As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide resistant and resilient society, an original GIS-based decision support system is put forth in order to help emergency managers better prepare for and respond to landslide disasters. The GIS-based landslide monitoring and management system includes a Central Repository System (CRS), Disaster Data Processing Modules (DDPM), a Command and Control System (CCS) and a Portal Management System (PMS). This architecture provides valuable insights into landslide early warning, landslide risk and vulnerability analyses, and critical infrastructure damage assessments. Finally, internet-based communications are used to support landslide disaster modelling, monitoring and management.

On 20 April 2010, the 5000-foot-deep Macondo Mississippi Canyon Block 252 (MC252) well erupted after a blowout caused a catastrophic explosion and fire aboard the BP PLC-leased Deepwater Horizon offshore oil drilling platform (owned by Transocean Ltd.) about 40 miles (64 km) southeast of the Louisiana coast. The 2010 Deepwater Horizon Gulf Coast oil spill now imperils ecologically sensitive lands, affects the livelihoods of thousands of workers, and threatens a way of life that has been passed down for generations. An oil spill management Decision Support System (DSS) architecture is put forth that integrates the latest advances in MCDA and geomatics engineering in order to cope with the worst environmental disaster in US history. It is shown that policy-makers should improve the sustainability and resilience of Gulf Coast ecosystems and communities so that they are healthier and more robust than before the oil spill. Policy recommendations with respect to the 2010 Deepwater Horizon spill are put forth. We discuss how to best assist disrupted businesses, rehabilitate polluted ecosystems, and improve regulatory oversight of the oil and gas industry in order to prevent future oil spill disasters.

Dust storms in North China result in high concentrations of airborne dust particles, which cause detrimental effects on human health as well as social and economic losses and environmental degradation. To investigate the impact of land surface processes on dust storms, we simulate two dust storm events in North China during spring 2002 using two versions of a dust storm prediction system developed by the Institute for Atmospheric Physics (IAP) in Beijing, China. The primary difference between the IAP Sandstorm Prediction System (IAPS 1.0) and more recent version (IAPS 2.0) is the land surface modeling. IAPS 1.0 is based on the Oregon State University (OSU) land surface model, whereas the latest version of the dust storm prediction (IAPS 2.0) uses NOAH land surface schemes for land surface modeling within a meteorological model, MM5. This work investigates whether the improved land surface modeling affects modeling of sandstorms. It is shown that an integrated sandstorm management system can be used to aid the following tasks: ensure sandstorm monitoring and warning; incorporate weather forecasts; ascertain the risk of a sandstorm disaster; integrate multiple technologies (for example, GIS, remote sensing, and information processing technology); track the progress of the storm in real-time; exhibit flexibility, accuracy and reliability (by using multiple sources of data, including in-situ meteorological observations); and monitor PM₁₀ and PM_2.5 dust concentrations in airborne dustfalls. The results indicate that with the new land surface scheme, the simulation of soil moisture is greatly improved, leading to a better estimate of the threshold frictional velocity, a key parameter for the estimating surface dust emissions. In this study, we also discuss specific mechanisms by which land surface processes affect dust storm modeling and make recommendations for further improvements to numerical dust storm simulations.

This study provides a comprehensive framework to mitigate or prevent forest and land (or wildland) fire disaster in Malaysia. This system supports emergency response and preparedness for wildland fire by means of integrated modelling, monitoring and mapping of fire danger. In this framework, multi-sensor applications for monitoring fire danger and fire activity are linked with decision-aid models in a Geographic Information System (GIS) environment to generate information required for wildland fire management. Using a customized version of the spatial Fire Management System software, components of the Malaysia Fire Danger Rating System (FDRS) are calculated to provide fire weather, fire behaviour and wildfire threat information. Wildfire threat ratings (WTRs) are assessed on the basis of fire occurrence risk, potential fire behaviour, suppression capability and values at risk. Outputs from the Malaysia FDRS were integrated with hotspots extracted from remote sensing data to generate combined maps of active fire locations, fire danger, potential fire behaviour and uncontrolled wildland fire (or wildfire) threat. In case of wildfire, remotely sensed data were also used to generate wildfire affected area and emergency response maps for emergency management. The system architecture and application models for wildfire analysis, which aid decision-making components for wildfire mitigation and relief, are described. These include early warning of fire, risk analysis, damage assessment and emergency response analysis. This article provides the first documentation of a national, operational system linking fire danger rating with socio-economic values, as defined by the WTR models, to guide fire and rescue decision-making during wildfire events. It is finally shown how the proposed system can reduce the risk offire management disputes in Malaysia by directing the conflict to a more favourable resolution.