A large-sample investigation into uncertain climate change impacts on high flows across Great Britain

Climate change may significantly increase flood risk globally, but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is analysed for Great Britain (GB) to provide the first spatially consistent GB projections to include both climate ensembles and hydrological model parameter uncertainties. We use the latest high-resolution (12 km) regional climate model ensemble from the UK Climate Projections (UKCP18). These projections are based on a perturbed-physics ensemble of 12 regional climate model simulations and allow exploration of climate model uncertainty beyond the variability caused by the use of different models. We model 346 larger (>144 km2) catchments across GB using the DECIPHeR hydrological modelling framework. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1, and annual maximum) along the western coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for western Scotland. In contrast, median flows (Q50) were projected to decrease across GB. Even when using an ensemble based on a single regional climate model (RCM) structure, all flow projections contained large uncertainties. While the RCM parameters were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in eastern and south-eastern England. Regional variations in flow projections were found to relate to (i) differences in climatic change and (ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, and highlight the impact of hydrological model parameter uncertainties when modelling climate change impact on high flows

Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities, yet they are also limited by their lack of comparability, uncertainty estimates and characterization of human impacts. This article (i) underscores the key role of LSH datasets in hydrological studies, (ii) provides a review of currently available LSH datasets, (iii) highlights current limitations of LSH datasets and (iv) proposes guidelines and coordinated actions to overcome these limitations. These guidelines and actions aim to standardize and automatize the creation of LSH datasets worldwide, and to enhance the reproducibility and comparability of hydrological studies

A snow and glacier hydrological model for large catchments – case study for the Naryn River, central Asia

In this paper we implement a degree day snowmelt and glacier melt model in the Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) model. The purpose is to develop a hydrological model that can be applied to large glaciated and snow-fed catchments yet is computationally efficient enough to include model uncertainty in streamflow predictions. The model is evaluated by simulating monthly discharge at six gauging stations in the Naryn River catchment (57 833 km2) in central Asia over the period 1951 to a variable end date between 1980 and 1995 depending on the availability of discharge observations. The spatial distribution of simulated snow cover is validated against MODIS weekly snow extent for the years 2001–2007. Discharge is calibrated by selecting parameter sets using Latin hypercube sampling and assessing the model performance using six evaluation metrics. The model shows good performance in simulating monthly discharge for the calibration period (NSE is 0.74&lt;NSE&lt;0.87) and validation period (0.7&lt;NSE&lt;0.9), where the range of NSE values represents the 5th–95th percentile prediction limits across the gauging stations. The exception is the Uch-Kurgan station, which exhibits a reduction in model performance during the validation period attributed to commissioning of the Toktogul reservoir in 1975 which impacted the observations. The model reproduces the spatial extent in seasonal snow cover well when evaluated against MODIS snow extent; 86 % of the snow extent is captured (mean 2001–2007) for the median ensemble member of the best 0.5 % calibration simulations. We establish the present-day contributions of glacier melt, snowmelt and rainfall to the total annual runoff and the timing of when these components dominate river flow. The model predicts well the observed increase in discharge during the spring (April–May) associated with the onset of snow melting and peak discharge during the summer (June, July and August) associated with glacier melting. Snow melting is the largest component of the annual runoff (89 %), followed by the rainfall (9 %) and the glacier melt component (2 %), where the values refer to the 50th percentile estimates at the catchment outlet gauging station Uch-Kurgan. In August, glacier melting can contribute up to 66 % of the total runoff at the highly glacierized Naryn headwater sub-catchment. The glaciated area predicted by the best 0.5 % calibration simulations overlaps the Landsat observations for the late 1990s and mid-2000s. Despite good predictions for discharge, the model produces a large range of estimates for the glaciated area (680–1196 km2) (5th–95th percentile limits) at the end of the simulation period. To constrain these estimates further, additional observations such as glacier mass balance, snow depth or snow extent should be used directly to constrain model simulations.</p

Spatial sensitivity of river flooding to changes in climate and land cover through explainable AI

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

Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales

Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow

A large-sample investigation into uncertain climate change impacts on high flows across Great Britain

Climate change may significantly increase flood risk globally, but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is analysed for Great Britain (GB) to provide the first spatially consistent GB projections to include both climate ensembles and hydrological model parameter uncertainties. We use the latest high-resolution (12 km) regional climate model ensemble from the UK Climate Projections (UKCP18). These projections are based on a perturbed-physics ensemble of 12 regional climate model simulations and allow exploration of climate model uncertainty beyond the variability caused by the use of different models. We model 346 larger (>144 km2) catchments across GB using the DECIPHeR hydrological modelling framework. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1, and annual maximum) along the western coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for western Scotland. In contrast, median flows (Q50) were projected to decrease across GB. Even when using an ensemble based on a single regional climate model (RCM) structure, all flow projections contained large uncertainties. While the RCM parameters were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in eastern and south-eastern England. Regional variations in flow projections were found to relate to (i) differences in climatic change and (ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, and highlight the impact of hydrological model parameter uncertainties when modelling climate change impact on high flows

Strategic analysis of the drought resilience of water supply systems

Severe droughts can result in shortages of water supplies, with widespread social and economic consequences. Here we use a coupled simulation model to assess the reliability of public water supplies in England, in the context of changing scenarios of water demand, water regulation and climate change. The coupled simulation model combines climate simulations, a national-scale hydrological model and a national-scale water resource systems model to demonstrate how extreme meteorological droughts translate into hydrological droughts and water shortages for water users. We use this model to explore the effectiveness of strategic water resource options that are being planned in England to secure water supplies to most of England's population up to a drought return period of 1 in 500 years. We conclude that it is possible to achieve a 1-in-500-years standard in locations where strategic resource options are used, while also reducing water abstraction to restore the aquatic environment. However, the target will be easier to achieve if effective steps are also taken to reduce water demand. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’

On doing hydrology with dragons:Realizing the value of perceptual models and knowledge accumulation

Our ability to fully and reliably observe and simulate the terrestrial hydrologic cycle is limited, and in-depth experimental studies cover only a tiny fraction of our landscape. On medieval maps, unexplored regions were shown as images of dragons—displaying a fear of the unknown. With time, cartographers dared to leave such areas blank, thus inviting explorations of what lay beyond the edge of current knowledge. In hydrology, we are still in a phase where maps of variables more likely contain hydrologic dragons than blank areas, which would acknowledge a lack of knowledge. In which regions is our ability to extrapolate well developed, and where is it poor? Where are available data sets informative, and where are they just poor approximations of likely system properties? How do we best identify and acknowledge these gaps to better understand and reduce the uncertainty in characterizing hydrologic systems? The accumulation of knowledge has been postulated as a fundamental mark of scientific advancement. In hydrology, we lack an effective strategy for knowledge accumulation as a community, and insufficiently focus on highlighting knowledge gaps where they exist. We propose two strategies to rectify these deficiencies. Firstly, the use of open and shared perceptual models to develop, debate, and test hypotheses. Secondly, improved knowledge accumulation in hydrology through a stronger focus on knowledge extraction and integration from available peer-reviewed articles. The latter should include metadata to tag journal articles complemented by a common hydro-meteorological database that would enable searching, organizing and analyzing previous studies in a hydrologically meaningful manner. This article is categorized under: Engineering Water &gt; Planning Water Science of Water &gt; Hydrological Processes Science of Water &gt; Methods.</p