Nina Hartman
Cosmetic Laser Dermatology
21 Papers
11 Citations
Nina Hartman is an academic researcher from Cosmetic Laser Dermatology. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 4, co-authored 21 publications. Previous affiliations of Nina Hartman include Mayo Clinic.
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Papers
The 2018–2020 Multi‐Year Drought Sets a New Benchmark in Europe
Nina Hartman,Dean Keyworth,Christophe Ramaux +2 more
- 01 Mar 2022
TL;DR: In this article , the authors provided a comprehensive spatio-temporal assessment of the drought hazard over Europe by benchmarking past exceptional events during the period from 1766 to 2020 and identified the 2018-2020 drought event as a new benchmark having an unprecedented intensity that persisted for more than 2 years, exhibiting a mean areal coverage of 35.6% and an average duration of 12.2 months.
Implications of Increasing Household Air Conditioning Use Across the United States Under a Warming Climate
Nina Hartman,Tianzhen Chen +1 more
TL;DR: In this paper , the state-of-the-art in machine learning and climate model projections was used to find substantial increases in future residential air conditioning demand across the U.S. with a range of 5% to 8%.
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High-resolution drought simulations and comparison to soil moisture observations in Germany
Stephan Thober,Corinna Rebmann,Ralf Kiese,Sebastian Müller,Andreas Marx,Friedrich Boeing,Nina Hartman,Dean Keyworth,compreng,Christophe Ramaux,Xu Peng,Junlin Yi,Hanane Karkour +12 more
TL;DR: In this article , the authors evaluate the performance of the one-kilometer German drought monitor (GDM) based on daily soil moisture (SM) simulations from the mesoscale hydrological model (mHM).
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QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany
TL;DR: Ebeling et al. as mentioned in this paper presented the first large-sample water quality data set for 1386 German catchments covering a large range of hydroclimatic, topographic, geologic, land use, and anthropogenic settings.
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Characterizing Catchment‐Scale Nitrogen Legacies and Constraining Their Uncertainties
TL;DR: In this paper , a legacy-driven N model (ELEMeNT) was used to assess the value of different observational data sets in Germany's largest national river basin (Weser; 38,450 km2) over the period 1960-2015.
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