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Table 1 Descriptions of meteorological and hydro-gauging stations data series homogeneity at different stations. Using man) homogeneity tests was important to minimize unreliability issues in the data series. data series homogeneity at different stations. Using many homogeneity tests was important to minimize unreliability issues in the data series.

Table 1 Descriptions of meteorological and hydro-gauging stations data series homogeneity at different stations. Using man) homogeneity tests was important to minimize unreliability issues in the data series. data series homogeneity at different stations. Using many homogeneity tests was important to minimize unreliability issues in the data series.

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Fig. 1 Locations of the Jemma sub-basin and meteorological and hydro-gauging stations Homogeneity assessment for hydro-climate data is crucial before test detection on account of the observed long- term data series affected by external factors. This test was adopted for pre-processing data to explore the statistical nature of data homogeneity [24-26]. The four homoge- neity tests viz., Pettitt’s, SNHT, Buishad range test, and Von Neumann test (VNT) were performed to assess the
Fig. 1 Locations of the Jemma sub-basin and meteorological and hydro-gauging stations Homogeneity assessment for hydro-climate data is crucial before test detection on account of the observed long- term data series affected by external factors. This test was adopted for pre-processing data to explore the statistical nature of data homogeneity [24-26]. The four homoge- neity tests viz., Pettitt’s, SNHT, Buishad range test, and Von Neumann test (VNT) were performed to assess the
Where, o is the variance the data; the year k is the change point if the value of the T,, is maximum. If the test statistic is greater than the critical value, the null hypothesis is rejected. Where, T(x) is used to compare the mean of the first k years with mean of the last n-k years, nis the last year and k is the first year. 2.6 Buishand’s test
Where, o is the variance the data; the year k is the change point if the value of the T,, is maximum. If the test statistic is greater than the critical value, the null hypothesis is rejected. Where, T(x) is used to compare the mean of the first k years with mean of the last n-k years, nis the last year and k is the first year. 2.6 Buishand’s test
where m is the number of the attached group; n is the number of data points; and t, represents the number of extent k.
where m is the number of the attached group; n is the number of data points; and t, represents the number of extent k.
The bold numbers indicate the test statistics significance level (p-value) at 0.05 for homogeneity tests
The bold numbers indicate the test statistics significance level (p-value) at 0.05 for homogeneity tests
Table 3 The annual trends of hydro-climatic variables
Table 3 The annual trends of hydro-climatic variables
Fig.2 Innovative trend analysis graph for streamflow: The path of red dots along the trend line is indicating the increasing /decreasin« trend of precipitation between 1990 and 2008
Fig.2 Innovative trend analysis graph for streamflow: The path of red dots along the trend line is indicating the increasing /decreasin« trend of precipitation between 1990 and 2008
Fig. 3 Innovative trend analysis graph for temperature: The path of red dots along the trend line is indicating the increasing /decreasin trend of temperature between 1980 and 2014
Fig. 3 Innovative trend analysis graph for temperature: The path of red dots along the trend line is indicating the increasing /decreasin trend of temperature between 1980 and 2014
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Environmental ScienceClimate ChangeTrend Analysis
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