Supporting reduction of risks of tailings dams using earth observation data

Mapping Intimacies ◽

10.5194/egusphere-egu2020-7658 ◽

2020 ◽

Author(s):

Mark Davison ◽

Marta Roca ◽

Gregor Petkovsek

Keyword(s):

Weather Forecasting ◽

Flood Hazard ◽

Disaster Risk ◽

Earth Observation ◽

Mitigation Measures ◽

Dam Failure ◽

Observation Data ◽

Irreversible Damage ◽

Loss Of Life ◽

Tailings Dams

Tailings dams are earth embankments used to store toxic mine waste and effluent. Their failure, as already seen in January 2019 with the fatal failure of Brumadinho dam in Brazil, can cause loss of life, irreversible damage to ecosystems and large economic damages. In countries with limited resources, it is challenging for the authorities to be able to assess the risk and effectively monitor this type of infrastructure, especially when located in remote areas.We are developing DAMSAT (Dam Monitoring from SATellites), a web-based system for a sustainable and cost effective way of remotely monitor tailings and water retention dams to support early decision making and reduce the social, economic and environmental impacts downstream of potential failures.DAMSAT monitors the displacement of the structures using earth observation technologies such as Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) technologies, combined with real-time in-situ devices. These observations combined with weather forecasting tools allow the issue of alerts for unusual behaviour or weather conditions that could lead to dam failure. These alerts are part of the Disaster Risk Management cycle to trigger the implementation of mitigation measures to reduce the likelihood of failure of the dam or the potential consequences downstream.&#160;&#160;In order to have a better understanding of these potential consequences and provide all the information necessary for asset managers to take decisions, DAMSAT also assesses the hazard component of disaster risk due to dam failure using a set of modelling tools. A dam breach simulation model (EMBREA) is combined with a mud flow model to spread the flood hazard downstream of the dam if a failure occurs.&#160; The consequences of the flood are assessed in terms of loss of life using an evacuation model, the Life Safety Model. Different flood warning scenarios and evacuation strategies are mapped to inform emergency planning.DAMSAT is currently being piloted in two mining regions in Peru with the involvement of government organisations and other relevant stakeholders.&#160;

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Modelling the Brumadinho tailings dam failure, the subsequent loss of life and how it could have been reduced

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-21-2021 ◽

2021 ◽

Vol 21 (1) ◽

pp. 21-37

Author(s):

Darren Lumbroso ◽

Mark Davison ◽

Richard Body ◽

Gregor Petkovšek

Keyword(s):

Mining Waste ◽

Dam Failure ◽

Tailings Dam ◽

Agent Based Model ◽

Emergency Plans ◽

Loss Of Life ◽

Agent Based ◽

Agent Based Modelling ◽

Tailings Dams ◽

Subsequent Loss

Abstract. In recent years the number of tailings dams failures has increased. On 25 January 2019, the Brumadinho tailings dam in Brazil suddenly failed, releasing a mudflow over 10 m deep comprising some 107 m3 of mining waste which killed between 270 and 320 people. This paper details the use of an agent-based model, known as the Life Safety Model (LSM), to estimate the risk to people downstream of the Brumadinho tailings dam and to assess if the number of fatalities could have been reduced if a warning had been received prior to or at time the dam failed. The LSM modelling indicates that even if a warning had been issued as the dam failed, the number of fatalities could have been reduced. Agent-based modelling tools such as the LSM can help to inform and improve emergency plans for tailings dams, which will help to reduce the risks posed by them in the future.

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Modelling the Brumadinho tailings dam failure, the subsequent loss of life and how it could have been reduced

10.5194/nhess-2020-159 ◽

2020 ◽

Author(s):

Darren Lumbroso ◽

Mark Davison ◽

Richard Body ◽

Gregor Petkovšek

Keyword(s):

Mining Waste ◽

Dam Failure ◽

Tailings Dam ◽

Agent Based Model ◽

Emergency Plans ◽

Loss Of Life ◽

Agent Based ◽

Agent Based Modelling ◽

Tailings Dams ◽

Subsequent Loss

Abstract. In recent years the number of tailings dams failures has increased. On 25 January 2019, the Brumadinho tailings dam in Brazil suddenly failed releasing a mudflow over 10 m deep comprising some 10 million m3 of mining waste which killed between 270 and 320 people. This paper details the use of an agent-based model, known as the Life Safety Model (LSM), to estimate the risk to people downstream of the Brumadinho tailings dam and to assess if the number of fatalities could have been reduced if a warning had been received prior to or at time the dam failed. The LSM modelling indicates that even if a warning had been issued as the dam failed the number of fatalities could have been reduced. Agent-based modelling tools such as the LSM can help to inform and improve emergency plans for tailings dams, which will help to reduce the risk posed by them in the future.

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Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽

10.26643/gis.v12i3.5187 ◽

2019 ◽

Vol 12 (3) ◽

pp. 12-14

Author(s):

Eicher, A

Keyword(s):

Earth Observation ◽

Observation Data ◽

Business World ◽

The Earth ◽

Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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Mapping slums and model population density using earth observation data and open source solutions

2019 Joint Urban Remote Sensing Event (JURSE) ◽

10.1109/jurse.2019.8808934 ◽

2019 ◽

Author(s):

Tais Grippa ◽

Stefanos Georganos ◽

Sabine Vanhuysse ◽

Moritz Lennert ◽

Nicholus Mboga ◽

...

Keyword(s):

Population Density ◽

Open Source ◽

Earth Observation ◽

Observation Data ◽

Model Population ◽

Earth Observation Data

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Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

Remote Sensing ◽

10.3390/rs13010005 ◽

2020 ◽

Vol 13 (1) ◽

pp. 5

Author(s):

William Straka ◽

Shobha Kondragunta ◽

Zigang Wei ◽

Hai Zhang ◽

Steven D. Miller ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Environmental Impacts ◽

Demographic Data ◽

The United States ◽

Earth Observation ◽

Observation Data ◽

Washington Dc ◽

Mobility Data ◽

Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

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2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

Remote Sensing ◽

10.3390/rs13071310 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1310

Author(s):

Gabriele Bitelli ◽

Emanuele Mandanici

Keyword(s):

High Resolution ◽

Exponential Growth ◽

Smart City ◽

Earth Observation ◽

Urban Environments ◽

Observation Data ◽

High Resolution Imagery ◽

Observatory Data ◽

Methods And Techniques ◽

Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

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Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07952 ◽

2020 ◽

Vol 3 (1) ◽

pp. 78

Author(s):

Francis Oloo ◽

Godwin Murithi ◽

Charlynne Jepkosgei

Keyword(s):

Land Cover ◽

Metropolitan Area ◽

Land Surface ◽

Urban Forest ◽

Urban Forests ◽

Earth Observation ◽

Tree Cover ◽

Observation Data ◽

Vegetation Health ◽

Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

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