Skilful seasonal forecasts of streamflow over Europe?

Arnal, Louise; Cloke, Hannah; Stephens, Elisabeth; Wetterhall, F.; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

doi:10.5194/hess-2017-610

Cited by 20 publications

(31 citation statements)

References 37 publications

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“…We develop our forecasting framework in the Midwest because the drivers of streamflow variability are relatively well understood in the region (e.g., Slater & Villarini, 2017). The climate exhibits average annual temperatures ranging from less than 3°C in northern Minnesota to over 15°C in southeastern Missouri and average annual precipitation from~500 mm to~1200 mm in the same areas (Andresen et al, 2012). The Midwest has also witnessed a notable increase in precipitation (Alter et al, 2018) and flooding (Mallakpour & Villarini, 2015) in recent decades.…”

Section: Data Sets Of Observed Streamflow Precipitation Temperaturementioning

confidence: 99%

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

Slater

Villarini

2018

Geophysical Research Letters

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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.

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Section: Data Sets Of Observed Streamflow Precipitation Temperaturementioning

confidence: 99%

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

Slater

Villarini

2018

Geophysical Research Letters

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“…The most favorable years could be chosen from snow measurements during winter and seasonal forecasts of the flow peak during spring. Monthly seasonal forecasts of spring flow show skills in the region (Arnal et al, 2017;Foster et al, 2017), although low, and the efficiency of the artificially induced floods will increase with help of natural high flow also from unregulated areas contributing to flooding of the floodplains.…”

Section: Discussionmentioning

confidence: 99%

Artificially Induced Floods to Manage Forest Habitats Under Climate Change

Arheimer

Hjerdt

Lindström

2018

Front. Environ. Sci.

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Global change is affecting agroforestry and its inherent ecosystems in Sweden. Here we examine the benefits of ecologically adjusted dam regulations to conserve biodiversity under climate change in floodplain habitats, including meadows and riparian mixed forests. The natural flood regime in snow-dominated regions has changed significantly during the last decades, in line with the projections for climate change. The ecosystems of temporary flooded forests show high biodiversity but are dependent on river high flows with long duration. These events are rare in the new climate scenario, but on the other hand, snow-fed rivers are also affected by hydropower dams and regulations. In this study we explored the potential of using reservoir regulation to artificially induce flood events; water management would then be a method to conserve biodiversity in forest habitats and adapt management to climate change. We made detailed calculations in lower Dalälven River, central Sweden, using observed time-series of river flow and dynamic scenario modeling for highly valuable Natura 2000 habitats. Here we show that long-term flooding is less frequent since extensive hydropower was introduced during the 1920s, and moreover, since the 1990's the spring floods are low due to low snow storage and short winter seasons. Sustainable management of 50% of the riparian forest requires flooding by 25 continuous days of 800 m 3 s −1 . We found that artificial floods using new ecological regulation regime of upstream hydropower reservoirs would help, but not be enough, to achieve this goal. The new regulation routines would correspond to a loss of 50-200 GWh in hydropower production for each artificial flood. Sustainable ecosystems in the study site do not request flooding every year, but some every fifth year. For practical implementation, the County Board is currently driving the process locally and we discuss the relevant social features, such as legal and funding aspects, of this adaptive management of water and forests. A smaller part of the forest could probably be rescued and costs could potentially be lowered by using only the most snow rich years and seasonal forecasting of river flow for optimal timing of water release from dams to induce flooding.

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“…They found that S4 performs better than both ESPs at longer lead times. Contrarily, Arnal et al (2018), who studied LISFL-OOD seasonal streamflow forecasts over Europe with a spatial resolution of 0.7°, found that, on average, ESPs perform better than S4 for longer lead times, though this differs per season. It remains to be explored if this would also be the case for high resolution data in Catalonia.…”

Section: The Use Of Lisflood and Ecmwf Seas4mentioning

confidence: 99%

“…In probabilistic hydrological forecasts, a hydrological model is forced by probabilistic weather forecasts. On the other hand, ESP indicates that a hydrological model is initialized with current hydrological conditions, that subsequently is driven with different samples of historical meteorological data (Wood and Lettenmaier, 2008;Shukla and Lettenmaier, 2011;Staudinger and Seibert, 2014;Arnal et al, 2018;Monhart et al, 2019). In this study, we use probabilistic hydrological forecasts, which are, from now on, referred to as "hydrological drought forecasts".…”

Section: Introductionmentioning

confidence: 99%

Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)

Lanen¹,

Hateren²,

Sutanto³

2020

Preprint

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<p>Robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders coping with water scarcity. Many studies have been conducted to evaluate the performance of hydrological forecasts, that is, streamflow. Nevertheless, only few studies evaluated the performance of hydrological drought forecasts, e.g. forecast of deficit volumes in river flow, or duration of deficits. The objective of this study, therefore, was to analyse the skill and robustness of meteorological and hydrological drought forecasts at the catchment scale (the Ter and Llobregat rivers in Catalonia, Spain), rather than at a continental or global scale. Meteorological droughts were forecasted using downscaled (5&#160;km) probabilistic seasonal weather reforecasts (ECMWF-SEAS4). These downscaled data were also used to drive the hydrological model (EFAS-LISFLOOD) to produce hydrological drought forecasts, which were derived from time series of simulated streamflow data. This resulted in seasonal hydro-meteorological reforecasts with a lead time up to 7 months, for the time period 2002-2010. These monthly reforecasts were compared to two datasets: 1) droughts derived from a proxy for observed data, including gridded precipitation data and streamflow simulated by the LISFLOOD model; and 2) droughts derived from in situ observed precipitation and streamflow. Results show that the skill of hydrological drought forecasts is higher than the climatology, up to 3-4 months lead time. On the contrary, meteorological drought forecasts, analysed using the Standardized Precipitation Index (SPI), do not show added value for short accumulation times (SPI1 and SPI3). The robustness analysis show that using either a less extreme or a more extreme threshold leads to a large change in forecasting skill, which points at a rather low robustness of the hydrological drought forecasts. Because the skill found in hydrological drought forecasts is higher than the meteorological ones in this case study, the use of hydrological drought forecasts in Catalonia is highly recommended to improve drought risk management. The results of this study have already been implemented by the Catalonian Water Agency to forecast reservoir volumes of two big reservoirs located in the Ter and Llobregat catchments, which supply the majority of water to the Barcelona metropolitan area.</p>

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Skilful seasonal forecasts of streamflow over Europe?

Cited by 20 publications

References 37 publications

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

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

Artificially Induced Floods to Manage Forest Habitats Under Climate Change

Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)

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Skilful seasonal forecasts of streamflow over Europe?

Cited by 20 publications

References 37 publications

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

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

Artificially Induced Floods to Manage Forest Habitats Under Climate Change

Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale &#8211; Case Catalonia (Spain)

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Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)