On the decadal predictability of the frequency of flood events across the U.S. Midwest

Neri, Andrea; Villarini, Gabriele; Salvi, Kaustubh; Slater, Louise; Napolitano, Francesco

doi:10.1002/joc.5915

Cited by 20 publications

(14 citation statements)

References 34 publications

(62 reference statements)

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“…Meanwhile, past studies (Gillies et al 2015, Gillies et al 2011 [10,11] have developed time series modeling to anticipate the GSL water level out to 5-10 years. Predictability of streamflow and water storage at decadal timescale was reported elsewhere, such as the Missouri River basin (Neri et al 2018;Wang et al 2014) [12,13]. Therefore, a compelling case can be made that the same multi-year forecast developed for the GSL water level could apply to the Colorado River WS.…”

Section: Introductionmentioning

confidence: 78%

Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling

Plucinski

Sun

Wang

et al. 2019

Water

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The future of the Colorado River water supply (WS) affects millions of people and the US economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a decline in the 10-year averaged WS since 2013 before starting to increase around 2020. Comparison between this WS prediction and the WS projection published in a 2012 government report (derived from climate models) reveals a widened imbalance between supply and demand by 2020, a tendency that is confirmed by updated WS observation. Such information could aid in management decision-making in the face of near-future water shortages.

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Section: Introductionmentioning

confidence: 78%

Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling

Plucinski

Sun

Wang

et al. 2019

Water

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“…Figure 3 shows the prediction skill of the models for different lead times (the same maps for all the lead times are shown in Figure S3). We obtain good correlation coefficients only when considering the shortest forecasts horizons (i.e., 0.5–2.5 months) during spring: this is because at many stations the selected model includes antecedent wetness conditions as an important predictor for the location parameter (see Figure S4; see also Neri, Villarini, Kaustubh, et al (2019) and Slater and Villarini (2017)), which means that observations are included in the forecasting as preconditioning for the season of interest. However, the prediction skill is lower for the other seasons, with no improvement as the forecast horizon shortens and with no area performing consistently better than others.…”

Section: Resultsmentioning

confidence: 97%

“…Similarly to Neri, Villarini, Kaustubh, Slater, and Napolitano (2019), we define different statistical models, each one assuming a different distribution function and a different relationship between its parameters and the three drivers (see Table 2), for a total of 22 models.…”

Section: Methodsmentioning

confidence: 99%

Intraseasonal predictability of the duration of flooding above National Weather Service flood warning levels across the U.S. Midwest

Neri

Villarini

Napolitano

2020

Hydrological Processes

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Unpreparedness is often the main cause of the economic and social damages caused by floods. To mitigate these impacts, short-term forecasting has been the focus of several studies during the past decades; however, less effort has been paid to flood predictions at longer lead times. Here, we use forecasts by six models from the North American Multi-Model Ensemble project with a lead time from 0.5 to 9.5 months to predict the seasonal duration of floods above four National Weather Service flood categories ("action," "flood," "moderate" and "major"). We focus on 202 U.S. Geological Survey gage stations across the U.S. Midwest and use a statistical framework which considers precipitation, temperature, and antecedent wetness conditions as predictors. We find that the prediction skill of the duration of floods for the "action" and "flood" categories is overall low, largely because of the low accuracy of the climate forecasts rather than of the errors introduced by the statistical models. The prediction skill slightly improves when considering the shortest lead times (i.e., from 0.5 to 2.5 months) during spring in the Northern Great Plains, where antecedent wetness conditions play an important role in influencing the generation of floods. It is very difficult to draw strong conclusions with respect to the "moderate" and "major" flood categories because of the limited number of available events.

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“…The flash flood events that caused property damage were randomly divided into two parts: training (85% of dataset) and testing (15% dataset). The result of the developed model are evaluated using two performance measures: correlation coefficient (R) and bias, both of which have been commonly used to measure the accuracy and performance of the ML models (Gavahi et al 2019, Neri et al 2019, Shastry and Durand 2019, Abbaszadeh et al 2019a. Here, the regression (i.e.…”

Section: Damage Prediction Modelmentioning

confidence: 99%

Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour

Ahmadalipour

Abbaszadeh

et al. 2020

Environ. Res. Lett.

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Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

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On the decadal predictability of the frequency of flood events across the U.S. Midwest

Cited by 20 publications

References 34 publications

Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling

Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling

Intraseasonal predictability of the duration of flooding above National Weather Service flood warning levels across the U.S. Midwest

Leveraging machine learning for predicting flash flood damage in the Southeast US

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