Projected mid-century rainfall erosivity under climate change over the southeastern United States

Takhellambam, Bijoychandra S.; Srivastava, Puneet; Lamba, Jasmeet; McGehee, Ryan P.; Kumar, Hemendra; Tian, Di

doi:10.1016/j.scitotenv.2022.161119

Cited by 16 publications

(10 citation statements)

References 57 publications

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“…Analogous to this study, there are studies which found underestimation and overestimation of observed values due to statistical bias correction [31][32][33]37]. For instance, there was a 19.2% difference between biascorrected climate model simulation and observed annual precipitation, while the difference between raw climate model simulation and observed annual precipitation was 3.5% [32]. Such differences in the skill of bias correction techniques could be partly attributed to the quality of the observed data.…”

Section: Evaluation Of Bias Correction Techniquessupporting

confidence: 77%

“…In the southeastern USA, bias correction increased the difference between observed and simulated annual precipitation and overestimated the annual R-factor by an average of 137%. In contrast, non-bias-corrected data underestimated the R-factor by an average of 62% compared to the observed annual R-factor [32]. Statistical bias correction methods also have a limitation where there is high dependence on the quality of the observational data used to develop scale and shape parameters during bias correction [34].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, bias correction methods may trigger biases [31][32][33]. For instance, the bias-corrected Weather Research and Forecasting (WRF) RCM resulted in a larger wet bias than the non-bias-corrected WRF simulation over Canada and Central North America [33].…”

Section: Introductionmentioning

confidence: 99%

“…Statistical bias correction methods also have a limitation where there is high dependence on the quality of the observational data used to develop scale and shape parameters during bias correction [34]. Another limitation is that bias correction methods consider biases, and bias correction algorithms are stationary over time [20,32]. The stationarity and non-stationarity of the biasadjusting algorithms are to be evaluated in different climate conditions using differential split-sample testing (DSST) and split-sample testing (SST) [35,36] before their use for climate change scenario development and climate change impact assessment.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

2023

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This study evaluates the effect of the statistical bias correction techniques of distribution mapping and linear scaling on climate change signals and extreme rainfall indices under different climate change scenarios in the Jemma sub-basin of the Upper Blue Nile Basin. The mean, cumulative distribution function (CDF), mean absolute error (MAE), probability of wet days (Prwet (%)), and 90th percentile (X90 (mm)) of observed rainfall and the regional climate model (RCM) simulations of rainfall with and without statistical bias correction were compared with the historical climate (1981–2005). For future (2071–2100) climate scenarios, the change in climate signal and extreme rainfall indices in the RCM simulations with and without bias correction were also evaluated using different statistical metrics. The result showed that the statistical bias correction techniques effectively adjusted the mean annual and monthly RCM simulations of rainfall to the observed rainfall. However, distribution mapping is effective and better than linear scaling for adjusting the probability of wet days and the 90th percentile of RCM simulations. In future climate scenarios, RCM simulations showed an increase in rainfall. However, the statistically bias-adjusted RCM outputs revealed a decrease in rainfall, which indicated that the statistical bias correction techniques triggered a change in climate signal. Statistical bias correction methods also result in changes in the extreme rainfall indices, such as frequency of wet days (R1mm), number of heavy precipitation days (R10mm), number of very heavy rainfall days (R20mm), and other intensity and frequency indices.

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Section: Evaluation Of Bias Correction Techniquessupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

2023

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“…Despite these limitations, our multi-pronged data approach has allowed us substantial insights into the history and patterns of change in Bayou Pierre. (Easterling et al, 2017;Nieto Ferreira et al, 2018), and changing climate has already altered rates of erosion in watersheds globally (Goode et al, 2012;Takhellambam et al, 2023), suggesting further acceleration of erosional processes might be expected. Second, our results suggest that change in Bayou Pierre is more complicated than knickpoint retreat and channel incision, and management strategies should reflect this.…”

Section: The History Of Mississippimentioning

confidence: 99%

Historical and contemporary drivers of knickpoint retreat and morphological evolution along Bayou Pierre, Mississippi

Stearman,

Heitmuller

2024

River Research & Apps

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Channel incision in rivers can cause marked ecological and economic damages. This phenomenon is abundant and generally well documented along impacted streams in the loess hills adjacent to the Lower Mississippi River valley. Bayou Pierre, an ecologically important small tributary of the Mississippi River, is currently incising but causes are not well understood. In this study, we examine diverse data sources to: (1) reconstruct a history of erosional stimuli and possible origins and (2) examine effects of contemporary controls. Review of long‐term planform and land use data for the Mississippi River revealed episodic foreshortening events followed by episodic deforestation and reforestation. Hydrologic data suggest an increase in rainfall over the last few decades. Estimates of knickpoint retreat place origins prior to Mississippi River channel straightening (1929–1944). Planform analysis in three focal reaches of Bayou Pierre demonstrates slow change prior to 1982, but accelerated changes after those periods. Mean rainfall and 3‐day storm intensity correlate to some planform changes; however, the storm of 1983 may be a better explanation of sudden planform change. We found some evidence of potential internal feedback loops in patterns of bar growth. Together, our analyses provide a synthesis of stimuli experienced by Bayou Pierre over the last ~200 years and suggest both channel migration events before straightening of the Mississippi River and more recent hydrologic events have influenced patterns of geomorphic change in Bayou Pierre.

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Trend analysis and forecasting of meteorological variables in the lower Thoubal river watershed, India using non-parametrical approach and machine learning models

Rahaman

Saha

Masroor

et al. 2023

Model. Earth Syst. Environ.

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Projected mid-century rainfall erosivity under climate change over the southeastern United States

Cited by 16 publications

References 57 publications

Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

Historical and contemporary drivers of knickpoint retreat and morphological evolution along Bayou Pierre, Mississippi

Trend analysis and forecasting of meteorological variables in the lower Thoubal river watershed, India using non-parametrical approach and machine learning models

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