The Iowa Flood Information System (IFIS) is a web-based platform developed at the Iowa Flood Center (IFC) in order to provide access to flood inundation maps, real-time flood conditions, flood ...forecasts, flood-related data, information, applications, and interactive visualizations for communities in Iowa. The IFIS provides community-centric watershed and river characteristics, rainfall conditions, and stream-flow data and visualization tools. Interactive interfaces allow access to inundation maps for different stage and return period values as well as to flooding scenarios with contributions from multiple rivers. Real-time and historical data of water levels, gauge heights, hourly and seasonal flood forecasts, and rainfall conditions are made available by integrating data from NEXRAD radars, IFC stream sensors, and USGS and National Weather Service (NWS) stream gauges. The IFIS provides customized flood-related data, information, and visualization for over 1000 communities in Iowa. To help reduce the damage from floods, the IFIS helps communities make better-informed decisions about the occurrence of floods and alerts communities in advance using NWS and IFC forecasts. The integrated and modular design and structure of the IFIS allows easy adaptation of the system in other regional and scientific domains. This paper provides an overview of the design and capabilities of the IFIS that was developed as a platform to provide one-stop access to flood-related information.
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•A comprehensive one-stop web platform is developed for flood related data and information.•Community centric approach provides a customized experience to communities.•The IFIS provides flood warnings, forecasts, inundation maps, and rainfall products.•The IFIS helps communities make better-informed decisions on the occurrence of floods.•The IFIS alerts communities in advance to help them reduce the damage of floods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
It is well acknowledged that there are large uncertainties associated with radar-based estimates of rainfall. Numerous sources of these errors are due to parameter estimation, the observational ...system and measurement principles, and not fully understood physical processes. Propagation of these uncertainties through all models for which radar-rainfall are used as input (e.g., hydrologic models) or as initial conditions (e.g., weather forecasting models) is necessary to enhance the understanding and interpretation of the obtained results. The aim of this paper is to provide an extensive literature review of the principal sources of error affecting single polarization radar-based rainfall estimates. These include radar miscalibration, attenuation, ground clutter and anomalous propagation, beam blockage, variability of the
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relation, range degradation, vertical variability of the precipitation system, vertical air motion and precipitation drift, and temporal sampling errors. Finally, the authors report some recent results from empirically-based modeling of the total radar-rainfall uncertainties. The bibliography comprises over 200 peer reviewed journal articles.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Annual peak discharge records from 50 stations in the continental United States with at least 100 years of record are used to investigate stationarity of flood peaks during the 20th century. We ...examine temporal trends in flood peaks and abrupt changes in the mean and/or variance of flood peak distributions. Change point analysis for detecting abrupt changes in flood distributions is performed using the nonparametric Pettitt test. Two nonparametric (Mann‐Kendall and Spearman) tests and one parametric (Pearson) test are used to detect the presence of temporal trends. Generalized additive models for location, scale, and shape (GAMLSS) are also used to parametrically model the annual peak data, exploiting their flexibility to account for abrupt changes and temporal trends in the parameters of the distribution functions. Additionally, the presence of long‐term persistence is investigated through estimation of the Hurst exponent, and an alternative interpretation of the results in terms of long‐term persistence is provided. Many of the drainage basins represented in this study have been affected by regulation through systems of reservoirs, and all of the drainage basins have experienced significant land use changes during the 20th century. Despite the profound changes that have occurred to drainage basins throughout the continental United States and the recognition that elements of the hydrologic cycle are being altered by human‐induced climate change, it is easier to proclaim the demise of stationarity of flood peaks than to prove it through analyses of annual flood peak data.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
AbstractExtreme rainfall in midwestern United States has gotten more common over the last half century, thus increasing flooding events across the region. As a result, traditional flood mitigation ...measures are commonly overwhelmed by highwater events, illustrating the need for new solutions. Of the 91,000 dams in the US, the vast majority are small and go unused for flood mitigation. Among those that are utilized in flood peak reduction, few are actively managed in which outflows are manipulated through a gated outlet. Instead, small storage locations typically use passive control, allowing impounded water levels to fluctuate without the use of a gated outlet, possibly squandering some of their flow-reduction potential. In this paper, we have evaluated actively managed storage within a distributed network of 130 small dams in a 660-km2 watershed in southeastern Iowa using three operation schemes to increase storage utilization and reduce downstream flows. We developed a module to simulate the dam operation into a distributed hydrologic model that is forced with soil conservation service (SCS) 24-h design storms distributed uniformly across the watershed with 0.2, 0.1, 0.02, and 0.01 exceedance probabilities to evaluate flow reductions. When compared with passive operation, outlet flows were reduced under each proposed iteration of the 24-h design storm. Using the most aggressive operation scheme, outlet flows were reduced by over 70%. These results showcase the need for better understanding of activated flood storage across midwestern watersheds and encourage further work in optimizing this technique for real-time management.
Flood frequency estimation forms the basis for engineering design of hydraulic structures, including bridges and culverts, local and regional development planning, and flood insurance. In the United ...States, the Water Resources Council recommends using the Log-Pearson Type III (LP3) distribution as a standard for use with the annual peak flow data. However, researchers have argued for the use of more than one streamflow value in a year thus increasing the sample size and decreasing the sampling error in the estimates of the flood quantiles. In this study, conducted over Iowa, the authors revisit the method proposed by Donald Turcotte and others to use power-law distribution applied to streamflow peak values for events separated by a time window. In contrast to those earlier studies, the authors applied formal statistical approach based on the maximum likelihood method and Kolmogorov-Smirnov statistic for parameter estimation. They also propose a novel simulation framework for the estimation of the sampling uncertainty of the power-law distribution. They apply the methodology to streamflow data from 62 USGS stream gauges in Iowa. The key finding of the study is that low-probability quantile estimates using Turcotte’s method result in conservative estimates when compared with LP3 distribution confirming the earlier outcomes.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Significant errors often arise when measuring streamflow during high flows and flood events. Such errors conflated by short records of observations may induce bias in the flood frequency estimates, ...leading to costly engineering design mistakes. This work illustrates how observational (measurement) errors affect the uncertainty of flood frequency estimation. The study used the Bulletin 17 C (US standard) method to estimate flood frequencies of historical peak flows modified to represent the measurement limitations. To perform the modifications, we explored, via Monte Carlo simulation, four hypothetical scenarios that mimic measurement errors, sample size limitations, and their combination. We used a multiplicative noise from a log-normal distribution to simulate the measurement errors and implemented a bootstrap approach to represent the sampling error. Then, we randomly selected M samples from the total N records of the observed peak flows of four gauging stations in Iowa in central USA. The observed data record ranges between 76 and 119 years for watersheds with drainage areas between 500 and 16,000 km
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. According to the results, measurement errors lead to more significant differences than sampling limitations. The scenarios exhibited differences with median magnitudes of up to 50%, with some cases reaching differences up to 100% for return periods above 50 years. The results raise a red flag regarding flood frequency estimation that warrants looking for further research on observational errors.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
Key Points
A total of 34 width function descriptors (WFDs), in addition to A, are examined as potential candidates for explaining the regional peak flow variability
The WFDs are capable of explaining spatial variability of peak flows for individual rainfall‐runoff events under restricted physical conditions
The WFDs did not sufficiently explain the regional variability of the peak flow quantiles
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This paper explores the performance of the analysis‐and‐assimilation configuration of the National Water Model (NWM) v1.0 in Iowa. The NWM assimilates streamflow observations from the United States ...Geological Survey (USGS), which increases the performance but also limits the available data for model evaluation. In this study, Iowa Flood Center Bridge Sensors (IFCBS) data provided an independent nonassimilated dataset for evaluation analyses. The authors compared NWM outputs for the period between May 2016 and April 2017, with two datasets: USGS streamflow and velocity observations; Stage and streamflow data from IFCBS. The distribution of Spearman rank correlation (rs), Nash–Sutcliffe efficiency (E), and Kling–Gupta efficiency (KGE) provided quantification of model performance. We found the performance was linked with the spatial scale of the basins. Analysis at USGS gauges showed the strongest performance in large (>10,000 km2) basins (rs = 0.9, E = 0.9, KGE = 0.8), with some decrease at small (<1,000 km2) basins (rs = 0.6, E = −0.25, KGE = −0.2). Analysis with independent IFCBS observations was used to report performance at large basins (rs = 0.6, KGE = 0.1) and small basins (rs = 0.2, KGE = −0.4). Data assimilation improves simulations at downstream basins. We found differences in the characterization of the model and observed data flow velocity distributions. The authors recommend checking the connection of USGS gauges and NHDPlus reaches for selected locations where performance is weak.
Research Impact Statement: Streamflow and velocity estimates from the National Water Model (NWM) are evaluated using an independent dataset. NWM predictions are better at larger watersheds and downstream basins in Iowa.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Precipitation frequency analysis is important to the design of infrastructure. While analysis has traditionally been conducted using rain gauge data, quantitative precipitation estimates (QPEs) based ...on multi-sensor radar offers an opportunity for improvement. This study seeks to evaluate the implications of windfarm locations and weather radar coverage areas on radar rainfall frequency estimation. The analyses are based on 19 years of hourly Stage-IV radar data over the state of Iowa in the Midwestern United States. The data were compiled using an annual maximum series approach and a generalized extreme value (GEV) distribution to estimate pixel return quantiles. Results showed that windfarm locations positively correlate to elevated GEV shape parameters, resulting in a wider upper tail causing possible overestimation of extreme events. Probability of detection analysis revealed that areas roughly equidistant from multiple radars were more likely to record rainfall accumulations over all hourly thresholds tested. Radar based quantile estimates at windfarm locations and distances far from WSR-88D radar sites tended to be greater than gauge derived values while radar quantiles underestimated those based on observed values across the Iowa domain. This underestimation has been outlined as “conditional bias” by previous studies. While our analysis shows that these issues are overcome with sufficient expansion of reference windows, it strengthens the concerns of earlier studies suggesting radar-rainfall alone is not yet adequate for the determination of rainfall recurrence intervals used in engineering design.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Rain gauge networks provide rainfall measurements with a high degree of accuracy at specific locations but, in most cases, the instruments are too sparsely distributed to accurately capture the high ...spatial and temporal variability of precipitation systems. Radar and satellite remote sensing of rainfall has become a viable approach to address this problem effectively. However, among other sources of uncertainties, the remote‐sensing based rainfall products are unavoidably affected by sampling errors that need to be evaluated and characterized. Using a large data set (more than six years) of rainfall measurements from a dense network of 50 rain gauges deployed over an area of about 135 km2 in the Brue catchment (south‐western England), this study sheds some light on the temporal and spatial sampling uncertainties: the former are defined as the errors resulting from temporal gaps in rainfall observations, while the latter as the uncertainties due to the approximation of an areal estimate using point measurements. It is shown that the temporal sampling uncertainties increase with the sampling interval according to a scaling law and decrease with increasing averaging area with no strong dependence on local orography. On the other hand, the spatial sampling uncertainties tend to decrease for increasing accumulation time, with no strong dependence on location of the gauge within the pixel or on the gauge elevation. For the evaluation of high resolution satellite rainfall products, a simple rule is proposed for the number of rain gauges required to estimate areal rainfall with a prescribed accuracy. Additionally, a description is given of the characteristics of the rainfall process in the area in terms of spatial correlation.
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