Modelling climate change impacts on the Brahmaputra streamflow resulting from change in snowpack attributes

The Tibetan Plateau (TP) plays a critical role in modulating the hydrology for a number of prominent river basins. Despite its importance, changes in hydrological processes of the region are not closely monitored. It is now well known that rising temperatures are impacting the water cycle in the Plateau. The Upper Brahmaputra Basin, originating from the TP, provides fresh water for a large population downstream and its likely change in reference to future water availability is the focus of this thesis. One possible way to ascertain and project such changes is to formulate hydrological models and use simulations from General Circulation Models (GCMs) and Regional Climate Models (RCMs) as inputs. This thesis seeks to investigate climate change impacts on snowpack and streamflow as its two key aims. The first part of the thesis explores snowpack changes in terms of within-year accumulation and depletion across the Northern Hemisphere using measured spatially distributed snow water equivalent (SWE) information. Following this, a catchment-scale investigation of uncertainties in precipitation downscaling across the TP is then presented. Such uncertainties affect future projections of precipitation, which in turn influence streamflow simulations. Next, an evaluation of GCM and RCM-derived SWE is reported, which reveals that both GCM and RCM products suffer from significant uncertainties and biases. Such uncertainties and biases in SWE and other climatic variables are reduced significantly using a multivariate bias correction approach. In the second part of the thesis, a conceptual hydrological model is proposed to assess the impact of temperature-driven changes in snowpack attributes on the streamflow, considering the lack of data available for the upper Brahmaputra basin. The model simulates snow cover fraction, SWE and streamflow using temperature and precipitation information. The results show that SWE is likely to decrease in the near future (2041 to 2064) as well as in the far future (2071 to 2094), which will impact streamflow, and hence water availability for a significant portion of the global population that depends on the water supplied by the Brahmaputra as well as the other major rivers originating from the Tibetan Plateau

Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh

Time-series analyses of temperature data are important for investigating temperature variation and predicting temperature change. Here, Mann–Kendall (M–K) analyses of temperature time-series data in northeastern Bangladesh indicated increasing trends (Sen’s slope of maximum and minimum yearly temperature at Sylhet of 0.03 °C and 0.026 °C, respectively, and a minimum temperature at Sreemangal of 0.024 °C) except for the maximum temperature at Sreemangal. The linear trends showed that the maximum temperature is increasing by 2.97 °C and 0.59 °C per hundred years, and the minimum, by 2.17 °C and 2.73 °C per hundred years at the Sylhet and Sreemangal stations, indicating that climate change is affecting temperature in this area. This paper presents an alternative method for temperature prediction by combining the wavelet technique with an autoregressive integrated moving average (ARIMA) model and an artificial neural network (ANN) applied to monthly maximum and minimum temperature data. The data are divided into a training dataset (1957–2000) to construct the models and a testing dataset (2001–2012) to estimate their performance. The calibration and validation performance of the models is evaluated statistically, and the relative performance based on the predictive capability of out-of-sample forecasts is assessed. The results indicate that the wavelet-ARIMA model is more effective than the wavelet-ANN model

BREAKTHROUGH COLUMN STUDIES FOR REMOVAL OF IRON (II) FROM GROUNDWATER USING WOODEN CHARCOAL AND SAND

ABSTRACT Groundwater is an attractive and easily accessible resource of water in Bangladesh. All rural water supplies and most of urban water supplies rely on groundwater for potable supplies. Though groundwater is much less prone to bacterial contamination but in many parts of country has a severe problem with metal contamination particularly higher concentration of iron. Many iron removal units are available but most of these methods are not feasible in the rural and semi-urban areas. As a part of low cost iron removal strategy, this study used processed wooden charcoal (PWC) and processed sand (PS) as filter media and evaluates adsorptive capacity of them for dissolved iron removal through continuous mode column studies. The experiments were carried out using synthetic water containing Fe (II) at a fixed pH of 5.5 and zero dissolved oxygen levels. Different bed depth used to obtain the adsorption breakthrough curves. An increase of breakthrough time and adsorption bed capacity were found with the increase in bed depths, while breakthrough time and uptake of Fe (II) ions onto the adsorbent decreases when the linear flow rate through the bed increases. At different bed depths, PWC shows higher adsorption capacity for Fe (II) as compared to PS. Breakthrough profiles of up-scaled columns and indigenous unit models indicate that for same bed heights and flow rate, the up-scaled columns perform better than indigenous unit models and yield higher breakthrough throughputs. In the meanwhile, the up-scaled columns also performed reasonably well to remove fluoride, turbidity, sulfate and alkalinity at breakthrough point of Fe (II)