What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?. Issue 6 (7th June 2020)

• Record Type: Journal Article
• Authors: Pechlivanidis, I. G.; Crochemore, L.; Rosberg, J.; Bosshard, T.
• Is Part Of: Water resources research. Volume 56:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-07
• Subjects: seasonal forecasting -- performance attribution -- pan‐European scale -- continental models -- hydroclimate services -- hydrologic similarities; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026987
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Recent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydroclimatic services. Given that many water‐related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35, 400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic‐hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions. Plain Language Summary: Hydrological information for the months ahead is of great value to existing decision‐making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate‐related risks. Currently, there is limited knowledge of the factors controlling the quality of the seasonal streamflow forecasts. We analyze such forecasts over Europe and link their predictability to basin descriptors and meteorological biases. This allows the identification of the key drivers along a strong hydroclimatic gradient. The seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. Predictability deteriorates with increasing lead time particularly in the winter months. The hydrological regime is strongly linked to the forecast quality, with quickly reacting basins showing low values. Basin climatology and precipitation forecast biases are also related to the predictability of streamflow. Key Points: Forecast quality of seasonal streamflow volume varies geographically and seasonally, while streamflow predictability can be regionalized Streamflow predictability is strongly dependent on the basin's hydrological regime, climatology, and precipitation forecast biases Predictability is higher in river systems of long streamflow memory than in systems immediately responding to the precipitation signal