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, Le et al. (2019) applied LSTM for flood forecasting based on 24 years of daily data with 18 years of data for training, 5 years for validation, and the remaining 2 years for testing. Tian et al. (2018) trained the LSTM models with 10 years of daily data and validated streamflow simulation at two river basins using another 5 years. Kratzert et al. (2019a) considered 30 years of daily data and trained LSTM on 15 years and tested the performance on the remaining 15 years. Regardless of the
, Le et al. (2019) applied LSTM for flood forecasting based on 24 years of daily data with 18 years of data for training, 5 years for validation, and the remaining 2 years for testing. Tian et al. (2018) trained the LSTM models with 10 years of daily data and validated streamflow simulation at two river basins using another 5 years. Kratzert et al. (2019a) considered 30 years of daily data and trained LSTM on 15 years and tested the performance on the remaining 15 years. Regardless of the
. Atlantic Forest is the second-largest rain forest of the American continent and one of the world’s regions hosting the biggest biodiversity. Annual rainfall is between 1000 and 3000 mm. The Brazilian Pampa represents 2.07% of the national territory and lies within the South Temperate Zone ( Roesch et al. 2009 ). The annual precipitation in the region is around 1200–1600 mm. The Pantanal wetland is a complex of seasonally inundated floodplains along the upper Paraguay River, located mostly in Brazil
. Atlantic Forest is the second-largest rain forest of the American continent and one of the world’s regions hosting the biggest biodiversity. Annual rainfall is between 1000 and 3000 mm. The Brazilian Pampa represents 2.07% of the national territory and lies within the South Temperate Zone ( Roesch et al. 2009 ). The annual precipitation in the region is around 1200–1600 mm. The Pantanal wetland is a complex of seasonally inundated floodplains along the upper Paraguay River, located mostly in Brazil
-020-10234-7 He , J. , X. Bian , Y. Fu , and Y. Qin , 2012 : Research on water consumption and its law of main crops in west Liaohe River plain . Jieshui Guan’gai , 11 , 1 – 4 . Hirschi , M. , and Coauthors , 2011 : Observational evidence for soil-moisture impact on hot extremes in southeastern Europe . Nat. Geosci. , 4 , 17 – 21 , https://doi.org/10.1038/ngeo1032 . 10.1038/ngeo1032 Huang , P. M. , Y. Li , and M. E. Sumner , 2011 : Handbook of Soil Sciences: Properties and
-020-10234-7 He , J. , X. Bian , Y. Fu , and Y. Qin , 2012 : Research on water consumption and its law of main crops in west Liaohe River plain . Jieshui Guan’gai , 11 , 1 – 4 . Hirschi , M. , and Coauthors , 2011 : Observational evidence for soil-moisture impact on hot extremes in southeastern Europe . Nat. Geosci. , 4 , 17 – 21 , https://doi.org/10.1038/ngeo1032 . 10.1038/ngeo1032 Huang , P. M. , Y. Li , and M. E. Sumner , 2011 : Handbook of Soil Sciences: Properties and
flash flooding, geomorphological parameters were derived from the National Elevation Dataset (NED; http://ned.usgs.gov/ ) digital elevation model (DEM) across the CONUS. To ensure compatibility between DEM-based flow accumulations and the actual river network, flow accumulation and direction was extracted by delineating basins with USGS stations, and the National Hydrography Dataset (NHD; http://nhd.usgs.gov/ ) was used to resample the 30-m DEM to a 1-km grid. The geomorphologic parameters for
flash flooding, geomorphological parameters were derived from the National Elevation Dataset (NED; http://ned.usgs.gov/ ) digital elevation model (DEM) across the CONUS. To ensure compatibility between DEM-based flow accumulations and the actual river network, flow accumulation and direction was extracted by delineating basins with USGS stations, and the National Hydrography Dataset (NHD; http://nhd.usgs.gov/ ) was used to resample the 30-m DEM to a 1-km grid. The geomorphologic parameters for
