Sanaz Imen

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.

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.

Developing a multi-scale modeling system for resilience assessment of green-grey drainage infrastructures under climate change and sea level rise impact

Multi-scale modeling analysis is often required for comprehensive resilience assessment of urban drainage infrastructures to account for global climate change impact and local watershed response. The goal of this study was to develop a multi-scale modeling platform for drainage infrastructure resilience assessment in a coastal watershed. The model employs scale-dependent informatics, including hydroinformatics, climate informatics, and geoinformatics, to support a comprehensive hydrodynamic stormwater and hydrologic model, called the Interconnected Channel and Pond Routing Model. Low Impact Development (LID), deemed as green drainage infrastructure, was adopted and assessed in the Cross Bayou Watershed, Florida. The Cross Bayou Canal is the grey infrastructure, which dissects the watershed and connects both Tampa Bay and Boca Ciega Bay on its northeastern and southwestern ends, respectively. Modeling scenarios are driven by watershed-scale rainfall/runoff, coastal high tide, and global sea level rise, respectively or collectively, to evaluate the green-grey drainage infrastructure system in response to current and future coastal flood hazards predicted for year 2030. The quantitative resilience metrics, such as peak inflow reduction at flood zone, were chosen to reflect storms that pose threats to the watershed, now and in the future year 2030, for climate change scenarios derived by the Statistical Downscaling Model. Results indicate that the effectiveness of LID depends on the rainfall type being considered, such as convective storm versus frontal rain, and sub-daily rainfall patterns, as well as a groundwater table analysis. LID implementation for flood mitigation is more effective in the short-term.LID implementation alters hydrologic response such as to offer increased resilience via peak outflow reduction.Variations in rainfall type such as convective versus frontal rain end up varying LID effectiveness.

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’s 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 τvalue 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.

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.

Developing a Model-Based Drinking Water Decision Support System Featuring Remote Sensing and Fast Learning Techniques

Timely adjustment of operating strategies in drinking water treatment in response to water quality variations in both natural and anthropogenic causes is a grand technical challenge. One essential approach is to develop and apply integrated sensing, monitoring, and modeling technologies to provide early warning messages to plant operators. This paper presents a thorough literature review of the technical methods, followed by the development of a model-based decision support system (DSS). The DSS aims to aid water treatment plant operators by analyzing source water impacts. This model-based DSS features remote sensing and fast learning techniques that can be easily applied by end-users and provide a visual depiction of spatiotemporal variations in source water quality parameters of interest. The system is able to forecast the trend of water quality one day into the future at a specific location and nowcast water quality at water intake locations, thus helping the assessment of water quality in finished water against treatment objectives. The model-based DSS was assessed in a case study at a water treatment plant in Las Vegas, United States.

Multisensor Satellite Image Fusion and Networking for All-Weather Environmental Monitoring

Given the advancements of remote sensing technology, large volumes of remotely sensed images with different spatial, temporal, and spectral resolutions are available. To better monitor and understand the changing Earths environment, fusion of remotely sensed images with different spatial, temporal, and spectral resolutions is critical for distinctive feature retrieval, interpretation, mapping, and decision analysis. A suite of methods have been developed to fuse multisensor satellite images for different purposes in the past few decades. This paper provides a thorough review of contemporary and classic image fusion methods and presents a summary of their phenomenological applications, with challenges and perspectives, for environmental systems analysis. Cross-mission satellite image fusion, networking, and missing value pixel reconstruction for environmental monitoring are described, and their complex integration is illustrated with a case study of Lake Nicaragua that elucidates the state-of-the-art remote sensing technologies for advancing water quality management.

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.

Spatiotemporal monitoring of TOC concentrations in lake mead with a near real-time multi-sensor network

Forest fires, soil erosion, and land use changes in watersheds nearby Lake Mead and inflows from Las Vegas Wash into the lake are considered as sources of the lakes water quality impairment. 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 the EPAs disinfection by-product rule. As a result of the threat posed to the drinking water used by the 25 million people downstream, recreational area, and wildlife habitat of Lake Mead, it is necessary to develop a method for near real-time monitoring of TOC in this 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, the multi-sensor remote sensing technology linking those ground-based TOC analyzers and two satellites with the aid of data fusion and mining techniques provides us with near real time 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 Landsats imageries which suffers from long overpass of 16 days. Consequently, near-real time Integrated Multi-sensor 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 updated by ground-based TOC analyzers and lead to sustainable water quality management with the aid of IDFM in Lake Mead watershed.

Linkages between turbidity levels in Lake Mead associated forest fire events in the lower Virgin watershed

Lake Mead provides the source of drinking water for over 25 million people in the western United States. Different forest fire events at the northern part of the lake may intensify the concentration of total suspended sediments (TSSs) in water bodies due to the abrupt changes of land covers with accelerated soil erosion. Therefore, it is important to assess the linkage between forest fire events and TSS concentration within the lake. For this purpose, the integrated data fusion and mining (IDFM) techniques were applied in this study to generate TSS concentration maps on a daily basis with the aid of remote sensing imageries. The results of this study confirm the reliability of the IDFM method for nowcasting of TSS concentrations within the lake based on these daily TSS concentration maps. It leads to the investigation of the probable impact of forest fire events on increasing TSS concentrations. Comparing these maps with time of forest fire occurrence showed the potential linkage between increasing TSS concentrations and forest fire events. However, the negative impacts of forest fire events on soil erosion may have lag time to show up.