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Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety ofpredictions from dynamical, physics-based models - such as numerical weather prediction, climate, land, hydrology, and Earth system models - into a final prediction product. They are recognized as a promising way of enhancing the 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 subseasonal to decadal scales, a better appreciation of the strengths of AI, and 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, benefit from the strengths of machine learning, 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 for further research. 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, incorporatinginitial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes., Hydrology and Earth System Sciences, 27 (9), ISSN:1027-5606, ISSN:1607-7938

Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes. Within an idealized simulation framework, these rainfall events are used as input to an operational distributed hydrologic model to evaluate the sensitivity of the hydrologic response to different forcing grid spacings. We consider the finest scale (i.e., 250 m) as reference, and compute event peak flows and volumes across a wide range of basin sizes. We find that the use of increasingly-coarser inputs leads to changes in the distribution of event peak flows and volumes, with the strongest sensitivity at the smallest catchment sizes. Overall, we find that 4-km rainfall simulations represent a good compromise between computational costs and hydrologic performance, providing basic information for future endeavors geared towards regional downscaling.

Trends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large-scale comparisons and minimize biases. Using process-based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro-climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro-climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls.

Floods claim a high toll in fatalities and economic impacts. Despite their societal relevance, there is much more to learn about the projected changes in discharge and flooding. Here we force an operational hydrologic model over the state of Iowa with high-resolution convection-permitting climate-model precipitation to evaluate the response of 140 watersheds to climate change. At the end of the century, under the most aggressive scenario in terms of fossil fuel use, we show that the transition from snow to rainfall and a ~30% increase in extreme precipitation rates lead to a doubling of maximum discharge during the spring and extended the flood season into the fall. Total discharge volumes are also expected to increase. Our results suggest that flood projections based on extreme precipitation increases alone substantially underestimate future risk due to the nonlinearity of the hydrologic response explained by long-term soil moisture memory and its feedbacks with precipitation.

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.

This study examines the response of the clustering behavior in heavy precipitation events across Europe to increasing atmospheric CO2 concentrations. We focus on four large-scale climate modes [Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic (EA) pattern, and Scandinavia pattern (SCAND)] that have been found to play a significant role in controlling the occurrence of these events. We use a peak over threshold (POT) method to define daily heavy precipitation events and Cox regression as the modeling framework. We consider three experiments from the Coupled Model Intercomparison Project Phase 5 (CMIP5): (1) pre-industrial control run experiments (PI-control); (2) a 1%/year increase in CO2 from the CO2 concentration in PI control to quadrupling (1pctCO2); and (3) an instantaneous quadrupling of CO2, then holding it fixed (abrupt4 × CO2). We measure the effects of CO2 by examining whether the Cox regression coefficients in the CO2 experiments are significantly different from those in the PI control. We find that (1) the increases in CO2 are unlikely to lead to changes in the spatial patterns of relationships between climate modes and heavy precipitation; (2) the increases in CO2 are likely to lead to a strengthening of the relationship between the AO/SCAND and the occurrence of heavy precipitation events, while increases in CO2 have weaker effects on the role of NAO and EA; (3) the response to an abrupt increase in CO2 is generally stronger compared to a more gradual one; (4) the responses of the integrated vapor transport and 500-mb geopotential height to increasing CO2 provide a physical mechanism to explain the enhanced relationship between climate modes and clustering of heavy precipitation events.

Understanding when floods occur is fundamental to reducing flood risk, yet depictions of flood seasonality rarely address two key issues: multiple seasons and the periodicity of seasonal data. We analyze the timing of extreme water levels at 47 presently operational tide gages located along the US coastlines with periods of record ranging from 29 to 118 years. Because the dates of extreme water levels are naturally multimodal periodic data, we model them with weighted mixtures of circular von Mises distributions. Fitting probability density functions on the circumference of a circle allow periodicity to be handled implicitly, while mixture models provide insight into the timing and importance of the various modes in the overall distribution. To our knowledge, this is the first application of circular statistical modeling to coastal water levels. We find clear spatial patterns in extreme water level seasonality along the US coasts. Gages located on the West Coast experience their main flood season in the winter and a secondary season in the summer, while those on the East and Gulf coasts have more uniform distributions, fewer seasons, and maximum flood likelihood in the late summer. Analysis of water level after removing tides and sea level change reduces the importance of secondary and higher seasons and yields greater uniformity compared with raw water levels. Finally, peaks-over-threshold datasets reveal secondary flood seasons that are obscured in annual maxima series, which may become increasingly impactful as sea levels rise. These findings are pertinent to coastal infrastructure design, risk sharing via financial instruments, and emergency preparedness and response.

This review provides a broad overview of the current state of flood research, current challenges, and future directions. Beginning with a discussion of flood-generating mechanisms, the review synthesizes the literature on flood forecasting, multivariate and nonstationary flood frequency analysis, urban flooding, and the remote sensing of floods. Challenges and future flood research directions are outlined and highlight emerging topics where more work is needed to help mitigate flood risks. It is anticipated that the future urban systems will likely have more significant flood risk due to the compounding effects of continued climate change and land-use intensification. The timely prediction of urban floods, quantification of the socioeconomic impacts of flooding, and developing mitigation strategies will continue to be challenging. There is a need to bridge the scales between model capabilities and end-user needs by integrating multiscale models, stakeholder input, and social and citizen science input for flood monitoring, mapping, and dissemination. Although much progress has been made in using remote sensing for flood applications, recent and upcoming Earth Observations provide excellent potential to unlock additional benefits for flood applications. The flood community can benefit from more downscaled, as well as ensemble scenarios that consider climate and land-use changes. Efforts are also needed for data assimilation approaches, especially to ingest local, citizen, and social media data. Also needed are enhanced capabilities to model compound hazards and assess as well as help reduce social vulnerability and impacts. The dynamic and complex interactions between climate, societal change, watershed processes, and human factors often confronted with deep uncertainty highlights the need for transdisciplinary research between science, policymakers, and stakeholders to reduce flood risk and social vulnerability

Baseflow is an important water resource because it supplies streamflow between precipitation events and during dry seasons. We report the first continental assessment of baseflow in Africa by analyzing the baseflow index, baseflow seasonality, and monthly baseflow trends for three periods (1950–2018, 1950–1980, and 1981–2018). To explain changes in baseflow, we analyze precipitation trends. Results highlight that the baseflow season in west-central Africa occurs during August–November. In southern and northern Africa, the baseflow season is similar (January–May and January–April, respectively). Trend analysis detected monthly baseflow decreases in west-central Africa over the entire record and from 1950 to 1980, with increases after 1980. A clear pattern was absent in southern Africa for the whole record, but increases were detected earlier and decreases from 1981 to 2018. In northern Africa, decreasing baseflow trends were pronounced over the whole record, with no clear shift present. Precipitation trends were only consistent with baseflow changes in west-central Africa.

The North Pacific Ocean is the most active region on our planet in terms of tropical cyclone (TC) activity. These storms are responsible for numerous fatalities and economic damages, affecting the livelihood of those living in the impacted areas. Historically the examination of TCs in the North Pacific Ocean has been performed separately for its two main sub-basins: the West North Pacific and the East North Pacific. Here, we consider the TC activity in the North Pacific as a single basin and examine the climate processes responsible for its number of TC days. We show that the Pacific Decadal Oscillation modulates the number of TC days in the North Pacific Ocean through its connection to the sea surface temperature. The insights from this work will advance the understanding of the climate processes responsible for these storms, and will provide valuable information toward our preparation and adaptation efforts on long timescales.

Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

The use of a long-term and high-quality precipitation dataset is crucial for hydrologic modeling and flood risk management. This study evaluates the Analysis of Period of Record for Calibration (AORC) dataset, a newly released product with high temporal and spatial resolutions. Our study region is centered on Louisiana because of the major flooding it has been experiencing. We compare the AORC hourly precipitation to other widely used gridded rainfall products and rain-gauge observations. To evaluate the performance of rainfall products according to different weather conditions causing severe flooding, we stratify the analyses depending on whether precipitation is associated with a tropical cyclone (TC) or not. Compared to observations, our results show that the AORC has the highest correlation coefficients (i.e., values above 0.75) with respect to observations among all rainfall products for both TC and non-TC periods. When the skill metric is decomposed into the potential skill and biases, the AORC clearly shows the highest potential skill with relatively small biases for the whole period. In addition, the AORC performs better for the TC period compared to the non-TC ones. Our results suggest that AORC precipitation shows good potential to be viable for hydrologic modeling and simulations of TC and non-TC events.

Africa is severely affected by floods, with an increasing vulnerability to these events in the most recent decades. Our improved preparation against and response to this hazard would benefit from an enhanced understanding of the physical processes at play. Here, a database of 399 African stream gauges is used to analyze the seasonality of observed annual maximum flood, precipitation and soil moisture between 1981 and 2018. The database includes a total of 11,302 flood events, covering most African regions. The analysis is based on directional statistics to compare the annual maximum river flood with annual maximum rainfall and soil moisture. The results show that the annual maximum flood in most areas is more strongly linked to the annual peak of soil moisture than of annual maximum precipitation. In addition, the interannual variability of flood magnitudes is better explained by the variability of annual maximum soil moisture than by the variability in the annual maximum precipitation. These results have important implications for flood forecasting and the analysis of the long-term evolution of these hydrological hazards in relation with their drivers.

Urbanization has a strong signal on the hydrologic cycle, leading to reduced infiltration, and faster and larger runoff. However, less is known in watersheds that have been experiencing such a large and rapid urbanization as those in China. Here we focus on the Wenyu watershed, a fast urbanizing basin located in the Beijing metropolitan area. Using a statistical attribution framework, we examine the hydrological response to the increasing urbanization across a wide range of discharge quantiles, from low to high flows; moreover, we perform analyses at the seasonal scale to capture differences in the physical processes at play during the year. In addition to impervious areas, we also consider precipitation, temperature, antecedent wetness, recycled water amount, and groundwater level as potential predictors. Results indicate that our models can capture well the variability in streamflow in this highly urbanized basin. Overall, urbanization played a different role for the different seasons and discharge quantiles. More specifically, we find its strongest impact to be in winter and spring, and for low and median quantiles. The role of precipitation is the strongest in summer, and it increases as we move towards the upper tail of the discharge distribution. Recycled water, on the other hand, tends to play a more dominant role in winter and spring.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K −1 ), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Concepts like the 100-year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20-, 50-, and 100-year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20-, 50-, and 100-year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20-/50-year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100-year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments.

The central USA experienced major flooding during spring 2019, with both the Missouri and Mississippi Rivers at major flood stage at several locations, causing levees to breach and widespread flooding. Here, we examine the total precipitation responsible for the spring 2019 flooding across the central USA from the perspective of weather types. We focus on the weather type (referred to as ‘Midwest Water Hose’ (MWH) (Zhang and Villarini. 2019 Climate Dynamics 53 , 4217–4232. ( doi:10.1007/s00382-019-04783-4 ))) that contributes the most to the total precipitation across the central USA. This weather type contributed to more than 70% of the total precipitation received across much of this region during January–May 2019, and it has been occurring increasingly frequently over the past 40 years. Furthermore, we found that climate model experiments with the historical change of greenhouse gas concentration can well reproduce the observed rising trend, while this is not the case for the natural forcing experiments. Therefore, the rising trend and the high frequency of the MWH can be mainly attributed to the rising greenhouse gases caused by human activities, rather than natural forcing. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Research into potential implications of climate change on flood hazard has made significant progress over the past decade, yet efforts to translate this research into practical guidance for flood estimation remain in their infancy. In this commentary, we address the question: how best can practical flood guidance be modified to incorporate the additional uncertainty due to climate change? We begin by summarizing the physical causes of changes in flooding and then discuss common methods of design flood estimation in the context of uncertainty. We find that although climate science operates across aleatory, epistemic and deep uncertainty, engineering practitioners generally only address aleatory uncertainty associated with natural variability through standards-based approaches. A review of existing literature and flood guidance reveals that although research efforts in hydrology do not always reflect the methods used in flood estimation, significant progress has been made with many jurisdictions around the world now incorporating climate change in their flood guidance. We conclude that the deep uncertainty that climate change brings signals a need to shift towards more flexible design and planning approaches, and future research effort should focus on providing information that supports the range of flood estimation methods used in practice. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Atlantic hurricanes are a major hazard to life and property, and a topic of intense scientific interest. Historical changes in observing practices limit the utility of century-scale records of Atlantic major hurricane frequency. To evaluate past changes in frequency, we have here developed a homogenization method for Atlantic hurricane and major hurricane frequency over 1851–2019. We find that recorded century-scale increases in Atlantic hurricane and major hurricane frequency, and associated decrease in USA hurricanes strike fraction, are consistent with changes in observing practices and not likely a true climate trend. After homogenization, increases in basin-wide hurricane and major hurricane activity since the 1970s are not part of a century-scale increase, but a recovery from a deep minimum in the 1960s–1980s. We suggest internal (e.g., Atlantic multidecadal) climate variability and aerosol-induced mid-to-late-20th century major hurricane frequency reductions have probably masked century-scale greenhouse-gas warming contributions to North Atlantic major hurricane frequency., How tropical cyclones have varied in intensity and frequency in the past is not well known as longer records are rare. Here, the authors show that changes in observing practices explain the recorded century scale increase in Atlantic major hurricane frequency, and recent increases are not part of a century-scale trend.