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.
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.
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.
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
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.
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.
Abstract 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.
Plain Language Summary 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.
Key Points 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
This study proposes a flood potential index suitable for use in streamflow forecasting at any location in a drainage network. We obtained the index by comparing the discharge magnitude derived from a ...hydrologic model and the expected mean annual peak flow at the spatial scale of the basin. We use the term “flood potential” to indicate that uncertainty is associated with this information. The index helps communicate flood potential alerts to communities near rivers where there are no quantitative records of historical floods to provide a reference. This method establishes a reference that we can compare to forecasted hydrographs and that facilitates communication of their relative importance. As a proof of concept, the authors present an assessment of the index as applied to the peak flows that caused severe floods in Iowa in June 2008. The Iowa Flood Center uses the proposed approach operationally as part of its real-time hydrologic forecasting system.
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