Modeling the effects of dispersal on predicted contemporary and future fisher (Martes pennanti) distribution in the U.S

Treesearch

Lucretia Olson; M. Schwartz

2013-01-01

Many species at high trophic levels are predicted to be impacted by shifts in habitat associated with climate change. While temperate coniferous forests are predicted to be one of the least affected ecosystems, the impact of shifting habitat on terrestrial carnivores that live within these ecosystems may depend on the dispersal rates of the species and the patchiness...

Insensitivity of evapotranspiration to seasonal rainfall distribution directs climate change impacts at water yield

NASA Astrophysics Data System (ADS)

Montaldo, N.; Oren, R.

2017-12-01

Over the past century, climate change is affecting precipitation regimes across the world. In the Mediterranean regions there is a persistent trend of precipitation and runoff decreases, generating a desertification process. Given the past winter precipitation shifts, the impacts on evapotranspiration (ET) need to be carefully evaluated, and the compelling question is what will be the impact of future climate change scenarios (predicting changes of precipitation and vapor pressure deficit, VPD) on evapotranspiration and water yield? Looking for the key elements of the climate change that are impacting annual ET, we investigate main climate conditions (e.g. precipitation and VPD) and basin physiographic properties contributing to annual ET. We propose a simplified model for annual ET predictions that accounts for the strong meteo seasonality typical of Mediterranean climates, using the steady state assumption of the basin water balance at mean annual scale. We investigate the Sardinia case study because the position of the island of Sardinia in the center of the western Mediterranean Sea basin and its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. Sardinian runoff decreased drastically over the 1975-2010 period, with mean yearly runoff reduced by more than 40% compared to the previous 1922-1974 period, and most yearly runoff in the Sardinian basins (70% on average) is produced by winter precipitation due to the seasonality typical of the Mediterranean climate regime. The use of our proposed model allows to predict future ET and water yield using future climate scenarios. We use the future climate scenarios predicted by Global climate models (GCM) in the Fifth Assessment report of the Intergovernmental Panel on Climate Change (IPCC), and we select most reliable models testing the past GCM predictions with historical data. Contrasting shifts of precipitation (both positive and negative) are predicted in the future scenarios by GCMs but these changes will produce significant changes (level of significance > 90%) only in runoff and not in ET. Surprisingly, we show that ET is insensitive to intra-annual rainfall distribution changes, and is insensitive to VPD scenario changes.

Impact of Climate Change on Potential Distribution of Chinese Caterpillar Fungus (Ophiocordyceps sinensis) in Nepal Himalaya

PubMed Central

Shrestha, Uttam Babu; Bawa, Kamaljit S.

2014-01-01

Climate change has already impacted ecosystems and species and substantial impacts of climate change in the future are expected. Species distribution modeling is widely used to map the current potential distribution of species as well as to model the impact of future climate change on distribution of species. Mapping current distribution is useful for conservation planning and understanding the change in distribution impacted by climate change is important for mitigation of future biodiversity losses. However, the current distribution of Chinese caterpillar fungus, a flagship species of the Himalaya with very high economic value, is unknown. Nor do we know the potential changes in suitable habitat of Chinese caterpillar fungus caused by future climate change. We used MaxEnt modeling to predict current distribution and changes in the future distributions of Chinese caterpillar fungus in three future climate change trajectories based on representative concentration pathways (RCPs: RCP 2.6, RCP 4.5, and RCP 6.0) in three different time periods (2030, 2050, and 2070) using species occurrence points, bioclimatic variables, and altitude. About 6.02% (8,989 km2) area of the Nepal Himalaya is suitable for Chinese caterpillar fungus habitat. Our model showed that across all future climate change trajectories over three different time periods, the area of predicted suitable habitat of Chinese caterpillar fungus would expand, with 0.11–4.87% expansion over current suitable habitat. Depending upon the representative concentration pathways, we observed both increase and decrease in average elevation of the suitable habitat range of the species. PMID:25180515

Impact of climate change on potential distribution of Chinese caterpillar fungus (Ophiocordyceps sinensis) in Nepal Himalaya.

PubMed

Shrestha, Uttam Babu; Bawa, Kamaljit S

2014-01-01

Climate change has already impacted ecosystems and species and substantial impacts of climate change in the future are expected. Species distribution modeling is widely used to map the current potential distribution of species as well as to model the impact of future climate change on distribution of species. Mapping current distribution is useful for conservation planning and understanding the change in distribution impacted by climate change is important for mitigation of future biodiversity losses. However, the current distribution of Chinese caterpillar fungus, a flagship species of the Himalaya with very high economic value, is unknown. Nor do we know the potential changes in suitable habitat of Chinese caterpillar fungus caused by future climate change. We used MaxEnt modeling to predict current distribution and changes in the future distributions of Chinese caterpillar fungus in three future climate change trajectories based on representative concentration pathways (RCPs: RCP 2.6, RCP 4.5, and RCP 6.0) in three different time periods (2030, 2050, and 2070) using species occurrence points, bioclimatic variables, and altitude. About 6.02% (8,989 km2) area of the Nepal Himalaya is suitable for Chinese caterpillar fungus habitat. Our model showed that across all future climate change trajectories over three different time periods, the area of predicted suitable habitat of Chinese caterpillar fungus would expand, with 0.11-4.87% expansion over current suitable habitat. Depending upon the representative concentration pathways, we observed both increase and decrease in average elevation of the suitable habitat range of the species.

IMF Prediction with Cosmic Rays

NASA Astrophysics Data System (ADS)

Bieber, J. W.; Evenson, P. A.; Kuwabara, T.; Pei, C.

2013-12-01

Cosmic rays impacting Earth have passed through and interacted with the interplanetary magnetic field (IMF) surrounding Earth, and in some sense they carry information on the three-dimensional structure of that field. This work uses neutron monitor data in an effort to extract that information and use it to predict the future behavior of the IMF, especially the north-south component (Bz) which is so crucial in determining geomagnetic activity. We consider 161 events from a published list of interplanetary coronal mass ejections and compare hourly averages of the predicted field with the actual field measured later. We find that the percentage of events with 'good' predictions of Bz (in the sense of having a positive correlation between the prediction and the subsequent measurement) varies from about 85% for predictions 1 hour into the future to about 60% for predictions 4 hours into the future. We present several ideas for how the method might be improved in future implementations. Supported by NASA grant NNX08AQ01G and NSF grant ANT-0739620.

Do We Need Better Climate Predictions to Adapt to a Changing Climate? (Invited)

NASA Astrophysics Data System (ADS)

Dessai, S.; Hulme, M.; Lempert, R.; Pielke, R., Jr.

2009-12-01

Based on a series of international scientific assessments, climate change has been presented to society as a major problem that needs urgently to be tackled. The science that underpins these assessments has been pre-dominantly from the realm of the natural sciences and central to this framing have been ‘projections’ of future climate change (and its impacts on environment and society) under various greenhouse gas emissions scenarios and using a variety of climate model predictions with embedded assumptions. Central to much of the discussion surrounding adaptation to climate change is the claim - explicit or implicit - that decision makers need accurate and increasingly precise assessments of future impacts of climate change in order to adapt successfully. If true, this claim places a high premium on accurate and precise climate predictions at a range of geographical and temporal scales; such predictions therefore become indispensable, and indeed a prerequisite for, effective adaptation decision-making. But is effective adaptation tied to the ability of the scientific enterprise to predict future climate with accuracy and precision? If so, this may impose a serious and intractable limit on adaptation. This paper proceeds in three sections. It first gathers evidence of claims that climate prediction is necessary for adaptation decision-making. This evidence is drawn from peer-reviewed literature and from published science funding strategies and government policy in a number of different countries. The second part discusses the challenges of climate prediction and why science will consistently be unable to provide accurate and precise predictions of future climate relevant for adaptation (usually at the local/regional level). Section three discusses whether these limits to future foresight represent a limit to adaptation, arguing that effective adaptation need not be limited by a general inability to predict future climate. Given the deep uncertainties involved in climate prediction (and even more so in the prediction of climate impacts) and given that climate is usually only one factor in decisions aimed at climate adaptation, we conclude that the ‘predict and provide’ approach to science in support of climate change adaptation is largely flawed. We consider other important areas of public policy fraught with uncertainty - e.g. earthquake risk, national security, public health - where such a ‘predict and provide’ approach is not attempted. Instead of relying on an approach which has climate prediction (and consequent risk assessment) at its heart - which because of the associated epistemological limits to prediction will consequently act as an apparent limit to adaptation - we need to view adaptation differently, in a manner that opens up options for decision making under uncertainty. We suggest an approach which examines the robustness of adaptation strategies/policies/activities to the myriad of uncertainties that face us in the future, only one of which is the state of climate.

Response of Groundwater Recharge to Potential Future Climate Change in the Grand River Watershed

NASA Astrophysics Data System (ADS)

Jyrkama, M. I.; Sykes, J. F.

2004-05-01

The Grand River watershed is situated in south-western Ontario, draining an area of nearly 7000 square kilometres into Lake Erie. Approximately eighty percent of the population in the watershed derive their drinking water from groundwater sources. Quantifying the recharge input to the groundwater system and the impact of climate variability due to climate change is, therefore, essential for ensuring the quantity and sustainability of the watershed's drinking water resources in the future. The primary goal of this study is to investigate the impact of potential future climate changes on groundwater recharge in the Grand River watershed. The physically based hydrologic model HELP3 is used in conjunction with GIS to simulate the past conditions and future changes in evapotranspiration, potential surface runoff, and groundwater recharge rates as a result of projected changes in the regions climate. The climate change projections are based on the general predictions reported by the Intergovernmental Panel on Climate Change (IPCC) in 2001. Forty years of daily historical weather data are used as the reference condition. The impact of climate change on the hydrologic cycle over a forty year study period is modelled by perturbing the HELP3 model input parameters using predicted future changes in precipitation, temperature, and solar radiation. The changes in land use and vegetation cover over time were not considered in the study. The results of the study indicate that the overall simulated rate of groundwater recharge is predicted to increase in the watershed as a result of the projected future climate change. Warmer winter temperatures will reduce the extent and duration of ground frost and shift the springmelt from spring toward winter months, allowing more water to infiltrate into the ground. This results in decreased surface runoff, higher infiltration, and subsequently increased groundwater recharge. The predicted higher intensity and frequency of future precipitation will not only contribute significantly to increased surface runoff, but also results in higher evapotranspiration and groundwater recharge rates due to increased amounts of available water. Changes in the incoming solar radiation have a minimal impact on the simulated hydrologic processes. The overall simulated average annual recharge in the watershed is predicted to increase by approximately 100 mm/year over the next forty years from 189 mm/year to 289 mm/year.

Motivation toward a Graduate Career in the Physical Sciences: Gender Differences and the Impact on Science Career Productivity

ERIC Educational Resources Information Center

Hazari, Zahra; Potvin, Geoff; Tai, Robert H.; Almarode, John T.

2012-01-01

What motivates individuals to embark on graduate careers in physics and chemistry and how could these motivations impact future productivity? This study examines gender differences in such motivations and their ability to predict select future success outcomes (publications and grant funding) for physical scientists. The data were obtained as part…

Impacts of climate change and renewable energy development on habitat of an endemic squirrel, Xerospermophilus mohavensis, in the Mojave Desert, USA

USGS Publications Warehouse

Inman, Richard D.; Esque, Todd C.; Nussear, Kenneth E.; Leitner, Philip; Matocq, Marjorie D.; Weisberg, Peter J.; Dilts, Thomas E.

2016-01-01

Predicting changes in species distributions under a changing climate is becoming widespread with the use of species distribution models (SDMs). The resulting predictions of future potential habitat can be cast in light of planned land use changes, such as urban expansion and energy development to identify areas with potential conflict. However, SDMs rarely incorporate an understanding of dispersal capacity, and therefore assume unlimited dispersal in potential range shifts under uncertain climate futures. We use SDMs to predict future distributions of the Mojave ground squirrel, Xerospermophilus mohavensis Merriam, and incorporate partial dispersal models informed by field data on juvenile dispersal to assess projected impact of climate change and energy development on future distributions of X. mohavensis. Our models predict loss of extant habitat, but also concurrent gains of new habitat under two scenarios of future climate change. Under the B1 emissions scenario- a storyline describing a convergent world with emphasis on curbing greenhouse gas emissions- our models predicted losses of up to 64% of extant habitat by 2080, while under the increased greenhouse gas emissions of the A2 scenario, we suggest losses of 56%. New potential habitat may become available to X. mohavensis, thereby offsetting as much as 6330 km2 (50%) of the current habitat lost. Habitat lost due to planned energy development was marginal compared to habitat lost from changing climates, but disproportionately affected current habitat. Future areas of overlap in potential habitat between the two climate change scenarios are identified and discussed in context of proposed energy development.

Translating weather extremes into the future - a case for Norway

NASA Astrophysics Data System (ADS)

Sillmann, Jana; Mueller, Malte; Gjertsen, Uta; Haarsma, Rein; Hazeleger, Wilco; Amundsen, Helene

2017-04-01

We introduce a new project "Translating weather extremes into the future - a case for Norway" (TWEX - http://www.cicero.uio.no/en/twex). In TWEX, we take a novel "Tales of future weather" approach in which we use future scenarios tailored to a specific region and stakeholder in order to gain a more realistic picture of what future weather extremes might look like in a particular context. We focus on hydroclimatic extremes associated with a particular circulation pattern (so-called "Atmospheric River") leading to heavy rainfall in fall and winter along the West Coast of Norway and causing high-impact floods in Norwegian communities. We translate selected past events into the future (e.g., 2090) by using an approach very similar to what is used today for weather prediction. The data generated in TWEX will be distributed by standard (weather prediction) communication channels of the Norwegian Meteorological Institute and thus, will be accessible by end-user in a well-known data format for analyzing the impact of the events in the future and support decision-making on hazard prevention and adaptation planning.

PREDICTING THE RELATIVE IMPACTS OF URBAN DEVELOPMENT POLICIES AND ON-ROAD VEHICLE TECHNOLOGIES ON AIR QUALITY IN THE UNITED STATES: MODELING AND ANALYSIS OF A CASE STUDY IN AUSTIN, TEXAS

EPA Science Inventory

Urban development results in changes to land use and land cover and, consequently, to biogenic and anthropogenic emissions, meteorological processes, and processes such as dry deposition that influence future predictions of air quality. This study examines the impacts of alter...

Prediction of future subsurface temperatures in Korea

NASA Astrophysics Data System (ADS)

Lee, Y.; Kim, S. K.; Jeong, J.; SHIN, E.

2017-12-01

The importance of climate change has been increasingly recognized because it has had the huge amount of impact on social, economic, and environmental aspect. For the reason, paleoclimate change has been studied intensively using different geological tools including borehole temperatures and future surface air temperatures (SATs) have been predicted for the local areas and the globe. Future subsurface temperatures can have also enormous impact on various areas and be predicted by an analytical method or a numerical simulation using measured and predicted SATs, and thermal diffusivity data of rocks. SATs have been measured at 73 meteorological observatories since 1907 in Korea and predicted at same locations up to the year of 2100. Measured SATs at the Seoul meteorological observatory increased by about 3.0 K from the year of 1907 to the present. Predicted SATs have 4 different scenarios depending on mainly CO2 concentration and national action plan on climate change in the future. The hottest scenario shows that SATs in Korea will increase by about 5.0 K from the present to the year of 2100. In addition, thermal diffusivity values have been measured on 2,903 rock samples collected from entire Korea. Data pretreatment based on autocorrelation analysis was conducted to control high frequency noise in thermal diffusivity data. Finally, future subsurface temperatures in Korea were predicted up to the year of 2100 by a FEM simulation code (COMSOL Multiphysics) using measured and predicted SATs, and thermal diffusivity data in Korea. At Seoul, the results of predictions show that subsurface temperatures will increase by about 5.4 K, 3.0 K, 1.5 K, and 0.2 K from the present to 2050 and then by about 7.9 K, 4.8 K, 2.5 K, and 0.5 K to 2100 at the depths of 10 m, 50 m, 100 m, and 200 m, respectively. We are now proceeding numerical simulations for subsurface temperature predictions for 73 locations in Korea.

Uncertainty of a hydrological climate change impact assessment - Is it really all about climate uncertainty?

NASA Astrophysics Data System (ADS)

Honti, Mark; Reichert, Peter; Scheidegger, Andreas; Stamm, Christian

2013-04-01

Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with another boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the close future. The ``standard'' workflow considers future climate under a specific IPCC emission scenario simulated by global circulation models (GCMs), possibly downscaled by a regional climate model (RCM) and/or a stochastic weather generator. The output from the climate models is typically corrected for bias before feeding it into a calibrated hydrological model, which is run on the past and future meteorological data to analyse the impacts of climate change on the hydrological indicators of interest. The impact predictions are as uncertain as any forecast that tries to describe the behaviour of an extremely complex system decades into the future. Future climate predictions are uncertain due to the scenario uncertainty and the GCM model uncertainty that is obvious on finer resolution than continental scale. Like in any hierarchical model system, uncertainty propagates through the descendant components. Downscaling increases uncertainty with the deficiencies of RCMs and/or weather generators. Bias correction adds a strong deterministic shift to the input data. Finally the predictive uncertainty of the hydrological model ends the cascade that leads to the total uncertainty of the hydrological impact assessment. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. There are only few studies, which found that the predictive uncertainty of hydrological models can be in the same range or even larger than climatic uncertainty. We carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on 2 small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment method with 2 different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was a likelihood function for the flow quantiles directly. Due to the better data coverage and smaller hydrological complexity in one of our test catchments we had better performance from the hydrological model and thus could observe that the relative importance of different uncertainty sources varied between sites, boundary conditions and flow indicators. The uncertainty of future climate was important, but not dominant. The deficiencies of the hydrological model were on the same scale, especially for the sites and flow components where model performance for the past observations was further from optimal (Nash-Sutcliffe index = 0.5 - 0.7). The overall uncertainty of predictions was well beyond the expected change signal even for the best performing site and flow indicator.

Does the OVX matter for volatility forecasting? Evidence from the crude oil market

NASA Astrophysics Data System (ADS)

Lv, Wendai

2018-02-01

In this paper, I investigate that whether the OVX and its truncated parts with a certain threshold can significantly help in forecasting the oil futures price volatility basing on the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). In-sample estimation results show that the OVX has a significantly positive impact on futures volatility. The impact of large OVX on future volatility has slightly powerful compared to the small ones. Moreover, the HARQ-RV model outperforms the HAR-RV in predicting the oil futures volatility. More importantly, the decomposed OVX have more powerful in forecasting the oil futures price volatility compared to the OVX itself.

Climatically-mediated landcover change: impacts on Brazilian territory.

PubMed

Zanin, Marina; Tessarolo, Geiziane; Machado, Nathália; Albernaz, Ana Luisa M

2017-01-01

In the face of climate change threats, governments are drawing attention to policies for mitigating its effects on biodiversity. However, the lack of distribution data makes predictions at species level a difficult task, mainly in regions of higher biodiversity. To overcome this problem, we use native landcover as a surrogate biodiversity, because it can represent specialized habitat for species, and investigate the effects of future climate change on Brazilian biomes. We characterize the climatic niches of native landcover and use ecological niche modeling to predict the potential distribution under current and future climate scenarios. Our results highlight expansion of the distribution of open vegetation and the contraction of closed forests. Drier Brazilian biomes, like Caatinga and Cerrado, are predicted to expand their distributions, being the most resistant to climate change impacts. However, these would also be affected by losses of their closed forest enclaves and their habitat-specific or endemic species. Replacement by open vegetation and overall reductions are a considerable risk for closed forest, threatening Amazon and Atlantic forest biomes. Here, we evidence the impacts of climate change on Brazilian biomes, and draw attention to the necessity for management and attenuation plans to guarantee the future of Brazilian biodiversity.

The impact of climatological model biases in the North Atlantic jet on predicted future circulation change

NASA Astrophysics Data System (ADS)

Simpson, I.

2015-12-01

A long standing bias among global climate models (GCMs) is their incorrect representation of the wintertime circulation of the North Atlantic region. Specifically models tend to exhibit a North Atlantic jet (and associated storm track) that is too zonal, extending across central Europe, when it should tilt northward toward Scandinavia. GCM's consistently predict substantial changes in the large scale circulation in this region, consisting of a localized anti-cyclonic circulation, centered over the Mediterranean and accompanied by increased aridity there and increased storminess over Northern Europe.Here, we present preliminary results from experiments that are designed to address the question of what the impact of the climatological circulation biases might be on this predicted future response. Climate change experiments will be compared in two versions of the Community Earth System Model: the first is a free running version of the model, as typically used in climate prediction; the second is a bias corrected version of the model in which a seasonally varying cycle of bias correction tendencies are applied to the wind and temperature fields. These bias correction tendencies are designed to account for deficiencies in the fast parameterized processes, with an aim to push the model toward a more realistic climatology.While these experiments come with the caveat that they assume the bias correction tendencies will remain constant with time, they allow for an initial assessment, through controlled experiments, of the impact that biases in the climatological circulation can have on future predictions in this region. They will also motivate future work that can make use of the bias correction tendencies to understand the underlying physical processes responsible for the incorrect tilt of the jet.

Using scenarios to assess possible future impacts of invasive species in the Laurentian Great Lakes

USGS Publications Warehouse

Lauber, T. Bruce; Stedman, Richard C.; Connelly, Nancy A; Rudstam, Lars G.; Ready, Richard C; Poe, Gregory L; Bunnell, David B.; Hook, Tomas O.; Koops, Marten A.; Ludsin, Stuart A.; Rutherford, Edward S; Wittmann, Marion E.

2016-01-01

The expected impacts of invasive species are key considerations in selecting policy responses to potential invasions. But predicting the impacts of invasive species is daunting, particularly in large systems threatened by multiple invasive species, such as North America’s Laurentian Great Lakes. We developed and evaluated a scenario-building process that relied on an expert panel to assess possible future impacts of aquatic invasive species on recreational fishing in the Great Lakes. To maximize its usefulness to policy makers, this process was designed to be implemented relatively rapidly and consider a range of species. The expert panel developed plausible, internally-consistent invasion scenarios for 5 aquatic invasive species, along with subjective probabilities of those scenarios. We describe these scenarios and evaluate this approach for assessing future invasive species impacts. The panel held diverse opinions about the likelihood of the scenarios, and only one scenario with impacts on sportfish species was considered likely by most of the experts. These outcomes are consistent with the literature on scenario building, which advocates for developing a range of plausible scenarios in decision making because the uncertainty of future conditions makes the likelihood of any particular scenario low. We believe that this scenario-building approach could contribute to policy decisions about whether and how to address the possible impacts of invasive species. In this case, scenarios could allow policy makers to narrow the range of possible impacts on Great Lakes fisheries they consider and help set a research agenda for further refining invasive species predictions.

Research notes : best practices for traffic impact studies.

DOT National Transportation Integrated Search

2006-11-01

Traffic Impact Studies (TISs) are used by the Oregon Department of Transportation (ODOT) and staff of other transportation agencies to forecast future system effects from proposed development projects and to predict the useful life of a transportatio...

Predicting climate change impacts on polar bear litter size.

PubMed

Molnár, Péter K; Derocher, Andrew E; Klanjscek, Tin; Lewis, Mark A

2011-02-08

Predicting the ecological impacts of climate warming is critical for species conservation. Incorporating future warming into population models, however, is challenging because reproduction and survival cannot be measured for yet unobserved environmental conditions. In this study, we use mechanistic energy budget models and data obtainable under current conditions to predict polar bear litter size under future conditions. In western Hudson Bay, we predict climate warming-induced litter size declines that jeopardize population viability: ∼28% of pregnant females failed to reproduce for energetic reasons during the early 1990s, but 40-73% could fail if spring sea ice break-up occurs 1 month earlier than during the 1990s, and 55-100% if break-up occurs 2 months earlier. Simultaneously, mean litter size would decrease by 22-67% and 44-100%, respectively. The expected timeline for these declines varies with climate-model-specific sea ice predictions. Similar litter size declines may occur in over one-third of the global polar bear population.

Predicting climate change impacts on polar bear litter size

PubMed Central

Molnár, Péter K.; Derocher, Andrew E.; Klanjscek, Tin; Lewis, Mark A.

2011-01-01

Predicting the ecological impacts of climate warming is critical for species conservation. Incorporating future warming into population models, however, is challenging because reproduction and survival cannot be measured for yet unobserved environmental conditions. In this study, we use mechanistic energy budget models and data obtainable under current conditions to predict polar bear litter size under future conditions. In western Hudson Bay, we predict climate warming-induced litter size declines that jeopardize population viability: ∼28% of pregnant females failed to reproduce for energetic reasons during the early 1990s, but 40–73% could fail if spring sea ice break-up occurs 1 month earlier than during the 1990s, and 55–100% if break-up occurs 2 months earlier. Simultaneously, mean litter size would decrease by 22–67% and 44–100%, respectively. The expected timeline for these declines varies with climate-model-specific sea ice predictions. Similar litter size declines may occur in over one-third of the global polar bear population. PMID:21304515

Motivated prediction of future feelings: effects of negative mood and mood orientation on affective forecasts.

PubMed

Buehler, Roger; McFarland, Cathy; Spyropoulos, Vassili; Lam, Kent C H

2007-09-01

This article examines the role of motivational factors in affective forecasting. The primary hypothesis was that people predict positive emotional reactions to future events when they are motivated to enhance their current feelings. Three experiments manipulated participants' moods (negative vs. neutral) and orientation toward their moods (reflective vs. ruminative) and then assessed the positivity of their affective predictions for future events. As hypothesized, when participants adopted a reflective orientation, and thus should have been motivated to engage in mood-regulation processes, they predicted more positive feelings in the negative than in the neutral mood condition. This pattern of mood-incongruent affective prediction was not exhibited when participants adopted a ruminative orientation. Additionally, within the negative mood condition, generating affective forecasts had a more positive emotional impact on reflectors than on ruminators. The findings suggest that affective predictions are sometimes driven by mood-regulatory motives.

Research prioritization through prediction of future impact on biomedical science: a position paper on inference-analytics.

PubMed

Ganapathiraju, Madhavi K; Orii, Naoki

2013-08-30

Advances in biotechnology have created "big-data" situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist's perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact. We propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI. This position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.

Predicting future glacial lakes in Austria using different modelling approaches

NASA Astrophysics Data System (ADS)

Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

2017-04-01

Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers intermediate results from the FUTURELAKES project, which aims at generating the first nation-wide data set on future glacial lakes in Austria.

The Future Institutional Research Office: Brave New Workplace or Electronic Sweatshop? AIR 1989 Annual Forum Paper.

ERIC Educational Resources Information Center

Liebmann, Jeffrey D.

Information technology is changing the workplace. Forecasts range from wondrous visions of future capabilities to dark scenarios of employment loss and dehumanization. Some predict revolutionary impacts, while others conclude that the way we do business will change only gradually if much at all. The less positive visions of the future workplace…

23 CFR 772.9 - Traffic noise prediction.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Average pavement type shall be used in the FHWA TNM for future noise level prediction unless a highway agency substantiates the use of a different pavement type for approval by the FHWA. (c) Noise contour... impact for the design year shall be used. ...

23 CFR 772.9 - Traffic noise prediction.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Average pavement type shall be used in the FHWA TNM for future noise level prediction unless a highway agency substantiates the use of a different pavement type for approval by the FHWA. (c) Noise contour... impact for the design year shall be used. ...

23 CFR 772.9 - Traffic noise prediction.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Average pavement type shall be used in the FHWA TNM for future noise level prediction unless a highway agency substantiates the use of a different pavement type for approval by the FHWA. (c) Noise contour... impact for the design year shall be used. ...

Characterizing bias correction uncertainty in wheat yield predictions

NASA Astrophysics Data System (ADS)

Ortiz, Andrea Monica; Jones, Julie; Freckleton, Robert; Scaife, Adam

2017-04-01

Farming systems are under increased pressure due to current and future climate change, variability and extremes. Research on the impacts of climate change on crop production typically rely on the output of complex Global and Regional Climate Models, which are used as input to crop impact models. Yield predictions from these top-down approaches can have high uncertainty for several reasons, including diverse model construction and parameterization, future emissions scenarios, and inherent or response uncertainty. These uncertainties propagate down each step of the 'cascade of uncertainty' that flows from climate input to impact predictions, leading to yield predictions that may be too complex for their intended use in practical adaptation options. In addition to uncertainty from impact models, uncertainty can also stem from the intermediate steps that are used in impact studies to adjust climate model simulations to become more realistic when compared to observations, or to correct the spatial or temporal resolution of climate simulations, which are often not directly applicable as input into impact models. These important steps of bias correction or calibration also add uncertainty to final yield predictions, given the various approaches that exist to correct climate model simulations. In order to address how much uncertainty the choice of bias correction method can add to yield predictions, we use several evaluation runs from Regional Climate Models from the Coordinated Regional Downscaling Experiment over Europe (EURO-CORDEX) at different resolutions together with different bias correction methods (linear and variance scaling, power transformation, quantile-quantile mapping) as input to a statistical crop model for wheat, a staple European food crop. The objective of our work is to compare the resulting simulation-driven hindcasted wheat yields to climate observation-driven wheat yield hindcasts from the UK and Germany in order to determine ranges of yield uncertainty that result from different climate model simulation input and bias correction methods. We simulate wheat yields using a General Linear Model that includes the effects of seasonal maximum temperatures and precipitation, since wheat is sensitive to heat stress during important developmental stages. We use the same statistical model to predict future wheat yields using the recently available bias-corrected simulations of EURO-CORDEX-Adjust. While statistical models are often criticized for their lack of complexity, an advantage is that we are here able to consider only the effect of the choice of climate model, resolution or bias correction method on yield. Initial results using both past and future bias-corrected climate simulations with a process-based model will also be presented. Through these methods, we make recommendations in preparing climate model output for crop models.

Climate change impact on soil erosion in the Mandakini River Basin, North India

NASA Astrophysics Data System (ADS)

Khare, Deepak; Mondal, Arun; Kundu, Sananda; Mishra, Prabhash Kumar

2017-09-01

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

The Impact of Current and Future Polar Orbiting Satellite Data on Numerical Weather Prediction at NASA/GSFC

NASA Technical Reports Server (NTRS)

Atlas, Robert

2004-01-01

The lack of adequate observational data continues to be recognized as a major factor limiting both atmospheric research and numerical prediction on a variety of temporal and spatial scales. Since the advent of meteorological satellites in the 1960's, a considerable research effort has been directed toward the design of space-borne meteorological sensors, the development of optimal methods for the utilization of these data, (and an assessment of the influence of existing satellite data and the potential influence of future satellite observations on numerical weather prediction. This has included both Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs). OSEs are conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. While OSEs are performed with existing data, OSSEs are conducted to evaluate the potential for future observing systems to improve-NWP, as well as to evaluate trade-offs in observing system design, and to develop and test improved methods for data assimilation. At the conference, results from OSEs to evaluate satellite data sets that have recently become available to the global observing system, such as AIRS and Seawinds, and results from OSSEs to determine the potential impact of space-based lidar winds will be presented.

Shale gas, wind and water: assessing the potential cumulative impacts of energy development on ecosystem services within the Marcellus play.

PubMed

Evans, Jeffrey S; Kiesecker, Joseph M

2014-01-01

Global demand for energy has increased by more than 50 percent in the last half-century, and a similar increase is projected by 2030. This demand will increasingly be met with alternative and unconventional energy sources. Development of these resources causes disturbances that strongly impact terrestrial and freshwater ecosystems. The Marcellus Shale gas play covers more than 160,934 km(2) in an area that provides drinking water for over 22 million people in several of the largest metropolitan areas in the United States (e.g. New York City, Washington DC, Philadelphia & Pittsburgh). Here we created probability surfaces representing development potential of wind and shale gas for portions of six states in the Central Appalachians. We used these predictions and published projections to model future energy build-out scenarios to quantify future potential impacts on surface drinking water. Our analysis predicts up to 106,004 new wells and 10,798 new wind turbines resulting up to 535,023 ha of impervious surface (3% of the study area) and upwards of 447,134 ha of impacted forest (2% of the study area). In light of this new energy future, mitigating the impacts of energy development will be one of the major challenges in the coming decades.

Shale Gas, Wind and Water: Assessing the Potential Cumulative Impacts of Energy Development on Ecosystem Services within the Marcellus Play

PubMed Central

Evans, Jeffrey S.; Kiesecker, Joseph M.

2014-01-01

Global demand for energy has increased by more than 50 percent in the last half-century, and a similar increase is projected by 2030. This demand will increasingly be met with alternative and unconventional energy sources. Development of these resources causes disturbances that strongly impact terrestrial and freshwater ecosystems. The Marcellus Shale gas play covers more than 160,934 km2 in an area that provides drinking water for over 22 million people in several of the largest metropolitan areas in the United States (e.g. New York City, Washington DC, Philadelphia & Pittsburgh). Here we created probability surfaces representing development potential of wind and shale gas for portions of six states in the Central Appalachians. We used these predictions and published projections to model future energy build-out scenarios to quantify future potential impacts on surface drinking water. Our analysis predicts up to 106,004 new wells and 10,798 new wind turbines resulting up to 535,023 ha of impervious surface (3% of the study area) and upwards of 447,134 ha of impacted forest (2% of the study area). In light of this new energy future, mitigating the impacts of energy development will be one of the major challenges in the coming decades. PMID:24586599

Neogeomorphology, prediction, and the anthropic landscape

NASA Astrophysics Data System (ADS)

Haff, P. K.

The surface of the earth is undergoing profound change due to human impact. By some measures the level of human impact is comparable to the effects of major classical geomorphic processes such as fluvial sediment transport. This change is occurring rapidly, has no geologic precedent, and may represent an irreversible transition to a new and novel landscape with which we have no experience. For these reasons prediction of future landscape evolution will be of increasing importance. The combination of physical and social forces that drive modern landscape change represents the Anthropic Force. Neogeomorphology is the study of the Anthropic Force and its present and likely future effects on the landscape. Unique properties associated with the Anthropic Force include consciousness, intention and design. These properties support the occurrence of nonclassical geomorphic phenomena, such as landscape planning, engineering, and management. The occurrence of short time-scale phenomena induced by anthropic landscape change, the direct effects of this change on society, and the ability to anticipate and intentionally influence the future trajectory of the global landscape underscore the importance of prediction in a neogeomorphic world.

Modification of Roberts' Theory for Rocket Exhaust Plumes Eroding Lunar Soil

NASA Technical Reports Server (NTRS)

Metzger, Philip T.; Lane, John E.; Immer, Christopher D.

2008-01-01

Roberts' model of lunar soil erosion beneath a landing rocket has been updated in several ways to predict the effects of future lunar landings. The model predicts, among other things, the number of divots that would result on surrounding hardware due to the impact of high velocity particulates, the amount and depth of surface material removed, the volume of ejected soil, its velocity, and the distance the particles travel on the Moon. The results are compared against measured results from the Apollo program and predictions are made for mitigating the spray around a future lunar outpost.

Evaluating Urban Resilience to Climate Change: A Multi-Sector Approach (External Review Draft)

EPA Science Inventory

Climate change impacts are diverse, long-term, and not easily predictable. Adapting to climate change requires making context specific and forward-looking decisions regarding a variety of climate change impacts and vulnerabilities when the future is highly uncertain. EPA scientis...

Is the future already here? The impact of climate change on the distribution of the eastern coral snake (Micrurus fulvius).

PubMed

Archis, Jennifer N; Akcali, Christopher; Stuart, Bryan L; Kikuchi, David; Chunco, Amanda J

2018-01-01

Anthropogenic climate change is a significant global driver of species distribution change. Although many species have undergone range expansion at their poleward limits, data on several taxonomic groups are still lacking. A common method for studying range shifts is using species distribution models to evaluate current, and predict future, distributions. Notably, many sources of 'current' climate data used in species distribution modeling use the years 1950-2000 to calculate climatic averages. However, this does not account for recent (post 2000) climate change. This study examines the influence of climate change on the eastern coral snake ( Micrurus fulvius ). Specifically, we: (1) identified the current range and suitable environment of M. fulvius in the Southeastern United States, (2) investigated the potential impacts of climate change on the distribution of M. fulvius , and (3) evaluated the utility of future models in predicting recent (2001-2015) records. We used the species distribution modeling program Maxent and compared both current (1950-2000) and future (2050) climate conditions. Future climate models showed a shift in the distribution of suitable habitat across a significant portion of the range; however, results also suggest that much of the Southeastern United States will be outside the range of current conditions, suggesting that there may be no-analog environments in the future. Most strikingly, future models were more effective than the current models at predicting recent records, suggesting that range shifts may already be occurring. These results have implications for both M. fulvius and its Batesian mimics. More broadly, we recommend future Maxent studies consider using future climate data along with current data to better estimate the current distribution.

SSAIS: A Program to Assess Adverse Impact in Multistage Selection Decisions

ERIC Educational Resources Information Center

De Corte, Wilfried

2004-01-01

The article describes a Windows program to estimate the expected value and sampling distribution function of the adverse impact ratio for general multistage selections. The results of the program can also be used to predict the risk that a future selection decision will result in an outcome that reflects the presence of adverse impact. The method…

The Impacts of Climate Variations on Military Operations in the Horn of Africa

DTIC Science & Technology

2006-03-01

variability in a region. Climate forecasts are predictions of the future state of the climate , much as we think of weather forecasts but at longer...arrive at accurate characterizations of the future state of the climate . Many of the civilian organizations that generate reanalysis data also

The Big and Future Picture(s).

ERIC Educational Resources Information Center

Lo Bianco, Joseph

1996-01-01

Examines how "big picture" scenarios of world futures in the new millennium that frame policymakers' views have an impact on language, culture, and education. Some of these scenarios predict homogenization, whereas others foretell conflict. In this changing scenario, the social and political effects of language planning and English as a…

A virtual test system representing the distribution of pedestrian impact configurations for future vehicle front-end optimization.

PubMed

Li, Guibing; Yang, Jikuang; Simms, Ciaran

2016-07-03

The purpose of this study is to define a computationally efficient virtual test system (VTS) to assess the aggressivity of vehicle front-end designs to pedestrians considering the distribution of pedestrian impact configurations for future vehicle front-end optimization. The VTS should represent real-world impact configurations in terms of the distribution of vehicle impact speeds, pedestrian walking speeds, pedestrian gait, and pedestrian height. The distribution of injuries as a function of body region, vehicle impact speed, and pedestrian size produced using this VTS should match the distribution of injuries observed in the accident data. The VTS should have the predictive ability to distinguish the aggressivity of different vehicle front-end designs to pedestrians. The proposed VTS includes 2 parts: a simulation test sample (STS) and an injury weighting system (IWS). The STS was defined based on MADYMO multibody vehicle to pedestrian impact simulations accounting for the range of vehicle impact speeds, pedestrian heights, pedestrian gait, and walking speed to represent real world impact configurations using the Pedestrian Crash Data Study (PCDS) and anthropometric data. In total 1,300 impact configurations were accounted for in the STS. Three vehicle shapes were then tested using the STS. The IWS was developed to weight the predicted injuries in the STS using the estimated proportion of each impact configuration in the PCDS accident data. A weighted injury number (WIN) was defined as the resulting output of the VTS. The WIN is the weighted number of average Abbreviated Injury Scale (AIS) 2+ injuries recorded per impact simulation in the STS. Then the predictive capability of the VTS was evaluated by comparing the distributions of AIS 2+ injuries to different pedestrian body regions and heights, as well as vehicle types and impact speeds, with that from the PCDS database. Further, a parametric analysis was performed with the VTS to assess the sensitivity of the injury predictions to changes in vehicle shape (type) and stiffness to establish the potential for using the VTS for future vehicle front-end optimization. An STS of 1,300 multibody simulations and an IWS based on the distribution of impact speed, pedestrian height, gait stance, and walking speed is broadly capable of predicting the distribution of pedestrian injuries observed in the PCDS database when the same vehicle type distribution as the accident data is employed. The sensitivity study shows significant variations in the WIN when either vehicle type or stiffness is altered. Injury predictions derived from the VTS give a good representation of the distribution of injuries observed in the PCDS and distinguishing ability on the aggressivity of vehicle front-end designs to pedestrians. The VTS can be considered as an effective approach for assessing pedestrian safety performance of vehicle front-end designs at the generalized level. However, the absolute injury number is substantially underpredicted by the VTS, and this needs further development.

The impact of future climate on historic interiors.

PubMed

Lankester, Paul; Brimblecombe, Peter

2012-02-15

The socio-economic significance of climate change is widely recognised. However, its potential to affect our cultural heritage has not been discussed in detail (i.e. not explicit in IPCC 4) even though the cultural impacts of future outdoor climate have been the focus of some European Commission projects (e.g. NOAH'S ARK) and World Heritage Centre reports. Recently there have been a few projects that have examined the changing environmental threats to tangible heritage indoors (e.g. Preparing Historic Collections for Climate Change and Climate for Culture). Here we predict future indoor temperature and humidity, and damage arising from changes to climate in historic rooms in Southern England with little climate control, using simple building simulations coupled with high resolution (~5 km) climate predictions. The calculations suggest an increase in indoor temperature over the next century that is slightly less than that outdoors. Annual relative humidity shows little change, but the seasonal cycles suggest drier summers and slightly damper winters indoors. Damage from mould growth and pests is likely to increase in the future, while humidity driven dimensional change to materials (e.g. wood) should decrease somewhat. The results allow collection managers to prepare for the impact of long-term climate change, putting strategic measures in place to prevent increased damage, and thus preserve our heritage for future generations. Copyright © 2011 Elsevier B.V. All rights reserved.

A changing climate: impacts on human exposures to O3 using an integrated modeling methodology

EPA Science Inventory

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposu...

Transboundary impacts on regional ground water modeling in Texas

USGS Publications Warehouse

Rainwater, K.; Stovall, J.; Frailey, S.; Urban, L.

2005-01-01

Recent legislation required regional grassroots water resources planning across the entire state of Texas. The Texas Water Development Board (TWDB), the state's primary water resource planning agency, divided the state into 16 planning regions. Each planning group developed plans to manage both ground water and surface water sources and to meet future demands of various combinations of domestic, agricultural, municipal, and industrial water consumers. This presentation describes the challenges in developing a ground water model for the Llano Estacado Regional Water Planning Group (LERWPG), whose region includes 21 counties in the Southern High Plains of Texas. While surface water is supplied to several cities in this region, the vast majority of the regional water use comes from the High Plains aquifer system, often locally referred to as the Ogallala Aquifer. Over 95% of the ground water demand is for irrigated agriculture. The LERWPG had to predict the impact of future TWDB-projected water demands, as provided by the TWDB, on the aquifer for the period 2000 to 2050. If detrimental impacts were noted, alternative management strategies must be proposed. While much effort was spent on evaluating the current status of the ground water reserves, an appropriate numerical model of the aquifer system was necessary to demonstrate future impacts of the predicted withdrawals as well as the effects of the alternative strategies. The modeling effort was completed in the summer of 2000. This presentation concentrates on the political, scientific, and nontechnical issues in this planning process that complicated the modeling effort. Uncertainties in data, most significantly in distribution and intensity of recharge and withdrawals, significantly impacted the calibration and predictive modeling efforts. Four predictive scenarios, including baseline projections, recurrence of the drought of record, precipitation enhancement, and reduced irrigation demand, were simulated to identify counties at risk of low final ground water storage volume or low levels of satisfied demand by 2050. Copyright ?? 2005 National Ground Water Association.

The future of planetary defense

NASA Astrophysics Data System (ADS)

Mainzer, A.

2017-04-01

Asteroids and comets have impacted Earth in the past and will do so in the future. While the frequency of impacts is reasonably well understood on geologic timescales, it is difficult to predict the next sizeable impact on human timescales by extrapolation from population statistics alone. Fortunately, by identifying and tracking individual objects, we can make precise predictions of any potential close encounters with Earth. As more advance notice is provided, the range of possible mitigation options expands. While the chance of an impact is very small, the potential consequences can be severe, meaning that sensible risk reduction measures should be undertaken. By implementing surveys, the risk of an unforeseen impact can be greatly reduced: the first step is finding the objects. Fortunately, the worldwide community of professional and amateur astronomers has made significant progress in discovering large near-Earth objects (NEOs). More than 95% of NEOs capable of causing global devastation (objects larger than 1 km in diameter) have been discovered, and none of these pose an impact hazard in the near future. Infrastructure is in place to link observations and compute close approaches in real time. Interagency and international collaborations have been undertaken to strengthen cooperative efforts to plan potential mitigation and civil defense campaigns. Yet much remains to be done. Approximately 70% of NEOs larger than 140 m (large enough to cause severe regional damage) remain undiscovered. With the existing surveys, it will take decades to identify the rest. Progress can be accelerated by undertaking new surveys with improved sensitivity.