Effect of River Network Geometry on Flood Frequency: A Tale of Two Watersheds in Iowa

Ayalew, Tibebu B.; Krajewski, Witold F.

doi:10.1061/(asce)he.1943-5584.0001544

Cited by 29 publications

(17 citation statements)

References 37 publications

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“…This agrees with the pattern observed in the results of hydrologic simulations with rainfall duration of less than 12 hr (top panel of Figure ). It also agrees with results presented by Ayalew and Krajewski () indicating that because these two watersheds have similar rainfall, soil properties, and land use, the variations in the drainage network topology are the dominant factor generating differences in peak flow quantiles between the two watersheds. However, this is not true for results obtained for the other two pairs of watersheds.…”

Section: Connections Between Wfds and Peak Flowssupporting

confidence: 92%

The Influence of Spatial Variability of Width Functions on Regional Peak Flow Regressions

Pérez

Mantilla

Krajewski

2018

Water Resources Research

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

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Section: Connections Between Wfds and Peak Flowssupporting

confidence: 92%

The Influence of Spatial Variability of Width Functions on Regional Peak Flow Regressions

Pérez

Mantilla

Krajewski

2018

Water Resources Research

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“…It is a geometric property of the river‐network showing the distribution of distance of stream‐link to the basin outlet. As Perez et al () and Ayalew and Krajewski () showed, basin scales show a strong relationship with WFD. Therefore, to avoid potential collinearity issues with drainage area, we explored the variability in persistence skill with the WFD at site and WFD at region (refer Perez et al for details).…”

Section: Resultsmentioning

confidence: 78%

Exploring Persistence in Streamflow Forecasting

Ghimire

Krajewski

2019

J American Water Resour Assoc

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In this study, the authors explore three persistence approaches in streamflow forecasting motivated by the need for forecasting model skill evaluation. The authors use streamflow observations with 15 min resolution from the year 2008 to 2017 at 140 United States Geological Survey streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 to 37,000 km2. The study explores three approaches: simple persistence, gradient persistence, and anomaly persistence. The study shows that persistence forecasts skill has strong dependence on basin scales and weaker but non‐negligible dependence on geometric properties of the river network for a given basin. Among the three approaches explored, anomaly persistence shows highest skill especially for small basins, under about 500 km2. The anomaly persistence can serve as a benchmark for model evaluations considering the effect of basin scales and geometric properties of river network of the basin. This study further reiterates that persistence forecasts are hard‐to‐beat methods for larger basin scales at short to medium forecast range.

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“…Design storm (DS) approaches use idealized rainfall scenarios of a given return period as inputs to a hydrologic model to simulate flood peaks. DS is widely used in practice due to its simplicity (Cudworth, 1989;Kjeldsen, 2007;Ball et al, 2016). To some extent, the flood-producing physical processes are captured via the hydrologic model, which also provides a complete simulated flood hydrograph, as opposed to only the peak discharge or volume provided by statistical approaches.…”

Section: Design Storm Approachesmentioning

confidence: 99%

Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Wright

Zhu

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

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Effect of River Network Geometry on Flood Frequency: A Tale of Two Watersheds in Iowa

Cited by 29 publications

References 37 publications

The Influence of Spatial Variability of Width Functions on Regional Peak Flow Regressions

The Influence of Spatial Variability of Width Functions on Regional Peak Flow Regressions

Exploring Persistence in Streamflow Forecasting

Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

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