Potential changes in rainfall erosivity under GCM climate change scenarios for the southern Appalachian region, USA

Hoomehr, Siavash; Schwartz, John S.; Yoder, Daniel C.

doi:10.1016/j.catena.2015.01.012

Cited by 46 publications

(21 citation statements)

References 35 publications

(48 reference statements)

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“…CLIGEN is a stochastic weather generator established by the United States Department of Agriculture (USDA) to provide a climate input for Water Erosion Prediction Project(WEPP) model [14,15], which includes daily precipitation, precipitation pattern, maximum and minimum temperature, dew temperature, wind direction, wind speed and solar radiation [16]. It has been used widely to downscale the monthly data of GCM outputs [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Shao

Liu

et al. 2020

Water

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Global Climate Models (GCMs) can provide essential meteorological data as inputs for simulating and assessing the impact of climate change on catchment hydrology. However, downscaling of GCM outputs is often required due to their coarse spatial and temporal resolution. As an effective downscaling method, stochastic weather generators can reproduce daily sequences with statistically similar statistical characteristics. Most weather generators can only simulate single-site meteorological data, which are spatially uncorrelated. Therefore, this study introduces a method for multi-site precipitation downscaling based on a combination of a single-site stochastic weather generator, CLIGEN (CLImate GENerator), and a modified shuffle procedure constrained with multi-model ensemble GCM monthly precipitation outputs. The applicability of the downscaling method is demonstrated in the Huangfuchuan Basin (arid to semi-arid climate) for a historical period (1976–2005) and a projection period (2021–2070, historical, the representative concentration path (RCP) 2.6, RCP4.5, RCP4.8 scenarios) to generate spatially correlated daily precipitation. The results show that the proposed downscaling method can accurately simulate the mean of daily, monthly and annual precipitation and the wet spell lengths, and the inter-station correlation among 10 sites in the basin. In addition, this combination method generated the projected precipitation and showed an increasing trend for future years. These findings could help us better cope with the potential risks of climate change.

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Section: Introductionmentioning

confidence: 99%

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Shao

Liu

et al. 2020

Water

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“…Climate change can affect soil erosion through changes to precipitation patterns [25]. The effects of climate change on soil erosion have been investigated using different methods [26][27][28]. In arid and semi-arid regions of Iran, climate change may significantly affect the amount of soil loss due to water erosion [29].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

et al. 2019

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Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha−1 h−1y−1 in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha−1 y−1, which will generate 5.52 t ha−1 y−1 sediment. The difference between estimated and observed sediment was 1.42 t ha−1 year−1 at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.

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“…; Hoomehr et al. ) to overcome this limitation. A frequently used alternative to the DC method is the QM method, which explicitly accounts for changes in the precipitation distribution.…”

Section: Discussionmentioning

confidence: 99%

The implications of bias correction methods and climate model ensembles on soil erosion projections under climate change

Eekhout

Vente

2019

Earth Surf Processes Landf

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Climate change will most likely cause an increase in extreme precipitation and consequently an increase in soil erosion in many locations worldwide. In most cases, climate model output is used to assess the impact of climate change on soil erosion; however, there is little knowledge of the implications of bias correction methods and climate model ensembles on projected soil erosion rates. Using a soil erosion model, we evaluated the implications of three bias correction methods (delta change, quantile mapping and scaled distribution mapping) and climate model selection on regional soil erosion projections in two contrasting Mediterranean catchments. Depending on the bias correction method, soil erosion is projected to decrease or increase. Scaled distribution mapping best projects the changes in extreme precipitation. While an increase in extreme precipitation does not always result in increased soil loss, it is an important soil erosion indicator. We suggest first establishing the deviation of the bias‐corrected climate signal with respect to the raw climate signal, in particular for extreme precipitation. Furthermore, individual climate models may project opposite changes with respect to the ensemble average; hence climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that the impact of climate change on soil erosion can only accurately be assessed with a bias correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models. © 2018 John Wiley & Sons, Ltd.

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Potential changes in rainfall erosivity under GCM climate change scenarios for the southern Appalachian region, USA

Cited by 46 publications

References 35 publications

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

The implications of bias correction methods and climate model ensembles on soil erosion projections under climate change

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