A Flood Potential Index for Effective Communication of Streamflow Forecasts at Ungauged Communities

Quintero, Felipe; Krajewski, Witold F.; Rojas, Marcela

doi:10.1175/jhm-d-19-0212.1

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…The subscripts “ s ” and “ o ” correspond to the simulated hydrographs from MRMS with and without wind turbine clutter, respectively. In addition, we report a new metric called flood potential index, FPI (see Quintero et al, 2020), that can be used to effectively communicate flood potential alerts to ungauged communities. We computed it as,

italicFPI = \frac{Q}{Q_{\max}}

where Q is the predicted streamflow and Q max is the average annual peak flow, often considered a bank full flow, corresponding to the same location.…”

Section: Methodsmentioning

confidence: 99%

“…We computed it as,

italicFPI = \frac{Q}{Q_{\max}}

where Q is the predicted streamflow and Q max is the average annual peak flow, often considered a bank full flow, corresponding to the same location. Quintero et al (2020) determined power law relationship to obtain Q max at any location of the drainage network in Iowa. They found it to be Q max = 3.12 A 0.57 , where A is the upstream drainage area.…”

Section: Methodsmentioning

confidence: 99%

“…They coined the term “flood potential” owing to the uncertainty associated with the streamflow prediction, Q , and average annual maximum, Q max . For details, refer to Quintero et al (2020).…”

Section: Methodsmentioning

confidence: 99%

“…We adopted the distributed hydrologic model used by Iowa Flood Center (IFC) for real-time streamflow (flood) forecasting in Iowa, called the Hillslope-Link Model (HLM). It is a nonlinear physically based model in which a landscape is decomposed into a system of hillslopes and links (refer to, e.g., Krajewski et al, 2017;Quintero et al, 2016Quintero et al, , 2020, for details on the model). The rainfall-runoff transformation occurs at hillslopes, while generated flows are routed through corresponding stream links by a nonlinear velocity formulation (Ghimire et al, 2018), aggregating water from upstream links.…”

Section: 1029/2020gl089188mentioning

confidence: 99%

“…The subscripts "s" and "o" correspond to the simulated hydrographs from MRMS with and without wind turbine clutter, respectively. In addition, we report a new metric called flood potential index, FPI (see Quintero et al, 2020), that can be used to effectively communicate flood potential alerts to ungauged communities. We computed it as,…”

Section: 1029/2020gl089188mentioning

confidence: 99%

See 4 more Smart Citations

Hydrologic Implications of Wind Farm Effect on Radar‐Rainfall Observations

Ghimire

Krajewski

2020

Geophysical Research Letters

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Despite efforts to detect and mitigate wind farm clutter in weather radar observations of rainfall, these signatures propagate to quantitative precipitation estimates. In this study, the authors investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor rainfall products. The study uses the probability of detection method to identify wind farm clutter in data from Iowa for the years 2016 and 2017. Using the physically based distributed hydrologic model called the Hillslope‐Link Model, the authors show that streamflow (flood) prediction errors are generally significant at smaller basin communities where wind farms occupy a large portion of the upstream basins. These errors due to wind farm clutter show systematic decrease with the basin scales. The results from this study have implications for real‐time streamflow forecasts provided automatically by the National Water Model at the National Oceanic and Atmospheric Administration, particularly at small riverine communities.

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italicFPI = \frac{Q}{Q_{\max}}

where Q is the predicted streamflow and Q max is the average annual peak flow, often considered a bank full flow, corresponding to the same location.…”

Section: Methodsmentioning

confidence: 99%

“…We computed it as,

italicFPI = \frac{Q}{Q_{\max}}

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: 1029/2020gl089188mentioning

confidence: 99%

Section: 1029/2020gl089188mentioning

confidence: 99%

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Hydrologic Implications of Wind Farm Effect on Radar‐Rainfall Observations

Ghimire

Krajewski

2020

Geophysical Research Letters

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Incorporating IMERG Satellite Precipitation Uncertainty into Seasonal and Peak Streamflow Predictions using the Hillslope Link Hydrological Model

Hartke

Wright

Quintero

et al. 2022

Preprint

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In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite 2 precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using groundreference forcing data across basin sizes.

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