Projected rainfall erosivity changes under climate change from multimodel and multiscenario projections in Northeast China

The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI 30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI 30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI 30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.

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“…It indicates the applicability of the use of synthetic rainfall series to estimate the R factor, corroborating Zhang et al (2010).…”

Section: Dif = Percentage Difference Of the Values Computed For Eachsupporting

confidence: 72%

“…yu (2002) and Zhang et al (2008bZhang et al ( , 2010) assessed the ability of stochastic weather generators to generate daily rainfall synthetic series used to calculate the R factor. These generators have the potential to be used in Brazil to extend the rainfall erosivity index database.…”

Section: Introductionmentioning

confidence: 99%

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Cecílio

Moreira

Pezzopane

et al. 2013

An. Acad. Bras. Ciênc.

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“…The impact of the climate change on frequency and extent of soil erosion processes has been estimated by several scientists (e.g. Klik and Eitzinger, 2010;Zhang et al, 2010;Mullan et al, 2012;Nunes et al, 2013). They emphasized fundamental limitations of several previous studies: the spatial and the temporal scale at which climate changes are represented; changes in land use and management.…”

Section: Resultsmentioning

confidence: 99%

Soil Erosion in the River Basin of Provala, Montenegro

Spalević¹,

Čurović²,

Barovic³

et al. 2015

AgricultForest

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Use of models in the research of soil erosion processes is at the moment a standard practice and models are necessary tool for better understanding of sediment production and runoff, although their interpretation is limited by large uncertainties, including model parameter uncertainties. In this research we studied the main ecological factors and anthropogenic influences in sediment yield on the Provala basin of Montenegro analyzing the relationships between the physical-geographical, geological, soil, land use, climate characteristics and soil erosion intensity. Real soil losses, G yr , were calculated on 7541 m 3 yr -1 , specific on 597 m 3 km -2 yr -1 . The value of Z coefficient was calculated on 0.482 what indicates that the river basin belongs to the 3 rd destruction category; erosion process is medium. There is a possibility for large flood waves to appear in the studied basin. The results presented in this paper can provide decision support for watershed managers about where the best conservation measures can be implemented effectively and at low cost in the studied area. The methods we used in this study can also be of interest in sediment modeling for other basins in the Region.

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“…Monthly mean precipitation (P, mm) and temperature (T, • C) for observed weather at the Topeka airport weather station 147007 , and weather adjustments (∆P, ∆T) from late-20th century historical period ) to mid-21st century projection (2046−2065, + indicates increase in future) for six climate change scenarios: 1a, 10% increase in precipitation; 1b, 10% decrease in precipitation; 2, ensemble means of 15 GCMs; 3ww, ensemble means of 4 GCMs with wetter spring and wetter summer; 3dd, ensemble means of 4 GCMs with drier spring and drier summer; 3wd, ensemble means of 6 GCMs with wetter spring and drier summer. The baseline scenario for the mid-21st century assumes no monthly changes in future temperature and precipitation from the late-20th century historical dataset [11,13,14,45]. The normalizing factor was assumed to be equal to unity.…”

Section: Future Scenariosmentioning

confidence: 99%

“…One family of downscaling methods involves a statistical approach of applying smaller-scale climatic observations to bias-correct the larger-scale GCM data [7,10,11]. Stochastic weather generators can be used to generate numerous instances of future daily climate variables by adjusting historical weather patterns based on GCM projections for mid-21st or late-21st century periods [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models

Sheshukov

Douglas-Mankin

2017

Climate

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Future climate forcing data at the temporal and spatial scales needed to drive hydrologic models are not readily available. Simple methods to derive these data from historical data or General Circulation Model (GCM) results may not adequately capture future hydrological variability. This study assessed streamflow response to daily future climate forcing data produced by a new method using subsets of multi-model GCM ensembles for the mid-21st century period in northeast Kansas. Daily timeseries of precipitation and temperature were developed for six future climate scenarios: stationary, uniform 10% changes in precipitation; shifts based on a 15-GCM ensemble-mean; and shifts based on three seasonally-consistent subsets of GCMs representing Spring-Summer combinations that were wetter or drier than the historical period. The analysis of daily streamflow and hydrologic index statistics were conducted. Stationary 10% precipitation shifts generally bounded the monthly mean streamflow projections of the other scenarios, and the 15-GCM ensemble-mean captured non-stationary effects of annual and seasonal hydrological response, but did not identify important intra-annual shifts in drought and flood characteristics. The seasonally-consistent subset ensembles produced a range of distinct monthly streamflow trends, particularly for extreme low-flow and high-flow events. Meaningful water management and planning for the future will require hydrological impact simulations that reflect the range of possible future climates. Use of GCM ensemble-mean climate forcing data without consideration of the range of seasonal patterns among models was demonstrated to remove important seasonal hydrologic patterns that were retained in the subset ensemble-mean approach.

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Projected rainfall erosivity changes under climate change from multimodel and multiscenario projections in Northeast China

Cited by 111 publications

References 24 publications

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Soil Erosion in the River Basin of Provala, Montenegro

Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models

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