Aldo Tapia Araya
Universidad de La Serena;University Of Arizona
| Subject Areas: | Catchment hydrology,Model calibration |
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ABSTRACT:
Accurate short-term streamflow forecasting is crucial for effective water resource management and mitigating hydrological extremes, such as floods and droughts. HidroCL DB is a comprehensive lumped hydrometeorological database designed to support short-term streamflow forecasting across 432 catchments in Continental Chile, with daily records spanning 2000 to 2025. The database integrates diverse data sources, including GIS layers, climatic reanalysis, satellite observations, and numerical weather predictions. Data are organized by catchment and grouped into static variables (e.g., catchment characteristics, water rights, soil properties), observed variables (e.g., satellite-derived indicators, meteorological data, reservoir surface area), forecasted variables (e.g., numerical weather prediction outputs), and streamflow measurements from gauge stations. HidroCL DB provides a valuable resource for researchers and practitioners to develop, validate, and benchmark hydrological models for operational streamflow forecasting in Chile.
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Created: July 17, 2025, 7:55 p.m.
Authors: Tapia Araya, Aldo · Jorge Arevalo · Jorge Saavedra-Garrido · Luis De LaFuente · ChristopherParedes-Arroyo
ABSTRACT:
Accurate short-term streamflow forecasting is crucial for effective water resource management and mitigating hydrological extremes, such as floods and droughts. HidroCL DB is a comprehensive lumped hydrometeorological database designed to support short-term streamflow forecasting across 432 catchments in Continental Chile, with daily records spanning 2000 to 2025. The database integrates diverse data sources, including GIS layers, climatic reanalysis, satellite observations, and numerical weather predictions. Data are organized by catchment and grouped into static variables (e.g., catchment characteristics, water rights, soil properties), observed variables (e.g., satellite-derived indicators, meteorological data, reservoir surface area), forecasted variables (e.g., numerical weather prediction outputs), and streamflow measurements from gauge stations. HidroCL DB provides a valuable resource for researchers and practitioners to develop, validate, and benchmark hydrological models for operational streamflow forecasting in Chile.