Louise J. Slater

Hydrologic versus geomorphic drivers of trends in flood hazard

Flooding is a major hazard to lives and infrastructure, but trends in flood hazard are poorly understood. The capacity of river channels to convey flood flows is typically assumed to be stationary, so changes in flood frequency are thought to be driven primarily by trends in streamflow. We have developed new methods for separately quantifying how trends in both streamflow and channel capacity have affected flood frequency at gauging sites across the United States Flood frequency was generally nonstationary, with increasing flood hazard at a statistically significant majority of sites. Changes in flood hazard driven by channel capacity were smaller, but more numerous, than those driven by streamflow. Our results demonstrate that accurately quantifying changes in flood hazard requires accounting separately for trends in both streamflow and channel capacity. They also show that channel capacity trends may have unforeseen consequences for flood management and for estimating flood insurance costs.

Objective extraction of channel heads from high-resolution topographic data

Fluvial landscapes are dissected by channels, and at their upstream termini are channel heads. Accurate reconstruction of the fluvial domain is fundamental to understanding runoff generation, storm hydrology, sediment transport, biogeochemical cycling, and landscape evolution. Many methods have been proposed for predicting channel head locations using topographic data, yet none have been tested against a robust field data set of mapped channel heads across multiple landscapes. In this study, four methods of channel head prediction were tested against field data from four sites with high-resolution DEMs: slope-area scaling relationships; two techniques based on landscape tangential curvature; and a new method presented here, which identifies the change from channel to hillslope topography along a profile using a transformed longitudinal coordinate system. Our method requires only two user-defined parameters, determined via independent statistical analysis. Slope-area plots are traditionally used to identify the fluvial-hillslope transition, but we observe no clear relationship between this transition and field-mapped channel heads. Of the four methods assessed, one of the tangential curvature methods and our new method most accurately reproduce the measured channel heads in all four field sites (Feather River CA, Mid Bailey Run OH, Indian Creek OH, Piedmont VA), with mean errors of −11, −7, 5, and −24 m and 34, 3, 12, and −58 m, respectively. Negative values indicate channel heads located upslope of those mapped in the field. Importantly, these two independent methods produce mutually consistent estimates, providing two tests of channel head locations based on independent topographic signatures.

Imprint of climate and climate change in alluvial riverbeds: Continental United States, 1950–2011

Alluvial riverbed elevation responds to the balance between sediment supply and transport capacity, which is largely dependent on climate and its translation into fl uvial discharge. We examine these relations using U.S. Geological Survey streamfl ow and channel measurements in conjunction with basin characteristics for 915 reference (“least disturbed”) measurement stations across the conterminous United States for the period A.D. 1950‐2011. We fi that (1) 68% of stations have bed elevation change (BEC) trends (p < 0.05) with median values of +0.5 cm/yr for aggradation and ‐0.6 cm/yr for degradation, with no obvious relation to drainage basin structure, physiography, or lithology; (2) BEC correlates with drainage basin area; (3) high-fl ow variability (Q 90 /Q 50 , where Q is discharge and 90 and 50 are annual fl ow percentiles) translates directly into the magnitude, though not the direction, of BEC, after accounting for the scale dependence; (4) Q 90 /Q 50 declines systematically from dry to wet climates, producing disproportionately high rates of BEC in drier regions; and (5) marked increases in precipitation and streamfl ow occurred disproportionately at dry sites, while streamfl ow declined disproportionately at wet sites. Climatic shifts in streamfl ow have the potential to increase/decrease sediment fl ux and thus affect riverbed elevation by altering fl ood frequency. These unforeseen responses of bed elevation to climate and climate change have important implications for sediment budgets, longitudinal profi les, ecology, and river management.

Recent trends in U.S. flood risk

Flooding is projected to become more frequent as warming temperatures amplify the atmospheres water holding capacity and increase the occurrence of extreme precipitation events. However, there is still little evidence of regional changes in flood risk across the USA. Here we present a novel approach assessing the trends in inundation frequency above the National Weather Services four flood level categories in 2042 catchments. Results reveal stark regional patterns of changing flood risk that are broadly consistent above the four flood categories. We show that these patterns are dependent on the overall wetness and potential water storage, with fundamental implications for water resources management, agriculture, insurance, navigation, ecology, and populations living in flood-affected areas. Our findings may assist in a better communication of changing flood patterns to a wider audience compared with the more traditional approach of stating trends in terms of discharge magnitudes and frequencies.

Evaluation of the skill of North-American multi-model ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA

This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales.

Improved ENSO Forecasting using Bayesian Updating and the North American Multi Model Ensemble (NMME)

AbstractThis study assesses the forecast skill of eight North American Multimodel Ensemble (NMME) models in predicting Nino-3/-3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows a strong dependence on lead (initial) month and target month and is quite promising in terms of correlation, root-mean-square error (RMSE), standard deviation ratio (SDRatio), and probabilistic Brier skill score, especially at short lead months. However, the skill decreases in target months from late spring to summer owing to the spring predictability barrier. When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Nino-3/-3.4 in terms of correlation, RMSE, and SDRatio. For Nino-3.4, the BU-Model outperforms NMME-EM forecasts for almost all leads (1–12; particularly for short leads) and target months (from January to December). However, for Nino-3, the BU-Model does not outperform N...

Examination of Changes in Annual Maximum Gauge Height in the Continental United States Using Quantile Regression

AbstractThis study focuses on the detection of temporal changes in annual maximum gauge height (GH) across the continental United States and their relationship to changes in short- and long-term pr...

Demystifying academics to enhance university–business collaborations in environmental science

In countries globally there is intense political interest in fostering effective university–business collaborations, but there has been scant attention devoted to exactly how an individual scientist’s workload (i.e. specified tasks) and incentive structures (i.e. assessment criteria) may act as a key barrier to this. To investigate this an original, empirical dataset is derived from UK job specifications and promotion criteria, which distil universities’ varied drivers into requirements upon academics. This work reveals the nature of the severe challenge posed by a heavily time-constrained culture; specifically, tension exists between opportunities presented by working with business and non-optional duties (e.g. administration and teaching). Thus, to justify the time to work with business, such work must inspire curiosity and facilitate future novel science in order to mitigate its conflict with the overriding imperative for academics to publish. It must also provide evidence of real-world changes (i.e. impact), and ideally other reportable outcomes (e.g. official status as a business’ advisor), to feed back into the scientist’s performance appraisals. Indicatively, amid 20–50 key duties, typical fullPublished by Copernicus Publications on behalf of the European Geosciences Union. 2 J. K. Hillier et al.: Demystifying academics to enhance university–business collaborations time scientists may be able to free up to 0.5 day per week for work with business. Thus specific, pragmatic actions, including short-term and time-efficient steps, are proposed in a “user guide” to help initiate and nurture a long-term collaboration between an earlyto mid-career environmental scientist and a practitioner in the insurance sector. These actions are mapped back to a tailored typology of impact and a newly created representative set of appraisal criteria to explain how they may be effective, mutually beneficial and overcome barriers. Throughout, the focus is on environmental science, with illustrative detail provided through the example of natural hazard risk modelling in the insurance sector. However, a new conceptual model of academics’ behaviour is developed, fusing perspectives from literature on academics’ motivations and performance assessment, which we propose is internationally applicable and transferable between sectors. Sector-specific details (e.g. list of relevant impacts and user guide) may serve as templates for how people may act differently to work more effectively together.

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

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 streamgauges 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.

Measuring the changing pulse of rivers

A 50-year data set shows changes in the seasonal timing of river floods in Europe River flood risks are expected to rise as climate change intensifies the global hydrological cycle and more people live in floodplains (1). Changing risk may be revealed by trends in flood frequency, magnitude, or seasonality, as well as by shifts in the mechanisms that generate inundations (2). However, detection and attribution of climate signals in flood records is often hampered by brief, incomplete, or poor-quality flood data (3). Additionally, it can be difficult to disentangle the effects of changing climate, land cover, channel morphology, and human activities (2, 4). On page 588 of this issue, Blöschl et al. (5) overcome these problems through a consistent pan-European assessment of observed flood seasonality trends between 1960 and 2010. They thus provide the first evaluation of how climatic changes are influencing flood regimes at the continental scale.