By Dr. Yang Hong
School of Civil Engineering and Environmental Science
School of Meteorology
University of Oklahoma
Norman, OK 73072
Global and Regional Climate Change
The Global Climate Change Impacts in the United States Report compiled by the U.S. Global Change Research Program claims that, “Climate changes are already affecting water, energy, transportation, agriculture, ecosystems, and health” and finds that the “global temperature has increased over the past 50 years.” Global climate change has profound effects on society’s physical systems and human activities, especially at regional and local scales (IPCC 2007, Rathore 2005, Piao et al. 2010, Patz et al. 2005, Karl et al. 2009). Regional climate is a combined product of global climate forcing and also of regional atmosphere-land surface feedbacks. Particularly in regional climate-water assessments, the linkage between climate and water resource management should be localized to allow for more hydrologically relevant assessments and management actions (Karl and Trenberth 2003). Additionally, the frequency and areal extent of local extreme weather is of great importance to regional social and economic systems; regional climate therefore plays a significant role in policy making and business management decisions (Giorgi and Mearns 1991, Karl and Trenberth 2003, Hellmuth 2007).
Global Climate Modeling
Global Climate Models (GCMs) have been developed over decades to study the global climate as a whole. The complexity of the Earth’s climate is demonstrated using a variety of dynamic, chemical and biological equations that form the computationally intensive GCMs (Phillips 1956). GCM outputs usually have coarse resolutions and perform poorly at smaller scales, and therefore are inappropriate for regional impact assessment (Maurer et al. 2007). To solve the problem, downscaling techniques were applied to a subset of global climate data for the specific study region. The two primary downscaling methods commonly used are dynamic and statistical downscaling (Wilby and Wigley 1997, Giorgi 2001). Dynamic downscaling techniques considers regional surface features by applying Regional Climate Models (RCM) to the GCMs outputs, and as a result performs better at capturing local processes and feedbacks. However, it is relatively computationally expensive to operate (Lo et al. 2008). Statistical downscaling, on the other hand, finds the statistical relationship between large-scale climate features and local climate, and directly applies the relationship to downscale the GCMs outputs. Therefore, it is computationally cost-effective, but less physically relevant and dependent on the quality of the observational data (Maurer et al. 2007). Normally, uncertainties are expected in climate simulations and projections produced by GCMs due to lack of understanding of the natural processes across scales. In addition, future human behavior is the most unpredictable component in climate modeling. For example, technology innovations that limit the amount of greenhouses gases, regulations that change the amount of pollutants, and how the population will increase in the future all remain somewhat unknown (IPCC 2007).
Climate Change Projections in Oklahoma
Fig. 1 shows the time series of the historical and projected temperature anomaly with respect to the 1950-99 mean (Liu et al. 2012a). The ensemble mean of the three scenarios tend to diverge around the year 2040, with the A2 and A1B accelerating in warming temperatures. The period 2040s is expected to experience a warming range of 1.4-1.9°C. The A1B and A2 scenarios remain close to each other until 2070s, where the A1B starts to level off and the A2 continues at the same rate. At end of the century, warming is projected to be within 2.3-4.5°C. Looking at the spatial distribution of temperature change averaged over the century, more warming is projected in the northern and western parts of Oklahoma for all scenarios (Fig. 2). The panhandle region in particular shows the most warming, possibly related to a transition towards a drier climate.
Future trends in precipitation are not expected to be as clear as temperature. Fig. 3 shows the annual anomaly time series, with no distinctly noticeable long-term change relative to the past half-century. Potential changes at end of the century are 1.4%, 0.38%, and -3.9% for B1, A1B, and A2 respectively, with high inter-model variability. Figure 4 shows the spatial distribution of precipitation change. Scenarios B1 and A1B show average precipitation to be greater than the 1950-99 mean across much of the state except for the panhandle and southeast. Under the A2 scenario the decrease is larger and covers much more area, which—like in the other scenarios—is centered in the panhandle and southeast regions.
Climate Change and Hydrological Extremes: Flood and Drought
According to the IPCC 2007 report, the global temperature rise has increased the evaporation rate and moisture-holding capacity in the atmosphere, which in turn alters the hydrological cycle, changes precipitation patterns, and thus streamflow and soil water content, which could potentially result in more severe extreme weather impact such as floods and droughts.
Drought is usually defined on the basis of the degree of dryness and the duration of the dry period (Palmer 1965). It is generally caused by precipitation deficits over an extended period of time, which might result in a water shortage for some activity, group, or environmental sector (Landsberg, 1982). Scientists have developed four classifications to describe different kinds of drought: meteorological drought, agricultural drought, hydrological drought, and socio-economic drought (Wilhite and Glantz 1985).
Meteorological drought is simply the departure from normal conditions of meteorological variables (e.g. precipitation) that induces drying of the surface. It is region-specific because the atmospheric conditions of different areas have high local variability in space and time (NDMC 2006). Agricultural drought occurs when the soil moisture fails to provide enough nourishment to the plants. It indicates whether the water quantity in soil can meet the demand of plants at various growing stages. Hydrological drought, initially caused by rainfall deficits, is normally associated with streamflow, reservoirs or lake levels within a basin (Rathore 2005). It is important to note that the hydrological responses normally are latent to precipitation deficiencies in a basin. Therefore, not all meteorological droughts will immediately trigger a hydrological drought. Socio-economic drought is different from the aforementioned types of droughts because it is an economic measure of the gap between water supply and demand. If the water supply cannot meet the demand of water consumption such as hydroelectric power, food production, and fishery activities etc., a socio-economic drought will occur due to the demand-supply unbalance (NDMC 2006).
Drought History in the Southern US
Historical records documented that Oklahoma, which is within Southern U.S., experienced six major droughts since the 20th century: 1909-1918, 1931-1941, 1950-1956, 2001-2002 2005-2006 and 2010-2011. While the drought of the 1930s is historically associated with the Dust Bowl of the Great Plains, statistics show that the drought of the 1950s was more severe for Oklahoma as indicated by record low drought indices values (Arndt 2002). However, socio-economic impacts were less severe as Oklahoma’s population learned to cope with the Dust Bowl and put into place agricultural and water management practices that mitigated many of the worst impacts of the Dust Bowl. The drought history in the Southern U.S. reveals that the Southern U.S. is a drought-prone region. This raises the concern of how the future will be in terms of drought and whether climate change plays a role in affecting drought conditions.
Drought Projection Cases Study: Blue River Basin, OK
The following is a case study to show how drought in the Southern U.S. is projected under a changing climate. The Blue River Basin (Fig. 5) is particularly important to the state of Oklahoma and local surrounding communities. Historically, several Native American tribal communities have used the river as their important water source. Recently, however, there have been increasingly competing demands from surrounding industrial and metropolitan areas located in Oklahoma and Texas (OWRB 2003). The Blue River Basin is also a drought-prone region. It is very essential to study the future drought in this basin given the water conflict in the region.
The three drought indices mentioned above are first validated against the historical records. Results show that the three indices all capture the historical droughts with SPI and SRI showing better agreement with the records (Liu et al., 2012b).
In terms of the drought projection, the three drought indices give similar but somewhat different drought projects in the Blue River Basin (Fig. 6). SPI indicates one minor drought in the early 2020s, and the frequency and intensity of drought appear to increase substantially after 2050. PDSI and SRI show similar results and many more droughts are projected after 2050. More drought events are displayed on the PDSI panel than on the SRI panel, and severe droughts on PDSI are projected to be more severe (PDSI<-5) than those on SRI, except for the early 2080s.
The Blue River Basin is projected to be nearly constantly under wet conditions before 2050 for both PDSI and SRI, with a slight decreasing trend of wetness from 2011 to 2050. It is not surprising to see that both PDSI and SRI demonstrate more severe and frequent drought after 2050, although the magnitude and timing of droughts are not exactly the same. Based on the projections from Thorthwaite Monthly Water Balance Model, which is a hydrological model that gives future changes of hydrological variables, the Blue River Basin is expected to have an increasing trend of evapotranspiration (ET) and decreasing trend in total runoff under A1B scenario. Actual ET is expected to increase by up to 8% on average and runoff is projected to decrease by more than 10% by the end of the 21st century (Fig. 7). Accordingly, more water is going out as ET and less water will be available for surface runoff.
Figure 7. (a) 10 years moving average of projected AET change as percentage of 1950-1999 mean (b) 10 years moving average of projected runoff change as percentage of 1950-1999 mean (Source: Liu et al., 2012b).
In conclusion, three types of drought indices (SPI, PDSI and SRI) capture the major droughts documented historically in the state. The results projected by the drought indices under the business as usual A1B scenario suggest that more drought events might occur in the second half of the 21st century. This could be caused by the fact that the precipitation predicted by the GCM GISS-ER shows a descending trend, while the temperature is slowly but constantly increasing after 2010. Moreover, the ET projected by the Thornthwaite MonthlyWater Balance Model also has a significant increasing trend under such a warming climate. Therefore, it is very likely that future drought in the Blue River Basin will be more severe and intense compared to the 1950–1999 period, especially for the second half of the 21st century.
Flood Risk under Changing Climate
Of the major flood-inducing factors which directly impact water availability and variability, changing climate and the alternation of extreme weather play the primary role in water related issues and are of more concern worldwide. According to the IPCC report (IPCC, 2007), the global temperature rise has increased the evaporation rate and moisture-holding capacity in the atmosphere, which in turn alters the hydrological cycle, changes precipitation, and thus streamflow and soil water content. Due to continuous accumulation of atmospheric greenhouse gases and aerosols, climate predictions consistently warn of increases in global temperature. This could potentially result in more severe extreme weather impacts such as floods. Although there is significant uncertainty related to climate change impacts, many regions across different continents are projected to experience increased flooding. For example, the upper Mississippi River Basin and the lower Missouri River Basin would be more likely to have intensified precipitation and flooding (Pan et al. 2004; Qiao et al. 2012; Stone et al. 2003 and Jha et al. 2004). In other regions like the U.K., Osborn et al. (2000) showed an upward trend in rainfall and related increase in magnitude for high streamflow since 1960s. By using statistical rainfall models and high resolution RCMs, a 20% increase in peak flow over the next 50 years were suggested in the U.K (Reynard et al., 2004). Likewise for Oklahoma, climate change (e.g., warming temperature) will increase water vapor contents in atmosphere, which could likely result in more intense rainfall patterns and potentially higher flood risk.
Arndt, D.S. (2002) The Oklahoma Drought of 2001-2002. Oklahoma Event Summary. Oklahoma Climatological Survey.
Giorgi F, Mearns LO. (1991) Approaches to the simulation of regional climate change–a review. Reviews of Geophysics 29: 191–216.
Giorgi, F. (2001) Regional climate information: Evaluation and projections, in Climate Change 2001: The Scientific Basis Contribution of Working Group I to the Third IPCC Assessment Report. 56.
Hellmuth ME, Moorhead A, Thomson MC, and Williams J (eds) (2007) Climate Risk Management in Africa: Learning from Practice. International Research Institute for Climate and Society (IRI), Columbia University, New York, USA
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, editied by Core Writing Team, Pachauri, R.K and Reisinger, A., 104 pp, Geneva, Switzerland
Jha, M., Pan Z., Takle E. S., and Gu R. (2004) Impacts of climate change on streamflow in the Upper Mississippi River Basin: A regional climate model perspective. J. Geophys. Res., 109(D9), D09105.
Karl TR, Melillo JM, Peterson TC (2009) Global climate change impacts in the United States. Cambridge University Press, New York
Karl TR, Trenberth KE (2003) Modern global climate change. Science:1719–1723
Landsberg HE (1982) Climatic aspects of drought. Bull Am Meteorol Soc 63:593–596
Liu, L., Hong, Y., Hocker, J., Shafer, M., Carter, L., Gourley, J., Bednarczyk, C., Yong, B. and Adhikari, P. (2012a) Analyzing projected changes and trends of temperature and precipitation in the southern USA from 16 downscaled global climate models. Theoretical and Applied Climatology.
Liu L., Y. Hong, J. E. Hocker, M. A. Shafer, C. N. Bednarczyk (2012b) Hydro-climatological Drought Analyses and Projection using Meteorological and Hydrological Drought Indices: A Case Study in Blue River Basin, Oklahoma, Water Resources Management. DOI: 10.1007/S11269-012-0044-y
Lo, J.C.-F., Yang, Z.-L. and Pielke, R.A., Sr. (2008) Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res. 113(D9), D09112.
Maurer, E.P., Brekke, L., Pruitt, T. and Duffy, P.B. (2007) Fine-resolution climate projections enhance regional climate change impact studies. Eos Trans. AGU 88(47).
NDMC (2006). http://drought.unl.edu/whatis/indices.htm
Osborn, T.J., Hulme, M., Jones, P.D., Basnett, T.A. (2000) Observed trends in the daily intensity of United Kingdom precipitation. Int. J. Climatol. 20, 347–364.
OWRB (2003) The Arbuckle-Simpson Hydrology Study: Management and protection of an Oklahoma water resource
Palmer, W.C. (1965) Meteorological drought. Research Paper No. 45. U.S. Weather Bureau.
Pan, Z., Arritt R. W., Takle E. S., Gutowski W. J., Anderson C. J., and Segal M., (2004) Altered hydrologic feedback in a warming climate introduces a ” warming hole”. Geophysical Research Letters, 31(17), L17109 17101-17104.
Patz, J.A., Campbell-Lendrum, D., Holloway, T. and Foley, J.A. (2005) Impact of regional climate change on human health. Nature 438(7066), 310-317.
Phillips, N.A. (1956) The general circulation of the atmosphere: A numerical experiment. Quarterly Journal of the Royal Meteorological Society 82(352), 123-164.
Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H., Ma, Y., Ding, Y., Friedlingstein, P., Liu, C., Tan, K., Yu, Y., Zhang, T. and Fang, J. (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311), 43-51.
Qiao et al., (2012) Hydrological variability and uncertainty induced by climate change in the lower Missouri River Basin based on NARCCAP simulations and SWAT model, Journal of Hydrology (submitted)
Rathore, M.S. (2005) State level analysis of drought policies and impacts in Rajasthan, India. International Water Management Institute.
Reynard, N., S. Crooks, R. L. Wilby, and A. Kay (2004), Climate change and flood frequency in the UK, paper presented at the Defra National Conference, Univ. of York, York, U. K.
Stone, M. C., Hotchkiss R. H., and Mearns L. O. (2003) Water yield responses to high and low spatial resolution climate change scenarios in the Missouri River Basin. Geophys. Res. Lett., 30(4), 1186.
Wilby, R.L. and Wigley, T.M.L. (1997) Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography 21(4), 530-548.
Wilhite, D.A. and Glantz, M.H. (1985) Understanding: the Drought Phenomenon: The Role of Definitions. Water International 10(3), 111-120.