Introduction
Review of Literature
- Climate Change Impact Studies
- Climate Change Studies in the North East India
- GCM for Water Resources Studies
- RCM for Water Resources Studies
- The direct use of GCM outputs in hydrological models
- Coupling GCMs and Macro-Scale Hydrologic Models
- Downscaling GCM to Force Hydrological Models
Teutschbein et al., (2011) and Teutschbein and Seibert, (2012) provided a recent review on the use of RCMs for hydrological models. However, some indicators show that the impacts are already visible in the region (Das et al. 2009).
Hydrological Modeling of Umiam Watershed
RES_ESA 980 Area of the reservoir when the reservoir is filled to the emergency flow (ha). RES_PSA 307 Area of the reservoir when the reservoir is filled to the main overflow (ha).
Impacts of Inter-Basin Water Transfer Reservoir on Streamflow
- Simulation of the natural flow
- Changes in the magnitude of monthly flows
- Daily flows and hydrologic extremes
- Hydrological alteration of extreme conditions
- Hydrologic alteration of high and low pulses
- Hydrologic alteration of rate and frequency of flow conditions change
- The overall degree of hydrological alteration
Therefore, a methodological framework is proposed in this paper to generate natural stream flow and to perform the analysis of the changes in the flow regime due to the reservoir. The changes in the hydrologic regime due to the reservoir are investigated using the Indicators of Hydrologic Alteration (IHA) software (The Nature Conservancy 2009). The monthly median flow (Figure 4.2) during the 1965-2015 simulations shows reduced flow in the presence of the reservoir.
This reduction can be attributed to the water collected in the reservoir and the diversion to the power plant. It can be observed that the presence of the reservoir has led to a reduction in the number of major floods (10 years return flow) and small floods (2 years return flow). 2006 to 2015 there are more frequent low flows in the presence of the reservoir than with natural flow.
They found that the number of capsizes was higher on steep terrain due to the rapid nature of the river.
Effects of Inter-Basin Water Transfer and Cascading Reservoirs on Streamflow of
- Effect of Kyrdemkulai Reservoir
- Effect of Nongmahir Reservoir
- Effect of Stage-IV Reservoir
- Effect of Umtru Reservoir
- Effect of all dams
Monthly flows appear to increase in all months due to IBWT and reservoir. The highest hydrological change values are seen in 1-day, 3-day, 7-day minimum flow and baseflow index (Figure 5.9) in the high RVA category, while there is a negative change in the middle RVA category. The high RVA category in monthly flows shows a positive hydrologic change as monthly flows increased in size due to IBWT and Umtru Reservoir (Figure 5.11).
The higher discharge in the case of the all-dams scenario may be due to storage of water in the reservoir. Parameters related to the size and duration of extreme annual water conditions have an important role in structuring the river channel morphology and physical habitat conditions (Figure 5.14. The effect of individual and cumulative effects of all reservoirs under different scenarios are presented in figure 5.15.
The analysis revealed an increase in monthly flows due to Kyrdemkulai, Nongmahir, Umiam Stage IV and Umtru Reservoirs.
Trend Analysis of Spatial and Temporal Rainfall Variations
Data
The analysis in this study was carried out using gridded rainfall data provided by the National Data Centre, India Meteorological Department (IMD), Pune. This gridded data was developed by Pai et al. from daily rainfall records at 6955 rain gauge stations in India. Due to differences in the density of rain gauges in different regions and time periods, the number of stations available to develop daily grid point data also varied.
On average about 2600 stations per year were used to generate homogeneous daily grid data. IMD performs standard quality checks on the data for errors from the sentinel level. The original data developed by Pai et al. 2014) was up to 2010, but is continuously updated using the same methods and made publicly available in the IMD web portal.
The time series plot for one of the grid points (P1) is shown in Figure 6.4 Figure 6.2.
Innovative Trend Analysis (ITA) Method
Unlike the most commonly used classical methods of trend analysis such as Mann-Kendal Test (Kendall 1948; Mann 1945) and Sen's slope (Sen 1968), the ITA method is free from assumptions such as serial correlation, non-normality, record length . , etc. The first half of the series (Xi) is plotted on the X-axis and the second half on the Y-axis. Points falling in the upper triangular area of the 1:1 line indicate an increasing trend in the time series, and data falling in the triangular area the bottom of the 1:1 line show a downward trend in the time series.
But when the time series has a non-monotonic trend, the scatter points may appear on the upper and lower regions of the 1:1 line. To determine the significance of the trend slope S, a null hypothesis (H0) of no significant trend and an alternative hypothesis (Ha) for the presence of a significant trend are considered. If α is the level of significance, the confidence limits (CL) of the trend slope are given by.
In equation (6.3, 𝜌𝑋̅𝑖𝑋̅𝑗 is the cross-correlation coefficient between the means of sorted Xi and Xj.
Mann-Kendall test
In the case of autocorrelated data, trends are more likely to be detected, even if there is no trend in reality (Hamed and Rao 1998). To minimize the effect of auto-correlation, a pre-whitening process proposed by Yue and Wang (2004) was applied to the dataset when significant (α=0.05) auto-correlation exists. If the autocorrelation is not significant, the MK test is applied to the original data set.
Sen’s slope estimator
Annual rainfall trend
In the "high" category, an increasing trend is visible for all grid points except for P18 (Figure 6.4). The same study found that medium and high rainfall for Otago and Westland contributed to the positive trend. The value of Z for all networks ranges from -8.77 to 2.96, and the variation of its statistical significance in the considered area is presented in Figure 6.5.
This shows that the ITA can detect hidden trends in the data series that are not detected by the MK test. Sen's slope magnitude was higher in the southern region with a maximum of -8.96 mm/yr at P1 (Figure 6.5b). If the area specified in their paper (25°41' N, 91° 55' E), which falls under grids P2 and P3 in this study, is considered, similar increasing trends can be observed, but significant at P3 (Figure 6.5b).
Differences in detecting a significant trend in the study area may be due to the length of the data considered in the analysis because the power of the MK test in its ability to detect trends increases with increasing sample size (Sheng Yue et al. 2002). .
Winter rainfall trend
After the autocorrelation check and pre-whitening of rainfall data, the commonly accepted MK test was performed to compare with the trends detected using ITA. The results of MK test show an increasing trend in 6 grids, while 14 grids were in decreasing trend with negative Z value (Table 6.2). The trends using MK test were only significant in 12 grids, while ITA detected significant trends in 18 grids.
However, only 5% of the networks show a significant increasing trend using the MK test as opposed to 45% using the aforementioned ITA test. 10% consisted of networks showing a markedly increasing trend, while the remaining 10% were evenly split between slightly increasing and decreasing trends. In the MK test, the winter precipitation trend tends to decrease on all grids (Figure 6.5d), of which 70% were characteristic and 30% insignificant (Figure 6.6g).
The sharpest decline was seen at P1 in both tests (Table did not find any significant trend for seasonal rainfall in the Shillong region, which falls under the P2 and P3 grids in this study.
Pre-Monsoon rainfall trend
However, both the ITA and MK tests in this study show significant declining trends for the winter season. It can be seen that the negative value of the ITA slope is in the majority, and most of them are significant except for P15 and P17. The maximum downtrend was seen at P1 while the maximum uptrend was located at P8 for the high category.
Low and medium rainfall shows a decreasing trend in most networks (Figure 6.8). In general, 60% of the networks show a significant downward trend, 30% showed a significant increasing trend and from the rest 20%, non-significant and significant account for 10% each (Figure 6.6c). Again, the number of non-significant trends reported by the MK test was 40%, while the ITA test reported only 10%.
The maximum changes are seen in the P1 network in both the ITA and MK test and both show a negative trend.
Monsoon rainfall trend
Post-monsoon rainfall trend
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Estimation of freshwater availability in the West African subcontinent using the SWAT hydrological model. Journal of Hydrology. An Innovative Analysis of Annual and Seasonal Precipitation Trends in the Yangtze River Delta of Eastern China.”. Effects of Cascade Reservoir Dams on Flow and Sediment Transport in the Wujiang River Basin of the Yangtze River, China.” Inland Waters.
Impact of projected climate change and human activities on streamflow
Summary and conclusion
After the hydrological analysis, the rainfall trend in the study area was analyzed which is one of the most important factors affecting stream flow. The analysis revealed that changes in land use and climate are increasing streamflow in the Umiam Basin. Evaluation of rainfall trends in the South Island of New Zealand using the innovative trend analysis (ITA). Theoretical and Applied Climatology, Theoretical and Applied Climatology.
Integrated water resources management under climate change scenarios in the Abaya-Chamo sub-basin, Ethiopia." Modeling Earth Systems and Environment, Springer International Publishing. Variation in the orographic extreme rainfall events over the Meghalaya Hills of Northeast India in the two halves of the twentieth century." Theoretical and applied climatology. Improving SWAT2000 Modeling to Assess the Impact of Dams and Locks on Flow in the Huai River Basin of China." Hydrological Processes.
Modeling the effects of climate change on water resources in central Sweden.” Water Resources Management.
