This paper presents a statistical model to estimate the volume of released tailings (V F) and the maximum distance travelled by the tailings (D max) in the event of a tailings dam failure, based on physical parameters of the dams. The dataset of historical tailings dam failures is updated from the one used by Rico et al., (Floods from tailings dam failures, Journal of Hazardous Materials, 154 (1) (2008) 79-87) for their regression model. It includes events out of the range of the dams contained in the previous dataset. A new linear regression model for the calculation of D max , which considers the potential energy associated with the released volume is proposed. A reduction in the uncertainty in the estimation of D max when large tailings dam failures are evaluated, is demonstrated. Since site conditions vary significantly it is important to directly consider the uncertainty associated with such predictions, rather than directly using these types of regression equations. Here, we formally quantify the uncertainty distribution for the conditional estimation of V F and D max , given tailings dam attributes, and advocate its use to better represent the tailings dam failure data and to characterize the risk associated with a potential failure.

Roof rainwater harvesting (RWH) has the potential to augment water supplies for urban and suburban uses throughout the United States (U.S.). Studies of the performance of RWH at the building and city scales in the U.S. are available, but a countrywide overview of the potential performance of RWH at the county scale has not been done before. Three approaches were taken: (1) assess the viability of RWH in terms of the rainfall that could be captured in relation to the water demand in each county (excluding agriculture), (2) evaluate the performance of a “typical” domestic RWH system across all counties with metrics related to its ability to supply the potable and nonpotable demand, and (3) evaluate the effect of adding a 50% rainwater reuse component to the analysis. We find RWH could be a viable supplemental water source in the U.S., particularly in counties of the Pacific Northwest, Central, and Eastern regions (percent demand covered >50%). Low population density counties have the potential to meet their annual water needs with RWH, while high‐density counties could only source a small portion (~20%) of their annual demand with RWH. Typical RWH systems in counties in the Central and Eastern U.S. performed better than in Western counties. Adding a reuse component can be a key factor in making RWH attractive in many areas of the country. This work can inform future water infrastructure investment and planning in the U.S.

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We assess the potential financial benefits of rooftop rainwater harvesting (RWH) in Mexico City from the perspective of property owners and entrepreneurs. A bottom‐up approach was followed by evaluating RWH at individual buildings and aggregating the results to a borough/city level. We consider sector‐specific water demands, potable and nonpotable uses, and user‐specific water tariffs. We find that RWH is economically most beneficial for nondomestic users rather than for small domestic users, who are often the target of RWH interventions. Based on a net present value analysis, a potable RWH system is not favored for most domestic users under the current subsidized municipal water tariff structure. Our analysis only considers capital and maintenance expenses, and not other benefits related to increased access to water and reliability, or social benefits from a switch to a RWH system. If the initial capital expense for RWH is partly financed by transferring the water subsidy to an entrepreneur, then RWH becomes financially attractive for a wide range of domestic users. To improve water access in Mexico City, RWH is attractive in the most marginalized boroughs where water use is currently lower and precipitation is higher. For domestic users relying on trucked water, RWH can have great financial benefits. Our approach provides quantitative data with high spatial specificity, highlighting the places and types of users that would benefit most from RWH.