Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop

Dasgupta, Antara; Arnal, Louise; Emerton, Rebecca; Harrigan, Shaun; Matthews, Gwyneth; Muhammad, Ameer; O'Regan, Karen; Pérez‐Ciria, Teresa; Valdez, Emixi; Osnabrugge, Bart van; Werner, Micha; Buontempo, Carlo; Cloke, Hannah; Pappenberger, Florian; Pechlivanidis, Ilias; Prudhomme, Christel; Ramos, Maria‐Helena; Salamon, Peter

doi:10.1111/jfr3.12880

Cited by 10 publications

(6 citation statements)

References 175 publications

(136 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the transferability of knowledge and seasonal forecasting techniques for identifying solutions across local studies is crucial to maximize the global efficacy (Jackson-Blake et al 2022, Dasgupta et al 2023, Göber et al 2023. Our study highlights the significance of attributing predictability to local hydrological characteristics and enables the knowledge to be regionalized and transferred to other (ungauged) geographical locations.…”

Section: Practical Implicationsmentioning

confidence: 81%

Hydrological regimes explain the seasonal predictability of streamflow extremes

Du,

Clemenzi,

Pechlivanidis

2023

Environ. Res. Lett.

Self Cite

View full text Add to dashboard Cite

Advances in hydrological modeling and numerical weather forecasting have allowed hydro-climate services to provide accurate impact simulations and skillful forecasts that can drive decisions at the local scale. To enhance early warnings and long-term risk reduction actions, it is imperative to better understand the hydrological extremes and explore the drivers for their predictability. Here, we investigate the seasonal forecast skill of streamflow extremes over the pan-European domain, and further attribute the discrepancy in their predictability to the local river system memory as described by the hydrological regimes. Streamflow forecasts at about 35 400 basins, generated from the E-HYPE hydrological model driven with bias-adjusted ECMWF SEAS5 meteorological forcing input, are explored. Overall the results show adequate predictability for both hydrological extremes over Europe, despite the spatial variability in skill. The skill of high streamflow extreme deteriorates faster as a function of lead time than that of low extreme, with a positive skill persisting up to 12 and 20 weeks ahead for high and low extremes, respectively. A strong link between the predictability of extremes and the underlying local hydrological regime is identified through comparative analysis, indicating that systems of analogous river memory, e.g. fast or slow response to rainfall, can similarly predict the high and low streamflow extremes. The results improve our understanding of the geographical areas and periods, where the seasonal forecasts can timely provide information on very high and low streamflow conditions, including the drivers controlling their predictability. This consequently benefits regional and national organizations to embrace seasonal prediction systems and improve the capacity to act in order to reduce disaster risk and support climate adaptation.

show abstract

Section: Practical Implicationsmentioning

confidence: 81%

Hydrological regimes explain the seasonal predictability of streamflow extremes

Du,

Clemenzi,

Pechlivanidis

2023

Environ. Res. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Progress in the hydro‐climatic forecasting of drought is mainly driven by advances in process‐based impact modeling, numerical weather prediction systems, postprocessing methods, and machine learning (ML) algorithms (Dasgupta et al, 2023; White et al, 2022). However, every year drought events cause unnecessary severe damages and fatalities.…”

Section: The Evolution Of the Emerging Field Of Ibf Of Droughtsmentioning

confidence: 99%

Advances and gaps in the science and practice of impact‐based forecasting of droughts

Shyrokaya,

Pappenberger,

Pechlivanidis

et al. 2023

WIREs Water

Self Cite

View full text Add to dashboard Cite

Advances in impact modeling and numerical weather forecasting have allowed accurate drought monitoring and skilful forecasts that can drive decisions at the regional scale. State‐of‐the‐art drought early‐warning systems are currently based on statistical drought indicators, which do not account for dynamic regional vulnerabilities, and hence neglect the socio‐economic impact for initiating actions. The transition from conventional physical forecasts of droughts toward impact‐based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of “what the weather will do” underpins the rising interest in drought IbF across all weather‐sensitive sectors. Despite the large expected socio‐economic benefits, migrating to this new paradigm presents myriad challenges. In this article, we provide a comprehensive overview of drought IbF, outlining the progress made in the field. Additionally, we present a road map highlighting current challenges and limitations in the science and practice of drought IbF and possible ways forward. We identify seven scientific and practical challenges/limitations: the contextual challenge (inadequate accounting for the spatio‐sectoral dynamics of vulnerability and exposure), the human‐water feedbacks challenge (neglecting how human activities influence the propagation of drought), the typology challenge (oversimplifying drought typology to meteorological), the model challenge (reliance on mainstream machine learning models), and the data challenge (mainly textual) with the linked sectoral and geographical limitations. Our vision is to facilitate the progress of drought IbF and its use in making informed and timely decisions on mitigation measures, thus minimizing the drought impacts globally.This article is categorized under: Science of Water > Water Extremes Science of Water > Methods Science of Water > Water and Environmental Change

show abstract

“…Progress in the hydro-climatic forecasting of drought is mainly driven by advances in process-based impact modeling, numerical weather prediction systems, postprocessing methods, and machine learning (ML) algorithms (Dasgupta et al, 2023;White et al, 2022). However, every year drought events cause unnecessary severe damages and fatalities.…”

Section: The Evolution Of the Emerging Field Of Ibf Of Droughtsmentioning

confidence: 99%

Advances and gaps in the science and practice of impact-based forecasting of droughts

Shyrokaya

Pappenberger

Pechlivanidis

et al. 2023

Preprint

View full text Add to dashboard Cite

Advances in impact modelling and numerical weather forecasting have allowed accurate drought monitoring and skillful forecasts that can drive decisions at the regional scale. State-of-the-art drought early warning systems are currently based on statistical drought indicators, which do not account for regional vulnerabilities, and hence neglect the socio-economic impact for initiating actions. The transition from conventional physical forecasts of droughts towards impact-based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of “what the weather will do” underpins the rising interest in drought IbF across all weather-sensitive sectors. Despite the socio-economic benefits are expected to be high, migrating to this new paradigm presents myriad challenges. In this paper, we outline the progress in IbF of drought, and present a road map highlighting current challenges and corresponding needs for advancing this emerging field. More specifically, we identify the main disconnects and biases of the science and practice of IbF of drought, namely contextual bias (inadequate accounting for the spatio-sectoral dynamics of vulnerability and exposure), data bias (mainly textual and lacking collection protocols for all sectors), model bias (mostly reliant on machine learning models), sectoral bias (mainly agriculture in the science), and geography (mainly Europe for the science, and developing regions in the practice). Our vision is to facilitate the IbF progress and its use in making informed and timely decisions on mitigation measures, thus minimizing the impacts of droughts globally.           

show abstract

Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop

Cited by 10 publications

References 175 publications

Hydrological regimes explain the seasonal predictability of streamflow extremes

Hydrological regimes explain the seasonal predictability of streamflow extremes

Advances and gaps in the science and practice of impact‐based forecasting of droughts

Advances and gaps in the science and practice of impact-based forecasting of droughts

Contact Info

Product

Resources

About