In most of arid and semi-arid regions, there are limited sources of available fresh water for different domestic and environmental demands. Strategic and parsimonious fresh water-use in water-scarce areas such as Southern New Mexico is crucially important. Elephant Butte and Caballo reservoirs are two integrated reservoirs in this region that provide water supply for many water users in downstream areas. Since Elephant Butte Reservoir is in a semi-arid region, it would be rational to utilize other energy sources such as wind energy to produce electricity and use the water supply to other critical demands in terms of time and availability. This study develops a strategy of optimal management of two integrated reservoirs to quantify the savable volume of water sources through optimal operation management. To optimize operations for the Elephant Butte and Caballo reservoirs as an integrated reservoir operation in New Mexico, the authors in this case study utilized two autoregressive integrated moving average models, one non-seasonal (daily, ARIMA model) and one seasonal (monthly, SARIMA model), to predict daily and monthly inflows to the Elephant Butte Reservoir. The coefficient of determination between predicted and observed daily values and the normalized mean of absolute error (NMAE) were 0.97 and 0.09, respectively, indicating that the daily ARIMA prediction model was significantly reliable and accurate for a univariate based streamflow forecast model. The developed time series prediction models were incorporated in a decision support system, which utilizes the predicted values for a day and a month ahead and leads to save significant amount of water volume by providing the optimal release schedule from Elephant Butte into the Caballo Reservoir. The predicted daily and monthly values from the developed ARIMA prediction models were integrated successfully with the dynamic operation model, which provides the optimal operation plans. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. The saved volume of the water would be considered as a significant water supply for environmental conservation actions in downstream of the Caballo Reservoir. Providing an integrated optimal management plan for two reservoirs led to save significant water sources in a region that water shortage has led to significant environmental consequences. Finally, since the models are univariate, they demonstrate an approach for reliable inflow prediction when information is limited to only streamflow values. We find that hydroelectric power generation forces the region to lose significant amount of water to evaporation and therefore hinder the optimal use of freshwater. Based on these findings, we conclude that a water scarce region like Southern New Mexico should gain independence from hydroelectric power and save the freshwater for supporting ecosystem services and environmental purposes.

This study illustrates the benefits of data pre-processing through supervised data-mining techniques and utilizing those processed data in an artificial neural networks (ANNs) for streamflow prediction. Two major categories of physical parameters such as snowpack data and time-dependent trend indices were utilized as predictors of streamflow values. Correlation analysis of different models indicate that, for the period of January to June, using fewer predictors led to simpler modeling with equivalent accuracy on daily prediction models. This did not hold in all periods. For monthly prediction models, accuracy was improved compared to earlier works done to predict monthly streamflow for the same case of Elephant Butte Reservoir (EB), NM. Overall, superior prediction performance was achieved by utilizing data-mining techniques for pre-processing historical data, extracting the most effective predictors, correlation analysis, extracting and utilizing combined climate variability indices, physical indices, and employing several developed ANNs for different prediction periods of the year.

Health data usually has missing or incomplete location information, which impacts the quality of research. Geoimputation methods are used by health professionals to increase the spatial resolution of address information for more accurate analyses. The objective of this study was to evaluate geo-imputation methods with respect to the demographic and spatial characteristics of the data. We evaluated four geoimputation methods for increasing spatial resolution of records with known locational information at a coarse level. In order to test and rigorously evaluate two stochastic and two deterministic strategies, we used the Texas Sex Offender registry database with over 50,000 records with known demographic and coordinate information. We reduced the spatial resolution of each record to a census block group and attempted to recover coordinate information using the four strategies. We rigorously evaluated the results in terms of the error distance between the original coordinates and recovered coordinates by studying the results by demographic sub groups and the characteristics of the underlying geography. We observed that in estimating the actual location of a case, the weighted mean method is the most superior for each demographic group followed by the maximum imputation centroid, the random point in matching sub-geographies and the random point in all sub-geographies methods. Higher accuracies were observed for minority populations because minorities tend to cluster in certain neighborhoods, which makes it easier to impute their location. Results are greatly affected by the population density of the underlying geographies. We observed high accuracies in high population density areas, which often exist within smaller census blocks, which makes the search space smaller. Similarly, mapping geoimputation accuracies in a spatially explicit manner reveals that metropolitan areas yield higher accuracy results. Based on gains in standard error, reduction in mean error and validation results, we conclude that characteristics of the estimated records such as the demographic profile and population density information provide a measure of certainty of geographic imputation.

The food, energy, and water (FEW) nexus has gained increased attention, resulting in numerous studies on management approaches. Themes of resource use, and their subsequent scarcity and economic rents, which are within the application domain of the World Trade Model, are ripe for study, with the continuing development of forward- and backward-facing economic data. Scenarios of future food and energy demand, relating to supply chains, as well as direct and indirect resource uses, are modelled in this paper. While it is possible to generate a substantial number of economic and environmental scenarios, our focus is on the development of an overarching approach involving a range of scenarios. We intend to establish a benchmark of possibilities in the context of the debates surrounding the Paris Climate Agreement (COP21) and the Green New Deal. Our approach draws heavily from the existing literature on international agreements and targets, notably that of COP21, whose application we associate with the Shared Socioeconomic Pathway (SSP). Relevant factor uses and scarcity rent increases are found and localized, e.g., on the optimal qualities of water, minerals, and land. A clear policy implication is that, in all scenarios, processes of energy transition, raw material use reduction, and recycling must be strengthened.