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  • 1
    Series available for loan
    Series available for loan
    Water quality in the Santa Ana Basin, California, 1999-2001 (2004)
    Reston, Va. : U.S. Dep. of the Interior, U.S. Geological Survey
    Associated volumes
    Details Availability
    Call number: S 90.0003(1238)
    In: U.S. Geological Survey circular
    Type of Medium: Series available for loan
    Pages: VI, 37 S. , Ill., graph. Darst.
    ISBN: 0607964073
    Series Statement: U.S. Geological Survey circular 1238
    Classification:
    Hydrology
    Location: Lower compact magazine
    Branch Library: GFZ Library
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    Link to Library Catalog
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  • 2
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    The Impact of Shared Vision on Leadership, Engagement, Organizational Citizenship and Coaching (2021)
    Frontiers Media SA
    Details
    Publication Date: 2023-12-20
    Description: According to management and psychology courses, as well as legions of consultants in organizational psychology, shared vision in dyads, teams and organizations can fill us with hope and inspire new possibilities, or delude us into following false prophets. However, few research studies have empirically examined the impact of shared vision on key organizational outcomes such as leadership effectiveness, employee engagement, organizational citizenship, coaching and organizational change. As a result, the field of organizational psychology has not yet established a causal pattern of whether, if, and how shared vision helps dyads, teams and organizations function more effectively. The lack of empirical work around shared vision is surprising given its long-standing history in the literature. Bennis and Nanus (1982) showed that distinctive leaders managed attention through vision. The practitioner literature has long proclaimed that vision is a key to change, while Conger and Kanungo (1998) discussed its link to charismatic leadership. Around the same time, positive psychology appeared in the forms of Appreciative Inquiry (Cooperrider, Sorensen, Whitney, & Yaeger, 2000) and Positive Organizational Scholarship (Cameron, Dutton, & Quinn, 2003). In this context, a shared vision or dream became a legitimate antecedent to sustainable change. But again, empirical measurement has been elusive. More recently, shared vision has been the focus of a number of dissertations and quantitative studies building on Intentional Change Theory (ICT) (Boyatzis, 2008) at dyad, team and organization levels of social systems. These studies are beginning to lay the foundations for a systematic body of empirical knowledge about the role of shared vision in an organizational context. For example, we now know that shared vision can activate neural networks that arouse endocrine systems and allow a person to consider the possibilities of a better future (Jack, Boyatzis, Leckie, Passarelli & Khawaja, 2013). Additionally, Boyatzis & Akrivou (2006) have discussed the role of a shared vision as the result of a well-developed set of factors that produce a desired image of the future. Outside of the organizational context, positive visioning has been known to help guide future behavior in sports psychology (Loehr & Schwartz, 2003), medical treatment (Roffe, Schmidt, & Ernst, 2005), musical performance (Meister, Krings, Foltys, Boroojerdi, Muller, Topper, & Thron, 2004), and academic performance (Curry, Snyder, Cook, Ruby, & Rehm, 1997). This Research Topic for Frontiers in Psychology is a collection of 14 original papers examining the role of vision and shared vision on a wide variety of desired dependent variables from leadership effectiveness and executive performance to organizational engagement, citizenship and corporate social responsibility, and how to develop it through coaching.
    Keywords: BF1-990 ; Q1-390 ; Leadership ; Family Business ; relationships ; engagement ; Vision ; citizenship ; coaching ; Emotional Intelligence ; prospection ; Shared Vision ; bic Book Industry Communication::J Society & social sciences::JM Psychology
    Language: English
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    E-BOOK
  • 3
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    Crop Traits for Defense against Pests and Disease: Durability, Breakdown and Future Prospects (2021)
    Frontiers Media SA
    Details
    Publication Date: 2024-04-05
    Description: With global populations expected to exceed 9.2 billion by 2050 and available land and water resources devoted to crop production dwindling, we face significant challenges to secure global food security. Only 12 plant species feed 80% of the world’s population, with just three crop species (wheat, rice and maize) accounting for food consumed by 50% of the global population. Annual losses to crop pests and pathogens are significant, thought to be equivalent to that required to feed a billion people, at a time when crop productivity has plateaued. With pesticide applications becoming increasingly unfeasible on cost, efficacy and environmental grounds, there is growing interest in exploiting plant resistance and tolerance traits for crop protection. Indeed, mankind has been selectively breeding plants for desirable traits for thousands of years. However, resistance and tolerance traits have not always been those most desired, and in many cases have been inadvertently lost during the domestication process: crops have been effectively ‘disarmed by domestication’. Moreover, mechanistic understanding of how resistance and tolerance traits operate is often incomplete, which makes identifying the right combination for crop protection difficult. We aimed to address this Research Topic by inviting authors to contribute their knowledge of appropriate resistance and tolerance traits, explore what is known about durability and breakdown of defensive traits and, finally, asking what are the prospects for exploiting these traits for crop protection. The research topic summarised in this book addresses some of the most important issues in the future sustainability of global crop production.
    Keywords: QK1-989 ; Q1-390 ; Integrated Pest Management ; crop protection ; Insect herbivore ; pathogen ; biological control ; global climate change ; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PST Botany and plant sciences
    Language: English
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  • 4
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    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In-situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. Therefore, we here provide four datasets comprising: 1. Harmonized, standardized and aggregated in situ observations of SEB components at 64 vegetated and glaciated sites north of 60° latitude, in the time period 1994-2021 2. A description of all study sites and associated environmental conditions, including the vegetation types, which correspond to the classification of the Circumpolar Arctic Vegetation Map (CAVM, Raynolds et al. 2019). 3. Data generated in a literature synthesis from 358 study sites on vegetation or glacier (〉=60°N latitude) covered by 148 publications. 4. Metadata, including data contributor information and measurement heights of variables associated with Oehri et al. 2022.
    Keywords: Arctic; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; dry tundra; Eddy covariance; eddy heat flux; glacier; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Land-Atmosphere; Land-cover; latent and sensible heat; latent heat flux; longwave radiation; meteorological data; observatory data; Peat bog; Radiation fluxes; Radiative energy budget; sensible heat flux; shortwave radiation; shrub tundra; surface energy balance; synthetic data; tundra vegetation; wetland
    Type: Dataset
    Format: application/zip, 4 datasets
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    DATA PANGAEA
  • 5
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    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites - Metadata (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset contains metadata information about surface energy budget components measured at 64 tundra and glacier sites 〉60° N across the Arctic. This information was taken from the open-access repositories FLUXNET, Ameriflux, AON, GC-Net and PROMICE. The contained datasets are associated with the publication vegetation type as an important predictor of the Arctic Summer Land Surface Energy Budget by Oehri et al. 2022, and intended to support research of surface energy budgets and their relationship with environmental conditions, in particular vegetation characteristics across the terrestrial Arctic.
    Keywords: Aggregation type; Arctic; Arctic_SEB_CA-SCB; Arctic_SEB_CP1; Arctic_SEB_Dye-2; Arctic_SEB_EGP; Arctic_SEB_FI-Lom; Arctic_SEB_GL-NuF; Arctic_SEB_GL-ZaF; Arctic_SEB_GL-ZaH; Arctic_SEB_KAN_B; Arctic_SEB_KAN_L; Arctic_SEB_KAN_M; Arctic_SEB_KAN_U; Arctic_SEB_KPC_L; Arctic_SEB_KPC_U; Arctic_SEB_MIT; Arctic_SEB_NASA-E; Arctic_SEB_NASA-SE; Arctic_SEB_NASA-U; Arctic_SEB_NUK_K; Arctic_SEB_NUK_L; Arctic_SEB_NUK_N; Arctic_SEB_NUK_U; Arctic_SEB_QAS_A; Arctic_SEB_QAS_L; Arctic_SEB_QAS_M; Arctic_SEB_QAS_U; Arctic_SEB_RU-Che; Arctic_SEB_RU-Cok; Arctic_SEB_RU-Sam; Arctic_SEB_RU-Tks; Arctic_SEB_RU-Vrk; Arctic_SEB_Saddle; Arctic_SEB_SCO_L; Arctic_SEB_SCO_U; Arctic_SEB_SE-St1; Arctic_SEB_SJ-Adv; Arctic_SEB_SJ-Blv; Arctic_SEB_SouthDome; Arctic_SEB_Summit; Arctic_SEB_TAS_A; Arctic_SEB_TAS_L; Arctic_SEB_TAS_U; Arctic_SEB_THU_L; Arctic_SEB_THU_U; Arctic_SEB_Tunu-N; Arctic_SEB_UPE_L; Arctic_SEB_UPE_U; Arctic_SEB_US-A03; Arctic_SEB_US-A10; Arctic_SEB_US-An1; Arctic_SEB_US-An2; Arctic_SEB_US-An3; Arctic_SEB_US-Atq; Arctic_SEB_US-Brw; Arctic_SEB_US-EML; Arctic_SEB_US-HVa; Arctic_SEB_US-ICh; Arctic_SEB_US-ICs; Arctic_SEB_US-ICt; Arctic_SEB_US-Ivo; Arctic_SEB_US-NGB; Arctic_SEB_US-Upa; Arctic_SEB_US-xHE; Arctic_SEB_US-xTL; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Author(s); Data source; Date/Time of event; Day of the year; Description; dry tundra; Eddy covariance; eddy heat flux; Event label; Field observation; First year of observation; glacier; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Institution; Instrument; Land-Atmosphere; Land-cover; Last year of observation; latent and sensible heat; latent heat flux; LATITUDE; Location ID; LONGITUDE; longwave radiation; meteorological data; Method comment; observatory data; Peat bog; Radiation fluxes; Radiative energy budget; Sample height; sensible heat flux; shortwave radiation; shrub tundra; surface energy balance; synthetic data; tundra vegetation; Type of study; Unit; Variable; wetland
    Type: Dataset
    Format: text/tab-separated-values, 20562 data points
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    DATA PANGAEA
  • 6
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    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites - Surface energy budget componenent data (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset comprises harmonized, standardized and aggregated in-situ observations of surface energy budget components measured at 64 sites on vegetated and glaciated sites north of 60° latitude, in the time period from 1994 till 2021. The surface energy budget components include net radiation, sensible heat flux, latent heat flux, ground heat flux, net shortwave radiation, net longwave radiation, surface temperature and albedo, which were aggregated to daily mean, minimum and maximum values from hourly and half-hourly measurements. Data were retrieved from the monitoring networks FLUXNET, AmeriFlux, AON, GC-Net and PROMICE.
    Keywords: Albedo; Albedo, maximum; Albedo, minimum; Arctic; Arctic_SEB_CA-SCB; Arctic_SEB_CP1; Arctic_SEB_Dye-2; Arctic_SEB_EGP; Arctic_SEB_FI-Lom; Arctic_SEB_GL-NuF; Arctic_SEB_GL-ZaF; Arctic_SEB_GL-ZaH; Arctic_SEB_KAN_B; Arctic_SEB_KAN_L; Arctic_SEB_KAN_M; Arctic_SEB_KAN_U; Arctic_SEB_KPC_L; Arctic_SEB_KPC_U; Arctic_SEB_MIT; Arctic_SEB_NASA-E; Arctic_SEB_NASA-SE; Arctic_SEB_NASA-U; Arctic_SEB_NUK_K; Arctic_SEB_NUK_L; Arctic_SEB_NUK_N; Arctic_SEB_NUK_U; Arctic_SEB_QAS_A; Arctic_SEB_QAS_L; Arctic_SEB_QAS_M; Arctic_SEB_QAS_U; Arctic_SEB_RU-Che; Arctic_SEB_RU-Cok; Arctic_SEB_RU-Sam; Arctic_SEB_RU-Tks; Arctic_SEB_RU-Vrk; Arctic_SEB_Saddle; Arctic_SEB_SCO_L; Arctic_SEB_SCO_U; Arctic_SEB_SE-St1; Arctic_SEB_SJ-Adv; Arctic_SEB_SJ-Blv; Arctic_SEB_SouthDome; Arctic_SEB_Summit; Arctic_SEB_TAS_A; Arctic_SEB_TAS_L; Arctic_SEB_TAS_U; Arctic_SEB_THU_L; Arctic_SEB_THU_U; Arctic_SEB_Tunu-N; Arctic_SEB_UPE_L; Arctic_SEB_UPE_U; Arctic_SEB_US-A03; Arctic_SEB_US-A10; Arctic_SEB_US-An1; Arctic_SEB_US-An2; Arctic_SEB_US-An3; Arctic_SEB_US-Atq; Arctic_SEB_US-Brw; Arctic_SEB_US-EML; Arctic_SEB_US-HVa; Arctic_SEB_US-ICh; Arctic_SEB_US-ICs; Arctic_SEB_US-ICt; Arctic_SEB_US-Ivo; Arctic_SEB_US-NGB; Arctic_SEB_US-Upa; Arctic_SEB_US-xHE; Arctic_SEB_US-xTL; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Bowen ratio; Calculated from Ground heat, flux / Net radiation; Calculated from Heat, flux, latent / Net radiation; Calculated from Heat, flux, sensible / Heat, flux, latent; Calculated from Heat, flux, sensible / Net radiation; Calculated from Heat, flux, sensible + Heat, flux, latent + Ground heat, flux; Calculated from Long-wave downward radiation, maximum - Long-wave upward radiation, maximum; Calculated from Long-wave downward radiation, minimum - Long-wave upward radiation, minimum; Calculated from Long-wave downward radiation - Long-wave upward radiation; Calculated from Long-wave net radiation / Net radiation; Calculated from Short-wave downward (GLOBAL) radiation, maximum - Short-wave upward (REFLEX) radiation, maximum; Calculated from Short-wave downward (GLOBAL) radiation, minimum - Short-wave upward (REFLEX) radiation, minimum; Calculated from Short-wave downward (GLOBAL) radiation - Short-wave upward (REFLEX) radiation; Calculated from Short-wave net radiation, maximum + Long-wave net radiation, maximum; Calculated from Short-wave net radiation, minimum + Long-wave net radiation, minimum; Calculated from Short-wave net radiation / Net radiation; Calculated from Short-wave net radiation + Long-wave net radiation; Calculated from Short-wave upward (REFLEX) radiation / Short-wave downward (GLOBAL) radiation; Calculated from Surface temperature, maximum - Temperature, air, maximum; Calculated from Surface temperature, minimum - Temperature, air, minimum; Calculated from Surface temperature - Temperature, air; Cloud coverage; Cloud coverage, maximum; Cloud coverage, minimum; Daily maximum; Daily mean; Daily minimum; Data source; DATE/TIME; Day of the year; dry tundra; Eddy covariance; eddy heat flux; ELEVATION; Event label; Field observation; glacier; graminoids; Ground heat, flux; Ground heat, flux, maximum; Ground heat, flux, minimum; Ground heat, flux/Net radiation ratio; ground heat flux and net radiation; harmonized data; Heat, flux, latent; Heat, flux, latent, maximum; Heat, flux, latent, minimum; Heat, flux, latent/Net radiation ratio; Heat, flux, sensible; Heat, flux, sensible, maximum; Heat, flux, sensible, minimum; Heat flux, sensible/Net radiation ratio; high latitude; Humidity, relative; Humidity, relative, maximum; Humidity, relative, minimum; Land-Atmosphere; Land-cover; latent and sensible heat; latent heat flux; LATITUDE; Location ID; LONGITUDE; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave net radiation; Long-wave net radiation, maximum; Long-wave net radiation, minimum; Long-wave net radiation/Net radiation ratio; longwave radiation; Long-wave upward radiation; Long-wave upward radiation, maximum; Long-wave upward radiation, minimum; meteorological data; Month; Net radiation; Net radiation, maximum; Net radiation, minimum; Normalized by X / Potential incoming solar radiation, maximum * 100; observatory data; Original variable; Peat bog; Potential incoming solar radiation; Potential incoming solar radiation, maximum; Potential incoming solar radiation, minimum; Precipitation; Precipitation, daily, maximum; Precipitation, daily, minimum; Pressure, atmospheric; Pressure, atmospheric, maximum; Pressure, atmospheric, minimum; Radiation fluxes; Radiative energy budget; sensible heat flux; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave net radiation; Short-wave net radiation, maximum; Short-wave net radiation, minimum; Short-wave net radiation/Net radiation ratio; shortwave radiation; Short-wave upward (REFLEX) radiation; Short-wave upward (REFLEX) radiation, maximum; Short-wave upward (REFLEX) radiation, minimum; shrub tundra; Soil water content, volumetric; Soil water content, volumetric, maximum; Soil water content, volumetric, minimum; surface energy balance; Surface temperature; Surface temperature, maximum; Surface temperature, minimum; synthetic data; Temperature, air; Temperature, air, maximum; Temperature, air, minimum; Temperature, soil; Temperature, soil, maximum; Temperature, soil, minimum; Temperature gradient, 0-2m above surface; Temperature gradient, 0-2m above surface, maximum; Temperature gradient, 0-2m above surface, minimum; tundra vegetation; Type of study; Vapour pressure deficit; Vapour pressure deficit, maximum; Vapour pressure deficit, minimum; wetland; Wind direction; Wind speed; Wind speed, maximum; Wind speed, minimum; Year of observation
    Type: Dataset
    Format: text/tab-separated-values, 17112737 data points
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  • 7
    facet.materialart.
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    Literature synthesis data of surface energy fluxes and environmental drivers from Arctic vegetation and glacier sites (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-12
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset describes the data generated in a literature synthesis, covering 358 study sites on vegetation or glacier (〉=60°N latitude), which contained surface energy budget observations. The literature synthesis comprised 148 publications searched on the ISI Web of Science Core Collection.
    Keywords: Arctic; Arctic_SEB_1; Arctic_SEB_1951-2009_1; Arctic_SEB_1965-2000_1; Arctic_SEB_1965-2000_2; Arctic_SEB_1965-2000_3; Arctic_SEB_1965-2000_4; Arctic_SEB_1969-2013_1; Arctic_SEB_1970-1972_1; Arctic_SEB_1970-1979_1; Arctic_SEB_1972-2004_1; Arctic_SEB_1972-2004_10; Arctic_SEB_1972-2004_11; Arctic_SEB_1972-2004_2; Arctic_SEB_1972-2004_3; Arctic_SEB_1972-2004_4; Arctic_SEB_1972-2004_5; Arctic_SEB_1972-2004_6; Arctic_SEB_1972-2004_7; Arctic_SEB_1972-2004_8; Arctic_SEB_1972-2004_9; Arctic_SEB_1979-1995_1; Arctic_SEB_1979-1995_2; Arctic_SEB_1979-1995_3; Arctic_SEB_1979-1995_4; Arctic_SEB_1979-2005_1; Arctic_SEB_1980-1981_1; Arctic_SEB_1981-1997_1; Arctic_SEB_1981-1997_2; Arctic_SEB_1983-2005_1; Arctic_SEB_1983-2005_2; Arctic_SEB_1983-2005_3; Arctic_SEB_1984-1991_1; Arctic_SEB_1985-1989_1; Arctic_SEB_1985-2016_1; Arctic_SEB_1988-1988_1; Arctic_SEB_1988-1988_2; Arctic_SEB_1988-1988_3; Arctic_SEB_1988-1988_4; Arctic_SEB_1988-1988_5; Arctic_SEB_1989-1990_1; Arctic_SEB_1990-1991_1; Arctic_SEB_1991-1991_1; Arctic_SEB_1991-1999_1; Arctic_SEB_1991-1999_2; Arctic_SEB_1991-1999_3; Arctic_SEB_1992-1992_1; Arctic_SEB_1992-1997_1; Arctic_SEB_1994-1994_1; Arctic_SEB_1994-1994_2; Arctic_SEB_1994-1994_3; Arctic_SEB_1994-1994_4; Arctic_SEB_1994-1996_1; Arctic_SEB_1994-1996_10; Arctic_SEB_1994-1996_11; Arctic_SEB_1994-1996_12; Arctic_SEB_1994-1996_13; Arctic_SEB_1994-1996_14; Arctic_SEB_1994-1996_15; Arctic_SEB_1994-1996_16; Arctic_SEB_1994-1996_17; Arctic_SEB_1994-1996_2; Arctic_SEB_1994-1996_3; Arctic_SEB_1994-1996_4; Arctic_SEB_1994-1996_5; Arctic_SEB_1994-1996_6; Arctic_SEB_1994-1996_7; Arctic_SEB_1994-1996_8; Arctic_SEB_1994-1996_9; Arctic_SEB_1994-2008_1; Arctic_SEB_1994-2008_2; Arctic_SEB_1994-2009_1; Arctic_SEB_1994-2015_1; Arctic_SEB_1994-2015_2; Arctic_SEB_1994-2015_3; Arctic_SEB_1994-2015_4; Arctic_SEB_1994-2015_5; Arctic_SEB_1994-2015_6; Arctic_SEB_1995-1995_1; Arctic_SEB_1995-1995_2; Arctic_SEB_1995-1996_1; Arctic_SEB_1995-1997_1; Arctic_SEB_1995-1997_2; Arctic_SEB_1995-1997_3; Arctic_SEB_1995-1997_4; Arctic_SEB_1995-1998_1; Arctic_SEB_1995-1999_1; Arctic_SEB_1996-1997_1; Arctic_SEB_1996-1999_1; Arctic_SEB_1996-2005_1; Arctic_SEB_1996-2005_2; Arctic_SEB_1996-2005_3; Arctic_SEB_1997-1998_1; Arctic_SEB_1997-1999_1; Arctic_SEB_1997-2018_1; Arctic_SEB_1997-2018_10; Arctic_SEB_1997-2018_11; Arctic_SEB_1997-2018_12; Arctic_SEB_1997-2018_13; Arctic_SEB_1997-2018_14; Arctic_SEB_1997-2018_15; Arctic_SEB_1997-2018_16; Arctic_SEB_1997-2018_17; Arctic_SEB_1997-2018_18; Arctic_SEB_1997-2018_19; Arctic_SEB_1997-2018_2; Arctic_SEB_1997-2018_20; Arctic_SEB_1997-2018_21; Arctic_SEB_1997-2018_22; Arctic_SEB_1997-2018_23; Arctic_SEB_1997-2018_24; Arctic_SEB_1997-2018_25; Arctic_SEB_1997-2018_3; Arctic_SEB_1997-2018_4; Arctic_SEB_1997-2018_5; Arctic_SEB_1997-2018_6; Arctic_SEB_1997-2018_7; Arctic_SEB_1997-2018_8; Arctic_SEB_1997-2018_9; Arctic_SEB_1998-1998_1; Arctic_SEB_1998-1999_1; Arctic_SEB_1998-2000_1; Arctic_SEB_1998-2001_1; Arctic_SEB_1998-2005_1; Arctic_SEB_1998-2011_1; Arctic_SEB_1998-2011_2; Arctic_SEB_1998-2011_3; Arctic_SEB_1998-2013_1; Arctic_SEB_1999-1999_1; Arctic_SEB_1999-2000_1; Arctic_SEB_1999-2008_1; Arctic_SEB_1999-2008_2; Arctic_SEB_1999-2009_1; Arctic_SEB_1999-2014_1; Arctic_SEB_2000-2000_1; Arctic_SEB_2000-2000_2; Arctic_SEB_2000-2000_3; Arctic_SEB_2000-2000_4; Arctic_SEB_2000-2002_1; Arctic_SEB_2000-2002_2; Arctic_SEB_2000-2002_3; Arctic_SEB_2000-2003_1; Arctic_SEB_2000-2003_2; Arctic_SEB_2000-2003_3; Arctic_SEB_2000-2007_1; Arctic_SEB_2000-2007_2; Arctic_SEB_2000-2007_3; Arctic_SEB_2000-2007_4; Arctic_SEB_2000-2008_1; Arctic_SEB_2000-2010_1; Arctic_SEB_2000-2011_1; Arctic_SEB_2000-2011_10; Arctic_SEB_2000-2011_11; Arctic_SEB_2000-2011_2; Arctic_SEB_2000-2011_3; Arctic_SEB_2000-2011_4; Arctic_SEB_2000-2011_5; Arctic_SEB_2000-2011_6; Arctic_SEB_2000-2011_7; Arctic_SEB_2000-2011_8; Arctic_SEB_2000-2011_9; Arctic_SEB_2000-2014_1; Arctic_SEB_2001-2003_1; Arctic_SEB_2002-2002_1; Arctic_SEB_2002-2003_1; Arctic_SEB_2002-2003_2; Arctic_SEB_2002-2004_1; Arctic_SEB_2002-2010_1; Arctic_SEB_2002-2012_1; Arctic_SEB_2002-2012_2; Arctic_SEB_2002-2012_3; Arctic_SEB_2003-2003_1; Arctic_SEB_2003-2004_1; Arctic_SEB_2003-2007_1; Arctic_SEB_2003-2008_1; Arctic_SEB_2003-2008_2; Arctic_SEB_2003-2010_1; Arctic_SEB_2003-2010_2; Arctic_SEB_2003-2010_3; Arctic_SEB_2003-2011_1; Arctic_SEB_2004-2004_1; Arctic_SEB_2004-2006_1; Arctic_SEB_2004-2013_1; Arctic_SEB_2005-2005_1; Arctic_SEB_2006-2006_1; Arctic_SEB_2006-2006_2; Arctic_SEB_2006-2007_1; Arctic_SEB_2006-2007_10; Arctic_SEB_2006-2007_11; Arctic_SEB_2006-2007_12; Arctic_SEB_2006-2007_13; Arctic_SEB_2006-2007_14; Arctic_SEB_2006-2007_2; Arctic_SEB_2006-2007_3; Arctic_SEB_2006-2007_4; Arctic_SEB_2006-2007_5; Arctic_SEB_2006-2007_6; Arctic_SEB_2006-2007_7; Arctic_SEB_2006-2007_8; Arctic_SEB_2006-2007_9; Arctic_SEB_2006-2008_1; Arctic_SEB_2006-2008_2; Arctic_SEB_2006-2009_1; Arctic_SEB_2007-2007_1; Arctic_SEB_2007-2008_1; Arctic_SEB_2007-2009_1; Arctic_SEB_2007-2009_2; Arctic_SEB_2007-2010_1; Arctic_SEB_2007-2014_1; Arctic_SEB_2007-2015_1; Arctic_SEB_2007-2015_2; Arctic_SEB_2008-2008_1; Arctic_SEB_2008-2008_2; Arctic_SEB_2008-2008_3; Arctic_SEB_2008-2009_1; Arctic_SEB_2008-2010_1; Arctic_SEB_2008-2010_2; Arctic_SEB_2008-2010_3; Arctic_SEB_2008-2011_1; Arctic_SEB_2008-2012_1; Arctic_SEB_2008-2012_2; Arctic_SEB_2008-2012_3; Arctic_SEB_2009-2012_1; Arctic_SEB_2009-2012_2; Arctic_SEB_2009-2012_3; Arctic_SEB_2009-2012_4; Arctic_SEB_2009-2012_5; Arctic_SEB_2009-2014_1; Arctic_SEB_2009-2014_2; Arctic_SEB_2010-2014_1; Arctic_SEB_2010-2014_2; Arctic_SEB_2010-2014_3; Arctic_SEB_2010-2014_4; Arctic_SEB_2010-2014_5; Arctic_SEB_2011-2011_1; Arctic_SEB_2011-2013_1; Arctic_SEB_2011-2014_1; Arctic_SEB_2012-2012_1; Arctic_SEB_2012-2013_1; Arctic_SEB_2012-2013_2; Arctic_SEB_2012-2013_3; Arctic_SEB_2012-2013_4; Arctic_SEB_2012-2014_1; Arctic_SEB_2012-2015_1; Arctic_SEB_2012-2015_2; Arctic_SEB_2012-2015_3; Arctic_SEB_2012-2015_4; Arctic_SEB_2012-2015_5; Arctic_SEB_2013-2013_1; Arctic_SEB_2013-2014_1; Arctic_SEB_2013-2015_1; Arctic_SEB_2013-2015_2; Arctic_SEB_2013-2015_3; Arctic_SEB_2014-2014_1; Arctic_SEB_2014-2015_1; Arctic_SEB_2014-2016_1; Arctic_SEB_2015-2015_1; Arctic_SEB_2015-2015_2; Arctic_SEB_2015-2015_3; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Author(s); Classification; Comment; Data collection methodology; Data type; Date/Time of event; dry tundra; Eddy covariance; eddy heat flux; ELEVATION; Energy budget, description; Event label; Field observation; First year of observation; glacier; glaciers; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Identification; Journal/report title; Land-Atmosphere; Land-cover; Last year of observation; latent and sensible heat; latent heat flux; LATITUDE; Location; LONGITUDE; longwave radiation; meteorological data; observatory data; Peat bog; Persistent Identifier; Publication type; Radiation fluxes; Radiative energy budget; Resolution; Season; sensible heat flux; shortwave radiation; shrub tundra; Spatial coverage; surface energy balance; synthetic data; Title; tundra vegetation; Type of study; Variable; Vegetation type; wetland; wetlands; Year of publication
    Type: Dataset
    Format: text/tab-separated-values, 8650 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 8
    facet.materialart.
    Unknown
    Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites - Environmental conditions (2022)
    PANGAEA
    Details
    Publication Date: 2024-05-05
    Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset describes the environmental conditions for 64 tundra and glacier sites (〉=60°N latitude) across the Arctic, for which in situ measurements of surface energy budget components were harmonized (see Oehri et al. 2022). These environmental conditions are (proxies of) potential drivers of SEB-components and could therefore be called SEB-drivers. The associated environmental conditions, include the vegetation types graminoid tundra, prostrate dwarf-shrub tundra, erect-shrub tundra, wetland complexes, barren complexes (≤ 40% horizontal plant cover), boreal peat bogs and glacier. These land surface types (apart from boreal peat bogs) correspond to the main classification units of the Circumpolar Arctic Vegetation Map (CAVM, Raynolds et al. 2019). For each site, additional climatic and biophysical variables are available, including cloud cover, snow cover duration, permafrost characteristics, climatic conditions and topographic conditions.
    Keywords: Arctic; Arctic_SEB_CA-SCB; Arctic_SEB_CP1; Arctic_SEB_Dye-2; Arctic_SEB_EGP; Arctic_SEB_FI-Lom; Arctic_SEB_GL-NuF; Arctic_SEB_GL-ZaF; Arctic_SEB_GL-ZaH; Arctic_SEB_KAN_B; Arctic_SEB_KAN_L; Arctic_SEB_KAN_M; Arctic_SEB_KAN_U; Arctic_SEB_KPC_L; Arctic_SEB_KPC_U; Arctic_SEB_MIT; Arctic_SEB_NASA-E; Arctic_SEB_NASA-SE; Arctic_SEB_NASA-U; Arctic_SEB_NUK_K; Arctic_SEB_NUK_L; Arctic_SEB_NUK_N; Arctic_SEB_NUK_U; Arctic_SEB_QAS_A; Arctic_SEB_QAS_L; Arctic_SEB_QAS_M; Arctic_SEB_QAS_U; Arctic_SEB_RU-Che; Arctic_SEB_RU-Cok; Arctic_SEB_RU-Sam; Arctic_SEB_RU-Tks; Arctic_SEB_RU-Vrk; Arctic_SEB_Saddle; Arctic_SEB_SCO_L; Arctic_SEB_SCO_U; Arctic_SEB_SE-St1; Arctic_SEB_SJ-Adv; Arctic_SEB_SJ-Blv; Arctic_SEB_SouthDome; Arctic_SEB_Summit; Arctic_SEB_TAS_A; Arctic_SEB_TAS_L; Arctic_SEB_TAS_U; Arctic_SEB_THU_L; Arctic_SEB_THU_U; Arctic_SEB_Tunu-N; Arctic_SEB_UPE_L; Arctic_SEB_UPE_U; Arctic_SEB_US-A03; Arctic_SEB_US-A10; Arctic_SEB_US-An1; Arctic_SEB_US-An2; Arctic_SEB_US-An3; Arctic_SEB_US-Atq; Arctic_SEB_US-Brw; Arctic_SEB_US-EML; Arctic_SEB_US-HVa; Arctic_SEB_US-ICh; Arctic_SEB_US-ICs; Arctic_SEB_US-ICt; Arctic_SEB_US-Ivo; Arctic_SEB_US-NGB; Arctic_SEB_US-Upa; Arctic_SEB_US-xHE; Arctic_SEB_US-xTL; ArcticTundraSEB; Arctic Tundra Surface Energy Budget; Aspect; Aspect, coefficient of variation; Calculated average/mean values; Cloud cover; Cloud cover, standard deviation; Cloud top pressure; Cloud top pressure, standard deviation; Cloud top temperature; Cloud top temperature, standard deviation; Conrad's continentality index; Daily maximum; Daily mean; Data source; Date/Time of event; dry tundra; Eddy covariance; eddy heat flux; ELEVATION; Elevation, standard deviation; Event label; Field observation; glacier; graminoids; ground heat flux and net radiation; harmonized data; high latitude; Humidity, relative; Land-Atmosphere; Land-cover; Land cover classes; Land cover type; latent and sensible heat; latent heat flux; LATITUDE; Location ID; LONGITUDE; longwave radiation; Mean values; Median values; meteorological data; Number of vegetation types; observatory data; Peat bog; Permafrost, type; Permafrost extent; Permafrost ice content, description; Precipitation; Precipitation, coefficient of variation; Precipitation, daily, maximum; Precipitation, snow; Precipitation, sum; Pressure, atmospheric; p-value; Radiation fluxes; Radiative energy budget; Reference/source; sensible heat flux; Shannon Diversity Index; Shannon Diversity Index, maximum; shortwave radiation; shrub tundra; Site; Slope; Slope, coefficient of variation; Slope, mathematical; Snow, onset, day of the year; Snow cover, number of days; Snowfall, coefficient of variation; Snow-free days; Snow type; Soil water content, volumetric; Species present; Summer warmth index; surface energy balance; synthetic data; Temperature, air, annual mean; Temperature, air, coefficient of variation; Temperature, annual mean range; tundra vegetation; Type of study; Uniform resource locator/link to reference; Vapour pressure deficit; Vegetation type; wetland; Wind speed; Zone
    Type: Dataset
    Format: text/tab-separated-values, 4705 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 9
    facet.materialart.
    Unknown
    Short-term impacts of active layer detachments on carbon exchange in a High Arctic ecosystem, Cape bounty, Nunavut, Canada (2014)
    In:  Polar Biology
    Details
    Publication Date: 2020-03-12
    Description: Localized permafrost disturbances such as active layer detachments (ALDs) are increasing in frequency and severity across the Canadian Arctic impacting terrestrial ecosystem functioning. However, the contribution of permafrost disturbance-carbon feedbacks to the carbon (C) balance of Arctic ecosystems is poorly understood. Here, we explore the short-term impact of active layer detachments (ALDs) on carbon dioxide (CO2) exchange in a High Arctic semi-desert ecosystem by comparing midday C exchange between undisturbed areas, moderately disturbed areas (intact islands of vegetation within an ALD), and highly disturbed areas (non-vegetated areas due to ALD). Midday C exchange was measured using a static chamber method between June 23 and August 8 during the 2009 and 2010 growing seasons. Results show that areas of high disturbance had significantly reduced gross ecosystem exchange and ecosystem respiration (RE) compared to control and moderately disturbed areas. Moderately disturbed areas showed significantly enhanced net ecosystem exchange compared to areas of high disturbance, but were not significantly different from control areas. Disturbance did not significantly impact soil thermal, physical or chemical properties. According to average midday fluxes, ALDs as a whole (moderately disturbed areas: ?1.942 \ensuremath{μ}}mol m?2 s?1+ highly disturbed areas: 2.969 {\ensuremath{μ}}mol m?2 s?1) were a small CO2 source of 1.027 {\ensuremath{μ}}mol m?2 s?1 which did not differ significantly from average midday fluxes in control areas 1.219 {\ensuremath{μ}mol m?2 s?1. The findings of this study provide evidence that the short-term impacts of ALDs on midday, net C exchange and soil properties in a High Arctic semi-desert are minimal.
    Type: info:eu-repo/semantics/article
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    PAPER (OA)
  • 10
    Electronic Resource
    Electronic Resource
    Structural requirements for neuropeptide Y18-36-evoked hypotension: a systematic study (1992)
    s.l. : American Chemical Society
    Journal of medicinal chemistry 35 (1992), S. 2836-2843 
    Details
    ISSN: 1520-4804
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/jm00093a017
    Location Call Number Expected Availability
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    Paper (German National Licenses)
    Fulltext
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