Uncertainty of Climate Change Impacts on Soil Erosion from Cropland in Central Oklahoma

Garbrecht, Jürgen; Nearing, M. A.; Zhang, X. C.; Steiner, Jean L.

doi:10.13031/aea.32.11613

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…The coefficient of dispersion increases in the future climate scenarios with respect to the reference scenario (Figure 6), meaning that soil erosion Here, we present the application of a soil erosion model ensemble to account for the uncertainty from model assumptions, which is suggested to be an important source of uncertainty in climate-change impact assessments on soil erosion (Shrestha et al, 2013;Garbrecht et al, 2016;Li & Fang, 2016;Op de Hipt et al, 2018 F I G U R E 3 Catchment-wide variability of the annual soil loss (Gg km −2 yr −1 ) for the reference scenario . The coloured boxes indicate the inter-quantile range (IQR) and the whiskers extend to ±1.…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…The soil erosion model uncertainty comes at the end of the modelling chain, which is also known as the 'uncertainty cascade' (Coulthard, Ramirez, Fowler, & Glenis, 2012). In the case of climate-change impact assessments, this cascade includes general circulation models, regional climate models, bias-correction methods, downscaling techniques, hydrological models and, ultimately, soil erosion models (Coulthard et al, 2012;Eekhout & de Vente, 2019;Garbrecht et al, 2016;Mondal et al, 2015;Op de Hipt et al, 2018;Scholz, Quinton, & Strauss, 2008;Simonneaux et al, 2015). Most recent climate-change impact assessments on soil erosion account for climate model uncertainty through the use of a climate model ensemble (GCM or RCM) (Eekhout & de Vente, 2019).…”

Section: Uncertainty Cascadementioning

confidence: 99%

“…By all means, this assumption depends on the size of the model ensemble and the range in model assumptions considered, where a larger model ensemble, consisting of a large variety of model assumptions, increases the confidence in the model outcome. Many sources of uncertainty are involved in climate change assessments on soil erosion, including the uncertainty arising from climate models, bias-correction methods and downscaling techniques (Eekhout & de Vente, 2019;Garbrecht, Nearing, Zhang, & Steiner, 2016;Mondal et al, 2015;Op de Hipt et al, 2018;Simonneaux et al, 2015). Soil erosion models are suggested to be a large source of uncertainty in climate-change impact assessments as well (Shrestha et al, 2013;Garbrecht et al, 2016;Li & Fang, 2016;Op de Hipt et al, 2018); however, only a few studies quantified this uncertainty.…”

mentioning

confidence: 99%

“…Many sources of uncertainty are involved in climate change assessments on soil erosion, including the uncertainty arising from climate models, bias-correction methods and downscaling techniques (Eekhout & de Vente, 2019;Garbrecht, Nearing, Zhang, & Steiner, 2016;Mondal et al, 2015;Op de Hipt et al, 2018;Simonneaux et al, 2015). Soil erosion models are suggested to be a large source of uncertainty in climate-change impact assessments as well (Shrestha et al, 2013;Garbrecht et al, 2016;Li & Fang, 2016;Op de Hipt et al, 2018); however, only a few studies quantified this uncertainty. For instance, Bussi et al (2016), Cho et al (2018) and Batista et al (2021) Govers, & Baudet, 1999).…”

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confidence: 99%

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A process‐based soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments

et al. 2021

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The impact of climate change on future soil loss is commonly assessed with soil erosion models, which are suggested to be an important source of uncertainty. Here, we present a novel soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments. The model ensemble consists of five continuous process‐based soil erosion models that run at a daily time step (i.e., DHSVM, HSPF, INCA, MMF, SHETRAN). The models were implemented in the SPHY hydrological model and simulate detachment by raindrop impact, detachment by runoff, and immediate deposition. The soil erosion model ensemble was applied in a semiarid catchment in the southeast of Spain. We applied three future climate scenarios based on global mean temperature rise (+1.5, +2 and +3°C). Data from two contrasting regional climate models were used to assess how an increase and a decrease in projected extreme precipitation affect model uncertainty. Soil loss is projected to increase (up to 95%) and decrease (up to −30%) under climate change, mostly reflecting the change in extreme precipitation. Model uncertainty is found to increase with increasing slope, extreme precipitation and runoff, which reveals some inherent differences in model assumptions among the five models. Moreover, the model uncertainty increases in all climate change scenarios, independent of the projected change in annual precipitation and extreme precipitation. This stresses the importance to consider model uncertainty through model ensembles of climate, hydrology, and soil erosion in climate‐change impact assessments.

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Section: Uncertainty Analysismentioning

confidence: 99%

Section: Uncertainty Cascadementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A process‐based soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments

et al. 2021

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“…These data were used to calibrate and validate the Water Erosion Prediction Project (WEPP) model to address various research questions 1984-19891, 5, 6, 7 Sharpley et al, 1991BPP, TP, PO, PI, TSP 1986-19875, 6, 7, 8 Sharpley et al, 1992TSP, PP, BAP, sediment 1985-1989All 8 Sharpley, 1993aBAP, DIP, DOP, TP 1988-1990 All 8 Sharpley, Daniel, & Edwards, 1993BAP, DIP, DOP, TP 1988-1990 All 8 Sharpley, Daniel, & Edwards, 1993 BPP, TSP, TP Unspecified Unspecified Sharpley, 1993bBAP, DIP, DOP, TP 1986-19875, 6, 7, 8 Robinson & Sharpley, 1994BAP, DIP, DOP, TP 1988-1990 All 8 Sharpley, Robinson, & Smith, 1995aTP, sediment 1977 All 8 Sharpley, Robinson, & Smith, 1995bBAP, DIP, DOP, TP 1988-1990 All 8 Note. BAP, bioavailable P; BPP, bioavailable particulate P; DIP, dissolved inorganic P; DOP, dissolved organic P; PI, inorganic P; PN, particulate N; PO, organic P; PP, particulate P; TKN, total Kjeldahl N; TN, total N; TP, total P; TRP, total reactive P; TSP, total soluble P. (Garbrecht, Nearing, Steiner, Zhang, & Nichols, 2015;Garbrecht, Nearing, Zhang, & Steiner, 2016;Zhang, 2016). The objectives of this paper are (a) to present and provide a description of management, water quantity, and water quality data collected in the WRE, (b) to provide summary analyses of the variability in each dataset, and (c) to provide details about how to access these datasets.…”

Section: Introductionmentioning

confidence: 99%

Runoff water quantity and quality data from native tallgrass prairie and crop–livestock systems in Oklahoma between 1977 and 1999

et al. 2020

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Erosion and sedimentation pose serious threats to soil and water quality worldwide, including in the U.S. southern Great Plains. To better understand these processes in agricultural landscapes, eight 1.6-ha watersheds were established and instrumented in 1976 at the USDA-ARS Grazinglands Research Laboratory, ∼50 km west of Oklahoma City near El Reno, OK, to measure precipitation and surface runoff quantity and quality. Prior to construction, all watersheds were in native grass, primarily big bluestem (Andropogon gerardii Vitman.), little bluestem [Schizachyrium scoparium (Michx.) Nash], and Indiangrass [Sorghastrum nutans (L.) Nash]; afterwards, four of the eight watersheds were cropped initially into winter wheat (Triticum aestivum L.) (two conventionally tilled and two minimally or no-till). Although there have been many peer-reviewed papers from the Water Resources and Erosion (WRE) watersheds, none included all the datasets collected during the period 1977-1999. The objectives of this paper were (a) to present and discuss all archived historical data, including methods of collection and analysis, (b) to provide summary analyses of the variability in each dataset, and (c) to provide details about how to access these datasets. These datasets are valuable resources to improve modeling in relation to land use and management changes, climate variability, and other environmental factors and may be useful in developing strategies to mitigate environmental impacts of agricultural systems. They are available at https://doi.org/10.15482/USDA.ADC/1518421.

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The LTAR Integrated Grazing Land Common Experiment at the Texas Gulf

Schantz,

Smith,

Harmel

et al. 2024

J of Env Quality

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Extreme weather and climate events have become more frequent and directly affect the ecological structure and function of integrated grazing lands. While the Great Plains have experienced a long history of regular disturbances from drought and floods, grazing, and fires, the increased frequency and magnitude of these disturbances can reduce ecological resilience, largely depending on management practices. Alternative strategies designed to adaptively manage grazing land resources based on the ecology of the system should increase the resistance and resilience to disturbances when compared to prevailing practices. Determining the ecologic and economic value of alternative strategies will require long‐term evaluations across large spatial scales. The Long‐Term Agroecosystem Research Network has been established to evaluate the differences between alternative and prevailing practices among 18 strategically located sites and across decadal time scales throughout the continental United States. A key integrated grazing land site within this network is the Texas Gulf located at the Riesel Watersheds in the Blackland Prairie of Central Texas. At this study site, the differences between alternative and prevailing grazing management strategies are now being evaluated. The alternative strategy was designed using a combination of knowledge of the site and species ecology with modern‐day tools and technologies. Alternatively, the prevailing practice implements a conventional year‐round continuous grazing system with heavy reliance on hay and supplemental protein during winter. Results will provide grazing land managers with economically viable adaptive management choices for increasing ecological resilience following extreme and frequent disturbance events.

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Uncertainty of Climate Change Impacts on Soil Erosion from Cropland in Central Oklahoma

Cited by 9 publications

References 34 publications

A process‐based soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments

A process‐based soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments

Runoff water quantity and quality data from native tallgrass prairie and crop–livestock systems in Oklahoma between 1977 and 1999

The LTAR Integrated Grazing Land Common Experiment at the Texas Gulf

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