No abstract

Improved sanitation is considered equally important for public health as is access to improved drinking water. However, the world has been slower to meet the challenge of sanitation provision for the world's poor. We analyze previously cited barriers to sanitation coverage including inadequate investment poor or nonexistent policies, governance, too few resources, gender disparities, and water availability. Analysis includes investigation of correlation between indicators of the mentioned barriers and sanitation coverage, correlations among the indicators themselves, and a geospatial assessment of the potential impacts of sanitation technology on global water resources under six scenarios of sanitation technology choice. The challenges studied were found to be significant barriers to sanitation coverage, but water availability was not a primary obstacle at a global scale. Analysis at a 0.5 degrees grid scale shows, however, that water availability is an important barrier to as many as 46 million people, depending on the sanitation technology selected. The majority of these people are urban dwellers in countries where water quality is already poor and may be further degraded by sewering vast populations. Water quality is especially important because this vulnerable population primarily resides in locations that depend on environmental income associated with fish consumption.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

The solutions to the world's current and future problems require that engineers and scientists design and construct ecologically and socially just systems within the carrying capacity of nature without compromising future generations. In addition, as governments move towards policies that promote an international marketplace, educators need to prepare students to succeed in the global economy. Young people entering the workforce in the upcoming decades will also have the opportunity to play a critical role in the eradication of poverty and hunger and facilitation of sustainable development, appropriate technology, beneficial infrastructure, and promotion of change that is environmentally and socially just.Many universities espouse the idea that discipline integration is a prerequisite for successful implementation of sustainability in education. However, few engineering curriculum have taken the step to integrate concepts of sustainable development with an international experience. This paper discusses the educational and global drivers for curricular change in this important area and demonstrates how several undergraduate and graduate programmes initiated at Michigan Technological University can provide a more interdisciplinary basis for educating engineers on global concepts of sustainability. To date, these programmes have taken place in 21 countries and reached approximately 300 students (49% women) that represent 11 engineering disciplines and nine non-engineering disciplines.