Ali, M. M., Nagamani, P. V., Sharma, N., Venu Gopal, R. T., Rajeevan, M., Goni, G. J., et al. (2015). Relationship between ocean mean temperatures and Indian summer monsoon rainfall: Ocean mean temperature and Indian summer monsoon rainfall. Atmos. Sci. Lett., 16(3), 408–413.
Abstract: Besides improving the understanding of the physics of the challenging problem of monsoon prediction, it is necessary to evaluate the efficiency of the input parameters used in models. Sea-surface temperature (SST) is the only oceanographic parameter applied in most of the monsoon forecasting models, which many times do not represent the heat energy available to the atmosphere. We studied the impacts of ocean mean temperature (OMT), representing the heat energy of the upper ocean, and SST on the all India summer monsoon rainfall through a statistical relation during 1993�2013 and found that OMT has a better link than SST.
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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.
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 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.
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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.
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.
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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.
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�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.
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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.
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.
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Bekele, D., Alamirew, T., Kebede, A., Zeleke, G., & Melese, A. M. (2017). Analysis of rainfall trend and variability for agricultural water management in Awash River Basin, Ethiopia. J Water Climate Change, 8(1), 127–141.
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Berhanu, B., Seleshi, Y., Demisse, S. S., & Melesse, A. M. (2016). Bias correction and characterization of climate forecast system re-analysis daily precipitation in Ethiopia using fuzzy overlay: Bias correction of climate forecast system re-analysis. Met. Apps, 23(2), 230–243.
Abstract: Knowledge of spatiotemporal variability of rainfall magnitude, pattern and trend is fundamental for understanding hydrological systems and runoff prediction for both gauged and ungauged catchments. These variables can be derived from rainfall-monitoring programmes with adequate spatial distribution and temporal coverage. However, rainfall-gauging stations in most developing countries are distributed sparsely. Remotely sensed rainfall datasets are becoming alternative rainfall data sources for larger area applications and are proven to have adequate spatiotemporal resolutions. Climate forecast system re-analysis (CFSR) is one such dataset provided by the National Center for Environmental Prediction (NCEP). This dataset captures the rainfall pattern in Ethiopia but with s magnitude bias of over- and underestimations. In this study, magnitude bias correction of the CFSR dataset with a linear scaling technique resulted in a rainfall grid of the country with ∼38 km spatial resolution of a 32 year (1979–2010) daily rainfall dataset. For the bias correction, observed annual rainfall from 930 and daily rainfall from 195 rain gauges were used. The study also attempted to understand the space and time variability of the rainfall through the construction of shape, magnitude and composite rainfall regimes for the entire country. The rainfall regimes of the country were developed using the fuzzy overlay technique with multi-indices of rainfall. The rainfall regimes address the frequency, duration, timing and magnitude variability of rainfall. The performance of the dataset generation and rainfall regime classification was evaluated using Nash–Sutcliffe Efficiency (NSE) and percent bias (PBIAS) values, which were found to be 0.8 and 1.3, respectively.
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Chen, R., Ebrahimi, H., & Jones, W. L. (2017). Creating a Multidecadal Ocean Microwave Brightness Dataset: Three-Way Intersatellite Radiometric Calibration Among GMI, TMI, and WindSat. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 10(6), 2623–2630.
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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.
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.
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Fill, J. M., Davis, C. N., & Crandall, R. M. (2019). Climate change lengthens southeastern USA lightning-ignited fire seasons. Glob Chang Biol, 25(10), 3562–3569.
Abstract: Trends in average annual or seasonal precipitation are insufficient for detecting changes in the climatic fire season, especially in regions where the fire season is defined by wet-dry seasonal cycles and lightning activity. Using an extensive dataset (1897-2017) in the Coastal Plain of the southeastern United States, we examined changes in annual dry season length, total precipitation, and (since 1945) the seasonal distribution of thunder-days as a correlate of lightning activity. We found that across the entire region, the dry season has lengthened by as much as 156 days (130% over 120 years), both starting earlier and ending later with less total precipitation. Less rainfall over a longer dry season, with no change in seasonal thunderstorm patterns, likely increases both the potential for lightning-ignited wildfires and fire severity. Global climate change could be having a hitherto undetected influence on fire regimes by altering the synchrony of climatic seasonal parameters.
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Guo, Q., & Matyas, C. J. (2016). Comparing the spatial extent of Atlantic basin tropical cyclone wind and rain fields prior to land interaction. Physical Geography, 37(1), 5–25.
Abstract: Understanding changes in the size of tropical cyclone (TC) wind and rain fields before landfall can improve identification of areas that may experience damage. We examine 25 Atlantic basin TCs for 36 h before gale-force winds (R17) cross land. Rain field extents are measured from satellite estimates of rain rates using a Geographic Information System. In each quadrant, R17 is obtained from the Extended Best Track data-set and correlated with the extent of the rain field. In general, both fields expand prior to landfall. The non-linearity of this trend poses problems for persistence forecast models. The largest wind fields are located over the Atlantic Ocean. Correlations between wind and rain field extent are strongly positive for Atlantic cases regardless of whether extratropical transition (ET) occurs and are associated with the direction of vertical wind shear. Poor correlations exist for Gulf observations. Rain fields extend farther towards the east during ET when vertical wind shear is stronger, but wind fields are not significantly different when separating cases based on whether or not ET occurs. As rain fields extend farther than wind fields in 33% of Gulf cases, moderately heavy rainfall may commence before damaging winds arrive, decreasing the time available for preparedness activities.
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Havens, K. E., Fulton III, R. S., Beaver, J. R., Samples, E. E., & Colee, J. (2016). Effects of climate variability on cladoceran zooplankton and cyanobacteria in a shallow subtropical lake. J. Plankton Res., 38(3), 418–430.
Abstract: In peninsular Florida, USA, rainfall is coupled with the Pacific Sea Surface Temperature Anomaly (SSTA), and rainfall affects mean depth and residence time of shallow lakes. We examined effects of two cycles of variation in rainfall using a 15-year data set from a shallow eutrophic lake dominated by small zooplankton, cyanobacteria and omnivorous fish. In high rainfall periods, the lake was deeper and cladoceran biomass was significantly higher than in dry periods. One factor was correlated with reduced biomass of cladocerans: a 3-fold higher biovolume of cyanobacteria. This led us to examine how variation in rainfall affects cyanobacteria. When cyanobacteria biovolume was high, the movement of water through the lake was low and invariant. Cyanobacteria grew unchecked. When cyanobacteria was reduced and cladocerans attained high biomass, there were intermittent flushing events that may have disrupted algal growth. Water color was elevated similar to 6-fold during the same time periods. Greater color may have made conditions less favorable for cyanobacteria by increasing light attenuation, and also more favorable for cladocerans, by reducing risk from fish. This study provides insight into how future variability in rainfall and drought, which may be exacerbated by global warming, could affect plankton in shallow subtropical lakes.
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Her, Y. G., Boote, K. J., Migliaccio, K. W., Fraisse, C., Letson, D., Mbuya, O., et al. (2017). Climate change impacts and adaptation in Florida's agriculture. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 235–267). Gainesville, FL: Florida Climate Institute.
Abstract: In this chapter, we describe Florida�s agriculture, the vulnerability of its crops and livestock to climate change and possible adaptation strategies. Much of Florida�s agricultural success is linked to its moderate climate, which allows vegetable and fruit crop production during the winter/spring season as well as the production of perennial crops such as citrus and sugarcane. In addition, there is a substantial livestock industry that uses the extensive perennial grasslands. While rising CO2 is generally beneficial to crop production but detrimental to nutritional quality, increase in temperature will cause mostly negative effects on yield. Florida�s agriculture faces additional challenges from climate change characterized by sea level rise and intensified extreme climate events, affecting land and irrigation water availability, livestock productivity and pest and disease pressure. New technologies and adaptation strategies are needed for sustainable agricultural production in Florida, including increased water and nutrient use efficiency in crops, crop and livestock breeding for heat stress, pest and disease resistance and reduced exposure of livestock to high temperature. Irrigation is a favored adaptation, but places an even greater burden or potential conflict between agriculture and community use of water resources.
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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.
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�2010. The maximum observed rainfall was also examined. Correlation analyses of the individual predictors, principal component regression (PCR) procedures and Mann�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 m s−1 or less. Extreme rainfall at a single location can occur when a TC's centre is over 450 km away.
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Hernández Ayala, J. J., & Matyas, C. J. (2017). Spatial distribution of tropical cyclone rainfall and its contribution to the climatology of Puerto Rico. Physical Geography, , 1–20.
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Hua, W., Zhou, L., Chen, H., Nicholson, S. E., Raghavendra, A., & Jiang, Y. (2016). Possible causes of the Central Equatorial African long-term drought. Environ. Res. Lett., 11(12), 124002.
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Infanti, J. M., & Kirtman, B. P. (2019). A comparison of CCSM4 high-resolution and low-resolution predictions for south Florida and southeast United States drought. Clim Dyn, 52(11), 6877–6892.
Abstract: It is important to have confidence in seasonal climate predictions of precipitation, particularly related to drought, as implications can be far-reaching and costlythis is particularly true for Florida. Precipitation can vary on fine spatial resolutions, and high-resolution coupled models may be needed to correctly represent precipitation variability. We study south Florida and southeast United States drought in Community Climate System version 4 low resolution (1 degrees atmosphere/ocean) and high-resolution (0.5 degrees atmosphere/0.1 degrees ocean) predictions for time means ranging from 3 to 36months. The very high-resolution in the ocean is of interest here given the potential importance of Gulf Stream on south Florida rainfall. Skill of shorter time-mean South Florida predictions (i.e. 3- and 12-months) are not impacted by increased resolution, but skill of 36-month mean south Florida precipitation is somewhat increased in the high resolution predictions. Notably, over the broader southeast United States the high-resolution model has higher skill for the 36-month mean rainfall predictions, associated with an improved relationship with tropical Pacific and Gulf Stream SSTA. Why this improvement in the broader southeast United States does not extend to Florida is an open question, but does suggest that even further resolution refinements may be needed.
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Ji, G., Havens, K., Beaver, J., & Fulton III, R. (2017). Response of Zooplankton to Climate Variability: Droughts Create a Perfect Storm for Cladocerans in Shallow Eutrophic Lakes. Water, 9(10), 764.
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Keener, V. W., Ingram, K. T., Jacobson, B., & Jones, J. W. (2007). Effects of El Nino/Southern Oscillation on simulated phosphorus loading in south Florida. Transactions of the Asabe, , 2081–2089.
Abstract: The El Nino/Southern Oscillation (ENSO) is a global climate phenomenon with strong effects on Florida's weather patterns. ENSO has been shown to have predictable effects on streamflow, rainfall, and crop yield; however, the relationship between N and P loading and ENSO has not been previously explored. Nutrient loads for a Lake Okeechobee sub-basin for 1965-2001 were simulated with the Watershed Assessment Model (WAM) and compared to measured P loads. The NS coefficients for simulated and measured monthly P loads were 0.73 for the calibration period and 0.63 for the validation period, which indicates "satisfactory" to "good" model performance. With a probable error range (PER) of +/-27.8% for measured P loads, the modified NS coefficients increased to 0.94 for the calibration period and 0.93 for the validation period. Results showed that ENSO strongly affected simulated seasonal and monthly phosphorus runoff. El Nino years produced seasonal peak loads of P runoff into Lake Okeechobee significant at the 99% level during the spring (February April), which indicates dominance of positive load anomalies. La Nina years produced significant seasonal peak loads in the summer (May-July) but with much greater variability in loads. Neutral years exhibited less predictable seasonal loading, although simulated P loads were generally similar to measured long-term means. Nutrient loading patterns during specific ENSO phases were comparable to previously explored precipitation and streamflow patterns in south Florida. This research has potential for use by land and water managers who can add short-term ENSO-based climate forecasts to their toolbox for reducing nutrient runoff.
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Mahjabin, T., & Abdul-Aziz, O. I. (2020). Trends in the Magnitude and Frequency of Extreme Rainfall Regimes in Florida. Water, .
Abstract: Trends in the extreme rainfall regimes were analyzed at 24 stations of Florida for four analysis periods: 1950–2010, 1960–2010, 1970–2010, and 1980–2010. A trend-free pre-whitening approach was utilized to correct data for autocorrelations. Non-parametric Mann-Kendall test and Theil-Sen approach were employed to detect and estimate trends in the magnitude of annual maximum rainfalls and in the number of annual above-threshold events (i.e., frequency). A bootstrap resampling approach was used to account for cross-correlations across sites and evaluate the global significance of trends at the 10% level (p-value ≤ 0.10). Dominant locally significant (p-value ≤ 0.10) increasing trends were found in the magnitudes of 1–12 h extreme rainfalls for the longest period, and in 6 h to 7 day rainfalls for the shortest period. The trends in 2–12 h rainfalls were also globally significant (i.e., exceeded the trends that could occur by chance). In contrast, globally significant decreasing trends were noted in the annual number of 1–3 h, 1–6 h, and 3–6 h extreme rainfalls during 1950–2010, 1960–2010, and 1980–2010, respectively. Trends in the number of 1–7 day extreme rainfalls were mixed, lacking global significance. Our findings would guide stormwater management in tropical/subtropical environments of Florida and around the world.
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