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Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

Multi-model studies are widespread in large-sample hydrology. However, significant challenges remain in identifying interpretable connections between high-performing model structures and catchment characteristics, and thus in developing a coherent strategy for developing tailored multi-model ensembles. Here, we assess the importance of selecting model structures that are consistent with the expected hydrological variability across the study domain. We compare results of two modular modelling frameworks across 998 catchments in Great Britain. The RRMT framework includes model structures historically evolved in the UK, while the FUSE framework employs model structures from diverse global origins. While both groups of model structures contain high-performing members, the historically evolved group members separate between catchments in line with our expectation of hydrologic differences. We find that four hydrologic signatures organize these distinctions. Our results emphasize (1) the importance of model structure selection based on explicit perceptual models, and (2) the need to look beyond statistical performance alone. [ABSTRACT FROM AUTHOR]

The comparison of models in geosciences involves refining a single model or comparing various model structures. However, such model comparison studies are potentially invalid without considering the uncertainty estimates of observations in evaluating relative model performance. The temporal sampling of the observation and simulation time series is an additional source of uncertainty as a few observation and simulation pairs, in the form of outliers, might have a disproportionate effect on the model skill score. In this study we highlight the importance of including observation uncertainty and temporal sampling uncertainty when comparing or evaluating hydrological models. In hydrology, large-sample hydrology datasets contain a collection of catchments with hydro-meteorological time series, catchment boundaries and catchment attributes that provide an excellent test-bed for model evaluation and comparison studies. In this study, two model experiments that cover different purposes for model evaluation are set up using 396 catchments from the CAMELS-GB dataset. The first experiment, intra-model , mimics a model refinement case by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model with and without additional calibration. The second experiment, inter-model , is a model comparison based on the streamflow estimates of the distributed PCR-GLOBWB and wflow_sbm hydrological models. The temporal sampling uncertainty, the result of outliers in observation and simulation pairs, is found to be substantial throughout the case study area. High temporal sampling uncertainty indicates that the model skill scores used to evaluate model performance are heavily influenced by only a few data points in the time series. This is the case for half of the simulations (210) of the first intra-model experiment and 53 catchment simulations of the second inter-model experiment as indicated by larger sampling uncertainty than the difference in the KGE-NP model skill score. These cases highlight the importance of reporting and determining the cause of temporal sampling uncertainty before drawing conclusions on large-sample hydrology based model performance. The streamflow observation uncertainty analysis shows similar results. One third of the catchments simulations (123) of the intra-model experiment contains smaller streamflow simulation differences between models than streamflow observation uncertainties, compared to only 4 catchment simulations of the inter-model experiment due to larger differences between streamflow simulations. These catchments simulations should be excluded before drawing conclusions based on large-samples of catchments. The results of this study demonstrate that it is crucial for benchmark efforts based on large-samples of catchments to include streamflow observation uncertainty and temporal sampling uncertainty to obtain more robust results. [ABSTRACT FROM AUTHOR]

• Current characterization of catchments largely ignores catchment location. • A new Strahler Sequence Index (SSI) accounts for location within a drainage basin. • The SSI adds information to analyze water imbalances in leaky catchments. • Location, geology, and topography shape GB catchment water balances beyond climate. • Results suggest need for emphasizing location-specific data in large-sample hydrology. The analysis of large samples of hydrologic catchments is regularly used to gain understanding of hydrologic variability and controlling processes. Several studies have pointed towards the problem that available catchment descriptors (such as mean topographic slope or average subsurface properties) are insufficient to capture hydrologically relevant properties. Here, we test the assumption that indicators of catchment location, i.e. the relative properties of catchments in relation to their surrounding neighbours, can provide additional information to reduce this problem. We test this idea in the context of Great Britain for a widely discussed problem, that of catchment water balance errors due to subsurface losses. We focus on three different locational aspects (i.e. location to coast, location within a wider basin and location to a neighboring catchment), utilizing only basic and widely available geological and topographical information to make the result useful for large sample hydrology. To achieve this, we introduce the Strahler Sequence Index to define catchment location within the wider river basin. Our results imply that location, geology and topography combine to define the differences of water balances of catchments in Great Britain compared to what we would expect from their climatic estimation alone. However, the given sample size limited our ability to derive robust conclusions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 > Planning WaterScience of Water > Hydrological ProcessesScience of Water > Methods [ABSTRACT FROM AUTHOR]

There is a no lack of significant open questions in the field of hydrology. How will hydrological connectivity between freshwater bodies be altered by future human alterations to the hydrological cycle? Where does water go when it rains? Or what is the future space–time variability of flood and drought events? However, the answers to these questions will vary with location due to the specific and often poorly understood local boundary conditions and system properties that control the functional behaviour of a catchment or any other hydrologic control volume. We suggest that an open, shared and evolving perceptual model of a region's hydrology is critical to tailor our science questions, as it would be for any other study domain from the plot to the continental scale. In this opinion piece, we begin to discuss the elements of and point out some knowledge gaps in the perceptual model of the terrestrial water cycle of Great Britain. We discuss six major knowledge gaps and propose four key ways to reduce them. While the specific knowledge gaps in our perceptual model do not necessarily transfer to other places, we believe that the development of such perceptual models should be at the core of the debate for all hydrologic communities, and we encourage others to have a similar debate for their hydrologic domain. [ABSTRACT FROM AUTHOR]

We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at 10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents DECIPHeR (Dynamic fluxEs and ConnectIvity for Predictions of HydRology), a new model framework that simulates and predicts hydrologic flows from spatial scales of small headwater catchments to entire continents. DECIPHeR can be adapted to specific hydrologic settings and to different levels of data availability. It is a flexible model framework which includes the capability to (1) change its representation of spatial variability and hydrologic connectivity by implementing hydrological response units in any configuration and (2) test different hypotheses of catchment behaviour by altering the model equations and parameters in different parts of the landscape. It has an automated build function that allows rapid set-up across large model domains and is open-source to help researchers and/or practitioners use the model. DECIPHeR is applied across Great Britain to demonstrate the model framework. It is evaluated against daily flow time series from 1366 gauges for four evaluation metrics to provide a benchmark of model performance. Results show that the model performs well across a range of catchment characteristics but particularly in wetter catchments in the west and north of Great Britain. Future model developments will focus on adding modules to DECIPHeR to improve the representation of groundwater dynamics and human influences. [ABSTRACT FROM AUTHOR]

Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of riskbased approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline (1900-2006), (ii) five nearfuture scenarios (2020-2049) and (iii) five far-future scenarios (2070-2099). The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period (> 3 months) and shorter-duration high precipitation (1-30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK. [ABSTRACT FROM AUTHOR]

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs. [ABSTRACT FROM AUTHOR]

We present a Matlab toolbox to calculate hydrologic signatures, which are metrics that quantify streamflow dynamics. Signatures are widely used for catchment characterisation, hydrologic model evaluation, and assessment of instream habitat, but standardisation across applications and advice on signature selection is lacking. The toolbox provides accessible, standardised signature calculations, with clear information on methodological decisions and recommended parameter values. The toolbox implements three categories of signatures: basic signatures that describe the five components of a natural streamflow regime, signatures from benchmark papers, and an extended set of process-based signatures. The toolbox is designed for ease of use, including documentation, workflow scripts and example data to demonstrate implementation procedures, and visualisation options. We demonstrate the accuracy and robustness of the signature calculations by applying reproducible workflows to large streamflow datasets. The modular design of the toolbox allows for flexibility and easy future expansion. The toolbox is available from https://github.com/TOSSHtoolbox/TOSSH (https://doi.org/10.5281/zenodo.4313275). • We present a Matlab toolbox to calculate hydrologic signatures, which are metrics that quantify streamflow dynamics. • The toolbox provides accessible, standardised signature calculations, with clear information on methodological decisions. • We demonstrate the accuracy and robustness of the toolbox by applying reproducible workflows to large streamflow datasets. [ABSTRACT FROM AUTHOR]

Data underpin our knowledge and understanding of the hydrological system; they are used to drive, test and evaluate hydrological models and advance our understanding of hydrological processes and dynamics. With the increasing availability of observational datasets, the integration of information from many catchments for data and modelling analyses is becoming increasingly common. The production of new, open source, datasets for large samples of catchments is vital to advance knowledge on hydrological processes, to underpin common frameworks for model evaluation across complex domains, and to ensure hydrological research is reusable and reproducible through the use of common datasets and code. However, the availability of open source, large-sample catchment datasets is notably sparse. In this study, we present the first large sample, open-source, hydro-meteorological catchment dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB integrates a wealth of different spatial and time-varying datasets formulated from national, continental and global products based on observational, satellite and derived products. The dataset consists of hydro-meteorological timeseries, catchment attributes and catchment boundaries for >800 catchments that cover a wide range of climatic, hydrological, landscape and human management characteristics across Great Britain. Daily timeseries covering 1970-2015 are provided for a range of hydro-meteorological data (including rainfall, potential evapotranspiration, temperature, radiation, humidity and flow) and cover several major hydrological episodes (i.e. nationally important drought and flood periods). A comprehensive set of summary attributes are quantified describing a range of catchment characteristics including topography, climate, hydrology, land cover, soils and (hydro)-geology. Importantly, we also derive human impact attributes (including abstraction returns, percentage urban and gauge distance from reservoir), as well as attributes describing the quality of the flow data (including discharge uncertainty estimates and out of bank flow). This ensures users can identify, for example, human impacted catchments when evaluating models or analysing data. The dataset and code used to derive the data will be made open source and provided with comprehensive metadata to allow its use in a wide range of hydro-meteorological data and environmental modelling analyses. These data are part of a wider international initiative to generate multi-national, large-sample datasets to facilitate reproducible and reusable hydrological research. [ABSTRACT FROM AUTHOR]

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs. [ABSTRACT FROM AUTHOR]