An Economic Assessment of the Global Potential for Seawater Desalination to 2050

Seawater desalination is a promising approach to satisfying water demand in coastal countries suffering from water scarcity. To clarify its potential future global scale, we perform a detailed investigation of the economic feasibility of desalination development for different countries using a feasibility index (Fi) that reflects a comparison between the price of water and the cost of production. We consider both past and future time periods. For historical validation, Fi is first evaluated for nine major desalination countries; its variation is in good agreement with the actual historical development of desalination in these countries on both spatial and temporal scales. We then simulate the period of 2015–2050 for a Shared Socioeconomic Pathway (SSP2) and two climate scenarios. Our projected results suggest that desalination will become more feasible for countries undergoing continued development by 2050. The corresponding total global desalination population will increase by 3.2-fold in 2050 compared to the present (from 551.6 × 106 in 2015 to 1768 × 106). The major spread of seawater desalination to more countries and its availability to larger populations is mainly attributed to the diminishing production costs and increasing water prices in these countries under the given socioeconomic/climate scenarios

Dynamical Behavior of Multivariate Time Series for SOI, Precipitation/Temperature in Fukuoka and Its Prediction by Artificial Neural Networks

Large atmospheric circulation has affected local/regional hydro-meteorological variables such as precipitation and temperature. The large-scale circulation represented by Southern Oscillation Index (SOI) in the present study has played a driving force affecting the local variables. The underlying interaction among them is difficult to detect directly due to the existence of noise and strong nonlinearity. In the present study, simultaneous predictability of SOI, precipitation, and temperature at Fukuoka was verified through noise reduction by low pass filtering and training of artificial neural networks (ANNs), presenting remarkable properties that can represent the nonlinearity in a system. Two types of transfer function (i. e., hyperbolic tangent and pure linear functions) were applied to hidden nodes, while only pure linear function was used for output layer. Possible extrapolation beyond the extreme values in training was verified with the testing and validation datasets. The observed and predicted values for the two cases were depicted in three-dimensional phase space to reveal the dynamical behavior of the interaction among the regional driving force and local hydrometeorological variables, as well as shown in the respective time series plots. The identified parameters from training of ANNs were verified in the testing and validation phase as well

Dynamical Behavior of Multivariate Time Series for SOI, Precipitation/Temperature in Fukuoka and Its Prediction by Artificial Neural Networks

Large atmospheric circulation has affected local/regional hydro-meteorological variables such as precipitation and temperature. The large-scale circulation represented by Southern Oscillation Index (SOI) in the present study has played a driving force affecting the local variables. The underlying interaction among them is difficult to detect directly due to the existence of noise and strong nonlinearity. In the present study, simultaneous predictability of SOI, precipitation, and temperature at Fukuoka was verified through noise reduction by low pass filtering and training of artificial neural networks (ANNs), presenting remarkable properties that can represent the nonlinearity in a system. Two types of transfer function (i. e., hyperbolic tangent and pure linear functions) were applied to hidden nodes, while only pure linear function was used for output layer. Possible extrapolation beyond the extreme values in training was verified with the testing and validation datasets. The observed and predicted values for the two cases were depicted in three-dimensional phase space to reveal the dynamical behavior of the interaction among the regional driving force and local hydrometeorological variables, as well as shown in the respective time series plots. The identified parameters from training of ANNs were verified in the testing and validation phase as well