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This study assesses streamflow predictions generated by two distributed hydrologic models, the Hillslope Link Model (HLM) and the National Water Model (NWM), driven by three radar-based precipitation forcing datasets. These forcing data include the Multi-Radar Multi-Sensor (MRMS), and the Iowa Flood Center's single-polarization-based (IFC-SP) and dual-polarization-based (IFC-DP) products. To examine forcing- and model-dependent aspects of the representation of hydrologic processes, we mixed and matched all forcing data and models, and simulated streamflow for 2016–18 based on six forcing–model combinations. The forcing product evaluation using independent ground reference data showed that the IFC-DP radar-only product's accuracy is comparable to MRMS, which is rain gauge corrected. Streamflow evaluation at 140 U.S. Geological Survey (USGS) stations in Iowa demonstrated that the HLM tended to perform slightly better than the NWM, generating streamflow with smaller volume errors and higher predictive power as measured by Kling–Gupta efficiency (KGE). The authors also inspected the effect of estimation errors in the forcing products on streamflow generation and found that MRMS's slight underestimation bias led to streamflow underestimation for all simulation years, particularly with the NWM. The less biased product (IFC-DP), which has higher error variability, resulted in increased runoff volumes with larger dispersion of errors compared to the ones derived from MRMS. Despite its tendency to underestimate, MRMS showed consistent performance with lower error variability as reflected by the KGE. The dispersion observed from the evaluation metrics (e.g., volume error and KGE) seems to decrease as scale becomes larger, implying that random errors in forcing are likely to average out at larger-scale basins. The evaluation of simulated peaks revealed that an accurate estimation of peak (e.g., time and magnitude) remains challenging, as demonstrated by the highly scattered distribution of peak errors for both hydrologic models. [ABSTRACT FROM AUTHOR]

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space–time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10–40 000 km2) over the state of Iowa in the midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1000 km2. The space–time scale, or reference time tr (ratio of forecast lead time to basin travel time), ~1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1. [ABSTRACT FROM AUTHOR]

The authors explore persistence in streamflow forecasting based on the real-time streamflow observations. They use 15-min streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges monitoring the streams and rivers throughout Iowa. The spatial scale of the basins ranges from about 7 to 37 000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of persistence schemes across spatial scales and lead times. They show that skill in temporal persistence forecasting has a strong dependence on basin size, and a weaker dependence on geometric properties of the river networks. Building on results from this temporal persistence, they extend the streamflow persistence forecasting to space through flow-connected river networks. The approach simply assumes that streamflow at a station in space will persist to another station which is flow connected; these are referred to as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF of streamflow is strongly dependent on the monitored versus predicted basin area ratio and lead times, and weakly related to the downstream flow distance between stations. River network topology shows some effect on the hydrograph timing and timing of the peaks, depending on the stream gauge configuration. The study shows that the skill depicted in terms of Kling-Gupta efficiency (KGE). 0.5 can be achieved for basin area ratio. 0.6 and lead time up to 3 days. The authors discuss the implications of their findings for assessment and improvements of rainfall-runoff models, data assimilation schemes, and stream gauging network design. [ABSTRACT FROM AUTHOR]

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 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 [ABSTRACT FROM AUTHOR]

This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product's algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation. [ABSTRACT FROM AUTHOR]

Accurate and timely flood prediction can reduce the risk of flooding, bolster preparedness, and help build resilience. In this study, we have developed a flood forecasting system prototype and checked its potential for carrying out operational flood forecasting in the state of Nebraska. This system builds upon some of the core components of the Iowa Flood Information System (IFIS), which is a state-of-the-art platform widely recognized around the world. We implemented our platform on a pilot basin in Nebraska (Elkhorn River basin) by installing eight stream sensors and setting up the hydrologic model component of IFIS, i.e., the Hillslope Link Model (HLM). Due to their importance in the Midwest, we particularly emphasized the snow processes and developed an improved HLM model that can account for different aspects of snow (rain-snow-partitioning, snowmelt, and snow accumulation) through simple parameterizations. Results show that the more thorough treatment of snow processes in the hydrologic model, as proposed herein, leads to better flood peak simulations. In this paper, we discuss different steps involved in developing the flood forecasting system prototype, along with the associated challenges and opportunities. • We developed a flood forecasting system prototype for a pilot basin in Nebraska. • We developed an improved version of the popular HLM model to include snow processes. • We identified the vulnerability of eight bridges to flooding. • We designed a simple web interface showing the streamflow in a distributed manner in the basin. [ABSTRACT FROM AUTHOR]