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Publications

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Ajaz Ahmed, M. A., Abd-Elrahman, A., Escobedo, F. J., Cropper Jr., W. P., Martin, T. A., & Timilsina, N. (2017). Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States. Journal of Environmental Management, 199, 158–171.
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Keywords: Trade-offs; Ecosystem services; Drivers; Geographically weighted regression; Watershed; Ecoregion
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Chiri, H., Abascal, A. J., Castanedo, S., Antolínez, J. A. A., Liu, Y., Weisberg, R. H., et al. (2019). Statistical simulation of ocean current patterns using autoregressive logistic regression models: A case study in the Gulf of Mexico. Ocean Modelling, 136, 1–12.
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Abstract: Autoregressive logistic regression models have been demonstrated to be a powerful tool for statistical simulation of spatial patterns in climate and meteorology fields. In this paper we introduce a statistical framework for the simulation of ocean current patterns based on the autoregressive logistic regression models, and apply it to the Gulf of Mexico Loop Current. The statistical model is forced by three autoregressive terms, the wind stress curl in the Gulf of Mexico and in the Caribbean Sea, and the sea level pressure anomalies over the North Atlantic. It is used to replicate the bi-weekly historical sequence of 8 Loop Current patterns, obtained from a 24-year altimetry derived dataset. The model reproduces the inter-annual and intra-annual variability of the original time series, showing notable fitting capacity. A point-by-point comparison between the actual and simulated pattern series confirms the capability of the model in analysing the evolution of ocean current patterns. The predictive skill of the model is also explored, and the preliminary forecast (up to 3 months) results are encouraging. The presented statistical framework may find more practical applications in the future, such as the generation of statistically sound climate-based oceanographic scenarios for risk analyses, and the mid-term probabilistic prediction of ocean current patterns.
Keywords: Statistical modelling; Autoregressive logistic regression; Ocean current patterns; Gulf of Mexico; Loop Current
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Cid-Serrano, L., Ramirez, S. M., Alfaro, E. J., & Enfield, D. B. (2015). Analysis of the Latin American west coast rainfall predictability using an ENSO index. Atmosfera, 28(3), 191–203.
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Abstract: The objective of this study was to determine the probability of occurrence of wet or dry season events, by means of estimating latitudinal profiles for the association between El Nino Southern-Oscillation and the rainfall along the west coast of Central and South America. The analysis was performed using multinomial linear regression and multinomial logit regression models. We used monthly time series of the Pacific equatorial sea surface temperature (SST), the Nino 3.4 index, a sea level pressure index (SOI) and rainfall anomalies over a 2.5 x 2.5 degrees grid along the west coast of Central and South America, for latitudes starting at 25 degrees N through 45 degrees S, from 1951 to 2011. We defined an ENSO index (NSO) as predictor and rainfall as response. Data was grouped into seasons and then categorized into terciles to construct 3 x 3 non-symmetrical three way contingency tables. As results, we generated latitudinal profiles of the predictability (association) of rainfall for the west coast of Central and South America, using the ENSO phases as predictor.
Keywords: El Nino; logit regression; rainfall; ENSO
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Fu, X., Song, J., Sun, B., & Peng, Z. - R. (2016). "Living on the edge": Estimating the economic cost of sea level rise on coastal real estate in the Tampa Bay region, Florida. Ocean & Coastal Management, 133, 11–17.
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Keywords: Sea level rise; Economic analysis; Hedonic price model; Spatial regression; Adaptation planning
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Goly, A., Teegavarapu, R. S. V., & Mondal, A. (2014). Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation. Earth Interact., 18(18), 1–28.
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Abstract: Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.
Keywords: Regression analysis; Statistical techniques; General circulation models; Model evaluation/performance; Numerical analysis/modeling; Regional models
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Guo, L., Cheng, J., Luedeling, E., Koerner, S. E., He, J. - S., Xu, J., et al. (2017). Critical climate periods for grassland productivity on China's Loess Plateau. Agricultural and Forest Meteorology, 233, 101–109.
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Keywords: Aboveground net primary productivity (ANPP); Partial least squares regression (PLS); Precipitation; Temperate grassland; Temperature; Timing of climate variability
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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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Mo, W., & Zhang, Q. (2016). Modeling the influence of various water stressors on regional water supply infrastructures and their embodied energy. Environ. Res. Lett., 11(6), 064018.
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Abstract: Water supply consumes a substantial amount of energy directly and indirectly. This study aims to provide an enhanced understanding of the influence of water stressors on the embodied energy of water supply (EEWS). To achieve this goal, the EEWS in 75 North Carolina counties was estimated through an economic input-output based hybrid life cycle assessment. Ten water stressor indicators related to population, economic development, climate, water source, and land use were obtained for the 75 counties. A multivariate analysis was performed to understand the correlations between water stressor indicators and the EEWS. A regression analysis was then conducted to identify the statistically significant indicators in describing the EEWS. It was found that the total amount of water supply energy varies significantly among selected counties. Water delivery presents the highest energy use and water storage presents the least. The total embodied energy was found to be highly correlated with total population. The regression analysis shows that the total embodied energy can be best described by total population and temperature indicators with a relatively high R square value of 0.69.
Keywords: embodied energy; water supply; climate; water source; land use; population; regression analysis
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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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Pathak, T. B., Jones, J. W., & Fraisse, C. W. (2012). Cotton yield forecasting for the southeastern United States using climate indices. Applied Engr. in Agriculture, 28(5), 711–723.
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Abstract: The United States cotton industry is one of the major economic drivers of the country accounting for more than $25 billion in products and services annually. The southeastern United States holds a major share of total cotton production in the United States. Although cotton is considered as a drought tolerant crop, climate variability may adversely impact cotton production. An effective way to reduce agricultural vulnerability to climate variability is through the implementation of effective adaptation strategies. Knowing cotton yield forecast in advance based on climate information such as using large scale climate indices, would aid the growers in making informed decisions to adapt to climate risk. The objectives of this study were to evaluate the relationships between large-scale climate indices and cotton yield and to evaluate the skill of cotton yield forecasts. Seven January and February month oceanic and atmospheric climate indices were correlated with May-September temperature, precipitation, and county average cotton yield for 64 counties in Georgia and Alabama. All climate indices were then summarized using a principal component analysis and regressed against historic cotton yield for 64 counties to obtain empirical models for cotton yield forecasting. The yield forecasts were evaluated using leave one out cross validation. Results indicated that January and February monthly climate indices exhibited statistically significant correlations with climate during the cotton growing season as well as with cotton yields. With a lead time of approximately 2 months before the typical planting period on the southeastern United States, about 77% of the counties in Georgia and 70% of the counties in Alabama showed statistically significant correlations between observed and forecasted cotton yields. Climate indices showed potential to forecast cotton yield in the southeastern United States with significant lead time.
Keywords: Climate indices, Cotton yield, Yield forecast, Principal component regression
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Regier, P., Briceño, H., & Jaffé, R. (2016). Long-term environmental drivers of DOC fluxes: Linkages between management, hydrology and climate in a subtropical coastal estuary. Estuarine, Coastal and Shelf Science, 182, 112–122.
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Keywords: Climate change; DOC flux; Everglades; Multivariate regression; Principal component analysis; Water management
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Roopsind, A., Caughlin, T. T. van der H., P., Arets, E., & Putz, F. E. (2018). Trade-offs between carbon stocks and timber recovery in tropical forests are mediated by logging intensity. Global Change Biology, .
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Keywords: Carbon stocks; Tropical forestry; Sustainable forest management; REDD+; Forest degradation; Climate change mitigation; Piecewise regression
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Song, J., Fu, X., Wang, R., Peng, Z. - R., & Gu, Z. (2018). Does planned retreat matter? Investigating land use change under the impacts of flooding induced by sea level rise. Mitig Adapt Strateg Glob Change, 23(5), 703–733.
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Keywords: Land use change; Sea level rise; Population relocation; Urban growth; Flooding; Multilayer perceptron; SimWeight; Logistic regression; Land use planning
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Tahsin, S., Medeiros, S. C., Hooshyar, M., & Singh, A. (2017). Optical Cloud Pixel Recovery via Machine Learning. Remote Sensing, 9(6), 527.
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Keywords: data reconstruction; random forest; NDVI; hydrology; regression; rainfall; temperature
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Tautenhahn, S., Lichstein, J. W., Jung, M., Kattge, J., Bohlman, S. A., Heilmeier, H., et al. (2016). Dispersal limitation drives successional pathways in Central Siberian forests under current and intensified fire regimes. Glob Change Biol, 22(6), 2178–2197.
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Abstract: Fire is a primary driver of boreal forest dynamics. Intensifying fire regimes due to climate change may cause a shift in boreal forest composition toward reduced dominance of conifers and greater abundance of deciduous hardwoods, with potential biogeochemical and biophysical feedbacks to regional and global climate. This shift has already been observed in some North American boreal forests and has been attributed to changes in site conditions. However, it is unknown if the mechanisms controlling fire-induced changes in deciduous hardwood cover are similar among different boreal forests, which differ in the ecological traits of the dominant tree species. To better understand the consequences of intensifying fire regimes in boreal forests, we studied postfire regeneration in five burns in the Central Siberian dark taiga, a vast but poorly studied boreal region. We combined field measurements, dendrochronological analysis, and seed-source maps derived from high-resolution satellite images to quantify the importance of site conditions (e.g., organic layer depth) vs. seed availability in shaping postfire regeneration. We show that dispersal limitation of evergreen conifers was the main factor determining postfire regeneration composition and density. Site conditions had significant but weaker effects. We used information on postfire regeneration to develop a classification scheme for successional pathways, representing the dominance of deciduous hardwoods vs. evergreen conifers at different successional stages. We estimated the spatial distribution of different successional pathways under alternative fire regime scenarios. Under intensified fire regimes, dispersal limitation of evergreen conifers is predicted to become more severe, primarily due to reduced abundance of surviving seed sources within burned areas. Increased dispersal limitation of evergreen conifers, in turn, is predicted to increase the prevalence of successional pathways dominated by deciduous hardwoods. The likely fire-induced shift toward greater deciduous hardwood cover may affect climate-vegetation feedbacks via surface albedo, Bowen ratio, and carbon cycling.
Keywords: boosted regression trees; boreal; dark taiga; fire regime; forest regeneration; land surface-climate feedback; postfire succession; residual seed trees; seed dispersal
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Wahl, T., Calafat, F. M., & Luther, M. E. (2014). Rapid changes in the seasonal sea level cycle along the US Gulf coast from the late 20th century. Geophys. Res. Lett., .
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Abstract: Temporal variations of the seasonal sea level harmonics throughout the 20th and early 21st century along the United States Gulf coast are investigated. A significant amplification of the annual sea level cycle from the 1990s onwards is found, with both lower winter and higher summer sea levels in the eastern Gulf. Ancillary data are used to build a set of multiple regression models to explore the mechanisms driving the decadal variability and recent increase in the annual cycle. The results suggest that changes in the air surface temperature towards warmer summers and colder winters and changes in mean sea level pressure explain most of the amplitude increase. The changes in the seasonal sea level cycle are shown to have almost doubled the risk of hurricane induced flooding associated with sea level rise since the 1990s for the eastern and north-eastern Gulf of Mexico coastlines.
Keywords: Gulf of Mexico; coastal seasonal sea level; tide gauge data; atmospheric reanalysis; multiple linear regression models; flood risk
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Youngflesh, C., Jenouvrier, S., Li, Y., Ji, R., Ainley, D. G., Ballard, G., et al. (2017). Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch. Ecology, 98(4), 940–951.
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Keywords: Anna Karenina Principle; Antarctica; asynchrony; Bayesian hierarchical model; climate change; phenology; Pygoscelis adeliae; quantile regression
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Zhang, C., Denka, S., Cooper, H., & Mishra, D. R. (2018). Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sensing of Environment, 204, 366–379.
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Keywords: Object-based biomass modeling; Ensemble analysis for biomass prediction; Machine learning regression algorithms; Coastal marshes
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