Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

Combining local- and large-scale models to predict the distributions of invasive plant species

Habitat-distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat-distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form ‘ensemble’ models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species’ native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington State, USA. We then combined the three methods to create ensemble models. We also developed climate-envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that \u3c4% of ONP is at high near-term risk of invasion while 67-99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the design of monitoring protocols and monitoring results can in turn be used to refine models. We propose that by using models from multiple scales to predict invasion risk and by explicitly linking model development to monitoring, it may be possible to overcome some of the limitations of habitat-distribution models

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

A GIS Model for Predicting Potential High Risk Areas of West Nile Virus by Identifying Ideal Mosquito Breeding Habitats

West Nile virus has become a major risk to humans since its first appearance in New York City in 1999. Physicians and state health officials are interested in new and more efficient methods for monitoring disease spread and predicting future outbreaks. This study modeled habitat suitability for mosquitoes that carry West Nile virus. Habitat characteristics were used to derive risk maps for the entire state of Mississippi. Statistical significance tests yielded objective evidence for choosing among many habitat variables. Variables that were significantly correlated with diagnosed human cases for 2002 were combined in weighted linear algebraic models using a geographic information system (GIS). Road density, slope, and summer precipitation minus evaporation (P-E) were the most significant variables. GIS-based model results were compared with results from logistic regression models. The algebraic model was preferred when validated by 2003 human cases. If adopted, GIS-based risk models can help guide mosquito control efforts

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

The impact of climate change on the archaeology of New Zealand’s coastline

Abstract: With rising sea levels, changes in precipitation patterns and an increased incidence of severe weather events being predicted as a result of global climate change, the Department of Conservation commissioned a study to determine the potential impacts of these effects on New Zealand’s archaeological sites, which are mostly located near the coast. A Geographic Information System (GIS)-based case study examined the distribution of archaeological sites in the Whangarei District and assessed the risk to the archaeological resource primarily from sea level rise associated with future climate change.The results of the analysis are fairly conclusive. Currently, the major threats to archaeological sites in coastal areas are erosion, flooding and ground instability, and some sites are at risk from more than one of these threats. Approximately one-third of the recorded site locations in the Whangarei District are potentially threatened by these hazards, regardless of any future climate change effects. Climate change will exacerbate existing coastal hazards, and increase the likelihood and severity of impacts on archaeological sites. An additional 2.5–10% of archaeological sites might be affected by increased threats due to predicted changes in climate, including rising sea levels. The types of sites that are most likely to be affected in the Whangarei District are coastal midden and small habitation sites relating to Māori occupation. Although these could be affected by all three of the major hazards identified, they are particularly susceptible to coastal erosion. Land stability issues and flooding are likely to affect a greater range of sites, including larger sites such as pā and sites relating to early European settlement. It is not possible to quantify the risk to sites from increased land instability as a result of global climate change, but it is noted that any increase in extreme weather events would not be confined to coastal areas. These sites potentially hold significant information relating to the history of both the district and New Zealand. The implications of the study are that coastal sites are already under considerable threat, and that important archaeological information is being lost at a rate that may increase significantly in the future. Action is needed now to protect or retrieve the information from significant sites under threat in coastal areas before these sites disappear completely

An Indirect Method for Predicting Road Surface Temperature in Coastal Areas with Snowy Winters

In places that experience snow and ice, road clearing and deicing operations are a necessity to ensure that road networks remain open and safe for travel. Such operations, however, are costly to both taxpayers and the environment making it all the more important that they are used in an efficient manner. Efficient use of road treatment resources takes experience on the part of the road network manager as well as access to reliable road surface temperature (RST) data which are used to determine when roads are conducive to snow and ice accumulation. On major roads and highways, road surface temperature is primarily obtained via road weather information systems (RWIS), thermal mapping, or a combination of the two methods. RWIS data are collected remotely from roadside weather stations which transmit meteorological readings and RST to a central computer running a predictive model such as HS4Cast (Hertl and Schaffar, 1998) or METRo (Crevier and Delage, 2001). RWIS are, however, limited in their usefulness because they only provide forecasts at their specific point locations. In reality, road surface temperatures can vary as much as 10°C at any given time depending on spatial location due to a number of interacting meteorological and geographical parameters (Shao et al., 1996). Thermal mapping was first described in the 1980s as a method to obtain RST in areas between roadside weather stations, thereby incorporating the spatial component of RST prediction (Gustavsson and Bogren, 1988). This method uses an infrared camera attached to a vehicle which travels along a subject route collecting data serving as a thermal “fingerprint” of the road surface that displays spatial variations of RST. When combined with RWIS data for verification, thermal mapping has proven to be an effective and economical method to visualize RST for large road networks (Shao et al., 1996). RWIS and thermal mapping, however, are not universally used and may be impractical for certain areas such as southeastern Massachusetts that are in close proximity to the ocean and have very limited access to in situ road temperature data. This region of New England frequently experiences dramatic horizontal gradients of air temperature within short distances especially along the coast due to the influence of relatively warm ocean winds. This, combined with the unpredictable nature of ocean storms, introduces complexity to models and creates a challenge for road network managers to identify where and when conditions are right for the accumulation of ice and snow on roadways. Roadside weather stations for RWIS exist in this area, but are usually restricted to major state roads and are too few to verify thermal maps. As a result, local jurisdictions are required to decide when to dispatch road crews primarily based on visual interpretations of road conditions, which can be inefficient for large areas. There is much research describing methods to create point specific forecasts of RST on major roads, but little addressing the needs of local road networks without RWIS. Considering this fact, this ongoing project attempts to develop an alternative to thermal mapping and RWIS by indirectly estimating road surface temperature using Geographic Information Systems (GIS) and numerical modeling with metrological and geographical parameters

Slope Assessment Systems: A Review and Evaluation of Current Techniques Used for Cut Slopes in the Mountainous Terrain of West Malaysia

In Malaysia, slope assessment systems (SAS) are widely used in assessing the instability of slopes or the probability of occurrence and likely severity of landslides. These SAS can be derived based on either one particular approach or combination of several approaches of landslide assessments and prediction. This paper overviews five slope assessment systems (SAS) developed in Malaysia for predicting landslide for large-scale assessments. They are the Slope Maintenance System (SMS), Slope Priority Ranking System (SPRS), Slope Information Management System (SIMS), the Slope Management and Risk Tracking System (SMART), and the Landslide Hazard and Risk Assessment (LHRA). An attempt is made to evaluate the accuracy of these SAS in predicting landslides based on slope inventory data from 139 cut slopes in granitic formations, and 47 cut slopes in meta-sediment formations, which are the two most common rock/soil formations found in West Malaysia. Based on this study, it was found that none of the existing SAS is satisfactory for predicting landslides of cut slopes in granitic formations, for various reasons such as the use of a hazard score developed from another country, an insufficient data base, an oversimplified approach, and the use of data base derived from different rock/soil formations. However for the case of cut slopes in meta-sediment, the Slope Management and Risk Tracking System (SMART) was found to be satisfactory with a 90% prediction accuracy. The current database of SMART is largely based on meta-sediment formations from the Kundusang area of Sabah, East Malaysia