• Arndt, D. S., J. B. Basara, R. A. McPherson, B. G. Illston, G. D. McManus, and D. B. Demko, 2009: Observations of the overland reintensification of Tropical Storm Erin (2007). Bull. Amer. Meteor. Soc., 90, 10791093, doi:10.1175/2009BAMS2644.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ayers, J., D. L. Ficklin, I. T. Stewart, and M. Strunk, 2016: Comparison of CMIP3 and CMIP5 projected hydrologic conditions over the upper Colorado River basin. Int. J. Climatol., 36, 38073818, doi:10.1002/joc.4594.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boone, K. M., R. A. McPherson, M. B. Richman, and D. J. Karoly, 2012: Spatial coherence of rainfall variations using the Oklahoma Mesonet. Int. J. Climatol., 32, 843853, doi:10.1002/joc.2322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cannon, A. J., S. R. Sobie, and T. Q. Murdock, 2015: Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? J. Climate, 28, 69386959, doi:10.1175/JCLI-D-14-00754.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C.-T., and T. Knutson, 2008: On the verification and comparison of extreme rainfall indices from climate models. J. Climate, 21, 16051621, doi:10.1175/2007JCLI1494.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Das, T., E. P. Maurer, D. W. Pierce, M. D. Dettinger, and D. R. Cayan, 2013: Increases in flood magnitudes in California under warming climates. J. Hydrol., 501, 101110, doi:10.1016/j.jhydrol.2013.07.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douglas, E. M., R. M. Vogel, and C. N. Kroll, 2000: Trends in floods and low flows in the United States: Impact of spatial correlation. J. Hydrol., 240, 90105, doi:10.1016/S0022-1694(00)00336-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaitán, C. F., 2016a: 1/10th of a degree observation-based dataset, version 1.0. University of Oklahoma Libraries, accessed 10 February 2016, doi:10.15763/DBS.SCCSC.RR.0001.

    • Crossref
    • Export Citation
  • Gaitán, C. F., 2016b: Downscaled climate variables from CCSM4 GCM, version 1.0. University of Oklahoma Libraries, accessed 10 February 2016, doi:10.15763/DBS.SCCSC.RR.0002.

    • Crossref
    • Export Citation
  • Gaitán, C. F., 2016c: Downscaled climate variables from MIROC5 GCM, version 1.0. University of Oklahoma Libraries, accessed 10 February 2016, doi:10.15763/DBS.SCCSC.RR.0003.

    • Crossref
    • Export Citation
  • Gaitán, C. F., 2016d: Downscaled climate variables from MPI-ESM-LR GCM, version 1.0. University of Oklahoma Libraries, accessed 10 February 2016, doi:10.15763/DBS.SCCSC.RR.0004.

    • Crossref
    • Export Citation
  • Gaitán, C. F., 2016e: Statistically downscaled time series for the Red River basin, version 1.0. University of Oklahoma Libraries, accessed 10 February 2016, doi:10.15763/DBS.SCCSC.RR.

    • Crossref
    • Export Citation
  • Gaitán, C. F., W. W. Hsieh, and A. J. Cannon, 2014: Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada. Climate Dyn., 43, 32013217, doi:10.1007/s00382-014-2098-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

  • Giorgetta, M. A., and Coauthors, 2013: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst., 5, 572597, doi:10.1002/jame.20038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., Y. Hong, Z. L. Flamig, L. Li, and J. Wang, 2010: Intercomparison of rainfall estimates from radar, satellite, gauge, and combinations for a season of record rainfall. J. Appl. Meteor. Climatol., 49, 437452, doi:10.1175/2009JAMC2302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Groisman, P. Y., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. N. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Climate, 18, 13261350, doi:10.1175/JCLI3339.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haerter, J. O., B. Eggert, C. Moseley, C. Piani, and P. Berg, 2015: Statistical precipitation bias correction of gridded model data using point measurements. Geophys. Res. Lett., 42, 19191929, doi:10.1002/2015GL063188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2011: The potential to narrow uncertainty in projections of regional precipitation change. Climate Dyn., 37, 407418, doi:10.1007/s00382-010-0810-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ho, C. K., D. B. Stephenson, M. Collins, C. A. T. Ferro, and S. J. Brown, 2012: Calibration strategies: A source of additional uncertainty in climate change projections. Bull. Amer. Meteor. Soc., 93, 2126, doi:10.1175/2011BAMS3110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janssen, E., R. L. Sriver, D. J. Wuebbles, and K. E. Kunkel, 2016: Seasonal and regional variations in extreme precipitation event frequency using CMIP5. Geophys. Res. Lett., 43, 53855393, doi:10.1002/2016GL069151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharel, G., and A. Kirilenko, 2015: Considering climate change in the estimation of long-term flood risks of Devils Lake in North Dakota. J. Amer. Water Resour. Assoc., 51, 12211234, doi:10.1111/1752-1688.12300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, A. K., and Coauthors, 2008: Consequences of more extreme precipitation regimes for terrestrial ecosystems. BioScience, 58, 811821, doi:10.1641/B580908.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., F. Zeng, and A. T. Wittenberg, 2014: Seasonal and annual mean precipitation extremes occurring during 2013: A U.S. focused analysis [in “Explaining Extreme Events of 2013 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 95 (9), S19S23.

    • Search Google Scholar
    • Export Citation
  • Li, H., J. Sheffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res., 115, D10101, doi:10.1029/2009JD012882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, L., and Coauthors, 2012: Analyzing projected changes and trends of temperature and precipitation in the southern USA from 16 downscaled global climate models. Theor. Appl. Climatol., 109, 345360, doi:10.1007/s00704-011-0567-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livneh, B., E. A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. M. Andreadis, E. P. Maurer, and D. P. Lettenmaier, 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, 26, 93849392, doi:10.1175/JCLI-D-12-00508.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and Coauthors, 2014: North American climate in CMIP5 experiments: Part III: Assessment of twenty-first-century projections. J. Climate, 27, 22302270, doi:10.1175/JCLI-D-13-00273.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., and D. W. Pierce, 2014: Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean. Hydrol. Earth Syst. Sci., 18, 915925, doi:10.5194/hess-18-915-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 32373251, doi:10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., L. Brekke, T. Pruitt, and P. B. Duffy, 2007: Fine-resolution climate projections enhance regional climate change impact studies. Eos, Trans. Amer. Geophys. Union, 88, 504, doi:10.1029/2007EO470006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCabe, G. J., and D. M. Wolock, 2002: Trends and temperature sensitivity of moisture conditions in the conterminous United States. Climate Res., 20, 1929, doi:10.3354/cr020019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., D. P. Lettenmaier, and S. McGinnis, 2015: Uses of results of regional climate model experiments for impacts and adaptation studies: The example of NARCCAP. Curr. Climate Change Rep., 1, 19, doi:10.1007/s40641-015-0004-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michelangeli, P. A., M. Vrac, and H. Loukos, 2009: Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophys. Res. Lett., 36, L11708, doi:10.1029/2009GL038401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378381, doi:10.1038/nature09763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ng, G.-H. C., D. McLaughlin, D. Entekhabi, and B. R. Scanlon, 2010: Probabilistic analysis of the effects of climate change on groundwater recharge. Water Resour. Res., 46, W07502, doi:10.1029/2009WR007904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiao, L., Y. Hong, S. Chen, C. B. Zou, J. J. Gourley, and B. Yong, 2014a: Performance assessment of the successive version 6 and version 7 TMPA products over the climate-transitional zone in the southern Great Plains, USA. J. Hydrol., 513, 446456, doi:10.1016/j.jhydrol.2014.03.040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiao, L., Y. Hong, R. McPherson, M. Shafer, D. Gade, D. Williams, S. Chen, and D. Lilly, 2014b: Climate change and hydrological response in the trans-state Oologah Lake watershed—Evaluating dynamically downscaled NARCCAP and statistically downscaled CMIP3 simulations with VIC model. Water Resour. Manage., 28, 32913305, doi:10.1007/s11269-014-0678-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiao, L., Z. Pan, R. B. Herrmann, and Y. Hong, 2014c: Hydrological variability and uncertainty of lower Missouri River basin under changing climate. J. Amer. Water Resour. Assoc., 50, 246260, doi:10.1111/jawr.12126.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenberg, N. J., R. A. Brown, R. C. Izaurralde, and A. M. Thomson, 2003: Integrated assessment of Hadley Centre (HadCM2) climate change projections on agricultural productivity and irrigation water supply in the conterminous United States: I. Climate change scenarios and impacts on irrigation water supply simulated with the HUMUS model. Agric. For. Meteor., 117, 7396, doi:10.1016/S0168-1923(03)00025-X.

    • Search Google Scholar
    • Export Citation
  • Ruiz Castillo, N., and C. F. Gaitán Ospina, 2016: Projecting future change in growing degree days for winter wheat. Agriculture, 6, 47, doi:10.3390/agriculture6030047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scanlon, B. R., J. B. Gates, R. C. Reedy, W. A. Jackson, and J. P. Bordovsky, 2010a: Effects of irrigated agroecosystems: 2. Quality of soil water and groundwater in the southern high plains, Texas. Water Resour. Res., 46, W09538, doi:10.1029/2009WR008428.

    • Search Google Scholar
    • Export Citation
  • Scanlon, B. R., R. C. Reedy, and J. B. Gates, 2010b: Effects of irrigated agroecosystems: 1. Quantity of soil water and groundwater in the southern high plains, Texas. Water Resour. Res., 46, W09537, doi:10.1029/2009WR008427.

    • Search Google Scholar
    • Export Citation
  • Shafer, M., D. Ojima, J. M. Antle, D. Kluck, R. A. McPherson, S. Petersen, B. Scanlon, and K. Sherman, 2014: Great Plains. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, T. C. Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 441–461, doi:10.7930/J0D798BC.

    • Crossref
    • Export Citation
  • Sheffield, J., and Coauthors, 2013: North American climate in CMIP5 experiments. Part I: Evaluation of historical simulations of continental and regional climatology. J. Climate, 26, 92099245, doi:10.1175/JCLI-D-12-00592.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stone, M. C., R. H. Hotchkiss, and L. O. Mearns, 2003: Water yield responses to high and low spatial resolution climate change scenarios in the Missouri River basin. Geophys. Res. Lett., 30, 1186, doi:10.1029/2002GL016122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takle, E. S., M. Jha, E. Lu, R. W. Arritt, and W. J. Gutowski, 2010: Streamflow in the upper Mississippi River basin as simulated by SWAT driven by 20th century contemporary results of global climate models and NARCCAP regional climate models. Meteor. Z., 19, 341346, doi:10.1127/0941-2948/2010/0464.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Venkataraman, K., S. Tummuri, A. Medina, and J. Perry, 2016: 21st century drought outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: Implications for water resource management. J. Hydrol., 534, 300316, doi:10.1016/j.jhydrol.2016.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S.-Y., R. R. Gillies, E. S. Takle, and W. J. Gutowski Jr., 2009: Evaluation of precipitation in the intermountain region as simulated by the NARCCAP regional climate models. Geophys. Res. Lett., 36, L11704, doi:10.1029/2009GL037930.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335, doi:10.1175/2010JCLI3679.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will global warming bring? Science, 317, 233235, doi:10.1126/science.1140746.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., and I. Harris, 2006: A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the river Thames, UK. Water Resour. Res., 42, W02419, doi:10.1029/2005WR004065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189216, doi:10.1023/B:CLIM.0000013685.99609.9e.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 530 149 9
PDF Downloads 232 106 4

Analysis of Precipitation Projections over the Climate Gradient of the Arkansas Red River Basin

Lei QiaoDepartment of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma

Search for other papers by Lei Qiao in
Current site
Google Scholar
PubMed
Close
,
Chris B. ZouDepartment of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma

Search for other papers by Chris B. Zou in
Current site
Google Scholar
PubMed
Close
,
Carlos F. GaitánSouth Central Climate Science Center, and College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, Oklahoma

Search for other papers by Carlos F. Gaitán in
Current site
Google Scholar
PubMed
Close
,
Yang HongSchool of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma

Search for other papers by Yang Hong in
Current site
Google Scholar
PubMed
Close
, and
Renee A. McPhersonSouth Central Climate Science Center, and Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma

Search for other papers by Renee A. McPherson in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Increases in the frequency and intensity of extreme precipitation are projected for most U.S. regions under climate change. There is a high degree of uncertainty, however, in precipitation regime changes across the large precipitation gradient of the Arkansas Red River basin (ARRB). The authors analyzed future precipitation regimes using two statistical downscaling datasets based on the scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Seasonal precipitation in low-to-high quantiles was calculated and compared for the southern ARRB where the downscaled data were available. The results showed a generally comparable shift in precipitation patterns and amounts between the two datasets. However, some spatial variation of precipitation amount change exists, and the direction of change could be opposite for the summer. Both datasets showed that the top 10% of monthly precipitation amounts could increase for the southern ARRB, mostly ranging from 5–10 mm month−1 for the early part of the century (2010–39) to 15–30 mm month−1 for the midcentury (2040–69) as compared with the historical period (1968–97). The maximum monthly precipitation could increase by up to 150 mm in both datasets by the midcentury. Precipitation was projected to increase regardless of quantile during both winter and spring but tended to decrease during summer and autumn. More-frequent and higher-intensity rainfall events were expected for the eastern part of the basin, and longer and drier dry periods were expected for the western basin. Conservation strategies and sustainable water management should consider the regional differences in the projected changes in precipitation regimes for the basin under climate change.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Lei Qiao, lei.qiao@okstate.edu

Abstract

Increases in the frequency and intensity of extreme precipitation are projected for most U.S. regions under climate change. There is a high degree of uncertainty, however, in precipitation regime changes across the large precipitation gradient of the Arkansas Red River basin (ARRB). The authors analyzed future precipitation regimes using two statistical downscaling datasets based on the scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Seasonal precipitation in low-to-high quantiles was calculated and compared for the southern ARRB where the downscaled data were available. The results showed a generally comparable shift in precipitation patterns and amounts between the two datasets. However, some spatial variation of precipitation amount change exists, and the direction of change could be opposite for the summer. Both datasets showed that the top 10% of monthly precipitation amounts could increase for the southern ARRB, mostly ranging from 5–10 mm month−1 for the early part of the century (2010–39) to 15–30 mm month−1 for the midcentury (2040–69) as compared with the historical period (1968–97). The maximum monthly precipitation could increase by up to 150 mm in both datasets by the midcentury. Precipitation was projected to increase regardless of quantile during both winter and spring but tended to decrease during summer and autumn. More-frequent and higher-intensity rainfall events were expected for the eastern part of the basin, and longer and drier dry periods were expected for the western basin. Conservation strategies and sustainable water management should consider the regional differences in the projected changes in precipitation regimes for the basin under climate change.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Lei Qiao, lei.qiao@okstate.edu
Save