Development and Adaption of Water Technologies in the Hills of Nepal - Research Proposal Example

Descriptive statistics Table 1 and figure 1 gives a summary of the variation of rainfall from station to station for =for various seasons. From table 1 it can be seen that monsoon rainfall has the highest mean of 1546.66 with a minimum rainfall of 135.4mm while the maximum is 4541.4. Second in rank is pre-monsoon rain with a mean of 253.17 the maximum value being 550.4mm and minimum being 58.8mm. The season with the amount of rain over the 25 stations is winter with a mean of 61.84 with the minimum being 24.3 and a maximum of 131.4. The rain from the four seasons translates to an annual rainfall mean for the 25 stations being 1954.74mm, the maximum of 5360.4mm and a minimum of 257.7mm. The pattern for the rain displayed over the 25 stations can be seen clearly in figure 1. It can be seen that the annual rainfall curve is above the other four curves in all the 25 stations. Winter curve is the lowest lying with pre-monsoon being just above it. Table 1 Descriptive Statistics N Minimum Maximum Mean Std. Deviation Annual rainfall 25 257.70 5360.40 1954.7440 1021.72939 Winter rainfall 25 24.30 131.40 61.8440 28.39101 Pre-monsoon rain 25 58.80 550.40 253.1720 131.45385 monsoon 25 135.40 4541.40 1546.6600 878.79600 Post-monsoon 25 39.10 236.70 93.0600 48.31295 Valid N (listwise) 25 Hypotheses There is a significant difference in the amount of rains experienced during winter and pre-monsoon There is significant difference in the level of rainfall experienced between winter and monsoon The difference in rains in winter and post monsoon is statistically significant There is no significant difference in level of rainfall in pre-monsoon and monsoon seasons There is no significant difference in the level of rainfall experienced between pre-monsoon and post-monsoon seasons There is no significant difference in the level of rainfall amount in monsoon and post-monsoon Results for paired samples tests In order to test the hypothesis a paired t-test was done with the results being as shown in table 2. From the table it can be seen that Pair 1 which is the comparison of means of winter and pre-monsoon shows that the difference in means is -191.32 and the difference is statistically significant with t(24)= -7.324 ( p=0.000). Pair 2 gives the result for comparison of mean rainfall between winter and monsoon which is statistically significant where t (24) = -8.471 (p=0.000) with the difference in mean being -1484.816. Comparison of winter rainfall and post-monsoon rainfall is labeled pair 3 and it can be seen that there is statistically significant difference between the mean with t (24) = -2.786 (p=0.000) with the mean difference being -31.216. There is also statistically significant difference in pair 4, pair 5 and pair 6 with the results being t(24)=-8.149 (p=0.000); t(24)= -7.825 (p=0.000) and t(24)= 8.69 (p=0.000) respectively. The difference in the mean for the three pairs is -1293.488, 160.112 and 1453.600 for pair 4, pair 5 and pair 6 respectively. The limitation of in this data is that the rains for entire seasons for the locations is given without the actual daily rains being given. This is makes it difficult to know the actual type of design of water system that would be appropriate. The significance difference in rains between seasons is and a clear indication that some of the seasons have lower rains that the other and thus there is a form of rain deficit in some of the seasons. There is a high mean difference between monsoon and post monsoon season of 1453.6. This shows that there a big difference in the amounts of rainfall registered for the two seasons for the 25 locations. From table 3 it can be seen that the mean for post-monsoon season is 93.06 which is very low in comparison of a mean of 1546.66 for monsoon. The mean of the winter which succeeds the post-monsoon is also very low at 61.84. This therefore means that most of the monsoon rains will be relied upon to supply water to serve the deficit in post monsoon and during the winter seasons. The level of rainfall between pre-monsoon and monsoon is also significant but the mean for pre-monsoon is not as low as that for winter and post-monsoon. Bearing in mind that if the excess water in the monsoon season is to be stored for use in this pre-monsoon season it will have to last through the both winter and post monsoon. Post management will have to resort to using the rains supplied in this season in the water needs of the season as the monsoon season is being waited for. Paired Samples Test Paired Differences t df Sig. (2-tailed) 95% Confidence Interval of the Difference Mean Std. Deviation Std. Error Mean Lower Upper Pair 1 winter - premons -191.3280 130.62439 26.12488 -245.24710 -137.408 -7.324 24 .000 Pair 2 winter - monso -1484.816 876.380 175.27602 -1846.567 -1123.064 -8.471 24 .000 Pair 3 winter - postmo -31.21600 56.029 11.20595 -54.34394 -8.08806 -2.786 24 .010 Pair 4 premons - monso -1293.488 793.672 158.73444 -1621.099 -965.87622 -8.149 24 .000 Pair 5 premons - postmo 160.112 102.305 20.46108 117.88241 202.3415 7.825 24 .000 Pair 6 monso - postmo 1453.600 835.606 167.12137 1108.67845 1798.52155 8.698 24 .000 Table 3 Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 winter 61.8440 25 28.39101 5.67820 premons 253.1720 25 131.45385 26.29077 Pair 2 winter 61.8440 25 28.39101 5.67820 monso 1546.6600 25 878.79600 175.75920 Pair 3 winter 61.8440 25 28.39101 5.67820 postmo 93.0600 25 48.31295 9.66259 Pair 4 premons 253.1720 25 131.45385 26.29077 monso 1546.6600 25 878.79600 175.75920 Pair 5 premons 253.1720 25 131.45385 26.29077 postmo 93.0600 25 48.31295 9.66259 Pair 6 monso 1546.6600 25 878.79600 175.75920 postmo 93.0600 25 48.31295 9.66259 Elevation It would be expected that areas at higher elevation would be associated with lower temperatures if conformation with the general principle “the higher you go the cooler it becomes”. The highly are expected to have low level of rainfall because of low pressures that might hinder condensation. Annual rainfall vs elevation Scatter plots Figure 2 gives the scatter plot for annual rainfall and elevation. From the plot it can be seen that there seem to be a general trend of annual rainfall decreasing with elevation even though this trend is not strongly portrayed. The trend line shows that actually there a negative correlation between the two variables with a correlation coefficient -0.314. The R-square value of 0.064 shows that the relationship between the two variables is weak and that only 6.4% of the variation in rainfall can be explained by elevation. Figure 2 Temperature vs elevation Scatter plots Figure 2 gives the scatter plot for temperature and elevation from which it can be seen that there is a general trend of temperature decrease with elevation. The trend line confirms there is a negative correlation between the two variables with a correlation coefficient -0.003. The R-square value of 0.541 shows that the relationship between the two variables of medium strength where 54.1% of change in temperature can be explained by elevation. Figure 3 Regression analysis of annual rainfall vs elevation The results of regression of between rainfall against elevation is given in the summary results which consist of table 4 which gives regression statistics and the ANOVA table in table 5. From table 4 it can be seen that R Square = 0.064 which means that there is a weak correlation between the variables. The ANOVA table gives F is 1.59 with p=0.219 meaning that the regression is not statistically significant. This is because the F value is small and p is greater than 0.05.This means that the hypothesis that rainfall is negatively correlated to elevation is not true. The comparison of the actual values and the predicted values are as shown in figure 4. SUMMARYOUTPUT Table 4 Regression Statistics Multiple R 0.25 R Square 0.064 Adjusted R Square 0.024 Standard Error 1009.250 Observations 25 Table 5: ANOVA   df SS MS F Significance F Regression 1 1626831 1626831.145 1.59 0.219 Residual 23 23427512 1018587.468 Total 24 25054343       Figure 4 Regression analysis of temperature vs elevation The results of regression between temperature against elevation is given in the summary results which consist of table 6 which gives regression statistics and the ANOVA table in table 7. From table 6 it can be seen that R Square = 0.541 54.1% of variation in temperature can be explained by elevation. The ANOVA table gives F is 27.20555 with p=0.000027 meaning that the Regression is statistically significant as the F value is relatively high and p Read More