Exploring the sensitivity of coastal inundation modelling to DEM vertical error

© 2018 Informa UK Limited, trading as Taylor & Francis Group. As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover

Impact of the introduction and withdrawal of financial incentives on the delivery of alcohol screening and brief advice in English primary health care : an interrupted time–series analysis

Aim To evaluate the impact of the introduction and withdrawal of financial incentives on alcohol screening and brief advice delivery in English primary care. Design Interrupted time–series using data from The Health Improvement Network (THIN) database. Data were split into three periods: (1) before the introduction of financial incentives (1 January 2006–31 March 2008); (2) during the implementation of financial incentives (1 April 2008–31 March 2015); and (3) after the withdrawal of financial incentives (1 April 2015–31 December 2016). Segmented regression models were fitted, with slope and step change coefficients at both intervention points. Setting England. Participants Newly registered patients (16+) in 500 primary care practices for 2006–16 (n = 4 278 723). Measurements The outcome measures were percentage of patients each month who: (1) were screened for alcohol use; (2) screened positive for higher‐risk drinking; and (3) were reported as having received brief advice on alcohol consumption. Findings There was no significant change in the percentage of newly registered patients who were screened for alcohol use when financial incentives were introduced. However, the percentage fell (P < 0.001) immediately when incentives were withdrawn, and fell by a further 2.96 [95% confidence interval (CI) = 2.21–3.70] patients per 1000 each month thereafter. After the introduction of incentives, there was an immediate increase of 9.05 (95% CI = 3.87–14.23) per 1000 patients screening positive for higher‐risk drinking, but no significant further change over time. Withdrawal of financial incentives was associated with an immediate fall in screen‐positive rates of 29.96 (95% CI = 19.56–40.35) per 1000 patients, followed by a rise each month thereafter of 2.14 (95% CI = 1.51–2.77) per 1000. Screen‐positive patients recorded as receiving alcohol brief advice increased by 20.15 (95% CI = 12.30–28.00) per 1000 following the introduction of financial incentives, and continued to increase by 0.39 (95% CI = 0.26–0.53) per 1000 monthly until withdrawal. At this point, delivery of brief advice fell by 18.33 (95% CI = 11.97–24.69) per 1000 patients and continued to fall by a further 0.70 (95% CI = 0.28–1.12) per 1000 per month. Conclusions Removing a financial incentive for alcohol prevention in English primary care was associated with an immediate and sustained reduction in the rate of screening for alcohol use and brief advice provision. This contrasts with no, or limited, increase in screening and brief advice delivery rates following the introduction of the scheme