The failures of tailings dams, used to store waste from mining operations, pose a significant risk to the health of people and the environment, especially in many low income countries where the extractive industry makes a significant contribution to the nation's wealth. Recently the rate of failure of tailings dams has increased. The demand for raw materials and increases in intense rainfall as a result of climate change will exacerbate this issue in the future. The monitoring of tailings dams is essential to reduce their probability of failure. Virtually all the recent tailings dams failures were preventable. However, there is generally a lack of transparency and accountability for these structures by mining companies. In the past 10 years an increase in the global coverage and accuracy of Earth Observation (EO) based information has made it technically possible to use EO-based data to remotely monitor critical aspects of tailings dams, such as their deformation and the leakage of pollutants. This paper describes the development of an EO-based service, being piloted in Peru, which would allow tailings dams to be monitored cost effectively, and also help to forecast any potentially risk inducing behaviour from tailings dams several weeks in advance. Many regulatory bodies in low income countries do not have the resources to adequately monitor mining operations. A low cost EO-based system could improve the transparency and safety of tailings dams, allowing timely preventative interventions to be made where the probability of failure is found to be high.
Every year, reservoir sedimentation causes an estimated 1% reduction in the total capacity of reservoirs. When sediment deposits approach the dam, further issues related to sediment passing through the turbines, blocking of the outlets and dam safety arise. One of the possible ways to tackle these problems is by management of the sediment in the reservoir itself through a selected operation strategy. A long-term reservoir sedimentation model, Ressass, is used to explore the impact of reservoir operation on sediment deposition. This paper explores the influence of different water level management strategies that result from water supply and hydropower demands.The results show that preserving the live storage may increase the rate of approach of sediment deposits towards the dam. The findings may support reservoir managers when facing these sometimes opposite objectives. To illustrate the analysis, different theoretical strategies are applied to a case study of Tarbela, the largest dam and reservoir on the River Indus in Pakistan.
Rainfall and runoff play an important role in the process of soil erosion, which is usually expressed with the R factor. To calculate the R factor, long-term precipitation data are needed with high temporal resolution, typically available for only few locations. The aim of this study was to obtain an approximate relationship between the more commonly available daily precipitation data and the R factor. A set of equations is presented for calculating monthly and annual R factor values based on daily precipitation data for a sub-Mediterranean region in southwest Slovenia, and their applicability is discussed. The sum of squares of daily precipitation was found to be the best descriptor of the monthly R factor. The ratio between the R factor and the sum of squares of daily precipitation varies throughout the year and generally follows the mean monthly temperature, with the efficiency coefficient, e, of 0.869 between predicted and observed data on the annual basis.Key words nonlinear regression; R factor; RUSLE; rainfall intensity; rainfall and runoff erosivity; soil erosion; Slovenia Estimation du facteur R à partir de données journalières de pluie dans le climat sub-méditerranéen du Sud-Ouest de la Slovénie Résumé La pluie et le ruissellement jouent un rôle important dans le processus d'érosion des sols, ce qui est habituellement traduit par le facteur R. Pour calculer le facteur R, de longues séries de données de pluie, avec une résolution temporelle élevée, sont nécessaires alors qu'elles ne sont généralement disponibles qu'en de rares endroits. Le but de cette étude était d'obtenir une relation approximative entre les données journalières de pluie disponibles plus couramment et le facteur R. Nous présentons un ensemble d'équations pour calculer des valeurs mensuelles et annuelles du facteur R, basées sur des données journalières de pluie, pour une région subméditerranéenne en Slovénie du Sud-Ouest, et nous discutons leur applicabilité. La somme des carrés des pluies journalières s'est révélée être le meilleur descripteur du facteur R mensuel. Le rapport entre le facteur R et la somme des carrés des pluies journalières varie au cours de l'année et suit généralement la température mensuelle moyenne, avec un coefficient d'efficacité e de 0.869 entre les données prévues et observées sur la base annuelle.Mots clefs régression non linéaire; facteur R; RUSLE; intensité des pluies; érosivité des pluies et du ruissellement; érosion du sol; Slovénie
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.