https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2016RG000549

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this paper, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focussing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydro-meteorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

/pdf/ensemble-flood-forecasting-current-status-and-future-179stuuctp.pdf

Ensemble flood forecasting: Current status and future opportunities

. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

/pdf/skilful-seasonal-forecasts-of-streamflow-over-europe-1c9qi9bkhk.pdf

Skilful seasonal forecasts of streamflow over Europe

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028392

Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of
predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating
initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.


Hybrid forecasting: blending climate predictions with AI models

. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

/pdf/an-intercomparison-of-approaches-for-improving-operational-3xuqqhox8p.pdf

An intercomparison of approaches for improving operational seasonal streamflow forecasts

For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHC and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the U.S. Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs, and model-based ensemble streamflow prediction (ESP) – and then systematically inter-compare WSFs across a range of lead times. Additional methods include: (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables; and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are: (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

/pdf/an-intercomparison-of-approaches-for-improving-456kqr6haf.pdf

An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting

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An intercomparison of approaches for improving operational seasonal streamflow forecasts

An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting