Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining

Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply

Monitoring Spatiotemporal Total Organic Carbon Concentrations In Lake Mead With Integrated Data Fusion And Mining (IDFM) Technique

Forest fires, soil erosion, and land use changes in Lake Mead watershed nearby Las Vegas wash are considered as sources of water quality impairment in the Lake Mead. These conditions result in higher concentration of Total Organic Carbon (TOC). TOC in contact with Chlorine which is often used for disinfection purposes of drinking water supply causes the formation of trihalomethanes (THMs). THM is one of the toxic carcinogens controlled by EPA’s Disinfection By-Product Rule. As a result of threat posed to drinking water of 25 million people downstream, recreation area, and wildlife habitat of Lake Mead, it is necessary to develop a method for near real-time monitoring of TOC in Lake Mead area. Monitoring through a limited number of ground-based monitoring stations on a weekly/monthly basis is insufficient to capture both spatial and temporal variations of water quality changes. In this study, remote sensing technology with the aid of data fusion and mining techniques provides us with information about the spatiotemporal distribution of TOC for the entire lake on a daily basis. A data fusion method was applied to bridge the gap of poor 250/500m spatial resolution for the land bands of Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the 30 m enhanced spatial resolution of Landsat’s imageries which suffers from long overpass of 16 days. Consequently, Integrated Data Fusion and Mining (IDFM) techniques produce synthetic fused images of MODIS and Landsat satellites with both high spatial and temporal resolution in order to create near-real time TOC distribution maps and lead to sustainable water quality management with the aid of IDFM in Lake Mead watershed

Precipitation Forecasting With Wavelet-Based Empirical Orthogonal Function And Artificial Neural Network (WEOF-ANN) Model

Western drought began since 2000 caused sharp decrease by about 100 feet in the largest reservoir of North America, Lake Mead due to the precipitation pattern shift in the upstream lower Virgin River Basin. Oceans play an important role on earth’s climate via oceanic-atmospheric interactions known as climate teleconnections, which deeply affect the terrestrial precipitation patterns. This issue signifies the necessity of developing a modern hydroinformatics tool - precipitation forecasting model - to account for teleconnection signals from climate change and mitigate drought hazards impact on lake water, quantitatively and qualitatively, which cannot be achieved by using traditional Global Circulation Model. Therefore, understanding the relationship between precipitation and teleconnection patterns could initial step for precipitation forecasting. However, highly nonlinear and non-stationary nature of teleconnection patterns result in large uncertainties in estimates, since simple linear analyses failed to capture underlying trends at sub-continental scales. For this purpose, the integrated high-resolution remote sensing imagery, spectral analysis techniques, and wavelet analysis were integrated to explore the nonstationary and nonlinear behavior of teleconnection signals between the Pacific and Atlantic sea surface temperature (SST) on a long-term basis (30 years) from which the precipitation pattern shift in the lower Virgin River Basin can be elucidated. These processes lead to the creation of linear-lagged correlation maps which specify index regions within the Atlantic and Pacific Oceans where SST anomaly can be statistically significant in correlation with terrestrial precipitation. These indexed regions delivering some kind of memory effects of SST were extracted to be inputs into an Artificial Neural Network (ANN). Advances in wavelet-based ANN (WEOF-ANN) model for rainfall prediction assists in local water management agencies to mitigate the drought impact and obtain sustainable development strategies 3-6 months ahead of the time in urban drinking water infrastructure assessment plan around Lake Mead area

What does landslide triggering rainfall mean?

Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides where none are expected) and false positives (no landslides despite thresholds being exceeded). Debris flows and shallow landslides impact communities and infrastructures worldwide. Refinement of the relation between rainfall intensity and landslide occurrence would help remove the imprecise nature of this tool moving forward. Continuous 6-hour gridded precipitation data from over a five-year interval 900 km2, combined with a complete, time-constrained, landslide data base over the same period, are used to derive relations for the probability of shallow landslides with rainfall intensity measured over 6-hour, 12-hour, or 24-hour durations. Previously published and widely used thresholds are quantified in terms of landslide probability per unit area and demonstrate, for different sized study areas, the likelihood that at least one landslide will be initiated at different intensities and durations. Probabilistic distribution of landslides for a given study area and rainfall intensity can be easily derived using the binomial method from these relations

Multi-Sensor Acquisition, Data Fusion, Criteria Mining And Alarm Triggering For Decision Support In Urban Water Infrastructure Systems

Frequent adjustment of the drinking water treatment process as a simultaneous response to climate variations, and the impact those variations have on water quality, has been a grand challenge in water resource management in recent years. An early warning system with the aid of satellite remote sensing and local sensor networks, which provides timely and quantitative knowledge to monitor the quality of water, may be a soluition to this challenge. The development of such an early warning system is addressed to discover and evaluate the severity in a discrete event mode in this paper. The early warning system in the current study is able to empower the urban water ifrastructure systems with the integration of advanced data science, environmental monitoring, computational intelligence, and satellite remote sensing data. By developing a graphical user interface, end-users who do not have knowledge or skill in the field of integrated sensing, monitoring, networking, modeling can take advantage of the user-friendly early warning system. Practical implementation of the proposed early warning system was assessed at the largest resrvoir, Lake Mead, in Las Vegas in the United States. It uniquely demonstrates how such a system can benefit the drinking water treatment plant throughout decision support actions via multi-sensor acquisition, data fusion, criteria mining and alarm trigerring

Developing A Cyber-Physical System For Smart And Sustainable Drinking Water Infrastructure Management

Frequent adjustment of operating strategies in water treatment plant and water distribution network as a simultaneous response to growing water scarcity has been a grand challenge. This challenge is emanated from transitioning the sporadic water quality samplings to self-awareness, self-adaptive, and fast response system. To bridge this gap, a cyber-physical system (CPS) is developed in this study to respond to the needs of smart and sustainable drinking water infrastructure management. This new CPS is able to gather the massive volumes of information from ground and aquatic reference data via advanced remote sensing and sensor network technologies to timely detect water pollution, exchange information through cyber interfaces, provide early-warning awareness with the aid of different models, and support actionable intelligence. Integrated 5-level CPS architecture is proposed in this study as an instruction of developing CPS for smart and sustainable drinking water infrastructure management

Stochastic Fuzzy Assessment For Managing Hydro-Environmental Systems Under Uncertainty And Ambiguity

This paper develops a stochastic fuzzy decision making method to solve a class of decision making problems which involve simultaneous uncertainty and ambiguity when screening water resources management alternatives. In these analyses, not only the performance value with respect to a specific criterion in a given alternative is uncertain, but also the performance implications of the given alternative may be vague with respect to the considered criterion by the decision maker. The integration between Monte-Carlo simulation and fuzzy decision making may provide a unique soft computing technique linking randomness and fuzziness together. Whereas the stochastic problem is mapped into a deterministic environment through the use of a Monte-Carlo simulation process, fuzzy decision making helps entail the linguistic uncertainty (i.e., ambiguity) in decision science. With this framework, the overall performance of a wealth of alternatives can be further evaluated, resulting in considerable reduction in the uncertainty and ambiguity in the decision making arena. Practical implementation has been assessed by a case study of the Sacramento-San Joaquin Delta in California in which the performance of four water export alternatives were evaluated to determine the satisfactory solution with respect to the environmental sustainability and cost-effectiveness criteria. © 2012 ASCE

Remote Sensing For Monitoring Surface Water Quality Status And Ecosystem State In Relation To The Nutrient Cycle: A 40-Year Perspective

Delineating accurate nutrient fluxes and distributions in multimedia environments requires the integration of vast amounts of information. Such nutrient flows may be related to atmospheric deposition, agricultural runoff, and urbanization effect on surface and groundwater systems. Two types of significant undertakings for nutrient management have been in place for sustainable development. While many environmental engineering technologies for nutrient removal have been developed to secure tap water sources and improve the drinking water quality, various watershed management strategies for eutrophication control are moving to highlight the acute need for monitoring the dynamics and complexities that arise from nutrient impacts on water quality status and ecosystem state, both spatially and temporally. These monitoring methods and data are associated with local point measurements, air-borne remote sensing, and space-borne satellite images of spatiotemporal nutrient distributions leading to the generation of accurate environmental patterns. Within this context, several key water quality constituents, including total nitrogen, total phosphorus, chlorophyll-a concentration, colored dissolved organic matter (dissolved organic carbon or total organic carbon), harmful algal blooms (e.g., cyanobacterial toxins or microcystin concentrations), and descriptors of ecosystem states, such as total suspended sediment (or turbidity), transparency (e.g., Secchi disk depth), and temperature, will be of major concern. Considering the advancements, challenges, and accomplishments related to remote sensing technologies in the past four decades, we present a thorough literature review of contemporary state-of-the-art technologies of remote sensing platforms and sensors that may be employed to support essential scientific missions, and provide an in-depth discussion and new insight into various inversion methods (or models) to improve the estimation accuracy. In this study, the spectrum of these remote sensing technologies and models is first divided into groups based on chronological order associated with different platforms and sensors, although some of them may be subject to mission-oriented arrangements. Case-based and location-based studies were cited, organized, and summarized to further elucidate tracks of application potential that support future, forward-looking, cost-effective, and risk-informed nutrient management plans. The comprehensive reviews presented here should echo real-world observational evidence by using integrated sensing, monitoring, and modeling techniques to improve environmental management, policy analysis, and decision making

Bringing Environmental Benefits Into Caspian Sea Negotiations For Resources Allocation: Cooperative Game Theory Insights

The five littoral Caspian Sea states, namely Azerbaijan, Iran, Kazakhstan, Russia, and Turkmenistan, have been in negotiations on establishing a legal regime for governing the sea for almost two decades. What makes the Caspian Sea conflict more complicated is the immense amount of valuable oil and gas resources in the sea. The previous studies of the conflict have intimately focused on finding an appropriate division rule for sharing the water as well as gas and oil resources in the seabed, ignoring the environmental utilities associated with the possible division rules. This is despite the fact that Caspian Sea is the home to the most precious sturgeons, supplying 90% of the world\u27s caviar. Therefore, this study bridges the gap of previous ones of the Caspian Sea conflict by adding the environmental dimension to the conflict analysis. Four different cooperative game theoretic solution concepts, including Nash-Harsanyi, Shapely, Nucleolus, and τ- value are used to find the fair and efficient allocation of the Caspian Sea resources to the five states. The results are finally compared with previous ones that ignored the environmental aspect of the problem to highlight the importance of environmental benefits in the Caspian Sea negotiations. © 2012 ASCE

Developing The Remote Sensing-Based Early Warning System For Monitoring Tss Concentrations In Lake Mead

Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake