[1] The “Millennium Drought” (2001–2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water, economic, and remote sensing data combined with biophysical modeling. Prevailing El Nino conditions explained about two thirds of rainfall deficit in east Australia. Results for south Australia were inconclusive; a contribution from global climate change remains plausible but unproven. Natural processes changed the timing and magnitude of soil moisture, streamflow, and groundwater deficits by up to several years, and caused the amplification of rainfall declines in streamflow to be greater than in normal dry years. By design, river management avoided impacts on some categories of water users, but did so by exacerbating the impacts on annual irrigation agriculture and, in particular, river ecosystems. Relative rainfall reductions were amplified 1.5–1.7 times in dryland wheat yields, but the impact was offset by steady increases in cropping area and crop water use efficiency (perhaps partly due to CO2 fertilization). Impacts beyond the agricultural sector occurred (e.g., forestry, tourism, utilities) but were often diffuse and not well quantified. Key causative pathways from physical drought to the degradation of ecological, economic, and social health remain poorly understood and quantified. Combined with the multiple dimensions of multiyear droughts and the specter of climate change, this means future droughts may well break records in ever new ways and not necessarily be managed better than past ones.

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The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society

Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis

Review of trend detection methods and their application to detect temperature changes in India

Stream flow in Minnesota : Indicator of climate change

Temporal variability of precipitation over Iran: 1966-2005

The non-parametric Mann-Kendall (MK) statistical test has been popularly used to assess the significance of trend in hydrological time series The test requires sample data to be serially independent When sample data are serially correlated, the presence of serial correlation in time series will affect the ability of the test to correctly assess the significance of trend To eliminate the effect of serial correlation on the MK test, effective sample size (ESS) has been proposed to modify the MK statistic This study investigates the ability of ESS to eliminate the influence of serial correlation on the MK test by Monte Carlo simulation Simulation demonstrates that when no trend exists within time series, ESS can effectively limit the effect of serial correlation on the MK test When trend exists within time series, the existence of trend will contaminate the estimate of the magnitude of sample serial correlation, and ESS computed from the contaminated serial correlation cannot properly eliminate the effect of serial correlation on the MK test However, if ESS is computed from the sample serial correlation that is estimated from the detrended series, ESS can still effectively reduce the influence of serial correlation on the MK test

The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series

For effective planning, design, and management of water resources engineering, the probability distribution of annual streamflow is necessary. The method of L-moments is applied to identify the probability distribution type of annual streamflow in different climatic regions of Canada. In the Pacific and southern British Columbia mountains (regions 1 and 2), the generalized extreme value (GEV) distribution fits the observations best with the 3-parameter lognormal (LN3) and log Pearson type III (LP3) as potential candidates. In Yukon and northern British Columbia (region 3), the LN3 distribution corresponds to observations best with the LP3 and P3 as potential candidates. In the northwestern forest (region 5), the LP3 distribution matches observations best with the P3 and GEV as potential candidates. In Arctic Tundra (region 10), the 3-parameter Weibull (W3) is the best one with the LN3 and P3 as potential candidates. The P3 distribution provides a best-fit to observations in the Prairies (region 4), northeastern forest (region 6), great Lakes and St. Lawrence (region 7), Atlantic (region 8), and Mackenzie (region 9) with the LN3, LP3, and GEV as potential candidates.

Sheng Yue

Papers

The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series

Possible Regional Probability Distribution Type of Canadian Annual Streamflow by L-moments