Chapter 2 : Our Changing Climate. Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II

Hayhoe, Katharine; Wuebbles, Donald J.; Easterling, David R.; Fahey, D. W.; Doherty, Sarah J.; Kossin, James P.; Sweet, William; Vose, Russell S.; Wehner, Michael

doi:10.7930/nca4.2018.ch2

Cited by 122 publications

(140 citation statements)

References 314 publications

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“…Spatial characteristics of temporal trends were estimated and statistical significance (α = 0.05) assessed over the entire time series (1900-2016) and during the first and second (1959-2016) halves of each time series at each observation location. Analyses for each half of the time series were performed because the data series was sufficiently long and the first half corresponded with reforestation whereas the second half corresponded with forest maturation and globally averaged warming exceeding 0.65 • C [4][5][6]23]. Except for New Cumberland (Table 1, Figure 1), each observation location had a more complete time series of each variable during the second half (93.8%) rather than the first half (71.7%) of the POR suggesting early data gaps (1900-1930) could have influenced results.…”

Section: Methodsmentioning

confidence: 99%

Climatic Trends of West Virginia: A Representative Appalachian Microcosm

Kutta

Hubbart

2019

Water

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During the late 19th and very early 20th centuries widespread deforestation occurred across the Appalachian region, USA. However, since the early 20th century, land cover rapidly changed from predominantly agricultural land use (72%; 1909) to forest. West Virginia (WV) is now the USA’s third most forested state by area (79%; 1989–present). It is well understood that land cover alterations feedback on climate with important implications for ecology, water resources, and watershed management. However, the spatiotemporal distribution of climatic changes during reforestation in WV remains unclear. To fill this knowledge gap, daily maximum temperature, minimum temperature, and precipitation data were acquired for eighteen observation sites with long periods of record (POR; ≥77 years). Results indicate an increasingly wet and temperate WV climate characterized by warming summertime minimum temperatures, cooling maximum temperatures year-round, and increased annual precipitation that accelerated during the second half (1959–2016) of the POR. Trends are elevation dependent and may be accelerating due to local to regional ecohydrological feedbacks including increasing forest age and density, changing forest species composition, and increasing globally averaged atmospheric moisture. Furthermore, results imply that excessive wetness may become the primary ecosystem stressor associated with climate change in the USA’s rugged and flood prone Appalachian region. The Appalachian region’s physiographic complexity and history of widespread land use changes makes climatic changes particularly dynamic. Therefore, mechanistic understanding of micro- to mesoscale climate changes is imperative to better inform decision makers and ensure preservation of the region’s rich natural resources.

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Section: Methodsmentioning

confidence: 99%

Climatic Trends of West Virginia: A Representative Appalachian Microcosm

Kutta

Hubbart

2019

Water

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“…States by 2050, leading to decreases in snowpack, increases in drought duration, and decreases in runoff (Seager & Vecchi, 2010;Hayhoe et al, 2018;Gonzalez et al, 2018). Consequently, climate change will likely stress regional water supplies that are already very sensitive to changes in runoff (McCabe & Wolock, 2007;Christensen & Lettenmaier, 2006;Woodhouse et al, 2016, Udall & Overpeck 2017.…”

Section: Model Performance Comparisonsmentioning

confidence: 99%

Improved Accuracy of Watershed-Scale General Circulation Model Runoff Using Deep Neural Networks

Rice¹,

Saia²,

Emanuel³

2019

Preprint

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Projecting impacts of climate change on water resources is a vital research task, and general circulation models (GCMs) are important tools for this work. However, the spatial resolution of downscaled GCMs makes them difficult to apply to non-grid conforming scales relevant to water resources management: individual watersheds. Machine learning techniques like deep neural networks (DNNs) may address this issue. Here we use a DNN to predict monthly watershed-scale runoff (i.e., stream discharge divided by watershed area) from monthly gridded and downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM hydroclimatic fluxes (i.e., precipitation, evapotranspiration, and temperature). We used hydroclimatic fluxes, biotic, and abiotic characteristics from 2,731 watersheds in the conterminous United States to train and test a DNN that can predict watershed-scale runoff. The DNN described 93% (Pearson’s correlation coefficient = 0.962) of the variability in observed runoff and was temporally and spatially robust. The median absolute error (MAE) of DNN predictions was approximately 25 percentage points lower than that of gridded, downscaled GCM runoff or monthly normal runoff (i.e., 30-year average of runoff observations at the watershed-outlet). DNN monthly runoff predictions had the lowest MAE of all the grid-to-watershed-scale conversion approaches we tested, including: linear ridge regression, support vector machines, extreme gradient boosting, and artificial neural networks. We demonstrated why using DNNs to convert gridded GCM hydroclimatic fluxes to watershed-scales is relevant to water resources research and management. We also provided a methods guide for hydrologists interested in implementing machine learning techniques.

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“…The frequency and severity of climate extremes such as drought, floods, and heat waves are projected to increase with global climate change (de Coninck et al, 2018;Hayhoe et al, 2018). These climate extremes can trigger rapid ecosystem responses (i.e., extreme climatic events; Smith, 2011), including widespread tree die-off (Allen et al, 2010(Allen et al, , 2015, algal blooms (Havens et al, 2016), wildfires (Moritz et al, 2010), plant invasions (Sheppard et al, 2012), and extensive soil erosion (Coppus and Imeson, 2002).…”

Section: Introductionmentioning

confidence: 99%

Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys

Redmond

Law

Field

et al. 2019

Front. Environ. Sci.

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Ecologists are improving predictive capability using near-term ecological forecasts, in which predictions are made iteratively and publically to increase transparency, rate of learning, and maximize utility. Ongoing ecological forecasting efforts focus mostly on long-term datasets of continuous variables, such as CO 2 fluxes, or more abrupt variables, such as phenological events or algal blooms. Generally lacking from these forecasting efforts is the integration of short-term, opportunistic data concurrent with developing climate extremes such as drought. We posit that incorporating targeted experiments and regional surveys, implemented rapidly during developing extreme events, into current forecasting efforts will ultimately enhance our ability to forecast ecological responses to climate extremes, which are projected to increase in both frequency and intensity. We highlight a project, "chasing tree die-off," in which we coupled an experiment with regional-scale observational field surveys during a developing severe drought to test and improve forecasts of tree die-off. General insights to consider in incorporating this approach include: (1) tracking developing climate extremes in near-real time to efficiently ramp up measurements rapidly and, if feasible, initiate an experiment quickly-including funding and site selection challenges; (2) accepting uncertainty in projected extreme climatic events and adjusting sampling design overtime as needed, especially given the spatially heterogeneous nature of many ecological disturbances; and (3) producing timely and iterative output. In summary, targeted experiments and regional surveys implemented rapidly during developing extreme climatic events offer promise to efficiently (both financially and logistically) improve our ability to forecast ecological responses to climate extremes.

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Chapter 2 : Our Changing Climate. Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II

Cited by 122 publications

References 314 publications

Climatic Trends of West Virginia: A Representative Appalachian Microcosm

Climatic Trends of West Virginia: A Representative Appalachian Microcosm

Improved Accuracy of Watershed-Scale General Circulation Model Runoff Using Deep Neural Networks

Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys

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