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Sources of predictability of winter river flows as reflected by the coefficients for a regression model of winter river flow on either or both of two predictors: long-range forecasts of atmospheric circulation over the North Atlantic as characterized by the NAO index (a), and observed monthly mean river flow for November (b). The aquifer outcrop areas (light blue shading) show where groundwater makes an important contribution to river flows.
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Seasonal river flow forecasts are beneficial for planning agricultural activities, river navigation, and for management of reservoirs for public water supply and hydropower generation. In the United Kingdom (UK), skilful seasonal river flow predictions have previously been limited to catchments in lowland (southern and eastern) regions. Here we sho...
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Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño...
Citations
... Previous research has found a positive correlation between the North Atlantic Oscillation (NAO) and winter rainfall, particularly for western UK 55 with more storms over northern Europe (e.g. Svensson et al. 2015;Hall and Hanna 2018). For southeast and eastern England, studies have found that variability of winter rainfall arises from the combined influence of various circulation indices, particularly the East Atlantic (EA) pattern which can either enhance or dampen the surface temperature and rainfall response to the NAO (Mellado-Cano et al. 2019;West et al. 2021 conditions (Folland et al. 2015). ...
The UK has experienced recurring periods of hydrological droughts in the past, including the latest drought declared in summer 2022. Seasonal hindcasts, consisting of a large sample of plausible weather sequences, can be used to add value to existing approaches to water resources planning. In this study, the drivers of winter rainfall in the Greater Anglia region are investigated using the ECMWF SEAS5 hindcast dataset, which includes 2850 plausible winters across 25 ensemble members and 3 lead times. Four winter clusters were defined using the hindcast winters based on possible combinations of various atmospheric circulation indices (such as North Atlantic Oscillation, East Atlantic Pattern and the El-Niño Southern Oscillation). Using the 2022 drought as a case study, we demonstrate how storylines of the event could be created in autumn 2022 to provide an outlook of drought conditions and to explore plausible worst cases over winter 2022/23 and beyond. The winter clusters span a range of temperature and rainfall response in the Anglian region and represent circulation storylines that could have happened over winter 2022/23. Although winter 2022/23 has now passed, we aim to demonstrate the added value of this approach to provide outlooks during an ongoing event with a brief retrospective of how winter 2022/23 transpired. Storylines created from the hindcast winters were simulated using the GR6J catchment hydrological model and the groundwater level model Aquimod at selected catchments and boreholes in the Anglian region. Results show that drier than average winters characterised by predominantly NAO-/EA- and NAO+/EA- circulation patterns would result in the continuation of the drought with a high likelihood of below normal to low river flows across all selected catchments and boreholes by spring and summer 2023. Catchments in Norfolk are particularly vulnerable to a dry summer in 2023 as river flows are not estimated to recover to normal levels even with wet La Niña winters characterised predominantly by NAO-/EA+ and NAO+/EA+ circulation patterns due to insufficient rainfall to overcome previous dry conditions and the slow response nature of groundwater-dominated catchments. Storylines constructed in this way provide outlooks of ongoing events and supplement traditional weather forecasts to explore a wider range of plausible outcomes.
... Decadal climate variability in northern Europe is strongly linked to the NAO, and NAO-matching improves the skill of precipitation and temperature predictions for this region. The NAO mainly influences UK winter streamflow in the north and northwest UK, and has less influence in east and south UK where antecedent conditions are a better predictor of winter streamflow (Svensson et al., 2015;West et al., 2019). Other climate modes may therefore have greater value for enhancing flood prediction in different regions of the UK and globally, but further work would be required to assess the extent to which they improve the predictability of climate variables. ...
Plain Language Summary
Reliable predictions of flooding can help society to manage the associated risk to lives and property. Seasonal predictions of flooding over the coming months already form the basis of many operational services around the world. In contrast, decadal predictions with lead times of up to 10 years are more challenging, due to the difficulty of simulating dynamic changes in atmospheric circulation at these timescales. Here, we show that a large ensemble of climate models can predict average winter flood conditions over the UK in the next decade. The climate models underestimate the magnitude of atmospheric variability in the north Atlantic and identifying a subset of skillful climate model simulations improves the ability to predict floods. Our results suggest that decadal climate predictions may be useful in the context of flood risk management. However, the use of multiyear averages for flood prediction is still poorly studied, and therefore further work should help determine how such predictions can be used in an operational setting.
... The North Atlantic Oscillation (NAO) is the dominant pattern of atmospheric circulation variability in the North Atlantic (Wallace & Gutzler, 1981), with positive phases associated with stormy, mild and wet conditions over north-west Europe and eastern United States, and negative phases associated with dry, cold conditions (and vice versa for southwest Europe and eastern Canada). Recently, the surface winter mean (December-February [DJF]) NAO has been demonstrated to be predictable 1 month ahead (Athanasiadis et al., 2017;Scaife et al., 2014), and this has been followed by real time operational predictions from the Met Office's seasonal prediction system, GloSea5, where the model ensemble mean has consistently predicted the correct sign of the anomaly Hardiman et al., 2020;Knight et al., 2021;Scaife et al., 2017), aiding decision makers in sectors such as transport (Palin et al., 2016), energy (Clark et al., 2017;Thornton et al., 2019) and water management (Stringer et al., 2020;Svensson et al., 2015). ...
Abstract Boreal winter (December–February) 2020/2021 in the North Atlantic/European region was characterised by a negative North Atlantic Oscillation (NAO) index. Although this was captured within the ensemble spread of predictions from the Met Office Global Seasonal forecast system (GloSea5), with 17% of ensemble members predicting an NAO less than zero, the forecast ensemble mean was shifted towards a positive NAO phase. The observed monthly NAO anomalies were particularly negative in January and February, following an early January sudden stratospheric warming (SSW), and a prolonged period of Phase 6 or 7 of the Madden Julian Oscillation (MJO) in late January/early February. In contrast, predictions showed the expected teleconnection from the observed La Niña, with a positive NAO signal resulting from a weakening of the Aleutian Low leading to a reduction in tropospheric wave activity, an increase in polar vortex strength and a reduced chance of an SSW. Forecasts initialised later in the winter season successfully predicted the negative NAO in January and February once the SSW and MJO were within the medium range timescale. GloSea5 likely over‐predicted the strength of the La Niña which we estimate caused a small negative bias in the SSW probability. However, this error is smaller than the uncertainty in SSW probability from the finite forecast ensemble size, emphasising the need for large forecast ensembles. This case study also demonstrates the advantage of continuously updated lagged ensemble forecasts over a ‘burst’ ensemble started on a fixed date, since a change in forecast signal due to events within the season can be detected early and promptly communicated to users.
... Many of these systems in Europe (e.g. Hall and Hanna, 2018;Svensson et al., 2015) rely on high-resolution hydrometeorological datasets for calibration of historical relationships, many of which are only available for recent decades (Rust et al., 2021b;Sun et al., 2018). We show here that frequency statistics potentially used as calibration bases for water resource early warning systems can exhibit both multidecadal periods of stability and abrupt sub-decadal non-stationarities, driven by multiannual behaviours in the NAO. ...
Drought forecasting and early warning systems for water resource extremes
are increasingly important tools in water resource management in Europe
where increased population density and climate change are expected to place
greater pressures on water supply. In this context, the North Atlantic
Oscillation (NAO) is often used to indicate future water resource behaviours (including droughts) over Europe, given its dominant control on winter rainfall totals in the North Atlantic region. Recent hydroclimate research has focused on the role of multiannual periodicities in the NAO in driving low frequency behaviours in some water resources, suggesting that notable improvements to lead-times in forecasting may be possible by incorporating these multiannual relationships. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here we quantify the time–frequency relationship between the NAO and a large dataset of water resources records to identify key non-stationarities that have dominated multiannual behaviour of water resource extremes over recent decades. The most dominant of these is a 7.5-year periodicity in water resource extremes since approximately 1970 but which has been diminishing since 2005. Furthermore, we show that the non-stationary relationship between the NAO and European rainfall is clearly expressed at multiannual periodicities in the water resource records assessed. These multiannual behaviours are found to have modulated historical water resource anomalies to an extent that is comparable to the projected effects of a worst-case climate change scenario. Furthermore, there is limited systematic understanding in existing atmospheric research for non-stationarities in these periodic behaviours which poses considerable implications to existing water resource forecasting and projection systems, as well as the use of these periodic behaviours as an indicator of future water resource drought.
... These drivers may induce a delay in the response of streamflow to climate variability Steirou et al., 2017). However, other local factors may control these differences at a more detailed spatial scale, such as topography (with mountain chains acting as barriers, but also influential through storage in ice and snow at high altitudes) and lithology (notably significant storages in permeable aquifers), which have been highlighted in previous studies over both southern (Blöschl et al., 2019;López-Moreno et al., 2013) and northern Europe (Hannaford et al., 2011;Svensson et al., 2015). Other relevant factors may be related to the consumption of water by vegetation, especially during summer, or anthropogenic activities associated with dam construction and reservoir use that can affect the distribution of the flow throughout the year (Bastos et al., 2016;Guerrieri et al., 2019;Lorenzo-Lacruz et al., 2013;Mankin et al., 2019). ...
This study presents a new data set of gauged streamflow (N = 3,224) for Europe spanning the period 1962–2017. The Monthly Streamflow of Europe Dataset (MSED) is freely available at http://msed.csic.es/. Based on this data set, changes in the characteristics of hydrological drought (i.e., frequency, duration, and severity) were assessed for different regions of Europe. Due to the density of the database, it is possible to delimit spatial patterns in hydrological droughts trend with the greatest detail available to date. Results reveal bidirectional changes in monthly streamflow, with negative changes predominating over central and southern Europe, while positive trends dominate over northern Europe. Temporally, two dominant patterns were noted. The first pattern corresponds to a consistent downward trend in all months, evident for southern Europe. A second pattern was noted over central and northern Europe and western France, with a predominant negative trend during warm months and a positive trend in cold months. For hydrological drought events, results suggest a positive trend toward more frequent and severe droughts in southern and central Europe and conversely a negative trend over northern Europe. This study emphasizes that hydrological droughts show complex spatial patterns across Europe over the past six decades, implying that hydrological drought behavior in Europe has a regional character. Accordingly it is challenging to adopt “efficient” strategies and policies to monitor and mitigate drought impacts at the continental level.
... Prominent teleconnection patterns that are inherently related to the surface weather conditions in Europe, and known as Euro-Atlantic Teleconnections (EATCs), have been identified in the North Atlantic sector (Barnston et al., 1987). Several previous studies have recognized the strong links between the North Atlantic Oscillation (NAO) (Walker et al., 1932;Lamb et al., 1987;Hurrell, 1995) -the most relevant EATC-and surface temperature, wind speed or precipitation anomalies over Europe on interannual timescales (Trigo et al., 2002;Scaife et al., 2008;Hurrell et al., 2009;Burningham and French, 2013;Svensson et al., 2015). However, more recent studies (Moore and Renfrew, 2012;Comas-Bru and McDermott, 2014;Zubiate et al., 2017;Comas-Bru and Hernández, 2018;Hall and Hanna, 2018;Rust et al., 2015) have shown that taking into account the second, third and fourth modes of variability -namely the East Atlantic (EA), East Atlantic/Western Russia (EAWR) and the Scandinavian (SCA) patterns-greatly improves the representation of the variability and our understanding of the impacts on surface climate. ...
The goal of this analysis is the better understanding of how the large-scale atmospheric patterns affect the renewable resources over Europe and to investigate to what extent the dynamical predictions of the large-scale variability might be used to formulate empirical prediction of local climate conditions (relevant for the energy sector). The increasing integration of renewable energy into the power mix is making the electricity supply more vulnerable to climate variability, therefore increasing the need for skillful weather and climate predictions. Forecasting seasonal variations of energy relevant climate variables can help the transition to renewable energy and the entire energy industry to make better informed decision-making. At seasonal timescale climate variability can be described by recurring and persistent, large-scale patterns of atmospheric pressure and circulation anomalies that interest vast geographical areas. The main patterns of the North Atlantic region (Euro Atlantic Teleconnections, EATCs) drive variations in the surface climate over Europe. We analyze reanalysis dataset ERA5 and the multi-system seasonal forecast service provided by Copernicus Climate Change Service (C3S). We found that the observed EATC indices are strongly correlated with surface variables. However, the observed relationship between EATC patterns and surface impacts is not accurately reproduced by seasonal prediction systems. This opens the door to employ hybrid dynamical-statistical methods. The idea consists in combining the dynamical seasonal predictions of EATC indices with the observed relationship between EATCs and surface variables. We reconstructed the surface anomalies for multiple seasonal prediction systems and benchmarked these hybrid forecasts with the direct variable forecasts from the systems and also with the climatology. The analysis suggests that hybrid methodology can bring several improvements to the predictions of energy relevant Essential Climate Variables.
... The aquifer types in these countries explain the different BFI values. North and west UK have little catchment storage compared to France, where major aquifer system occur in some areas 32,33 . ...
... The moderate skill of winter hydrological drought forecasts might be linked to large-scale teleconnection patterns that affect European climate in winter. Some studies show that the North Atlantic Oscillation (NAO), the occurrence of blocking, and the North Atlantic jet stream are conjectured as strong predictors in winter meteorological forecasts in Europe 33,51,52 . The high skill in prediction of winter precipitation and temperature is then translated to better prediction of streamflow and subsequently streamflow drought. ...
Hydrological drought forecasts outperform meteorological ones, which is anticipated coming from catchment memory. Yet, the importance of catchment memory in explaining hydrological drought forecast performance has not been studied. Here, we use the Basefow Index (BFI) and the groundwater Recession Coefcient (gRC), which through the streamfow, give information on the catchment memory. Performance of streamfow drought forecasts was evaluated using the Brier Score (BS) for rivers across Europe. We found that BS is negatively correlated with BFI, meaning that rivers with high BFI (large memory) yield better drought prediction (low BS). A signifcant positive correlation between gRC and BS demonstrates that catchments slowly releasing groundwater to
streams (low gRC), i.e. large memory, generates higher drought forecast performance. The higher performance of hydrological drought forecasts in catchments with relatively large memory (high BFI and low gRC) implies that Drought Early Warning Systems have more potential to be implemented there and will appear to be more useful.
... Consequently, recent decades have seen considerable advances in the development of monthly and seasonal forecasting systems at global to regional scales (e.g., MacLachlan et al., 2015;Yuan et al., 2015;Tompkins et al., 2017;Emerton et al., 2018). There is recognition that seasonal climate variability can be attributed to atmospheric teleconnections, with many studies showing relationships between local climate conditions and large-scale modes as predictors for skilful seasonal precipitation and streamflow forecasting (e.g., Svensson et al., 2015;Mekanik et al., 2016;Bell et al., 2017;Mariotti et al., 2018). ...
Seasonal precipitation forecasting is highly challenging for the northwest fringes of Europe due to complex dynamical drivers. Hybrid dynamical–statistical approaches offer potential to improve forecast skill. Here, hindcasts of mean sea level pressure (MSLP) from two dynamical systems (GloSea5 and SEAS5) are used to derive two distinct sets of indices for forecasting winter (DJF) and summer (JJA) precipitation over lead‐times of 1–4 months. These indices provide predictors of seasonal precipitation via a multiple linear regression model (MLR) and an artificial neural network (ANN) applied to four Irish rainfall regions and the Island of Ireland. Forecast skill for each model, lead time, and region was evaluated using the correlation coefficient (r) and mean absolute error (MAE), benchmarked against (a) climatology, (b) bias corrected precipitation hindcasts from both GloSea5 and SEAS5, and (c) a zero‐order forecast based on rainfall persistence. The MLR and ANN models produced skilful precipitation forecasts with leads of up to 4 months. In all tests, our hybrid method based on MSLP indices outperformed the three benchmarks (i.e., climatology, bias corrected, and persistence). With correlation coefficients ranging between 0.38 and 0.81 in winter, and between 0.24 and 0.78 in summer, the ANN model outperformed MLR in both seasons in most regions and lead‐times. Forecast skill for summer was comparable to that in winter and for some regions/lead times even superior. Our results also show that climatology and persistence performed better than direct use of bias corrected dynamical outputs in most regions and lead‐times in terms of MAE. We conclude that the hybrid dynamical–statistical approach developed here—by leveraging useful information about MSLP from dynamical systems—enables more skilful seasonal precipitation forecasts for Ireland, and possibly other locations in western Europe, in both winter and summer.
... Reliable forecast information in the S2S range has been identified as high value to a wide range of industries and users (White, 2017). For example, in addition to hydropower operations, S2S precipitation and streamflow forecasts can play an important role in the management of public water supplies and activation of early warning and response systems for floods and droughts (Arnal et al., 2018;Bell et al., 2017;Svensson, 2015;Vitart, 2017Vitart, , 2017White, 2017). S2S forecasts can help end users in many sectors develop proactive management strategies, on weekly timescales, to mitigate weather-related risks. ...
... Streamflow measures the flow of water in a river channel or stream, whereas inflow measures the flow of water into a reservoir. For river systems with a large water storage capacity, it is possible to produce skilful streamflow forecasts on seasonal or even annual timescales (Bell et al., 2017;Harrigan et al., 2018;Svensson, 2015;Svensson, 2016;Wood & Lettenmaier, 2008). Water storage within a river catchment could include features such as mountain snow cover, aquifers, and soil moisture. ...
... As an example, recent studies have explored how snow depth measurements could improve inflow forecasts and operations for hydropower reservoirs in Norway (Magnusson et al., 2020;Ødegård et al., 2019). Streamflow forecasts for river systems with large water storage capacities are typically produced using a hydrological model, or assuming that present day streamflow anomalies will persist, or selecting historical streamflow time series that are analogous to the present day conditions (Bell et al., 2017;Harrigan et al., 2018;Magnusson et al., 2020;Svensson, 2015). ...
Abstract Inflow forecasts play an integral role in the management and operations of hydropower reservoirs. In Scotland, the horizon of inflow forecasts is limited in range to approximately 2 weeks ahead. Additional forecast information in the sub‐seasonal to seasonal (S2S) range would allow operators to take proactive action to mitigate weather‐related risks, thereby improving water management and increasing revenue. The aim of this study is to develop methods of deriving skilful S2S probabilistic inflow forecasts for hydropower reservoirs in Scotland, without the application of a hydrological model. We forecast inflow for a case study reservoir using a linear regression model, trained on historical S2S precipitation predictions and observed inflow rates. Ensemble inflow forecasts generated from the regression model are post‐processed using Ensemble Model Output Statistics, to create calibrated S2S probabilistic forecasts. We evaluate forecast skill for 11 different horizons, using inflow observations. Probabilistic forecasts of weekly average inflow rates hold fair skill relative to climatology up to 6 weeks ahead (fCRPSS = 0.01). Forecasts of 28‐day average inflow rates hold good skill (fCRPSS = 0.19). The S2S probabilistic inflow forecasts are most skilful during winter, when there is greatest risk of reservoirs spilling. Forecasts struggle to predict high summer inflows even at short lead times. The potential for the S2S probabilistic inflow forecasts to improve water management and deliver increased economic value is explored using a stylized cost model. While applied to hydropower forecasting, the results and methods presented here are relevant to broader fields of water management and S2S forecasting applications.
... These drivers may induce a delay in the response of streamflow to climate variability Steirou et al., 2017). However, other local factors may control these differences at a more detailed spatial scale, such as topography (with mountain chains acting as barriers, but also influential through storage in ice and snow at high altitudes) and lithology (notably significant storages in permeable aquifers), which have been highlighted in previous studies over both southern (Blöschl et al., 2019;López-Moreno et al., 2013) and northern Europe (Hannaford et al., 2011;Svensson et al., 2015). Other relevant factors may be related to the consumption of water by vegetation, especially during summer, or anthropogenic activities associated with dam construction and reservoir use that can affect the distribution of the flow throughout the year (Bastos et al., 2016;Guerrieri et al., 2019;Lorenzo-Lacruz et al., 2013;Mankin et al., 2019). ...
