Asefa T.,575 Enterprise Rd. |
Wanakule N.,575 Enterprise Rd. |
Adams A.,575 Enterprise Rd. |
Shelby J.,575 Enterprise Rd. |
Clayton J.,Hazen and Sawyer
Journal of Water Resources Planning and Management | Year: 2014
A novel approach on quantifying the value of an incremental surface water-use permit in an integrated water resources system consisting of groundwater, surface water, off-stream reservoir, and desalinated seawater sources is presented. First, a stochastic framework that accounts for demand uncertainties and variations in climatic parameters was used to generate regional demand and supply realizations. Second, a linked optimization-simulation model was used to navigate through complex system constraints and sustainable operational constraints. The resulting decision variables were then used to calculate system performance metrics, demonstrating the benefit of an increase in surface-water withdrawals at high flows. The Monte Carlo-based model took advantage of distributed computing capabilities on a private cloud computing system to significantly reduce the total run time. The model codes were developed in two different software environments, executed on different platforms, in which information was exchanged through an inter-process communication (IPC) protocol. The major contribution of this research is toward the practical use of stochastic-based integrated surface/groundwater-use permit application for a complex water resources system. The approach is demonstrated using Tampa Bay Water's integrated water resources system. © 2014 American Society of Civil Engineers.
Tian D.,Princeton University |
Martinez C.J.,University of Florida |
Asefa T.,575 Enterprise Rd.
Journal of Water Resources Planning and Management | Year: 2016
Urban water demand forecasting is key to municipal water supply management. Short-term urban water demands are influencedby weather conditions. Thus, short-term urban water demand forecasting could be improved by using accurate weather forecasting information.This study explores the potential of using an analog approach with a newly developed retrospective weather forecast (reforecast) ofa numerical weather prediction (NWP) for improving short-term urban water demand forecasting. The analog method derives an analogensemble forecast resampled from observed data (analogs) based on the reforecast of a NWP: the Global Ensemble Forecast System (GEFS).The probabilistic and ensemble mean forecasts generated from analogs of weekly total rainfall (WeekRain), number of rainy days in one week(RainDays), number of consecutive rainy days in one week (CosRainDays), number of hot days in one week (HotDays), and daily meantemperature of the first seven lead days (T) from the reforecast were evaluated using in situ observations. The analog ensemble forecasts wereused to drive seven water demand forecasting models based on autoregressive integrated moving average with exogenous inputs (ARIMAX)to make water demand forecasts in the Tampa Bay region of Florida. The GEFS-based analog forecast generally showed moderately high skillfor WeekRain, RainDays, CosRainDays, and T but no skill for HotDays. The water demand forecasts driven by analog forecasts mostlyshowed higher skill than the original ARIMAX forecasts. Besides improving forecast accuracy, the analog-driven water demand forecastsaccounted for the uncertainty of the weather forecasts, allowing for the assessment of demand forecast uncertainty. These results indicated thatNWP-based analogs showed promising features for advancing the accuracy of short-term urban water demand forecasts. © 2016 American Society of Civil Engineers.