Hydrological Ensemble Prediction Systems Around the Globe

Pappenberger, Florian; Pagano, T. S.; Brown, James Dean; Alfieri, Lorenzo; Lavers, David A.; Berthet, Lionel; Bressand, F.; Cloke, Hannah; Cranston, Michael; Daňhelka, Jan; Demargne, Julie; Demuth, N.; Saint-Aubin, C. de; Feikema, Paul; Fresch,; Garçon, Rémy; Gelfan, Alexander; He, Yi; Hu, Ya; Janet, Bruno; Jurdy, N.; Javelle, Pierre; Kuchment, L. S.; Laborda, Y.; Langsholt, Elin; Lay, Matthieu Le; Li, Z.J.; Mannessiez, F.; Marchandise, Arthur; Marty, Renaud; Meißner, Dennis; Manful, Desmond; Organde, Didier; Pourret, Vivien; Rademacher, S.; Ramos, Maria‐Helena; Reinbold, D.; Tibaldi, S.; Silvano, P.; Salamon, Peter; Shin, Daehyok; Sorbet, C.; Sprokkereef, Eric; Thiemig, Vera; Tuteja, Narendra; Andel, Schalk Jan van; Verkade, Jan; Vehviläinen, Bertel; Vogelbacher, A.; Wetterhall, Fredrik; Zappa, Massimiliano; Zwan, R.E. Van der; Pozo, Jutta Thielen-del

doi:10.1007/978-3-642-40457-3_47-1

Cited by 17 publications

(9 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the inland waterway navigation is most of the time dependent on long-term investments and long-term management and the related infrastructure and money cannot be easily relocated, it is imperative to forecast low flows so that the shipping companies are aware of navigation restrictions and have the opportunity to provide alternative means of transport. Currently, there are a large number of drought warning systems available at a global scale 8, but these systems are providing monthly/seasonal forecasts based either on meteorological drought indices (e.g. Standardized Precipitation and Evapotranspiration Index and/or Palmer Drought Severity Index) or agricultural drought-related indices (e.g.…”

mentioning

confidence: 99%

Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018

Ionita

Nagavciuc

2020

Sci Rep

View full text Add to dashboard Cite

Over the past decades, Europe has been affected by several low flow periods which had substantial impacts on the hydrology of the rivers themselves as well as on the society and economy. Low flow periods have a direct impact on the environment, on the inland waterway navigation, on the hydropower production as well as on the sediment management, among others. Similar to floods, low flows are naturally occurring phenomena which can significantly hinder different uses and functions of the rivers and impact the aquatic system and the water quality. Moreover, it is projected that, in the future, climate change might lead to drier summers over the european region and therefore to more frequent and severe low flow periods. The results presented here show that the summer 2018 low flow situation, over the Rhine and Elbe Rivers basin, could have been predicted up to two seasons ahead by using previous months' sea surface temperature, sea level pressure, precipitation, mean air temperature and soil moisture. the lagged relationship between the predictand (e.g. seasonal streamflow) and the climate and oceanic predictors varies between 1 month (e.g. precipitation) up to 6 months (e.g. sea surface temperature). Taking into account that all predictors are available in realtime, the forecast scheme can be used to provide early warnings for the upcoming low flow situations, thus offering the possibility for better management of the water resources. Drought is among the costliest and damaging disasters in the world and is a naturally occurring phenomenon that can affect large land areas and can prevail over extended periods (e.g. several months up to a few years). Over the last two decades, the European region has witnessed a series of long-lasting dry and hot summers (2003, 2010, 2015, and 2018, among others) 1-3. For example, the year 2018 over the central part of Europe, especially Germany, was extraordinarily hot and dry. For Germany, the period April-July 2018 was the warmest since 1,880 and different meteorological stations have reached all-time maximum temperature records. This situation was also exacerbated by a rainfall deficit since February 2018. Overall, in Europe, the monetary losses caused by hydrological and meteorological extremes, over the period 1980-2017, amounted to ~ 453 billion Euro 4. Prolonged dry periods, such as the ones observed in the last decades (e.g. 2003, 2015, and 2018) have emphasized the degree of vulnerability of society to this natural hazard and alerted different governmental agencies and stakeholders regarding the damaging effects drought can have on the economy and society 5,6. Moreover, the IPCC fifth assessment report 7 concludes that the magnitude and frequency of extreme events (e.g. droughts, floods, heatwaves, compound events) will increase globally in the future and large areas of the European continent will be exposed to increased drought risk and possibly to more frequent and long-lasting low flow periods. Prolonged low flow periods may result in several types of issues for the soc...

show abstract

mentioning

confidence: 99%

Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018

Ionita

Nagavciuc

2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…CRHM provides a framework that allows the integration of physically based parameterizations of hydrological processes. Current implementations consider coldregion-specific processes such as blowing snow, snow interception in forest canopies, sublimation, snowmelt, infiltration into frozen soils, and hillslope water movement over permafrost (Pomeroy et al, 2007). CRHM supports both spatially distributed and aggregated model approaches.…”

Section: Other Frameworkmentioning

confidence: 99%

Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology

et al. 2021

View full text Add to dashboard Cite

Abstract. This paper presents Shyft, a novel hydrologic modeling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables rapid development and implementation in operational settings and the capability to perform distributed hydrologic modeling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well-known routines or through the creation of new routines, each defined for processes such as evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an application programming interface (API) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The API allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide enterprise-level software, strong focus is given to computational efficiency, code quality, documentation, and test coverage. Shyft is released open-source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os (last access: 22 November 2020), facilitating effective cooperation between core developers, industry, and research institutions.

show abstract

“…To date, hydrological modelling has proven to be a reliable solution for a plethora of tasks, both research and applied, such as water resources planning, flood hazard analysis, civil engineering, structure design, operational forecasting, impact analysis, land-atmosphere interaction analysis, cold season processes, and ecohydrology [1][2][3]. For more than 70 years, the hydrological community has been developing a variety of model designs for the aforementioned applications, from simple data-driven mechanistic models (referred to as 'black boxes') to complex spatially distributed process-based models [2].…”

Section: Introductionmentioning

confidence: 99%

How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting

Moreido

Гарцман

Solomatine

et al. 2021

Water

View full text Add to dashboard Cite

With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors or only with those pre-selected by a hydrologist. The models used in this study were multivariate linear regression, the M5 model tree, multilayer perceptron (MLP) artificial neural network, and the long short-term memory (LSTM) model. We used two river catchments in contrasting runoff generation conditions to try to infer the ability of different model structures to automatically select the best predictor set from all those available in the dataset and compared models’ performance with that of a model operating on predictors prescribed by a hydrologist. Additionally, we tested how shuffling of the initial dataset improved model performance. We can conclude that in rainfall-driven catchments, the models performed generally better on a dataset prescribed by a hydrologist, while in mixed-snowmelt and baseflow-driven catchments, the automatic selection of predictors was preferable.

show abstract

Hydrological Ensemble Prediction Systems Around the Globe

Cited by 17 publications

References 59 publications

Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018

Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018

Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology

How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting

Contact Info

Product

Resources

About