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Abstract Heavy precipitation, which is changing significantly as Earth's climate warms, can result in flooding that seriously damages societies. However, little is known about how heavy precipitation of varying durations responds to the diverse gradients of urban development in China. Through statistical analyses spanning from 1990 to 2021, we have examined shorter‐duration (≤3 days) and longer‐duration (>3 days) heavy precipitation across a spectrum of urban development, encompassing long‐term built‐up (LTB), recently built‐up (RTB), and rural background catchments within each urban agglomeration catchment (UAC) across China. We find that urbanization primarily influences shorter‐duration heavy precipitation, with a more pronounced effect observed in the LTB catchments. Conversely, the influence of urbanization on longer‐duration heavy precipitation appears to be more weakened in the RTB catchments. The intensification of shorter‐duration heavy precipitation induced by urbanization is more pronounced in humid regions and within larger UACs, while the urban effect on longer‐duration heavy precipitation is weaker in humid regions and within larger UACs. Notably, the attribution analysis results of the geographical detector model confirm our findings. Anthropogenic‐related factors (population density, nighttime light data, impervious surface percent, land surface temperature) significantly influence shorter‐duration heavy precipitation in more UACs than natural factors (distance from the coast, wind and elevation), while natural factors dominate longer‐duration events in larger UACs across China. Our results highlight the necessity of considering the spatial difference between the UAC center and UAC periphery for accurate projections and effective prevention of heavy precipitation and potential flood risks in the future.

Study region: The island of Taiwan. Study focus: This study presents long-term and high-resolution modeling of flood occurrence, interdecadal patterns of river-floodplain dynamics, and analysis of flooding during two typhoon events—Nari and Morakot over Taiwan. The modeling system combines a hydrological model (HiGW-MAT) and a river hydrodynamics model (CaMa-Flood), simulating hydrologic-hydrodynamic processes at ∼ 5 km resolution with flood occurrences downscaled to ∼ 90 m. New hydrological insights for the region: As the first investigation to conduct spatially comprehensive and temporally continuous modeling in Taiwan, this study presents important advances on the application of large-scale hydrological-hydrodynamic models in settings like that of Taiwan with important implications on flood prediction under climate change. The assessment of interdecadal changes in streamflow indicates no consistent trends over the past four decades; however, the variabilities in monthly-scale streamflow are significant and regionally diverse across Taiwan. Decadal changes in flood occurrence are also minimal at the island-scale, but the changes vary substantially across different regions and exhibit an increased variability over time. Furthermore, the simulated flood patterns in response to Typhoons Nari and Morakot suggest that the modeling framework can be used to reproduce the spatial dynamics and temporal progression of flooding under extreme events in relatively small regions like Taiwan.

Abstract T‐cell lymphoma in the gastrointestinal tract (intestinal T‐cell lymphoma, [ITCL]) is rare. ITCL, not otherwise specified (ITCL, NOS) which is a type of ITCL is particularly rare. There are few case reports of ITCL, NOS but no previous reports describe its endoscopic features. In this report, the 69‐year‐old man was diagnosed with ITCL, NOS. Colonoscopy revealed the elevated legion and edematous mucosa with focal depressions in the lower rectum. On the depressed legions, magnifying endoscopy combined with narrow‐band imaging detected the disappearance of glandular structure and branching abnormal blood vessels like a tree. These findings were similar to the tree‐like appearance, which has been described as a unique feature of gastric mucosal‐associated lymphoid tissue lymphoma. The targeted biopsy of the tree‐like appearance showed abnormal histopathological findings which fit the definition of ITCL, NOS. He was treated with chemotherapy and achieved complete remission. As is the case of gastric mucosal‐associated lymphoid tissue lymphoma, the tree‐like appearance is possibly the unique sign of ITCL, NOS. We report the endoscopic features of ITCL, NOS and show characteristic findings by magnifying endoscopy combined with narrow‐band imaging.

Abstract Urban land will face high fluvial flood risk against the background of climate change and urban expansion. The effect of urban spatial expansion, instead of densification of assets within existing urban cells, on flood risk has rarely been reported. Here, we project the future flood risk of seven urban agglomerations in China, home to over 750 million people. The inundated urban land areas in the future are projected to be 4 to 19 times that at present. Without considering the urban spatial expansion, the inundated urban land areas will be underestimated by 10-50%. Urban land is more likely to be inundated than non-urban land, and the newly-developed urban land will be inundated more easily than the historical urban land. The results demonstrate the urgency of integrating climate change mitigation, reasonable urban land expansion, and increased flood protection levels to minimize the flood risk in urban land.

Variation in global water scarcity is strongly dependent on the spatial distribution of population and economic conditions, according to a quantitative assessment of combined physical and economic water scarcity under multiple future emissions scenarios.

Abstract Global terrain classification data have been used for various issues related to topography such as the estimation of soil types and of ground vulnerability to earthquakes and the creation of seismic hazard maps. However, due to the resolution of digital elevation models (DEMs), the terrain classification data from previous studies could not discriminate small landforms such as plains at the bottom of narrow valleys and small rises in plains. Owing to the greater regional variation of small landforms, there is trade-off between DEMs of higher resolution and the creation of global geomorphological legends. To address this problem, we first merged regions with similar topographic characteristics using slope gradients and HAND (height above the nearest drainage) calculated by the 90-m-spatial-resolution DEMs interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM), and united the polygons with the unit catchments of the MERIT-Basins dataset, so that the polygons contain calculated terrain measurements (slope gradient, HAND, surface texture, local convexity, sinks) and noise types as attributes, as well as the ID number of the unit catchment. In addition, we performed k-means clustering on the dataset using slope gradient, HAND, and surface texture, which can be combined with the dataset as a simple terrain classification. The clustering results were prepared in 15 and 40 global uniform clusters and 15 and 40 clusters for each basin to understand the global appearance of the terrain and provide zoning data for regional problem-solving. The 15 clusters were prepared to observe the outline of the terrain without any processing, whereas the 40 clusters were prepared to group and reclassify the polygons to create zoning data for each region. This dataset showed improvements in terms of capturing the small rises in plains compared to the authors' previous global terrain classification data. This dataset can be used as a proxy and is expected to contribute to modeling and estimation in various applications that are known to be related to topography. The datasets of this article are available at [ https://gisstar.gsi.go.jp/terrain2021/ ].

Abstract River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26‐year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin‐wide terrestrial water storage variability (median 2.78%, range 0.04%–12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5% (CaMa‐Flood) and 30.7% (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Abstract The ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

Abstract Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.

Abstract Estimates of future flood risk rely on projections from climate models. The relatively few climate models used to analyze future flood risk cannot easily quantify of their associated uncertainties. In this study, we demonstrated that the projected fluvial flood changes estimated by a new generation of climate models, the collectively known as Coupled Model Intercomparison Project Phase 6 (CMIP6), are similar to those estimated by CMIP5. The spatial patterns of the multi-model median signs of change (+ or −) were also very consistent, implying greater confidence in the projections. The model spread changed little over the course of model development, suggesting irreducibility of the model spread due to internal climate variability, and the consistent projections of models from the same institute suggest the potential to reduce uncertainties caused by model differences. Potential global exposure to flooding is projected to be proportional to the degree of warming, and a greater threat is anticipated as populations increase, demonstrating the need for immediate decisions.

Abstract This study aims to create a terrain classification of Japan that allows both geomorphological and geoengineering classifications coexist without large contradictions and to distinguish landform elements even in urban plains which include noise associated with digital elevation models (DEMs). Because Japan is susceptible to natural disasters, we designed the classification to reflect the ground vulnerability of both alluvial plains and mountains through the application of terrain classification data to landslide susceptibility and seismic zoning. We updated an existing DEM-based terrain classification method for application in the high-resolution 30 m DEM. We used topographic measurements that do not amplify manmade unevenness or noise, which are usually the main problems associated with the use of high-resolution DEMs with high vertical accuracies. We selected the height above the nearest drainage (HAND), slope gradient, surface texture, and local convexity as geometric signatures, which were devised so as not to detect noise. Segment polygon data of terrain units were derived from the raster data of slope and HAND. The polygon data were classified into 40 clusters using the attributes of slope, HAND, and surface texture; then they were grouped into 16 legends following comparisons with the existing geological and geomorphological maps and supplementary reclassification by HAND and local convexity. The derived terrain classification, except for manmade cuts and fills, showed similarities with the existing expert-driven maps and some association with areas where shallow landslides or floods frequently occur. Based on a trial in California using a 30 m DEM, we concluded that the proposed method can be adopted in other regions outside of Japan.

Measurement(s) vector river network • river network drainage density Technology Type(s) computational modeling technique • machine learning Sample Characteristic - Environment river • watershed • drainage basin Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13377329

Global flood risk is assessed in this study; in particular, the authors describe, based on a modeling approach, the positive effect of river dams on mitigating flood hazards to people.

Abstract The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events is investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39 h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto Region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

Abstract Integrating reservoir flood control operations in global flood forecasting systems is important for accurately estimating discharge and other variables. Because existing modeling operational rules and parameters do not reflect the actual variability due to a lack of associated data, globally applicable modeling of flood regulation needs to be studied further. In this study, we developed a flood control operation scheme with refined parameters and algorithms to tackle this problem. We used recently developed objective data sets of reservoir dynamics to more realistically estimate different reservoir parameters, such as flood storage capacity. Furthermore, the algorithm for the operational rule was modified by introducing a new release coefficient such that the peak attenuation depended on the reservoir's ability to regulate floods. The operation scheme for 2,169 dams was introduced into the CaMa‐Flood global hydrodynamic model. Compared to the simulation without reservoirs, the Nash‐Sutcliffe Efficiency and peak discharge error in the daily river discharge improved at 62.2% and 49.9% of the gauges, respectively, after the integration. Compared to the parameter estimated by a method in previous studies, the newly estimated flood storage capacity parameters reduced the peak discharge error at 47% of the gauges downstream of the reservoirs where the flood storage capacity ratio is small (≤50%). Furthermore, the modification of operational rules had the effect of mitigating people exposed to flooding by ∼10%. This study informs the integration and the refinements of the reservoir operation scheme to improve global flood simulation and forecasting.

Bangladesh is highly vulnerable to flood hazards, and its flood risk is projected to increase with global warming. In addition to climate change, internal climate variation, such as the El Niño–Southern Oscillation (ENSO), influences flooding in many rivers worldwide. However, the impact of internal climate variability on flooding in Bangladesh remains unclear due to the limited observations. Here, we assess the impacts of ENSO and climate change on flood occurrence in Bangladesh using a large-ensemble climate simulation dataset and a global river model (CaMa-Flood). After separating 6000 years of simulation (100-member ensemble river simulations for 1950–2010) into El Niño, La Niña, and neutral years, we calculated the extent to which each ENSO stage increased flood occurrence probability relative to the neutral state using the fraction of attributable risk method. In addition, we estimated the impact of historical climate change on past flood occurrence through a comparison of simulations with and without historical global warming. Under the no-global-warming climate, La Niña increased the occurrence probability of a 10 year return period flood at Hardinge Bridge on the Ganges River by 38% compared to neutral years. The influence of La Niña or El Niño state on flood occurrence probability in the Brahmaputra River at Bahadurabad station is negligible. Historical global warming increased the occurrence of flooding in the Ganges River, the Brahmaputra River, and their confluence by 59%, 44%, and 55%, respectively. The impact of ENSO on flood occurrence probability decreased in the historical simulation, likely due to the conflation of ENSO and climate change signals, and no significant correlation between ENSO and flood occurrence was detected when only small-ensemble simulations were used. These findings suggest that the use of large-ensemble climate simulation datasets is essential for precise attribution of the impacts of internal climate variability on flooding in Bangladesh.

Extensive floodplains throughout the Amazon basin support important ecosystem services and influence global water and carbon cycles. A recent change in the hydroclimatic regime of the region, with increased rainfall in the northern portions of the basin, has produced record-breaking high water levels on the Amazon River mainstem. Yet, the implications for the magnitude and duration of floodplain inundation across the basin remain unknown. Here we leverage state-of-the-art hydrological models, supported by in-situ and remote sensing observations, to show that the maximum annual inundation extent along the central Amazon increased by 26% since 1980. We further reveal increased flood duration and greater connectivity among open water areas in multiple Amazon floodplain regions. These changes in the hydrological regime of the world’s largest river system have major implications for ecology and biogeochemistry, and require rapid adaptation by vulnerable populations living along Amazonian rivers.

Abstract A vector‐river network explicitly uses realistic geometries of river reaches and catchments for spatial discretization in a river model. This enables improving the accuracy of the physical properties of the modeled river system, compared to a gridded river network that has been used in Earth System Models. With a finer‐scale river network, resolving smaller‐scale river reaches, there is a need for efficient methods to route streamflow and its constituents throughout the river network. The purpose of this study is twofold: (1) develop a new method to decompose river networks into hydrologically independent tributary domains, where routing computations can be performed in parallel; and (2) perform global river routing simulations with two global river networks, with different scales, to examine the computational efficiency and the differences in discharge simulations at various temporal scales. The new parallelization method uses a hierarchical decomposition strategy, where each decomposed tributary is further decomposed into many sub‐tributary domains, enabling hybrid parallel computing. This parallelization scheme has excellent computational scaling for the global domain where it is straightforward to distribute computations across many independent river basins. However, parallel computing for a single large basin remains challenging. The global routing experiments show that the scale of the vector‐river network has less impact on the discharge simulations than the runoff input that is generated by the combination of land surface model and meteorological forcing. The scale of vector‐river networks needs to consider the scale of local hydrologic features such as lakes that are to be resolved in the network.

Abstract Polygon-based terrain classification data were created globally using 280 m digital elevation models (DEMs) interpolated from the multi-error-removed improved-terrain DEM (MERIT DEM). First, area segmentation was performed globally with the logarithmic value of slope gradient and the local convexity calculated from the DEM. Next, by adding surface texture, k-means clustering was performed globally and the polygons were grouped into 40 clusters. Then, we tried to reclassify these 40 clusters into geomorphologic terrain groups. In this study, we attempted reclassification and grouping using local information from Japan as a test case. The 40 clusters were compared with Japanese geological and geomorphological data and were then reclassified into 12 groups that had different geomorphological and geological characteristics. In addition, large shape landforms, mountains, and hills were subdivided by using the combined texture. Finally, 15 groups were created as terrain groups. Cross tabulations were performed with geological or lithological maps of California and Australia in order to investigate if the Japanese grouping of the clusters was also meaningful for other regions. The classification is improved from previous studies that used 1-km DEMs, especially for the representation of terrace shapes and landform elements smaller than 1 km. The results were generally suitable for distinguishing bedrock mountains, hills, large highland slopes, intermediate landforms (plateaus, terraces, large lowland slopes), and plains. On the other hand, the cross tabulations indicate that in the case of gentler landforms under different geologic provinces/climates, similar topographies may not always indicate similar formative mechanisms and lithology. This may be solved by locally replacing the legend; however, care is necessary for mixed areas where both depositional and erosional gentle plains exist. Moreover, the limit of the description of geometric signatures still appears in failure to detect narrow valley bottom plains, metropolitan areas, and slight rises in gentle plains. Therefore, both global and local perspectives regarding geologic province and climate are necessary for better grouping of the clusters, and additional parameters or higher resolution DEMs are necessary. Successful classification of terrain types of geomorphology may lead to a better understanding of terrain susceptibility to natural hazards and land development.

An amendment to this paper has been published and can be accessed via the original article.

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand, which would facilitate modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, for example, Copernicus, is presented. This paper introduces recent examples of novel ECLand developments on (i) vegetation; (ii) snow; (iii) soil; (iv) open water/lake; (v) river/inundation; and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation and prediction framework.

Constellations of small satellites equipped with synthetic aperture radar (SAR) payloads can realize observations in short time intervals independently from daylight and weather conditions and this technology is now in the early stages of development. This tool would greatly contribute to rapid flood monitoring, which is usually one of the main missions in upcoming plans, but few studies have focused on this potential application and a required observation performance for flood disaster monitoring has been unclear. In this study, we propose an unprecedented method for investigating how flood extents would be temporally and spatially observed with a SAR small-satellite constellation and for evaluating that observation performance via an original index. The virtual experiments of flood monitoring with designed constellations were conducted using two case studies of flood events in Japan. Experimental results showed that a SAR small-satellite constellation with sun-synchronous orbit at 570 km altitude, 30-km swath, 15–30° incidence angle, and 20 satellites can achieve 87% acquisition of cumulative flood extent in total observations. There is a difference between the results of observation performance in two cases because of each flood’s characteristics and a SAR satellite’s observation system, which implies the necessity of individual assessments for various types of rivers.

Current global riverine flood risk studies assume a constant mean sea level boundary. In reality high sea levels can propagate up a river, impede high river discharge, thus leading to elevated water levels. Riverine flood risk in deltas may therefore be underestimated. This paper presents the first global scale assessment of the joint influence of riverine and coastal drivers of flooding in deltas. We show that if storm surge is ignored, flood depths are significantly underestimated for 9.3% of the expected annual population exposed to riverine flooding. The assessment is based on extreme water levels at 3433 river mouth locations as modeled by a state-of-the-art global river routing model, forced with a multi-model runoff ensemble and bounded by dynamic sea level conditions derived from a global tide and surge reanalysis. We first classified the drivers of riverine flooding at each location into four classes: surge-dominant, discharge-dominant, compound-dominant or insignificant. We then developed a model experiment to quantify the effect of surge on flood hazard and impacts. Drivers of riverine flooding are compound-dominant at 19.7% of the locations analyzed, discharge-dominant at 69.2%, and surge-dominant at 7.8%. Compared to locations with either surge- or discharge-dominant flood drivers, locations with compound-dominant flood drivers generally have larger surge extremes and are located in basins with faster discharge response and/or flat topography. Globally, surge exacerbates 1-in-10 years flood levels at 64.0% of the locations analyzed, with a mean increase of 11 cm. While this increase is generally larger at locations with compound- or surge-dominant flood drivers, flood levels also increase at locations with discharge-dominant flood drivers. This study underlines the importance of including dynamic downstream sea level boundaries in (global) riverine flood risk studies.

The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In this paper, we analyze the spatiotemporal variations in the terrestrial water storage (TWS) of the ACTR and three of the largest pan-Arctic river basins (Lena, Mackenzie, Yukon). To do this, we utilize monthly Gravity Recovery and Climate Experiment (GRACE) data from 2002 to 2016. Together with global land reanalysis, and river runoff data, we identify declining TWS trends throughout the ACTR that we attribute largely to increasing evapotranspiration driven by increasing summer air temperatures. In terms of regional changes, large and significant negative trends in TWS are observed mainly over the North American continent. At basin scale, we show that, in the Lena River basin, the autumnal TWS signal persists until the spring of the following year, while in the Mackenzie River basin, the TWS level in the autumn and winter has no significant impact on the following year. As expected global warming is expected to be particularly significant in the northern regions, our results are important for understanding future TWS trends, with possible further decline.

Global flood models (GFMs) are becoming increasingly important for disaster risk management internationally. However, these models have had little validation against observed flood events, making it difficult to compare model performance. In this paper, we introduce the first collective validation of multiple GFMs against the same events and we analyse how different model structures influence performance. We identify three hydraulically diverse regions in Africa with recent large scale flood events: Lokoja, Nigeria; Idah, Nigeria; and Chemba, Mozambique. We then evaluate the flood extent output provided by six GFMs against satellite observations of historical flood extents in these regions. The critical success index of individual models across the three regions ranges from 0.45 to 0.7 and the percentage of flood captured ranges from 52% to 97%. Site specific conditions influence performance as the models score better in the confined floodplain of Lokoja but score poorly in Idah’s flat extensive floodplain. 2D hydrodynamic models are shown to perform favourably. The models forced by gauged flow data show a greater level of return period accuracy compared to those forced by climate reanalysis data. Using the results of our analysis, we create and validate a three-model ensemble to investigate the usefulness of ensemble modelling in a flood hazard context. We find the ensemble model performs similarly to the best individual and aggregated models. In the three study regions, we found no correlation between performance and the spatial resolution of the models. The best individual models show an acceptable level of performance for these large rivers.

The neutron instruments suite, installed at the spallation neutron source of the Materials and Life Science Experimental Facility (MLF) at the Japan Proton Accelerator Research Complex (J-PARC), is reviewed. MLF has 23 neutron beam ports and 21 instruments are in operation for user programs or are under commissioning. A unique and challenging instrumental suite in MLF has been realized via combination of a high-performance neutron source, optimized for neutron scattering, and unique instruments using cutting-edge technologies. All instruments are/will serve in world-leading investigations in a broad range of fields, from fundamental physics to industrial applications. In this review, overviews, characteristic features, and typical applications of the individual instruments are mentioned.

Neutron devices such as neutron detectors, optical devices including supermirror devices and 3He neutron spin filters, and choppers are successfully developed and installed at the Materials Life Science Facility (MLF) of the Japan Proton Accelerator Research Complex (J-PARC), Tokai, Japan. Four software components of MLF computational environment, instrument control, data acquisition, data analysis, and a database, have been developed and equipped at MLF. MLF also provides a wide variety of sample environment options including high and low temperatures, high magnetic fields, and high pressures. This paper describes the current status of neutron devices, computational and sample environments at MLF.

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Global warming is likely to exacerbate future fluvial floods in the world’s mega-delta regions due to both changing climate and rising sea levels. However, the effects of sea level rise (SLR) on fluvial floods in such regions have not been taken into account in current global assessments of future flood risk, due to the difficulties in modeling channel bifurcation and the backwater effect. We used a state-of-the-art global river routing model to demonstrate how these complexities contribute to future flood hazard associated with changing climate and SLR in the world’s largest mega-delta region, the Ganges-Brahmaputra-Meghna Delta. The model demonstrated that flood water in the main channels flows into tributaries through bifurcation channels, which resulted in an increase in inundation depth in deltaic regions. We found that there were large areas that experienced an increase in inundation depth and period not directly from the SLR itself but from the backwater effect of SLR, and the effect propagated upstream to locations far from the river mouth. Projections under future climate scenarios as well as SLR indicated that exposure to fluvial floods will increase in the last part of the 21st century, and both SLR and channel bifurcation make meaningful contributions.

In global hydrological models, river discharge is accumulated by following the river network, which presumes that there is one downstream destination for each grid. Implementing "diversions" where there are multiple downstream destinations, such as bifurcations and inter-basin water transfers, requires an extension of river routing algorithms. Previous global water resources models that implemented diversions typically used a semi-implicit numerical scheme where river discharge should be calculated in an upstream-to-downstream order. The major obstacle to flexible application to any river network with diversions was that ad-hoc modification of the model's code was required because the river sequence map to specify calculation order was developed without considering diversions. To overcome this limitation, we developed two new river routing algorithms that can generally represent diversions. One is automatically updating river sequence considering diversions and the other is introducing the Forward Time Centered Space scheme. The former has the advantage of stability under longer time steps, while the latter's strength lies in its easy implementation and applicability where backwater happens. We confirmed that both algorithms efficiently handle the complex canal network in the Indus River. These approaches allow us to flexibly implement diversions in river routing algorithms. [ABSTRACT FROM AUTHOR]

The current global water body maps have a resolution of approximately 30 m depending on the available remote sensing data. A water body map with higher spatial resolution is required to distinguish smaller rivers for advanced applications involving global carbon cycle and real-time flood predictions. Conventional water extraction methods use water indices that combine visible and infrared spectra. State-of-the-art remote sensing data including aerial photography, offers a few-meter spatial resolution but only contains the visible spectrum. Here, we established a water extraction method at 60-cm resolution via Bayesian inference, using aerial photography's visible spectrum combining the Landsat-based Global 1-second Water Body Map and the Open Street Map (OSM). The Japan Flow Direction Map (J-FlwDir) was used to link the water bodies. Our method detected the main streams of the Tsurumi and Tama rivers and their tributaries, which were not resolved by the Landsat-based dataset. With our approach, rivers with widths >10 m were detected and water extent was obtained for 37% of small rivers, represented as lines on the OSM. These findings indicate that our method, solely based on visible spectra with hydrography data and existing water body maps, improves the spatial resolution of water mapping without requiring infrared. [ABSTRACT FROM AUTHOR]