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Publications

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Dzotsi, K. A., Basso, B., & Jones, J. W. (2013). Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecological Modelling, 260, 62–76.
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Abstract: Simplified approaches to modeling crop growth and development have recently received more attention due to increased interest in applying crop models at large scales for various agricultural assessments. In this study, we integrated the simple version of SALUS (System Approach to Land Use Sustainability) crop model in the widely-used Decision Support System for Agrotechnology Transfer (DSSAT) to enhance the capability of DSSAT to simulate additional crops without requiring detailed parameterization. An uncertainty and sensitivity analysis was conducted using the integrated DSSAT-simple SALUS model to assess the variability in model outputs and crop parameter ranking in response to uncertainties associated with crop parameters required by the model. The influence of year, production level, and location on the effect of crop parameter uncertainty was also investigated. Parameter uncertainty resulted in a high variability in modeled outputs. Simulated potential aboveground biomass ranged from 1.2 t ha&#8722;1 to 38 t ha&#8722;1 for maize and 4 t ha&#8722;1 to 26.5 t ha&#8722;1 for peanut and cotton, all locations and years considered. The degree of variability was dependent upon the production level, the location, the year, and the crop. Ranking of crop parameters was not significantly affected by the year of study but was strongly related to the production level, location, and crop. The model was not sensitive to parameters related to prediction of the timing of germination and emergence. The most influential parameters were related to leaf area index growth, crop duration, and thermal time accumulation. Findings from this study contributed to understanding the effects of crop parameter uncertainty on the model's outputs under different environmental conditions.
Keywords: Crop modeling; Maize; Peanut; Cotton; Latin hypercube; Correlation
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Hernandez Ayala, J. J., & Matyas, C. J. (2016). Tropical cyclone rainfall over Puerto Rico and its relations to environmental and storm-specific factors. Int. J. Climatol., 36(5), 2223–2237.
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Abstract: Although tropical cyclone rainfall (TCR) is common over Puerto Rico, the factors that cause this rain to vary from one storm to another have not been studied. The aim of this article is to understand how storm-specific characteristics including storm location, duration, storm centre proximity to land, intensity, horizontal translation speed (HTS) and environmental factors like moisture and vertical wind shear affect TCR variability over Puerto Rico. TCR was determined at rain gauge locations for days when storms were within a 500 km radius of Puerto Rico. The station data were then used to calculate an island-averaged total rainfall value for 86 storms during 1970&#65533;2010. The maximum observed rainfall was also examined. Correlation analyses of the individual predictors, principal component regression (PCR) procedures and Mann&#65533;Whitney U tests identified precipitable water, storm centre proximity to land, mid-level relative humidity (MRH), duration, HTS and longitude as the predictors with the strongest influence on rainfall. The PCR showed that a component comprised of precipitable water, MRH and longitude accounted for more than 60% in TCR variability. When an additional component comprised of storm duration, storm centre proximity to land and translation speed was considered, the PCR model explained 70% (52%) of the variability in mean (maximum) TCR. Key threshold values for high rainfall across Puerto Rico are a storm centre distance of 233 km or less and moisture exceeding 44.5 mm of precipitable water and 44.5% of relative humidity with forward speeds of 6.4&#8201;m&#8201;s&#8722;1 or less. Extreme rainfall at a single location can occur when a TC's centre is over 450 km away.
Keywords: tropical cyclones; rainfall; Puerto Rico; correlation analysis; principal component regression
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Li, Y., Zhang, L., Qiu, J., Yan, J., Wan, L., Wang, P., et al. (2017). Spatially explicit quantification of the interactions among ecosystem services. Landscape Ecol, 32(6), 1181–1199.
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Keywords: Multiple ecosystem services; Trade-off; Synergy; Temporal dynamics; Spatial heterogeneity; Partial correlation; LULC change; Loess Plateau
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Onyejekwe, O., Holman, B., & Kachouie, N. N. (2017). Multivariate models for predicting glacier termini. Environ Earth Sci, 76(23).
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Keywords: Climate change; Mountain glaciers; Statistical analysis; Regression; Multivariate models; Correlation; Prediction; Terminus location; Climate factors
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van Bruggen, A. H. C., He, M., Zelenev, V. V., Semenov, V. M., Semenov, A. M., Semenova, E. V., et al. (2017). Relationships between greenhouse gas emissions and cultivable bacterial populations in conventional, organic and long-term grass plots as affected by environmental variables and disturbances. Soil Biology and Biochemistry, 114, 145–159.
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Keywords: Carbon dioxide; Nitrous oxide; Methane; Cross correlations; Harmonics analysis; Canonical correspondence analysis
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