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The past decade has seen considerable advances in the field of global flood modelling. In the 2010s, it began as a niche academic endeavour building models of the order 103 m horizontal resolution. In the 2020s, it is maturing into an established scientific discipline and yields profitable commercial ventures, with global models emerging of the order 101 m resolution.Building on the original 102 resolution global inland flood model of Sampson et al. (2015) – with a hydraulic engine based on the sub-grid version of the LISFLOOD-FP local inertial formulation of the shallow water equations (Bates et al., 2010; Neal et al., 2012) – we present the critical advances required to create a ~30 m resolution model of considerably greater fidelity and functionality:Using FABDEM as the underlying elevation grid, a machine-learning correction of the Copernicus global digital surface model to a digital terrain model (Hawker et al., 2022). Representing river hydrography with MERIT-Hydro (Yamazaki et al., 2019), ensuring the correct alignment of river channels with valley bottoms. Estimating river bathymetry prior to inundation modelling with a gradually varied flow solver (Neal et al., 2021). Updating boundary condition generation models with new hydrometric datasets and machine-learning hydrologic regionalization techniques (e.g. Zhao et al., 2021). Driving a global coastal flood model with a tide–surge–wave regional frequency analysis using tide gauges and reanalyses (Sweet et al., 2020). Implementing known and estimated flood protection measures as a rapid and adaptable post-process. Generating global climate change factors for fluvial, pluvial, and coastal floods for any plausible 21st century climate state. Applying climate change factors as a tractable post-process to a set of multi-frequency flood maps. These updates form the third version of Fathom's global flood maps. We show that these herald a new era of global flood modelling precision and accuracy, with additional utility wrought from linking climate projections to high-resolution true hydrodynamic models at the global scale for the first time. We also chart the road ahead for global flood modelling: outlining the significant data and modelling challenges our community must address to continue on this unprecedented development trajectory. References:Bates, P., et al. (2010) https://doi.org/10.1016/j.jhydrol.2010.03.027Hawker, L. & Uhe, P., et al. (2022) https://doi.org/10.1088/1748-9326/ac4d4fNeal, J., et al. (2012) https://doi.org/10.1029/2012WR012514Neal, J., et al. (2021) https://doi.org/10.1029/2020WR028301Sampson, C., et al. (2015) https://doi.org/10.1002/2015WR016954Sweet, W., et al. (2020) https://doi.org/10.3389/fmars.2020.581769Yamazaki, D., et al. (2019) https://doi.org/10.1029/2019WR024873Zhao, G., et al. (2021) https://doi.org/10.5194/hess-25-5981-202

In recent years there have been many new global datasets and methodological advancements that could be utilised by hydraulic models to help better understand global flood risk both in the present day and in the future. A major challenge facing modellers is how to incorporate these new datasets to improve the understanding of flood risk in both well, and less well, developed countries using a consistent approach, particularly as the latter of these contain increasingly larger exposures to floods.This new framework presents a computationally efficient yet flexible approach that seeks to utilise new global datasets and allows flood hazard maps to be calculated anywhere in the world, for any event severity (within a pre-defined range) and for any future climate scenario. The framework can be applied to all three of the major flood perils; fluvial, pluvial and coastal.At the heart of the framework is an efficient post-processing methodology that incorporates outputs from leading climate models, flood defence datasets and a baseline set of simulations spanning a range of evert severities. Furthermore, the flexible approach allows users to modify assumptions of flood defences and incorporate new climate simulations as and when they become available to quickly re-calculate flood hazard.We present here the full modelling chain, from input data through to flood hazard outputs covering all aspects of modelling, from determining model boundary conditions and estimating channel bathymetry, to post-processing the presence of flood defences and interpolation to future climate scenarios. We show that such an approach is able to replicate explicitly modelling the scenarios required at a fraction of the computation cost and demonstrate how this is crucial to anyone wanting to understand how exposure to floods may change into the future.

Air can hold more moisture as temperature increases, leading to more extreme rainfall events. Yet, this does not necessarily result in larger river floods. Here we use model projections to explore differences in the response of the atmosphere and catchments to an increase in global mean temperature. For both extreme rainfall and flow, we compute relative changes per °C, often called change factors (CFs) or scaling factors. Unlike some other studies, the multidecadal temperature mean is used instead of the temperature during the extreme event. This allows us to use CFs to produce maps of future changes for any emission scenario and future period.We relied on rainfall projected by 4 high-resolution GCMs from CMIP6 HighResMIP post-processed using 3 levels spatial smoothing (low to high smoothing). We also used hydrological simulations from 3 global hydrological models (GHMs) forced by 4 GCMs and produced as part of the ISIMIP2b project. We computed changes in the median of annual maxima based on periods of 31 years on 0.25° (HighResMIP) and 0.5° (ISIMIP2b) global grids. Working with two 12-member ensembles enables us to assess uncertainties in future changes.We found that whilst extreme rainfall is projected to increase over 87% of the land area (ensemble median), only 69% of the land area is projected to show an increase in extreme flow magnitude. Importantly, while there is high model agreement (at least ¾ of the models agree) that extreme rainfall will increase over 76% of the land area, high agreement that future flows will increase is only found over 40% of the land area. We show that these discrepancies are caused by changes in soil moisture and snow pack projected by the GHMs, highlighting the importance of river flood drivers other than extreme rainfall.

Over recent years, numerous open catchment datasets have been published. In 2017, the first CAMELS (catchment attributes and meteorology for large-sample studies) dataset was released for the continental US by Addor et al. (2017). It comprises data for several hundreds of catchments including dynamic time series of daily resolution over several decades for discharge, precipitation and temperature - originally compiled by Newman et al. (2015) - as well as static basin attributes such as indices on topography, soil, geology and climate. Subsequently, similar datasets for several other countries were made or will be made publicly available. Some of these also contain additional data such as attributes on glaciers or human influence like, e.g., the CAMELS datasets for Chile (Alvarez-Garreton et al., 2018) and Great Britain (Coxon et al., 2020). Such datasets build an accessible and unified basis for reproducible and complementary research. They led to a high stimulation of hydrological research with methodologies that could not be applied before, like the joint evaluation of a large number of catchments.We present CAMELS-CH, a new dataset for about 200 basins in Switzerland that will be released in 2022. In this collaborative project, several academic institutions and agencies work together to publish a hydro-meteorological dataset that covers both dynamic and static catchment data, and that accounts for the wide range of hydrological regimes in Switzerland, e.g., alpine environment, hydropower usage, densely populated and cultivated regions, etc. We summarize the current state of the project, remaining challenges, in particular regarding translating base data into the CAMELS format, and the final steps toward dataset publication. ReferencesAddor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrology and Earth System Sciences, 21, 5293-5313, 2017Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Cortes, G., Garreaud, R., McPhee, J., Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies-Chile dataset, Hydrology and Earth System Sciences, 22, 5817–5846, 2018Coxon, G., Addor, N., Bloomfield, J., Freer, J., Fry, M., Hannaford, J., Howden, N., Lane, R., Lewis, M., Robinson, E., Wagener, T.,and Woods, R.: CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth System Science Data 12, 2459–2483, 2020Newman, A., Clark, M., Sampson, K., Wood, A., Hay, L., Bock, A., Viger, R., Blodgett, D., Brekke, L., Arnold, J.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences, 19, 209-223, 2015

We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3713 gauges, as well as meteorological forcing (precipitation, evapotranspiration and temperature) for 897 selected catchments. It also includes 63 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile and Great Britain. CAMELS-BR complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and in the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and to facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709338 (Chagas et al., 2020).

Extreme droughts can cause enormous ecological and economic damage, and are expected to become more severe in some regions due to climate change. For water managers, it is crucial to understand extreme droughts and how they are projected to change compared to previous droughts, in order to plan for resilience to these events. Changes in water resources do not only result from changes in precipitation and periods of below normal precipitation (meteorological droughts), they are also shaped by changes in atmospheric moisture demand, characterized here by potential evaporation. Therefore we use two standardized indicators, the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evaporation Index (SPEI) to isolate the impact of projected changes in precipitation and potential evaporation. We consider the contribution of precipitation deficits and potential evaporation changes to projected changes in future drought duration, severity and frequency. We explore droughts and their development across different time scales, as their diversity – from flash droughts to creeping multi-year droughts – adds to the challenge. We make use of the recently released 12-member 12-km horizontal resolution perturbed parameter ensemble of spatially coherent regional UKCP18 climate projections (with and without bias adjustment). This ensemble of projections was produced by the UK Met Office by dynamically downscaling a perturbed parameter ensemble of HadGEM3-GC3.05 simulations with a regional variant. The skill of the UKCP18 regional ensemble members for drought simulation is evaluated by comparison with observed drought metrics for the baseline period. Projected changes in UK climate according to the UKCP18 projections include wetter winters, drier summers and generally stronger temperature increases in summer than in winter. We assess how these changes contribute to changes in drought characteristics using SPI and SPEI for each member of the ensemble. While this work focusses on meteorological droughts, it will be followed by a future analysis of their propagation to hydrological droughts. This project aims to support adaptation to droughts in the region of East Anglia and is conducted in collaboration with the water company Anglian Water.

Hundreds of millions of people depend strongly on hydrological inputs in the mountainous regions of China and central Asia. Glacier runoff is a major contributor to this hydrological forcing, yet many glaciers in the region have undergone mass loss in recent years and this mass loss is expected to continue or increase in response to climatological change. As such it is important to assess the large-scale response of High Mountain Asia glaciers to climate change , and its effects on hydrology. We present here preliminary modelling investigations of glacier change and hydrological impacts in response to high-resolution climate model projections over the 21st century as a component of the project SWARM (Impacts Assessment to Support WAter Resources Management and Climate Change Adaptation for China). Our model chain consists of i) Open Global Glacier Model (OGGM), which allows for high-resolution glacier flowline modelling of multiple glaciers, and ii) the Framework for Understanding Structural Errors (FUSE) a modular framework for snow and hydrology modelling, which we used to assemble and run three hydrological models over the whole of China. Both FUSE and OGGM are forced by an ensemble of bias-corrected CORDEX-East Asia regional climate models (in turn forced by CMIP5 general circulation models), and outputs of OGGM are provided to FUSE. We discuss our application of OGGM to 80,000 glaciers in Chinese river catchments; our efforts to calibrate the mass balance model using an expanded set of geodetic mass balance constraints; and finally the projections of glacier, snow and streamflow changes in the 21st century. In particular, we discuss the robustness and uncertainties in the projections as sampled by our multi-model ensemble.

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 and provide the foundation for improved understanding of the link between catchment characteristics, climate and hydrological responses. 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 for open hydrology, yet they are also limited by their lack of comparability, accessibility, uncertainty estimates and characterization of human impacts.Here, we underscore the key role of LSH datasets in open hydrologic science and highlight their potential to enhance the transparency and reproducibility of hydrological studies. We provide a review of current LSH datasets and identify their limitations, including the current difficulties of inter-dataset comparison and limited accessibility of hydrological observations. To overcome these limitations, we propose simple guidelines alongside long-term coordinated actions for the community, which aim to standardize and automatize the creation of LSH datasets worldwide. This presentation will highlight how, by producing and using common LSH datasets, the community can increase the comparability and reproducibility of hydrological research.This research was performed as part of the Panta Rhei Working Group on large-sample hydrology and is based on https://doi.org/10.1080/02626667.2019.1683182.

Hydrological models (HMs) are essential tools to explore terrestrial water dynamics and to anticipate future hydrological events. Since their inception, HMs have been developed in parallel by different institutions. There is now a plethora of HMs, yet a relative absence of cross-model developments (code is almost never portable between models) and of guidance on model selection (modellers typically stick to the model they are most familiar with). Furthermore, traditional HMs, developed over the last decades by successive code additions, are rarely adapted to modern hydrological challenges, principally because they lack modularity. These HMs typically rely on a single model structure (most processes are simulated by a single set of equations), which make it difficult to i) understand differences between models, ii) run a large ensemble of models, iii) capture the spatial variability of hydrological processes and iv) develop and improve hydrological models in a coordinated fashion across the community.These limitations can be overcome by modular modelling frameworks (MMFs), which are master templates for model generation. MMFs offer several options for each important modelling decision. They also allow users to add functionalities when they are required, by loading libraries developed and maintained by the community. This presentation uses FUSE (Framework for Understanding Structural Error) as an example of MMF for hydrology. FUSE enables the generation of a myriad of conceptual HMs by recombining elements from four commonly-used models. This presentation will summarize the development of FUSE version 2 (FUSE2), which was created with users in mind and significantly increases the usability and range of applicability of the original FUSE. In FUSE2, NetCDF output files contain a detailed description of the modelling decisions (e.g., selected modules, numerical scheme, parameter values), which improves reproducibility. FUSE2 also makes code re-usable, as modules can be used across the community and are not limited to a single model structure. After decades of siloed model development, we argue that MMFs are essential to develop and improve hydrological models in a coordinated fashion across the community.

Over the last decade or so many studies of the hydrologic characteristics of basins have been driven by the desire to develop models that enable prediction of particular signatures, such as baseflow and Base Flow Index (BFI), in ungauged basins (PUB). These studies typically focus on understanding how readily available mapped or remotely sensed data can be used to infer hydrologic signals. However, in the specific case of baseflow, there is a recognition that we still have a poor understanding of the relative influences of underlying hydrological processes at appropriate scales, particularly in anthropogenically impacted catchments. New opportunities are being offered to better understand relationships between BFI and various controls on baseflow through the production of large sample catchment datasets. Here we present the results of an analysis of one such large-sample dataset, CAMELS-GB, investigating the relative importance of different hydrogeological controls on baseflow, including factors such as: climatology; hydrogeology; geophysical catchment characteristics, e.g. soil characteristics and land cover; and, anthropogenic influences, e.g. discharge from reservoirs and from sewage treatment works (STWs), abstraction, and mains leakage.CAMELS-GB consists of daily hydrometerorological time series for the period 1970-2015 and landscape, catchment and hydrogeological attributes for 671 catchments in Great Britain. Machine learning approaches, including random forest algorithms, are used to investigate the influence of catchment characteristics on BFI and to inform the selection of hydrologically reasonable parameters to quantify relationships using linear regression models. We describe how the regression models can be used to investigate and characterise the sensitivity of estimates of BFI to: i.) the underlying hydrogeological mapping; ii.) the spatial support scale of the analysis; and iii.) anthropogenic influences.

Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, climate change impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of individual catchments, and more importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability, and thereby provide limited insights into climate change adaptation. Here, we collaborated with Groupe E, a hydropower company operating several reservoirs in the Swiss pre-Alps and worked with them to produce hydroclimatic projections tailored to support their upcoming water concession negotiations. We started by identifying the vulnerabilities of their activities to climate change and then together chose streamflow and energy indices to characterize the associated risks. We provided Groupe E with figures showing the projected climate change impacts, which were refined over several meetings. The selected indices enabled us to simultaneously assess a variety of impacts induced by changes on i) the seasonal water volume distribution, ii) low flows, iii) high flows, and iv) energy demand. We were hence able to identify key opportunities (e.g., the future increase of reservoir inflow in winter, when electricity prices are historically high) and risks (e.g., the expected increase of consecutive days of low flows in summer and fall, which is likely to make it more difficult to meet residual flow requirements). This study highlights that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. We also illustrate the importance of identifying stakeholder needs and using them to inform the production of climate impact projections. Our user-centered approach is transferable to other impact modeling studies, in the field of water resources and beyond.

Hydrologic projections are of vital socio-economic importance. Yet, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in constrained hydrologic projections for 605 basins throughout the contiguous United States. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070–2100 compared to 1985–2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty on the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more wide-spread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resources planning in defining policy pathways.