Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

Abstract

Seasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large‐scale climate indices as predictors in statistical models. In contrast, hybrid statistical‐dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon, and their skill is largely unknown. Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.

On the decadal predictability of the frequency of flood events across the U.S. Midwest

“…In contrast to the precipitation, downscaling and bias‐correcting the temperature ensemble time series leads to an overall increase of the prediction skill (compare Figures S3 and S4); however, shorter lead times are not always associated with higher values of the correlation coefficient. Overall, the skill in predicting precipitation and temperature at long lead times is comparable with what observed for their seasonal predictions (Slater and Villarini (), figs. S2 and S3), suggesting that the challenges in predicting these variables are shared between seasonal and decadal lead times.…”

“…The vast majority of published studies related to flood predictions have focused on predictions with short (e.g., days) to medium (e.g., months) lead times, generally using outputs from numerical weather prediction models (NWP) or general circulation models (GCMs) as input to hydrological or statistical models (e.g., Cloke and Pappenberger, ; Slater and Villarini, ). For instance, He et al .…”

“…Data‐driven approaches of this kind are recognized to be straightforward to implement, especially given the availability of climate data, and they may yield results as accurate as those obtained by physically based models (Pagano et al ., ; Duethmann et al ., ). One key component in statistical flood forecasting is identifying which physical drivers are responsible for the observed inter‐annual variability: there have been growing efforts towards attribution studies of this kind, indicating that precipitation and antecedent moisture conditions are dominant flood drivers (e.g., Mallakpour and Villarini, ; Slater and Villarini, ; ; ). Building on the insights gained from these attribution studies, the research questions we aim to answer in this study are: Is it possible to skilfully predict the frequency of flood events across the central United States?…”

“…The vast majority of published studies related to flood predictions have focused on predictions with short (e.g., days) to medium (e.g., months) lead times, generally using outputs from numerical weather prediction models (NWP) or general circulation models (GCMs) as input to hydrological or statistical models (e.g., Cloke and Pappenberger, 2009;Slater and Villarini, 2018). For instance, He et al (2010) used outputs from The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE; Bougeault et al, 2010) data set to predict flood discharge in the Huai River catchment (China) up to 10 days in advance using the Xinanjiang hydrological model, with promising results.…”

What is the most useful approach for forecasting hydrological extremes during El Niño?

“…topography, river flow observations, lakes and reservoirs), seasonal precipitation forecasts and hydrological models could result in a dynamical forecasting system that consistently provides a more useful forecast of hydrological extremes, with the benefit that such dynamical forecasts are not constrained to periods of time when there is an El Niño. A third approach, not considered in this study, could be to combine statistical and dynamical forecasts to produce a hybrid system; recent studies suggest this approach could enhance prediction skill at seasonal timescales [45,46]. Research shows that seasonal hydrological forecasts are able to inform local decisions and actions, and that while uncertainty is not necessarily a barrier to the use of such forecasts, a range of information, including forecast skill, different forecast types and local knowledge are important, alongside a need for higher resolutions to aid local decision-making [47].…”