar radiation to reach the surface. Aerosol absorption of radiation in the lower layers of the atmosphere delayed convective evolution but produced higher maximum rainfall rates due to increased instability. In addition, the intensity of the surface sensible heat flux, as well as that of the latent heat flux, was reduced by the lower temperature difference between surface and air, producing greater energy stores at the surface.

TOWARD UNDERSTANDING THE B[e] PHENOMENON. IV. MODELING OF IRAS 00470+6429

FS CMa type stars are a recently described group of objects with the B[e] phenomenon which exhibits strong emission-line spectra and strong IR excesses. In this paper, we report the first attempt for a detailed modeling of IRAS 00470+6429, for which we have the best set of observations. Our modeling is based on two key assumptions: the star has a main-sequence luminosity for its spectral type (B2) and the circumstellar (CS) envelope is bimodal, composed of a slowly outflowing disklike wind and a fast polar wind. Both outflows are assumed to be purely radial. We adopt a novel approach to describe the dust formation site in the wind that employs timescale arguments for grain condensation and a self-consistent solution for the dust destruction surface. With the above assumptions we were able to satisfactorily reproduce many observational properties of IRAS 00470+6429, including the Hi line profiles and the overall shape of the spectral energy distribution. Our adopted recipe for dust formation proved successful in reproducing the correct amount of dust formed in the CS envelope. Possible shortcomings of our model, as well as suggestions for future improvements, are discussed.

MODELING VERY LONG BASELINE INTERFEROMETRIC IMAGES WITH THE CROSS-ENTROPY GLOBAL OPTIMIZATION TECHNIQUE

We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N(s) elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e. g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images constructed from a poorly sampled (u, v) plane.