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specific hydrologic processes. For example, the Flash Flood Guidance (FFG) system used worldwide estimates runoff generation ( Sweeney 1992 ), However, FFG only addresses parts of the flood’s characteristics and does not focus on water propagation overland or along streams. It misses any occurrence of flooding downstream of the rainfall, especially the delay, magnitude, and duration of the flood. Because a flood forecasting system needs to describe these characteristics ahead of time, modern instances
specific hydrologic processes. For example, the Flash Flood Guidance (FFG) system used worldwide estimates runoff generation ( Sweeney 1992 ), However, FFG only addresses parts of the flood’s characteristics and does not focus on water propagation overland or along streams. It misses any occurrence of flooding downstream of the rainfall, especially the delay, magnitude, and duration of the flood. Because a flood forecasting system needs to describe these characteristics ahead of time, modern instances
techniques and logistic regression techniques for postprocessing GEFS precipitation forecasts. For selecting optimal postprocessing methods, it would be informative to compare the performance of analog methods, which requires long-term reforecast archives, with logistic regression, which only needs a small set of training data. Given the research gaps we have identified, this study aims to 1) document the performance of the GEFS and ECMWF daily precipitation ensemble forecasts using Brazil as case study
techniques and logistic regression techniques for postprocessing GEFS precipitation forecasts. For selecting optimal postprocessing methods, it would be informative to compare the performance of analog methods, which requires long-term reforecast archives, with logistic regression, which only needs a small set of training data. Given the research gaps we have identified, this study aims to 1) document the performance of the GEFS and ECMWF daily precipitation ensemble forecasts using Brazil as case study
observations and in absence of detailed and reliable catchment attributes. Hydrologic models allow simulating and forecasting of streamflow based on meteorological observations ( Beven 2001 ). The classical physics-based hydrologic approach is to first develop conceptual models having fixed structures and parameterizations that reflect our physical understanding of internal catchment structure and functioning such as rainfall–runoff processes and their interactions, and then to apply these prespecified
observations and in absence of detailed and reliable catchment attributes. Hydrologic models allow simulating and forecasting of streamflow based on meteorological observations ( Beven 2001 ). The classical physics-based hydrologic approach is to first develop conceptual models having fixed structures and parameterizations that reflect our physical understanding of internal catchment structure and functioning such as rainfall–runoff processes and their interactions, and then to apply these prespecified
; Miralles et al. 2014 ), and they may also affect the intensity, frequency, and distribution of precipitation ( Findell et al. 2011 ; Guillod et al. 2015 ; Taylor et al. 2012 ). Further, a shortage or excess of SM could trigger the occurrence of droughts ( Wang et al. 2011 ) or floods ( Koster et al. 2010 ). As such, SM is crucial for weather prediction, climate forecasting and ecosystem dynamics assessment. Moreover, SM is vital for agricultural production since it is the only direct source for crop
; Miralles et al. 2014 ), and they may also affect the intensity, frequency, and distribution of precipitation ( Findell et al. 2011 ; Guillod et al. 2015 ; Taylor et al. 2012 ). Further, a shortage or excess of SM could trigger the occurrence of droughts ( Wang et al. 2011 ) or floods ( Koster et al. 2010 ). As such, SM is crucial for weather prediction, climate forecasting and ecosystem dynamics assessment. Moreover, SM is vital for agricultural production since it is the only direct source for crop
