Contribution of urbanisation to non-stationary river flow in the UK

Han, Shasha; Slater, Louise; Wilby, Robert L.; Faulkner, Duncan

doi:10.1016/j.jhydrol.2022.128417

Cited by 20 publications

(8 citation statements)

References 49 publications

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“…The impacts of LC are secondary but non-negligible (Figures 1a-1d), consistent with previous work (e.g., Blum et al, 2020;Buechel et al, 2022). On average, urbanization tends to increase the seasonal maximum flood, also in line with previous findings (Anderson et al, 2022;Blum et al, 2020;Han et al, 2022), except in the catchments that have limited urban extent to begin with. Perhaps more surprisingly, we find that forest cover exhibits a nonlinear association with flooding, where specific discharge decreases with afforestation up to about 20% of forest extent.…”

Section: Relative Contribution Of Climate and Land Cover To Flooding ...supporting

confidence: 91%

“…Severe floods may also include snowrelated processes, which tend to affect floods over a larger contiguous spatial area than rainfall-driven events (Brunner & Fischer, 2022). Alongside these climatic drivers, changes in forest or urban LC also play a vital role in modulating flood risk, but the effects of LC change can be challenging to measure (e.g., Anderson et al, 2022;Buechel et al, 2022Buechel et al, , 2023Han et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Spatial Sensitivity of River Flooding to Changes in Climate and Land Cover Through Explainable AI

Slater,

Coxon,

Brunner

et al. 2024

Earth's Future

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Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments.

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Section: Relative Contribution Of Climate and Land Cover To Flooding ...supporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Spatial Sensitivity of River Flooding to Changes in Climate and Land Cover Through Explainable AI

Slater,

Coxon,

Brunner

et al. 2024

Earth's Future

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“…In some places (e.g., Scotland, north England) this could be related to snowmelt‐dominated flood generation (Berghuijs et al., 2019), as the decadal forecasts pick up a monotonic trend of increasing temperature from around 1970 (Figure S3 in Supporting Information ). However, it is also possible that streamflow is responding to a concurrent trend such as urbanization (Han et al., 2022). Although the UKBN2 streamflow data set controls for non‐climatic drivers of streamflow to a reasonable extent, few UK catchments are unaffected by anthropogenic activity (Harrigan et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

Skillful Decadal Flood Prediction

Moulds

Slater

Dunstone

et al. 2023

Geophysical Research Letters

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Accurate long‐term flood predictions are increasingly needed for flood risk management in a changing climate, but are hindered by the underestimation of climate variability by climate models. Here, we drive a statistical flood model with a large ensemble of dynamical CMIP5‐6 predictions of precipitation and temperature. Predictions of UK winter flooding (95th streamflow percentile) have low skill when using the raw 676‐member ensemble averaged over lead times of 2–5 years from the initialization date. Sub‐selecting 20 ensemble members that adequately represent the multiyear temporal variability in the North Atlantic Oscillation (NAO) significantly improves the flood predictions. Applying this method we show positive skill in 46% of stations compared to 26% using the raw ensemble, primarily in regions most strongly influenced by the NAO. Our findings reveal the potential of decadal predictions to inform flood risk management at long lead times.

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“…One option is to develop proxies to detect and model human influence. For instance, Han et al (2022) employed census information on the number of households to extend UK urbanisation records. Similarly, Slater and Villarini (2018) used population density data as a proxy for urbanisation and considered the extent to which seasonal streamflow predictability might benefit from 'anthropogenic' predictors such as land cover change alongside seasonal climate forecasts.…”

Section: Assimilating Human Influencesmentioning

confidence: 99%

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

Slater

Arnal²,

Boucher³

et al. 2022

Preprint

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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

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Contribution of urbanisation to non-stationary river flow in the UK

Cited by 20 publications

References 49 publications

Spatial Sensitivity of River Flooding to Changes in Climate and Land Cover Through Explainable AI

Spatial Sensitivity of River Flooding to Changes in Climate and Land Cover Through Explainable AI

Skillful Decadal Flood Prediction

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

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