Ecosystem resilience despite large-scale altered hydroclimatic conditions

Climate change is predicted to increase both drought frequency and duration, and when coupled with substantial warming, will establish a new hydroclimatological model for many regions. Large-scale, warm droughts have recently occurred in North America, Africa, Europe, Amazonia and Australia, resulting in major effects on terrestrial ecosystems, carbon balance and food security. Here we compare the functional response of above-ground net primary production to contrasting hydroclimatic periods in the late twentieth century (1975-1998), and drier, warmer conditions in the early twenty-first century (2000-2009) in the Northern and Southern Hemispheres. We find a common ecosystem water-use efficiency (WUE e: Above-ground net primary production/ evapotranspiration) across biomes ranging from grassland to forest that indicates an intrinsic system sensitivity to water availability across rainfall regimes, regardless of hydroclimatic conditions. We found higher WUE e in drier years that increased significantly with drought to a maximum WUE e across all biomes; and a minimum native state in wetter years that was common across hydroclimatic periods. This indicates biome-scale resilience to the interannual variability associated with the early twenty-first century drought - that is, the capacity to tolerate low, annual precipitation and to respond to subsequent periods of favourable water balance. These findings provide a conceptual model of ecosystem properties at the decadal scale applicable to the widespread altered hydroclimatic conditions that are predicted for later this century. Understanding the hydroclimatic threshold that will break down ecosystem resilience and alter maximum WUE e may allow us to predict land-surface consequences as large regions become more arid, starting with water-limited, low-productivity grasslands. © 2013 Macmillan Publishers Limited. All rights reserved

Electric Grid Vulnerability Analysis to Identify Communities Prone to Wildfires

Natural hazards, like wildfires, present various challenges to the electric grid that can leave many communities without power. To identify vulnerabilities in the grid and the corresponding at-risk communities, this work considers the implementation of two Graph Theory assessment approaches, namely betweenness centrality and minimum cut, and combines the results from each with spatial fire probability data to produce a novel assessment of communities at-risk of losing service because of a wildfire. The results from a betweenness centrality analysis identified at-risk communities whose critical lines, necessary for routing power to the community from the numerous generators, were found to be at-risk if they were located within high probability burn zones. Communities at-risk of separation from the grid with one cut (or electrical shorting) of a transmission line due its proximity to a high burn probability (BP) area were also identified using the minimum cut Graph Theory algorithm. When the methodologies were applied to a demonstration transmission grid, the results found that about one third of the 585 substations had centrally located lines in high BP areas. About 46% of the substations require just one cut to be removed from the grid, and the average length of these one-cut segments was 37 km and the longest was 188 km

Geospatial Assessment Methodology to Estimate Power Line Restoration Access Vulnerabilities After a Hurricane in Puerto Rico

Limited access to transmission lines after a major contingency event can inhibit restoration efforts. After Hurricane Maria, for example, flooding and landslides damaged roads and thus limited travel. Transmission lines are also often situated far from maintained roadways, further limiting the ability to access and repair them. Therefore, this paper proposes a methodology for assessing Puerto Rico’s infrastructure (i.e., roads and transmission lines) to identify potentially hard to reach areas due to natural risks or distance to roads. The approach uses geographic information system (GIS) data to define vulnerable areas, that may experience excessive restoration times. The methodology also uses graph theory analysis to find transmission lines with high centrality (or importance). Comparison of these important transmission lines with the vulnerability results found that many reside near roads that are at risk for landslides or floods