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Figure 2. The stations labelled according to the season analysed

Figure 2 The stations labelled according to the season analysed

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Figure 1. The datasets for the four different periods
Figure 1. The datasets for the four different periods
Figure 3. The threshold level approach defining drought parameters [modified from Tallaksen, 2000 (modified from Tallaksen ef al., 1997), Advances in Natural and Technological Hazards Research, 14, p. 104, Figure 1. Reproduced with kind permission by Kluwer Academic Publishers].
Figure 3. The threshold level approach defining drought parameters [modified from Tallaksen, 2000 (modified from Tallaksen ef al., 1997), Advances in Natural and Technological Hazards Research, 14, p. 104, Figure 1. Reproduced with kind permission by Kluwer Academic Publishers].
Figure 4. Summary of the significant trends in all datasets, calculated with (a) the Mann—Kendall test and (b) the resampling test. The dotted horizontal lines indicate «/2 = 2.5% The percentages of stations with significant positive and negative trends are summarized in Figure 4. A significance level of 5% implies that significant trends for 5% of the records can be expected, even if no trends are present. However, the trend tests for all drought parameters (except one) resulted in higher percentages than 5%, with the exact figure varying with period, parameter and test. Overall, the number of negative significant trends pointing towards decreasing drought deficit volumes or fewer drought events exceeded the number of positive significant trends (increasing drought deficit volumes or more drought
Figure 4. Summary of the significant trends in all datasets, calculated with (a) the Mann—Kendall test and (b) the resampling test. The dotted horizontal lines indicate «/2 = 2.5% The percentages of stations with significant positive and negative trends are summarized in Figure 4. A significance level of 5% implies that significant trends for 5% of the records can be expected, even if no trends are present. However, the trend tests for all drought parameters (except one) resulted in higher percentages than 5%, with the exact figure varying with period, parameter and test. Overall, the number of negative significant trends pointing towards decreasing drought deficit volumes or fewer drought events exceeded the number of positive significant trends (increasing drought deficit volumes or more drought
Table I. Difference between the Mann—Kendall and the resampling test results (% of stations with significant trends) Copyright © 2001 Royal Meteorological Society
Table I. Difference between the Mann—Kendall and the resampling test results (% of stations with significant trends) Copyright © 2001 Royal Meteorological Society
Figure 6. The spatial distribution of the significant trends found for AMV in period 2 (1962-1990) and period 3 (1930-1995) area stretching along the northern foothills of the Alps. In Eastern Europe, the aforementioned trends towards less severe droughts coincide with trends towards fewer events per year. — -* 4g ae, 84 pr 41 —f~ 7 1er 4 Aw-CaAtrT * * #¥ e gome 1
Figure 6. The spatial distribution of the significant trends found for AMV in period 2 (1962-1990) and period 3 (1930-1995) area stretching along the northern foothills of the Alps. In Eastern Europe, the aforementioned trends towards less severe droughts coincide with trends towards fewer events per year. — -* 4g ae, 84 pr 41 —f~ 7 1er 4 Aw-CaAtrT * * #¥ e gome 1
Figure 7. The series of AMV for Vils in Germany (upper) and the trend test statistics calculated for consecutive 30-year periods (lower) Changes in streamflow drought severity and frequency might occur as a result of changes in climate (mainly precipitation and temperature) and artificial influences in the catchment such as groundwater abstraction, irrigation and urbanization. In order to ensure the quality of the flow data in the EWA, ‘the extent of artificial influences on the flows is assessed and only those basins whose flow regimes approximate to natural conditions are accepted’ (Roald et al., 1993, p. 12). Even so, low flow data are especially prone to artificial influences in a catchment, and the results presented in this paper may have been affected by this. The causes of a change in riverflow behaviour often do not have a simple explanation, and a further study would require a detailed analysis at the catchment scale, which is beyond the scope of this paper. However, the spatial consistency in the results does indicate some systematic factors that can be evaluated at a qualitative, regional level.
Figure 7. The series of AMV for Vils in Germany (upper) and the trend test statistics calculated for consecutive 30-year periods (lower) Changes in streamflow drought severity and frequency might occur as a result of changes in climate (mainly precipitation and temperature) and artificial influences in the catchment such as groundwater abstraction, irrigation and urbanization. In order to ensure the quality of the flow data in the EWA, ‘the extent of artificial influences on the flows is assessed and only those basins whose flow regimes approximate to natural conditions are accepted’ (Roald et al., 1993, p. 12). Even so, low flow data are especially prone to artificial influences in a catchment, and the results presented in this paper may have been affected by this. The causes of a change in riverflow behaviour often do not have a simple explanation, and a further study would require a detailed analysis at the catchment scale, which is beyond the scope of this paper. However, the spatial consistency in the results does indicate some systematic factors that can be evaluated at a qualitative, regional level.
Related topics:
Environmental EngineeringCivil EngineeringEastern EuropeClimatologyAquatic EcosystemAtmospheric sciencesTrend AnalysisTrend detection
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