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|Title:||CLIMATE CHANGE SCENARIO GENERATION USING STATISTICAL DOWNSCALING|
|Authors:||Gautam, Narayan Prasad|
CLIMATE CHANGE SCENARIO GENERATION
|Abstract:||Climate change has been emerging as one of the challenges in the global environment. Information of predicted climatic changes in basin scale is highly useful to know the future climatic condition in the basin that ultimately becomes helpful to perform planning and management of the water resources available in the basin. Observed warming over several decades has been linked to changes in the large-scale hydrological cycle such as: increasing atmospheric water vapour content; changing precipitation patterns, intensity and extremes; reduced snow cover and widespread melting of ice; and changes in soil moisture and runoff. Precipitation changes show substantial spatial and inter-decadal variability. The frequency of heavy precipitation events has increased over most areas. There have been significant decreases in water storage in mountain glaciers and Northern Hemisphere snow cover. Shifts in the amplitude and timing of runoff in glacier and snowmelt-fed rivers, and in ice-related phenomena in rivers and lakes, have been observed. General Circulation Models (GCMs), representing physical processes in the atmosphere, ocean, cryosphere and land surface, are the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations. Recent interest in global warming has also increased concerns about the possible changes of flood and drought patterns including the rainfall amount. This study based on statistical downscaling, provide good example focusing on predicting the rainfall and runoff patterns, using the input of coarse general circulation model (GCM) outputs. The outputs of the GCMs are utilized to study the impact of climate change on water resources. The present study has been taken up to understand the climatic changes, climatic scenario generation and their inter-relationships based on the data of Satluj river basin. The findings of this study can be summarized as follows: 1. Statistical downscaling has been conducted in Bhakra region of Satluj river basin and predicted the future rainfalls from 2001 to 2100. 2. The predicted rainfalls have shown that there will be decrease in rainfall for winter season, partially decreases for pre monsoon and post monsoon and increases for monsoon season in the Bhakra region of Satluj river. 3. Rainfall variability as based on the observed rainfall has indicated that the rainfall values of December, January, February, March, July and August are on decreasing order and the rainfall values of remaining six months are on increasing order. 4. Future discharges from 2001 to 2100 have been predicted by rainfall-discharge and rainfall-temperature-discharge relationships by using ANN and MLR techniques. It has shown a better relationship by considering rainfall and temperature as inputs in compared to only rainfall as input. 5. In this study, comparison of the results of the ANN models with MLR for rainfall-runoff relationships has shown that the ANN model is better than MLR. 6. The predicted rainfalls and discharges for first 30 years from 2001 to 2030 are observed with having higher rainfall and discharge values in compared to the remaining prediction periods. 7. The Z-statistics of Mann Kendall test has shown that there are no trends in the predicted rainfalls and discharges from 2001 to 2100 at 5 % significance level. Likewise, there is no trend found out at 5 % significance level in the observed rainfall, discharge, temperature and predicted temperature data set used in this study.|
|Appears in Collections:||MASTERS' DISSERTATIONS (Hydrology)|
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