Articles | Volume 19, issue 6
https://doi.org/10.5194/hess-19-2945-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-2945-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Projected changes in US rainfall erosivity
Lamont Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY, USA
R. Seager
Lamont Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY, USA
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PAGES Hydro2k Consortium
Clim. Past, 13, 1851–1900, https://doi.org/10.5194/cp-13-1851-2017, https://doi.org/10.5194/cp-13-1851-2017, 2017
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Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited due to a paucity of modern instrumental observations. We review how proxy records of past climate and climate model simulations can be used in tandem to understand hydroclimate variability over the last 2000 years and how these tools can also inform risk assessments of future hydroclimatic extremes.
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Subject: Water Resources Management | Techniques and Approaches: Uncertainty analysis
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Dissolved oxygen prediction using a possibility theory based fuzzy neural network
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Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong
Hydrol. Earth Syst. Sci., 28, 3739–3753, https://doi.org/10.5194/hess-28-3739-2024, https://doi.org/10.5194/hess-28-3739-2024, 2024
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Faced with the problem of uncertainty in the field of water resources management, this paper proposes the Copula Multi-objective Robust Optimization and Probabilistic Analysis of Robustness (CM-ROPAR) approach to obtain robust water allocation schemes based on the uncertainty of drought and wet encounters and the uncertainty of inflow. We believe that this research article not only highlights the significance of the CM-ROPAR approach but also provides a new concept for uncertainty analysis.
Laura Gil-García, Nazaret M. Montilla-López, Carlos Gutiérrez-Martín, Ángel Sánchez-Daniel, Pablo Saiz-Santiago, Josué M. Polanco-Martínez, Julio Pindado, and C. Dionisio Pérez-Blanco
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-61, https://doi.org/10.5194/hess-2024-61, 2024
Revised manuscript accepted for HESS
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This paper presents an interdisciplinary model for quantifying uncertainties in water allocation under climate change. It combines climate, hydrological, and microeconomic experiments with a decision support system. Multi-model analyses reveal potential futures for water management policies, emphasizing nonlinear climate responses. As illustrated in the Douro River Basin, minor water allocation changes have significant economic impacts, stresssing the need for adaptation strategies.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
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Marina R. L. Mautner, Laura Foglia, and Jonathan D. Herman
Hydrol. Earth Syst. Sci., 26, 1319–1340, https://doi.org/10.5194/hess-26-1319-2022, https://doi.org/10.5194/hess-26-1319-2022, 2022
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Alessandro Amaranto, Dinis Juizo, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 26, 245–263, https://doi.org/10.5194/hess-26-245-2022, https://doi.org/10.5194/hess-26-245-2022, 2022
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Thibaut Lachaut and Amaury Tilmant
Hydrol. Earth Syst. Sci., 25, 6421–6435, https://doi.org/10.5194/hess-25-6421-2021, https://doi.org/10.5194/hess-25-6421-2021, 2021
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Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, https://doi.org/10.5194/hess-22-5387-2018, 2018
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Seungwoo Chang, Wendy Graham, Jeffrey Geurink, Nisai Wanakule, and Tirusew Asefa
Hydrol. Earth Syst. Sci., 22, 4793–4813, https://doi.org/10.5194/hess-22-4793-2018, https://doi.org/10.5194/hess-22-4793-2018, 2018
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It is important to understand potential impacts of climate change and human water use on streamflow and groundwater levels. This study used climate models with an integrated hydrologic model to project future streamflow and groundwater level in Tampa Bay for a variety of future water use scenarios. Impacts of different climate projections on streamflow were found to be much stronger than the impacts of different human water use scenarios, but both were significant for groundwater projection.
Jessica E. Cherry, Corrie Knapp, Sarah Trainor, Andrea J. Ray, Molly Tedesche, and Susan Walker
Hydrol. Earth Syst. Sci., 21, 133–151, https://doi.org/10.5194/hess-21-133-2017, https://doi.org/10.5194/hess-21-133-2017, 2017
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We know that climate is changing quickly in the Far North (the Arctic and sub-Arctic). Hydropower continues to grow in this region because water resources are perceived to be plentiful. However, with changes in glacier extent and permafrost, and more extreme events, will those resources prove reliable into the future? This study amasses the evidence that quantitative hydrology modeling and uncertainty assessment have matured to the point where they should be used in water resource planning.
Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
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We show that the derived distribution approach is able to characterize the interannual variability of precipitation much better than fitting a probabilistic model to annual rainfall totals, as long as continuously gauged data are available. The method is a useful tool for describing temporal changes in the distribution of annual rainfall, as it works for records as short as 5 years, and therefore does not require any stationarity assumption over long periods.
Usman T. Khan and Caterina Valeo
Hydrol. Earth Syst. Sci., 20, 2267–2293, https://doi.org/10.5194/hess-20-2267-2016, https://doi.org/10.5194/hess-20-2267-2016, 2016
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This paper contains a new two-step method to construct fuzzy numbers using observational data. In addition an existing fuzzy neural network is modified to account for fuzzy number inputs. This is combined with possibility-theory based intervals to train the network. Furthermore, model output and a defuzzification technique is used to estimate the risk of low Dissolved Oxygen so that water resource managers can implement strategies to prevent the occurrence of low Dissolved Oxygen.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
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We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
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Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
C. A. Scott, S. Vicuña, I. Blanco-Gutiérrez, F. Meza, and C. Varela-Ortega
Hydrol. Earth Syst. Sci., 18, 1339–1348, https://doi.org/10.5194/hess-18-1339-2014, https://doi.org/10.5194/hess-18-1339-2014, 2014
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
D. Zhu, D. Z. Peng, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 1445–1453, https://doi.org/10.5194/hess-17-1445-2013, https://doi.org/10.5194/hess-17-1445-2013, 2013
B. L. Harding, A. W. Wood, and J. R. Prairie
Hydrol. Earth Syst. Sci., 16, 3989–4007, https://doi.org/10.5194/hess-16-3989-2012, https://doi.org/10.5194/hess-16-3989-2012, 2012
J.-S. Yang, E.-S. Chung, S.-U. Kim, and T.-W. Kim
Hydrol. Earth Syst. Sci., 16, 801–814, https://doi.org/10.5194/hess-16-801-2012, https://doi.org/10.5194/hess-16-801-2012, 2012
S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
Hydrol. Earth Syst. Sci., 15, 505–518, https://doi.org/10.5194/hess-15-505-2011, https://doi.org/10.5194/hess-15-505-2011, 2011
Cited articles
Alexander, L., Zhang, X., Peterson, T., Caesar, J., Gleason, B., Tank, A., Haylock, M., Collins, D., Trewin, B., and Rahimzadeh, F.: Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006.
Angel, J. R., Palecki, M. A., and Hollinger, S. E.: Storm precipitation in the United States. Part II: Soil erosion characteristics, J. Appl. Meteorol., 44, 947–959, 2005.
Arnoldus, H. M. J.: An approximation of the rainfall factor in the Universal Soil Loss Equation, in: Assessment of Erosion, edited by: De Boodt, M. and Gabriels, D., 127–132, Chichester, New York, 1980.
Asadieh, B. and Krakauer, N. Y.: Global trends in extreme precipitation: climate models versus observations, Hydrol. Earth Syst. Sci., 19, 877–891, https://doi.org/10.5194/hess-19-877-2015, 2015.
Biasutti, M., Sobel, A. H., Camarago, S. J., and Creyts, T. T.: Projected Changes in the Physical Climate of the Gulf Coast and Caribbean, Climatic Change, 112–, 819–845, https://doi.org/10.1007/s10584-011-0254-y, 2011.
Bridges, E. M. and Oldeman, L. R.: Global Assessment of Human-Induced Soil Degradation, Arid Soil Res. Rehabil., 13, 319–325, 1999.
Chou, C. and Lan, C.-W.: Changes in the Annual Range of Precipitation under Global Warming, J. Climate, 25, 222–235, 2012.
Christensen, J., Kumar, K. K., Aldrian, E., An, S. I., Cavalcanti, I. F. A., de Castro, M., Dong, W., Goswami, P., Hall, A., Kanyanga, J. K., Kitoh, A., Kossin, J., Lau, N. C., Renwick, J., Stephenson, D. B., Xie, S.-P., and Zhou, T.: Climate Phenomena and their Relevance for Future Regional Climate Change, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., 1–92, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.
Dabney, S. M., Yoder, D. C., and Vieira, D. A. N.: The application of the Revised Universal Soil Loss Equation, Version 2, to evaluate the impacts of alternative climate change scenarios on runoff and sediment yield, J. Soil Water Conserv., 67, 343–353, 2012.
Dai, A.: Precipitation characteristics in eighteen coupled climate models, J. Climate, 19, 4605–4630, 2006.
Daly, C. and Taylor, G. H.: Development of New Spatial Grids of R-factor and 10-yr EI30 for the Conterminous United States, Internal EPA Report, NERL-LV. Spatial Climate Analysis Service, Oregon State University, Corvallis, Oregon, 2002.
Daly, C., Gibson, W. P., Taylor, G. H., Johnson, G. L., and Pasteris, P.: A knowledge-based approach to the statistical mapping of climate, Clim. Res., 22, 99–113, 2002.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031–2064, 2008.
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate change projections: the role of internal variability, Clim. Dynam., 38, 527–546, https://doi.org/10.1007/s00382-010-0977-x, 2010.
Dissmeyer, G. E. and Foster, G. R.: A guide for predicting sheet and rill erosion on forestland., Tech. Rep. Technical Publication SA-TP-11, 1980.
Fournier: Climat et Erosion, Presses Universitaires de France, Paris, 1960.
Gyssels, G., Poesen, J., Bochet, E., and Li, Y.: Impact of plant roots on the resistance of soils to erosion by water: a review, Progr. Phys. Geogr., 29, 189–217, 2005.
Hollinger, S. E., Angel, J. R., and Palecki, M. A.: Spatial Distribution, Variation, and Trends in Storm Precipitation Characteristics Associated with Soil Erosion in the United States, Tech. Rep., 2002.
Judson, S. and Ritter, D. F.: Rates of regional denudation in the United States, J. Geophys. Res., 69, 3395–3401, 1964.
Lenderink, G., Mok, H. Y., Lee, T. C., and van Oldenborgh, G. J.: Scaling and trends of hourly precipitation extremes in two different climate zones – Hong Kong and the Netherlands, Hydrol. Earth Syst. Sci., 15, 3033–3041, https://doi.org/10.5194/hess-15-3033-2011, 2011.
Maloney, E. D., Camargo, S. J., Chang, E., Colle, B., Fu, R., Geil, K. L., Hu, Q., Jiang, X., Johnson, N., Karnauskas, K. B., Kinter, J., Kirtman, B., Kumar, S., Langenbrunner, B., Lombardo, K., Long, L. N., Mariotti, A., Meyerson, J. E., Mo, K. C., Neelin, J. D., Pan, Z., Seager, R., Serra, Y., Seth, A., Sheffield, J., Stroeve, J., Thibeault, J., Xie, S.-P., Wang, C., Wyman, B., and Zhao, M.: North American Climate in CMIP5 Experiments: Part III: Assessment of Twenty-First-Century Projections*, J. Climate, 27, 2230–2270, 2014.
Maurer, E. P., Hidalgo, H. G., Das, T., Dettinger, M. D., and Cayan, D. R.: The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California, Hydrol. Earth Syst. Sci., 14, 1125–1138, https://doi.org/10.5194/hess-14-1125-2010, 2010.
Maurer, E., Brekke, L., Pruitt, T., Thrasher, B., Long, J., Duffy, P., Dettinger, M., Cayan, D., and Arnold, J.: An Enhanced Archive Facilitating Climate Impacts and Adaptation Analysis, B. Am. Meteorol. Soc., 95, 1011–1019, 2014.
Maurer, E. P., Brekke, L., Pruitt, T., and Duffy, P. B.: Fine-resolution climate projections enhance regional climate change impact studies, Eos Trans. AGU, 88, 504–504, 2007.
Montgomery, D. R.: Soil erosion and agricultural sustainability, Proc. Natl. Acad. Sci., 104, 13268–13272, 2007.
Mullan, D., Favis-Mortlock, D., and Fealy, R.: Addressing key limitations associated with modelling soil erosion under the impacts of future climate change, Agr. Forest Meteorol., 156, 18–30, 2012.
Nearing, M. A.: Potential changes in rainfall erosivity in the U.S. with climate change during the 21st century, J. Soil Water Conserv., 56, 229–232, 2001.
Nearing, M. A., Pruski, F. F., and O'neal, M. R.: Expected climate change impacts on soil erosion rates: a review, J. Soil Water Conserv., 59, 43–50, 2004.
Nearing, M. A., Jetten, V., Baffaut, C., Cerdan, O., Couturier, A., Hernandez, M., Le Bissonnais, Y., Nichols, M. H., Nunes, J. P., Renschler, C. S., Souchère, V., and van Oost, K.: Modeling response of soil erosion and runoff to changes in precipitation and cover, CATENA, 61, 131–154, 2005.
O'Gorman, P. A.: Sensitivity of tropical precipitation extremes to climate change, Nat. Geosci., 5, 697–700, 2012.
O'Gorman, P. A. and Schneider, T.: The physical basis for increases in precipitation extremes in simulations of 21st-century climate change, Proc. Natl. Acad. Sci., 106, 14773–14777, https://doi.org/10.1073/pnas.0907610106, 2009.
Palecki, M. A., Angel, J. R., and Hollinger, S. E.: Storm Precipitation in the United States. Part I: Meteorological Characteristics, J. Appl. Meteorol., 44, 933–946, 2005.
Pruski, F. F. and Nearing, M. A.: Climate-induced changes in erosion during the 21st century for eight U.S. locations, Water Resour. Res., 38, 34-1–34-11, 2002.
Renard, K. G. and Freimund, J. R.: Using monthly precipitation data to estimate the R-factor in the revised USLE, J. Hydrol., 157, 287–306, 1994.
Schönbrodt-Stitt, S., Bosch, A., Behrens, T., Hartmann, H., Shi, X., and Scholten, T.: Approximation and spatial regionalization of rainfall erosivity based on sparse data in a mountainous catchment of the Yangtze River in Central China, Environ. Sci. Pollut. Res., 20, 6917–6933, 2013.
Seager, R., Ting, M., Held, I., Kushnir, Y., Lu, J., Vecchi, G., Huang, H., Harnik, N., Leetmaa, A., Lau, N., et al.: Model Projections of an Imminent Transition to a More Arid Climate in Southwestern North America, Science, 316, 1181–1184, https://doi.org/10.1126/science.1139601, 2007.
Seager, R., Neelin, D., Simpson, I., Liu, H., Henderson, N., Shaw, T., Kushnir, Y., Ting, M., and Cook, B.: Dynamical and Thermodynamical Causes of Large-Scale Changes in the Hydrological Cycle over North America in Response to Global Warming*, J. Climate, 27, 7921–7948, 2014.
Segura, C., Sun, G., McNulty, S., and Zhang, Y.: Potential impacts of climate change on soil erosion vulnerability across the conterminous United States, J. Soil Water Conserv., 69, 171–181, 2014.
Sillmann, J., Kharin, V., Zwiers, F., Zhang, X., and Bronaugh, D.: Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections, J. Geophys. Res.-Atmos., 118, 2473–2493, 2013.
Smithen, A. A. and Schulze, R. E.: The spatial distribution in southern Africa of rainfall erosivity for use in the Universal Soil Loss Equation, Water SA, 8, 74–78, 1982.
Soil and Water Conservation Society: Conservation Implications of Climate Change: Soil Erosion and Runoff from Cropland, Tech. Rep., 2003.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Tebaldi, C., Hayhoe, K., Arblaster, J., and Meehl, G.: Going to the extremes, Climatic Change, 79, 185–211, 2006.
Trenberth, K. E.: Conceptual framework for changes of extremes of the hydrological cycle with climate change, Climatic Change, 42, 327–339, 1999.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1217, 2003.
USDA Agricultural Research Service: Revised Universal Soil Loss Equation Version 2 (RUSLE2), Tech. Rep., 2013.
van Dijk, A. I. J. M., Bruijnzeel, L. A., and Rosewell, C. J.: Rainfall intensity-kinetic energy relationships: a critical literature appraisal, J. Hydrol., 261, 1–23, 2002.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall-erosion losses from cropland east of the Rocky Mountains, Vol. 282 of Agriculture Handbooks, U.S. Department of Agriculture, Agricultural Research Service, 1965.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall erosion losses-A guide to conservation planning, Vol. 537 of Agriculture Handbooks, U.S. Department of Agriculture, Agricultural Research Service, 1978.
Wuebbles, D., Meehl, G., Hayhoe, K., Karl, T. R., Kunkel, K., Santer, B., Wehner, M., Colle, B., Fischer, E. M., Fu, R., Goodman, A., Janssen, E., Kharin, V., Lee, H., Li, W., Long, L. N., Olsen, S. C., Pan, Z., Seth, A., Sheffield, J., and Sun, L.: CMIP5 Climate Model Analyses: Climate Extremes in the United States, B. Am. Meteorol. Soc., 95, 571–583, 2014.
Yang, D., Kanae, S., Oki, T., Koike, T., and Musiake, K.: Global potential soil erosion with reference to land use and climate changes, Hydrol. Process., 17, 2913–2928, 2003.
Zhang, X. C.: A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments, Climatic Change, 84, 337–363, 2007.
Zhang, X. C., Liu, W. Z., Li, Z., and Chen, J.: Trend and uncertainty analysis of simulated climate change impacts with multiple GCMs and emission scenarios, Agr. Forest Meteorol., 151, 1297–1304, 2011.
Zhang, Y., Hernandez, M., Anson, E., Nearing, M. A., Wei, H., Stone, J. J., and Heilman, P.: Modeling climate change effects on runoff and soil erosion in southeastern Arizona rangelands and implications for mitigation with conservation practices, J. Soil Water Conserv., 67, 390–405, 2012.
Short summary
We estimate future changes in US erosivity from the most recent ensemble projections of daily and monthly rainfall accumulation. The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations, and leads to changes of uncertain sign in parts of the south.
We estimate future changes in US erosivity from the most recent ensemble projections of daily...