This study proposes a new deep recurrent neural network approach, Neural Runoff Model (NRM), which has been applied on 125 USGS streamflow gages in the State of Iowa for predicting the next 120 h due to the difficult nature of accurate streamflow forecasting. The proposed model outperforms the streamflow persistence, ridge regression and random forest regression on majority of the gages. The model has also shown strong predictive power and can be used for long-term streamflow predictions.
Peer-Reviewed Research Publications
As an academic center focused on flood-related research and education, Iowa Flood Center faculty, staff, and students produce many scholarly peer-reviewed journal publications, conference proceedings, and books each year. The following is a sample of this important work, which makes IFC innovations and research available to others in our field and moves the science forward toward improved service for Iowans and others.
Peer-Reviewed Publications
2023
Post, R., and W.F. Krajewski, Towards using radar data for probabilistic analysis of rainfall: A case study over Iowa, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-023-02439-8, 2023.
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.
Post, R., F. Quintero, and W.F. Krajewski, Evaluating the efficacy of actively managed distributed storage systems through hydrologic simulation using spatially uniform design storms, Journal of Hydrologic Engineering, 2023 (in press)
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.
Michalek, A.T., G. Villarini, T. Kim, F. Quintero, and W.F. Krajewski, Disentangling the sources of uncertainties in the projection of flood risk across the central United States (Iowa), Geophysical Research Letters, 50, e2023GL105852, 2023.
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.
Michalek, A., G. Villarini, F. Quintero, and W.F. Krajewski, Projected changes in annual maximum discharge for Iowa communities, Journal of Hydrology, 625 129957, 2023.
In this study we use a hydrologic model to evaluate the projected changes in annual maximum peak discharge at the community-level across Iowa under two emission scenarios, Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5).
2022
Michalek, A., F. Quintero, G. Villarini, and W.F. Krajewski, Advantages of physically based flood frequency analysis with long-term simulations for Iowa, Journal of Hydrologic Engineering, 27(12), 05022021, 2022.
The authors explore an alternative approach to regional flood quantile estimation analysis by analyzing the performance of the Iowa Flood Center’s physically based, calibration-free, and spatially distributed Hillslope-Link Model (HLM).
Quintero, F., G. Villarini, A.F. Prein, W.F. Krajewski, and W. Zhang, On the role of atmospheric simulations horizontal grid spacing for flood modeling, Climate Dynamics, 59(11-12), 3167-3174, 2022.
Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes.
Quintero, F., G. Villarini, A.F. Prein, W. Zhang, and W.F. Krajewski, Discharge and floods in Iowa projected to increase more than precipitation extremes, Hydrologic Processes, e14738, 2022.
Floods claim a high toll in fatalities and economic impacts. Despite their societal relevance, there is much more to learn about the projected changes in discharge and flooding. Here we force an operational hydrologic model over the state of Iowa with high-resolution convection-permitting climate-model precipitation to evaluate the response of 140 watersheds to climate change.
2021
Alabbad, Y., Mount, J., Campbell, A., Demir, I., Assessment of transportation system disruption and accessibility to critical amenities during flooding: Iowa case study. Science of the Total Environment, 2021, 793.
This paper presents a comprehensive analysis of flood impacts on road network topology and accessibility to amenities for major communities in the State of Iowa using graph-theoretic methods, including single-source shortest path analyses under 100 and 500-year flood scenarios.
Ewing, G., Demir, I., An ethical decision-making framework with serious gaming: A smart water case study on flooding. Hydroinformatics, 2021, 23(3), pp. 466–482.
As sensor networks control more within water environments, humans are releasing more control over decision-making skills to AI networks. the Water Ethics Web Engine (WE)2, an integrated and generalized web framework incorporates voting-based ethical and normative preferences into water resources decision support.
Ghimire, G., Krajewski, W., Quintero, F., Scale-dependent value of QPF for real-time streamflow forecasting. Journal of Hydrometeorology, 2021, 22(7), pp. 1931–1947.
Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. 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.
Ghimire, G.R., N. Jadidoleslam, R. Goska, and W.F. Krajewski, Insights on storm direction effect on flood peak response, Journal of Hydrology, 600 (2021) 126683, 1-11, 2021.
In this study, we investigate the directional influence of storm movement on catchment flood peak response using the synthetic circular basin. Due to the complexity in defining storm movements that require meteorological modeling, we adopt a novel approach of combining the basin rotation method (BRM) with a circular basin construct.
Ghimire, G.R., Predictability of streamflow across space and time scales, Iowa Research Online, 2019.
Over the years, accurate prediction of streamflow in both space and time has been a challenge despite being one of the most studied topics in water engineering sciences. Despite significant contributions in the field of streamflow forecasting, the challenge has been to identify the trade-off between the forecast time-horizon, basin scale, and streamflow forecasting accuracy.
Hu, A., Demir, I., Real-time flood mapping on client-Side Web Systems Using HAND Model. Hydrology, 2021, 8(2), p. 65.
The Height Above Nearest Drainage (HAND) model is used to analyze soil and predict flood inundation extents. HAND produced inundation maps comparable to advanced hydrodynamic models in practice in Iowa, and would be helpful in the absence of detailed hydrological data.
Quintero , F., Rojas, M., Muste, M., Krajewski, W., Development of synthetic rating curves: Case study in Iowa. Journal of Hydraulic Engineering, 2021, 26(1).
The authors of the case study discuss an economically feasible approach to generate synthetic rating curves that enhance utility of stage-only river gauges. There are 250 bridge-mounted river-stage sensors deployed by IFC in Iowa. Those sensors, in concert with USGS and other related sensor data, are used to determine discharge and other errors or triumphs of the measurement system.
Teague, A., Sermet, Y., Demir, I., Muste, M., A collaborative serious game for water resources planning and hazard mitigation. International Journal of Disaster Risk Reduction, 2021, 53.
Hydrological hazards are enormous risks for communities. A Multi-Hazard Tournament (MHT) allows members of a watershed community to evaluate adaptation options to develop mitigation strategies for multiple water-related hazards such as floods, drought, and water pollution. Hazard risk assessment and minimization of water quality issues and water resources are all parts of the plan.
Velasquez, N., R. Mantilla, W.F. Krajewski, M. Fonley, and F. Quintero, Improvements in performance of the Hillslope-Link Model in Iowa using a non-linear representation of natural and artificially drained subsurface flows, Hydrology, 8(4), 187, https://doi.org/10.3390/hydrology8040187, 2021.
This study evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM).
2020
Agliamzanov, R., Sit, M., Demir, I., Hydrology@Home: A distributed volunteer computing framework for hydrological research and applications. Hydroinformatics, 2020, 22(2), pp. 235–248.
Web-based distributed volunteer computing enables scientists to constitute platforms that can be used for computational tasks by using potentially millions of computers connected to the internet. The framework provides distribution and scaling capabilities for projects with user bases of thousands of volunteers. As a case study, we tested and evaluated the proposed framework with a large-scale hydrological flood forecasting model.
Ghimire, G.R., Jadidoleslam, N., Krajewski W., Tsonis, A., Insights on streamflow predictability across scales using horizontal visibility graph based networks. Frontiers in Water, 2020, 2, p. 17.
The authors characterize the dynamics associated with streamflow time-series data from 64 U.S. Geological Survey (USGS) unregulated stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG) that uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical systems.
Ghimire, G., Krajewski, W., Exploring persistence in streamflow forecasting. Journal of the American Water Resources Association, 2020, 56, pp. 542–550.
This paper explores three approaches for streamflow forecasting: simple persistence, gradient persistence, and anomaly persistence. The basin scales clearly have an impact on the persistence modeling and a weaker, but non‐negligible dependence on geometric properties of the river network for a given basin.
Ghimire, G.R., Krajewski, W., Hydrologic implications of wind farm effect on radar-rainfall observations. Geophysical Research Letters, 2020.
In this study, the authors investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor (MRMS) rainfall products. The study uses the probability of detection (POD) method to identify wind farm clutter in data from Iowa for the years 2016 and 2017.
Grimley, L., F. Quintero, and W.F. Krajewski, Streamflow predictions in a small urban-rural watershed: the effects of radar-rainfall resolution and urban rainfall-runoff dynamics, Atmosphere, 11, 774-795; doi:10.3390/atmos11080774, 2020.
The authors predicted streamflow in an urban–rural watershed using a nested regional–local modeling approach for the community of Manchester, Iowa, which is downstream of a largely rural watershed.
Krajewski, W., Ghimire, G., Quintero, F., Streamflow forecasting without models. Journal of Hydrometeorology, 2020, 21(8), pp. 1689–1704.
The authors used 16 years of river measurements to explore persistence in streamflow forecasting based on the real-time streamflow observations.
Muste, M., Lee, K., Kim, D., Bacotiu, C., Oliveros, M., Cheng, Z., Revisiting hysteresis of flow variables in monitoring unsteady streamflows. Journal of Hydraulic Research, 2021, 58(6), pp. 867–887.
Steady and unsteady streamflows are monitored through combining direct flow measurements and statistical analyses. Flow variables displaying inherent hysteretic behavior is indicative of non-kinematic waves passing through the gauging station. The paper demonstrates the index-velocity and continuous slope-area methods are more suitable to monitor unsteady flows in comparison with the widely used stage-discharge approach.
Otto, Lindsay, An application of power-law distributions to the tail of flood frequency data: a search for a physical connection in flood frequency statistics, Iowa Research Online, 2020.
The past several decades have showered riverine communities across Iowa with historic floods and billions of dollars in damages. Inspired by the recent historic flood records at gages on many rivers, this thesis seeks to better understand the tail of flood frequency curves, or rather the low probability flood events.
Quintero, F., W.F. Krajewski, and M. Rojas, A flood potential index for effective communication of streamflow forecasts at ungauged communities, Journal of Hydrometeorology, 21(4), 807–814, 2020.
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.
Quintero, F., W.F. Krajewski, B.-C. Seo, and R. Mantilla, A long-term evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach, Journal of Hydrology, 584, 124686, https://doi.org/10.1016/j.jhydrol.2020.124686, 2020.
This study evaluates the performance of Iowa Flood Center’s real-time distributed hydrologic model, Hillslope-Link Model (HLM). The HLM provides information about current and future streamflow conditions for over 1000 locations in Iowa, including small communities and stream gauge locations.
Rojas, M., Quintero, F., Young, N., Analysis of stage–discharge relationship stability based on historical ratings, Hydrology, 2020, 7(2), p. 31.
The article explores the stability of the rating curves at six streamflow gauging sites in the state of Iowa, USA, to examine temporal variability of their stage–discharge relationships. The analyzed sites have up to 10 years of rating and shift records. Rating curve shifts reflect the alteration of channel geometry caused by scouring and sediment deposition.
Rojas, M., F. Quintero, and W.F. Krajewski, Performance of the National Water Model Analysis and Assimilation configuration over Iowa, Journal of American Water Resources Association, 56(4), 568-585, 2020.
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.
Sermet, Y., Demir, I., Muste, M., A serious gaming framework for decision support on hydrological hazards, Science of the Total Environment, 2020, 728, p. 138895.
In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process regarding hydrological hazard preparedness and response. The framework is an engaging, accessible, and collaborative serious game environment facilitating the relationship between the environment and communities.
Sermet, Yusuf, Knowledge generation and communication in intelligent and immersive systems: a case study on flooding, Iowa Research Online, 2020.
In this dissertation, we present a generalized intelligent and immersive framework to augment the information systems on any domain. The framework enables the realization of a futuristic vision of a voice-controlled assistant with immersive capabilities to create the next-generation information systems that can be intuitively accessed from any device via the internet.
Varmaghani, A., Eichinger, W.E., Prueger, J.H.,(2020). A meteorological-based crop coefficient model for estimation of daily evapotranspiration. Hydrological Processes, DOI: 10.1002/hyp.14025
Analysis of six years of micrometeorological records and data revealed strong interactions between relative humidity and evapotranspiration. Daily evapotranspiration estimates for cloudy regions need more information that relies solely on meteorological data, a primary focus of this study.
Villarini, G., W. Zhang, F. Quintero, W.F. Krajewski, and G.A. Vecchi, Attribution of the impacts of the 2008 flooding in Cedar Rapids (Iowa) to anthropogenic forcing, Environmental Research Letters, 15, 114057, 2020
The City of Cedar Rapids was significantly affected by the June 2008 flood. However, little is known about the role anthropogenic warming during this event, not only in terms of hydrologic response but also of impacts. Here we use a continuous distributed hydrologic model forced with precipitation with and without external forcing and show that the impacts of this flood were likely magnified because of increased anthropogenic warming.
Xiang, Z., Demir, I., Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa, Environmental Modelling & Software, 2020, 131, p. 104761.
Xiang, Z., Yan, J., Demir, I., A rainfall-runoff model with LSTM-based sequence-to-sequence learning, Water Resources Research, 2020, 56(1).
Researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets, and this study presents an application of a prediction model based on long short-term memory (LSTM) and the sequence-to-sequence modeling (seq2seq) structure to estimate hourly rainfall‐runoff.
Xu, H., Windsor, M., Muste, M., Demir, I., A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming. Journal of Environmental Management, 2020, 255, p.109887.
The paper focuses on a participation-based serious gaming application developed to enhance multi-jurisdictional collaborative planning and decision making for mitigation of multiple hazards related to water, such as flooding, soil erosion, and water quality. The game is integrated into the Iowa Watershed Decision Support System (IoWaDSS).
2019
Ayers, J.R., Villarini, G., Jones, C.S., Schilling, K.E., Changes in monthly baseflow across the U.S. Midwest, Hydrological Processes, 2019, 33(5), pp. 748–758.
Characterizing streamflow changes in the agricultural U.S. Midwest is critical for effective planning and management of water resources throughout the region. The objective of this study is to determine if and how baseflow has responded to land alteration and climate changes across the study area during the 50‐year study period by exploring hydrologic variations based on long‐term stream gage data.
Barth, N.A., Villarini, G., White, K., Accounting for mixed populations in flood frequency analysis: A Bulletin 17C perspective, Journal of Hydrologic Engineering, 2019, 24(3), pp. 1–12.
This study provides a general statistical framework to perform a process-driven flood frequency analysis using a weighted mixed population approach. Furthermore, it allows for accounting for both sampling and mixing uncertainties.
Brammeier, John R., On the performance of X-band dual-polarization radar-rainfall estimation algorithms during the SMAPVEX-16 field campaign, Iowa Research Online, 2019.
Soil moisture estimates from space on a continuous spatial domain could afford researchers with insight about agricultural productivity, flood vulnerability, and biological processes. To evaluate satellite soil moisture estimates, the SMAPVEX-16 experiment was one of a suite of verification data collection campaigns for NASA’s Soil Moisture Active Passive satellite.
Brenner, Iris, Application of storm transposition to the Middle Cedar Watershed: a reanalysis of the 2008 Cedar Rapids Flood, Iowa Research Online, 2019.
This thesis project modeled a variety of rainfall patterns on June 12, 2008, to determine the effect of varying rainfall intensity and location on the magnitude of the 2008 flood of Cedar Rapids. Using a method known as Stochastic Storm Transposition (SST), I overwrote precipitation data in a hydrologic model of the Middle Cedar Watershed with rainfall data extracted from specific storm events that occurred in the Upper Midwest.
Chen, B., Krajewski, W.F., Helmers, M., Zhang, Z., Spatial variability and temporal persistence of event runoff coefficients over agricultural land, Water Resources Research, 2019, 55(2), pp. 1583–1597.
The analysis of unique rainfall-runoff data can offer more in-depth knowledge than the current internal spatial variability of small-scale runoff yield. Through the use of 12 unique hills, the authors intend to reduce the error of runoff data.
Jadidoleslam, N., Mantilla, R., Krajewski, W.F., Cosh, M., Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture, Journal of Hydrology, 2019, 576, pp. 85–97.
A stochastic model was created to create high resolution surface soil moisture information.
Jadidoleslam, N., Mantilla, R., Krajewski, W.F., Goska, R., Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region. Journal of Hydrology, 2019, 579, p. 124210.
The goal of the paper is to confirm relationships identified by a paper written in 2017 (Crow et al.) that relates antecedent soil moisture to runoff production. The paper investigates total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments in Iowa.
Keem, M., Seo, B.-C., Krajewski, W.F., Morris, K.R., Inter-comparison of reflectivity measurements between GPM DPR and NEXRAD Radars, Atmospheric Research, 2019, 226, pp. 49–65.
Reflectivity measurements are one example of a measurement that can be taken from GPM, DPR, and NEXRAD radars. This study demonstrates the potential use of the NASA’s Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) to examine ground radar (GR) miscalibration and other uncertainty sources (e.g., partial beam blockage).
Krasowski, Michael, Continuous watershed-scale hydrologic modeling of conservation practices for peak flow reduction, Iowa Research Online, 2019.
Watersheds in Iowa, and the Midwest at large, have been drastically altered hydrologically—through land use change, tile drainage, digging of drainage ditches, and channelizing of meandering streams. Though drainage practices maximize arable land, they also induce higher flood peaks.
Neri, A., Villarini, G., Salvi, K., Slater, L.J., Napolitano, F., On the decadal predictability of the frequency of flood events across the U.S. Midwest, International Journal of Climatology, 2019, 39(3), pp. 1796–1804.
Skillful predictions of the frequency of flood events over long lead times (e.g., from 1 to 10 years ahead) are essential for governments and institutions making near‐term flood risk plans. However, little is known about current flood prediction capabilities over annual to decadal timescales. Here we address this knowledge gap at 286 U.S. Geological Survey gaging stations across the U.S. Midwest using precipitation and temperature decadal predictions from the Coupled Model Intercomparison Project (CMIP) phase 5 models.
Perez Mesa, Gabriel Jaime, Explaining the physics behind regional peak flow equations using the scaling theory of floods and river network descriptors, Iowa Research Online, 2019.
I present a series of studies based on hydrologic simulations and peak flow observations that illustrate several aspects related to the application and use of the scaling theory of floods, which include the following: (1) description of spatial patterns of scaling parameters; (2) inclusion of river network descriptors in flood frequency equations; and (3) evaluation of sampling errors and epistemic errors in the construction of flood frequency equations.
Rojas, M., Quintero, F., Krajewski, W., Performance of the National Water Model in Iowa using independent observations, Journal of the American Water Resources Association, 2019, 56(4), pp. 568–585.
This paper looks at the performance of the National Water Model in Iowa. The model pulls data from the United States Geological Survey (USGS), which provides a limited amount of data, but increases the performance. The Iowa bridge sensors provide independent data for evaluation analyses.
Seo, B.-C., Keem, M., Hammond, R., Demir, I., Krajewski, W.F., A pilot infrastructure for searching rainfall metadata and generating rainfall product using the big data of NEXRAD, Environmental Modeling and Software, 2019, 117, pp. 69–75.
NEXRAD collects and stores rainfall data in a cloud, allowing the IFC to develop a program to retrieve the data and create a map. The map has the benefit of allowing researchers to choose a specific area of study.
Sermet, Y., Villanueva, P., Sit, M., Demir, I., Crowdsourced approaches for stage measurements at ungauged locations using smartphones, Special Issue: Hydrological Data: Opportunities and Barriers, 2019, 65(5), pp. 813–822.
Citizen science opportunities for environmental monitoring have increased with the advances in smart phone capabilities and their growing availability. This project describes a new method to accurately measure river levels using smartphone sensors. Pictures of the same point on the river’s surface are taken to perform calculations based on the GPS location and spatial orientation of the smartphone. The proposed implementation is significantly more accessible than existing water measuring systems while offering similar accuracy.
Slater, L.J., Villarini, G., Bradley, A.A., Evaluation of the skill of North-American Multi-Model Ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA, Climate Dynamics, 2019, 54, pp. 7381–7396.
This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States.
Slater, L.J., Villarini, G., Bradley, A.A., Vecchi, G.A., A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed, Climate Dynamics, 2019, 53, pp. 7429–7445.
The state of Iowa in the US Midwest is regularly affected by major floods and has seen a notable increase in agricultural land cover over the twentieth century. We present a novel statistical-dynamical approach for probabilistic seasonal streamflow forecasting using land cover and General Circulation Model (GCM) precipitation forecasts.
Varmaghani, A., Eichinger, W. E., Prueger, J. H., Modification of FAO Penman-Monteith equation for minor components of energy, Hydrology Research, 2019, 50(2), pp. 607–615, DOI: 10.2166/nh.2018.093.
The findings in this study suggest a fundamental modification of FAO P-M formula by considering the inclusion of MECs in the energy term.
2018
Carson, A., Windsor, M., Hill, H., Haigh, T., Wall, N., Smith, J., Olsen, R., Bathke, D., Demir, I., Muste, M., Serious gaming based participatory planning for mitigation of flood, drought, and water quality, International Journal of River Basin Management, 2018, 16(3), pp. 379–391.
Collaborative, holistic, and proactive planning for basin-wide water management solutions addressing multiple water-related hazards is challenging. Shared vision planning (SVP) and decision support systems (DSSs) are two approaches that have been used to address the challenges described. SVP is a participatory planning process. DSSs can efficiently support the integration of multiple and vast amounts of information and interactively illustrate the trade-offs between alternative mitigation plans.
Demir, I., Yildirim, E., Sermet, Y., Sit, M., FLOODSS: Iowa Flood Information System as a generalized flood cyberinfrastructure, International Journal of River Basin Management, 2018, 16(3), pp. 393–400.
This article presents the vision, implementation, and case studies of the Iowa Flood Information System (IFIS) towards the vision for next-generation decision support systems for flooding. The IFIS is an end-to-end web-based platform that incorporates various aspects of the decision-making process for flood risk management and mitigation for the State of Iowa. The IFIS provides real-time information on streams and weather conditions, incorporates advanced hydrological models for flood prediction and mapping, and several data analytics and visualization tools to support effective decision-making process.
Dhanya, C.T., G. Villarini, On the inherent predictability of precipitation across the United States, Theoretical and Applied Climatology, 2018, 133, pp. 1035–1050.
This study investigates the spatial distribution of predictability of daily precipitation across the USA. The emphasis is on determining the rate of increase in predictability with spatio-temporal averaging, by defining three predictability statistics (maximum predictability, predictive error, and predictive instability) based on the nonlinear finite-time Lyapunov exponent.
ElSadaani, M., Krajewski, W.F., Goska, R., Smith, M., An investigation of errors in the NFIE-Hydro frameworks’ stream discharge prediction due to channel routing, Journal of the American Water Resources Association, 2018, 54(1), pp. 1–10.
The authors substitute RAPID which is based on the simplified linear Muskingum routing method by the nonlinear routing component the Iowa Flood Center have incorporated in their full hydrologic Hillslope‐Link Model. The results show improvement in model performance across scales due to incorporating new routing methodology.
ElSadaani, M., Krajewski, W.F., Zimmerman, D.L., River network based characterization of errors in remotely sensed rainfall products in hydrological applications, Remote Sensing Letters, 2018, 9(8), pp. 743–752.
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.
Ghimire, G.R., Krajewski, W.F., Mantilla, R., A power law model for river water velocity in U.S. Upper Midwestern basins, Journal of the American Water Resources Association, 2018, 54(3), pp. 1–13.
This study explores power law relationships to estimate water flow velocity as a function of discharge and drainage area across river networks. We test the model using empirical data from 214 United States (U.S.) Geological Survey gauging stations distributed over the state of Iowa in the U.S.
Giuntoli, I., Villarini, G., Prudhomme, C., Hannah, D.M., Uncertainties in projected runoff over the conterminous United States, Climatic Change, 2018, 150(3), pp. 149–162.
Using a set of GIMs driven by GCMs under different representative concentration pathways (RCPs), this study aims to partition the uncertainty of future flows coming from GIMs, GCMs, RCPs, and internal variability over the CONterminous United States (CONUS).
Nayak, M.A., Villarini, G., Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States, Journal of Hydrology, 2018, 556, pp. 1038–1049.
This study fills this major scientific gap by describing the rainfall during ARs over the central United States using five remote sensing-based precipitation products over a 12-year study period. The products we consider are: Stage IV, Tropical Rainfall Measuring Mission – Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH).
Perez, G., Mantilla, R., Krajewski, W.F., Estimating annual and monthly scale-invariant flow duration curves for the State of Iowa, Journal of Hydrologic Engineering, 2018, 23(12), 05018021.
This paper presents a procedure to derive historical-annual and historical-monthly flow duration curves (FDC) that are monotonic and continuous for agricultural, unregulated, ungauged sites. The authors explore the performance and the regional dependence of four different regression models for the estimation of daily flow quantiles (QpQp), with probabilities of exceedance (pp) ranging from 0.01 to 0.99.
Perez, G., Mantilla, R., Krajewski, W.F., The influence of spatial variability of width functions on regional peak flow regressions. Water Resources Research, 2018, 54, pp. 1–19. https://doi.org/10.1029/2018WR023509.
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.
Quintero, F., Krajewski, W.F., Mapping outlets of Iowa Flood Center and National Water Center river networks for hydrologic model comparison, Journal of the American Water Resources Association, 2018, 54(1), pp. 28–39.
This study explores methods that identify the scale where networks obtained by different methods agree within some margin of error. The problem is relevant for comparing hydrologic models built around the two networks.
Quintero, F., Mantilla, R., Anderson, C., Claman, D., Krajewski, W.F., Assessment of changes in flood frequency due to the effects of climate change: Implications for engineering design, Hydrology, 2018, 5(19), pp. 1–16, doi:10.3390/hydrology5010019.
The authors explore the uncertainty implied in the estimation of changes in flood frequency due to climate change at the basins of the Cedar River and Skunk River in Iowa, United States. The study focuses on the influence of climate change on the 100-year flood, used broadly as a reference flow for civil engineering design.
Seo, B.-C., Krajewski, W.F., Quintero, F., ElSaadani, M., Goska, R., Cunha, L.K., et al., Comprehensive evaluation of the IFloodS radar-rainfall products for hydrologic applications, Journal of Hydrometeorology, 2018, 19(11), pp. 1793–1813.
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.
Seo, B.-C., Quintero, F., Krajewski, W.F. High-resolution QPF uncertainty and its implications for flood prediction: A case study for the Eastern Iowa flood of 2016, Journal of Hydrometeorology, 2018, 19(8), pp. 1289–1304.
This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016.
Sermet, Y., Demir, I., An intelligent system on knowledge generation and communication for flooding, Environmental Modeling and Software, 2018, 108, pp. 51–60.
Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societal preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding.
Slater, L.J., Villarini, G. Enhancing the predictability of seasonal streamflow via a statistical-dynamical approach, Geophysical Research Letters, 2018, 45, pp. 6504–6513.
Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.
Villarini, G., Slater, L.J., Examination of changes in annual maximum gauge height in the continental United States using quantile regression, Journal of Hydrologic Engineering, 2018, 23(3), pp. 1–5.
This study focuses on the detection of temporal changes in annual maximum gauge height (GH) across the continental United States and their relationship to changes in short- and long-term precipitation. Analyses are based on 1,805 U.S. Geological Survey records over the 1985–2015 period and are performed using quantile regression. Trends were significant only at a limited number of sites, with a higher number of detections at the tails of the distribution.
Zhang, W., Villarini, G., Vecchi, G.A., Smith, J.A., Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston, Nature, 2018, 563, pp. 384–388.
Using the Weather Research and Forecast model—a numerical model for simulating weather and climate at regional scales—and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25–30 August 2017 because of urbanization.
2017
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Zimmerman, D.L., Can floods at large river basin be predicted from floods observed at small subbasins? Journal of Flood Risk Management, 2017, 11(3), pp. 331–338, DOI: 10.1111/jfr3.12327.
In this article, we show that a log‐linear relationship between α(e) and θ(e) can be used to simplify the problem of predicting α(e) and θ(e) from the physical characteristics of the catchment and rainfall. In particular, we show that α(e) can be predicted from peak floods observed in the smallest gauged subcatchment in the basin and its log‐linear relationship with θ(e) can be used to predict peak flood at any location in the basin. We demonstrate this using observed peak floods from the Iowa River basin in the Upper Midwest part of United States.
Chen, B., Krajewski, W.F., Goska, R., Young, N., Using LiDAR surveys to document floods: A case study of the 2008 Iowa flood, Journal of Hydrology, 2017, 553, pp. 338–349.
Can we use Light Detection and Ranging (LiDAR), an emergent remote sensing technology with wide applications, to document floods with high accuracy? To explore the feasibility of this application, we propose a method to extract distributed inundation depths from a LiDAR survey conducted during flooding.
Demir, I., Szczepanek, R., Optimization of river network representation data models for web-based systems, Earth and Space Science, 2017, 4(6).
This paper presents a detailed study of widely used methods for representing generic networks in relational databases and benchmarking common queries on river network data using these methods.
Dhanya, C.T., Villarini, G. An investigation of predictability dynamics of temperature and precipitation in reanalysis datasets over the continental United States, Atmospheric Research, 2017, 183, pp. 341–350.
Reanalysis datasets have been under critical scrutiny due to their widespread use in various climatic and hydrological modeling applications, in particular over many areas of the globe with limited or absent reliable observational data.
Krajewski, W.F., Ceynar, D., Demir, I., Goska, R., Kruger, A., Langel, C., Mantilla, R., Niemeier, F., Quintero, F., Seo, B.C., Small, S., Weber, L., Young, N., Real-time flood forecasting and information system for the State of Iowa, Bulletin of the American Meteorological Society, 2017, 98, pp. 539–554.
The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links.
Lee, K., Muste, M., Refinement of the Fread method for improved tracking of stream discharges during unsteady flows, Journal of Hydraulic Engineering, 2017, 143(6). DOI: 10.1061/(ASCE)HY.1943-7900.0001280.
This paper presents an extension of the method that can be applicable to arbitrary geometry of the channel cross section by replacing the hydraulic depth with the hydraulic radius and adjusting formulations associated with wave celerity coefficient and kinematic wave equation in the original Fread method.
Mallakpour, I., Villarini,G., Analysis of changes in the magnitude, frequency, and seasonality of heavy precipitation over the contiguous United States, Theoretical and Applied Climatology, 2017, 130, pp. 345–363.
Gridded daily precipitation observations over the contiguous USA are used to investigate the past observed changes in the frequency and magnitude of heavy precipitation, and to examine its seasonality. Analyses are based on the Climate Prediction Center (CPC) daily precipitation data from 1948 to 2012.
Mallakpour, I., Villarini, G., Jones, M.P., Smith, J.A., On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States, Global and Planetary Change, 2017, 155, pp. 98–108.
The findings of this work highlight that variations in the climate system play a critical role in explaining the occurrence of flood and heavy precipitation events at the sub-seasonal scale over the central United States.
Muste, M., Hoitink, T., Measuring flood discharge, chapter in Oxford Research Encyclopedia of Natural Hazard Science, Oxford University Press, doi: 10.1093/acrefore/9780199389407.013.121, 2017.
This book chapter reviews the methodologies for the measurement of discharges in streams with special focus on the issues relevant to acquisition and estimation of streamflows during flood conditions.
Nayak, M.A, Villarini, G., A long-term perspective of the hydroclimatological impacts of atmospheric rivers over the central United States, Water Resources Research, 2017, 53, pp. 1144–1166.
The focus of this study is on the climatology of atmospheric rivers (ARs) over the central United States using six atmospheric reanalysis products. This climatology is used to understand the long‐term impacts of ARs on annual precipitation, precipitation extremes, and flooding over the central United States.
Quintero, F., Krajewski, W.F., Mapping outlets of Iowa Flood Center and National Water Center river networks for hydrologic model comparison, Journal of the American Water Resources Association, 2017, .
The authors propose a methodology to find corresponding outlets where the hydrologic simulations of the IFC model, and the National Water Center model, can be compared.
Quintero, F., Krajewski, W.F., Mantilla, R., Small, S., Seo. B.-C., A spatial-dynamical framework for evaluation of satellite rainfall products for flood prediction, Journal of Hydrometeorology, 2017, 17(8). doi.org/10.1175/JHM-D-15-0195.1
The authors report a novel framework that provides insights about the spatial and temporal propagation of errors in hydrologic simulations across the drainage network.
Salvi, K., Villarini, G., Vecchi, G.A., High resolution decadal precipitation predictions over the continental United States for impacts assessment, Journal of Hydrology, 2017, 553, pp. 559–573.
Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches.
Salvi, K., Villarini, G., Vecchi, G.A., Ghosh, S., Decadal temperature predictions over the continental United States: Analysis and enhancement, Climate Dynamics, 2017, 49, pp. 3587–3604.
Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling.
Slater, L.J., Villarini, G., Evaluating the drivers of seasonal streamflow in the U.S. Midwest, Water, 2017, 9(9), pp. 1–22.
Streamflows have increased notably across the U.S. Midwest over the past century, fueling a debate on the relative influences of changes in precipitation and land cover on the flow distribution. Here, we propose a simple modeling framework to evaluate the main drivers of streamflow rates.
Villarini, G., Khouakhi, A., Cunningham, E., On the impacts of computing daily temperatures as the average of the daily minimum and maximum temperatures, Atmospheric Research, 2017, 198, pp. 145–150.
Daily temperature values are generally computed as the average of the daily minimum and maximum observations, which can lead to biases in the estimation of daily averaged values. This study examines the impacts of these biases on the calculation of climatology and trends in temperature extremes at 409 sites in North America with at least 25 years of complete hourly records. Our results show that the calculation of daily temperature based on the average of minimum and maximum daily readings leads to an overestimation of the daily values of ~ 10+ % when focusing on extremes and values above (below) high (low) thresholds.
Weber, L., Muste, M., Bradley, A.A., Amado, A. A., Demir, I., Drake, C., Krajewski, W.F., Loeser, T., Politano, M., Shea, B., Thomas, N., The Iowa Watersheds Project: Iowa’s prototype for engaging communities and professionals in watershed hazard mitigation. International Journal of River Basin Management, 2017. DOI: 10.1080/15715124.2017.1387127.
After more than a century of intensive changes in the state’s agricultural watersheds, repeated record floods motivated Iowa to innovate in its flood recovery and disaster mitigation efforts following the 2008 floods. The state created the Iowa Flood Center (IFC) and authorized the creation of Watershed Management Authorities.
Zalenski, G., Krajewski, W.F., Quintero, F., Restrepo, P., Buan, S., Analysis of National Weather Service stage forecast errors, Weather and Forecasting, August 2017, pp. 1441–1465. doi.org/10.1175/WAF-D-16-0219.1
The authors report the skill of the river stage forecasts produced by National Weather Service at 51 locations in Iowa. They also analyze how the skill obtained at particular locations is related to the characteristics of the basin.
Zhang, W., Villarini, G., Heavy precipitation is highly sensitive to the magnitude of future warming, Climatic Change, 2017, 145, pp. 249–257.
Here, we investigate the changes in heavy precipitation events with the Community Earth System Model (CESM) climate experiments using the scenarios consistent with the 1.5 and 2 °C temperature targets. We find that the frequency of annual heavy precipitation at a global scale increases in both 1.5 and 2 °C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative.
Zhang, W., Villarini, G., On the unseasonal flooding over the central United States during December 2015 and January 2016, Atmospheric Research, 2017, 196, pp. 23–28.
The unseasonal winter heavy rainfall and flooding that occurred during December 2015–January 2016 had large socio-economic impacts for the central United States. Here we examine the climatic conditions that led to the observed extreme precipitation, and compare and contrast them with the 1982/1983 and 2011/2012 winters.
Zhang, W., Villarini, G., Scoccimarro, E., Vecchi, G.A., Stronger influences of increased CO2 on sub-daily precipitation extremes than at the daily scale, Geophysical Research Letters, 2017, 44, pp. 7464–7471.
We find that the increased CO2 concentration substantially increases the odds of the occurrence of subdaily precipitation extremes compared to the daily scale in most areas of the world, with the exception of some regions in the subtropics, likely in relation to the subsidence of the Hadley Cell. These results point to the large role that atmospheric CO2 plays in extreme precipitation under an idealized framework.
2016
Faraji, S., Sadri, B., Vajdi Hokmabad, B., Jadidoleslam, N., Esmaeilzadeh, E., Experimental study on the role of electrical conductivity in pulsating models of electrospraying, Science Direct, 2016, 81, pp. 327–335, DOI: 10.1016/j/expthermflusci.2016.10.030.
In this study, we have presented insight to the role of physicochemical properties on the pulsating modes of spraying.
Gil, Y., David, C., Demir I., Essawy, B., Fulweiler, R., Goodall, J., Karlstrom, L., Lee. H., Mills, H., Oh, J., Pierce, S., Pope, A., Tzeng, M., Villamizar, S., Yu, X., Towards the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance, Earth and Space Science, 2016, 3(10), pp. 388–415.
Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented.
Mallakpour, I., Villarini, G., Investigating the relationship between the frequency of flooding over the central United States and large-scale climate, Advances in Water Resources, 2016, 92, pp. 159–171.
The aim of this study is to examine whether the climatic driving forces can describe the observed variability in the frequency of flooding over the central United States. Results are based on daily streamflow records from 774 U.S. Geological Survey (USGS) stations with at least 50 years of data and ending no earlier than 2011.
Mishra, K.V., Krajewski, W.F., Goska, R., Ceynar, D., Seo, B.-C., Kruger, A., Niemeier, J., Galvez, M.B., Thurai, M., Bringi, V.N., Tolstoy, L., Kucera, P., Petersen, W., Grazioli, J., Pazmany, A., Deployment and performance analyses of high-resolution Iowa XPOL radar system during the NASA IFloodS campaign, Journal of Hydrometeorology, 2016, 17(2), pp. 455–479.
This article presents the data collected and analyzed using the University of Iowa’s X-band weather radars that were part of the spring 2013 Iowa Flood Studies (IFloodS) field campaign, sponsored by the NASA’s Global Precipitation Measurement (GPM) satellite mission.
Nayak, M.A., Villarini, G., Bradley, A.A., Atmospheric rivers and rainfall during NASA’s Iowa Flood Studies (IFloodS) campaign, Journal of Hydrometeorology, 2016, 17(1), pp. 257–271.
Atmospheric rivers (ARs) play a major role in causing extreme precipitation and flooding over the central United States (e.g., Midwest floods of 1993 and 2008). The goal of this study is to characterize rainfall associated with ARs over this region during the Iowa Flood Studies (IFloodS) campaign that took place in April–June 2013.
Slater, L.J., Villarini, G., Recent trends in US flood risk, Geophysical Research Letters, 2016, 43(24), pp. 12428–12436.
Here we present a novel approach assessing the trends in inundation frequency above the National Weather Service’s four flood level categories in 2042 catchments. Results reveal stark regional patterns of changing flood risk that are broadly consistent above the four flood categories.
Sloan, B. P., Basu, N. B., Mantilla, R., Hydrologic impacts of subsurface drainage at the field scale: Climate, landscape and anthropogenic controls, Agricultural Water Management, 2016, 165, pp. 1–10.
Installation of subsurface drainage systems is one of the most common modifications of the agricultural landscape, and while it is well accepted that these systems alter the hydrologic regime, the nature and magnitude of such alterations remains poorly understood. We explore the impact of drainage systems using the field-scale model DRAINMOD and rainfall and soils data for Iowa.
Varmaghani, A., Eichinger, W.E. Early-season classification of corn and soybean using Bayesian discriminant analysis on satellite images, Agronomy Journal, 2016, 108(5), pp. 1880–1889, DOI: 10.2134/AGRONJ2015.0454.
This study investigated early season crop classification for corn and soybeans using vegetation maps and land cover data to construct “agricultural units.”
Varmaghani, A., Eichinger, W.E., and Prueger, J.H. A diagnostic approach towards the causes of energy balance closure problem, Open Journal of Modern Hydrology, 2016, 6, pp. 101–114.
The results obtained in this study suggest that a-posteriori analysis may offer a superior methodology to correct measured eddy-correlation measurements. Furthermore, the overall trends in the correction of LE measurements suggest that there is a potential for rough monthly corrections of LE, irrespective of the type of crop.
Villarini, G., On the seasonality of flooding across the continental United States, Advances in Water Resources, 2016, 87, pp. 80–91.
This study examines the seasonality of flooding across the continental United States using circular statistics. Analyses are based on 7506 USGS stream gauge stations with a record of least 30 years of annual maximum instantaneous peak discharge.
2015
Anderson, C., Claman, D., Mantilla, R., Iowa’s bridge and highway climate change and extreme weather vulnerability assessment pilot. Iowa Publications Online, 2015, pp. 1–61.
A pilot study was conducted for six bridges in two Iowa river basins—the Cedar River Basin and the South Skunk River Basin—to develop a methodology to evaluate their vulnerability to climate change and extreme weather. The six bridges had been either closed or severely stressed by record streamflow within the past seven years.
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Insights into expected changes in regulated flood frequencies due to the spatial configuration of flood retention ponds, Journal of Hydrologic Engineering, 2015, DOI:10.1061/(ASCE)HE.1943-5584.0001173.
This study examines the effects that the spatial configuration of flood retention ponds have on the reduction of flood peaks across different spatial scales in the catchment.
Demir, I., Conover, H., Krajewski, W., Seo, B., Goska, R., He, Y., McEniry, M.F., Graves, S.J., Peterson, W., Data-enabled field experiment planning, management, and research using cyberinfrastructure, Journal of Hydrometeorology, 2015, 3, pp. 1155–1170.
This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments.
Giuntoli, I., Villarini, G., Prudhomme, C., Mallakpour, I., Hannah, D., Evaluation of global impact models ability to reproduce runoff characteristics over the central United States, Journal of Geophysical Research, 2015, 120, pp. 9138–9159.
This study aims to evaluate the ability of a set of global impact models (GIMs) from the Water Model Intercomparison Project to reproduce the regional hydrology of the central United States for the period 1963–2001.
Kim, D., Muste, M., Merwade, V., A GIS-based relational data model for multi-dimensional representation or river hydrodynamics and morphodynamics, Environmental Modelling and Software, 2015, 65, pp. 79–93.
This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology.
Lavers, D.A., Villarini, G., The contribution of atmospheric rivers to precipitation in Europe and the United States, Journal of Hydrology, 2015, 522, pp. 382–390.
Using gridded precipitation products across Europe and the continental United States and the ERA-Interim reanalysis, we investigate the fraction of precipitation from 1979 to 2012 that is related to ARs in these regions. The results are region- and month-dependent, with the largest contribution generally occurring during the winter season and being on the order of 30–50%.
Mallakpour, I., Villarini, G., The changing nature of flooding across the central United States, Nature Climate Change, 2015, 5, pp. 250–254.
Here, we show that while observational records (774 stream gauge stations) from the central United States present limited evidence of significant changes in the magnitude of floodpeaks, strong evidence points to an increasing frequency of flooding. These changes in flood hydrology result from changes in both seasonal rainfall and temperature across this region.
Moser, B., Gallus Jr., W.A., Mantilla, R., An initial assessment of radar data assimilation on warm season rainfall forecasts for use in hydrologic models, American Meteorological Society, 2015, DOI: 10.1175/WAF-D-14-00125.1.
The initial results of this study indicate that radar assimilation improves WRF’s ability to capture the character of storms, promising more accurate guidance for flash flood warnings.
Seo, B.-C., Krajewski, W.F., Mishra, K.V., Using the new dual-polarimetric capability of WSR-88D to eliminate anomalous propagation and wind turbine effects in radar-rainfall, Atmospheric Research, 2015, 153, pp. 296–309.
This study addresses the effect that the interaction between anomalous radar beam propagation (AP) and wind turbines that are located far from the radar has on radar-rainfall estimates.
Villarini, G., Scoccimarro, E., White, K.D., Arnold, J.R., Schilling, K.E., Ghosh, J., Projected changes in discharge in an agricultural watershed in Iowa, Journal of the American Water Resources Association, 2015, 51(5), pp. 1361–1371.
Our improved capability to adapt to the future changes in discharge is linked to our capability to predict the magnitude or at least the direction of these changes. For the agricultural United States Midwest, too much or too little water has severe socioeconomic impacts. Here, we focus on the Raccoon River at Van Meter, Iowa, and use a statistical approach to examine projected changes in discharge. We build on statistical models using rainfall and harvested corn and soybean acreage to explain the observed discharge variability. We then use projections of these two predictors to examine the projected discharge response.
2014
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Connecting the power-law scaling structure of peak-discharges to spatially variable rainfall and catchment physical properties, Advances in Water Resources, 2014, 71, pp. 32–43.
We have conducted extensive hydrologic simulation experiments in order to investigate how the flood scaling parameters in the power-law relationship Q(A)=αAθ, between peak-discharges resulting from a single rainfall–runoff event Q(A) and upstream area A, change as a function of rainfall, runoff coefficient (Cr) that we use as a proxy for catchment antecedent moisture state, hillslope overland flow velocity (vh), and channel flow velocity (vc), all of which are variable in space.
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Small, S.J., Exploring the effects of hillslope-channel link dynamics and excess rainfall properties on the scaling structure of peak-discharge, Advances in Water Resources, 2014, 64, pp. 9–20.
We use the rainfall-runoff model CUENCAS and apply it to three different river basins in Iowa to investigate how the interplay among rainfall intensity, duration, hillslope overland flow velocity, channel flow velocity, and the drainage network structure affects these parameters.
Gupta, V.K., Mesa, O.J., Horton laws for hydraulic–geometric variables and their scaling exponents in self-similar Tokunaga river networks, Nonlinear Processes Geophysics, 2014, 21, pp. 1007–1025.
We used the observed exponents of depth and slope to predict the Manning friction exponent and to test it against field exponents from three studies. Finally, we briefly sketch how the two anomalous scaling exponents could be estimated from the transport of suspended sediment load and the bed load.
Muste, M., Hauet, A., Fujita, I., Legout, C., Ho, H.-C., Capabilities of large-scale particle image velocimetry to characterize shallow free-surface flows, Advances in Water Resources, 2014, 70, pp. 160–171.
Irrespective of their spatial extent, free-surface shallow flows are challenging measurement environments for most instruments due to the relatively small depths and velocities typically associated with these flows. A promising candidate for enabling measurements in such conditions is Large-scale Particle Image Velocimetry (LSPIV).
Nayak, M.A., Villarini, G., Lavers, D.A., On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States, Geophysical Research Letter, 2014, 41, pp. 4354–4362.
This study focuses on the verification of the skill of five numerical weather prediction models in forecasting AR activity over the central United States. We find that these models generally forecast AR occurrences well at short lead times, with location errors increasing from one to three decimal degrees as the lead time increases to about 1 week.
Seo, B.-C., Krajewski, W.F., Smith, J.A., Four-dimensional reflectivity data comparison between two ground-based radars: Methodology and statistical analysis, Hydrological Sciences Journal, 2014, 59, pp. 1320–132.
To identify radar calibration differences, radar reflectivity is compared for well-matched radar sampling volumes viewing common meteorological targets.334.
Varmaghani, A., Ghiassi, R., Release time component of a hydrograph. Journal of Hydrologic Engineering, 2014, pp. 444–447, DOI:10.1061/(ASCE)HE.1943-5584.0000790.
This research introduces another concept of partial release time, and a parametrical equation for its estimation is suggested. Furthermore, the fundamental relation between time base and excess rainfall duration for linear systems is clarified, giving rise to a formula for time base of Clark hydrograph. Finally, it will be theoretically shown that release time is the temporal dependency between the rainfall and runoff in each event, and hence it plays a crucial role in event-based rainfall–runoff-forecasting models.
Villarini, G., Goska, R., Smith, J.A., Vecchi, G.A., North Atlantic tropical cyclones and U.S. flooding, Bulletin of the American Meteorological Society, 2014, 95(9), pp. 1381–1388.
These results indicate that flooding from TCs is not solely a coastal phenomenon but affects much larger areas of the United States, as far inland as Illinois, Wisconsin, and Michigan. Moreover, the authors highlight the dependence of the frequency and magnitude of TC flood peaks on large-scale climate indices, and the role played by the North Atlantic Oscillation and the El Niño–Southern Oscillation phenomenon (ENSO), suggesting potential sources of extended-range predictability.
Villarini, G., Seo, B.-C., Serinaldi, F., Krajewski, W.F., Spatial and temporal modeling of radar rainfall uncertainties, Atmospheric Research, 2014, 135-136, pp. 91–101.
Building on earlier efforts, the authors apply a data-driven multiplicative model in which the relationship between true rainfall and radar rainfall can be described in terms of the product of a systematic and random component. For the first time, the authors present a methodology based on conditional copulas to generate ensembles of random error fields with the prescribed marginal probability distribution and spatio-temporal dependencies.
Villarini, G., Strong, A., Roles of climate and agricultural practices in discharge changes in an agricultural watershed in Iowa, Agriculture, Ecosystems and Environment, 2014, 188, pp. 204–211.
An outstanding question is related to the contribution of changes in the climate system and in land use/land cover and agricultural practices in explaining changes in discharge. We address this question by developing statistical models to describe the changes in different parts of the discharge distribution. We use rainfall and harvested corn and soybean acreage to explain the observed stream flow variability.
2013
Ayalew, T. B., Krajewski, W.F., Mantilla, R., Exploring the effect of reservoir storage on peak discharge frequency, Journal of Hydrologic Engineering, 2013, 18(12), pp. 1697–1708.
In this paper, a simple hydrologic example is employed to illustrate the important features of reservoir regulated flood frequency.
Demir, I., Krajewski, W.F., Towards an integrated flood information system: Centralized data access, analysis, and visualization, Environmental Modeling and Software, 2013, 50, pp. 77–84.
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.
Gourley, J., Hong, Y., Flamig, Z., Arthur, A., Clark, R., Calianno, M., Ruin, I., Ortel, T., Wieczorek, M., Kirstetter, P.-E., Clark, E., Krajewski, W., A unified flash flood database across the United States, Bulletin of the American Meteorological Society, 2013, 94, pp. 799–805.
This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts.
Krajewski, W.F., Kruger, A., Singh, S., Seo, B.-C., Smith, J.A., Hydro-NEXRAD-2: Real-time access to customized radar-rainfall for hydrologic applications, Journal of Hydroinformatics, 2013, 15(2), pp. 580–590.
This paper describes the challenges involved in HNX2’s development and implementation, which include real-time error-handling, time-synchronization of data from multiple asynchronous sources, generation of multiple-radar metadata products, and distribution of products to a user base with diverse needs and constraints.
Lavers, D.A., Villarini, G., Atmospheric rivers and flooding over the Central United States, Journal of Climate, 2013, 26(12), pp. 7829–7836.
Based on the findings of this study, ARs are a major flood agent over the central United States.
Peterson, T.C., Heim, R.R., Hirsch, R., Kaiser, D.P., Brooks, H., Diffenbaugh, N.S., Dole, R.M., Giovannettone, J.P., Guirguis, J., Karl, T.R., Katz, R.W., Kunkel, K., Lettenmaier, D., McCabe, G.J., Paciorek, C.J., Ryberg, K.R., Schubert, S., Silva, V.B.S., Stewart, B.C., Vecchia, A.V., Villarini, G., Vose, R.S., Walsh, J., Wehner, M., Wolock, D., Wolter, K., Woodhouse, C.A., Wuebbles, D., Monitoring and understanding changes in heat waves, cold waves, floods and droughts in the United States: State of knowledge, Bulletin of the American Meteorological Society, 2013, 94(6), pp. 821–834.
In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country.
Rowe, S.T., Villarini, G., Flooding associated with predecessor rain events over the Midwest United States, Environmental Research Letters, 2013, 8, pp. 1–5.
This paper examines the severity and extent of flooding caused by six predecessor rain events (PREs) over the Midwest United States. PREs are areas of heavy rainfall that occur about 1000 km ahead of landfalling tropical cyclones. While recent studies have mostly focused on the synoptic conditions associated with PREs, little is known about the hydrologic impacts of these events.
Seo, B.-C., Cunha, L.K., Krajewski, W.F., Uncertainty in radar-rainfall composite and its impact on hydrologic prediction for the Eastern Iowa flood of 2008, Water Resources Research, 2013, 49, pp. 2747–2764.
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.
Small, S.J., Jay, L.O., Mantilla, R., Curtu, R., Cunha, L.K., Fonley, M., Krajewski, W.F., An asynchronous solver for systems of ODEs linked by a directed tree structure, Advances in Water Resources, 2013, 53, pp. 23–32.
This paper documents our development and evaluation of a numerical solver for systems of sparsely linked ordinary differential equations in which the connectivity between equations is determined by a directed tree.
Villarini, G., Scoccimarro, E., Gualdi, S., Projections of heavy rainfall over the Central United States based on CMIP5 models, Atmospheric Science Letters, 2013, 14(3), pp. 200–205.
Several studies based on observational records found increasing trends over the central United States. Recently, Villarini et al. found a large increase in the number of rainfall days exceeding the 95th percentile of the rainfall distribution over the Upper Mississippi River Basin, and a much weaker signal in the Lower Mississippi River Basin.
Villarini, G., Smith, J.A., Vecchi, G.A., Changing frequency of heavy rainfall over the Central United States, Journal of Climate, 2013, 26(1), pp. 343–350.
Villarini et al. used daily rainfall measurements from 447 rain gauges with a record of least 50 years throughout the central United States to examine the presence of changes in the frequency of heavy rainfall, which they defined as days exceeding the 95th percentile of the at-site rainfall distribution. The observational records covered at least the second half of the 20th century and the first decade of the 21st century, providing information about the most recent changes in heavy rainfall events over this area. They found a generally increasing trend in the northern part of the study region (roughly the Upper Mississippi River basin).
“We tried to explain these results and the differences between the northern and southern parts of the study region in light of changes in temperature,” Villarini says. “We found that the northern region is experiencing large increasing trends in temperature, resulting in an increase in atmospheric water vapor. Therefore, there is more water vapor available for precipitation.” In addition to increasing temperatures, they also indicated the increased irrigation over the Ogallala Aquifer, which likely resulted in an increase in water vapor in the area.
Villarini, G., Smith, J.A., Vitolo, R., Stephenson, D.B., On the temporal clustering of U.S. floods and its relationship to climate teleconnection patterns, International Journal of Climatology, 2013, 33(3), pp. 629–640.
This article examines whether the temporal clustering of flood events can be explained in terms of climate variability or time‐varying land‐surface state variables.
2012
Cunha, L.K., Mandapaka, P.V., Krajewski, W.F., Mantilla, R., Bradley, A.A., Impact of radar rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model, Water Resources Research, 2012, 48(10), W10515.
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.
Ferguson, C.R., Villarini, G., Detecting inhomogeneities in the 20th-Century reanalysis over the Central United States, Journal of Geophysical Research, 2012, 117, D05123.
We use three statistical methods (Pettitt and Bai‐Perron tests and segmented regression) to detect abrupt shifts in multiple hydrometeorological variable mean and uncertainty fields over the central United States. For surface air temperature and precipitation, we use the Climate Research Unit (CRU) time series data set for comparison. We find that for warm‐season months, there is a consensus change point among all variables between 1940 and 1950, which is not substantiated by the CRU record.
Varmaghani, A., An analytical formula for potential water vapor in an atmosphere of constant lapse rate, Terrestrial, Atmospheric, and Oceanic Sciences, 2012, 23(1), pp. 17–24.
Accurate calculation of precipitable water vapor (PWV) in the atmosphere has always been a matter of importance for meteorologists. Potential water vapor (POWV) or maximum precipitable water vapor can be an appropriate base for estimation of probable maximum precipitation (PMP) in an area, leading to probable maximum flood (PMF) and flash flood management systems.
2011
Cunha, L.K., Mandapaka, P.V., Krajewski, W.F., Mantilla, R., Bradley, A.A., A framework for flood risk assessment in ungauged basins, Journal of Flood Risk Management, 2011, 4(1), pp. 3–22.
We present a diagnostic framework to assess changes in flood risk across multiple scales in a river network, under nonstationary conditions or in the absence of historical hydro-meteorological data. The framework combines calibration-free hydrological and hydraulic models with urban development information to demonstrate altered flood risk.
Gilles, D.G., Young, N.C., Piotrowski, J.A., Schroeder, H.S., Chang, Y.J. Inundation mapping initiatives of the Iowa Flood Center: Statewide coverage and detailed urban flooding analysis, Water, 2011, 4(1), pp. 85–106.
The State of Iowa, located in the Midwestern United States, has experienced an increased frequency of large floods in recent decades. After extreme flooding in the summer of 2008, the Iowa Flood Center (IFC) was established for advanced research and education specifically related to floods.
Villarini, G., Smith, J.A., Baeck, M.L., Krajewski, W.F., Examining regional flood frequency in the U.S. Midwest, Journal of the American Water Resources Association, 2011, 47(3), pp. 447–463.
The focus of this study is to evaluate: (1) “mixtures” of flood peak distributions, (2) upper tail and scaling properties of the flood peak distributions, and (3) presence of temporal nonstationarities in the flood peak records.
Villarini, G., Smith, J.A., Baeck, M.L., Vitolo, R., Stephenson, B., Krajewski, W.F., On the frequency of heavy rainfall for the Midwest of the United States, Journal of Hydrology, 2011, 400(1-2), pp. 103–120.
The results point to increasing trends in heavy rainfall over the northern part of the study domain. Examination of the surface temperature record suggests that these increasing trends occur over the area with the largest increasing trends in temperature and, consequently, with an increase in atmospheric water vapor.
2010
Gupta, V.K., Mantilla, R., Troutman, B.M., Dawdy, D., Krajewski,W.F., Generalizing a nonlinear geophysical flood theory to medium size river basins, Geophysical Research Letters, 2010, 37, L11402.
The central hypothesis of a nonlinear geophysical flood theory postulates that, given space‐time rainfall intensity for a rainfall‐runoff event, solutions of coupled mass and momentum conservation differential equations governing runoff generation and transport in a self‐similar river network produce spatial scaling, or a power law, relation between peak discharge and drainage area in the limit of large area.
We show scaling in mean annual peak discharges, and briefly discuss that it is physically connected with scaling in multiple rainfall‐runoff events. Scaling in peak discharges would hold in a non‐stationary climate due to global warming but its slope and intercept would change.
Books
Muste, M., et al. Experimental Hydraulics: Methods, Instrumentation, Data Processing, and Management (Vols. I and II), CRC Press, Taylor & Francis Group, 2017.
Mutel, C.F. (ed.). A Watershed Year: Anatomy of the Iowa Floods of 2008, The University of Iowa Press, 2010.
Post, R., and W.F. Krajewski, Towards using radar data for probabilistic analysis of rainfall: A case study over Iowa, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-023-02439-8, 2023.
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.
Post, R., F. Quintero, and W.F. Krajewski, Evaluating the efficacy of actively managed distributed storage systems through hydrologic simulation using spatially uniform design storms, Journal of Hydrologic Engineering, 2023 (in press)
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.
Michalek, A.T., G. Villarini, T. Kim, F. Quintero, and W.F. Krajewski, Disentangling the sources of uncertainties in the projection of flood risk across the central United States (Iowa), Geophysical Research Letters, 50, e2023GL105852, 2023.
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.
Michalek, A., G. Villarini, F. Quintero, and W.F. Krajewski, Projected changes in annual maximum discharge for Iowa communities, Journal of Hydrology, 625 129957, 2023.
In this study we use a hydrologic model to evaluate the projected changes in annual maximum peak discharge at the community-level across Iowa under two emission scenarios, Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5).
Michalek, A., F. Quintero, G. Villarini, and W.F. Krajewski, Advantages of physically based flood frequency analysis with long-term simulations for Iowa, Journal of Hydrologic Engineering, 27(12), 05022021, 2022.
The authors explore an alternative approach to regional flood quantile estimation analysis by analyzing the performance of the Iowa Flood Center’s physically based, calibration-free, and spatially distributed Hillslope-Link Model (HLM).
Quintero, F., G. Villarini, A.F. Prein, W.F. Krajewski, and W. Zhang, On the role of atmospheric simulations horizontal grid spacing for flood modeling, Climate Dynamics, 59(11-12), 3167-3174, 2022.
Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes.
Quintero, F., G. Villarini, A.F. Prein, W. Zhang, and W.F. Krajewski, Discharge and floods in Iowa projected to increase more than precipitation extremes, Hydrologic Processes, e14738, 2022.
Floods claim a high toll in fatalities and economic impacts. Despite their societal relevance, there is much more to learn about the projected changes in discharge and flooding. Here we force an operational hydrologic model over the state of Iowa with high-resolution convection-permitting climate-model precipitation to evaluate the response of 140 watersheds to climate change.
Alabbad, Y., Mount, J., Campbell, A., Demir, I., Assessment of transportation system disruption and accessibility to critical amenities during flooding: Iowa case study. Science of the Total Environment, 2021, 793.
This paper presents a comprehensive analysis of flood impacts on road network topology and accessibility to amenities for major communities in the State of Iowa using graph-theoretic methods, including single-source shortest path analyses under 100 and 500-year flood scenarios.
Ewing, G., Demir, I., An ethical decision-making framework with serious gaming: A smart water case study on flooding. Hydroinformatics, 2021, 23(3), pp. 466–482.
As sensor networks control more within water environments, humans are releasing more control over decision-making skills to AI networks. the Water Ethics Web Engine (WE)2, an integrated and generalized web framework incorporates voting-based ethical and normative preferences into water resources decision support.
Ghimire, G., Krajewski, W., Quintero, F., Scale-dependent value of QPF for real-time streamflow forecasting. Journal of Hydrometeorology, 2021, 22(7), pp. 1931–1947.
Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. 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.
Ghimire, G.R., N. Jadidoleslam, R. Goska, and W.F. Krajewski, Insights on storm direction effect on flood peak response, Journal of Hydrology, 600 (2021) 126683, 1-11, 2021.
In this study, we investigate the directional influence of storm movement on catchment flood peak response using the synthetic circular basin. Due to the complexity in defining storm movements that require meteorological modeling, we adopt a novel approach of combining the basin rotation method (BRM) with a circular basin construct.
Ghimire, G.R., Predictability of streamflow across space and time scales, Iowa Research Online, 2019.
Over the years, accurate prediction of streamflow in both space and time has been a challenge despite being one of the most studied topics in water engineering sciences. Despite significant contributions in the field of streamflow forecasting, the challenge has been to identify the trade-off between the forecast time-horizon, basin scale, and streamflow forecasting accuracy.
Hu, A., Demir, I., Real-time flood mapping on client-Side Web Systems Using HAND Model. Hydrology, 2021, 8(2), p. 65.
The Height Above Nearest Drainage (HAND) model is used to analyze soil and predict flood inundation extents. HAND produced inundation maps comparable to advanced hydrodynamic models in practice in Iowa, and would be helpful in the absence of detailed hydrological data.
Quintero , F., Rojas, M., Muste, M., Krajewski, W., Development of synthetic rating curves: Case study in Iowa. Journal of Hydraulic Engineering, 2021, 26(1).
The authors of the case study discuss an economically feasible approach to generate synthetic rating curves that enhance utility of stage-only river gauges. There are 250 bridge-mounted river-stage sensors deployed by IFC in Iowa. Those sensors, in concert with USGS and other related sensor data, are used to determine discharge and other errors or triumphs of the measurement system.
Teague, A., Sermet, Y., Demir, I., Muste, M., A collaborative serious game for water resources planning and hazard mitigation. International Journal of Disaster Risk Reduction, 2021, 53.
Hydrological hazards are enormous risks for communities. A Multi-Hazard Tournament (MHT) allows members of a watershed community to evaluate adaptation options to develop mitigation strategies for multiple water-related hazards such as floods, drought, and water pollution. Hazard risk assessment and minimization of water quality issues and water resources are all parts of the plan.
Velasquez, N., R. Mantilla, W.F. Krajewski, M. Fonley, and F. Quintero, Improvements in performance of the Hillslope-Link Model in Iowa using a non-linear representation of natural and artificially drained subsurface flows, Hydrology, 8(4), 187, https://doi.org/10.3390/hydrology8040187, 2021.
This study evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM).
Agliamzanov, R., Sit, M., Demir, I., Hydrology@Home: A distributed volunteer computing framework for hydrological research and applications. Hydroinformatics, 2020, 22(2), pp. 235–248.
Web-based distributed volunteer computing enables scientists to constitute platforms that can be used for computational tasks by using potentially millions of computers connected to the internet. The framework provides distribution and scaling capabilities for projects with user bases of thousands of volunteers. As a case study, we tested and evaluated the proposed framework with a large-scale hydrological flood forecasting model.
Ghimire, G.R., Jadidoleslam, N., Krajewski W., Tsonis, A., Insights on streamflow predictability across scales using horizontal visibility graph based networks. Frontiers in Water, 2020, 2, p. 17.
The authors characterize the dynamics associated with streamflow time-series data from 64 U.S. Geological Survey (USGS) unregulated stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG) that uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical systems.
Ghimire, G., Krajewski, W., Exploring persistence in streamflow forecasting. Journal of the American Water Resources Association, 2020, 56, pp. 542–550.
This paper explores three approaches for streamflow forecasting: simple persistence, gradient persistence, and anomaly persistence. The basin scales clearly have an impact on the persistence modeling and a weaker, but non‐negligible dependence on geometric properties of the river network for a given basin.
Ghimire, G.R., Krajewski, W., Hydrologic implications of wind farm effect on radar-rainfall observations. Geophysical Research Letters, 2020.
In this study, the authors investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor (MRMS) rainfall products. The study uses the probability of detection (POD) method to identify wind farm clutter in data from Iowa for the years 2016 and 2017.
Grimley, L., F. Quintero, and W.F. Krajewski, Streamflow predictions in a small urban-rural watershed: the effects of radar-rainfall resolution and urban rainfall-runoff dynamics, Atmosphere, 11, 774-795; doi:10.3390/atmos11080774, 2020.
The authors predicted streamflow in an urban–rural watershed using a nested regional–local modeling approach for the community of Manchester, Iowa, which is downstream of a largely rural watershed.
Krajewski, W., Ghimire, G., Quintero, F., Streamflow forecasting without models. Journal of Hydrometeorology, 2020, 21(8), pp. 1689–1704.
The authors used 16 years of river measurements to explore persistence in streamflow forecasting based on the real-time streamflow observations.
Muste, M., Lee, K., Kim, D., Bacotiu, C., Oliveros, M., Cheng, Z., Revisiting hysteresis of flow variables in monitoring unsteady streamflows. Journal of Hydraulic Research, 2021, 58(6), pp. 867–887.
Steady and unsteady streamflows are monitored through combining direct flow measurements and statistical analyses. Flow variables displaying inherent hysteretic behavior is indicative of non-kinematic waves passing through the gauging station. The paper demonstrates the index-velocity and continuous slope-area methods are more suitable to monitor unsteady flows in comparison with the widely used stage-discharge approach.
Otto, Lindsay, An application of power-law distributions to the tail of flood frequency data: a search for a physical connection in flood frequency statistics, Iowa Research Online, 2020.
The past several decades have showered riverine communities across Iowa with historic floods and billions of dollars in damages. Inspired by the recent historic flood records at gages on many rivers, this thesis seeks to better understand the tail of flood frequency curves, or rather the low probability flood events.
Quintero, F., W.F. Krajewski, and M. Rojas, A flood potential index for effective communication of streamflow forecasts at ungauged communities, Journal of Hydrometeorology, 21(4), 807–814, 2020.
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.
Quintero, F., W.F. Krajewski, B.-C. Seo, and R. Mantilla, A long-term evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach, Journal of Hydrology, 584, 124686, https://doi.org/10.1016/j.jhydrol.2020.124686, 2020.
This study evaluates the performance of Iowa Flood Center’s real-time distributed hydrologic model, Hillslope-Link Model (HLM). The HLM provides information about current and future streamflow conditions for over 1000 locations in Iowa, including small communities and stream gauge locations.
Rojas, M., Quintero, F., Young, N., Analysis of stage–discharge relationship stability based on historical ratings, Hydrology, 2020, 7(2), p. 31.
The article explores the stability of the rating curves at six streamflow gauging sites in the state of Iowa, USA, to examine temporal variability of their stage–discharge relationships. The analyzed sites have up to 10 years of rating and shift records. Rating curve shifts reflect the alteration of channel geometry caused by scouring and sediment deposition.
Rojas, M., F. Quintero, and W.F. Krajewski, Performance of the National Water Model Analysis and Assimilation configuration over Iowa, Journal of American Water Resources Association, 56(4), 568-585, 2020.
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.
Sermet, Y., Demir, I., Muste, M., A serious gaming framework for decision support on hydrological hazards, Science of the Total Environment, 2020, 728, p. 138895.
In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process regarding hydrological hazard preparedness and response. The framework is an engaging, accessible, and collaborative serious game environment facilitating the relationship between the environment and communities.
Sermet, Yusuf, Knowledge generation and communication in intelligent and immersive systems: a case study on flooding, Iowa Research Online, 2020.
In this dissertation, we present a generalized intelligent and immersive framework to augment the information systems on any domain. The framework enables the realization of a futuristic vision of a voice-controlled assistant with immersive capabilities to create the next-generation information systems that can be intuitively accessed from any device via the internet.
Varmaghani, A., Eichinger, W.E., Prueger, J.H.,(2020). A meteorological-based crop coefficient model for estimation of daily evapotranspiration. Hydrological Processes, DOI: 10.1002/hyp.14025
Analysis of six years of micrometeorological records and data revealed strong interactions between relative humidity and evapotranspiration. Daily evapotranspiration estimates for cloudy regions need more information that relies solely on meteorological data, a primary focus of this study.
Villarini, G., W. Zhang, F. Quintero, W.F. Krajewski, and G.A. Vecchi, Attribution of the impacts of the 2008 flooding in Cedar Rapids (Iowa) to anthropogenic forcing, Environmental Research Letters, 15, 114057, 2020
The City of Cedar Rapids was significantly affected by the June 2008 flood. However, little is known about the role anthropogenic warming during this event, not only in terms of hydrologic response but also of impacts. Here we use a continuous distributed hydrologic model forced with precipitation with and without external forcing and show that the impacts of this flood were likely magnified because of increased anthropogenic warming.
Xiang, Z., Demir, I., Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa, Environmental Modelling & Software, 2020, 131, p. 104761.
This study proposes a new deep recurrent neural network approach, Neural Runoff Model (NRM), which has been applied on 125 USGS streamflow gages in the State of Iowa for predicting the next 120 h due to the difficult nature of accurate streamflow forecasting. The proposed model outperforms the streamflow persistence, ridge regression and random forest regression on majority of the gages. The model has also shown strong predictive power and can be used for long-term streamflow predictions.
Xiang, Z., Yan, J., Demir, I., A rainfall-runoff model with LSTM-based sequence-to-sequence learning, Water Resources Research, 2020, 56(1).
Researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets, and this study presents an application of a prediction model based on long short-term memory (LSTM) and the sequence-to-sequence modeling (seq2seq) structure to estimate hourly rainfall‐runoff.
Xu, H., Windsor, M., Muste, M., Demir, I., A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming. Journal of Environmental Management, 2020, 255, p.109887.
The paper focuses on a participation-based serious gaming application developed to enhance multi-jurisdictional collaborative planning and decision making for mitigation of multiple hazards related to water, such as flooding, soil erosion, and water quality. The game is integrated into the Iowa Watershed Decision Support System (IoWaDSS).
Ayers, J.R., Villarini, G., Jones, C.S., Schilling, K.E., Changes in monthly baseflow across the U.S. Midwest, Hydrological Processes, 2019, 33(5), pp. 748–758.
Characterizing streamflow changes in the agricultural U.S. Midwest is critical for effective planning and management of water resources throughout the region. The objective of this study is to determine if and how baseflow has responded to land alteration and climate changes across the study area during the 50‐year study period by exploring hydrologic variations based on long‐term stream gage data.
Barth, N.A., Villarini, G., White, K., Accounting for mixed populations in flood frequency analysis: A Bulletin 17C perspective, Journal of Hydrologic Engineering, 2019, 24(3), pp. 1–12.
This study provides a general statistical framework to perform a process-driven flood frequency analysis using a weighted mixed population approach. Furthermore, it allows for accounting for both sampling and mixing uncertainties.
Brammeier, John R., On the performance of X-band dual-polarization radar-rainfall estimation algorithms during the SMAPVEX-16 field campaign, Iowa Research Online, 2019.
Soil moisture estimates from space on a continuous spatial domain could afford researchers with insight about agricultural productivity, flood vulnerability, and biological processes. To evaluate satellite soil moisture estimates, the SMAPVEX-16 experiment was one of a suite of verification data collection campaigns for NASA’s Soil Moisture Active Passive satellite.
Brenner, Iris, Application of storm transposition to the Middle Cedar Watershed: a reanalysis of the 2008 Cedar Rapids Flood, Iowa Research Online, 2019.
This thesis project modeled a variety of rainfall patterns on June 12, 2008, to determine the effect of varying rainfall intensity and location on the magnitude of the 2008 flood of Cedar Rapids. Using a method known as Stochastic Storm Transposition (SST), I overwrote precipitation data in a hydrologic model of the Middle Cedar Watershed with rainfall data extracted from specific storm events that occurred in the Upper Midwest.
Chen, B., Krajewski, W.F., Helmers, M., Zhang, Z., Spatial variability and temporal persistence of event runoff coefficients over agricultural land, Water Resources Research, 2019, 55(2), pp. 1583–1597.
The analysis of unique rainfall-runoff data can offer more in-depth knowledge than the current internal spatial variability of small-scale runoff yield. Through the use of 12 unique hills, the authors intend to reduce the error of runoff data.
Jadidoleslam, N., Mantilla, R., Krajewski, W.F., Cosh, M., Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture, Journal of Hydrology, 2019, 576, pp. 85–97.
A stochastic model was created to create high resolution surface soil moisture information.
Jadidoleslam, N., Mantilla, R., Krajewski, W.F., Goska, R., Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region. Journal of Hydrology, 2019, 579, p. 124210.
The goal of the paper is to confirm relationships identified by a paper written in 2017 (Crow et al.) that relates antecedent soil moisture to runoff production. The paper investigates total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments in Iowa.
Keem, M., Seo, B.-C., Krajewski, W.F., Morris, K.R., Inter-comparison of reflectivity measurements between GPM DPR and NEXRAD Radars, Atmospheric Research, 2019, 226, pp. 49–65.
Reflectivity measurements are one example of a measurement that can be taken from GPM, DPR, and NEXRAD radars. This study demonstrates the potential use of the NASA’s Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) to examine ground radar (GR) miscalibration and other uncertainty sources (e.g., partial beam blockage).
Krasowski, Michael, Continuous watershed-scale hydrologic modeling of conservation practices for peak flow reduction, Iowa Research Online, 2019.
Watersheds in Iowa, and the Midwest at large, have been drastically altered hydrologically—through land use change, tile drainage, digging of drainage ditches, and channelizing of meandering streams. Though drainage practices maximize arable land, they also induce higher flood peaks.
Neri, A., Villarini, G., Salvi, K., Slater, L.J., Napolitano, F., On the decadal predictability of the frequency of flood events across the U.S. Midwest, International Journal of Climatology, 2019, 39(3), pp. 1796–1804.
Skillful predictions of the frequency of flood events over long lead times (e.g., from 1 to 10 years ahead) are essential for governments and institutions making near‐term flood risk plans. However, little is known about current flood prediction capabilities over annual to decadal timescales. Here we address this knowledge gap at 286 U.S. Geological Survey gaging stations across the U.S. Midwest using precipitation and temperature decadal predictions from the Coupled Model Intercomparison Project (CMIP) phase 5 models.
Perez Mesa, Gabriel Jaime, Explaining the physics behind regional peak flow equations using the scaling theory of floods and river network descriptors, Iowa Research Online, 2019.
I present a series of studies based on hydrologic simulations and peak flow observations that illustrate several aspects related to the application and use of the scaling theory of floods, which include the following: (1) description of spatial patterns of scaling parameters; (2) inclusion of river network descriptors in flood frequency equations; and (3) evaluation of sampling errors and epistemic errors in the construction of flood frequency equations.
Rojas, M., Quintero, F., Krajewski, W., Performance of the National Water Model in Iowa using independent observations, Journal of the American Water Resources Association, 2019, 56(4), pp. 568–585.
This paper looks at the performance of the National Water Model in Iowa. The model pulls data from the United States Geological Survey (USGS), which provides a limited amount of data, but increases the performance. The Iowa bridge sensors provide independent data for evaluation analyses.
Seo, B.-C., Keem, M., Hammond, R., Demir, I., Krajewski, W.F., A pilot infrastructure for searching rainfall metadata and generating rainfall product using the big data of NEXRAD, Environmental Modeling and Software, 2019, 117, pp. 69–75.
NEXRAD collects and stores rainfall data in a cloud, allowing the IFC to develop a program to retrieve the data and create a map. The map has the benefit of allowing researchers to choose a specific area of study.
Sermet, Y., Villanueva, P., Sit, M., Demir, I., Crowdsourced approaches for stage measurements at ungauged locations using smartphones, Special Issue: Hydrological Data: Opportunities and Barriers, 2019, 65(5), pp. 813–822.
Citizen science opportunities for environmental monitoring have increased with the advances in smart phone capabilities and their growing availability. This project describes a new method to accurately measure river levels using smartphone sensors. Pictures of the same point on the river’s surface are taken to perform calculations based on the GPS location and spatial orientation of the smartphone. The proposed implementation is significantly more accessible than existing water measuring systems while offering similar accuracy.
Slater, L.J., Villarini, G., Bradley, A.A., Evaluation of the skill of North-American Multi-Model Ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA, Climate Dynamics, 2019, 54, pp. 7381–7396.
This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States.
Slater, L.J., Villarini, G., Bradley, A.A., Vecchi, G.A., A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed, Climate Dynamics, 2019, 53, pp. 7429–7445.
The state of Iowa in the US Midwest is regularly affected by major floods and has seen a notable increase in agricultural land cover over the twentieth century. We present a novel statistical-dynamical approach for probabilistic seasonal streamflow forecasting using land cover and General Circulation Model (GCM) precipitation forecasts.
Varmaghani, A., Eichinger, W. E., Prueger, J. H., Modification of FAO Penman-Monteith equation for minor components of energy, Hydrology Research, 2019, 50(2), pp. 607–615, DOI: 10.2166/nh.2018.093.
The findings in this study suggest a fundamental modification of FAO P-M formula by considering the inclusion of MECs in the energy term.
Carson, A., Windsor, M., Hill, H., Haigh, T., Wall, N., Smith, J., Olsen, R., Bathke, D., Demir, I., Muste, M., Serious gaming based participatory planning for mitigation of flood, drought, and water quality, International Journal of River Basin Management, 2018, 16(3), pp. 379–391.
Collaborative, holistic, and proactive planning for basin-wide water management solutions addressing multiple water-related hazards is challenging. Shared vision planning (SVP) and decision support systems (DSSs) are two approaches that have been used to address the challenges described. SVP is a participatory planning process. DSSs can efficiently support the integration of multiple and vast amounts of information and interactively illustrate the trade-offs between alternative mitigation plans.
Demir, I., Yildirim, E., Sermet, Y., Sit, M., FLOODSS: Iowa Flood Information System as a generalized flood cyberinfrastructure, International Journal of River Basin Management, 2018, 16(3), pp. 393–400.
This article presents the vision, implementation, and case studies of the Iowa Flood Information System (IFIS) towards the vision for next-generation decision support systems for flooding. The IFIS is an end-to-end web-based platform that incorporates various aspects of the decision-making process for flood risk management and mitigation for the State of Iowa. The IFIS provides real-time information on streams and weather conditions, incorporates advanced hydrological models for flood prediction and mapping, and several data analytics and visualization tools to support effective decision-making process.
Dhanya, C.T., G. Villarini, On the inherent predictability of precipitation across the United States, Theoretical and Applied Climatology, 2018, 133, pp. 1035–1050.
This study investigates the spatial distribution of predictability of daily precipitation across the USA. The emphasis is on determining the rate of increase in predictability with spatio-temporal averaging, by defining three predictability statistics (maximum predictability, predictive error, and predictive instability) based on the nonlinear finite-time Lyapunov exponent.
ElSadaani, M., Krajewski, W.F., Goska, R., Smith, M., An investigation of errors in the NFIE-Hydro frameworks’ stream discharge prediction due to channel routing, Journal of the American Water Resources Association, 2018, 54(1), pp. 1–10.
The authors substitute RAPID which is based on the simplified linear Muskingum routing method by the nonlinear routing component the Iowa Flood Center have incorporated in their full hydrologic Hillslope‐Link Model. The results show improvement in model performance across scales due to incorporating new routing methodology.
ElSadaani, M., Krajewski, W.F., Zimmerman, D.L., River network based characterization of errors in remotely sensed rainfall products in hydrological applications, Remote Sensing Letters, 2018, 9(8), pp. 743–752.
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.
Ghimire, G.R., Krajewski, W.F., Mantilla, R., A power law model for river water velocity in U.S. Upper Midwestern basins, Journal of the American Water Resources Association, 2018, 54(3), pp. 1–13.
This study explores power law relationships to estimate water flow velocity as a function of discharge and drainage area across river networks. We test the model using empirical data from 214 United States (U.S.) Geological Survey gauging stations distributed over the state of Iowa in the U.S.
Giuntoli, I., Villarini, G., Prudhomme, C., Hannah, D.M., Uncertainties in projected runoff over the conterminous United States, Climatic Change, 2018, 150(3), pp. 149–162.
Using a set of GIMs driven by GCMs under different representative concentration pathways (RCPs), this study aims to partition the uncertainty of future flows coming from GIMs, GCMs, RCPs, and internal variability over the CONterminous United States (CONUS).
Nayak, M.A., Villarini, G., Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States, Journal of Hydrology, 2018, 556, pp. 1038–1049.
This study fills this major scientific gap by describing the rainfall during ARs over the central United States using five remote sensing-based precipitation products over a 12-year study period. The products we consider are: Stage IV, Tropical Rainfall Measuring Mission – Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH).
Perez, G., Mantilla, R., Krajewski, W.F., Estimating annual and monthly scale-invariant flow duration curves for the State of Iowa, Journal of Hydrologic Engineering, 2018, 23(12), 05018021.
This paper presents a procedure to derive historical-annual and historical-monthly flow duration curves (FDC) that are monotonic and continuous for agricultural, unregulated, ungauged sites. The authors explore the performance and the regional dependence of four different regression models for the estimation of daily flow quantiles (QpQp), with probabilities of exceedance (pp) ranging from 0.01 to 0.99.
Perez, G., Mantilla, R., Krajewski, W.F., The influence of spatial variability of width functions on regional peak flow regressions. Water Resources Research, 2018, 54, pp. 1–19. https://doi.org/10.1029/2018WR023509.
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.
Quintero, F., Krajewski, W.F., Mapping outlets of Iowa Flood Center and National Water Center river networks for hydrologic model comparison, Journal of the American Water Resources Association, 2018, 54(1), pp. 28–39.
This study explores methods that identify the scale where networks obtained by different methods agree within some margin of error. The problem is relevant for comparing hydrologic models built around the two networks.
Quintero, F., Mantilla, R., Anderson, C., Claman, D., Krajewski, W.F., Assessment of changes in flood frequency due to the effects of climate change: Implications for engineering design, Hydrology, 2018, 5(19), pp. 1–16, doi:10.3390/hydrology5010019.
The authors explore the uncertainty implied in the estimation of changes in flood frequency due to climate change at the basins of the Cedar River and Skunk River in Iowa, United States. The study focuses on the influence of climate change on the 100-year flood, used broadly as a reference flow for civil engineering design.
Seo, B.-C., Krajewski, W.F., Quintero, F., ElSaadani, M., Goska, R., Cunha, L.K., et al., Comprehensive evaluation of the IFloodS radar-rainfall products for hydrologic applications, Journal of Hydrometeorology, 2018, 19(11), pp. 1793–1813.
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.
Seo, B.-C., Quintero, F., Krajewski, W.F. High-resolution QPF uncertainty and its implications for flood prediction: A case study for the Eastern Iowa flood of 2016, Journal of Hydrometeorology, 2018, 19(8), pp. 1289–1304.
This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016.
Sermet, Y., Demir, I., An intelligent system on knowledge generation and communication for flooding, Environmental Modeling and Software, 2018, 108, pp. 51–60.
Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societal preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding.
Slater, L.J., Villarini, G. Enhancing the predictability of seasonal streamflow via a statistical-dynamical approach, Geophysical Research Letters, 2018, 45, pp. 6504–6513.
Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.
Villarini, G., Slater, L.J., Examination of changes in annual maximum gauge height in the continental United States using quantile regression, Journal of Hydrologic Engineering, 2018, 23(3), pp. 1–5.
This study focuses on the detection of temporal changes in annual maximum gauge height (GH) across the continental United States and their relationship to changes in short- and long-term precipitation. Analyses are based on 1,805 U.S. Geological Survey records over the 1985–2015 period and are performed using quantile regression. Trends were significant only at a limited number of sites, with a higher number of detections at the tails of the distribution.
Zhang, W., Villarini, G., Vecchi, G.A., Smith, J.A., Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston, Nature, 2018, 563, pp. 384–388.
Using the Weather Research and Forecast model—a numerical model for simulating weather and climate at regional scales—and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25–30 August 2017 because of urbanization.
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Zimmerman, D.L., Can floods at large river basin be predicted from floods observed at small subbasins? Journal of Flood Risk Management, 2017, 11(3), pp. 331–338, DOI: 10.1111/jfr3.12327.
In this article, we show that a log‐linear relationship between α(e) and θ(e) can be used to simplify the problem of predicting α(e) and θ(e) from the physical characteristics of the catchment and rainfall. In particular, we show that α(e) can be predicted from peak floods observed in the smallest gauged subcatchment in the basin and its log‐linear relationship with θ(e) can be used to predict peak flood at any location in the basin. We demonstrate this using observed peak floods from the Iowa River basin in the Upper Midwest part of United States.
Chen, B., Krajewski, W.F., Goska, R., Young, N., Using LiDAR surveys to document floods: A case study of the 2008 Iowa flood, Journal of Hydrology, 2017, 553, pp. 338–349.
Can we use Light Detection and Ranging (LiDAR), an emergent remote sensing technology with wide applications, to document floods with high accuracy? To explore the feasibility of this application, we propose a method to extract distributed inundation depths from a LiDAR survey conducted during flooding.
Demir, I., Szczepanek, R., Optimization of river network representation data models for web-based systems, Earth and Space Science, 2017, 4(6).
This paper presents a detailed study of widely used methods for representing generic networks in relational databases and benchmarking common queries on river network data using these methods.
Dhanya, C.T., Villarini, G. An investigation of predictability dynamics of temperature and precipitation in reanalysis datasets over the continental United States, Atmospheric Research, 2017, 183, pp. 341–350.
Reanalysis datasets have been under critical scrutiny due to their widespread use in various climatic and hydrological modeling applications, in particular over many areas of the globe with limited or absent reliable observational data.
Krajewski, W.F., Ceynar, D., Demir, I., Goska, R., Kruger, A., Langel, C., Mantilla, R., Niemeier, F., Quintero, F., Seo, B.C., Small, S., Weber, L., Young, N., Real-time flood forecasting and information system for the State of Iowa, Bulletin of the American Meteorological Society, 2017, 98, pp. 539–554.
The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links.
Lee, K., Muste, M., Refinement of the Fread method for improved tracking of stream discharges during unsteady flows, Journal of Hydraulic Engineering, 2017, 143(6). DOI: 10.1061/(ASCE)HY.1943-7900.0001280.
This paper presents an extension of the method that can be applicable to arbitrary geometry of the channel cross section by replacing the hydraulic depth with the hydraulic radius and adjusting formulations associated with wave celerity coefficient and kinematic wave equation in the original Fread method.
Mallakpour, I., Villarini,G., Analysis of changes in the magnitude, frequency, and seasonality of heavy precipitation over the contiguous United States, Theoretical and Applied Climatology, 2017, 130, pp. 345–363.
Gridded daily precipitation observations over the contiguous USA are used to investigate the past observed changes in the frequency and magnitude of heavy precipitation, and to examine its seasonality. Analyses are based on the Climate Prediction Center (CPC) daily precipitation data from 1948 to 2012.
Mallakpour, I., Villarini, G., Jones, M.P., Smith, J.A., On the use of Cox regression to examine the temporal clustering of flooding and heavy precipitation across the central United States, Global and Planetary Change, 2017, 155, pp. 98–108.
The findings of this work highlight that variations in the climate system play a critical role in explaining the occurrence of flood and heavy precipitation events at the sub-seasonal scale over the central United States.
Muste, M., Hoitink, T., Measuring flood discharge, chapter in Oxford Research Encyclopedia of Natural Hazard Science, Oxford University Press, doi: 10.1093/acrefore/9780199389407.013.121, 2017.
This book chapter reviews the methodologies for the measurement of discharges in streams with special focus on the issues relevant to acquisition and estimation of streamflows during flood conditions.
Nayak, M.A, Villarini, G., A long-term perspective of the hydroclimatological impacts of atmospheric rivers over the central United States, Water Resources Research, 2017, 53, pp. 1144–1166.
The focus of this study is on the climatology of atmospheric rivers (ARs) over the central United States using six atmospheric reanalysis products. This climatology is used to understand the long‐term impacts of ARs on annual precipitation, precipitation extremes, and flooding over the central United States.
Quintero, F., Krajewski, W.F., Mapping outlets of Iowa Flood Center and National Water Center river networks for hydrologic model comparison, Journal of the American Water Resources Association, 2017, .
The authors propose a methodology to find corresponding outlets where the hydrologic simulations of the IFC model, and the National Water Center model, can be compared.
Quintero, F., Krajewski, W.F., Mantilla, R., Small, S., Seo. B.-C., A spatial-dynamical framework for evaluation of satellite rainfall products for flood prediction, Journal of Hydrometeorology, 2017, 17(8). doi.org/10.1175/JHM-D-15-0195.1
The authors report a novel framework that provides insights about the spatial and temporal propagation of errors in hydrologic simulations across the drainage network.
Salvi, K., Villarini, G., Vecchi, G.A., High resolution decadal precipitation predictions over the continental United States for impacts assessment, Journal of Hydrology, 2017, 553, pp. 559–573.
Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches.
Salvi, K., Villarini, G., Vecchi, G.A., Ghosh, S., Decadal temperature predictions over the continental United States: Analysis and enhancement, Climate Dynamics, 2017, 49, pp. 3587–3604.
Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling.
Slater, L.J., Villarini, G., Evaluating the drivers of seasonal streamflow in the U.S. Midwest, Water, 2017, 9(9), pp. 1–22.
Streamflows have increased notably across the U.S. Midwest over the past century, fueling a debate on the relative influences of changes in precipitation and land cover on the flow distribution. Here, we propose a simple modeling framework to evaluate the main drivers of streamflow rates.
Villarini, G., Khouakhi, A., Cunningham, E., On the impacts of computing daily temperatures as the average of the daily minimum and maximum temperatures, Atmospheric Research, 2017, 198, pp. 145–150.
Daily temperature values are generally computed as the average of the daily minimum and maximum observations, which can lead to biases in the estimation of daily averaged values. This study examines the impacts of these biases on the calculation of climatology and trends in temperature extremes at 409 sites in North America with at least 25 years of complete hourly records. Our results show that the calculation of daily temperature based on the average of minimum and maximum daily readings leads to an overestimation of the daily values of ~ 10+ % when focusing on extremes and values above (below) high (low) thresholds.
Weber, L., Muste, M., Bradley, A.A., Amado, A. A., Demir, I., Drake, C., Krajewski, W.F., Loeser, T., Politano, M., Shea, B., Thomas, N., The Iowa Watersheds Project: Iowa’s prototype for engaging communities and professionals in watershed hazard mitigation. International Journal of River Basin Management, 2017. DOI: 10.1080/15715124.2017.1387127.
After more than a century of intensive changes in the state’s agricultural watersheds, repeated record floods motivated Iowa to innovate in its flood recovery and disaster mitigation efforts following the 2008 floods. The state created the Iowa Flood Center (IFC) and authorized the creation of Watershed Management Authorities.
Zalenski, G., Krajewski, W.F., Quintero, F., Restrepo, P., Buan, S., Analysis of National Weather Service stage forecast errors, Weather and Forecasting, August 2017, pp. 1441–1465. doi.org/10.1175/WAF-D-16-0219.1
The authors report the skill of the river stage forecasts produced by National Weather Service at 51 locations in Iowa. They also analyze how the skill obtained at particular locations is related to the characteristics of the basin.
Zhang, W., Villarini, G., Heavy precipitation is highly sensitive to the magnitude of future warming, Climatic Change, 2017, 145, pp. 249–257.
Here, we investigate the changes in heavy precipitation events with the Community Earth System Model (CESM) climate experiments using the scenarios consistent with the 1.5 and 2 °C temperature targets. We find that the frequency of annual heavy precipitation at a global scale increases in both 1.5 and 2 °C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative.
Zhang, W., Villarini, G., On the unseasonal flooding over the central United States during December 2015 and January 2016, Atmospheric Research, 2017, 196, pp. 23–28.
The unseasonal winter heavy rainfall and flooding that occurred during December 2015–January 2016 had large socio-economic impacts for the central United States. Here we examine the climatic conditions that led to the observed extreme precipitation, and compare and contrast them with the 1982/1983 and 2011/2012 winters.
Zhang, W., Villarini, G., Scoccimarro, E., Vecchi, G.A., Stronger influences of increased CO2 on sub-daily precipitation extremes than at the daily scale, Geophysical Research Letters, 2017, 44, pp. 7464–7471.
We find that the increased CO2 concentration substantially increases the odds of the occurrence of subdaily precipitation extremes compared to the daily scale in most areas of the world, with the exception of some regions in the subtropics, likely in relation to the subsidence of the Hadley Cell. These results point to the large role that atmospheric CO2 plays in extreme precipitation under an idealized framework.
Faraji, S., Sadri, B., Vajdi Hokmabad, B., Jadidoleslam, N., Esmaeilzadeh, E., Experimental study on the role of electrical conductivity in pulsating models of electrospraying, Science Direct, 2016, 81, pp. 327–335, DOI: 10.1016/j/expthermflusci.2016.10.030.
In this study, we have presented insight to the role of physicochemical properties on the pulsating modes of spraying.
Gil, Y., David, C., Demir I., Essawy, B., Fulweiler, R., Goodall, J., Karlstrom, L., Lee. H., Mills, H., Oh, J., Pierce, S., Pope, A., Tzeng, M., Villamizar, S., Yu, X., Towards the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance, Earth and Space Science, 2016, 3(10), pp. 388–415.
Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented.
Mallakpour, I., Villarini, G., Investigating the relationship between the frequency of flooding over the central United States and large-scale climate, Advances in Water Resources, 2016, 92, pp. 159–171.
The aim of this study is to examine whether the climatic driving forces can describe the observed variability in the frequency of flooding over the central United States. Results are based on daily streamflow records from 774 U.S. Geological Survey (USGS) stations with at least 50 years of data and ending no earlier than 2011.
Mishra, K.V., Krajewski, W.F., Goska, R., Ceynar, D., Seo, B.-C., Kruger, A., Niemeier, J., Galvez, M.B., Thurai, M., Bringi, V.N., Tolstoy, L., Kucera, P., Petersen, W., Grazioli, J., Pazmany, A., Deployment and performance analyses of high-resolution Iowa XPOL radar system during the NASA IFloodS campaign, Journal of Hydrometeorology, 2016, 17(2), pp. 455–479.
This article presents the data collected and analyzed using the University of Iowa’s X-band weather radars that were part of the spring 2013 Iowa Flood Studies (IFloodS) field campaign, sponsored by the NASA’s Global Precipitation Measurement (GPM) satellite mission.
Nayak, M.A., Villarini, G., Bradley, A.A., Atmospheric rivers and rainfall during NASA’s Iowa Flood Studies (IFloodS) campaign, Journal of Hydrometeorology, 2016, 17(1), pp. 257–271.
Atmospheric rivers (ARs) play a major role in causing extreme precipitation and flooding over the central United States (e.g., Midwest floods of 1993 and 2008). The goal of this study is to characterize rainfall associated with ARs over this region during the Iowa Flood Studies (IFloodS) campaign that took place in April–June 2013.
Slater, L.J., Villarini, G., Recent trends in US flood risk, Geophysical Research Letters, 2016, 43(24), pp. 12428–12436.
Here we present a novel approach assessing the trends in inundation frequency above the National Weather Service’s four flood level categories in 2042 catchments. Results reveal stark regional patterns of changing flood risk that are broadly consistent above the four flood categories.
Sloan, B. P., Basu, N. B., Mantilla, R., Hydrologic impacts of subsurface drainage at the field scale: Climate, landscape and anthropogenic controls, Agricultural Water Management, 2016, 165, pp. 1–10.
Installation of subsurface drainage systems is one of the most common modifications of the agricultural landscape, and while it is well accepted that these systems alter the hydrologic regime, the nature and magnitude of such alterations remains poorly understood. We explore the impact of drainage systems using the field-scale model DRAINMOD and rainfall and soils data for Iowa.
Varmaghani, A., Eichinger, W.E. Early-season classification of corn and soybean using Bayesian discriminant analysis on satellite images, Agronomy Journal, 2016, 108(5), pp. 1880–1889, DOI: 10.2134/AGRONJ2015.0454.
This study investigated early season crop classification for corn and soybeans using vegetation maps and land cover data to construct “agricultural units.”
Varmaghani, A., Eichinger, W.E., and Prueger, J.H. A diagnostic approach towards the causes of energy balance closure problem, Open Journal of Modern Hydrology, 2016, 6, pp. 101–114.
The results obtained in this study suggest that a-posteriori analysis may offer a superior methodology to correct measured eddy-correlation measurements. Furthermore, the overall trends in the correction of LE measurements suggest that there is a potential for rough monthly corrections of LE, irrespective of the type of crop.
Villarini, G., On the seasonality of flooding across the continental United States, Advances in Water Resources, 2016, 87, pp. 80–91.
This study examines the seasonality of flooding across the continental United States using circular statistics. Analyses are based on 7506 USGS stream gauge stations with a record of least 30 years of annual maximum instantaneous peak discharge.
Anderson, C., Claman, D., Mantilla, R., Iowa’s bridge and highway climate change and extreme weather vulnerability assessment pilot. Iowa Publications Online, 2015, pp. 1–61.
A pilot study was conducted for six bridges in two Iowa river basins—the Cedar River Basin and the South Skunk River Basin—to develop a methodology to evaluate their vulnerability to climate change and extreme weather. The six bridges had been either closed or severely stressed by record streamflow within the past seven years.
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Insights into expected changes in regulated flood frequencies due to the spatial configuration of flood retention ponds, Journal of Hydrologic Engineering, 2015, DOI:10.1061/(ASCE)HE.1943-5584.0001173.
This study examines the effects that the spatial configuration of flood retention ponds have on the reduction of flood peaks across different spatial scales in the catchment.
Demir, I., Conover, H., Krajewski, W., Seo, B., Goska, R., He, Y., McEniry, M.F., Graves, S.J., Peterson, W., Data-enabled field experiment planning, management, and research using cyberinfrastructure, Journal of Hydrometeorology, 2015, 3, pp. 1155–1170.
This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments.
Giuntoli, I., Villarini, G., Prudhomme, C., Mallakpour, I., Hannah, D., Evaluation of global impact models ability to reproduce runoff characteristics over the central United States, Journal of Geophysical Research, 2015, 120, pp. 9138–9159.
This study aims to evaluate the ability of a set of global impact models (GIMs) from the Water Model Intercomparison Project to reproduce the regional hydrology of the central United States for the period 1963–2001.
Kim, D., Muste, M., Merwade, V., A GIS-based relational data model for multi-dimensional representation or river hydrodynamics and morphodynamics, Environmental Modelling and Software, 2015, 65, pp. 79–93.
This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology.
Lavers, D.A., Villarini, G., The contribution of atmospheric rivers to precipitation in Europe and the United States, Journal of Hydrology, 2015, 522, pp. 382–390.
Using gridded precipitation products across Europe and the continental United States and the ERA-Interim reanalysis, we investigate the fraction of precipitation from 1979 to 2012 that is related to ARs in these regions. The results are region- and month-dependent, with the largest contribution generally occurring during the winter season and being on the order of 30–50%.
Mallakpour, I., Villarini, G., The changing nature of flooding across the central United States, Nature Climate Change, 2015, 5, pp. 250–254.
Here, we show that while observational records (774 stream gauge stations) from the central United States present limited evidence of significant changes in the magnitude of floodpeaks, strong evidence points to an increasing frequency of flooding. These changes in flood hydrology result from changes in both seasonal rainfall and temperature across this region.
Moser, B., Gallus Jr., W.A., Mantilla, R., An initial assessment of radar data assimilation on warm season rainfall forecasts for use in hydrologic models, American Meteorological Society, 2015, DOI: 10.1175/WAF-D-14-00125.1.
The initial results of this study indicate that radar assimilation improves WRF’s ability to capture the character of storms, promising more accurate guidance for flash flood warnings.
Seo, B.-C., Krajewski, W.F., Mishra, K.V., Using the new dual-polarimetric capability of WSR-88D to eliminate anomalous propagation and wind turbine effects in radar-rainfall, Atmospheric Research, 2015, 153, pp. 296–309.
This study addresses the effect that the interaction between anomalous radar beam propagation (AP) and wind turbines that are located far from the radar has on radar-rainfall estimates.
Villarini, G., Scoccimarro, E., White, K.D., Arnold, J.R., Schilling, K.E., Ghosh, J., Projected changes in discharge in an agricultural watershed in Iowa, Journal of the American Water Resources Association, 2015, 51(5), pp. 1361–1371.
Our improved capability to adapt to the future changes in discharge is linked to our capability to predict the magnitude or at least the direction of these changes. For the agricultural United States Midwest, too much or too little water has severe socioeconomic impacts. Here, we focus on the Raccoon River at Van Meter, Iowa, and use a statistical approach to examine projected changes in discharge. We build on statistical models using rainfall and harvested corn and soybean acreage to explain the observed discharge variability. We then use projections of these two predictors to examine the projected discharge response.
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Connecting the power-law scaling structure of peak-discharges to spatially variable rainfall and catchment physical properties, Advances in Water Resources, 2014, 71, pp. 32–43.
We have conducted extensive hydrologic simulation experiments in order to investigate how the flood scaling parameters in the power-law relationship Q(A)=αAθ, between peak-discharges resulting from a single rainfall–runoff event Q(A) and upstream area A, change as a function of rainfall, runoff coefficient (Cr) that we use as a proxy for catchment antecedent moisture state, hillslope overland flow velocity (vh), and channel flow velocity (vc), all of which are variable in space.
Ayalew, T.B., Krajewski, W.F., Mantilla, R., Small, S.J., Exploring the effects of hillslope-channel link dynamics and excess rainfall properties on the scaling structure of peak-discharge, Advances in Water Resources, 2014, 64, pp. 9–20.
We use the rainfall-runoff model CUENCAS and apply it to three different river basins in Iowa to investigate how the interplay among rainfall intensity, duration, hillslope overland flow velocity, channel flow velocity, and the drainage network structure affects these parameters.
Gupta, V.K., Mesa, O.J., Horton laws for hydraulic–geometric variables and their scaling exponents in self-similar Tokunaga river networks, Nonlinear Processes Geophysics, 2014, 21, pp. 1007–1025.
We used the observed exponents of depth and slope to predict the Manning friction exponent and to test it against field exponents from three studies. Finally, we briefly sketch how the two anomalous scaling exponents could be estimated from the transport of suspended sediment load and the bed load.
Muste, M., Hauet, A., Fujita, I., Legout, C., Ho, H.-C., Capabilities of large-scale particle image velocimetry to characterize shallow free-surface flows, Advances in Water Resources, 2014, 70, pp. 160–171.
Irrespective of their spatial extent, free-surface shallow flows are challenging measurement environments for most instruments due to the relatively small depths and velocities typically associated with these flows. A promising candidate for enabling measurements in such conditions is Large-scale Particle Image Velocimetry (LSPIV).
Nayak, M.A., Villarini, G., Lavers, D.A., On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States, Geophysical Research Letter, 2014, 41, pp. 4354–4362.
This study focuses on the verification of the skill of five numerical weather prediction models in forecasting AR activity over the central United States. We find that these models generally forecast AR occurrences well at short lead times, with location errors increasing from one to three decimal degrees as the lead time increases to about 1 week.
Seo, B.-C., Krajewski, W.F., Smith, J.A., Four-dimensional reflectivity data comparison between two ground-based radars: Methodology and statistical analysis, Hydrological Sciences Journal, 2014, 59, pp. 1320–132.
To identify radar calibration differences, radar reflectivity is compared for well-matched radar sampling volumes viewing common meteorological targets.334.
Varmaghani, A., Ghiassi, R., Release time component of a hydrograph. Journal of Hydrologic Engineering, 2014, pp. 444–447, DOI:10.1061/(ASCE)HE.1943-5584.0000790.
This research introduces another concept of partial release time, and a parametrical equation for its estimation is suggested. Furthermore, the fundamental relation between time base and excess rainfall duration for linear systems is clarified, giving rise to a formula for time base of Clark hydrograph. Finally, it will be theoretically shown that release time is the temporal dependency between the rainfall and runoff in each event, and hence it plays a crucial role in event-based rainfall–runoff-forecasting models.
Villarini, G., Goska, R., Smith, J.A., Vecchi, G.A., North Atlantic tropical cyclones and U.S. flooding, Bulletin of the American Meteorological Society, 2014, 95(9), pp. 1381–1388.
These results indicate that flooding from TCs is not solely a coastal phenomenon but affects much larger areas of the United States, as far inland as Illinois, Wisconsin, and Michigan. Moreover, the authors highlight the dependence of the frequency and magnitude of TC flood peaks on large-scale climate indices, and the role played by the North Atlantic Oscillation and the El Niño–Southern Oscillation phenomenon (ENSO), suggesting potential sources of extended-range predictability.
Villarini, G., Seo, B.-C., Serinaldi, F., Krajewski, W.F., Spatial and temporal modeling of radar rainfall uncertainties, Atmospheric Research, 2014, 135-136, pp. 91–101.
Building on earlier efforts, the authors apply a data-driven multiplicative model in which the relationship between true rainfall and radar rainfall can be described in terms of the product of a systematic and random component. For the first time, the authors present a methodology based on conditional copulas to generate ensembles of random error fields with the prescribed marginal probability distribution and spatio-temporal dependencies.
Villarini, G., Strong, A., Roles of climate and agricultural practices in discharge changes in an agricultural watershed in Iowa, Agriculture, Ecosystems and Environment, 2014, 188, pp. 204–211.
An outstanding question is related to the contribution of changes in the climate system and in land use/land cover and agricultural practices in explaining changes in discharge. We address this question by developing statistical models to describe the changes in different parts of the discharge distribution. We use rainfall and harvested corn and soybean acreage to explain the observed stream flow variability.
Ayalew, T. B., Krajewski, W.F., Mantilla, R., Exploring the effect of reservoir storage on peak discharge frequency, Journal of Hydrologic Engineering, 2013, 18(12), pp. 1697–1708.
In this paper, a simple hydrologic example is employed to illustrate the important features of reservoir regulated flood frequency.
Demir, I., Krajewski, W.F., Towards an integrated flood information system: Centralized data access, analysis, and visualization, Environmental Modeling and Software, 2013, 50, pp. 77–84.
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.
Gourley, J., Hong, Y., Flamig, Z., Arthur, A., Clark, R., Calianno, M., Ruin, I., Ortel, T., Wieczorek, M., Kirstetter, P.-E., Clark, E., Krajewski, W., A unified flash flood database across the United States, Bulletin of the American Meteorological Society, 2013, 94, pp. 799–805.
This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts.
Krajewski, W.F., Kruger, A., Singh, S., Seo, B.-C., Smith, J.A., Hydro-NEXRAD-2: Real-time access to customized radar-rainfall for hydrologic applications, Journal of Hydroinformatics, 2013, 15(2), pp. 580–590.
This paper describes the challenges involved in HNX2’s development and implementation, which include real-time error-handling, time-synchronization of data from multiple asynchronous sources, generation of multiple-radar metadata products, and distribution of products to a user base with diverse needs and constraints.
Lavers, D.A., Villarini, G., Atmospheric rivers and flooding over the Central United States, Journal of Climate, 2013, 26(12), pp. 7829–7836.
Based on the findings of this study, ARs are a major flood agent over the central United States.
Peterson, T.C., Heim, R.R., Hirsch, R., Kaiser, D.P., Brooks, H., Diffenbaugh, N.S., Dole, R.M., Giovannettone, J.P., Guirguis, J., Karl, T.R., Katz, R.W., Kunkel, K., Lettenmaier, D., McCabe, G.J., Paciorek, C.J., Ryberg, K.R., Schubert, S., Silva, V.B.S., Stewart, B.C., Vecchia, A.V., Villarini, G., Vose, R.S., Walsh, J., Wehner, M., Wolock, D., Wolter, K., Woodhouse, C.A., Wuebbles, D., Monitoring and understanding changes in heat waves, cold waves, floods and droughts in the United States: State of knowledge, Bulletin of the American Meteorological Society, 2013, 94(6), pp. 821–834.
In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country.
Rowe, S.T., Villarini, G., Flooding associated with predecessor rain events over the Midwest United States, Environmental Research Letters, 2013, 8, pp. 1–5.
This paper examines the severity and extent of flooding caused by six predecessor rain events (PREs) over the Midwest United States. PREs are areas of heavy rainfall that occur about 1000 km ahead of landfalling tropical cyclones. While recent studies have mostly focused on the synoptic conditions associated with PREs, little is known about the hydrologic impacts of these events.
Seo, B.-C., Cunha, L.K., Krajewski, W.F., Uncertainty in radar-rainfall composite and its impact on hydrologic prediction for the Eastern Iowa flood of 2008, Water Resources Research, 2013, 49, pp. 2747–2764.
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.
Small, S.J., Jay, L.O., Mantilla, R., Curtu, R., Cunha, L.K., Fonley, M., Krajewski, W.F., An asynchronous solver for systems of ODEs linked by a directed tree structure, Advances in Water Resources, 2013, 53, pp. 23–32.
This paper documents our development and evaluation of a numerical solver for systems of sparsely linked ordinary differential equations in which the connectivity between equations is determined by a directed tree.
Villarini, G., Scoccimarro, E., Gualdi, S., Projections of heavy rainfall over the Central United States based on CMIP5 models, Atmospheric Science Letters, 2013, 14(3), pp. 200–205.
Several studies based on observational records found increasing trends over the central United States. Recently, Villarini et al. found a large increase in the number of rainfall days exceeding the 95th percentile of the rainfall distribution over the Upper Mississippi River Basin, and a much weaker signal in the Lower Mississippi River Basin.
Villarini, G., Smith, J.A., Vecchi, G.A., Changing frequency of heavy rainfall over the Central United States, Journal of Climate, 2013, 26(1), pp. 343–350.
Villarini et al. used daily rainfall measurements from 447 rain gauges with a record of least 50 years throughout the central United States to examine the presence of changes in the frequency of heavy rainfall, which they defined as days exceeding the 95th percentile of the at-site rainfall distribution. The observational records covered at least the second half of the 20th century and the first decade of the 21st century, providing information about the most recent changes in heavy rainfall events over this area. They found a generally increasing trend in the northern part of the study region (roughly the Upper Mississippi River basin).
“We tried to explain these results and the differences between the northern and southern parts of the study region in light of changes in temperature,” Villarini says. “We found that the northern region is experiencing large increasing trends in temperature, resulting in an increase in atmospheric water vapor. Therefore, there is more water vapor available for precipitation.” In addition to increasing temperatures, they also indicated the increased irrigation over the Ogallala Aquifer, which likely resulted in an increase in water vapor in the area.
Villarini, G., Smith, J.A., Vitolo, R., Stephenson, D.B., On the temporal clustering of U.S. floods and its relationship to climate teleconnection patterns, International Journal of Climatology, 2013, 33(3), pp. 629–640.
This article examines whether the temporal clustering of flood events can be explained in terms of climate variability or time‐varying land‐surface state variables.
Cunha, L.K., Mandapaka, P.V., Krajewski, W.F., Mantilla, R., Bradley, A.A., Impact of radar rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model, Water Resources Research, 2012, 48(10), W10515.
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.
Ferguson, C.R., Villarini, G., Detecting inhomogeneities in the 20th-Century reanalysis over the Central United States, Journal of Geophysical Research, 2012, 117, D05123.
We use three statistical methods (Pettitt and Bai‐Perron tests and segmented regression) to detect abrupt shifts in multiple hydrometeorological variable mean and uncertainty fields over the central United States. For surface air temperature and precipitation, we use the Climate Research Unit (CRU) time series data set for comparison. We find that for warm‐season months, there is a consensus change point among all variables between 1940 and 1950, which is not substantiated by the CRU record.
Varmaghani, A., An analytical formula for potential water vapor in an atmosphere of constant lapse rate, Terrestrial, Atmospheric, and Oceanic Sciences, 2012, 23(1), pp. 17–24.
Accurate calculation of precipitable water vapor (PWV) in the atmosphere has always been a matter of importance for meteorologists. Potential water vapor (POWV) or maximum precipitable water vapor can be an appropriate base for estimation of probable maximum precipitation (PMP) in an area, leading to probable maximum flood (PMF) and flash flood management systems.
Cunha, L.K., Mandapaka, P.V., Krajewski, W.F., Mantilla, R., Bradley, A.A., A framework for flood risk assessment in ungauged basins, Journal of Flood Risk Management, 2011, 4(1), pp. 3–22.
We present a diagnostic framework to assess changes in flood risk across multiple scales in a river network, under nonstationary conditions or in the absence of historical hydro-meteorological data. The framework combines calibration-free hydrological and hydraulic models with urban development information to demonstrate altered flood risk.
Gilles, D.G., Young, N.C., Piotrowski, J.A., Schroeder, H.S., Chang, Y.J. Inundation mapping initiatives of the Iowa Flood Center: Statewide coverage and detailed urban flooding analysis, Water, 2011, 4(1), pp. 85–106.
The State of Iowa, located in the Midwestern United States, has experienced an increased frequency of large floods in recent decades. After extreme flooding in the summer of 2008, the Iowa Flood Center (IFC) was established for advanced research and education specifically related to floods.
Villarini, G., Smith, J.A., Baeck, M.L., Krajewski, W.F., Examining regional flood frequency in the U.S. Midwest, Journal of the American Water Resources Association, 2011, 47(3), pp. 447–463.
The focus of this study is to evaluate: (1) “mixtures” of flood peak distributions, (2) upper tail and scaling properties of the flood peak distributions, and (3) presence of temporal nonstationarities in the flood peak records.
Villarini, G., Smith, J.A., Baeck, M.L., Vitolo, R., Stephenson, B., Krajewski, W.F., On the frequency of heavy rainfall for the Midwest of the United States, Journal of Hydrology, 2011, 400(1-2), pp. 103–120.
The results point to increasing trends in heavy rainfall over the northern part of the study domain. Examination of the surface temperature record suggests that these increasing trends occur over the area with the largest increasing trends in temperature and, consequently, with an increase in atmospheric water vapor.
Gupta, V.K., Mantilla, R., Troutman, B.M., Dawdy, D., Krajewski,W.F., Generalizing a nonlinear geophysical flood theory to medium size river basins, Geophysical Research Letters, 2010, 37, L11402.
The central hypothesis of a nonlinear geophysical flood theory postulates that, given space‐time rainfall intensity for a rainfall‐runoff event, solutions of coupled mass and momentum conservation differential equations governing runoff generation and transport in a self‐similar river network produce spatial scaling, or a power law, relation between peak discharge and drainage area in the limit of large area.
We show scaling in mean annual peak discharges, and briefly discuss that it is physically connected with scaling in multiple rainfall‐runoff events. Scaling in peak discharges would hold in a non‐stationary climate due to global warming but its slope and intercept would change.
Books
Muste, M., et al. Experimental Hydraulics: Methods, Instrumentation, Data Processing, and Management (Vols. I and II), CRC Press, Taylor & Francis Group, 2017.
Mutel, C.F. (ed.). A Watershed Year: Anatomy of the Iowa Floods of 2008, The University of Iowa Press, 2010.