management in Fitzroy Basin

Transcription

1 Climate variability, climate change and grazing management in Fitzroy Basin Xike Zhang, David Cobon, Nathan Toombs Queensland Climate Change Centre of Excellence, Office of Climate Change, EPA

2 Outlines: Rainfall variability, extremely dry and wet events and their association with El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) Climate change and future climate projections for the region Impacts of climate change on grazing system Recommended adaptation strategies

3 rainfall anomaly from long-term average (mm) E l N iñ o Y e a r L a N iñ a y e a r E N S O -N e u tra l ye a r Among 34 El Niño years, 28 years had below-median rainfall, the probability is 82% 1950 Y e a r In 28 La Niña years, 21 years had above-median rainfall, 75% During 63 ENSO-Neutral years, the chance to get above or below median rainfall is During 63 ENSO-Neutral years, the chance to get above or below median rainfall is nearly equal

4 Extremely dry event Duration (months) ENSO (EN, El Niño; LN, La Niña; N, Neutral) PDO Phase Extremely wet event Duration (months) ENSO (EN, El Niño; LN, La Niña; N, Neutral) PDO Phase Jan 1883 to Jun 42 N-EN-N Unknown Mar 1886 to May 27 LN-N Unknown Jun 1900 to Nov N-N-EN Cool Jul 1888 to Jan EN-LN-LN Cool Jan 1914 to Oct 34 EN-N Cool Dec 1892 to Feb 39 LN-LN-N-N Cool Feb 1918 to Dec 1920 Nov 1924 to Nov 1927 Jul 1929 to Jun 1933 Sep 1963 to Dec EN-EN-N Warm May 1897 to Sep EN-N-N Warm Apr 1905 to Jul N-N-N-EN Cool Nov 1915 to Feb EN-LN-EN Warm Jul 1927 to Oct 1929 Mar 1991 to Feb 36 EN-EN-EN Warm Aug 1948 to Sep Apr 1994 to Apr EN-N Warm Mar 1953 to Jan 1958 May 2000 to 71 LN-N-EN-N-EN-N Warm Feb 1972 to Dec Mar N-N Cool 40 EN-LN-N Cool 40 N-LN-LN-EN Cool 28 N-N Warm 38 N-N-LN Cool 59 EN-N-LN-LN-EN Cool 59 EN-LN-LN-LN-N Cool Extreme drought is likely to happen in El Niño dominant periods within either Cool or Warm PDO phase Extremely wet event is likely to occur in La Niña dominant years, and 8/9 events happened during Cool PDO phase in the history

5 Is the climate changing in this region?

6 5-year moving average minimum temperature from May to October at Emerald Min nimum Tem mperature (o oc) Mann-Kendall: Data show statistically significant lo n g - te r m a v e ra g e increasing trend (at a<0.01) 5 - y e a r m o v in g a v e r a g e Z-statistic: Statistical table: a=0.10 a=1.96 a=0.01 Critical value: Y e a r

7 Number of days with minimum temperature below 2 o C from May to October at Emerald Mann-Kendall: Data show statistically significant decreasing trend (at a<0.01) Z-statistic: Statistical table: a=0.10 a=1.96 a=0.01 Critical value: Da ays Y e a r

8 5-year moving average maximum temperature from November to April at Emerald Maximum m temperat ture (oc) Mann-Kendall Test: Data show statistically significant increasing trend (at a<0.1) Z-statistic: long-term average Statistical table: 5-year a=0.10 m oving a=1.96 average a=0.01 Critical value: Year

9 Number of days with maximum temperature above 35 o C from November to April at Emerald Cusum Test: Data show a statistically significant step jump in 1978 (a<0.01) Data in the later years (mean 74 days) are higher than earlier years (61 days) Day ys Y e a r

10 5-year moving average annual rainfall at Emerald Annual ra ainful (mm) )8 0 0 Mann-Kendall Test: Data show statistically significant decreasing lo n g -te rm trend a ve(at raa<0.1) g e Z-statistic: ye a r m o vin g a ve ra g e Statistical table: a=0.10 a=1.96 a= Critical value: Y e a r

11 Climate change projections in 2030 for Nogoa Subcatchment Global Climate Models (GCMs) and a statistical downscaling method: monthly patterns of climate change to a 25 km grid over Australia for 5-year increment until 2100 (OZClim) 8 GCMs forced by different emission scenarios and levels of climate sensitivity Producing a range of climate change patterns for the region: the driest, wettest and the average scenarios

12 By 2030, the average 2.00 Projected monthly changes in temperature at 2030 Monthly Changes in Temperature for Dry, Wet and Average Models for Nogoa sub-catchment annual temperature will 1.80 increase by o C 1.60 in Nogoa sub- catchment, compared 1.40 with By 2030, the days with maximum temperature 35 o C will increase by up to 15 days, from mean 67days in Temperature Chang ge (Deg C) January February March April May June July August September October November December Month GFDL CM2 SRES A1F1 (HiSens) MUIB KMA SRES B1 (LowSens) AVERAGE SRES A1B (MedSens)

13 By 2030, the worst (dry) scenario: annual rainfall will decrease by 17%, the best Projected monthly changes in rainfall at 2030 for Nogoa sub-catchment Monthly Rainfall Changes for Dry, Wet and Average Models scenario: increase by 0.0 2%, compared with All GCMs predict reduction of winterspring rainfall Rainfall Cha ange (%) Janua ary Februa ary Mar rch Ap pril May Ju une J uly Augu ust Septemb ber Octob ber Novemb ber Decemb ber Month GFDL CM2 SRES A1F1 (HiSens) MUIB KMA SRES B1 (LowSens) AVERAGE SRES A1B (MedSens)

14 Under climate change conditions ENSO will be a robust mode of climate variability and El Niño events will be a bit of stronger Multi-decadal-long drying period will be intensified (CSIRO Report, 2003)

15 We use GRASP model to simulate a grazing system located at Emerald: Native grass species, no tree, medium soil fertility and light soil texture Compared the simulations for 2030 with 1990 Two stocking strategies constant, responsive (utilisation levels 10, 20 and 40%)

16 Annual pasture growth Higher temperature and low rainfall in 2030 will increase the variability of annual pasture growth Wet scenario can produce more growth when nitrogen is not limiting Higher temperature will produce more growth in wet years but less during dry years

17 Grass basal area Dry scenario and 40% utilisation level will reduce the basal area by 10% and increase risk of <2.5% basal area from nearly zero in 1990 to 1 in every 3 years Sustainable stocking rate (10-20% utilisation) will help maintain basal area Live weight gain of cattle The high temperature will increase LWG/head if rainfall is not limiting Dry scenario will increase the variability of LWG/ha In dry scenario using RSS at 40% utilisation level will reduce LWG/ha

18 Intensified Drought: Over-utilisation in pre-drought period: damaging perennial species, collapse of stock numbers High utilisation in first years of drought: losses of perennial species, worsening drought effect Continued retention of stock number: delaying recovery, irreversible degradation Higher temperature and sustained grazing pressure: lower grass basal area and reduce ground cover, reduced infiltration, local flooding, pasture and land degradation.

19 Impacts of intensified wet periods Over-expectation of carrying capacity Livestock number built up in the wet periods Weakening valuable species Invasion of undesirable grasses and weeds Uncontrolled wildfires Reduced beef price

20 Adaptation Strategies to climate variability and climate change Monitoring climate signals MJO ( SOI, SST, IOD ( PDO/IPO ( Seasonal outlooks and SPOTA-1 ( Maintaining pastures Take advantage of wet years (e.g. La Niña years in Cool PDO phase). Revegetation of degraded land (carbon farming ETS) Diversify grass species composition: C3 and C4 species (C3:wallaby grass, legumes, forbs; C4: Qld bluegrass, Mitchell grass, black spear grass, kangaroo grass)

21 Continued: Adaptation Strategies to climate variability and climate change Managing pasture utilisation and stocking rate Adopting conservative and safe stocking rate Using highly responsive stock management strategies Using rotational wet season spelling Delaying restocking after drought Using decision-support tools in management e.g. GRASP model, AussieGRASS model product, FORAGE ( GLM workshops, Managing livestock Monitoring the increased temperature and hot days: joining, calving and Monitoring the increased temperature and hot days: joining, calving and branding, time to travel to water points, and virus and disease

22 Climate Change Matrix: tools for adaptation to climate change developed by QCCCE

23 Continued: Climate Change Matrix: tools for adaptation to climate change developed by QCCCE

24 We thank: Ian Partridge (DPI&F) Grant Stone (QCCCE) Baisen Zhang (QCCCE) Lynne Turner (QCCCE) Paul Pinjuh (QCCCE) and other colleagues from QCCCE for their input and support in preparing the presentation

25 Cool and warm Pacific Decadal Oscillation (PDO) phases and 5-year moving average of rainfall anomaly at Emerald b e lo w -a v e ra g e ra in fa ll a b o v e -a v e ra g e ra in fa ll P D O Unclear with the end Of this episode 3 2 Rainfall anomaly (mm) ? PDO Y e a r

26 Atmospheric CO 2 : 280ppm in 1750 to 379ppm in 2005; CH 4, 715ppb to 1774ppb; N 2 O, 270ppb to 319ppb The warming effect measured in radiative forcing by GHG: +2.3W/m 2 ; by aerosols: -0.5W/m 2 by solar irradiance +0.12W/m 2 CO2 (ppm m) CH4 (ppb) IPCC, 2007 N2O (p ppb) Time (before 2005)

27 Emission scenarios: 450~950ppm in 2100 The climate sensitivity: 2~4.5 o C for doubling CO 2 Global warming: increased by 1.1~6.4 o C at , compared with (IPCC, 2007) Projected global average surface warming at 2100 Projected atmospheric CO2 level by different emission scenarios in 21 st century. TAR 2001