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Barnes, B. B., Hu, C., Holekamp, K. L., Blonski, S., Spiering, B. A., Palandro, D., et al. (2014). Use of Landsat data to track historical water quality changes in Florida Keys marine environments. Remote Sensing of Environment, 140, 485–496.
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Abstract: Satellite remote sensing has shown the advantage of water quality assessment at synoptic scales in coastal regions, yet modern sensors such as SeaWiFS or MODIS did not start until the late 1990s. For non-interrupted observations, only the Landsat series have the potential to detect major water quality events since the 1980s. However, such ability is hindered by the unknown data quality or consistency through time. Here, using the Florida Keys as a case study, we demonstrate an approach to identify historical water quality events through improved atmospheric correction of Landsat data and cross-validation with concurrent MODIS data. After aggregation of the Landsat-5 Thematic Mapper (TM) 30-m pixels to 240-m pixels (to increase the signal-to-noise ratio), a MODIS-like atmospheric correction approach using the Landsat shortwave-infrared (SWIR) bands was developed and applied to the entire Landsat-5 TM data series between 1985 and 2010. Remote sensing reflectance (RRS) anomalies from Landsat (2 standard deviations from a pixel-specific monthly climatology) were found to detect MODIS RRS anomalies with over 90% accuracy for all three bands for the same period of 2002–2010. Extending this analysis for the entire Landsat-5 time-series revealed RRS anomaly events in the 1980s and 1990s, some of which are corroborated by known ecosystem changes due in part to changes in local freshwater flow. Indeed, TM RRS anomalies were shown to be useful in detecting shifts in seagrass density, turbidity increases, black water events, and phytoplankton blooms. These findings have large implications for ongoing and future water quality assessment in the Florida Keys as well as in many other coastal regions.
Keywords: Water quality; Remote sensing; Atmospheric correction; Seagrass
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Barr, J. G., Engel, V., Smith, T. J., & Fuentes, J. D. (2012). Hurricane disturbance and recovery of energy balance, CO2 fluxes and canopy structure in a mangrove forest of the Florida Everglades. Agricultural and Forest Meteorology, 153, 54–66.
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Abstract: Eddy covariance (EC) estimates of carbon dioxide (CO2) &#64258;uxes and energy balance are examined to investigate the functional responses of a mature mangrove forest to a disturbance generated by Hurricane Wilma on October 24, 2005 in the Florida Everglades. At the EC site, high winds from the hurricane caused nearly 100% defoliation in the upper canopy and widespread tree mortality. Soil temperatures down to &#8722;50 cm increased, and air temperature lapse rates within the forest canopy switched from statically stable to statically unstable conditions following the disturbance. Unstable conditions allowed more ef&#64257;cient transport of water vapor and CO2 from the surface up to the upper canopy layer. Signi&#64257;- cant increases in latent heat &#64258;uxes (LE) and nighttime net ecosystem exchange (NEE) were also observed and sensible heat &#64258;uxes (H) as a proportion of net radiation decreased signi&#64257;cantly in response to the disturbance. Many of these impacts persisted through much of the study period through 2009. However, local albedo and MODIS (Moderate Resolution Imaging Spectro-radiometer) data (the Enhanced Vegetation Index) indicated a substantial proportion of active leaf area recovered before the EC measurements began 1 year after the storm. Observed changes in the vertical distribution and the degree of clumping in newly emerged leaves may have affected the energy balance. Direct comparisons of daytime NEE values from before the storm and after our measurements resumed did not show substantial or consistent differences that could be attributed to the disturbance. Regression analyses on seasonal time scales were required to differentiate the storm’s impact onmonthly average daytime NEE fromthe changes caused by interannual variability in other environmental drivers. The effects of the storm were apparent on annual time scales, and CO2 uptake remained approximately 250 g Cm&#8722;2 yr&#8722;1 lower in 2009 compared to the average annual values measured in 2004–2005. Dry season CO2 uptake was relatively more affected by the disturbance than wet season values. Complex leaf regeneration dynamics on damaged trees during ecosystem recovery are hypothesized to lead to the variable dry versus wet season impacts on daytime NEE. In contrast, nighttime CO2 release (i.e., nighttime respiration) was consistently and signi&#64257;cantly greater, possibly as a result of the enhanced decomposition of litter and coarse woody debris generated by the storm, and this effect was most apparent in the wet seasons compared to the dry seasons. The largest pre- and post-storm differences in NEE coincided roughly with the delayed peak in cumulative mortality of stems in 2007–2008. Across the hurricane-impacted region, cumulative tree mortality rates were also closely correlated with declines in peat surface elevation. Mangrove forest–atmosphere interactions are interpreted with respect to the damage and recovery of stand dynamics and soil accretion processes following the hurricane.
Keywords: Mangrove; Hurricane; Carbon dioxide; Energy balance; Disturbance; Sediment elevation; Carbon cycling; Sea level rise; Climate change
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Barreca, A., Deschenes, O., & Guldi, M. (2018). Maybe Next Month? Temperature Shocks and Dynamic Adjustments in Birth Rates. Demography, 55(4), 1269–1293.
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Abstract: We estimate the effects of temperature shocks on birth rates in the United States between 1931 and 2010. We find that days with a mean temperature above 80 degrees F cause a large decline in birth rates 8 to 10 months later. Unlike prior studies, we demonstrate that the initial decline is followed by a partial rebound in births over the next few months, implying that populations mitigate some of the fertility cost by shifting conception month. This shift helps explain the observed peak in late-summer births in the United States. We also present new evidence that hot weather most likely harms fertility via reproductive health as opposed to sexual activity. Historical evidence suggests that air conditioning could be used to substantially offset the fertility costs of high temperatures.
Keywords: Birth rates; Birth seasonality; Fertility; Temperature
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Barrett, P. M., Resing, J. A., Buck, N. J., Feely, R. A., Bullister, J. L., Buck, C. S., et al. (2014). Calcium carbonate dissolution in the upper 1000 m of the eastern North Atlantic: Atlantic Ocean CaCO3 dissolution. Global Biogeochem. Cycles, 28(4), 386–397.
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Abstract: Recent analyses suggest that considerable CaCO3 dissolution may occur in the upper water column of the ocean (<1500m). This study uses the distribution of particulate calcium from high-resolution suspended matter sampling along the Climate Variability and Predictability/CO2 Repeat Hydrography A16N transect in 2003 to estimate CaCO3 dissolution in the top 1000m of the North Atlantic. Dissolution rates were also approximated using changes in total alkalinity measurements along isopycnal surfaces. Water masses were found to be undersaturated with respect to aragonite at intermediate depths (400-1000m) in the eastern tropical North Atlantic. The CaCO3 dissolution rate in this region is estimated to be 0.9mmol CaCO3 m(-2) d(-1), indicating this region is a hotspot for upper water column CaCO3 dissolution compared to the Atlantic basin as a whole. Dissolution rates calculated from particulate calcium distributions outside of this region were significantly lower (0.2mmol CaCO3 m(-2) d(-1)) and are comparable to previous estimates of CaCO3 dissolution flux for the Atlantic Ocean. The magnitude of upper water column dissolution rates compared to measured surface ocean CaCO3 standing stocks suggests that biologically mediated CaCO3 dissolution may be occurring in the top 1000m of the Atlantic.
Keywords: CaCO3 dissolution; Atlantic Ocean; suspended particulate matter
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Barrett, R. T. S., Hollister, R. D., Oberbauer, S. F., & Tweedie, C. E. (2015). Arctic plant responses to changing abiotic factors in northern Alaska. American Journal of Botany, 102(12), 2020–2031.
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Abstract: PREMISE OF THE STUDY: Understanding the relationship between plants and changing abiotic factors is necessary to document and anticipate the impacts of climate change. METHODS: We used data from long-term research sites at Barrow and Atqasuk, Alaska, to investigate trends in abiotic factors (snow melt and freeze-up dates, air and soil temperature, thaw depth, and soil moisture) and their relationships with plant traits (inflorescence height, leaf length, reproductive effort, and reproductive phenology) over time. KEY RESULTS: Several abiotic factors, including increasing air and soil temperatures, earlier snowmelt, delayed freeze-up, drier soils, and increasing thaw depths, showed nonsignificant tendencies over time that were consistent with the regional warming pattern observed in the Barrow area. Over the same period, plants showed consistent, although typically nonsignificant tendencies toward increasing inflorescence heights and reproductive efforts. Air and soil temperatures, measured as degree days, were consistently correlated with plant growth and reproductive effort. Reproductive effort was best predicted using abiotic conditions from the previous year. We also found that varying the base temperature used to calculate degree days changed the number of significant relationships between temperature and the trait: in general, reproductive phenologies in colder sites were better predicted using lower base temperatures, but the opposite held for those in warmer sites. CONCLUSIONS: Plant response to changing abiotic factors is complex and varies by species, site, and trait; however, for six plant species, we have strong evidence that climate change will cause significant shifts in their growth and reproductive effort as the region continues to warm.
Keywords: abiotic factors; climate change; ITEX; LMM; northern Alaska; phenology; tundra plants
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Bartels, W. L., Furman, C. A., Diehl, D. D., Royce, F. S., Dourte, D. R., Ortiz, B. V., et al. (2013). Warming up to climate change: A participatory approach to engaging with agricultural stakeholders in the Southeast US. Reg. Environ. Change, 13(1), 45–55.
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Abstract: Within the context of a changing climate, scientists are called to engage directly with agricultural stakeholders for the coproduction of relevant information that will support decision making and adaptation. However, values, beliefs, identities, goals, and social networks shape perceptions and actions about climate change. Engagement processes that ignore the socio-cultural context within which stakeholders are embedded may fail to guide adaptive responses. To facilitate dialog around these issues, the Southeast Climate Consortium and the Florida Climate Institute formed a climate learning network consisting of row crop farmers, agricultural extension specialists, researchers, and climate scientists working in the Southeast US. Regional in scope, the learning network engages researchers and practitioners from Alabama, Georgia, and Florida as partners in adaptation science. This paper describes the ongoing interactions, dialog, and experiential learning among the network&#65533;s diverse participants. We illustrate how participatory tools have been used in a series of workshops to create interactive spaces for knowledge coproduction. For example, historical timelines, climate scenarios, and technology exchanges stimulated discussions about climate-related risk management. We present findings from the workshops related to participants&#65533; perspectives on climate change and adaptation. Finally, we discuss lessons learned that may be applicable to other groups involved in climate education, communication, and stakeholder engagement. We suggest that the thoughtful design of stakeholder engagement processes can become a powerful social tool for improving decision support and strengthening adaptive capacity within rural communities.
Keywords: Climate adaptation; Participatory process; Stakeholder network; Experiential learning
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Barton, M. B., Moran, J. R., Vollenweider, J. J., Heintz, R. A., & Boswell, K. M. (2017). Latitudinal dependence of body condition, growth rate, and stable isotopes of juvenile capelin (Mallotus villosus) in the Bering and Chukchi Seas. Polar Biol, 40(7), 1451–1463.
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Keywords: Capelin; Latitude; Isotopes; Nitrogen; Carbon; Food web; Energy allocation; Growth; Lipid; RNA/DNA
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Basso,, Dumont,, Maestrini,, Shcherbak,, Robertson,, Porter,, et al. (2018). Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields under Climate Change. AEL, 3(1), 180026.
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Abstract: A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.
Keywords: AgMIP; Agricultural Model Intercomparison and Improvement Project; SOC; soil organic carbon
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Basso, B., Gargiulo, O., Paustian, K., Robertson, P. G., Porter, C., Grace, P. R., et al. (2011). Procedures for initializing soil organic carbon pools in DSSAT-Century model for agricultural systems. Sssaj, 75.
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Basso, B., Ritchie, J. T., & Jones, J. W. (2012). On modeling approaches for effective assessment of hydrology of bioenergy crops: Comments on Le et al. (2011) Proc Natl Acad Sci USA 108:15085-15090. European Journal of Agronomy, 38, 64–65.
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Keywords: Maize; Hydrology; Bioenergy; Miscanthus; Switchgrass; ET
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Bassu, S., Brisson, N., Durand, J. - L., Boote, K., Lizaso, J., Jones, J. W., et al. (2014). How do various maize crop models vary in their responses to climate change factors? Glob Change Biol, 20(7), 2301–2320.
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Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly &#8722;0.5 Mg ha&#8722;1 per °C. Doubling [CO2] from 360 to 720 &#956;mol mol&#8722;1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Keywords: CO2; AgMIP; climate; maize; model intercomparison; simulation; temperature; uncertainty
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Bastola, S. (2013). Hydrologic impacts of future climate change on Southeast US watersheds. Reg. Environ. Change, 13(S1), S131–S139.
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Abstract: The hydrological impact of climate change is assessed for 28 watersheds located within the Southeast United States using output from global climate models (GCMs) from the Climate Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) run. Subsequently, the impact of projected change in seasonal streamflow is derived by propagating projected scenarios, generated using changes derived from GCMs and weather generators, through a suite of conceptual hydrological models. Analysis shows that the spread in the magnitude of change in temperature and rainfall for CMIP3 is wider than that for CMIP5. The reduction in the spread among many factors may be attributed to improved physics, model number and resolution, and emission scenarios. The spread in projected change in temperature (precipitation) increases (decreases) from southernmost to northernmost latitude. Hydrological projection with CMIP3 output for the 2070s shows that streamflow decreases for most of the watersheds in spring and summer and increased in fall. In contrast, CMIP5 results show an increase in flow for all seasons except with the high-end scenarios in spring. However, the uncertainty in projections in streamflow is high with model uncertainty dominating emission scenario. The variability in prediction uncertainty among watersheds is partly explained by the variability in wetness index. The probability distribution function for projected seasonal flow for each scenario is markedly wide and therefore reflects that the uncertainty associated with using multiple GCMs from both CMIP3 and CMIP5 experiment is high which makes design and implementation of adaption measure a difficult task.
Keywords: CMIP3; CMIP5; Hydrological models; GLUE
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Bastola, S. (2014). Uncertainty in Climate Change Studies. In S. Shrestha, M. S. Babel, & V. P. Pandey (Eds.), Climate Change and Water Resources (pp. 81–108). Boca Raton, FL: CRC Press.
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Bastola, S., & Misra, V. (2013). Sensitivity of Hydrological Simulations of Southeastern United States Watersheds to Temporal Aggregation of Rainfall. J. Hydrometeor, 14(4), 1334–1344.
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Abstract: This study investigates the sensitivity of the performance of hydrological models to certain temporal variations of precipitation over the SouthEastern United States (SEUS). Because of observational uncertainty in the estimates of rainfall variability at subdaily scales, the analysis is conducted with two independent rainfall datasets that resolve the diurnal variations. In addition, three hydrological models are used to account for model uncertainty. Results show that the temporal aggregation of subdaily rainfall can translate into a markedly higher volume error in flow simulated by the hydrological models. For the selected watersheds in the SEUS, the volume error is found high (~ 35%) for a 30-day aggregation in some of the selected watersheds. Hydrological models tend to underestimate flow in these watersheds with a decrease in temporal variability in precipitation. Furthermore, diminishing diurnal amplitude by removing subdaily rainfall corresponding to times of climatological daily maximum and minimum has a detrimental effect on the hydrological simulation. This theoretical experiment resulted in the underestimation of flow, with a disproportionate volume error (of as high as 77% in some watersheds). Observations indicate that over the SEUS variations of diurnal variability of rainfall explain a significant fraction of the seasonal variance throughout the year, with especially strong fractional variance explained in the boreal summer season. The results suggest that should diurnal variations of precipitation get modulated either from anthropogenic or natural causes in the SEUS, there will be a significant impact on the streamflow in the watersheds. These conclusions are quite robust since both observational and model uncertainties have been considered in the analysis.
Keywords: Hydrologic models
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Bastola, S., & Misra, V. (2014). Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application. Hydrol. Process., 28(4), 1989–2002.
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Abstract: Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium-Range Weather Forecasts 40-year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10-km grid resolution; stochastically generated data from weather generator; bias-corrected dynamically downscaled; and bias-corrected global reanalysis. The reanalysis products are considered as surrogates for large-scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias-corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22&#8201;years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales.
Keywords: reanalysis; bias correction; rainfall; runoff model; dynamic downscaling
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Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States. Environmental Modelling & Software, 73, 90–102.
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Abstract: We show useful seasonal deterministic and probabilistic prediction skill of streamflow and nutrient loading over watersheds in the Southeastern United States (SEUS) for the winter and spring seasons. The study accounts for forecast uncertainties stemming from the meteorological forcing and hydrological model uncertainty. Multi-model estimation from three hydrological models, each forced with an ensemble of forcing derived by matching observed analogues of forecasted quartile rainfall anomalies from a seasonal climate forecast is used. The attained useful hydrological prediction skill is despite the climate model overestimating rainfall by over 23% over these SEUS watersheds in December–May period. The prediction skill in the month of April and May is deteriorated as compared to the period from December–March (zero lead forecast). A nutrient streamflow rating curve is developed using a log linear tool for this purpose. The skill in the prediction of seasonal nutrient loading is identical to the skill of seasonal streamflow forecast.
Keywords: Rainfall-runoff model; Seasonal hydrologic forecasting; Southeastern United States; Water quality; Seasonal predictability
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Bastola, S., Misra, V., & Li, H. (2013). Seasonal hydrological forecasts for watersheds over the Southeastern United States for boreal summer and fall seasons. Earth Interact., 17, 25.
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Abstract: We evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the Southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts (the Florida Climate Institute&#65533;Florida State University Seasonal Hindcast at 50 km [FISH50]) is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall-runoff models. The hydrological models are forced with rainfall from FISH50, (quantile-based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogues of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, we find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root mean square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. We contend that given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.
Keywords: Seasonal climate forecast; Ensemble streamflow prediction; Rainfall-runoff model
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Batouli, M., & Mostafavi, A. (2018). Multiagent Simulation for Complex Adaptive Modeling of Road Infrastructure Resilience to Sea-Level Rise. Computer-Aided Civil and Infrastructure Engineering, 33(5), 393–410.
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Battisti, M., Delgado, M. S., & Parmeter, C. F. (2015). Evolution of the global distribution of carbon dioxide: A finite mixture analysis. Resource and Energy Economics, 42, 31–52.
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Abstract: Economists and environmental policymakers have recently begun advocating a bottom-up approach to climate change mitigation, focusing on reduction targets for groups of nations, rather than large scale global policies. We advance this discussion by conducting a rigorous empirical analysis of the global distribution of carbon emissions along several important dimensions: groupings, polarization, mobility, and volatility. In contrast to previous work, our empirical analysis is both comprehensive and data-driven. We discuss how robust empirical evidence may aid policymakers in forging a heterogeneous carbon abatement policy.
Keywords: Carbon emissions; Emissions groups; Heterogeneity; Abatement policy; Finite mixture models
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Battisti, R., Sentelhas, P. C., & Boote, K. J. (2018). Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. Int J Biometeorol, 62(5), 823–832.
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Abstract: Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 degrees C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha(-1) for the ensemble at + 6 degrees C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.
Keywords: Carbon dioxide; Future climate scenarios; Multi-model ensemble; Rainfall; Solar radiation; Temperature
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