Search

With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening. Deep learning-based approaches have been established as the most promising methods in this regard. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An intelligent feature merging scheme is introduced in the proposed merging network. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia. The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification.

Risks from human intervention in the climate system are raising concerns with respect to individual species and ecosystem health and resiliency. A dominant approach uses global climate models to predict changes in climate in the coming decades and then to downscale this information to assess impacts to plant communities, animal habitats, agricultural and urban ecosystems, and other parts of the Earth’s life system. To achieve robust assessments of the threats to these systems in this top-down, outcome vulnerability approach, however, requires skillful prediction, and representation of changes in regional and local climate processes, which has not yet been satisfactorily achieved. Moreover, threats to biodiversity and ecosystem function, such as from invasive species, are in general, not adequately included in the assessments. We discuss a complementary assessment framework that builds on a bottom-up vulnerability concept that requires the determination of the major human and natural forcings on the environment including extreme events, and the interactions between these forcings. After these forcings and interactions are identified, then the relative risks of each issue can be compared with other risks or forcings in order to adopt optimal mitigation/adaptation strategies. This framework is a more inclusive way of assessing risks, including climate variability and longer-term natural and anthropogenic-driven change, than the outcome vulnerability approach which is mainly based on multi-decadal global and regional climate model predictions. We therefore conclude that the top-down approach alone is outmoded as it is inadequate for robustly assessing risks to biodiversity and ecosystem function. In contrast the bottom-up, integrative approach is feasible and much more in line with the needs of the assessment and conservation community. A key message of our paper is to emphasize the need to consider coupled feedbacks since the Earth is a dynamically interactive system. This should be done not just in the model structure, but also in its application and subsequent analyses. We recognize that the community is moving toward that goal and we urge an accelerated pace.

When: 26 May–1 June 2020 Where: Virtual (organized by the University of Washington) While the primary mission of NASA’s Earth Science Division (ESD) is to develop a scientific understanding of the Earth system and how it is responding to human and natural drivers of change, an implicit need is also to understand how best to promote the use of NASA satellite mission products and information for application activities that directly benefit society and our environment [ ]the 2018 National Academies on Science, Engineering, and Medicine Decadal Survey for NASA, “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observations from Space,” clearly underscores the tremendous value of satellite mission data, which “enabled societal applications that provide tremendous value to individuals, businesses, the nation, and the world By virtue of its stated scientific goals, SWOT satellite data are expected to have tremendous societal impact in various sectors that involve water, such as disaster management, reservoir operations, water management, ecosystem services planning, hydropower and navigation, fisheries (freshwater and marine), and marine shipping [ ]in this proposed virtual hackathon architecture, the last 2 days involve intense one-on-one help sessions via a web-conferencing tool such as Zoom to “look under the hood” by highly qualified trainers (known hereafter as “helper hackers”) into the EA’s local device to examine tools/data/models used for the SWOT EA project

Background and aims Dyspnea is one of the most common symptoms associated with the COVID‐19 caused by novel coronavirus SARS‐CoV‐2. This study aimed to assess the prevalence of dyspnea, observe co‐variables, and find predictors of dyspnea after 2 months of recovery from COVID‐19. Methods A total of 377 patients were included in the study based on their responses and clinical findings during initial admission to the hospital with COVID‐19. After excluding five deceased patients, a total of 327 patients were interviewed through telephone using a 12‐point dyspnea scale and using relevant questions to gauge the patient clinically. Interviews were carried out by trained physicians, and responses were recorded and stored. All analyses were carried out using the statistical programming language R. Results Of the total 327 participants in the study, 34% had stated that they were suffering from respiratory symptoms even after 2 months of COVID‐19. The study demonstrated that patient oxygen saturation level SpO2 (P = .03), D‐dimer (P = .001), serum ferritin (P = .006), and the presence and severity of dyspnea are significantly correlated. In addition to that, patient smoking history (P = .012) and comorbidities such as chronic obstructive pulmonary disease (COPD) (P = .021) were found to be statistically significant among groups. Conclusion These findings of this study can be useful for predicting and managing long‐term complications of COVID‐19.

Selective water release from the deeper pools of reservoirs alters the temperature of downstream rivers. Thermal destabilization of downstream rivers can be detrimental to the riverine ecosystem by disturbing the growth stages of various aquatic species. To predict this impact of planned hydropower dams worldwide, we present a framework called “FUture Temperatures Using River hISTory (FUTURIST).” The framework used historical records of in‐situ river temperatures for existing dams in the U.S. and remote sensing observations for those in other regions to train an artificial neural network (ANN) model that predicts temperature change between upstream and downstream rivers. Validation of FUTURIST‐modeled impacts for dams worldwide showed promising results with a root mean squared error of 2.5°C (0.9°C) and categorical accuracy of 63% (88%) during the summer (winter) season. The trained ANN model afforded prediction of the likely thermal impacts of 216 planned dams. Results suggest that during the summer season, 73% of future dams will potentially cool downstream rivers by up to 6.6°C. Winter season operations were predicted to consistently warm downstream rivers by temperatures of up to 2°C. Reservoirs that experience strong stratification have the most potential to impact downstream pre‐dam thermal regimes. For copious existing or planned dams worldwide that are yet to be mapped of their thermal impacts, FUTURIST provides an efficient path forward to carry out a global thermal assessment and design sustainable hydropower expansion plans so that the upcoming dams can be operated in a more eco‐sensitive manner than the existing ones.

Challenges to manage and secure a sustainable water supply are expected to become more acute in Egypt as the lowermost riparian country of the Nile basin with the construction of new transboundary water infrastructures in Ethiopia and Sudan. To understand the impact of such transboundary water projects on Egypt, it is first necessary to develop a modeling tool that can simulate potential flow and reservoir scenarios inside Egypt without requiring in situ hydrologic or transboundary dam data that are typically unavailable. This study presents the water management value of a modeling framework to predict the current and future reservoir operating rules in the lower Nile basin using satellite Earth observations and hydrologic models. The platform comprises the Variable Infiltration Capacity (VIC) hydrologic model driven by high spatial and temporal resolution of satellite observations. Reservoir storage change is estimated using altimeter and visible imagery of lake area for Lake Nasser and then applied to infer reservoir operation for High Aswan Dam (HAD). The modeling framework based on satellite observations yielded a simulated streamflow at the outlet for Blue Nile basin (BNB) with a Nash–Sutcliffe efficiency of 0.68 with a correlation and RMSE of 0.94 and 1095 m3 s−1, respectively. Storage and outflow discharge of HAD were estimated for the period of 1998–2002 within 1.4% accuracy (0.076 km3 month−1) when compared with published reports. Because BNB controls the lion’s share of the variability to HAD inflow inside Egypt, the proposed modeling framework is appropriate for policy-makers to understand the implications of transboundary projects on the future water security of Egypt.

A generic and scalable scheme is proposed for forecasting reservoir inflow to optimize reservoir operations for hydropower maximization. Short-term weather forecasts and antecedent hydrological variables were inputs to a three-layered hydrologically-relevant Artificial Neural Network (ANN) to forecast inflow for 7-days of lead-time. Application of the scheme was demonstrated over 23 dams in U.S. with varying hydrological characteristics and climate regimes. Probabilistic forecast was also explored by feeding ANN with ensembles of weather forecast fields. Results suggest forecasting skill improves with decreasing coefficient of variation in inflow and increasing drainage area. Forecast-informed operations were simulated using a rolling horizon scheme and assessed against benchmark control rules. Over two years of operations from Pensacola dam (Oklahoma), additional 47,253 MWh of energy could have been harvested without compromising flood risk with optimal operations. This study reinforces the potential of a numerically efficient and skillful reservoir inflow forecasting scheme to address water-energy security challenges.

The current situation in the Lancang–Mekong River Basin is emblematic of the issues faced by many transboundary basins around the world: riparian countries prioritize national water-energy policies and provide limited information on how major infrastructures are operated. In turn, such infrastructures and their management become a source of controversy. Here, we turn our attention to the Upper Mekong River, or Lancang, where a system of eleven mainstream dams controls about 55 % of the annual flow to Northern Thailand and Laos. Yet, assessing their actual impact is a challenging task because of the chronic lack of data on reservoir storage and dam release decisions. To overcome this challenge, we focus on the ten largest reservoirs and leverage satellite observations to infer 13-year time series of monthly storage variations. Specifically, we use area-storage curves (derived from a Digital Elevation Model) and time series of water surface area, which we estimate from Landsat images through a novel algorithm that removes the effects of clouds and other disturbances. We also use satellite radar altimetry data (Jason) to validate the results obtained from satellite imagery. Our results describe the evolution of the hydropower system and highlight the pivotal role played by Xiaowan and Nuozhadu reservoirs, which make up to ~85 % of the total system's storage in the Lancang River Basin. We show that these two reservoirs were filled in only two years, and that their operations did not change in response to the drought that occurred in the region in 2019–2020. Deciphering these operating strategies could help enrich existing monitoring tools and hydrological models, thereby supporting riparian countries in the design of more cooperative water-energy policies.

Radiology examination of chest radiography or chest X-ray (CXR), is currently performed manually by radiologists. With the onset of the COVID-19 pandemic, there is now a need to automate this process which is currently one of the key methods of primary detection of the SARS-Cov-2 virus. This will lead to shorter diagnosis time and less human error. In this study, we try to perform three-class image classification on a dataset of chest X-rays of confirmed COVID-19 patients(408 images), confirmed pneumonia patients(4273 images), and chest X-rays of healthy people(1590 images). In total the dataset consists of 6271 people. We aim to use a Convolutional Neural Network(CNN) and transfer learning to perform this image classification task. Our model is based on a pre-trained InceptionV3 network with weights trained on the ImageNet dataset. We fine-tune the layers of the Inception network to train it to our specific task. We try fine-tuning the network to different extents by freezing a different number of layers and then comparing accuracy for each variation of the network. To evaluate the performance of our network we use several metrics which include Classification accuracy, Precision, Sensitivity, and Specificity. Our proposed method achieves an accuracy of 96.33% on a 3-class classification task (Normal, COVID-19, Pneumonia) and an accuracy of 99.39% on a 2-class (COVID and Non-COVID) classification task.

For clean hydropower generation while sustaining ecosystems, minimizing harmful impacts and balancing multiple water needs is an integral component. One particularly harmful effect not managed explicitly by hydropower operations is thermal destabilization of downstream waters. To demonstrate that the thermal destabilization by hydropower dams can be managed while maximizing energy production, we modelled thermal change in downstream waters as a function of decision variables for hydropower operation (reservoir level, powered/spillway release, storage), forecast reservoir inflow and air temperature for a dam site with in situ thermal measurements. For data-limited regions, remote sensing-based temperature estimation algorithm was established using thermal infrared band of Landsat ETM+ over multiple dams. The model for water temperature change was used to impose additional constraints of tolerable downstream cooling or warming (1–6 °C of change) on multi-objective optimization to maximize hydropower. A reservoir release policy adaptive to thermally optimum levels for aquatic species was derived. The novel concept was implemented for Detroit dam in Oregon (USA). Resulting benefits to hydropower generation strongly correlated with allowable flexibility in temperature constraints. Wet years were able to satisfy stringent temperature constraints and produce substantial hydropower benefits, while dry years, in contrast, were challenging to adhere to the upstream thermal regime.

This entry presents a review of what is currently known regarding the potential impact of large artificial reservoirs and dams on the local climate from the paradigm of land use and land cover (LULC) change. The review is based on the premise of systematic change in climate-sensitive LULC that most dams initiate after their construction and operation. To better understand the implication of change in local climate near dams and artificial reservoirs, it is, therefore, important to have a broader view of the changes to the landscape in the post-dam era. However, in the future, other factors, such as urbanization, atmospheric aerosols, and global warming, need to be explored as well.

Rice is one of the most important agricultural commodities throughout the Mekong River Basin including the Tonle Sap Lake floodplains in Cambodia. Recent increases in hydropower dams along the Mekong River have likely altered the surface water hydrology impacting the arable areas and soil qualities for rice production in the Tonle Sap lowland. Along with the hydrological impacts, the region’s rice farming is facing a rapidly changing climate. It is critical to understand how the hydrological changes associated with dam development impact the region’s rice production in a changing climate. The aims of this study were to assess the impacts of recent increases in hydropower dams on the timing and areas of rice cropping in the Tonle Sap floodplains and to evaluate the effects of changing hydrology, rising temperature, and adaptive farming practices on rice productivity using a process-based rice crop model: ORYZA (v3). The effect of dams on arable areas for rice was identified by a remote-sensing method based on the PhenoRice algorithm for the period of 2001–2019 in two rice-growing provinces: Kampong Thom and Battambang. The PhenoRice method identified an increase in rice growing areas as well as shifts in both timing and location of rice cropping towards the sources of irrigation during the dry season since 2010. The ORYZA model simulated a substantial yield reduction with an increase of 2 °C in air temperature in the region. The model predicted that the rice productivity in the region is sensitive to soil organic carbon content which is expected to change with surface water hydrology. The model also predicted that region’s rice yield can increase by optimizing the timing and amount of N fertilization. The findings from this study highlight how hydrology, climate, and agronomic practices can interact to impact rice production in the Lower Mekong Region and provide insights for effective water management and agronomic practices to attain food security in the region in a changing climate.

The present study was conducted for the first time to appraise the water quality, irrigation feasibility, and human health risk of surface water in heavy industrial and urban establishments from a coastal city of Bangladesh. Surface water samples were collected from the hilly terrain comprised of Plio-Pleistocene to Miocene-sediments throughout the study area. The ranges of pH were 5.35 to 7.58, EC values were 42.3 to 2440 µS cm−1, and turbidity were 2.53 to 1570 NTU, respectively. The overall physicochemical condition of water samples revealed that the water was unsuitable and also unsafe for domestic use. A bias-free systematic sampling and analyzed parameters invoke that surface water quality is deteriorated by Cr (52.2), Mn (532), Fe (4797), Mg (31445), K (29213), Co (69.0), Ni (39.5), and Pb (22.4 µg L−1) contamination and make the surface water unsuitable for drinking, cooking, fisheries, and other purposes. Anthropogenic activities (e.g., domestic, industrial, agricultural, etc.) intensified by climate change-induced sea-level rise are mainly responsible for the source apportionment of metal pollution in the study region. However, the surface waters of the studied sites are allowed to use for irrigations, except for Karnaphuli river due to high alkali hazards. Furthermore, the existence of toxic metal-induced non-carcinogenic and carcinogenic risks does not allow inhabitants to use this water for household works.

Objective There are no validated approaches to predict benefit from adjuvant chemotherapy for resected patients with non-small-cell lung cancer (NSCLC). The aim of this study was to translate a 15-gene mRNA expression profile published by Zhu et al, shown to be prognostic and predictive of benefit, into a readily applicable immunohistochemistry (IHC) panel. Methods For seven of the genes in the gene expression profile (GEP) for which suitable commercial antibodies were available, we semiquantitatively assessed the IHC expression and prognostic significance for 173 patients treated at the Saint John Regional Hospital (SJRH). Cut-offs for high and low expression were defined for each marker and applied to IHC scores from 291 of the 482 patients in JBR.10, including patients on both the adjuvant chemotherapy and observation arms. The prognostic and predictive value of these markers on overall survival (OS) or recurrence-free survival (RFS) was assessed by Cox regression models. Results In the SJRH cohort, in 62 patients with resected stage II–III NSCLC, the prognostic significance of IHC assays for four proteins were concordant with Zhu’s GEP results. Low FOSL2 (OS, HR=0.15; p=0.0001; RFS, HR=0.14; p

This chapter presents the survey results from a cross section of experienced water managers using a set of carefully crafted questions. These questions covered water resources management, infrastructure resiliency, and recommendations for inclusion in education and curriculum. The chapter describes the specifics of the survey and the results obtained in the form of statistical averages on the ‘perception’ of these managers. Finally, these ‘perception’ averages may help focus the ASCE community on issues required for stewardship of the civil engineering profession. The survey and the responses gathered are not exhaustive nor do they represent the ASCE-endorsed viewpoint. However, the survey provides a critical first step in developing the framework of a research and education plan for ASCE. Given the Water Resources Reform and Development Act passed in 2014, the engineering community should now take into account the perceived concerns of the water management community.

Over the past 100 years, numerous water management infrastructures have been constructed to serve the water-related needs of people worldwide (Mitchell 1990). The larger ones are typically reservoirs with a dam and are often built for multiple purposes (e.g., water supply, disaster control, energy production, recreation, and navigation). These large water management infrastructures are the center of local and regional water resources management (Grigg 1996; Asmal et al. 2000). With the projected increase of water usage in the coming decades due to population growth and economic development, dams and reservoirs will remain one of the most ubiquitous and centralized solutions to satisfy water demands (Graf et al. 2010; Schlosser et al. 2014).

We propose the adoption of a bottom-up, resource-based vulnerability approach in evaluating the effect of climate and other environmental and societal threats to large water management infrastructure. To effectively reduce risk and increase resiliency requires as a prerequisite the determination of the major threats to local and regional water supplies and quality from weather including those from extreme flood and drought events, but also from other social and environmental issues. After these threats are identified, the relative risks can be compared in order to adopt optimal preferred mitigation/adaptation strategies. This is a more inclusive way of assessing risks, including from climate variability and human and natural climate change, than using the outcome vulnerability approach adopted by the Intergovernmental Panel on Climate Change (IPCC). This “contextual vulnerability” assessment using the bottom-up, resource-based framework is a more inclusive approach for policymakers dealing with water management infrastructure to adopt effective mitigation and adaptation methodologies to deal with the complexity of the spectrum of social and environmental events that will occur in the coming decades.

This chapter presents a compilation of work conducted by the ASCE Task Committee ‘Infrastructure Impacts of Landscape-driven Weather Change’ under the ASCE Watershed Management Technical Committee and the ASCE Hydroclimate Technical Committee. The chapter argues for explicitly considering the well-established feedbacks triggered by infrastructure systems to the land-atmosphere system via landscape change. A definition for Infrastructure Resilience (IR) at the intersection of extreme weather and climate is provided for the engineering community. The broader range of views and issues than what is currently in the front view of engineering practice is expected to ensure more robust approaches for resilience assessment by the engineering community by affording a greater number of ‘scenarios’ in its decision-making. The engineering community needs to understand the predictive uncertainty of changes to extreme weather and climate and how it can be addressed to improve infrastructure design and operations.

Dam construction in developing nations is on the rise. Monitoring these dams is essential to understanding downstream hydrologic impacts and for better planning and management of water resources. Satellite observations and advancements in information technology now present a unique opportunity to overcome the traditional limitations of reservoir monitoring. In this study, a global reservoir monitoring framework was developed as an online tool for near real-time monitoring and impact analysis of existing and planned reservoirs based on publicly available and global satellite observations. The framework used a mass balance approach to monitor 1598 reservoirs in South America, Africa, and Southeast Asia. Simulated streamflow of the developed tool was validated in 25 river basins against a multidecadal record of in-situ discharge. The simulated storage change was validated against in-situ data from 77 reservoirs. The framework was able to capture reservoir state realistically for more than 75% of these reservoirs. At most in-situ gaging locations, the reservoir tool was able to capture streamflow with a correlation of more than 0.9 and a normalized root mean square error of 50% or less. The tool can now be used to study existing or planned reservoirs for short and long-term decision making and policy analysis.

Background and objectives: Recurrent pterygium is an important ocular problem in our country. There are different modalities of treatment for recurrent pterygium. The present study was undertaken to determine the effect of intraoperative mitomycin C along with conjunctival auto graft to prevent recurrence of pterygium. Methods: Patients with recurrent pterygium attending a tertiary care hospital in Dhaka from January 2013 to June 2015 were included in the study. Cases were randomized into two groups. Group 1 had pterygium surgery with conjunctival auto graft and Group 2 had auto graft and intraoperative mitomycin C (0.02%). All cases were followed up for one year to assess recurrence of pterygium. Results: A total of 54 recurrent pterygia cases were included in the study. The age of study population was 25 to 65 years. The recurrence rate of pterygium after 12 months was 77.7% in group 1 and none in group 2. No major postoperative complication was observed. Conclusion: The study demonstrated that the use of intraoperative mitomycin C along with conjunctival auto graft had significant effect in preventing the recurrence of pterygium. IMC J Med Sci 2016; 10(2): 49-52

Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.

Remotely sensed data can be used to understand and devise measures to address important global issues such as climate change, disaster and disease outbreak. National Aeronautics and Space Administration (NASA) is one of the largest producer and gatekeeper of satellite earth observation (EO) data that plays a crucial role in ensuring that these resources are used for solving global societal problems. However, the extent of remote sensing application is highly disparate in different parts of the world. This paper provides a general overview of key societal applications that have been enabled globally with the use of EO data. It also summarizes the impact of various NASA-supported programs for promoting applications on the targeted beneficiary communities. The themes addressed here are land cover/land use mapping, carbon biomass assessment, food security, disaster management, water resources, ocean management and health and air quality. The paper also argues for capacity building that is crucial to building sustainable solutions when using EO data for science-based decision making.

One of the largest hydropower projects in Africa is the Grand Ethiopian Renaissance Dam (GERD), which is currently under construction in the Upper Blue Nile (UBN) basin in Ethiopia. The GERD has been billed as a hydropower project that will significantly improve electricity supply in Ethiopia and neighboring countries with a total capacity of 5150 MW. This paper evaluates the hydrological potential of the UBN basin for meeting the declared hydropower production design from the GERD. Our investigation indicated that the hydrology of the UBN can sustain the inflow to the GERD that would produce 13 629 GWh per annum (capacity factor = 0.30). Investigations further revealed that the GERD operation in the current design configuration will likely result in eight (out of 14) idle turbines every year. Our study also demonstrated that current GERD capacity (5150 MW) is more reasonable than previous designs (e.g., 6000 and 6450 MW).

Environmental decision-support tools often fail to be effectively applied in society because tools are “use-inspired” rather than “user-ready.” Stakeholder engagement by researchers is frequently the missing, critical component. Here, university researchers collaborated with the Bangladesh Water Development Board (BWDB) in developing a cloud-based, satellite remote sensing tool for monitoring suspended sediment concentrations (SSC) in major Bangladesh rivers. Bangladesh faces complex river erosion and sediment management challenges. The tool helps to overcome BWDB's constraints of limited resources and ground-based monitoring capacity. The tool maps estimated SSC over satellite images and provides long-term estimated SSC time series at user-designated points. The key elements of success in the engagement process were the partnership and trust between the researchers and stakeholder; stakeholder leadership and ownership; diversity of stakeholder roles engaged; researcher's understanding of the stakeholder, problem, and context; and open-source, easy-to-use tool. Weaknesses in the engagement process can be improved on in future work.

Despite efforts to understand the hydrologic impact of hydropower dams, their influence on downstream river temperatures has gone unnoticed in data limited regions. Using 30 years of Landsat thermal infrared observations (1988–2018), we identified a relationship between dry season water temperature cooling trends and dam development in the 3S Basin, a major tributary of the Mekong River. Within a year of the beginning of operations of major dams in the 3S River Basin, rapid decreases in annual average dry season river temperature were observed ranging between 0.7 ° C and 2 ° C. Furthermore, in situ water temperature observations confirmed decreasing river temperature for two major dam development events. Evidence was found that the 3S outflow has been cooling the Mekong River downstream of the confluence, by as much as 0.8 ° C in recent years. Our findings are critically important for understanding how fish and aquatic ecosystems will behave in the future as more hydropower dams are built in the Mekong River Basin.

Anthropogenic activities affect the water cycle and water supply at global and regional spatial scales, and approaches to water management must consider anthropogenic inputs. One of the major inputs in local-to-regional availability of water and the water cycle is land use land cover change as a result of urbanization, artificial reservoirs, and irrigation activity. To understand evolving trends in local hydrologic cycle for water sustainability of growing cities, this study employed a multi-factorial approach involving population trends, water use (and demand), streamflow, and various satellite-derived water-relevant variables. These variables are daily precipitation (from Tropical Rainfall Measuring Mission-TRMM, 3B42.V7), Normalized Difference Vegetation Index (NDVI) (from Moderate Resolution Imaging Spectroradiometer-MODIS-MOD13A1), land surface temperature (LST) (from MODIS-MOD11A2), and land cover (MODIS-MCD12Q1). Long term trends in such data were used to understand temporal and spatial trends in impounded watersheds hosting a large and growing city. The cities studied for water sustainability were Atlanta, Georgia and Buford dam; Columbia, South Carolina and Saluda dam; Columbus, Ohio and Alum Creek dam; Montgomery, Alabama and Jordan dam; Tulsa, Oklahoma and Keystone dam; and Tuscaloosa, Alabama and Tuscaloosa dam. Our study reveals that daily mean stream flow has been decreasing in all but one (Tulsa) of the areas selected. Satellite data trends between 2000 and 2012 showed a steady decrease in precipitation and NDVI, while LST has gradually increased. We attribute the NDVI (i.e., gradual decrease in vegetation cover) to LST rather than precipitation trends. The results of this research suggest that future temperature projections from climate models can be used in understanding vegetation activity and water availability over the study areas. Cities with larger upstream watershed area are potentially more sustainable and resilient (than those with small watersheds) as a result of spatial variability of water resources' response to climate change. Inter-basin water resources transfer is a possible solution to vulnerable cities in the future. The study results also emphasize the need to establish a sustainable and resilient water resources management system that includes narrowing the information and perception gap between the engineering community and the general public.

NASA and the French space agency, CNES, with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA) are developing new wide swath altimetry technology that will cover most of the world’s ocean and surface freshwater bodies. The proposed Surface Water and Ocean Topography (SWOT) mission will have the capability to make observations of surface water (lakes, rivers, wetland) heights and measurements of ocean surface topography with unprecedented spatial coverage, temporal sampling, and spatial resolution compared to existing technologies. These data will be useful for monitoring the hydrologic cycle, flooding, and characterizing human impacts on a changing environment. The applied science community is a key element in the success of the SWOT mission, demonstrating the high value of the science and data products in addressing societal issues and needs. The SWOT applications framework includes a working group made up of applications specialists, SWOT science team members, academics and SWOT Project members to promote applications research and engage a broad community of potential SWOT data users. A defined plan and a guide describing a program to engage early adopters in using proxies for SWOT data, including sophisticated ocean and hydrology simulators, an airborne analogue for SWOT (AirSWOT), and existing satellite datasets, are cornerstones for the program. A user survey is in development and the first user workshop was held in 2015, with annual workshops planned. The anticipated science and engineering advances that SWOT will provide can be transformed into valuable services to decision makers and civic organizations focused on addressing global disaster risk reduction initiatives and potential science-based mitigation activities for water resources challenges of the future. With the surface water measurements anticipated from SWOT, a broad range of applications can inform inland and coastal managers and marine operators of terrestrial and oceanic phenomena relevant to their work.

In this brief communication, we discuss the implication of the hypothesis that "non-linearity and multi-point pattern recognition can improve the spatial mapping of complex patterns of groundwater contamination". The discussion is based on our recently published work in Stochastic Environmental Research and Risk Assessment. Therein we have found that the use of a highly non-linear pattern learning technique in the form of an artificial neural network (ANN) can yield significantly superior results under the same set of constraints when compared to the more linear two-point ordinary kriging method.

Researchers from the University of Washington and Pakistan are using 21st century technology to revive farming as a profitable profession in the Indus Valley.

Gepotidacin is a novel, first-in-class triazaacenaphthylene antibiotic in development for treatment of conventional and biothreat infections. This was a single-dose, crossover thorough QT study in healthy subjects who were administered intravenous (i.v.) gepotidacin as a therapeutic (1,000-mg) dose and supratherapeutic (1,800-mg) dose, placebo, and 400 mg oral moxifloxacin in 4 separate treatment periods. Gepotidacin caused a mild effect on heart rate, with a largest placebo-corrected change-from-baseline heart rate of 7 and 10 beats per minute at the end of the 1,000-mg and 1,800-mg infusion, respectively. Gepotidacin caused an increase of change-from-baseline QTcF (ΔQTcF), with a peak effect at the end of infusion. The largest mean placebo-corrected ΔQTcF (ΔΔQTcF) was 12.1 ms (90% confidence interval [CI], 9.5 to 14.8) and 22.2 ms (90% CI, 19.6 to 24.9) after 1,000 mg and 1,800 mg, respectively. ΔΔQTcF rapidly fell after the end of the infusion, with a mean ΔΔQTcF of 6.1 ms 60 min after the 1,800-mg dose. Exposure-response analysis demonstrated a statistically significant positive relationship between gepotidacin plasma levels and ΔΔQTcF, with a slope of 1.45 ms per μg/ml (90% CI, 1.30 to 1.61). Using this model, the effect on ΔΔQTcF can be predicted to be 11 and 20 ms at the observed mean peak plasma concentration after the infusion of gepotidacin at 1,000 mg (7 μg/ml) and 1,800 mg (13 μg/ml), respectively. In conclusion, gepotidacin caused QT prolongation in this thorough QT study, and a mean effect can be predicted to less than 15 ms at the highest expected plasma concentration, 9 μg/ml. (This study has been registered at ClinicalTrials.gov under identifier NCT02257398.)

This study presents a scheme for co-optimizing the long-term (seasonal) reservoir operating objectives with the short-term (daily) objectives for multi-dam networks to maximize hydropower generation. Long-term optimal reservoir storage provides temporal space to optimize operation of the dams at short-term based on forecasted reservoir inflow. This study asks if there is an added benefit of co-optimization of operations at long- and short-term scales for hydropower generation. The multi-objective optimization problem at both the temporal scales was simultaneously solved considering Pareto optimality between conflicting objectives. Constraints pertaining to flood control, dam safety, and environmental flow were imposed. The proposed scheme was implemented over a network of Blue Mesa, Morrow Point, and Crystal dams in the Upper Colorado Basin. Ensemble forecast forcings from publicly available numerical weather prediction and climate models were used as inputs for the daily and monthly scale inflow forecasts. The results show improvements of 41%, 27%, and 15% in hydropower generation using the co-optimization during wet, moderate, and dry years, respectively, against a benchmark that neglects inflow forecasts. This study demonstrates added benefit of co-optimizing the operations for hydropower generation based on short- and long-term forecasted reservoir inflow. Given that most dams today operate as a network in a river basin, we recommend moving away from single dam and single time scale optimization to a multiple-dam with long- and short-term scale co-optimization-based operations to make renewable energy generation more efficient.

The ongoing drought in California seems to indicate that water managers are now paying greater attention to the use of numerical models of the atmosphere for short-term (7–10 days) weather forecasts. At the time of writing this article, a bill to Congress was being formulated that essentially aims to make the rule curves for large dams more adaptive through the use of numerical models for weather forecast. The use of such models is expected to reduce wastage of impounded water for dam managers and allow more flexibility in water storage and release during periods of anomalous/ off-season big droughts or floods. The purpose of this opinion article is to shed light on the current state of hydropower generation in the United States and discuss how the use of such numerical models of the atmosphere can also maximize energy production while conserving water or protecting against floods. As a clean and renewable energy source, hydropower has been extensively exploited by human beings for over 100 years. According to the 2014 Hydropower Market Report released by the Department of Energy (DOE), there are 2,198 active hydropower plants with a total operational capacity of 79.64 gigawatts (GW) in the United States. Hydropower accounts approximately 7% of the total power generated in the United States (Uria-Martinez et al. 2015). Unfortunately, hydropower generation capacity has stagnated the last two decades since the 1990s owing to lower economic growth (Hall et al. 2003), stricter environmental regulations, a stagnant energy market, and recent breakthroughs in the shale gas and oil industries (such as fracking). Nevertheless, hydropower remains the single largest source of renewable energy because of its relatively low-cost and sustainable characteristics. Compared to other renewable energy sources, hydropower has several unique advantages (USBR 2005). For example, a dam, which is normally considered an expensive investment to build, has a long service life spanning at least 50–100 years. Hydropower remains a more stable and durable source compared to wind or solar power, which are vulnerable to changing or unpredictable weather conditions. Hydropower production is relatively easier to ramp up or scale back depending on the transient nature of power demand. There are also no greenhouse gas emissions as byproducts during hydropower generation. A few U.S. states that are gifted with abundant water resources and topography have already harnessed hydropower as a clean and reliable electricity generation source, such as Washington, Oregon, and California (Fig. 1). Collectively, these three states have the largest installed hydropower capacity, which is equivalent to half of the total installed capacity across the United States. Among these three states, Washington has the largest share with approximately 30.4% of total hydropower generation in the United States, which also amounts to 70% of total electricity generation in the state of Washington. Oregon and California contribute 13.5% and 6.3%, respectively to the national grid in hydropower sector. Compared to the Northwest’s large amount of installed capacity, the Northeast has the most hydropower facilities, which typically are aged and small-capacity. The states that rely more on locally available hydropower apparently have lower electricity price compared to other states that have limited access to hydropower generating resources (UriaMartinez et al. 2015). Because building a new power plant is expensive, the low prices are usually in the states that have extensive power facilities where owners have already paid off the capital cost. The three northwestern states (Washington, Oregon, Idaho) are clear examples of cheap electricity pricing due to abundant hydropower resources.

Postural transitions are transitory movements with limited duration bounded by two physical activities, which represent the transition between two activities. Recognition of postural transitions is a challenging task due to low occurrence rate of transitions and short duration than physical activities. Postural transitions may decrease recognition accuracy when postural transitions are unspecified in the recognition system. This paper deals with the occurrence of postural transitions during activity performance. Hence, this paper proposes importance score driven random forests to recognize activities and postural transitions on the smartphone. The proposed model first selects relevant features rely on the relative importance of features, and then feed selected features to random forests. This model shows 100% recognition accuracy on a benchmark dataset and indicates that features created from accelerometer signals are more relevant for recognition than features created from gyroscope signals.

HydroViz is a Web-based, student-centered, educational tool designed to support active learning in the field of Engineering Hydrology. The design of HydroViz is guided by a learning model that is based on learning with data and simulations, using real-world natural hydrologic systems to convey theoretical concepts, and using Web-based technologies for dissemination of the hydrologic education developments. This model, while being used in a hydrologic education context, can be adapted in other engineering educational settings. HydroViz leverages the free Google Earth resources to enable presentation of geospatial data layers and embed them in web pages that have the same look and feel of Google Earth. These design features significantly facilitate the dissemination and adoption of HydroViz by any interested educational institutions regardless of their access to data or computer models. To facilitate classroom usage, HydroViz is populated with a set of course modules that can be used incrementally within different stages of an engineering hydrology curriculum. A pilot evaluation study was conducted to determine the effectiveness of the HydroViz tool in delivering its educational content, to examine the buy-in of the program by faculty and students, and to identify specific project components that need to be further pursued and improved. A total of 182 students from seven freshmen and senior-level undergraduate classes in three universities participated in the study. HydroViz was effective in facilitating students' learning and understanding of hydrologic concepts and increasing related skills. Students had positive perceptions of various features of HydroViz and they believe that HydroViz fits well in the curriculum. In general, HydroViz tend to be more effective with students in senior-level classes than students in freshmen classes. Lessons gained from this pilot study provide guidance for future adaptation and expansion studies to scale-up the application and utility of HydroViz and other similar systems into various hydrology and water-resource engineering curriculum settings. The paper presents a set of design principles that contribute to the development of other active hydrology educational systems.

As the world’s population increases, the rising demand for water will be compounded further by the need to sustain economic growth (Vorosmarty et al. 2000). According to one report by the United Nations Environment Program (UNEP), the stress on freshwater resources is expected to significantly magnify and spread to other regions of the world by 2025 (see Fig. 1; UNEP 2002). Historically, one of the common engineering solutions to guarantee a steady water supply against a rising demand has been to construct surface water impoundments on rivers. Such large-scale infrastructure, commonly known as dams and artificial reservoirs, trap a sufficiently large amount of water from the local hydrologic cycle to make up for a shortfall when demand exceeds the variable supply from nature. In other words, dams can be regarded as a strategic (long-term) solution to resolve the tactical (short-term) challenges of balancing the water deficit compounded by population growth and economic activity. In the United States, statistics suggest that building dams is outdated and considered a twentieth-century construct by the civil engineering profession (Fig. 2) Graf et al. 2010; Graf 1999). However, for vast regions of the underdeveloped or developing world, large dam-construction projects are being implemented in increasing numbers for tackling the rising water deficit in emerging economies (Fig. 3). Examples of such large dam projects are the Southeast Anatolia Project, or GAP (Turkish acronym) project, in Turkey, comprising 22 dams on the Tigris and Euphrates rivers (Unver 1997), the Three Gorges Dam (TGD) in China (Shen and Xie 2004), Itaipu Dam in Brazil (Pierce 1995), and the proposed Indian River Linking Project (Misra et al. 2007). From a global perspective, dam operations and water management in impounded basins remain relevant worldwide, while dam design and building are pertinent mostly to the developing world, comprising Africa, South America, and Asia, where most of the rivers remain unregulated. The heritage of modern dam building is nearly a century old. For example, the construction of the oldest dam in the Tennessee River Valley, called the Wilson Dam in Alabama, began in 1918 (Gebregiorgis and Hossain 2012). With a long heritage built on knowledge gained from previous failures and success stories, the civil engineering profession has made tremendous progress in dam safety against hazards of earthquakes (e.g., Marcuson et al. 1996), piping/seepage (e.g., Casagrande 1961; Sherard 1987), structural instability (e.g., Terzaghi and LaCroix 1964; Vick and Bromwell 1989), and optimization of dam operations to serve multiple, but competing, applications (Dai and Labadie 2001; Datta and Burges 1984). Similarly, much is now known about the management of postdam effects on aquatic ecology (e.g., Ligon et al. 1995; Richter et al. 1996), riparian vegetation (e.g., Merritt and Cooper 2000), geomorphology (e.g., Graf 2006), and dam removal as a result of sedimentation (Morris and Fan 1998; Graf et al. 2010). In general, the aspects of dam design and operations that have improved during the last century are those that are directly visible or have instantaneous impact on the land surface. This is not surprising, as the essence of engineering is hands-on in nature. What can be touched, sensed, and immediately visualized in the real world can be accounted for in the design and operation of an infrastructure. For example, the importance of fish ladders to minimize the disturbance to predam fish-migration paths was quickly appreciated by the engineering community during the early history of dam building. Now fish ladders are a common provision during the planning of a dam along a river. Similarly, when the Teton Dam failed (Sherard 1987), the importance of design provisions to minimize seepage, particularly in karstic geology, has now become a standard engineering practice. The Wolf Creek Dam, the largest artificial reservoir east of the Mississippi River, has periodically undergone grouting of seepage holes throughout its existence (Boynton and Hossain 2010). With increased fluctuation of flows downstream of dams, it did not take long for the concept of environmental flow (Tharme 2003) and indicators of hydrologic alteration (IHA) (Richter et al. 1996) to be devised for better ecosystem-centric dam operations in impounded basins. When more residential and commercial development is planned in an impounded river basin, it is intuitive to the engineer that the increase in imperviousness of the land surface may require larger detention basins at select locations to account for the increased runoff and erosion from excess rainfall. The climatic impacts (i.e., feedbacks) of dams, however, are unique areas that have received little consideration by the engineering profession for dam building and operations. Climate, by virtue of its definition, represents anything but a hands-on phenomenon. Unlike weather, climate impacts are not measured instantaneously. Given the current breadth of engineering curricula that exclude atmospheric and climate-science subjects as prerequisites at the freshmen and sophomore levels, a large artificial lake having an

This case study presents a risk analysis reassessment for the oldest dam in the Tennessee River basin—the Wilson Dam—based on postdam flow data. The hydrologic risk of old Wilson Dam was computed from historical flow data (spanning pre and postdam periods) and reservoir volume at the dam site. Additional flow data not previously used in the design phase of the dam helped to update more robustly the probability of flood occurrence that exceeded a particular return period during the life of a dam. The generalized extreme value (GEV) distribution was fitted to historical peak flow (at the dam site) and annual maximum reservoir volume using the L -moment method. This reassessment approach has wide application in reservoir water and safety management for aging dams. The study underscores the need for a review of risk analysis for aging dams that have extensive postdam flow data, particularly in the United States. Furthermore, this case study also demonstrates the unique value of the L -moment method in incorpo...

The purpose of this article is to explain the design and implementation of an open-source engineering education software called Stochastic Theory Education through Visualization Environment (STEVE), version 2.0. In an earlier article, a proof-of-concept for a computer-aided visualization tool (also named STEVE, version 1.0) for stochastic theory education in water resources engineering was articulated [see, Schwenk et al. Comput. Appl. Eng. Educ., 2008, in press). Using Java Native Interfacing, it was shown that STEVE 1.0 could wrap a space–time stochastic model written in any computer language and be independent of any specific language compiler during tool usage. This article describes the general philosophy, software design, and classroom usage for STEVE with significant improvements on visualization and user-friendliness (hence, rightfully called version 2.0). The software was created using the C++ programming language with the Microsoft Windows Applications Programming Interface (API). OpenGL was used for the visualization display, and the OpenGL Utility Toolkit (GLUT) was used to visualize text inside the OpenGL window. The instructor-specified simulation program on stochastic theory was written in Fortran 77. The application has user-friendly options for modifying input data and parameter specifications as desired by the instructor or the student user. STEVE 2.0 has been tested with the Windows XP and Windows Vista operating systems. For the benefit of interested users and software makers, we also provide the software application, a short tutorial and all pertinent source codes as freeware for download on our STEVE homepage at http://iweb.tntech.edu/saswe/steve.html. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 48–55, 2011

This article summarizes the changes in landscape structure because of human land managementoverthelastseveralcenturies,andusingobservedandmodeleddata, documents how these changes have altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large-scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation.  2011 John Wiley & Sons, Ltd.

Hands-on engineering education is an upcoming trend among K-12 schools throughout the United States to stimulate interest in science, technology, engineering, and mathematics (STEM). This study used unconventional methods to stimulate interest in STEM fields among high school students in a rural county of Tennessee. First, the study began by assessing enrollment data from each of Tennessee’s 95 counties to determine the factors that influenced the decision by high school applicants to choose engineering at Tennessee Technological University (TTU). The findings revealed that, while there were no significant differences between the aptitude scores of applicants from rural and urban counties, TTU had a higher representation of freshmen from urban areas. This indicated that motivational programs targeted at counties with low population, income level, and education level were needed to boost enrollment from rural counties. As a response, we introduced next a hands-on engineering class at Jackson County High Sc...

This paper used ordinary kriging to spatially map arsenic contamination in shallow aquifers of Northwestern Bangladesh (total area ∼ 35,000 km 2 ). The Northwestern region was selected because it represents a relatively safer source of large-scale and affordable water supply for the rest of Bangladesh currently faced with extensive arsenic contamination in drinking water (such as the Southern regions). Hence, the work appropriately explored sustainability issues by building upon a previously published study ( Hossain et al., 2007 ; Water Resources Management , vol. 21: 1245–1261) where a more general nation-wide assessment afforded by kriging was identified. The arsenic database for reference comprised the nation-wide survey (of 3534 drinking wells) completed in 1999 by the British Geological Survey (BGS) in collaboration with the Department of Public Health Engineering (DPHE) of Bangladesh. Randomly sampled networks of zones from this reference database were used to develop an empirical variogram and develop maps of zonal arsenic concentration for the Northwestern region. The remaining non-sampled zones from the reference database were used to assess the accuracy of the kriged maps. Two additional criteria were explored: (1) the ability of geostatistical interpolators such as kriging to extrapolate information on spatial structure of arsenic contamination beyond small-scale exploratory domains; (2) the impact of a priori knowledge of anisotropic variability on the effectiveness of geostatistically based management. On the average, the kriging method was found to have a 90% probability of successful prediction of safe zones according to the WHO safe limit of 10 ppb while for the Bangladesh safe limit of 50 ppb, the safe zone prediction probability was 97%. Compared to the previous study by Hossain et al. (2007) over the rest of the contaminated country side, the probability of successful detection of safe zones in the Northwest is observed to be about 25% higher. An a priori knowledge of anisotropy was found to have inconclusive impact on the effectiveness of kriging. It was, however, hypothesized that a preferential sampling strategy that honored anisotropy could be necessary to reach a more definitive conclusion in regards to this issue.

A comprehensive forensic analysis of AS contamination in Bangladesh was undertaken using geographic information system (GIS), univariate, and bivariate statistics. The assessment provided the background needed for fine-tuning emerging/evolving methods of spatial mapping based on nonlinear deterministic dynamics and multivariate factorial analyses. Data were derived from a nationwide AS survey completed in 1999 by the British Geological Survey (BGS) in collaboration with the Department of Public Health Engineering (DPHE) of Bangladesh. It was found that the standard deviation of arsenic concentration is highest along the river flood plains of the major rivers. The distribution of arsenic concentration was found to be symmetric in regions of very high mean concentration, while the regions of low and moderate contamination reveal a highly skewed distribution, suggesting the need for data transformation at these points prior to spatial interpolation. The mean value of Ca and Mg concentrations showed a strong ...

In this letter, we investigated the optimal configuration of conceptual hydrologic models for satellite-rainfall-based flood prediction in the 970-km2 Upper Cumberland basin of Kentucky. We explored the impact of integrating NASA's real-time global satellite rainfall product (IR-3B41RT), available at 0.25deg-hourly resolution, in four conceptual model configurations: three built using the modular Hydrologic Modeling System of the Hydrologic Engineering Center that focused on structural differences in infiltration schemes (i.e., National Resources Conservation Service (NRCS) curve number (CN) method, Green-Ampt infiltration method, and deficit/constant loss method) and the fourth being the topographic-index-based TOPMODEL. For the case presented in this letter, a spatially distributed model application did not appear to yield greater accuracy than lumped approaches when using spatially distributed satellite rainfall data for such a medium-sized basin. In general, the NRCS CN method was found to be most effective in terms of minimizing flood prediction uncertainty, followed by the Green-Ampt infiltration and deficit/constant loss methods.

In order to meet the ever increasing demand of drinking water, Dhaka Water Supply Authority (DWASA) of Bangladesh has installed a number of deep tube wells that tap the upper aquifers. However, in most parts of the city, the current groundwater abstraction exceeds the recharge rate, causing the groundwater to be mined systematically and be depleted of its reserve. Thus, there is an urgent need to alleviate the demand on the upper aquifers and explore more sustainable sources to augment the present water supply. This implies a conjunctive use of groundwater and surface water in order to maintain the balance between anthropogenic demand and water’s natural availability. However, the surface water along these peripheral rivers is known to be highly polluted due to municipal and industrial untreated wastewaters that are discharged. This study analyzes the present water quality scenario along the surrounding rivers of Dhaka City pertaining to a 2-day field survey during the dry season of 2005. It uses a Geographic Information System (GIS) as a tool to arrive at a solution for relocation of the current intake point for surface water withdrawal. Derivation of water quality profiles (as a function of distance) along the downstream and upstream reaches of the current intake location indicated that a new location 12 km upstream of the present intake point could potentially be ideal for withdrawing surface water during the monsoon season. Such a proposed location was considered optimal due to the anticipated moderate construction costs of the transmission system that would be necessary to draw water to the current treatment plant. The study lays the foundations for the Dhaka City planners and designers to make a qualitative resource assessment of surface water. Such an assessment can eventually evolve to a long-term monitoring system of water supply sources for any city using GIS tools.

The systematic decline of in situ networks for hydrologic measurements has been recognized as a crucial limitation to advancing hydrologic monitoring in medium to large basins, especially those that are already sparsely instrumented. As a collective response, sections of the hydrologic community have recently forged partnerships for the development of space-borne missions for cost-effective, yet global, hydrologic measurements by building upon the technological advancements since the last two decades. In this article, we review the state-of-the-art on flood monitoring in medium and large ungauged basins where satellite remote sensing can facilitate development of a cost-effective mechanism. We present our review in the context of the current hydro-political situation of flood monitoring in flood-prone developing nations situated in international river basins (IRBs). Given the large number of such basins and the difficulty in acquisition of multi-faceted geophysical data, we argue that the conventional data-intensive implementation of physically based hydrologic models that are complex and distributed is time-consuming for global assessment of the utility of proposed global satellite hydrologic missions. A more parsimonious approach is justified at the tolerable expense of accuracy before such missions begin operation. Such a parsimonious approach can subsequently motivate the identified international basins to invest greater effort in conventional and detailed hydrologic studies to design a prototype flood forecasting system in an effort to overcome the hydro-political hurdles to flood monitoring. Through a modeling exercise involving an open-book watershed concept, we demonstrate the value of a parsimonious approach in understanding the utility of NASA-derived satellite rainfall products. It is critical now that real-world operational flood forecasting agencies in the under-developed world come forward to collaborate with the research community in order to leverage satellite rainfall data for greater societal benefit for inhabitants in IRBs.

This article represents the second report by an ASCE Task Committee 'Infrastructure Impacts of Landscape-drivenWeather Change' under the ASCE Watershed Management Technical Committee and the ASCE Hydroclimate Technical Committee. Herein, the 'infrastructure impacts' are referred to as infrastructure-sensitive changes in weather and climate patterns (extremes and non-extremes) that are modulated, among other factors, by changes in landscape, land use and land cover change. In this first report, the article argued for explicitly considering the well-established feedbacks triggered by infrastructure systems to the land-atmosphere system via landscape change. In this report by the ASCE Task Committee (TC), we present the results of this ASCE TC's survey of a cross section of experienced water managers using a set of carefully crafted questions. These questions covered water resources management, infrastructure resiliency and recommendations for inclusion in education and curriculum. We describe here the specifics of the survey and the results obtained in the formof statistical averages on the 'perception' of thesemanagers. Finally, we discuss what these 'perception' averages may indicate to the ASCE TC and community as a whole for stewardship of the civil engineering profession. The survey and the responses gathered are not exhaustive nor do they represent the ASCE-endorsed viewpoint. However, the survey provides a critical first step to developing the framework of a research and education plan for ASCE. Given the Water Resources Reform and Development Act passed in 2014, we must now take into account the perceived concerns of the water management community., published_or_final_version

Using remotely-sensed data from the Tropical Rainfall Measuring Mission (TRMM), a cloud classification study was undertaken employing neural network schemes. The objective of this study was to assess the accuracy of each scheme for classifying clouds. In the first phase, a data preprocessing and feature selection scheme was undertaken to identify a suitable set of features that could be useful in cloud classification. In the next phase, seven neural network classification schemes were implemented to understand the utility of each of these schemes. Parametric schemes performed poorly, while the perceptron, K-nearest neighbor approaches and the least means square algorithm yielded promising results. Further study is proposed so as to improve rainfall prediction.

More than a decade ago, a National Research Council (NRC) report popularized the term “valley of death” to describe the region where research on weather satellites had struggled to reach maturity for societal applications. A similar analogy can be drawn for other satellite missions, since their vantage point in space can be highly useful for some of the world's otherwise fundamentally intractable operational problems. One such intractable problem is flood forecasting for downstream nations where the f looding is transboundary. Bangladesh fits in this category by virtue of its small size and location at the sink of the mighty Ganges and Brahmaputra. There has been the claim made that satellites can be a solution for Bangladesh in achieving forecasts with lead times beyond three days. This claim has been backed up by scientific research done by numerous researchers, who have shown proof of concept of using satellite data for extending flood forecasting range. This article aims to take the reader on a journe...