Change in Rainfall Patterns in the Hilly Region of Uttarakhand due to the Impact of Climate Change

Uttarakhand, a Himalayan state of India, may experience an increase in temperature of 1.4°C to 5.8°C by 2100 due to global warming, which will melt the glaciers of the state. As such, we have tried to quantify the changes that have already been happened and also tried to quantify the future changes that may occur due to the impact of climate change. The future rainfall may be estimated by using a global circulation model (GCM). However, due to the very coarse spatial resolution of the different GCM, we cannot use them in their natural form. For matching this spatial inequality between the GCM output and historical precipitation data, we used the statistical downscaling technique. In the present study, we also tried to examine the suitability of the artificial neural network with principal component analysis for downscaling the rainfall for different hilly districts of Uttarakhand, India. We used the Canadian Earth System Model of the IPCC Fifth Assessment Report, as the GCM model, and the Indian metrological department gridded data as the observed rainfall. We performed the analysis for the different scenarios to visualize the impact of climate change on rainfall trends for all nine hilly districts of Uttarakhand. Results show that there was a clear indication of climate change in upper Himalayan districts like Pithoragarh, Rudraprayag, and Chamoli, which was observed from the pattern of the peak of monthly rainfall. The percentage change in monsoon rainfall may go up to 200 % in the case of RCP8.5 in comparison with the observation data. Also, the volume of rainfall may increase in the case of RCP8.5 from July to September as compared to the historical data, i.e., there may be a shifting of monsoon rainfall in the future

Statistical downscaling of climate change scenarios of rainfall and temperature over Indira Sagar Canal Command area in Madhya Pradesh, India

General circulation models (GCMs) have been employed by climate agencies to predict future climate change. A challenging issue with GCM output for local relevance is their coarse spatial resolution of the projected variables. Statistical Downscaling Model (SDSM) identifies relationships between large-scale predictors (i.e., GCM-based) and local-scale predictands using multiple linear regression models. In this study (SDSM) was applied to downscale rainfall and temperature from GCMs. The data from single station located in the Indira Sagar canal command area at Madhya Pradesh, India were used as input of the SDSM. The study included calibration and validation with large-scale atmospheric variables encompassing the NCEP reanalysis data, the future estimation due to a climate scenario, which is HadCM3 A2. Results of the downscaling experiment demonstrate that during the calibration and validation stages, the SDSM model can be well acceptable regard its performance in the downscaling of daily rainfall and temperature. For a future period (2010-2099), the SDSM model estimated an increase in total average annual rainfall and annual average temperature for station. This indicates that the area of station considered will be wet and humid in the future. Also, the mean temperature is projected to rise to 1.50 C to 2.50 C for present study area. However, the model projections show a rise in mean daily precipitation with varying percentage in the months of July (0.59% to 2.09%) and August (0.79% to 1.19) under A2 of HadCM3 model for future periods

Hydrologic responses to climate and land use/cover changes in world heritage site of Ngorongoro conservation area and surrounding catchments, northern Tanzania

A Dissertation submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Hydrology and Water Resources Engineering of the Nelson Mandela African Institution of Science and TechnologyIn Tanzania, various studies have analyzed the impact of climate and land use/cover changes on water resources. However, information on the interactions between climate and land use/cover change, temporal and spatial variability of hydrological components and water quality at the local scale is insufficient. The objective of this study was to evaluate the hydrological response to climate and land use/cover changes in Ngorongoro Conservation Area (NCA) and surroundings. The study performed climate change analysis using outputs from a multi-model ensemble of Regional Climate Models (RCMs) and statistically downscaled Global Climate Models (GCMs). The CA–Markov model applied to project Land use/cover for the future 2025 and 2035. This study further used the Soil Water Assessment Tool (SWAT) modelling approaches to analyse the hydrological responses and HYDRUS 1D to determine the change in Groundwater quality due to climate and land use/cover changes. The analysis of climate change between historical period (1982-2011) and future period (2021-2050) indicated an increase in the mean annual rainfall and temperature, seasonal rainfall except June to September (JJAS) season which showed a decreasing trend. Spatially, rainfall and temperatures would increase over the entire area. The projected Land use/cover change for the period 2025 to 2035 compared to the baseline 2016, showed a reduction in bushland, forest, water, and woodland, but an intensification in cultivated land, grassland, bare land, and the built-up area. The surface runoff, evapotranspiration, lateral flow, and water yield would significantly increase in the future, while groundwater would decrease under combined climate and land use/cover change. It is predicted that two anions (Cl− and PO4 −3 ) and two cations (Na+ and K+ ) would exceed the permissible limits for the drinking water set by the World Health organisation (WHO) and Tanzania Bureau of Standards (TBS), from 2036 to 2050. Changes in groundwater quality due to major cations and anions is significantly correlated to evapotranspiration and temperature with Pearson correlation (r) between 0.35 and 0.85. Furthermore, correlate to the changes in all land use/ cover types with Pearson correlation (r) between 0.56 and 0.96. The results obtained provide further insight into future water resources management planning and adaptation strategi