Fig.5.17 Trends showing the percentage of total area covered by different LULC classes for the Subansiri River basin. Fig.5.21 Trends showing the percentage of total area covered by different LULC classes for the Subansiri River basin.
General
As a result of global warming, this area of snow cover in the basin changes, causing a change in the discharge of the Subansiri River in its lower reaches. Not only the effect of snowmelt and precipitation, the change in land use/land cover that is happening in the Subansiri basin also affects the change in river discharge.
Objectives of the thesis
Study of tracking land use/land cover change of the Brahmaputra and the Subansiri river basins from 2002 to 2012 for January, April and October using Remote Sensing and GIS techniques. Impact of change in land use/land cover of the basin on the flow scenario of the river.
Research queries
Organization of the thesis
Brahmaputra basin was determined snow cover area in Subansiri basin and change of snow cover area with temperature change was analyzed. The energy potential was analyzed up to the year 2099 for four different time steps and for different land use/land cover scenarios.
Review of Literature……………………………………………………... 9-52
Climate change and its significance
Heydari et al (2015) developed a model based on the mixed linear programming (MILP) technique for the systematic operation of several reservoirs (Laar, Lartian and Karaj dams) in the Tehran-Karaj plain. MOD09A1.5 (MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid) of 500m resolution consisting of seven bands (band-1 to band-7) used to determine the snow cover area in the Subansiri River basin.
Snow and its study using remote sensing
- Snow properties in the electromagnetic spectrum
- Mapping of snow extent from remotely sensed data
- MODIS data and its application in snow cover mapping
- Himalayan glaciers and its study using remote sensing and GIS
Climate change and its impact on snow/glacier
- Impact of climate change on Himalayan glaciers
Mountain glaciers play an important role in detecting and monitoring climate change in regions not typically monitored by instrumentation (Haeberli et al., 2007). Afzal et al., (2014) used MODIS 8 day composite data to identify spatio-temporal trends in snow cover in the upper Indus Basin from 2001 to 2005.
Impact of change in snow cover area on discharge of rivers
They found that the snow cover of the Indus Basin showed an increasing trend from west to east. They found that total streamflow as well as snowmelt runoff for this basin increases with temperature.
Land use/land cover change detection study
- Application of remote sensing and GIS on land use/land
- Impact of change in land use/land cover on hydrology
- Application of hydrologic models to study the impact
A relationship between the change in snow cover surface and flow was established. The results show that for the month of January, the flow shows a decreasing trend in relation to the increasing trend of the snow cover area. Figure 4.4: Subansiri watershed showing flow direction and longest flow path. 4.3.2 Study of Snow Cover Area in Subansiri River Basin. A STUDY ON THE DETECTION OF LAND COVER CHANGE IN THE BRAHMAPUTRA AND SUBANSIRI RIVER BASINS.
The land use/land cover maps of Subansiri basin for the month of October from 2002 to 2012 are shown in the figure.
Rainfall-runoff modeling
- SCS-CN model and its application on runoff estimation
- Application of Artificial Neural Network model
- Scenario-based simulation of hydrological response
General Circulation Models/Global Climatic Models
- Approaches for downscaling of GCMs
- Dynamic downscaling
- Statistical downscaling
- Downscaling by Artificial Neural Network
Reservoir operation study
- Linear Programming (LP) model
- Non-linear Programming (NLP) model
- Dynamic Programming (DP) model
- Simulation model
- Models used as combination of two or more methods
- Artificial Neural Network Models
- Fuzzy rule based modeling
- Genetic Algorithm (GA) model
The reservoir operation policy determines the amount of water to be released based on the reservoir condition, demand, and likely inflow into the reservoir. First, he proposed a way to obtain general operating rules from the results of a deterministic optimization model. Panigrahi and Majumdar (2000) developed a rule-based fuzzy model for the operation of the Malaprabha single-purpose irrigation reservoir in Karnataka, India.
The results of the analysis showed that fuzzy logic can be effectively applied for developing reservoir operation rules.
Conclusions
Snow cover area variation study of the Brahmaputra River Basin and
Materials and Method
- Data used
- Normalized Difference Snow Index (NDSI)
- Preparation of snow map
- Comparison of the snow maps with Landsat data
The gridded temperature data from the HadCM3 (Hadley center Coupled Model version 3) model of the A2 scenario was used to obtain the average temperature of the upper part of the Brahmaputra River basin. There were fifteen points falling in and around the upper basin of the Brahmaputra basin. The snow maps of the study area were created in the Erdas Imagine remote sensing software.
Two small areas of the MODIS snow map have been compared with high-resolution Landsat data.
Results and Discussions
- Variation of snow cover area with respect to temperature
- Effect of change in snow cover area on discharge of the River
Again, for the month of October, the minimum snow cover area was found to be 9180 sq. For other years, the snow cover areas were less compared to the snow cover area in 2002. For the rest of the years, the average snow cover area of the basin was reduced compared to the snow cover area in 2002.
Increasing trends in discharge compared to decreasing trends in snow cover were observed in April, July, and October.
Conclusions
Accelerated melting and accelerated retreat of snowpack under warmer climates have direct implications for water resources and their distribution over time as well as regional climate. Reducing the area of snow cover under warmer climates will reduce the volume of snowmelt runoff. Evaporation will increase significantly due to higher temperatures and the rapid conversion of snow-covered areas to snow-free areas.
The enhanced ability of MODIS data to accurately detect snow cover area will have the greatest implications for hydrological modeling and forecasting in the Brahmaputra River Basin.
Status of snow cover of Subansiri basin and its impact on the
Materials used
- SRTM data
- MODIS satellite data
- Temperature data
- Discharge data
The gridded temperature data from the Hadley center Coupled Model version 3 (Hadley center Coupled Model version 3) of the A2 scenario with a spatial resolution of 2.5˚ to obtain the Subansiri. Three other HadCM3 points falling around the basin were also taken into account to determine the average temperature of the basin. The daily discharge data of Subansiri River at Khabulighat gauging station from 1990 to 2015 has been collected by Water Resources Department, Govt.
Methodology
- Watershed delineation
- Snow cover area study of the Subansiri River Basin
- Effect of change in snow cover area on discharge of
The SRTM DEM has been processed in the above seven stages as shown in the flowchart to get the catchment area of the Subansiri river basin. The NDSI method as explained in Chapter 3 to determine the snow cover area of the Brahmaputra river basin has also been used to determine the snow cover area of the Subansiri river basin. The four HadCM3 GCM points considered (Table 4.2) to determine the basin mean temperature are shown in Fig.
The snow cover areas obtained by the NDSI method were used to study the effect of snow cover area variations on the flow of the Subansiri River.
Results and Discussions
- Geomorphologic parameters of the Subansiri River
- Variation of snow cover area with respect to change
- Effect of change in snow cover area on discharge of the
It has been observed that the snow cover area for the month of January shows an increasing trend against the decreasing temperature trend. Like the month of April, the snow cover areas from 2003 to 2015 for the month of October are also less compared to the snow cover areas of 2002. The increasing and decreasing trend of discharge of Subansiri River with respect to the change in snow cover area for the months January, April, July and October from 2002 to 2015 are shown in Figure 4.13 (a, b, c and d).
The graphs show that for the month of January the discharge shows a decreasing trend compared to the increasing trend of the snow cover area.
Conclusions 96
- Methods of LULC change detection study
- Image classification
- Deterministic Classification
- Knowledge Base Classification
- Fuzzy/Soft Classification
Land use/land cover assessment has become one of the most important parameters for proper and meaningful management of land resources. It is considered to be one of the most appropriate and widely used methods for detecting changes (Jensen, 1996). The Maximum Likelihood Classification tool considers the variances and covariances of class signatures when assigning each cell to one of the classes represented in the signature file.
When the default EQUAL a priori option is specified, each cell is classified to the class of which it has the highest probability of being a member. The advantage of Maximum Likelihood method is the use of well-developed probability theory.
Materials and Methods
- Data used
- Database preparation
- Land use/land cover change detection and analysis
Unlike conventional classification techniques where each pixel is treated as a full member of one of the classes in the thematic map or completely excluded from those classes, fuzzy logic assigns the degree of membership of each pixel to each land cover class (Kruse, 1993). In this study an attempt has been made to map the land use/cover status of both the Brahmaputra and Subansir river basins in order to detect the changes that have occurred in the basins during the period 2002-2012 for three different. months ie January, April and October using geospatial techniques. Five different types of land use/cover are identified in the study area, viz., (i) bare soil (BS) (ii) surface water bodies (SWB) (iii) dense vegetation (DV) ( iv) light vegetation (LV) and (v) snow cover area (SCA). To perform LULC change detection, a post-classification detection method was used.
Classified images from different months were compared to identify quantitative aspects of change for the periods from 2002 to 2012 for the months of January, April and October.
Results and Discussions
- LULC change detection analysis for the Brahmaputra
- Change detection analysis for the month of January
- Change detection analysis for the month of April
- Change detection analysis for the month of October
- LULC change detection analysis for the Subansiri
- Change detection analysis for the month of January
- Change detection analysis for the month of April
- Change detection analysis for the month of October
- General Analysis
The area covered by each LULC class from 2002 to 2012 for the month of April has been determined and shown in Table 5.4. The areas covered under different LULC classes in the Subansiri Basin for the month of January from 2002 to 2012 are given in Table 5.10. The LULC maps prepared for the Subansiri basin for the month of January from 2002 to 2012 are shown in Fig.
The area covered by each LULC class from 2002 to 2012 for the Subansiri Basin for the month of April was determined and is given in Table 5.13.
Conclusions
In other words, the area covered by dense vegetation is decreasing due to its conversion to arable land. The reason for a decrease in area covered by surface water bodies may be the encroachment of slum dwellers and land developers. This can be attributed to the fact that the temperature is rising, which is the consequence of climate change.
As the snowmelt water accumulates in the pits, the area covered by the surface water bodies increases in January and April in the Brahmaputra basin.
Prediction of Rainfall over Subansiri River Basin using
General Circulation Models and their downscaling
The regression method is one of the most widely used statistical techniques (Driver and Tasker, 1990; Saget, 1994; Mendelhall and Beaver, 1994). The goal of multiple regression analysis is to use independent variables whose values are known to predict a single dependent variable. The impact of climate change on the hydrological cycle in river basins has been extensively analyzed in different parts of the world based on different emission scenarios and climate models.
Devastating floods accompanied by heavy rainfall in the upper catchments of the Subansiri River basin are a common phenomenon.
Materials and Methods
- Data and software used
- Downscaling of rainfall
- Selection of predictors
- Model calibration and validation for regression analysis
- Multiple Linear Regression Model
- Multiple Non-linear Regression Model
- Downscaling using Artificial Neural Network
- Prediction of rainfall
The primary objective is to determine the best set of parameters so that the model predicts experimental values of the dependent variable as accurately as possible. Nonlinear regression is a form of regression analysis in which observational data is modeled by a function that is a nonlinear combination of the model parameters and depends on either. In the test phase, the model is tested using the data set that was not used in the training. The most useful neural network in function approximation is multilayer perception (MLP) (Sharda and Patil, 1990).
During the training phase, the weights and biases of the network are optimized using an optimization algorithm.
Results and Discussions
- Determination of correlation coefficient
- Comparison of regression based statistical downscaling method and
- Prediction of future rainfall
- Future rainfall analysis at all the APHRODITE
- Future average rainfall analysis over the
- Spatial variation of rainfall over the Subansiri Basin
- Future Rainfall Analysis at the APHRODITE points on the basis of wet
Calibration and Validation were performed at all 24 APHRODITE rainfall points for both the multiple linear and multiple non-linear regression methods. Fig.6.4 shows the calibration and validation plots for (a) multiple linear regression and (b) multiple non-linear regression at the APHRODITE's point (91.875°E, 28.625°N) for the one GCM point that falls within the study area . The calibration and validation plots for (a) multiple linear regression and (b) multiple nonlinear regression performed by averaging the weighted average of all four GCM points at the APHRODITE point (91.875°E, 28.625°N) take, is shown in Fig. 6.5.
Fig.6.5: Calibration and validation graphs for (a) multiple linear regression and (b) multiple non-linear regression at the APHRODITE point 91.875°E; 28.625°N for the weighted one.
Conclusions
Introduction
Materials and Method
- Data used
- Runoff simulation of Subansiri
- Development of the Artificial Neural Network based rainfall-runoff
- Development of an ANN-SCS based Hybrid Model
Results and Discussions
- Sub-catchment wise rainfall analysis for each time step
- Runoff analysis for ANN based rainfall-runoff model
- Runoff analysis for ANN-SCS based hybrid model
- Curve numbers for different sub-catchments
- Performance of ANN-SCS based hybrid model and
- Comparison of ANN based rainfall-runoff model and
Conclusions
Determination of Future Power Potential of the
Hydroelectric Power Plant
Downstream impact of reservoir operation
Subansiri Lower Hydroelectric Project
- Principal features of Subansiri Lower Hydroelectric Project
Materials and Methods
- Reservoir simulation model
- Data used
- Methodology
- Capacity-Area-Elevation relationship for the reservoir
- Problem formulation
Results and Discussions
Conclusions
Summary and Conclusions……………………………………………… 233-237
Conclusions
Recommendations for future work
