What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?

Pechlivanidis, Ilias; Crochemore, Louise; Rosberg, Jörgen; Bosshard, Thomas

doi:10.1029/2019wr026987

Cited by 56 publications

(52 citation statements)

References 98 publications

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“…The Köppen-Geiger climate classification has been built based on observed global temperature and precipitation data used in Peel et al (2007). There are four main climate types found in Europe, which are cold (D), arid (B), temperate (C), and polar (E).…”

Section: Köppen-geiger Climate Classificationmentioning

confidence: 99%

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Streamflow drought: implication of drought definitions and its application for drought forecasting

Sutanto

Lanen

2021

Hydrol. Earth Syst. Sci.

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Abstract. Streamflow drought forecasting is a key element of contemporary drought early warning systems (DEWS). The term streamflow drought forecasting (not streamflow forecasting), however, has created confusion within the scientific hydrometeorological community as well as in operational weather and water management services. Streamflow drought forecasting requires an additional step, which is the application of a drought identification method to the forecasted streamflow time series. The way streamflow drought is identified is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the differences between different drought identification approaches to identify droughts in European rivers, including an analysis of both historical drought and implications for forecasting. Streamflow data were obtained from the LISFLOOD hydrological model forced with gridded meteorological observations (known as LISFLOOD-Simulation Forced with Observed, SFO). The same model fed with seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts system 5 (ECMWF SEAS 5) was used to obtain the forecasted streamflow. Streamflow droughts were analyzed using the daily and monthly variable threshold methods (VTD and VTM, respectively), the daily and monthly fixed threshold methods (FTD and FTM, respectively), and the Standardized Streamflow Index (SSI). Our results clearly show that streamflow droughts derived from different approaches deviate from each other in their characteristics, which also vary in different climate regions across Europe. The daily threshold methods (FTD and VTD) identify 25 %–50 % more drought events than the monthly threshold methods (FTM and VTM), and accordingly the average drought duration is longer for the monthly than for the daily threshold methods. The FTD and FTM, in general, identify drought occurrences earlier in the year than the VTD and VTM. In addition, the droughts obtained with the VTM and FTM approaches also have higher drought deficit volumes (about 25 %–30 %) than the VTD and FTD approaches. Overall, the characteristics of SSI-1 drought are close to what is being identified by the VTM. The different outcome obtained with the drought identification methods illustrated with the historical analysis is also found in drought forecasting, as documented for the 2003 drought across Europe and for the Rhine River specifically. In the end, there is no unique hydrological drought definition (identification method) that fits all purposes, and hence developers of DEWS and end-users should clearly agree in the co-design phase upon a sharp definition of which type of streamflow drought is required to be forecasted for a specific application.

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Section: Köppen-geiger Climate Classificationmentioning

confidence: 99%

“…1. For detailed information about climate classifications used in the study, see the Köppen-Geiger climate classification presented in Peel et al (2007).…”

Section: Köppen-geiger Climate Classificationmentioning

confidence: 99%

Streamflow drought: implication of drought definitions and its application for drought forecasting

Sutanto

Lanen

2021

Hydrol. Earth Syst. Sci.

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“…The seasonal forecasting skill for two pan-European hydrological systems (i.e., from the EFAS and the SMHI services) was evaluated during IMPREX [4,29]. Results showed these forecasts can have skillful seasonal predictions of anomalously high or low river flows (i.e., flows above or below average) in winter in Europe.…”

Section: Origin Of Seasonal Hydrological Forecast Skill Across Europementioning

confidence: 99%

A Vision for Hydrological Prediction

et al. 2020

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IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society’s ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skillful, thus leading to socioeconomic benefits for the citizens of Europe and beyond.

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“…EWSs, as defined by the United Nations International Strategy for Disasters Reduction, are based on four pillars: (1) risk knowledge, (2) risk monitoring and warning, (3) risk information dissemination and communication, and (4) response capacity. Among these pillars, risk monitoring and warning has been addressed in recent years at global [20][21][22] continental [23,24] or river basin [25] levels developing hydrological models that allow lead times much longer than those obtainable with local observations [15]. Despite these improvements, in situ measured hydrological data are often considered more reliable then hydrological model outputs for operational applications, particularly if coupled with hydraulic propagation models [26].…”

Section: Introductionmentioning

confidence: 99%

Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River

Massazza

Tarchiani²,

Andersson

et al. 2020

Water

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In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models—Niger HYPE (NH) and World-Wide HYPE (WWH)—in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash–Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to “very good” for NH (Heidke skill score HSS = 0.53) and “good” for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10–20% of forecasts were unfortunately not produced in 2019, impacting operational availability.

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What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?

Cited by 56 publications

References 98 publications

Streamflow drought: implication of drought definitions and its application for drought forecasting

Streamflow drought: implication of drought definitions and its application for drought forecasting

A Vision for Hydrological Prediction

Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River

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