scholarly journals LamaH | <i>La</i>rge-Sa<i>m</i>ple D<i>a</i>ta for <i>H</i>ydrology and Environmental Sciences for Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Time Series ◽  
Central Europe ◽  
Hydrological Cycle ◽  
Human Impacts ◽  
Small Scale ◽  
High Temporal Resolution ◽  
Large Sample ◽  
Potential Applications ◽  
The Individual ◽  
Headwater Basins

Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH (Large-Sample Data for Hydrology) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border Austria/Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of 170 000 km2 in 9 different countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 observed catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with daily and also hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses, also with a high temporal resolution. The runoff time series are classified by over 20 different attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS datasets. In contrast, however, LamaH does not only consider headwater basins. Intermediate catchments are also covered, allowing, for the first time within a hydrological large sample dataset, to consider the hydrological network and river topology in applications. We discuss not only the data basis and the methodology of data preparation, but also focus on possible limitations and uncertainties. Potential applications of LamaH are also outlined, since it is intended to serve as a uniform basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

scholarly journals LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe

2021 ◽  
Vol 13 (9) ◽  
pp. 4529-4565
Author(s):  
Christoph Klingler ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Time Series ◽  
Central Europe ◽  
Hydrological Cycle ◽  
Human Impacts ◽  
Small Scale ◽  
High Temporal Resolution ◽  
Environmental Sciences ◽  
Large Sample ◽  
Sample Data ◽  
The Individual

Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

LamaH: Large-sample Data for Hydrology in Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Mathew Herrnegger ◽  
Frederik Kratzert ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
River Network ◽  
Series Data ◽  
High Temporal Resolution ◽  
Stream Length ◽  
Discharge Data ◽  
Large Sample ◽  
Access To Data

&lt;p&gt;Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.&lt;/p&gt;&lt;p&gt;Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.&lt;/p&gt;&lt;p&gt;Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km&amp;#178; in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).&lt;/p&gt;&lt;p&gt;Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.&lt;/p&gt;&lt;p&gt;Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.&lt;/p&gt;

scholarly journals The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6

2021 ◽  
Author(s):  
Alberto Caldas-Alvarez ◽  
Samiro Khodayar ◽  
Peter Knippertz
Keyword(s):  
High Resolution ◽  
Temporal Resolution ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Convective Precipitation ◽  
Small Scale ◽  
High Temporal Resolution ◽  
The Impact

Abstract. Heavy precipitation is one of the most devastating weather extremes in the western Mediterranean region. Our capacity to prevent negative impacts from such extreme events requires advancements in numerical weather prediction, data assimilation and new observation techniques. In this paper we investigate the impact of two state-of-the-art data sets with very high resolution, Global Positioning System-Zenith Total Delays (GPS-ZTD) with a 10 min temporal resolution and radiosondes with ~700 levels, on the representation of convective precipitation in nudging experiments. Specifically, we investigate whether the high temporal resolution, quality, and coverage of GPS-ZTDs can outweigh their lack of vertical information or if radiosonde profiles are more valuable despite their scarce coverage and low temporal resolution (24 h to 6 h). The study focuses on the Intensive Observation Period 6 (IOP6) of the Hydrological Cycle in the Mediterranean eXperiment (HyMeX; 24 September 2012). This event is selected due to its severity (100 mm/12 h), the availability of observations for nudging and validation, and the large observation impact found in preliminary sensitivity experiments. We systematically compare simulations performed with the COnsortium for Small scale MOdelling (COSMO) model assimilating GPS, high- and low vertical resolution radiosoundings in model resolutions of 7 km, 2.8 km and 500 m. The results show that the additional GPS and radiosonde observations cannot compensate errors in the model dynamics and physics. In this regard the reference COSMO runs have an atmospheric moisture wet bias prior to precipitation onset but a negative bias in rainfall, indicative of deficiencies in the numerics and physics, unable to convert the moisture excess into sufficient precipitation. Nudging GPS and high-resolution soundings corrects atmospheric humidity, but even further reduces total precipitation. This case study also demonstrates the potential impact of individual observations in highly unstable environments. We show that assimilating a low-resolution sounding from Nimes (southern France) while precipitation is taking place induces a 40 % increase in precipitation during the subsequent three hours. This precipitation increase is brought about by the moistening of the 700  hPa level (7.5 g kg−1) upstream of the main precipitating systems, reducing the entrainment of dry air above the boundary layer. The moist layer was missed by GPS observations and high-resolution soundings alike, pointing to the importance of profile information and timing. However, assimilating GPS was beneficial for simulating the temporal evolution of precipitation. Finally, regarding the scale dependency, no resolution is particularly sensitive to a specific observation type, however the 2.8 km run has overall better scores, possibly as this is the optimally tuned operational version of COSMO. In follow-up experiments the Icosahedral Nonhydrostatic Model (ICON) will be investigated for this case study to assert whether its numerical and physics updates, compared to its predecessor COSMO, are able to improve the quality of the simulations.

Meteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe

2016 ◽  
Vol 55 (10) ◽  
pp. 2181-2195 ◽  
Author(s):  
Sebastian Bley ◽  
Hartwig Deneke ◽  
Fabian Senf
Keyword(s):  
Time Series ◽  
Central Europe ◽  
Convective Cloud ◽  
Horizontal Wind ◽  
Small Scale ◽  
Liquid Water Path ◽  
Spatiotemporal Evolution ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Time Lagged

AbstractThe spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 × 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-μm reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a Lagrangian one. The Eulerian decorrelation time contains contributions from the spatial decorrelation of the cloud field advected by the horizontal wind. A typical spatial decorrelation length of 7 km is observed, which suggests that sampling of SEVIRI observations is better in the temporal domain than in the spatial domain when investigating small-scale convective clouds. An along-track time series of box-averaged cloud liquid water path is derived and compared with the time series that would be measured at a fixed location. Supported by previous results, it is argued that this makes it possible to discriminate between local changes such as condensation and evaporation on the one hand and advective changes on the other hand.

scholarly journals CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

2020 ◽  
Vol 12 (3) ◽  
pp. 2075-2096 ◽  
Author(s):  
Vinícius B. P. Chagas ◽  
Pedro L. B. Chaffe ◽  
Nans Addor ◽  
Fernando M. Fan ◽  
Ayan S. Fleischmann ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Human Impacts ◽  
The United States ◽  
Meteorological Forcing ◽  
Large Sample ◽  
Spatial Features ◽  
Combining Data ◽  
The Tropics ◽  
Daily Time

Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile, and Great Britain. CAMELS-BR (Brazil) complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products, and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).

scholarly journals Recent evolution and associated hydrological dynamics of a vanishing tropical Andean glacier: Glaciar de Conejeras, Colombia

2018 ◽  
Vol 22 (10) ◽  
pp. 5445-5461 ◽  
Author(s):  
Enrique Morán-Tejeda ◽  
Jorge Luis Ceballos ◽  
Katherine Peña ◽  
Jorge Lorenzo-Lacruz ◽  
Juan Ignacio López-Moreno
Keyword(s):  
El Niño ◽  
Hydrological Cycle ◽  
El Nino ◽  
Southern Oscillation ◽  
Glacier Retreat ◽  
Small Scale ◽  
High Temporal Resolution ◽  
Special Focus ◽  
Hydrological Response ◽  
Great Loss

Abstract. Glaciers in the inner tropics are rapidly retreating due to atmospheric warming. In Colombia, this retreat is accelerated by volcanic activity, and most glaciers are in their last stages of existence. There is general concern about the hydrological implications of receding glaciers, as they constitute important freshwater reservoirs and, after an initial increase in melting flows due to glacier retreat, a decrease in water resources is expected in the long term as glaciers become smaller. In this paper, we perform a comprehensive study of the evolution of a small Colombian glacier, Conejeras (Parque Nacional Natural de los Nevados) that has been monitored since 2006, with a special focus on the hydrological response of the glacierized catchment. The glacier shows great sensitivity to changes in temperature and especially to the evolution of the El Niño–Southern Oscillation (ENSO) phenomenon, with great loss of mass and area during El Niño warm events. Since 2006, it has suffered a 37 % reduction, from 22.45 ha in 2006 to 12 ha in 2017, with an especially abrupt reduction since 2014. During the period of hydrological monitoring (June 2013 to December 2017), streamflow at the outlet of the catchment experienced a noticeable cycle of increasing flows up to mid-2016 and decreasing flows afterwards. The same cycle was observed for other hydrological indicators, including the slope of the rising flow limb and the monthly variability of flows. We observed an evident change in the daily hydrograph, from a predominance of days with a purely melt-driven hydrograph up to mid-2016, to an increase in the frequency of days with flows less influenced by melt after 2016. Such a hydrological cycle is not directly related to fluctuations of temperature or precipitation; therefore, it is reasonable to consider that it is the response of the glacierized catchment to retreat of the glacier. Results confirm the necessity for small-scale studies at a high temporal resolution, in order to understand the hydrological response of glacier-covered catchments to glacier retreat and imminent glacier extinction.

scholarly journals The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6

2021 ◽  
Vol 2 (3) ◽  
pp. 561-580
Author(s):  
Alberto Caldas-Alvarez ◽  
Samiro Khodayar ◽  
Peter Knippertz
Keyword(s):  
High Resolution ◽  
Temporal Resolution ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Convective Precipitation ◽  
Small Scale ◽  
High Temporal Resolution ◽  
The Impact

Abstract. Heavy precipitation is one of the most devastating weather extremes in the western Mediterranean region. Our capacity to prevent negative impacts from such extreme events requires advancements in numerical weather prediction, data assimilation, and new observation techniques. In this paper we investigate the impact of two state-of-the-art data sets with very high resolution, Global Positioning System (GPS)-derived zenith total delays (GPS-ZTD) with a 10 min temporal resolution and radiosondes with ∼ 700 levels, on the representation of convective precipitation in nudging experiments. Specifically, we investigate whether the high temporal resolution, quality, and coverage of GPS-ZTDs can outweigh their lack of vertical information or if radiosonde profiles are more valuable despite their scarce coverage and low temporal resolution (24 to 6 h). The study focuses on the Intensive Observation Period 6 (IOP6) of the Hydrological cycle in the Mediterranean eXperiment (HyMeX; 24 September 2012). This event is selected due to its severity (100 mm/12 h), the availability of observations for nudging and validation, and the large observation impact found in preliminary sensitivity experiments. We systematically compare simulations performed with the Consortium for Small-scale Modeling (COSMO) model assimilating GPS, high- and low-vertical-resolution radiosoundings in model resolutions of 7 km, 2.8 km, and 500 m. The results show that the additional GPS and radiosonde observations cannot compensate for errors in the model dynamics and physics. In this regard the reference COSMO runs have an atmospheric moisture wet bias prior to precipitation onset but a negative bias in rainfall, indicative of deficiencies in the numerics and physics, unable to convert the moisture excess into sufficient precipitation. Nudging GPS and high-resolution soundings corrects atmospheric humidity but even further reduces total precipitation. This case study also demonstrates the potential impact of individual observations in highly unstable environments. We show that assimilating a low-resolution sounding from Nîmes (southern France) while precipitation is taking place induces a 40 % increase in precipitation during the subsequent 3 h. This precipitation increase is brought about by the moistening of the 700 hPa level (7.5 g kg−1) upstream of the main precipitating systems, reducing the entrainment of dry air above the boundary layer. The moist layer was missed by GPS observations and high-resolution soundings alike, pointing to the importance of profile information and timing. However, assimilating GPS was beneficial for simulating the temporal evolution of precipitation. Finally, regarding the scale dependency, no resolution is particularly sensitive to a specific observation type; however, the 2.8 km run has overall better scores, possibly as this is the optimally tuned operational version of COSMO. Future work will aim at a generalization of these conclusions, investigating further cases of the autumn 2012, and the Icosahedral Nonhydrostatic Model (ICON) will be investigated for this case study to assert whether its updates are able to improve the quality of the simulations.

scholarly journals CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil

2020 ◽  
Author(s):  
Vinícius B. P. Chagas ◽  
Pedro L. B. Chaffe ◽  
Nans Addor ◽  
Fernando M. Fan ◽  
Ayan S. Fleischmann ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Human Impacts ◽  
The United States ◽  
Meteorological Forcing ◽  
Large Sample ◽  
Spatial Features ◽  
Combining Data ◽  
The Tropics ◽  
Daily Time

Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3713 gauges, as well as meteorological forcing (precipitation, evapotranspiration and temperature) for 897 selected catchments. It also includes 63 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile and Great Britain. CAMELS-BR complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and in the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and to facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709338 (Chagas et al., 2020).

scholarly journals Recent evolution and associated hydrological dynamics of a vanishing Tropical Andean glacier: Glaciar de Conejeras, Colombia

2018 ◽  
Author(s):  
Enrique Morán-Tejeda ◽  
Jorge Luis Ceballos ◽  
Katherine Peña ◽  
Jorge Lorenzo-Lacruz ◽  
Juan Ignacio López-Moreno
Keyword(s):  
Hydrological Cycle ◽  
Great Sensitivity ◽  
Glacier Retreat ◽  
Small Scale ◽  
Initial Increase ◽  
High Temporal Resolution ◽  
Special Focus ◽  
Hydrological Response ◽  
Great Loss ◽  
Loss Of Mass

Abstract. Glaciers in the inner tropics are rapidly retreating due to atmospheric warming. In Colombia, this retreat is accelerated by volcanic activity, and most glaciers are in their last stages of existence. There is general concern about the hydrological implications of receding glaciers, as they constitute important freshwater reservoirs and, after an initial increase in melting flows due to glacier retreat, a decrease in water resources is expected in the long term as glaciers become smaller. In this paper, we perform a comprehensive study of the evolution of a small Colombian glacier, Conejeras (Parque Nacional Natural de los Nevados), that has been monitored since 2006, with special focus on the hydrological response of the glacierized catchment. The glacier shows great sensitivity to changes in temperature and especially to the evolution of the ENSO phenomenon, with great loss of mass and area during El Niño warm events. Since 2006 it has suffered a 37% reduction from 22.45 ha to 12 ha in 2017, with an especially abrupt reduction since 2014. During the period of hydrological monitoring (June 2013 to December 2017) streamflows at the outlet of the catchment experienced a noticeable cycle of increasing flows up to mid-2016 and decreasing flows afterwards. The same kind of cycle was observed for other hydrological indicators, such as slope of the rising flow limb or the monthly variability of flows. We observed an evident change in the daily hydrograph: from a predominance of days with a pure melt-driven hydrograph up to mid-2016, to an increase in the frequency of days with flows less influenced by melt after 2016. Such a hydrological cycle is not directly related to fluctuations of temperature or precipitation; therefore, it is reasonable to consider that it is the response of the glacierized catchment to retreat of the glacier. Results confirm the necessity for small-scale studies at a high temporal resolution in order to understand the hydrological response of glacier-covered catchments to glacier retreat and imminent glacier extinction.

Towards a New Approach to Cross-Cultural Distinctiveness and Typicality of Human Faces: The Cross-Group Typicality/Distinctiveness Metric

2018 ◽  
Author(s):  
Karel Kleisner ◽  
Šimon Pokorný ◽  
Selahattin Adil Saribay
Keyword(s):  
Cross Cultural ◽  
Facial Appearance ◽  
Alternative Measure ◽  
Home Culture ◽  
Facial Stimuli ◽  
The Two Cultures ◽  
Potential Applications ◽  
The Individual ◽  
Human Faces ◽  
Ingroup Outgroup

In present research, we took advantage of geometric morphometrics to propose a data-driven method for estimating the individual degree of facial typicality/distinctiveness for cross-cultural (and other cross-group) comparisons. Looking like a stranger in one’s home culture may be somewhat stressful. The same facial appearance, however, might become advantageous within an outgroup population. To address this fit between facial appearance and cultural setting, we propose a simple measure of distinctiveness/typicality based on position of an individual along the axis connecting the facial averages of two populations under comparison. The more distant a face is from its ingroup population mean towards the outgroup mean the more distinct it is (vis-à-vis the ingroup) and the more it resembles the outgroup standards. We compared this new measure with an alternative measure based on distance from outgroup mean. The new measure showed stronger association with rated facial distinctiveness than distance from outgroup mean. Subsequently, we manipulated facial stimuli to reflect different levels of ingroup-outgroup distinctiveness and tested them in one of the target cultures. Perceivers were able to successfully distinguish outgroup from ingroup faces in a two-alternative forced-choice task. There was also some evidence that this task was harder when the two faces were closer along the axis connecting the facial averages from the two cultures. Future directions and potential applications of our proposed approach are discussed.

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