Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

Armal, Saman; Devineni, Naresh; Khanbilvardi, R.

doi:10.1175/jcli-d-17-0106.1

Cited by 64 publications

(46 citation statements)

References 64 publications

(67 reference statements)

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“…However, it should be noted that similarly to the MDB, many other large catchments worldwide have a complex river network and experience large interannual and interdecadal hydrological variability associated with climate modes such as El Niño‐Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. Moreover, current climate change projection studies highlight a possible amplification of wet and dry extremes (e.g., Armal et al, ; Betts et al, ; Lu et al, ; Taye et al, ; Trenberth et al, ). Furthermore, the large impact of local morphologic features highlighted in this study alludes to the importance of accounting for the potential impact of flood defenses in flood inundation prediction models, although a complete and accessible database of such infrastructure is currently not available, except for a very limited number of countries or regions.…”

Section: Discussionmentioning

confidence: 99%

Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

Grimaldi

Schumann

Shokri

et al. 2019

Water Resources Research

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Flood modeling at the regional to global scale is a key requirement for equitable emergency and land management. Coupled hydrological‐hydraulic models are at the core of flood forecasting and risk assessment models. Nevertheless, each model is subject to uncertainties from different sources (e.g., model structure, parameters, and inputs). Understanding how uncertainties propagate through the modeling cascade is essential to invest in data collection, increase flood modeling accuracy, and comprehensively communicate modeling results to end users. This study used a numerical experiment to quantify the propagation of errors when coupling hydrological and hydraulic models for multiyear flood event modeling in a large basin, with large morphological and hydrological variability. A coupled modeling chain consisting of the hydrological model Hydrologiska Byråns Vattenbalansavdelning and the hydraulic model LISFLOOD‐FP was used for the prediction of floodplain inundation in the Murray Darling Basin (Australia), from 2006 to 2012. The impacts of discrepancies between simulated and measured flow hydrographs on the predicted inundation patterns were analyzed by moving from small upstream catchments to large lowland catchments. The numerical experiment was able to identify areas requiring tailored modeling solutions or data collection. Moreover, this study highlighted the high sensitivity of inundation volume and extent prediction to uncertainties in flood peak values and explored challenges in time‐continuous modeling. Accurate flood peak predictions, knowledge of critical morphological features, and an event‐based modeling approach were outlined as pragmatic solutions for more accurate prediction of large‐scale spatiotemporal patterns of flood dynamics, particularly in the presence of low‐accuracy elevation data.

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Section: Discussionmentioning

confidence: 99%

Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

Grimaldi

Schumann

Shokri

et al. 2019

Water Resources Research

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“…

\log ((), Y_{italicit}) \sim N ((),, μ_{italicit}, σ_{i}^{2})

μ_{it} = α^{i} + β_{1}^{i} ((), {italicGDP}_{italicit}) + β_{2}^{i} \log {({CO}_{2})}_{t} + β_{3}^{i} {ENSO}_{t} + β_{4}^{i} {PDSI}_{it} + β_{5}^{i} {GPH}_{it}

β_{2}^{i} \sim N ((),, a_{1} + b_{11} {CV}_{i} + b_{12} {DTR}_{i} + b_{13} γ_{i} + b_{14} {IF}_{i}, σ_{β_{2}}^{2})

β_{3}^{i} \sim N ((),, a_{2} + b_{21} {CV}_{i} + b_{22} {DTR}_{i} + b_{23} γ_{i} + b_{24} {IF}_{i}, σ_{β_{3}}^{2})

where Y it is the average yield in year t in country i , CV i , DTR i , γ i , and IF i are the coefficient of variability of annual rainfall, average DTR, the aridity index and the fraction of croplands under irrigation of the country i . Since the effect of the ENSO and CO 2 are at the larger spatial scales impacting many countries, the second level helps pool this information by constraining the response parameter using the regional characteristics (Armal et al, ; Renard et al, ). The errors with variance

σ_{β_{2}}^{2}

, and

σ_{β_{3}}^{2}

represent variation in the ENSO and CO 2 coefficients between countries beyond what is explained by the aridity index, DTR, variabilit...…”

Section: Methodsmentioning

confidence: 99%

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

et al. 2018

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During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large‐scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño‐southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country‐based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out‐of‐sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

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“…In other words, they reveal the variability beyond what could be attributed to exogenous climate factors. The analysis of the time trends in the residuals will help discern any unexplained trend after accounting for background variability due to the climatic modulation (e.g., Merz et al, 2012;Armal et al, 2017). The models are fit using the "stepwiseglm" toolbox in MATLAB 2017a (McCullagh, 1984) that uses the forward and backward regression algorithm.…”

Section: The Generalized Linear Model (Glm) Frameworkmentioning

confidence: 99%

“…Natural climate variability often causes periods of increasing extremes (flood-rich cycle) or decreasing extreme events (flood-poor cycle) depending on the phase of the climate Hall et al, 2014;Blöschl et al, 2015;Cioffi et al, 2016;Armal et al, 2017).…”

Section: Millionsmentioning

confidence: 99%

“…Hence, in an effort to investigate any significant relationship between the observed trend in the flood data (characterized in H1 and H2) and the variability in the climate and atmospheric circulation patterns, we considered large-scale atmospheric teleconnections and climate indices (with quasiperiodicity in nature that can lead to wet-dry regimes) to explain the trend, i.e., to place the short-term trends within a longer climate variability context as argued by Merz et al (2012) and Armal et al (2017).…”

Section: Millionsmentioning

confidence: 99%

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Recent trends in the frequency and duration of global floods

2018

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Abstract. Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985-2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1-7, moderate: 8-20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann-Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. Results show that flood frequency and the tails of the flood duration (long duration) have increased at both the global and the latitudinal scales. In the tropics, floods have increased 4-fold since the 2000s. This increase is 2.5-fold in the north midlatitudes. However, much of the trend in frequency and duration of the floods can be placed within the long-term climate variability context since the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were the main atmospheric teleconnections explaining this trend. There is no monotonic trend in the frequency of short-duration floods across all the global and latitudinal scales. There is a significant increasing trend in the annual median of flood durations globally and each latitudinal belt, and this trend is not related to these teleconnections. While the DFO data come with a certain level of epistemic uncertainty due to imprecision in the estimation of floods, overall, the analysis provides insights for understanding the frequency and persistence in hydrologic extremes and how they relate to changes in the climate, organization of global and local dynamical systems, and country-scale socioeconomic factors.

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Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

Cited by 64 publications

References 64 publications

Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

Recent trends in the frequency and duration of global floods

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