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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.

We present here the analysis of 20 years of high-resolution experimental winter seasonal CLImate reForecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10km grid spacing from a global model run at T62 spectral resolution (~210km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida.CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November-December-January (NDJ) and one-month lead for December-January-February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

Reference evapotranspiration (ETo) is one of the most important factors in the hydrologic cycle and water balance studies. In this study, the performance of three simple and three wavelet hybrid models were compared to estimate ETo in three different climates in Iran, based on different combinations of input variables. It was found that the wavelet-artificial neural network was the best model, and multiple linear regression (MLR) was the worst model in most cases, although the performance of the models was related to the climate and the input variables used for modeling. Overall, it was found that all models had good accuracy in terms of estimating daily ETo. Also, it was found in this study that large numbers of decomposition levels via the wavelet transform had noticeable negative effects on the performance of the wavelet-based models, especially for the wavelet-adaptive network-based fuzzy inference system and wavelet-MLR, but in contrast, the type of db wavelet function did not have a detectable effect on the performance of the wavelet-based models.

Solar radiation (SRAD) plays a pivotal role for crop growth and development, and is, therefore, an indispensable input for crop simulation models. Lack of weather data, particularly SRAD, is a problematic issue in many agricultural zones. To handle this dearth, there have been recent advances in the improvement and application of gridded weather data, which provide realistic alternatives to observed weather data. The main objective of the present research was to identify the most accurate gridded SRAD data (GRD) to be used in crop simulation modeling. For this purpose, six GRD products, comprising AgCFSR, AgERA5, AgMERRA, GLDAS, PGF and NASA POWER, were evaluated for irrigated and rainfed wheat simulation in six sites across Iran using the NWheat model. This is one of the newest models that have recently been added to Decision Support System for Agrotechnology Transfer (DSSAT) application. Some of the GRDs were evaluated for the first time in the current study. For a comprehensive assessment, six different rainfed and irrigated wheat cultivars were simulated for the entire range of GRDs. The simulations of grain yield and leaf area were compared with simulations using observed SRAD. The findings showed that the quality of all GRD products was appropriate for crop simulation studies; however, AgCFSR, AgERA5, and AgMERRA, with normalized root mean square error (NRMSE) ≤ 3.8% and Nash-Sutcliffe Efficiency (NSE) ≥ 0.96, had the highest accuracy for grain yield simulation, and PGF with NRMSE of 5.2% and NSE of 0.92 had the least quality. The major disadvantage of AgCFSR and AgMERRA is their limited temporal period until 2010, so that they cannot be used for the studies after 2010. Thus, all the assessed GRD, especially AgERA5 can be recommended for applying in crop simulation models in regions with unavailable SRAD data.

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...

Large-scale climatic teleconnections have noticeable effects on meteorological events in different regions of the world. In this study, the linkages between three major climatic indices, Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI), and precipitation in Iran were assessed from 1960 to 2014, at 30 synoptic stations in a time-frequency space, using wavelet coherence (WCO). The results showed that the SOI is the most effective climatic teleconnection on precipitation in Iran, although the other studied climatic indices have noticeable effects as well. The predominant and effective period of AO on precipitation was equal to or greater than 32 months at most of the stations, while the major effective period of NAO was equal to or greater than 64 months. For the SOI, most parts of the country were affected by a period of less than 64 months, while the predominant period of SOI for the northwestern part of the country was greater than 64 months. A uniform phase difference was not observed between the three studied climatic indices and precipitation in the country; instead the phase differences were usually random. For long-term periods of SOI, an anti-phase situation was detected at most of the stations. The study suggested that the WCO is a very powerful and flexible method for studying the relationship between multiple time series in a time–frequency space, and its application in hydrological and meteorological research is expected to increase in the near future.

Atmospheric visibility (AV) is an indicator for assessing air quality and is measured in standard weather stations. The AV can change as a result of two main factors: air pollution and atmospheric humidity. This study aimed to investigate trends in the number of days with AV equal or less than 2 km (DAV2) in Iran during 1968–2013. Consequently, 43 weather stations with different climates were evaluated across the country, using the Mann–Kendall (MK) trend test. The results show that the number of stations with positive (i.e., significant or non-significant) MK z values was equal to, or greater than, those with negative MK z values, in all months and seasons of the year, as well as annually. Furthermore, summer and autumn had, respectively, the least and most stations with positive MK z values. Fewer trends in DAV2 were detected in the central, east, and northeast regions of the country. Analyzing the DAV2 and relative humidity together indicated that over 30% of stations had at-risk air quality in January, and that the largest number of stations with at-risk air quality was in the autumn and winter. These results are useful for better environmental planning to improve air quality, especially in developing countries such as Iran, where reduced air quality has been a major problem in recent decades.

Achieving total phosphorus (TP) total maximum daily loads (TMDL) for Lake Okeechobee (Florida, FL, USA), a large freshwater lake, is a key component of the greater Everglades ecosystem restoration and sustainability of south Florida. This study was aimed at identification of a cost-effective restoration alternative using four TP control strategies&mdash, Best Management Practices (BMPs), Dispersed Water Management (DWM), Wetland Restoration, and Stormwater Treatment Areas (STAs)&mdash, to achieve a flow-weighted mean TP concentration of 40 &micro, g/L at lake inflow points, through a phased scenario analysis approach. The Watershed Assessment Model was used to simulate flow and phosphorus dynamics. The 10-year (1998&ndash, 2007) &lsquo, Base&rsquo, scenario calibration indicated &lsquo, acceptable&rsquo, to &lsquo, good&rsquo, performance with simulated annual average flows and TP load of 2.64 &times, 109 m3 and 428.6 metric tons, respectively. Scenario results showed that TP load reduction without STAs would be around 11&ndash, 40% with respect to Base compared to over 75% reduction requirement to achieve TMDL, indicating STAs as a necessary component to achieve restoration. The most cost-effective alternative to achieve TP target consisted of implementation of nutrient management BMPs, continuation of existing DWM projects, and the construction of ~200 km2 of STAs for a total project cost of US $4.26 billion.

Sensitivity analysis (SA) plays a vital role in hydrologic and water quality (H/WQ) model development and reliability assessment. This paper performed the first formal global SA (GSA) evalu...

In this work, the applicability of 12 solar radiation (RS) estimation models and their impacts on daily reference evapotranspiration (ETo) estimates using the Penman‐Monteith FAO-56 (PMF-56) method were tested under cool arid and semi-arid conditions in Iran. The results indicated that the average increase in accuracy of the ETo estimates by the calibrated RS models, quantified by the decrease in RMSE, was 2.8% and 6.4% for semi-arid and arid climates, respectively. Mean daily deviations in the estimated ETo by the calibrated RS equations in semi-arid climates varied from −0.283 mm/d-1 for the Glover‐McCulloch model to 0.080 mm/d for the El-Sebaii model, with an average of −0.109 mm/d-1, and in arid climates, they ranged from −0.522 mm/d-1 for the Samani model to 0.668 mm/d for the El-Sebaii model, with an average of 0.125 mm/d-1.Editor D. Koutsyiannis; Associate editor Not assigned

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.

CLIP 4 Robotics is an icon-based Visual Programming Language (VPL) that is designed for the novice programmer. The VPL program is written as a series of icons that represent program instruction syntax. The images allow for a wide age range to use the VPL from children to adults. Icons allow for users to feel less intimated with programming than with text-based languages. CLIP 4 Robotics has a user-defined syntax that helps the novice comprehend the language especially children. Icons allow children that have let master reading to program. Example programs are shown that demonstrate the CLIP 4 Robotics language in two different scenarios.

The method of Elementary Effects (EE) is a parameter screening type sensitivity analysis technique that combines advantages of inexpensive one-at-a time methods and expensive variance decomposition based global Sensitivity Analysis (SA) techniques. Most of the sampling strategies for EE either use random sampling or maximize sample spread through oversampling. The Sampling for Uniformity (SU) is the only available strategy that combines the principle of sample spread with the principle of uniformity. In this work, we proposed modifications to SU (eSU) to further improve sample uniformity. Performance of eSU was compared to that of SU based on uniformity, sample spread, sample generation time, and screening efficiency. Importance of the concept of uniformity was strengthened as eSU outperformed SU across all evaluation criteria. Further, it was found that eSU does not need oversampling and can result in better screening with relatively few trajectories indicating significantly reduced requirement on computational resources.

High-value urban zones in coastal South Florida are considered particularly vulnerable to salt water intrusion into the groundwater-based, public water supplies caused by sea level rise (SLR) in combination with the low topography, existing high water table, and permeable karst substrate. Managers in the region closely regulate water depths in the extensive South Florida canal network to control closely coupled groundwater levels and thereby reduce the risk of saltwater intrusion into the karst aquifer. Potential SLR adaptation strategies developed by local managers suggest canal and groundwater levels may have to be increased over time to prevent the increased salt water intrusion risk to groundwater resources. However, higher canal and groundwater levels cause the loss of unsaturated zone storage and lead to an increased risk of inland flooding when the recharge from rainfall exceeds the capacity of the unsaturated zone to absorb it and the water table reaches the surface. Consequently, higher canal and groundwater levels are also associated with increased risk of economic losses, especially during the annual wet seasons. To help water managers and urban planners in this region better understand this trade-off, this study models the relationships between flood insurance claims and groundwater levels in Miami-Dade County. Via regression analyses, we relate the incurred number of monthly flood claims in 16 Miami-Dade County watersheds to monthly groundwater levels over the period from 1996 to 2010. We utilize these estimated statistical relationships to further illustrate various monthly flood loss scenarios that could plausibly result, thereby providing an economic quantification of a “too much water” trade-off. Importantly, this understanding is the first of its kind in South Florida and is exceedingly useful for regional-scale hydro-economic optimization models analyzing trade-offs associated with high water levels.

Watershed scale models have become important tools to assess the effectiveness of regional and farm level remediation strategies for ecological restoration. The Everglades is an ecologically unique and complex system posing numerous challenges to water managers in South Florida. The Feeder Canal Basin (FCB) in Hendry County, Florida, drains 277 km2 of land into the central Everglades. During 2008–2017, the average annual total phosphorus (TP) concentration at the FCB outflow was approximately 89 μg/L, almost an order of magnitude higher than the restoration target for the Everglades marshes. This study aimed at evaluating remediation strategies - Best Management Practices (BMPs) and Stormwater Treatment Areas (STAs) to achieve TP targets for ecosystem restoration using an integrated modeling framework with a top-down approach. The baseline regime was modeled using the Watershed Assessment Model with multi-gage calibration (2000−2003) and validation (2004–2010). With the model performing satisfactory to very good for all gage locations, results indicated that row crops, groves & orchards, and improved pastures were largely responsible for TP concentrations and/or loads. BMP scenario results showed that TP loads could be reduced by 8% to 24% at the FCB outlet under minimal or very aggressive BMPs, respectively, indicating the need for additional water quality improvement projects to meet restoration targets. A preliminary design of two STAs, using the Dynamic Model for Stormwater Treatment Areas, estimated that a total of 52 km2 and 11.3 km2 of STAs will be required to achieve the TP concentration targets. The analysis framework employed in this study can be replicated in other watersheds to provide a holistic picture of remediation measures required to achieve water quality targets.

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.