Search

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 regionalizedStreamflow predictability is strongly dependent on the basin's hydrological regime, climatology, and precipitation forecast biasesPredictability is higher in river systems of long streamflow memory than in systems immediately responding to the precipitation signal [ABSTRACT FROM AUTHOR]

Abstract: Climate warming may alter the quantity and timing of groundwater discharge to streams in high alpine watersheds due to changes in the timing of the duration of seasonal freezing in the subsurface and snowmelt recharge. It is imperative to understand the effects of seasonal freezing and recharge on groundwater discharge to streams in warming alpine watersheds as streamflow originating from these watersheds is a critical water resource for downstream users. This study evaluates how climate warming may alter groundwater discharge due to changes in seasonally frozen ground and snowmelt using a 2‐D coupled flow and heat transport model with freeze and thaw capabilities for variably saturated media. The model is applied to a representative snowmelt‐dominated watershed in the Rocky Mountains of central Colorado, USA, with snowmelt time series reconstructed from a 12 year data set of hydrometeorological records and satellite‐derived snow covered area. Model analyses indicate that the duration of seasonal freezing in the subsurface controls groundwater discharge to streams, while snowmelt timing controls groundwater discharge to hillslope faces. Climate warming causes changes to subsurface ice content and duration, rerouting groundwater flow paths but not altering the total magnitude of future groundwater discharge outside of the bounds of hydrologic parameter uncertainties. These findings suggest that frozen soil routines play an important role for predicting the future location of groundwater discharge in watersheds underlain by seasonally frozen ground. [ABSTRACT FROM AUTHOR]

Environmental models tend to require increasing computational time and resources as physical process descriptions are improved or new descriptions are incorporated. Many-query applications such as sensitivity analysis or model calibration usually require a large number of model evaluations leading to high computational demand. This often limits the feasibility of rigorous analyses. Here we present a fully automated sequential screening method that selects only informative parameters for a given model output. The method requires a number of model evaluations that is approximately 10 times the number of model parameters. It was tested using the mesoscale hydrologic model mHM in three hydrologically unique European river catchments. It identified around 20 informative parameters out of 52, with different informative parameters in each catchment. The screening method was evaluated with subsequent analyses using all 52 as well as only the informative parameters. Subsequent Sobol's global sensitivity analysis led to almost identical results yet required 40% fewer model evaluations after screening. mHM was calibrated with all and with only informative parameters in the three catchments. Model performances for daily discharge were equally high in both cases with Nash-Sutcliffe efficiencies above 0.82. Calibration using only the informative parameters needed just one third of the number of model evaluations. The universality of the sequential screening method was demonstrated using several general test functions from the literature. We therefore recommend the use of the computationally inexpensive sequential screening method prior to rigorous analyses on complex environmental models. [ABSTRACT FROM AUTHOR]