A cyclone climatology of the British-Irish Isles 1871-2012

The British-Irish Isles (BI) lie beneath the North Atlantic storm track year-round and thus are impacted by the passage of extra-tropical cyclones. Given recent extreme storminess and projections of enhanced winter cyclone activity for this region, there is much interest in assessing the extent to which the cyclone climate of the region may be changing. We address this by assessing a 142-year (1871-2012) record of cyclone frequency, intensity and 'storminess' derived from the 20th Century Reanalysis V2 (20CR) dataset. We also use this long-term record to examine associations between cyclone activity and regional hydroclimate. Our results confirm the importance of cyclone frequency in driving seasonal precipitation totals which we find to be greatest during summer months. Cyclone frequency and storminess are characterized by pronounced interannual and multi-decadal variability which are strongly coupled to atmospheric blocking in the Euro-Atlantic region, but we detect no evidence of an increasing trend. We observe an upward trend in cyclone intensity for the BI region, which is strongest in winter and consistent with model projections, but promote caution interpreting this given the changing data quality in the 20CR over time. Nonetheless, we assert that long-term reconstruction is helpful for contextualizing recent storminess and for identifying emerging changes in regional hydroclimate linked to cyclones

Attribution of detected changes in streamflow using multiple working hypotheses

This paper revisits a widely cited study of the Boyne catchment in east Ireland that attributed greater streamflow from the mid-1970s to increased precipitation linked to a shift in the North Atlantic Oscillation. Using the method of multiple working hypotheses we explore a wider set of potential drivers of hydrological change. Rainfall-runoff models are used to reconstruct streamflow to isolate the effect of climate, taking account of both model structure and parameter uncertainty. The Mann-Kendall test for monotonic trend and Pettitt change point test are applied to explore signatures of change. Contrary to earlier work, arterial drainage and simultaneous onset of field drainage in the 1970s and early 1980s are now invoked as the predominant drivers of change in annual mean and high flows within the Boyne. However, a change in precipitation regime is also present in March, thereby amplifying the effect of drainage. This new explanation posits that multiple drivers acting simultaneously were responsible for the observed change, with the relative contribution of each driver dependant on the timescale investigated. This work demonstrates that valuable insights can be gained from a systematic application of the method of multiple working hypotheses in an effort to move towards more rigorous attribution, which is an important part of managing emerging impacts on hydrological systems. © Author(s) 2014

Extreme multi-basin flooding linked with extra-tropical cyclones [Poster]

Fluvial floods are typically investigated as ‘events’ at the single basin-scale, hence flood management authorities may underestimate the threat of flooding across multiple basins driven by large-scale and nearly concurrent atmospheric event(s). We pilot a national-scale statistical analysis of the spatio-temporal characteristics of extreme multi-basin flooding (MBF) episodes, using peak river flow data for 260 basins in Great Britain (1975-2014), a sentinel region for storms impacting northwest and central Europe. During the most widespread MBF episode, 108 basins (~46% of the study area) recorded Annual Maximum (AMAX) discharge within a 16-day window. Such episodes are associated with persistent cyclonic and westerly atmospheric circulations, atmospheric rivers, and precipitation falling onto previously saturated ground, leading to hydrological response times <40h and documented flood impacts. Furthermore, peak flows tend to occur after 0-13 days of very severe gales causing combined and spatially-distributed, yet differentially time-lagged, wind and flood damages. These findings have implications for emergency responders, insurers and contingency planners worldwide

An evaluation of persistent meteorological drought using a homogeneous Island of Ireland precipitation network

This paper investigates the spatial and temporal properties of persistent meteorological droughts using the homogeneous Island of Ireland Precipitation (IIP) network. Relative to a 1961–1990 baseline period it is shown that the longest observed run of below average precipitation since the 1850s lasted up to 5 years (10 half-year seasons) at sites in southeast and east Ireland, or 3 years across the network as a whole. Dry spell and wet spell length distributions were represented by a first-order Markov model which yields realistic runs of below average rainfall for individual sites and IIP series. This model shows that there is relatively high likelihood (p = 0.125) of a 5-year dry spell at Dublin, and that near unbroken dry runs of 10 years or more are conceivable. We suggest that the IIP network and attendant rainfall deficit modelling provide credible data for stress testing water supply and drought plans under extreme conditions

The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces uncertainty regarding the magnitude, rate and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/ measurement procedures, post-processing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation and comparison between sites, outlier and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation or urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data

Observations relating extreme multi-basin river flows to very severe gales [Abstract]

Observations relating extreme multi-basin river flows to very severe gales [Abstract

Extreme multi-basin fluvial flows and their relationship to extra-tropical cyclones [Abstract]

Extreme multi-basin fluvial flows and their relationship to extra-tropical cyclones [Abstract

Dynamical–statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland

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

Benchmarking seasonal forecasting skill using river flow persistence in Irish catchments

This study assesses the seasonal forecast skill of river flow persistence in 46 catchments representing a range of hydrogeological conditions across Ireland. Skill is evaluated against a climatology benchmark forecast and by examining correlations between predicted and observed flow anomalies. Forecasts perform best when initialised in drier summer months, 87% of which show greater skill relative to the benchmark at a 1-month horizon. Such skill declines as forecast horizon increases due to the longer time a catchment has to “forget” initial anomalous flow conditions and/or to be impacted by “new” events. Skill is related to physical catchment descriptors such as the Baseflow Index (correlation ρ= 0.86) and is greatest in permeable high-storage catchments. The distinct seasonal and spatial variations in persistence skill allows us to pinpoint when and where this method can provide a useful benchmark in the future development of more complex seasonal hydrological forecasting approaches in Ireland. </div

Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores (CRPSS) of 0.8 (1 day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low and high flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office’s Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7–18% were possible over lead times of 1 to 3 months, and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.</div