Tirusew Asefa

Tampa Bay Water · Planning & System Decision Support

Water managers must constantly balance investment in infrastructure upgrades to ensure reliable water supply with the affordability of water rates for consumers. As a result, trade-offs between water supply and financial objectives have become central to the long-term sustainability of utility operations. Few studies, however, have directly quantif...

This study is motivated by multipleobjective optimization in short-term water management for a regional water utility. Although an increasing application of multi-objective evolutionary algorithm (MOEA) has been reported in the literature, we are not aware of its use for short-term water management by water utilities with diverse supply sources. Th...

Increasing water demand due to socio-economic development often requires structure-level interventions, e.g., infrastructure expansion of water supply systems to bridge the gap between demand and water supply. Evaluating different expansion options is critical to making informed decisions. This study focuses on evaluating infrastructure investment...

The Grand Ethiopian Renaissance Dam (GERD) filling and operation is a highly sensitive issue for Egypt and Sudan. A recently accepted manuscript by Heggy et al (2021 Environ. Res. Lett. 16 074022) assessed the water deficit for Egypt based on different scenarios for the first filling of GERD lake and estimated 31 billion cubic meters per year under...

Understanding the relationship between raw water quality and chemical dosage is especially important for drinking water treatment plants (DWTP) that have multiple water sources where the ratio of different supply sources could change with seasons or in a matter of weeks in response to changing hydrologic conditions. In this study, the potential for...

Although there is extensive work related to design and implementation of model-driven optimal groundwater management, evaluation of the performance of those models is less reported. This hinders transferability of lessons learned and wide adoption of specific policies. This paper reports a study designed to evaluate the effectiveness of a real-time...

This study focuses on investigating how climate variabilities exhibited in summer and winter precipitation in the Southern United States (SEUS) are possibly modulated by large scale atmospheric activities, including El Niño-Southern Oscillation (ENSO) and cyclonic activities measured in Accumulated Cyclone Energy (ACE) Index. A stringent criterion...

Scientific findings indicated there is climate change that affects given hydrology and, hence, water availability worldwide. To quantify its impact on a specific catchment scale, since spatial and temporal variability of climate change impact, this study was carried out at Ribb catchment, Lake Tana basin, Ethiopia. The catchment hydrology was repre...

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 s...

Monthly-to-seasonal precipitation forecasts are important in water resource management. Hidden Markov models (HMM) are widely applied in precipitation simulation due to its simplicity and advancements in associated computing techniques. HMMs, however, lack the flexibility to accommodate external factors in the dynamics. In this study, we consider a...

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...

Water shortage has become more frequent with occurrence of severe and frequent droughts and increasing water demand due to many factors, e.g., population growth and socio-economic development. Adaptive water management plans, including both supply-side and demand-side measures, provide water supply utilities guidance during water shortage situation...

Public water utilities face uncertain decisions every day in their efforts to meet drinking water needs of customers. Real-time decision-support tools (DST) are often used by water managers to solve a variety of water management challenges, including meeting customers' demands, forecasting floods, and developing reservoir operating rules. The incor...

Water supply managers are attempting to mitigate impacts of climate change on their systems for future planning and management. Employing general circulation models to simulate streamflow, one can observe impacts on water supply. This chapter used Tampa Bay, Florida as the case study to simulate climate changes impacts on streamflow and reservoir o...

A complete water shortage mitigation plan which includes water shortage indicators and mitigation measures is essential in adaptive water resources management. Current water shortage triggers used to manage water supply shortages typically recognize only past and current hydrologic conditions and system states, e.g., reservoir storage or water leve...

General circulation models (GCMs) have been widely used to simulate current and future climate at the global scale. However, the development of frameworks to apply GCMs to assess potential climate change impacts on regional hydrologic systems, ability to meet future water demand, and compliance with water resource regulations is more recent. In thi...

General circulation models (GCMs) have been widely used to simulate current and future climate at the global scale. However, the development of frameworks to apply GCMs to assess potential climate change impacts on regional hydrologic systems and compliance with water resource regulations is more recent. It is important to predict potential impacts...

There is a vast amount of research on downscaling and bias-correcting General Circulation Model (GCM) data to a regional scale, but research is lacking on whether these techniques alter precipitation signals embedded in these models or reproduce climate states that are viable for water resource planning and management. Using the Tampa, Florida regi...

Water resources systems in Florida are unique and exhibit significant diversity in hydrogeologic characteristics and in rainfall and emperature patterns. In many parts of the state, both surface and groundwater systems are complex, highly interconnected, and any change in hydrologic drivers such as rainfall or temperature has the potential to impac...

Recent advancement in climate sciences has discovered two different types of El Niño events, namely the eastern Pacific El Niño (EP-El Niño) and central Pacific El Niño (CP-El Niño). Identifying how watershed responses to different El Niño-Southern Oscillation (ENSO) events, including both El Niño and La Niña conditions, is critical in understandin...

The occurrence of regional water shortage situations where supply source cannot satisfy demand has become more frequently across the United States. Defining appropriate water shortage triggers is critical to initiate specific mitigation strategies to guide through those water shortage periods. • Water shortage triggers used by most water utilities...

Short-term water demand forecasts are often employed for operational planning purposes. The success of operational decision-making depends in part on the quality of those forecasts. This study examines the accuracy of one-week-ahead demand projections produced between June 2006 and February 2014 by Tampa Bay Water, a large wholesale water supplier...

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 retro...

In 2010, four water utilities: Tampa Bay Water (TBW), Portland Water Bureau (PWB), Seattle Public Utilities (SPU), and NYC Department of Environmental Protection (NYDEP) embarked on the Piloting Utility Modeling Applications (PUMA) project to track in real time the process and challenges of co-producing actionable climate change data for decision m...

One of the most challenging tasks of water supply utilities is planning the timing and quantity of new water supply sources as demand for water consumption grows. Many water supply utilities target on meeting 100% of their customers' needs based on scenario-based deterministic demand projections numbers even though there are uncertainties in both s...

In 1998, Tampa Bay Water, the largest wholesale water provider in South East USA with over 2.3 million customers, assumed the role of planning, developing, and operating water supply sources from six local water supply utilities through an Interlocal Agreement. Under the agreement, cities and counties served by the agency would have their water sup...

As introduced by Hashimoto et al. (1982), Reliability, Resilience, and Vulnerability (RRV) metrics measure different aspects of a water resources system performance. Together, RRV metrics provide one of the most comprehensive approaches for analyzing the probability of success or failure of a system, the rate of recovery (or rebound) of a system fr...

In this paper, a Bayesian-based indicator-weighting approach is developed to reduce the uncertainty resulting from bias-correcting projection outputs from multiple general circulations models (GCMs). The approach decides whether or not a projection from a given GCM output should be used depending on how close output from the GCM’s retrospective run...

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 use...

This paper introduced the flow forecast modeling system that a water management agency in west central Florida, Tampa Bay Water has been operated to forecast monthly rainfall and streamflow in the Tampa Bay region, Florida. We evaluated current 1-year monthly rainfall forecasts and flow forecasts and actual observations to investigate the benefits...

Water utilities that rely on surface water supplies often experience variable production due to weather. Future drinking water supply for such systems is mathematically uncertain. However, system simulations using flow data can determine the probability of sufficient future supply in support of planning and management activities, such as evaluating...

Nino 3.4 (120 – 170W and 5N- 5S) Sea Surface Temperature (SST) anomalies are the main indicator of ENSO states. Since January 2005 the International Research Institute (IRI) of Columbia University compiles Nino3.4 forecasts from several Dynamical and Statistical models. These models forecast SST anomalies from three to nine overlapping 3-month peri...

  While training a Neural Network to model a rainfall-runoff process, generally two aspects are considered: its capability to be able to describe the complex nature of the processes being modeled and the ability to generalize so that novel samples could be mapped correctly. The general conclusion is that, the smallest size network capable of repres...

Tampa Bay Water (www.tampabaywater.org), the largest wholesale water provider in Florida, along with its member governments (Hillsborough, Pasco, Pinellas Counties, and the cities of St. Petersburg, New Port Richey and Tampa) provide water for more than 2 million customers. Tampa Bay Water supplies over 180 millions of gallons per day (mgd) annuall...

In this paper, a field-scale applicability of three forms of artificial neural network algorithms in forecasting short-term ground-water levels at specific control points is presented. These algorithms are the feed-forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground-water level predictions...

[1] A common practice in preprocessing of data for use in hydrological modeling is to ignore observations with any missing variable values at any given time step, even if it is only one of the independent variables that is missing. In most cases, these rows of data are labeled incomplete and would not be used in either model building or subsequent...

Water scarcity in the Sevier River Basin in south-central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecas...

Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound appro...

Accurate short-term water demand forecasts are an important component of sound decision making by Tampa Bay Water, Florida's largest wholesale water supplier. Tampa Bay Water and its members serve more than two million people in the Northern Tampa Bay Tri-county area with over 250 million gallons of water per day on average annually. Week-to-week w...

Effective lead-time stream flow forecast is one of the key aspects of successful water resources management in arid regions. In this research, we present new data-driven models based on Statistical Learning Theory that were used to forecast flows at two time scales: seasonal flow volumes and hourly stream flows. The models, known as Support Vector...

Sparse learning machines provide a viable framework for modeling chaotic time-series systems. A powerful state-space reconstruction methodology using both support vector machines (SVM) and relevance vector machines (RVM) within a multiobjective optimization framework is presented in this paper. The utility and practicality of the proposed approache...

[1] The reconstruction of low-order nonlinear dynamics from the time series of a state variable has been an active area of research in the last decade. The 154 year long, biweekly time series of the Great Salt Lake volume has been analyzed by many researchers from this perspective. In this study, we present the application of a powerful state space...

Water scarcity and uncertainties in forecasting future water availabilities present serious problems for basin-scale water management. These problems create a need for intelligent prediction models that learn and adapt to their environment in order to provide water managers with decision-relevant information related to the operation of river system...

In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer's conditions on the kernels, the corresponding optimization prob...

This study presents a methodology for designing long-term groundwater head monitoring networks in order to reduce spatial redundancy. A spatially redundant well does not change the potentiometric surface estimation error appreciably, if not sampled. This methodology, based on Support Vector Machines, makes use of a uniquely solvable quadratic optim...

In this paper we present the application of a new statistical learning methodology called Support Vector Machines (SVMs). SVMs are learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space, trained with learning algorithm from optimization theory. They are based on a principle that aims at...

The recharge/discharge relation is one of the key factors in protecting ecologically valuable areas. But, it is also a prerequisite for an integrated land and water management. An approach which combines hydrological modeling and vegetation mapping within a GIS framework for characterizing discharge and recharge areas is promoted and has been appli...

Research in the ecology of wetlands is quite established. However, research in the hydrology of wetlands is much less developed. In Flanders, Belgium, a research project was initiated to investigate ecohydrological differences in wetlands. Three wetlands (Doode Bemde, Vors- donkbos, and Zwarte Beek Valley) have been examined in detail. As these wet...

There is an increasing interest in the hydrology community in integrating Geographic Information System (GIS) technology and hydrological models. The spatial dimensionality of most hydrological and environmental problems is addressed by spatially distributed models. One such model is WetSpass (stands for Water and Energy Transfer between Soil Plant...

Includes vita. Thesis (Ph. D.)--Utah State University, Dept. of Civil and Environmental Engineering, 2004. Includes bibliographical references and appendix.