Scaling of Floods With Geomorphologic Characteristics and Precipitation Variability Across the Conterminous United States

Najibi, Nasser; Devineni, Naresh

doi:10.1029/2022wr032815

Cited by 5 publications

(6 citation statements)

References 110 publications

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“…For our dataset, the catchment area explains most of the variation in A P . The decreasing relationship between A P and median flood can therefore be explained by the well-known spatial scaling of floods (Alexander, 1972;Blöschl and Sivapalan, 1995;Robinson and Sivapalan, 1997a, b;Tsonis et al, 2007;Tarasova et al, 2018;Stein et al, 2021;Najibi and Devineni, 2023).…”

Section: Hydrologic Interpretation Of Predictor-response Relationship...mentioning

confidence: 98%

Regional index flood estimation at multiple durations with generalized additive models

Barna,

Engeland,

Kneib

et al. 2023

Preprint

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Abstract. Estimation of flood quantiles at ungauged basins is often achieved through regression based methods. In situations where flood retention is important, e.g. floodplain management and reservoir design, flood quantile estimates are often needed at multiple durations. This poses a problem for regression-based models as the form of the functional relationship between catchment descriptors and the response may not be constant across different durations. A particular type of regression model that is well-suited to this situation is a generalized additive model (GAM), which allows for flexible, semi-parametric modeling and visualization of the relationship between predictors and the response. However, in practice, selecting predictors for such a flexible model can be challenging, particularly given the characteristics of available catchment descriptor datasets. We employ a machine learning-based variable pre-selection tool which, when combined with domain knowledge, enhances the practicality of constructing GAMs. In this study, we develop a GAM for index (median) flood estimation with the primary objective of investigating duration-specific differences in how catchment descriptors influence the median flood. As the accuracy of this explainable approach is dependent on the fitted GAM being adequate, the secondary objective of our study is prediction of the median flood at ungauged locations and multiple durations, where predictive performance and reliability at ungauged locations are used as proxies for adequacy of the GAM. Predictive performance of the GAM is compared to two benchmark models: the existing log-linear model for median flood estimation in Norway and a fully data-driven machine learning model (an extreme gradient boosting tree ensemble, XGBoost). We find that the predictive accuracy and reliability of the GAM matched or exceeded that of the benchmark models at both durations studied. Within the predictor set selected for this study, we observe duration-specific differences in the relationship between the median flood and the two catchment descriptors effective lake percentage and catchment shape. Ignoring these differences results in a statistically significant decline in predictive performance. This suggests that models developed and estimated for prediction of the index flood at one duration may have reduced performance when applied directly to situations outside of that specific duration.

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Section: Hydrologic Interpretation Of Predictor-response Relationship...mentioning

confidence: 98%

Regional index flood estimation at multiple durations with generalized additive models

Barna,

Engeland,

Kneib

et al. 2023

Preprint

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“…Historical flood risk and costs data (1978-present) are publicly available in the FEMA's National Flood Insurance Program (Brown, 2016). The processed data (e.g., peak streamflow, compound flood risk models) and climate teleconnections to reproduce the table and figures in this work are available at Zenodo (Najibi et al, 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

Compound Continental Risk of Multiple Extreme Floods in the United States

Najibi,

Devineni,

Lall

2023

Geophysical Research Letters

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Understanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods.

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“…Antecedent soil moisture and rainfall are the important factors influencing riverine flow discharge [14,19], and the combination of antecedent rainfall and intra-event rainfall is vital to generate riverine flow discharge and sediment loads [14,20]. Larger floods caused by extreme rainfall respond more strongly to antecedent rainfall [21,22]. Riverine sediment is also affected by antecedent rainfall that accumulates in the soil [23], which is an easily overlooked effect [24].…”

Section: Introductionmentioning

confidence: 99%

“…Riverine sediment is also affected by antecedent rainfall that accumulates in the soil [23], which is an easily overlooked effect [24]. Rainfall intensity and antecedent rainfall are also important contributors to changes in riverine flow discharge and riverine sediment loads [2,25,26], and their specific roles can differ depending on the region [8,22,27,28]. Only sufficiently accumulated antecedent rainfall in the soil can trigger massive riverine sediment in response to extreme rainfall [29,30].…”

Section: Introductionmentioning

confidence: 99%

The Lag Effect of Riverine Flow-Discharge and Sediment-Load Response to Antecedent Rainfall with Different Cumulative Durations in Red Hilly Area in China

Zhao,

Nie,

Zheng

et al. 2023

Water

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Rainfall is an important factor that causes riverine flow and sediment transport, and extreme rainfall has a particularly significant effect on the fluctuations of riverine flow and sediment load. Based on the daily rainfall from 1990 to 2020, in the upper watershed of the Lianjiang River, which is one of the source tributaries of China’s largest freshwater lake (Poyang Lake), the 95th percentile method and minimum event interval time were employed to identify extreme rainfall events. Mann–Kendall test was used to check for abrupt changes in annual rainfall, riverine flow discharge, and riverine sediment loads, and to identify abrupt-change years; thus, different periods were divided via the abrupt change years. Multiple linear regression was applied to explore the lag effect of riverine flow-discharge and sediment-load response to antecedent rainfall, with different cumulative durations for each period. The results of the study indicated that (1) the expansion of garden land in 1995 caused a significant and abrupt change in sediment load. (2) Extreme rainfall events had a greater impact on riverine flow and sediment load as compared to ordinary rainfall. These events were found to explain more variations in riverine flow and sediment load, which led to longer lag times for both riverine flow and sediment transport. (3) The expansion of garden land under extreme rainfall conditions resulted in longer lag times for riverine flow and sediment transport, and reduced the need for antecedent rainfall with a longer pre-event time. Therefore, the analysis of antecedent rainfall and the lag response of riverine flow discharge and sediment load can help in understanding the response mechanism of riverine flow discharge and sediment load for the current era of increasing extreme rainfall. This analysis is crucial for improving the accuracy of simulating riverine flow and sediment under extreme rainfall conditions. Ultimately, it can contribute to effective watershed management during extreme rainfall events.

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Scaling of Floods With Geomorphologic Characteristics and Precipitation Variability Across the Conterminous United States

Cited by 5 publications

References 110 publications

Regional index flood estimation at multiple durations with generalized additive models

Regional index flood estimation at multiple durations with generalized additive models

Compound Continental Risk of Multiple Extreme Floods in the United States

The Lag Effect of Riverine Flow-Discharge and Sediment-Load Response to Antecedent Rainfall with Different Cumulative Durations in Red Hilly Area in China

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