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The Florida Water and Climate Alliance (FloridaWCA) is a stakeholder–scientist partnership committed to increasing the relevance of climate science data and tools at time and space scales needed to support decision-making in water resource management, planning, and supply operations in Florida. Since 2010, a group of university researchers, public utility water resource managers and operators, water management district personnel, and local planners have engaged in a sustained collaboration for the development, sharing, and application of cutting-edge research to the practical issues of water management and distribution in the highly urbanized state of Florida. The authors, all members of FloridaWCA, present a case study of the organization’s history, its achievements, and lessons learned at the organizational, scientific/technical, and personal levels. Their goals are to 1) describe how the organizational process has contributed to actionable science based on posing and answering questions of importance; 2) share its scientific impact and technical contributions; 3) demonstrate the value of such a stakeholder–scientist partnership, and 4) identify organizational and structural components that have influenced its effectiveness, including personal reflections. The FloridaWCA, having reached its tenth anniversary, continues to evolve today as a sustained stakeholder–scientist partnership resulting in both guiding researchers of what is applicable in the field (creating an area of research that is useful to society) while also helping the practitioners to push the envelope on the state-of-the practices that can be informed by current research.

Cropping system models (CSMs) are useful tools for developing and optimizing crop management strategies under specific soil and climate conditions. This requires good quality weather data, which is often lacking in many agricultural areas of the world. Studies exposed the capability of gridded weather data to be a proper source in situations where measured weather data is not available. This study assessed the accuracy of a recently published gridded weather dataset called MSWX for modeling of wheat performance in Iran. MSWX has a 0.1°× 0.1° spatial and 3–hourly temporal resolution and covers 1979 to present, and it was initially generated using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 gridded data. For this study, the CSM-NWheat model within Decision Support System for Agrotechnology Transfer (DSSAT) was applied for modeling of wheat production of three irrigated and three rainfed cultivars at six sites in Iran during 2000–2010 with observed daily weather data. Results showed that MSWX maximum and minimum temperatures (Tmax and Tmin) had higher accuracy than AgERA5, while MSWX solar radiation had weaker performance than AgERA5. The bias of MSWX solar radiation was significantly greater than AgERA5 in some of sites. Comparing the NWheat model outputs based on measured and MSWX temperature (solar radiation) data, grain yield was estimated using MSWX with a MAE of 172 (133) kg/ha and a NRMSE of 11.4% (7.6%). The MSWX temperature (solar radiation) data had high accuracy for simulation of flowering and maturity dates with MAE of 3 and 5 (1 and 3) days and NRMSE of 5.4% and 7% (2.1% and 4.5%). The MSWX temperature data was found as the best replacement for measured temperature data in the study area. Also, it seems that MSWX solar radiation has the potential to be an adequate substitution for measured solar radiation data after further bias correction.

Soil temperature (Ts) is an important meteorological variable that noticeably impacts ecology, agriculture, and hydrology. In contrast to air temperature, there has been little focus on the projection of Ts in the future under climate change. In this study, future Ts were estimated at three weather stations (Ghoochan, Gonabad and Mashhad) in northeast Iran using an ensemble of 17 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). These Ts were analyzed under different representative concentration pathway (RCP) scenarios by applying the Soil Temperature and Moisture Model (STM 2 ). For each site, projected estimates were made for 10-year periods between 2025 and 2095, at depths of 5, 10, 20, 50, and 100 cm. Analysis of the data from the three stations yielded Ts increments ranging from 0.8 to 1.5 °C, 1.2–2.3 °C, and 2.4–4.4 °C, based on RCP scenarios RCP4.5, RCP6.0 and RCP8.5, respectively. Increased Ts may enhance crop development, especially during crop emergence and during parts of the vegetative period, but can also impact soil processes such as the rate of nutrient and CO 2 release from soil organic matter, and the rate of evaporation. Due to this phenomenon, increased Ts can create faster and higher water deficits in soil, especially in arid to semi-arid climates. The method used in this study is applicable to diverse geographical contexts and the authors recommend similar studies be undertaken elsewhere in order to obtain further projected Ts data. The optimization of the current method may be particularly useful for future agro-climatological studies, as Ts impacts both plant and soil processes in addition to soil-atmosphere exchanges.

Temperature plays a critical role for plant development and growth, and maximum and minimum temperature are two of the key inputs for the application of crop simulation models. The lack of good observed weather data is a challenging issue in many agricultural regions. Recent studies have used gridded weather data as an alternative to observed weather data for application in agricultural systems modeling. The aim of this study was to identify the most appropriate gridded temperature data (GTD) to be applied in crop simulation modeling. Therefore, nine global GTDs were evaluated for rainfed and irrigated wheat production for six sites across Iran using the Cropping System Model (CSM)-NWheat model. The GTDs included AgCFSR, AgERA5, AgMERRA, CPC, GLDAS, NCEP RII, PGF, POWER, and S14FD. The observed data from the weather stations located in the selected sites were used to evaluate the GTDs. To increase the robustness of the final results, six different rainfed and irrigated wheat cultivars were considered for modeling. Results showed that AgCFSR, AgMERRA, PGF, and AgERA5 had the best performance, while NCEP and GLDAS had the weakest performance. Using AgCFSR and AgERA5, grain yield was simulated with NRMSE ≤ 11.5% and NSE ≥ 0.7. The overall performance of AgCFSR, AgMERRA, and PGF was better that for AgERA5; however, these three GTDs are not recommended for agricultural modeling applications, especially if current weather data are needed. AgCFSR and AgMERRA are only available until 2010, and PGF is available until 2016. Therefore, AgERA5 can be recommended for temperature-related agricultural simulations for the study area in Iran and similar environmental regions, due to its overall performance compared to the observed temperature data and with availability since 1979 to present. Further studies are necessary to assess differences in performance for various environments.

Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short-term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network (ANN) and wavelet transform artificial neural network (WANN) models. The hourly data were collected from the Mashhad synoptic station in Khorasan Razavi province in Iran between 2010 and 2013. The results of this study showed that using a wavelet transform for preprocessing improved the accuracy of soil temperature forecasting. It was also found that changing the temporal increment in forecasting time did not have a noticeable effect on errors in the WANN models. WANN models can be used as accurate tools to forecast soil temperature 1–7 days ahead at depths of 5–30 cm.

Analysis of 20-year time series of water levels in the northeastern Gulf of Mexico has revealed that meteotsunamis are ubiquitous in this region. On average, 1–3 meteotsunamis with wave heights >0.5 m occur each year in this area. The probability of meteotsunami occurrence is highest during March–April and June–August. Meteotsunamis in the northeastern Gulf of Mexico can be triggered by winter and summer extra-tropical storms and by tropical cyclones. In northwestern Florida most of the events are triggered by winter storms, while in west and southwest Florida they appear both in winter and summer. Atmospheric pressure and wind anomalies (periods

We developed a comprehensive coupling framework with a multi-objective optimization that bridges a water balance model (WBM) and a wetland service model (WSM) to supporting wetland management. The framework was tested for management in Tram Chim National Park (with four wetland zones) where hydro-economic optimization was needed. The framework used (1) a model coupling process bridging WBM and WSM to create a modular hydro-economic model (MHEM), (2) a multi-objective optimization, and (3) an anneal scheduling for scenario optimization. The framework demonstrated its competency in identifying cause–effect/interaction flows (bridges) between WBM and WSM to design MHEM to simulate optimized scenarios; for the case study, the multi-objective optimization was met for all wetland zones. Results suggested a flexible consideration of management scales for optimization, i.e. hydrologic optimization at a zone level and net benefit optimization at a Park level. Our framework is applicable to supporting complex wetla...

Linking decadal variability with short-term variability could be potentially exploited for improving seasonal climate forecasting for assisting crop management decisions. The objective of this study was to explore whether there are decadal variations in wheat (winter crop) and maize (summer crop) production and whether these decadal variations correlate with any known variations of climate. Over one hundred years of wheat and maize yields were simulated using process-based crop models with dynamically downscaled daily reanalysis data over four locations in the southeast USA. Using wavelet and cross-wavelet analysis, we found that winter crop yields were dominated by 10- and 22-year decadal oscillations; the decadal variations of winter crop yields were driven by decadal variations of winter temperature and spring precipitation; no decadal variations were detected for summer crop yields and summer precipitation and temperature. Cross-wavelet analysis showed that the decadal variations of winter crop yields were correlated with indices of the annual Atlantic Multi-decadal Oscillation (AMO), the annual Pacific Decadal Oscillation (PDO), and the winter North Atlantic Oscillation (NAO). Therefore, this knowledge of decadal climate variability could potentially be leveraged to predict winter seasonal yields of crops.

This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

Summary Large-scale climate can provide predictive information for streamflow forecasts in many parts of the world. However, the optimal selection of predictors can be problematic when focusing on a localized region. This work evaluated multiple gridded climate datasets in order to determine optimal predictors of seasonal streamflow in West-Central Florida. Using persistence in streamflow, existing indices of climate, and sea surface temperature (SST) expansion coefficient time-series from singular value decomposition (SVD) analysis, this work developed probability of exceedance streamflow forecasts for multiple stations, seasons, and lead-times. Forecasts were found to be generally skillful between the September–November and April–June seasons with this range narrowing as lead time increased and skill was mainly related to the impact of the El Nino-Southern Oscillation (ENSO) on the region. Using multiple indices of ENSO that were determined by correlation and composite analyses to track its evolution from the west Pacific at long lead-times to the east Pacific at short lead-times was not found to appreciably improve forecasts over using the Nino 3.4 index alone. Using SST expansion coefficient time-series from SVD analysis was found to capture the evolution of ENSO from west to east and to provide skillful forecasts of streamflow at earlier leads (up to 7 months in advance) compared to that found by pre-defined indices, indicating the importance of predictor selection in achieving optimal forecast skill.

NOAA’s second-generation retrospective forecast (reforecast) dataset was created using the currently operational Global Ensemble Forecast System (GEFS). It has the potential to accurately forecast daily reference evapotranspiration ETo and can be useful for water management. This study was conducted to evaluate daily ETo forecasts using the GEFS reforecasts in the southeastern United States (SEUS) and to incorporate the ETo forecasts into irrigation scheduling to explore the usefulness of the forecasts for water management. ETo was estimated using the Penman–Monteith equation, and ensemble forecasts were downscaled and bias corrected using a forecast analog approach. The overall forecast skill was evaluated using the linear error in probability space skill score, and the forecast in five categories (terciles and 10th and 90th percentiles) was evaluated using the Brier skill score, relative operating characteristic, and reliability diagrams. Irrigation scheduling was evaluated by water deficit WD forecasts, which were determined based on the agricultural reference index for drought (ARID) model driven by the GEFS-based ETo forecasts. All forecast skill was generally positive up to lead day 7 throughout the year, with higher skill in cooler months compared to warmer months. The GEFS reforecast improved ETo forecast skill for all lead days over the SEUS compared to the first-generation reforecast. The WD forecasts driven by the ETo forecasts showed higher accuracy and less uncertainty than the forecasts driven by climatology, indicating their usefulness for irrigation scheduling, hydrological forecasting, and water demand forecasting in the SEUS.

Reference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman–Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño–Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.

A lumped hydrologic model (ACRU2K.1), developed from ACRU2000, was extended and evaluated to simulate water dynamics in a plastic mulch bedded system for vegetable production under high water table environments. The updated model (ACRU2K.2) and ACRU2K.1 were calibrated using data collected during the fall 2005 and spring 2006 crop seasons of a field study for a vegetable production system in south Florida. Both model versions were evaluated using data from the fall 2006 crop season. Graphical and statistical methods were applied to evaluate and compare the performance of ACRU2K.1 and ACRU2K.2 using observed and simulated water table depths (WTD). Graphical analyses showed no added advantage in using ACRU2K.2 with its new irrigation process. However, the introduction of a drainage process for ACRU2K.2 showed some improvement over ACRU2K.1 in simulating changes in the water table. For the calibration period, Nash-Sutcliffe coefficient (NS) values for ACRU2K.1 and ACRU2K.2 were 0.73 and 0.65, respect...

Large scale climate phenomena can provide valuable information for regional climate and streamflow in many parts of the world. Several climate phenomena may impact a given area and their value for providing information on streamflow is dependent on first establishing the local relationship. This study was conducted to establish the individual and coupled impacts of the El Nino–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) on streamflow in the Apalachicola–Chattahoochee–Flint (ACF) river basin. Differences in annual and seasonal streamflow using two unimpaired streamflow datasets based on the phase(s) of ENSO, the PDO, and the AMO were evaluated using the nonparametric rank-sum test. Few statistical differences were found for the individual impacts of ENSO and the PDO on annual and seasonal streamflow; differences based on ENSO were largely confined to the southern portion of the basin. Significant differences in annual streamflow based on the AMO were largely confined to the northern half of the basin. Differences in seasonal streamflow based on the AMO were found for much of the year in the northern portion of the basin but were confined to the winter season in the southern portion. Significant differences in annual and seasonal streamflow were found between the La Nina/positive AMO phase and the El Nino/negative AMO phase, between the positive AMO/negative PDO phase and the negative AMO/negative PDO phase, and there appears to be a modulation of the impacts of La Nina by the phase of the AMO. A greater number of stations and a greater magnitude of significant differences were found for the coupled impacts than for the individual impacts of ENSO, the PDO, and the AMO; indicating the importance of the coupled impacts on regional streamflow when establishing the role of annual, decadal, and multidecadal climate variability.

Despite nearly 20 years of work on understanding the climate information needs of stakeholders and simultaneous development of decision support tools, a relatively low level of awareness of specific climate information products exists among many water managers in the southeastern United States. This paper presents results from an assessment of key water management decisions, awareness of, perception of, use of, and barriers and opportunities for using climate information among water managers in Alabama, Florida, and Georgia. Through an online survey, we collected data from 141 water managers across the region. Our findings reveal that water managers surveyed, for the most part, are unaware of most sources of seasonal climate forecasts as well as specific sources of long-term climate change information. From our analysis, we found that the use of seasonal climate forecast products remains mostly in the realm of water managers with high levels of expertise and training generally employed at large water management agencies. In addition to limited awareness, some reasons for nonuse are lack of understanding of forecasts and limitations in temporal and spatial scales of available climate information. We conclude with recommendations on how our findings can be used to align goals of climate science research by ensuring that it is relevant to decision-makers’ needs and decisions and by identifying opportunities for improvements in forecast dissemination and climate risk management. For example, the need for more interactions among scientists and decision-makers, and the identification of opportunities within the decision-making frameworks where climate information can be input are manifest throughout the study.

This literature review focuses on the prevalence of nitrogen and phosphorus in urban environments and the complex relationships between land use and water quality. Extensive research in urban watersheds has broadened our knowledge about point and non-point pollutant sources, but the fate of nutrients is not completely understood. For example, it is not known how long-term nutrient cycling processes in turfgrass landscapes influence nitrogen retention rates or the relative atmospheric contribution to urban nitrogen exports. The effect of prolonged reclaimed water irrigation is also unknown. Stable isotopes have been used to trace pollutants, but distinguishing sources (e.g., fertilizers, wastewater, etc.) can be difficult. Identifying pollutant sources may aid our understanding of harmful algal blooms because the extent of the relationship between urban nutrient sources and algal blooms is unclear. Further research on the delivery and fate of nutrients within urban watersheds is needed to address manageable water quality impacts.

Accurate estimation of reference evapotranspiration (ET0) is needed for determining agricultural water demand and reservoir losses and driving hydrologic simulation models. This study was conducted to explore the application of the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS) retrospective forecast (reforecast) dataset combined with the NCEP–U.S. Department of Energy (DOE) Reanalysis 2 dataset (R2) to forecast ET0 in the southeastern United States using a forecast analog approach. Seven approaches of estimating ET0 using the Penman–Monteith (PM) and Thornthwaite equations were evaluated by substitution of climatological mean values of variables or by bias correcting variables including solar radiation, maximum temperature, and minimum temperature using the R2 dataset. The skill of both terciles and extremes (10th and 90th percentiles) were evaluated. Overall, for the ET0 forecast approaches that combined R2 solar radiation with temperature, relative humidity, and wind speed from GFS, the reforecasts produced higher skill than methods that estimated parameters using GFS the reforecasts data only. The primary increase in skill was due to the use of relative humidity from the GFS reforecasts and long-term climatological mean values of solar radiation from the R2 dataset, indicating its importance in forecasting ET0 in the region. While the five categorical forecasts were skillful, the skill of upper and lower tercile forecasts was greater than that of lower and upper extreme forecasts and middle tercile forecasts. Most of the forecasts were skillful in the first 5 lead days.

Summary The objective of this study was to compare the performance of natural analog (NA) and constructed analog (CA) methods to produce both probabilistic and deterministic downscaled daily reference evapotranspiration (ET o ) forecasts in the southeastern United States. The 1–15 day, 15-member ET o forecasts were produced from 1979 to 2009 using the Penman–Monteith equation and a forecast analog approach with a combination of the Global Forecast System (GFS) reforecasts and NCEP-DOE Reanalysis 2 climatology, and were downscaled using the North American Regional Reanalysis (NARR). The Pearson correlation coefficient (R), mean squared error skill score (MSESS), and Bias were used to evaluate the skill of downscaled deterministic forecasts. The Linear Error in Probability Space (LEPS) skill score, Brier Skill Score (BSS), relative operating characteristic, and reliability diagrams were used to evaluate the skill of downscaled probabilistic forecasts. Overall, CA showed slightly higher skill than NA in terms of the metrics for deterministic forecasts, while for probabilistic forecasts NA showed higher skill than CA regarding the BSS in five categories (terciles, and 10th and 90th percentiles) and lower skill than CA regarding the LEPS skill score. Both CA and NA produced skillful deterministic results in the first 3 lead days, while the skill was higher for CA than for NA. Probabilistic NA forecasts exhibited higher resolution and reliability than CA, likely due to a larger ensemble size. Forecasts by both methods showed the lowest skill in the Florida peninsula and in mountainous areas, likely due to the fact that these features were not well-resolved in the model forecast.

Reference evapotranspiration and solar radiation estimation methods that use only measured temperature have been demonstrated to require regional calibration for acceptable performance. This study was conducted to evaluate regional calibration of incoming solar radiation and reference evapotranspiration in Florida, USA. Using satellite-derived estimates of solar radiation and reference evapotranspiration as the standard for comparison, regional calibration of the Hargreaves-Samani solar radiation model and the Turc temperature-based reference evapotranspiration equation were evaluated using monthly temperature data from 22 weather stations in Florida. Station-specific and regional calibration of the Hargreaves-Samani solar radiation model was found to produce comparable results, indicating the validity of this approach in regional calibration of radiation estimates. Using the regionally calibrated radiation estimates, the original Turc equation was found to underestimate reference evapotranspiration, indi...

This research quantitatively evaluated the ability of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to reproduce observed spatiotemporal variability of precipitation in the Tampa Bay region over the 1986–2008 period. Raw MM5 model results were positively biased; therefore, the raw model precipitation outputs were bias corrected at 53 long-term precipitation stations in the region using the cumulative distribution function (CDF) mapping approach. CDF mapping effectively removed the bias in the mean daily, monthly, and annual precipitation totals and improved the RMSE of these rainfall totals. Observed daily precipitation transition probabilities were also well predicted by the bias-corrected MM5 results. Nevertheless, significant error remained in predicting specific daily, monthly, and annual total time series. After bias correction, MM5 successfully reproduced seasonal geostatistical precipitation patterns, with higher spatial variance of daily precipitation in the wet season and lower spatial variance of daily precipitation in the dry season. Bias-corrected daily precipitation fields were kriged over the study area to produce spatiotemporally distributed precipitation fields over the dense grids needed to drive hydrologic models in the Tampa Bay region. Cross validation at the 53 long-term precipitation gauges showed that kriging reproduced observed rainfall with average RMSEs lower than the RMSEs of individually bias-corrected point predictions. Results indicate that although significant error remains in predicting actual daily precipitation at rain gauges, kriging the bias-corrected MM5 predictions over a hydrologic model grid produces distributed precipitation fields with sufficient realism in the daily, seasonal, and interannual patterns to be useful for multidecadal water resource planning in the Tampa Bay region.

In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the state transition function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.

Sea surface temperatures (SSTs) can provide valuable predictive information for regional climate and agricultural yields in many parts of the world. This study was conducted to identify relationships between Atlantic and Pacific SSTs and corn yields in Alabama, Florida, and Georgia and to use these relationships to develop forecasts that can be used at lead times prior to spring planting. The relationships between seasonal SSTs and detrended county corn yields were analysed using singular value decomposition (SVD) analysis and confirmed using principal component analysis (PCA). Using a Monte Carlo approach, field-significant results were found with SSTs in the July–September (JAS−1) and October–December (OND−1) seasons in the previous year and with the January–March (JFM) season of the current. A significant portion of the variability of SSTs with corn yield residuals was connected with anomalous heating and cooling associated with the El Nino Southern Oscillation (ENSO). Based on the results found by SVD analysis and confirmed by PCA, indices of spatially averaged SSTs in regions of the North Pacific and Atlantic Oceans were derived. Using these indices along with the Nino3.4 index to represent tropical Pacific SSTs, cross-validated multiple linear regression models were developed to predict corn yield residuals using index values in the JAS−1 and OND−1 seasons. Using the cross-validated models 91.5 and 98.4% of forecasted county corn yield residuals showed predictive skill (based on tercile hit scores) with seasonal index values in the JAS−1 and OND−1 seasons, respectively. The results of the models indicate that the indices of SSTs show significant predictability with corn yield residuals at lead times up to 4–7 months prior to spring planting and are a significant improvement over the use of an index of ENSO alone. Copyright © 2010 Royal Meteorological Society

Reference evapotranspiration estimation methods that require minimal data are necessary when climatic data sets are incomplete, inaccurate, or unavailable. This study was conducted to evaluate temp...

Computer networks have continued to make substantial advances in the past couple of decades through better technologies and methodologies employed. As the usage of the networks continues to increase exponentially, high throughput of the networks has to be maintained with various performance-efficient network algorithms. IP address lookup is one of the processes, the performance of which dearly affects the overall network performance. Hashing has been widely used for fast IP address lookup due to its simplicity, but mostly assuming on hashing from an address set with uniformly distributed key values. Performance from these known hashing techniques is far from optimal due to the high nonuniformity in actual IP address distribution. In this paper, we propose a preprocessing method for the IP address databases to extract certain regularity to allow for design of more efficient hashing algorithms based on XOR operations. Simulation results show an improvement in performance ranging from 35% to 72% on randomly generated addresses and several sample IP address databases. The paper also shows that the proposed algorithms deliver comparable performance to other well-known hashing algorithms such as the CRC and RS hashing while requiring much less hardware to implement and a much shorter time to perform.

The impact of large-scale oceanic and atmospheric climate patterns on county corn yields in Alabama, Florida, and Georgia were evaluated for the period 1970–2005. Associations between detrended corn yield residuals, precipitation, surface temperature, and climate indices were explored by correlation analysis. Significant correlations were found with indices of the Pacific–North American pattern, tropical North Atlantic and eastern tropical Pacific sea surface temperatures, and two indices of the Bermuda high. The summer index of the Bermuda high (BHI) was the only index to be significantly correlated with yield residuals during the critical summer tasselling period; a time when corn is most susceptible to water stress. Due to the high degree of multi-collinearity found between the five indices, leave-n-out cross-validated principal component regression was conducted using all possible combinations of indices to predict corn yield residuals. Three indices produced models with the greatest skill using both lagged (known prior to planting) and concurrent indices (cross-validated Pearson's r: 0.679) and using lagged indices only (cross-validated Pearson's r: 0.569). Using the cross-validated models 99.2 and 96.9% of predicted county yields showed predictive skill (based on tercile hit scores) using both concurrent and lagged indices, and lagged-only indices, respectively. The cross-validated model using lagged-only indices indicated the results that can be achieved using known climate index values as early as the winter before the spring planting. Copyright © 2008 Royal Meteorological Society

In this paper, we present a case study of the execution time characteristics of several popular commercial audio and video applications on a state of the art microprocessor, the Intel Pentium 4. The on-chip performance counters on the Pentium 4 processor are used to perform this study using actual real-world workloads. While the Pentium 4 is capable of executing 3-4 instructions in one cycle, it was observed that commercial audio and video applications take between 1.4 and 3.5 cycles (per instruction) to execute. Despite using large caches and sophisticated out of ordering techniques, the average cycles per instruction is higher than a predecessor like Pentium II. This indicates that while clock frequency has improved, real speedups are not scaling. The performance of multimedia programs is compared with execution characteristics of SPEC CPU 2000 programs. Performance impact of branch predictors, caches and trace caches on the Pentium 4 are analyzed for multimedia and SPEC CPU applications.

Summary Humid, shallow water-table environments such as the flatwoods of the southeastern United States are characterized by flat topography and moderately to poorly drained soils where significant interaction between surface water and groundwater occurs. A Java-based, object-oriented hydrologic model has been created specifically for shallow water-table environments within the framework of the ACRU2000 model. Using the object-oriented design concept of inheritance allows for new modeling approaches to be incorporated into a model by extension, without modifying existing functionality. The object-oriented programming properties of inheritance, aggregation, and association provide a flexible, modular, and readily extensible platform for model development. The physically based, conceptual model represents the effect of the water-table on evapotranspiration, local groundwater flow, and runoff generation. The model is physically based in that it uses physically measurable parameters and is conceptual to the extent that the model domain is idealized. Model validation to two experimental sites in the southeastern US indicated the model’s ability to predict water-table depths, soil moisture distributions, evapotranspiration, and saturation-excess runoff.

Hashing algorithms have been widely adopted to provide a fast address lookup process which involves a search through a large database to find a record associated with a given key. Modern examples include address lookup in network routers for a forwarding outgoing link, rule-matching in intrusion detection systems comparing incoming packets with a large database, etc. Hashing algorithms involve transforming a key inside each target data to a hash value hoping that the hashing would render the database a uniform distribution with respect to this new hash value. When the database are already key-wise uniformly distributed, any regular hashing algorithm would easily lead to perfectly uniform distribution after the hashing. On the other hand, if records in the database are instead not uniformly distributed, then different hashing functions would lead to different performance. This paper addresses the cases when such distribution follows a natural negative linear distribution, a partial negative linear distribution, or an exponential distribution which are found to closely approximate many real-life database distributions. For each of these distributions, we derive a general formula for calculating the distribution variance produced by any given non-overlapping bit-grouping XOR hashing function. Such a distribution variance from the hashing directly translates to performance variations in searching. Through this, the best XOR hashing function can be easily determined for any given key size and any given hashing target size.

The Orlando Easterly Wetland (OEW), one of the largest constructed wetlands for the treatment of wastewater in Florida, started operation in 1987 for the reduction of nutrient loads in tertiary treated domestic wastewater produced by the City of Orlando. The wetland has performed better than design expectations, but phosphorus removal effectiveness experienced some seasonal declines beginning with the winter of 1999. Subsequent studies indicated that the OEW treatment capacity was hindered by inefficient phosphorus removal in the upstream cells of one of three flow trains. Therefore, rejuvenating management activities were initiated on these cells in 2002. The management included the removal of plants and organic top sediments, site grading in the interior of the cells, construction of baffles and islands, and re-vegetation. This study evaluates the improvement in hydraulic and phosphorus removal performance realized from the wetland modifications. Improvement of hydraulic performance was evaluated based on tracer tests, and improvement of phosphorus removal performance was evaluated based on episodic spatially distributed water samples as well as model prediction. The results showed that both the hydraulic efficiency and the phosphorus removal effectiveness of the rejuvenated wetland were significantly increased. However, the wetland has likely re-entered a start-up phase and long-term observation will be necessary to determine eventual steady-state conditions.

The model one-dimensional transport with inflow and storage (OTIS) was calibrated to results from tracer experiments conducted in the Orlando Easterly Wetland in order to quantify short-circuiting of the treatment volume and temporary storage of tracer in isolated, low-flow regions. OTIS was found to fit experimental data very well for both steady and unsteady flow conditions. The model calibrations indicate the presence of three different hydraulic ‘zones’ of the wetland. The first zone is the actively flowing main channel; the second, a temporary storage zone where water and constituents are exchanged with the main flow channel; and the third, completely isolated, ‘dead’ water. The uncertainty of storage zone parameters was determined to be low due to the experimental Damkohler number that quantifies intra-zone mass transfer processes. The appropriateness of the use of a one-dimensional model for treatment wetlands is also addressed.

Hydraulic testing of the Orlando Easterly Wetland, a constructed treatment wetland located near Christmas, Fla., was performed as part of a more comprehensive study of treatment efficacy of the system. The wetland serves to reduce nutrient loading from tertiary treated wastewater to the St. Johns River, the receiving body. Residence time distribution analysis of bromide tracer tests revealed and quantified inefficiencies (short circuiting and dead zones) in the hydraulic performance within individual treatment cells and the wetland system under the operating conditions studied. Hydraulic efficiencies (ratios of experimentally determined residence times to nominal residence times) of the cells ranged from 11 to 88%, while overall, the wetland was operating at near 50% efficiency during the tracer tests. Short circuiting and dead zones within the wetland are largely the results of historic land alterations, such as ditches, that were not removed during the conversion of the site from drained land to managed wetland. Volume- and area-based system-referenced metrics were developed to identify and prioritize opportunities to improve hydraulics on both cell-by-cell and system scales.