Cecilia Svensson

Uncertainty and climate change impact on the flood regime of small UK catchments

A rigorous methodology is described for quantifying some of the uncertainties of climate change impact studies, excluding those due to downscaling techniques, and applied on a set of five catchments in Great Britain. Uncertainties in climate change are calculated from a set of 25,000 climate scenarios randomly generated by a Monte Carlo simulation, using several Global Climate Models, SRES-98 emission scenarios and climate sensitivities. Flow series representative of current and future conditions were simulated using a conceptual hydrological model. Generalised Pareto Distributions were fitted to Peak-Over-Threshold series for each scenario, and future flood scenarios were compared to current conditions for four typical flood events. Most scenarios show an increase in both the magnitude and the frequency of flood events, generally not greater than the 95% confidence limits. The largest uncertainty can be attributed to the type of GCM used, with the magnitude of changes varying by up to a factor 9 in Northern England and Scotland. It is therefore essential that climate change impact studies consider a range of climate scenarios derived from different GCMs, and that adaptation policies do not rely on results from only very few scenarios.

Trend detection in river flow series: 1. Annual maximum flow / Détection de tendance dans des séries de débit fluvial: 1. Débit maximum annuel

Abstract Results of a study on change detection in hydrological time series of annual maximum river flow are presented. Out of more than a thousand long time series made available by the Global Runoff Data Centre (GRDC) in Koblenz, Germany, a worldwide data set consisting of 195 long series of daily mean flow records was selected, based on such criteria as length of series, currency, lack of gaps and missing values, adequate geographical distribution, and priority to smaller catchments. The analysis of annual maximum flows does not support the hypothesis of ubiquitous growth of high flows. Although 27 cases of strong, statistically significant increase were identified by the Mann-Kendall test, there are 31 decreases as well, and most (137) time series do not show any significant changes (at the 10% level). Caution is advised in interpreting these results as flooding is a complex phenomenon, caused by a number of factors that can be associated with local, regional, and hemispheric climatic processes. Moreover, river flow has strong natural variability and exhibits long-term persistence which can confound the results of trend and significance tests.

Trend detection in river flow series: 2. Flood and low-flow index series / Détection de tendance dans des séries de débit fluvial: 2. Séries d'indices de crue et d'étiage

Abstract Major floods in Europe and North America during the past decade have provoked the question of whether or not they are an effect of a changing climate. This study investigates changes in observational data, using up to 100-year-long daily mean river flow records at 21 stations worldwide. Trends in seven flood and low-flow index series are assessed using Mann-Kendall and linear regression methods. Emphasis was on the comparison of trends in these flow index series, particularly in peak-over-threshold (POT) series as opposed to annual maximum (AM) river flow series. There is a larger number of significant trends in the AM than in the POT flood magnitude series, probably relating to the way the series are constructed. Low flood peaks occurring at the beginning or end of a time series with trend may be too low to be selected for the POT analysis. However, one peak per year will always be selected for the AM series, making the slope steeper and/or the series longer, resulting in a more significant trend. There is no general pattern of increasing or decreasing numbers or magnitudes of floods, but there are significant increases in half of the low-flow series.

Multifractal Properties of Daily Rainfall in Two Different Climates

The multifractal properties of daily rainfall were investigated in two contrasting climates: an east Asian monsoon climate (China) with an extreme rainfall variability and a temperate climate (Sweden) with a moderate rainfall variability. First, daily time series were studied. The results showed that daily rainfall in both climates can be viewed as the result of a multiplicative cascade process for the range 1–32 days. The temporal data exhibited scaling for moments of orders up to 2.5 in the monsoon area and up to 4.0 in the temperate area and showed clear multifractal properties in both climates. Second, daily spatial rainfall distributions were pooled into different rainfall-generating mechanism groups, and each group was analyzed separately. The spatial data for all rainfall mechanisms in the two climates exhibited scaling for moments of orders up to 4.0. The scaling regime was 15–180 km (225–32,400 km2) in the monsoon climate and 7.5–90 km (55–8100 km2) in the temperate climate. A multifractal framework seemed well suited for description of convective-type rainfall in both climates, but its suitability for frontal rainfall in the two regions was less clear. Although the frontal rainfall exhibited scaling, the almost linear τ(q) functions suggested monofractality.

Long-range forecasts of UK winter hydrology

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 show that skilful long-range forecasts of winter flows can now be achieved across the whole of the UK. This is due to a remarkable geographical complementarity between the regional geological and meteorological sources of predictability for river flows. Forecast skill derives from the hydrogeological memory of antecedent conditions in southern and eastern parts of the UK and from meteorological predictability in northern and western areas. Specifically, it is the predictions of the atmospheric circulation over the North Atlantic that provides the skill at the seasonal timescale. In addition, significant levels of skill in predicting the frequency of winter high flow events is demonstrated, which has the potential to allow flood adaptation measures to be put in place.

Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues

Abstract Seasonal river flow forecasting methods are currently being developed for country-wide application in the United Kingdom, using several different techniques. In this paper, methods based on persistence and historical flow analogues are presented. New 1- and 3-month forecasts are made each month using monthly river flows at 93 stations with records at least 30 years long. The method that performs best is selected for each separate month, catchment and forecast duration. The forecasts based on persistence of the previous month’s flow generally outperform the analogues approach, particularly for slowly responding catchments (mainly in the southeast) with large underground water storage in aquifers. Historical analogues make a useful contribution to the forecasts in the northwest of the country. Correlations between hindcasts and observations that exceed 0.23 and are significant at the 5% level for a one-sided test are found for 81% (70%) of the station–month combinations for the 1-month (3-month) forecast. Editor Z. W. Kundzewicz Associate editor Not assigned

Flood frequency estimation using a joint probability approach within a Monte Carlo framework

Abstract Event-based methods are used in flood estimation to obtain the entire flood hydrograph. Previously, such methods adopted in the UK have relied on pre-determined values of the input variables (e.g. rainfall and antecedent conditions) to a rainfall–runoff model, which is expected to result in an output flood of a particular return period. In contrast, this paper presents a method that allows all the input variables to take on values across the full range of their individual distributions. These values are then brought together in all possible combinations as input to an event-based rainfall–runoff model in a Monte Carlo simulation approach. Further, this simulation strategy produces a long string of events (on average 10 per year), where dependencies from one event to the next, as well as between different variables within a single event, are accounted for. Frequency analysis is then applied to the annual maximum peak flows and flow volumes. Citation Svensson, C., Kjeldsen, T.R., and Jones, D.A., 2013. Flood frequency estimation using a joint probability approach within a Monte Carlo framework. Hydrological Sciences Journal, 58 (1), 1–20.

Statistical distributions for monthly aggregations of precipitation and streamflow in drought indicator applications

Drought indicators are used as triggers for action and so are the foundation of drought monitoring and early warning. The computation of drought indicators like the standardized precipitation index (SPI) and standardized streamflow index (SSI) require a statistical probability distribution to be fitted to the observed data. Both precipitation and streamflow have a lower bound at zero, and their empirical distributions tend to have positive skewness. For deriving the SPI, the Gamma distribution has therefore often been a natural choice. The concept of the SSI is newer and there is no consensus regarding distribution. In the present study, twelve different probability distributions are fitted to streamflow and catchment average precipitation for four durations (1, 3, 6, and 12 months), for 121 catchments throughout the United Kingdom. The more flexible three- and four-parameter distributions generally do not have a lower bound at zero, and hence may attach some probability to values below zero. As a result, there is a censoring of the possible values of the calculated SPIs and SSIs. This can be avoided by using one of the bounded distributions, such as the reasonably flexible three-parameter Tweedie distribution, which has a lower bound (and potentially mass) at zero. The Tweedie distribution has only recently been applied to precipitation data, and only for a few sites. We find it fits both precipitation and streamflow data nearly as well as the best of the traditionally used three-parameter distributions, and should improve the accuracy of drought indices used for monitoring and early warning.

Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales

ABSTRACT This paper describes the development of the first operational seasonal hydrological forecasting service for the UK, the Hydrological Outlook UK (HOUK). Since June 2013, this service has delivered monthly forecasts of streamflow and groundwater levels, with an emphasis on forecasting hydrological conditions over the next three months, accompanied by outlooks over longer time horizons. This system is based on three complementary approaches combined to produce the outlooks: (i) national-scale modelling of streamflow and groundwater levels based on dynamic seasonal rainfall forecasts, (ii) catchment-scale modelling where streamflow and groundwater level models are driven by historical meteorological forcings (i.e. the Ensemble Streamflow Prediction, ESP, approach), and (iii) a catchment-scale statistical method based on persistence and historical analogues. This paper provides the background to the Hydrological Outlook, describes the various component methods in detail and then considers the impact and usefulness of the product. As an example of a multi-method, operational seasonal hydrological forecasting system, it is hoped that this overview provides useful information and context for other forecasting initiatives around the world.

Developing drought impact functions for drought risk management

Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socio-economic consequences of drought. This study tests the potential for 15 developing empirical “drought impact functions” based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors, and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using SouthEast England as a case study we tested the potential of three different data-driven models for predicting drought impacts 20 quantified from text-based reports; logistic regression, zero-altered negative binomial regression (“hurdle model”), and an ensemble regression tree approach (“random forest”). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be predictable within reasonable 25 limits. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross-validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts as well as for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated, and were found to have little influence on the prediction skill. For all models 30 we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2017-187, 2017 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 31 May 2017 c