Drought In Australia 2006 Case Study

Drought – MEDC – Australia 2002-2009

What is a drought?

Long period of rainfall shortage.

Fact: Over a long time, Australia has three good rainfall years and three bad rainfall years out of ten.

Australia drought case study – the ‘Big Dry’ – Driest period in 125 years

Profile

Date: 2002-2009

Location: Murray-Darling Basin (MDB)- South East of the country

Details about the Murray-Darling Basin (MDB)

  • It usually provides 40% of Australia’s agricultural produce
  • The basin makes up more than 70% of Australia’s irrigated cropland and pasture
  • Is the size of France and Spain combined
  • Provides 75% of Australia’s water
  • Home to almost 2 million people

Causes 

Physical:

  • In El Nino years trade winds reverse, this caused Australia to experience very high pressure. The moist arm had been felts by South America which usually would have come to Australia. This change between normal years and El Nino years is called the Southern Oscillation Index.

Human:

  • Some scientist believe climate change is behind intensifying and prolonging the El Nino experience. Evidence that is was the driest and warmest year (2002) in Australia has been collated.

Impacts

Social

  • There used to be 19 dairy farms now there are a mere six
  • Water shortage = less agricultural produce
  • The rural suicide rate soared.
  • People in rural areas left due to the lack of water
  • Legislation was implemented e.g. car washing bans and 4-minute-long showers.

Economic

  • Farmers had to sell of cattle as they couldn’t afford straw to feed them
  • Food prices rose and Australia became reliant on imports
  • Prices of energy soared. Water bills were expected to rise by 20% in 2008.
  • Government spending increased to help farmers and small businesses cope with this scarcity.
  • Wiped 1% of Australia’s economy
  • Tourism was adversely affected.

Environmental

-Grassland turned into ‘scrubland’.

  • Energy from HEP sources was reduced and more polluted methods such as carbon fuels were used
  • Loss of vegetation, wildlife, biodiversity alongside soil erosion.
  • Water quality declined and there was toxic algal outbreaks in depleted rivers, dams and lakes.
  • Increasing number of wildfires due to the intensity of the heat
  • There is a negative spiral of decline e.g. saltwater reduces causing plants and migrating birds to decline as well

Management

The government announced a $10 billion national water management plan in 2007. It will build on work under two other organisations; the Living Murray Initiative and Australian Government water fund.

Hard Engineering

  • Lake Brewster Water Efficiency Project- it aims to improve Lake Brewster for storage on the Lachlan River in New South Wales. (building an embankment to split the lake, deepening of the channel, creation of 2 wetlands).
  • Numerous water smart projects involving smart technologies are being discussed including the Lake Brewster Water Efficient Project.

Soft Engineering

  • The government is going to control water usage – minimising the water usage in agriculture.
  • Modernising irrigation methods – adapting more accurate water meters to improve measurement and reduce over- watering and piping and lining channels to make watering methods efficient.
  • Addressing over-allocation- areas which are not viable e.g. salt-affected areas will be classed as retired and many farmers will be given support to leave the industry.
  • Reformation will take place to ensure accurate and precise equipment is available to monitor and forecast water levels. A national database will also be created to report water usage.
  • Key ecosystems, indigenous people and communities will be identified and it will be examined whether the MDB can meet and sustain the needs of these communities.
  • Research and development from The Australia Commonwealth Scientific and Industrial Research Organisation (CSIRO) has been key in introducing new technologies such as drought resistant plants to manage the drought, crop and pasture management technology for conserving water in different regions and Precision farming which uses technology to ensure farmers are making informed and sustainable decisions.
  • GPS are being used to make sure seeds are planted accurately to make farming efficient.




Download this essay: Drought Essay and highlight with a marker in different colours (or digitally – up to you!):

Physical causes

Human Cause

Effects

Management

2.1. Causes of the Meteorological Drought

[12] It is common practice to evaluate plausible linkages between observed regional rainfall anomalies and any skewness or changes in large-scale modes of variability previously identified to influence interannual rainfall patterns [Nicholls, 2006]. While such analyses are not attribution studies in any strict sense, they can help to interpret observed rainfall anomalies. Following this approach, the anomalously low rainfall conditions in southeastern Australia during the Millennium Drought have been linked to a combination of intensification of the mean sea level pressure across southern Australia [Hope et al., 2010] and particularly the subtropical ridge, a belt of high-pressure systems that expresses the descending branch of the Hadley cell [Timbal et al., 2010], as well as to ENSO [e.g., Verdon-Kidd and Kiem, 2009b]. An influence of the Indian Ocean Dipole (IOD) has been proposed [Cai et al., 2009; Ummenhofer et al., 2009] but also questioned [Smith and Timbal, 2012; Timbal and Hendon, 2011]; the same is true for the Southern Annular Mode (SAM) [Hendon et al., 2007; Meneghini et al., 2007; Nicholls, 2010; Verdon-Kidd and Kiem, 2009a]. To a considerable extent, different conclusions about the relative importance of different drivers appear to be a result of methodological differences: in the analysis method; in the metrics (indices) used to describe each phenomenon; in the region, time period, and time scale of variability considered; and other, more subtle choices made in the analysis.

[13] We examined if stronger inferences could be made if both predictand and predictors were averaged over larger areas and periods. This should reduce random components and noise in the data and allow for lagged correlations due to landscape hydrometeorological memory and any delayed atmospheric response to ocean circulation indices [Koster et al., 2004; Timbal et al., 2002]. We compared our statistical results based on indices with published research on the drivers of Australian rainfall in general, and during the drought in particular.

[14] Daily rainfall estimates for the Australian continent were derived by interpolation of daily rainfall gauge readings to a 0.05° grid [Jeffrey et al., 2001]. The gridded data were combined with a vector map showing 245 river basins identified by the Australian Water Resources Council (Map 5) [AWRC, 1975]. For each AWRC basin, a time series of basin-average annual rainfall was calculated for 1900–2009. A matrix of fraction covariance (squared correlation coefficient, R2) between each time series pair was calculated. The software package MultiDendrograms 2.1 [Fernández and Gómez, 2008] was used to cluster basins by interannual rainfall patterns. As a distance measure, the fraction of uncorrelated variance (1 − R2) was used. Seven metaclusters were derived from the cluster dendrogram, and these were merged into six large, contiguous regions through minor editing (see supporting information).

[15] For each of the six regions a time series of annual average rainfall was calculated. These time series were compared to time series of nine predictor indices describing six candidate phenomena: the ENSO (Nino3.4 [Kaplan et al., 1998] and Southern Oscillation Index (SOI)); IOD mode index and the classification of Ummenhofer et al. [2009]; Pacific decadal Oscillation (PDO) [Zhang et al., 1997]; SAM [Marshall, 2003; Visbeck, 2009]; global mean temperature (GMT); Hansen index); and the intensity and location of the Southern Hemisphere Subtropical Ridge (STRI and STRL) [Drosdowsky, 2005]. Full details and data sources are listed in the supporting information. Where monthly or seasonal predictor data were available, these were used to first calculate mean seasonal values (December-February and so on) as well as annual average values, producing five candidate predictor variables. All resulting climate predictor time series were normalized by their mean and standard deviation and any missing values replaced by the mean (i.e. zero, after normalizing).

[16] Five-year rolling averages were calculated for the predictors (x1..n) as well as the regional rainfall averages (y1..6) for the period 1900–2009 (shorter and longer integration periods were also tested, with very similar results). For each region, a multivariate model was fitted by regression against residuals in four steps:

(1)

where indices x1..4 were selected at each respective step as those having the greatest correlation with the residual unexplained variance (i.e., the highest partial correlation). The coefficients c0..4 were found through linear regression. From the associated fraction of variance explained (R2), we calculated Akaike's information criterion (AIC) [Akaike, 1974] to decide whether to accept the four-variable model or whether to select one with fewer predictors:

(2)

where k is the number of free parameters (c0..ci; 2 to 5) and n the number of independent observations. Although the total number of years was 110, the 5 year averaging would have introduced autocorrelation; therefore, we conservatively estimated n as 22 (110 years divided by 5). It is noted that the assumption of n = 110 for the original time series should be considered a rough estimate only: effective sample size could potentially be further reduced by serial correlation in the original time series [Yue and Wang, 2004], and calculating the 5 year rolling averages may not have removed all of that serial correlation.

[17] The contribution of each of the climate phenomena to the meteorological drought was estimated by multiplying the observed 2001–2009 anomaly in x1..4 by the sensitivity of rainfall to x1..4, defined by the slope c1..4 of the regression model (equation (1)).

[18] To assess whether the drought reflected a gradual change, linear trends and associated significance were calculated for each region using annual rainfall data for 1950–2009. The contribution to each identified driver to rainfall trends was estimated as

(3)

[19] That is, the contribution of each phenomenon to observed rainfall trends (dP/dt) was estimated as the product of the trend in the index (dxi/dt) and the sensitivity ci. It should be emphasized that the calculation of linear trends is inevitably contingent on the period chosen, regardless of statistical significance. For example, in southeast Australia 1950 marks the start of a comparatively wet period (see section 3); therefore, choosing an earlier or later start date would likely have led to a diminished and enhanced trend, respectively.

2.2. Hydrological Drought Impacts

[20] Due to nonlinearity in catchment hydrological functioning, relative changes in annual rainfall are typically amplified a few times in streamflow generation during nondrought years [Budyko, 1974; Chiew, 2006]. To determine the amplification of rainfall deficits in streamflow during the drought, we used observed catchment streamflow data as well as two alternative modeling approaches.

[21] Daily streamflow data was obtained from government agencies in New South Wales, Queensland, Victoria, Tasmania, and West Australia. Out of the available streamflow data, initially data was selected for 466 gauged catchments in the five drainage divisions most affected by the drought: Southeast Coast, Tasmania, MDB, South Australian Gulf, and Southwest Coast. All data were quality controlled and any interpolated data were removed. Terrain analysis was carried out using a digital elevation model to determine the catchment area of each of the catchments. Each individual catchment was visually assessed against topographic maps and satellite photography to ensure it was not affected by significant irrigation, impoundments, or other forms of regulation. For each catchment, streamflow (Q in mm) was aggregated from daily to monthly totals (by multiplying mean daily Q by the number of days in the month, provided more than 70% of days had data) and subsequently to annual totals. Missing months were gap-filled by considering the runoff coefficient (rc, that is, the ratio of total streamflow Q over total rainfall, P, for the year) and subsequently multiplying rc with P for the missing months. Gaps were filled only if less than 4 months were missing for a given year. Out of the 466 gauged catchments, 126 catchments were selected that had 30 or more years of observations before the drought and at least three years during the drought (2001–2009). Annual rainfall for the same catchments and years was derived by overlaying the catchment map with the rainfall grids.

[22] To determine to what extent streamflow reductions during the drought were different from those that could be expected in normal dry years, the observed relative streamflow declines were compared to predictions by two alternative modeling methods: a simple conceptual/statistical model that ignores temporal correlation or subannual rainfall patterns, and a daily time step process model.

[23] The simple model was a two-parameter rational function fitted to predrought annual rainfall and streamflow data. The nonlinearity between rainfall and streamflow expected under stationary conditions was estimated by fitting the model:

(4)

[24] This model is mathematically near identical to the model proposed by Zhang et al. [2001] based on Budyko [1974] theory, where a would represent potential evapotranspiration (PET) and b a fitting parameter. For each catchment, we fitted values for both a and b rather than prescribing a value. Hence, the resulting estimate represents the influence of rainfall changes, but potentially including any covariance between P and PET. The model was fitted to the rainfall and observed streamflow data before 2001. Subsequently, the fitted model was used to predict streamflow for the drought years. For most catchments, records before 2001 did not include a drought as severe as the Millennium Drought and this could influence the fitted model. This was indeed the intention: comparing observed and model-predicted impacts should indicate to what extent catchment behavior during the drought was different from normal dry years.

[25] The process model used is the landscape hydrological model of the Australian Water Resources Assessment (AWRA) system [AWRA-L version 0.5; Van Dijk, 2010; Van Dijk and Warren, 2010; Van Dijk and Renzullo, 2011]. It considers catchment storage dynamics and observed weather patterns, including the potential role of increased radiation or temperature [e.g., Cai and Cowan, 2008; Potter and Chiew, 2011]. AWRA-L may be described as a hybrid between a simplified land surface model and a lumped catchment model. Grid resolution, domain, and the number of subgrid land cover classes are not prescribed but defined by the model inputs. The model evolves on a daily time step, and for each cover class simulates the water balance of a top soil, shallow soil, and deep soil compartment as well as vegetation phenology in response to water availability; whereas groundwater and surface water dynamics are estimated at grid resolution. It considers two land cover classes (deep- and shallow-rooted vegetation). Forcing was from the daily rainfall grids (section 2.1) and similarly interpolated grids of shortwave radiation and minimum and maximum temperature [Jeffrey et al., 2001]. The model was run for the period 1895–2010 with default parameterization [Van Dijk, 2010], that is, the model was not optimized to reproduce the streamflow observations used in the analysis. The daily streamflow grids were combined with the catchment map and time series of catchment average streamflow (in millimeters per day) were calculated for each catchment.

[26] For each catchment, the observed and predicted reductions were estimated as the relative difference between the streamflow observed or predicted for the drought years (2001–2009) and the predrought years. In each case, only those years for which observations were available were selected. A test was done to assess the differences in model-estimated and observed runoff declines against those predicted for normal dry years: for each catchment the pre-2001 years with rainfall in the lowest quintile were selected, and the relative reductions in rainfall, model-estimated streamflow, and observed streamflow were compared.

[27] To help interpret the AWRA streamflow estimates, we compared model estimated total water storage with satellite observations. Satellite terrestrial water storage (TWS) data were available from the Gravity Recovery and Climate Experiment satellite mission (GRACE) [Tapley et al., 2004]. GRACE provides integrated estimates of variations in total TWS based on precise observations of Earth's time variable gravity field. We used the 1° resolution gridded estimates provided by the GRACE Tellus website and produced by the University of Texas Centre for Space Research (CSR). The data preprocessing and analysis was described in Van Dijk et al. [2011].

2.3. Ecological Drought Impacts

[28] We considered impacts on dryland and riverine ecosystems separately. Apart from the impacts of the drought on dryland agriculture (section 2.4), we did not attempt to quantify impacts on dryland ecology. Judging by the impact on living biomass, they are likely to have been widespread, however (Figure 1a; see section 4).

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