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The execution of hydraulic models at large spatial scales has yielded a step change in our understanding of flood risk. Yet their necessary simplification through the use of coarsened terrain data results in an artificially smooth digital elevation model with diminished representation of flood defense structures. Current approaches in dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socioeconomic data. Here, we introduce a novel solution for application at scale. The geomorphometric characteristics of defense structures are sampled, and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source digital elevation model. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed U.S. levee crest heights. When incorporated into a continental‐scale hydrodynamic model based on LISFLOOD‐FP and compared to local flood models in Iowa (USA), median correspondence was 69% for high‐frequency floods and 80% for low‐frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted, and risk‐based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering‐grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved‐accuracy simulations of flood inundation. Traditional flood risk assessments are carried out using computer models built with local data, but their spatial coverage is impaired by how expensive and time‐consuming they are to produce. Recent advances in data availability, understanding of necessary physical process representation, and computational capacity have enabled hydraulic models of the entire globe to be built in an automated fashion at a fraction of the financial and human cost. However, their accuracy can be significantly impaired by a lack of information on flood defenses. As the model is built, elevation data are coarsened to reduce the number of calculations required to simulate flooding over such wide areas. This results in flood defense structures being smoothed out of the terrain information used in the model. Publicly available defense inventories are of insufficient coverage to ameliorate this issue. In this paper, a method is presented, which automatically detects levee‐like features in high‐resolution elevation data and accurately represents their heights during this necessary coarsening process. Simulating flood inundation over this “defended” topography results in high correspondence between local models and observations for test cases in the United States and Italy, with improvements particularly felt where a lack of defense information is the dominant source of error. Flood defense representation is presently poor in large‐scale flood models, impairing their ability to map flood hazard accuratelyA new method is presented, which automatically identifies hydraulic structures in terrain data and accurately preserves their elevationsHydraulic simulations where a lack of defense data is the dominant error show significant improvements in skill when incorporating this method

The scaling of peak flows associated with a probability of exceedance (Qp) or a specific rainfall‐runoff event (QR) with respect to drainage area (A) is known as flood scaling and it has been widely used in peak flow regionalization. The attenuation and aggregation processes within the hillslopes and river network in a rainfall‐runoff event, provide a framework to test the scaling of QR. Although scaling of Qphas been reported in empirical studies, its physical interpretation is compromised, since Qpat each site could come from different rainfall‐runoff events. To address this problem, the authors explored the effect of actual variabilities of rainfall and soil moisture fields, and the effect of the river network structure, in the scaling of peak flows of 85 rainfall‐runoff events and peak flow quantiles that were observed in the Iowa River Basin at 43 streamflow gauges. The authors established empirical evidence that addresses two questions: (1) What does control the performance of the scaling of observed QR? (2) What is the interplay between sampling errors and the selection of explanatory variables in the construction of regional regression models for QRand Qp? For the first question, the authors found that the slope magnitude in the scaling of the rainfall intensity fields with respect to Acontrols the scaling' performance of QR. Regarding the second question, the authors demonstrate that the inclusion of river network descriptors should improve the regional equations to estimate peak flow quantiles unless stream gauging sampling errors affect the analysis. The performance of the scaling of peak flows from rainfall‐runoff events is explained by the scaling structure of rainfall intensity fieldsThe river network structure represented by width function descriptors can improve the regional equations to estimate peak flow quantilesThe selection of width function descriptors in regional equations is hampered by sampling errors and limited number of streamflow gauges

Although prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of pooledsites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis. Synthetic peak flows are used to investigate the epistemic and sampling errors associated with local and regional methods to estimate PFQsError components stemming from epistemic assumptions, parameter estimation method, sample size, and the number of pooled sites are evaluatedThe spatial variability of the skewness of the peak flows is partially explained by river network structure and orientation of the watershed

The Surface Water and Ocean Topography (SWOT) mission will measure water surface elevations and inundation extents of rivers of the world but with limited temporal sampling. By comparing flood location and duration of 4,664 past flood events recorded by the Dartmouth Flood Observatory to SWOT's orbit ephemeris, we estimate that SWOT would have seen 55% of these, with higher probabilities associated with more extreme events and with those that displaced more than 10,000 people. However, SWOT measurements will exhibit uneven temporal sampling and may require a combination of data obtained at different times to accurately characterize large events. This is illustrated using recent flooding in the United States, in eastern Iowa and in Houston and surrounding areas from Hurricane Harvey. SWOT data have significant potential to improve flood forecasting models by offering data needed to enhance flow routing modeling, provided that users can overcome the potential hurdles associated with its temporal and spatial sampling characteristics. The Surface Water and Ocean Topography (SWOT) satellite mission will simultaneously measure water surface elevations and inundated areas for the Earth's land surface. Such information can be valuable for improving flood models and their calibration; however, SWOT temporal sampling will be limited, with most locations in the world being seen once every 7 to 10 days, which may cause it to miss floods. Using a record of global flood information, including duration and location, compiled by the Dartmouth Flood Observatory and the expected satellite orbit, we estimated that, if already operational, SWOT would have collected at least one measurement over 55% of these events. We illustrate SWOT data coverage using flood inundation maps generated for flooding in eastern Iowa (2008) and in Houston and surrounding areas, Texas, caused by Hurricane Harvey (2017). Due to the novelty of this kind of hydrological information, particularly in the way SWOT samples rivers in time and space, early engagement of potential users may be instrumental to maximize the utility of this open source of worldwide observations of rivers, lakes, and inundated land. SWOT temporal and spatial observation capabilities and limitations are evaluated using recent floods in eastern Iowa and from Hurricane HarveyDuring its life cycle, SWOT may observe hundreds of flood events, including over data‐scarce regionsDartmouth Flood Observatory data suggest that more destructive floods tend to last longer and are more likely to be observed by SWOT

Using data collected from collocated hillslopes in central Iowa, the United States, the authors (1) explored the spatial variability of runoff coefficient at the event scale by examining the relationships between the standard deviation and coefficient of variation of runoff coefficient and the mean and (2) analyzed the temporal persistence of spatial pattern of runoff coefficient using Spearman rank and Pearson correlation coefficient. This study considered 12 cropland hillslopes with 0–20% native prairie vegetation coverage distributed at different hillslope locations. Seventy runoff events over the period 2008–2011 were investigated, of which 51 occurred during crop active growing season, when the hydrologic responses of crops and prairie vegetation are similar. For these events, the spatial coefficient of variation had a median value of 0.80, which indicate high variation of event‐scale runoff coefficients across neighboring hillslopes. This spatial variation largely cannot be consistently explained by the individual hillslope structural properties investigated. The standard deviation and mean of runoff coefficient showed a convex upward relationship across the range of runoff coefficients, with the maximum standard deviation value at the mean runoff coefficient of about 0.48. The coefficient of variation exponentially decreased with increasing runoff coefficient. For 71% of the cases, the results of both correlation analyses were statistically significant (p≤ 0.05), which indicate stable spatial pattern of runoff coefficient across events. This temporal persistence could be disrupted under extremely dry and wet conditions. The spatial variation‐mean empirical relation and the temporal persistence of spatial pattern provide insight for parameterizing spatial variability of runoff coefficient in distributed hydrologic models. Event‐scale runoff coefficient was highly variable across neighboring hillslopes with spatial separation ranging from tens of meters to 3000 mSpatial variability of runoff coefficient followed predictable patterns with respect to spatial mean of runoff coefficientTemporal persistence of the spatial pattern of runoff coefficient was observed and it could be disrupted under extremely dry and wet conditions

We explore the projected changes in flood impacts across Iowa (central United States) and the associated uncertainties by forcing a hydrologic model with downscaled global climate model outputs and four Shared Socioeconomic Pathways. Our results point to projected increasing magnitude and variability in flooding across the state, especially for high‐emission scenarios. Next, we partition the flood impacts' projections into: (a) the response of the global climate models to anthropogenic forcing, (b) scenario uncertainty due to emissions, and (c) internal climate variability. We find scenario uncertainty plays a small role, while climate model uncertainty and internal climate variability dominate the flood impacts' projections, with the contribution of model uncertainty increasing toward the end of this century. Insights from our work can be utilized by stakeholders to understand the current limitations of flood impact projections and provide suggestions about where modelers should focus efforts to reduce uncertainty. This study looks at how climate change is projected to affect floods in Iowa (central United States). The results suggest that flooding is projected to worsen and become more unpredictable, especially with higher greenhouse gas emissions. The main sources of uncertainty in these projections are the differences in climate models' response to forcings and natural climate variability. Understanding these uncertainties can help improve future climate change assessments for flood risk stakeholders such as agencies working toward climate adaptation and water management and improve related risk assessments and planning analysis. Climate models producing larger increases in flood peak magnitude typically produce larger changes in varianceClimate model uncertainty is dominant in the early 21st century, while internal climate variability dominates the later part of the 21st centuryUncertainty in flood peaks directly translates to flood risk across Iowa Climate models producing larger increases in flood peak magnitude typically produce larger changes in variance Climate model uncertainty is dominant in the early 21st century, while internal climate variability dominates the later part of the 21st century Uncertainty in flood peaks directly translates to flood risk across Iowa

Structural modification of ternary aluminium – zinc – coper alloys influences their wear properties. In a series of studies, Al – 30 wt. % Zn – 3 wt. % Cu alloy (Al-30Zn-3Cu) has been doped with 1 wt.% Mn introduced with AlMn-based master alloys. The alloy microstructure and mechanical properties have been studied using light and SEM/EBSD microscopy, and measurements of wear resistance and dimensional changes. Reducing Cu content to 3 wt.% and doping with 1 wt.% Mn allows obtaining alloy of significantly refined grains, improved wear resistance and preserved high dimensional stability.

The authors investigated the relation between the width function and the regional variability of peak flows. The authors explored 34 width function descriptors (WFDs), in addition to drainage area, as potential candidates for explaining the regional peak flow variability. First, using hydrologic simulations of uniform rainfall events with variable rainfall duration and constant rainfall intensity for 147 watersheds across the state of Iowa, they demonstrated that WFDs are capable of explaining spatial variability of peak flows for individual rainfall‐runoff events under idealized physical conditions. This theoretical exercise indicates that the inclusion of WFDs should drastically improve regional peak flow estimates with a reduction of the root mean square error by more than half in comparison with a regression model based on drainage area only. The authors followed the simulation with an analysis of estimated peak flow quantiles from 94 stream gauges in Iowa to determine if the WFDs have a similar explanatory power. The correlations between WFDs and peak flow quantiles are not as high as those found for simulated events, which indicates that results from event scale simulations do not translate directly to peak flow quantiles. The spatial variability of peak flow quantiles is influenced by other physical and statistical processes that are also variable in space. These results are consistent with recent work on event‐based scaling of peak flows that shows that the spatiotemporal variability of flood mechanisms is larger than the one expected from geomorphology alone. A total of 34 width function descriptors (WFDs), in addition to A, are examined as potential candidates for explaining the regional peak flow variabilityThe WFDs are capable of explaining spatial variability of peak flows for individual rainfall‐runoff events under restricted physical conditionsThe WFDs did not sufficiently explain the regional variability of the peak flow quantiles

ABSTRACTThe authors propose a hydrologic evaluation framework for gridded rainfall products. This framework makes use of the Spatial Stream Network (SSN) statistical method to provide spatial characterization of the discrepancies between two gridded rainfall products. The SSN method relies on using stream network length rather than the traditionally used Euclidean distances.It also accounts for the flow connectivity information between the network segments. This concept is relevant in hydrological modeling since rivers transport accumulated precipitation that occurred over different parts of the basins, and stream networks do not represent Euclidean space. To demonstrate, we used this framework to compare the satellite rainfall product called Integrated Multi-satellitE Retrievals for GPM (IMERG) with the ground-based Multi-Radar/Multi-Sensor (MRMS) rainfall product. The results show that the magnitudes of the rainfall discrepancies tend to decrease as rainfall accumulates in the downstream direction. However, the covariance range between these discrepancies is much larger along flow-connected stream network segments than in flow-unconnected stream segments. This in turn could have an effect on the error correlation of the predicted discharges. In addition, the spatial linear models of rainfall errors improved significantly with SSN based models in comparison to pure Euclidean separation distance models.

The High‐Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25‐ and 50‐km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope‐Link Model (HLM) at ∼1,000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff‐based simulations can capture the empirical distribution of flood peaks in five of the 10 models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community‐level assessment, we use nine statistical approaches to bias‐correct and downscale HighResMIP precipitation to a 4‐km resolution. The bias‐correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state. High‐Resolution Model Intercomparison Project (HighResMIP) runoff routed through a hydrologic model generates realistic distributions of annual maximum discharge for 5 of 11 realizations assessedHighResMIP precipitation forcings generate realistic flood peaks for all realizations assessed after bias‐correction and statistical downscalingChanges in flood peaks are not detected for the future but there is an overall increase in variance at 24% of the location in Iowa High‐Resolution Model Intercomparison Project (HighResMIP) runoff routed through a hydrologic model generates realistic distributions of annual maximum discharge for 5 of 11 realizations assessed HighResMIP precipitation forcings generate realistic flood peaks for all realizations assessed after bias‐correction and statistical downscaling Changes in flood peaks are not detected for the future but there is an overall increase in variance at 24% of the location in Iowa

The presented work is aimed at determining thermal diffusivity, thermal conductivity and heat capacity coefficients of silica quartz bentonite foundry sand. The values of the above thermo-physical properties were determined for temperature range of about 30 - 450 °C using the Casting measuring method [1-. The results obtained during the examinations presented in the paper can be useful when formulating boundary conditions in numerical models of heat and mass transfer in the system: casting mould ambient. The Casting method allows preserving real conditions during the experiment, i.e. contact of the mass with liquid metal and solidifying casting, and the obtained results are in a good agreement with the mean values available in literature. From the obtained results it follows that examinations should be also focused on thermo-physical properties vs. mass density dependency.

A significant potential source of error exists in mosaicked radar‐rainfall maps.Different radar calibration offsets lead to misestimation of rainfall amounts.Systematic error in rainfall significantly affects hydrologic predictions. This study addresses a significant potential source of error that exists in radar‐rainfall maps that are combined using data from multiple WSR‐88D radars of the Next Generation Radar (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to misestimation of rainfall over basins and subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar‐rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar‐rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the borderline that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically based, data‐intensive, calibration‐free, hillslope‐link hydrologic model called CUENCAS and demonstrated CUENCAS's ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the misestimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions.

Microstructure and mechanical properties of NiAlV alloys of the composition belonging to the pseudo-binary Ni3Al-Ni3V cross-section were investigated. The samples were prepared by the cold crucible levitation melting (CCLM) and by re-melting and crystallizing in the small volume copper mould. The phase composition of the samples, with should result from the eutectoidal decomposition was not found. Instead the Ni3(Al,V) and Ni(Al,V) solid solution or seldom disordered solid solution were retained due to the relatively high cooling rates. In the compression test the NiAlV alloys crystallized in the copper mould revealed high ductility and strength.

The presented work is aimed at determining thermal diffusivity, thermal conductivity and heat capacity of insulating sleeves used in Polish metallurgical/foundry practice. On basis of the theory elaborated in [1] the mean values of thermophysical properties for temperatures range of about 150-1000 oC were obtained. The results obtained during the examinations presented in the paper can be helpful when formulating boundary conditions during the computer aided simulation of the processes of heat and mass transfer in the system: casting (ingot) – mould riser (ingot head) – ambient, which uses the investigated insulating sleeves [2, 3]. The method of determining thermal properties can be also used for other foundry materials, e.g. sands or cores.

The paper brings data about heat balance of the killed steel ingot head. The balance is obtained on basis of the temperature measurement in the system: ingot body - ingot head - ingot mould - insulating sleeves – radiation shield - ambient. The measurements were performed using model sys-tem (1:5) of the 20000 kg flat ingot. The balance shows that about 86% of the heat issued during solidification of the ingot head is transferred to the ingot mould through the insulating sleeves. In order to decrease this heat, insulating sleeves of low thermal conductivity are required, which should allow reducing dimensions of the ingot head and increasing the metal yield.

The atoms site preference in the Al3Ti-Zn system has been studied using H. Rietveld method. The L12 Ti(Al, Zn)3 particles evolve in a ZnAl25 melt from the L12TiZn3 particles in the ZnTi4 master alloy. It is found that Zn is replaced by Al during the transformation TiZn3 → Ti(Al, Zn)3. In the evolving fcc L12 Ti(Al, Zn)3 phase Ti occupies (0, 0, 0) position while Al and Zn occupy (0, 0.5, 0.5) position, similarly to Al and X in the Al3Ti-X systems, where X = Ni, Cr, Mn, Cu, Ag, Pd [1, 2].

The microstructure of cast Zn-25wt%Al alloy inoculated by addition of a Zn-4wt%Ti master alloy (ZnTi4) has been studied using scanning electron microscopy and electron back-scatter diffraction (EBSD). It is found that Ti(Al,Zn)3 particles act as nucleation centres for grains of the primary solid solution of Zn in Al (α' phase). The Ti(Al,Zn)3 particles evolve in melt from the TiZn3 particles in the ZnTi4 master alloy. EBSD shows that α' phase dendrites are in the same crystallographic orientation as the Ti(Al,Zn)3 particles on which they nucleate. It is also found that some of the Ti(Al,Zn)3 particles do not have any well-defined crystallographic orientation relationship with the α' phase. These particles were probably pushed and then engulfed by growing α' grains which had already nucleated on other particles.

Statistical characteristics of spatial variability in rain fields at the scales below 5 km2 are discussed based on experimental data from five locations worldwide. The work is motivated by the practical needs of remote sensing applications using radar and satellite technologies, and by the scientific needs of rainfall modelling. Two types of rain-field characteristics are described: spatial correlations and conditional probabilities of occurrence. The accumulation intervals range from 5 min to 1 h. The analysed data samples were collected in Brazil, Florida, Iowa, Oklahoma, and on Guam.

The present work reports on the application of a computational fluid dynamics-based method as a tool to improve the aerodynamic design of rainfall measurement devices. The focus is on a new instrument, a two-dimensional video disdrometer that provides information about raindrop size distribution. The distorted wind field around and inside the instrument's body is simulated using a three-dimensional numerical model. A modified geometry of the instrument, suggested for operational purposes, is tested numerically. Trajectories of raindrops are simulated to investigate the wind effect on the catchment efficiency of the instrument. A stochastic Lagrangian particle-tracking model that accounts for the turbulence effect is examined. General guidelines related to aerodynamic aspects of the design of in-situ rainfall measuring devices are discussed.

The availability of satellite-derived rainfall products to hydrologists and other natural scientists has increased enormously in the last five years. The purpose of this paper is to review concisely the current state-of-the-art of satellite precipitation estimation over land and the availability of standard products, and to highlight some of the strengths and limitations of satellite-derived rainfall data for hydrological applications. Methods based on infrared, visible and passive microwave radiation measurements are discussed. Results of several international activities aimed at evaluating the performance of the estimation algorithms are briefly summarized.

To meet environmental pollution standards, the operators of industrial plants must include environmental criteria in their operating objectives. This involves dealing with the variability and uncertainty of natural processes, and requires mathematical modeling to quantify operational effects. An example is given in which optimal operation of a once‐through‐cooling power plant is affected by the uncertainty in the hydrometeorological conditions that control the heat exchange between the river water surface and the atmosphere. Real‐time optimal control of the power plant is based on simulations provided by a mathematical model of the river thermal regime. It is demonstrated that optimal results are achieved when correct information is provided on the uncertainty in the forecasts of the hydrometeorological conditions. Example study results are given for the Joliet, Illinois, power station and hydrometeorological data for the drought period of July 1988.

This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σchanges in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time. We develop a method to estimate past, present, and future flood risk for all properties in the conterminous United States whether affected by river, coastal or rainfall flooding. The analysis accounts for variability within environmental factors including changes in sea level rise, hurricane intensity and landfall locations, precipitation patterns, and river discharge. We show that even for a conservative climate change trajectory we can expect locally significant changes in the land area at risk from floods by 2050, and by this time defenses protecting 2,200 km2of land may be compromised. The complete dataset has been made available via a website (https://floodfactor.com/) created by the First Street Foundation in order to increase public awareness of the threat posed by flooding to safety and livelihoods. First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal)In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D modelsBy 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal) In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D models By 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West

Despite efforts to detect and mitigate wind farm clutter in weather radar observations of rainfall, these signatures propagate to quantitative precipitation estimates. In this study, the authors investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor rainfall products. The study uses the probability of detection method to identify wind farm clutter in data from Iowa for the years 2016 and 2017. Using the physically based distributed hydrologic model called the Hillslope‐Link Model, the authors show that streamflow (flood) prediction errors are generally significant at smaller basin communities where wind farms occupy a large portion of the upstream basins. These errors due to wind farm clutter show systematic decrease with the basin scales. The results from this study have implications for real‐time streamflow forecasts provided automatically by the National Water Model at the National Oceanic and Atmospheric Administration, particularly at small riverine communities. Wind farms corrupt radar‐rainfall estimates used for flood forecasting. Erroneous streamflow forecasts affect small communities where wind farms occupy a large portion of the upstream basins. The effect is difficult to detect at larger scales typically monitored by the USGS streamflow gauging network as the wind farms occupy a small fraction of the basins at those scales. Impact of wind farm clutter on streamflow predictions is higher at communities where wind farms cover a large portion of the upstream basinsDetection of wind farm effect is difficult at the USGS‐monitored basin scales as they occupy a small fraction of the drainage areaWind farm effect on streamflow predictions shows systematic decrease with increasing basin scales

The goal of this study is to diagnose the manner in which radar‐rainfall input affects peak flow simulation uncertainties across scales. We used the distributed physically based hydrological model CUENCAS with parameters that are estimated from available data and without fitting the model output to discharge observations. We evaluated the model's performance using (1) observed streamflow at the outlet of nested basins ranging in scale from 20 to 16,000 km2and (2) streamflow simulated by a well‐established and extensively calibrated hydrological model used by the US National Weather Service (SAC‐SMA). To mimic radar‐rainfall uncertainty, we applied a recently proposed statistical model of radar‐rainfall error to produce rainfall ensembles based on different expected error scenarios. We used the generated ensembles as input for the hydrological model and summarized the effects on flow sensitivities using a relative measure of the ensemble peak flow dispersion for every link in the river network. Results show that peak flow simulation uncertainty is strongly dependent on the catchment scale. Uncertainty decreases with increasing catchment drainage area due to the aggregation effect of the river network that filters out small‐scale uncertainties. The rate at which uncertainty changes depends on the error structure of the input rainfall fields. We found that random errors that are uncorrelated in space produce high peak flow variability for small scale basins, but uncertainties decrease rapidly as scale increases. In contrast, spatially correlated errors produce less scatter in peak flows for small scales, but uncertainty decreases slowly with increasing catchment size. This study demonstrates the large impact of scale on uncertainty in hydrological simulations and demonstrates the need for a more robust characterization of the uncertainty structure in radar‐rainfall. Our results are diagnostic and illustrate the benefits of using the calibration‐free, multiscale framework to investigate uncertainty propagation with hydrological models. Calibration‐free, multi‐scale framework to investigate uncertainty propagation wEmpirical radar rainfall error model to produce input ensemblesImpact of scale on peak flow simulation uncertainty

The goals of the Global Energy and Water Cycle Experiment Continental‐Scale International Project (GCIP) point to the need for high‐resolution data sets on all elements of the land surface and atmospheric hydrologic cycle. A high‐resolution precipitation data set has been derived from radar reflectivity observations taken from the National Weather Service WSR‐88D radars in the continental U.S. The data set is available for a continuous five‐year period (1996–2000) at an hourly, 4 × 4 km2resolution for the Mississippi River Basin. Development of the data set involved data management and quality control of input radar‐reflectivity, parameter estimation for radar‐reflectivity transformation, and product accumulation and quality control of the precipitation product. Quality control algorithms for the input radar‐reflectivity included procedures to deal with radar calibration differences, an especially important problem in developing a long‐term, continental‐scale data set for diverse hydroclimatological applications. Rainfall estimation was based on a Z‐R conversion algorithm that involved an optimization technique to determine the parameters for the transformation of radar‐reflectivity to rainfall. Rainfall accumulation involved integrating to hourly, 4 × 4 km2resolution and then visually inspecting the final product. Some limitations of the algorithm are presented and suggestions are proposed for improving the development of a long‐term, large‐scale precipitation product. Initial comparisons of the radar‐based product with a rain gauge based product after a quality analysis of both products show good agreement in the Mississippi River Basin.