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Nans Addor

  1. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset
    2018/11/13 by Camila Álvarez-Garretón, Pablo A. Mendoza, Juan Pablo Boisier +9 · 4 citations
    Environmental Science · Earth and Planetary Sciences · #Hydrology and Watershed Management Studies #Cryospheric studies and observations #Hydrology and Drought Analysis
  2. The CAMELS data set: catchment attributes and meteorology for large-sample studies
    2017/10/20 by Nans Addor, Andrew J. Newman, Naoki Mizukami +1 · 2 citations
    Environmental Science · #Hydrology and Watershed Management Studies #Flood Risk Assessment and Management #Hydrology and Drought Analysis
  3. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil
    2020/09/08 by Vinícius B. P. Chagas, Pedro Luiz Borges Chaffe, Nans Addor +4 · 3 citations
    Environmental Science · #Hydrology and Watershed Management Studies #Flood Risk Assessment and Management #Hydrology and Drought Analysis
  4. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain
    2020/10/12 by Gemma Coxon, Nans Addor, John P. Bloomfield +9 · 2 citations
    Environmental Science · #Hydrology and Watershed Management Studies #Flood Risk Assessment and Management #Hydrology and Drought Analysis
  5. Panta Rhei: a decade of progress in research on change in hydrology and society
    2025/04/03 by Heidi Kreibich, Murugesu Sivapalan, Amir AghaKouchak +123 · 1 voice · 1 citation
    Engineering · Environmental Science · #Flood Risk Assessment and Management #Hydrology and Watershed Management Studies #Water resources management and optimization
  6. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia
    2021/08/06 by Keirnan Fowler, Suwash Chandra Acharya, Nans Addor +2 · 1 citation
    Environmental Science · #Hydrology and Watershed Management Studies #Flood Risk Assessment and Management #Hydrological Forecasting Using AI