Summer monsoon rainfall scenario over Bangladesh using a high-resolution AGCM

Summer monsoon rainfall was simulated by a global 20 km-mesh atmospheric general circulation model (AGCM), focusing on the changes in the summer monsoon rainfall of Bangladesh. Calibration and validation of AGCM were performed over Bangladesh for generating summer monsoon rainfall scenarios. The model-produced summer monsoon rainfall was calibrated with a ground-based observational data in Bangladesh during the period 1979–2003. The TRMM 3B43 V6 data are also used for understanding the model performance. The AGCM output obtained through validation process made it confident to be used for near future and future summer monsoon rainfall projection in Bangladesh. In the present-day (1979–2003) climate simulations, the high-resolution AGCM produces the summer monsoon rainfall better as a spatial distribution over SAARC region in comparison with TRMM but magnitude may be different. Summer monsoon rainfall projection for Bangladesh was experimentally obtained for near future and future during the period 2015–2034 and 2075–2099, respectively. This work reveals that summer monsoon rainfall simulated by a high-resolution AGCM is not directly applicable to application purpose. However, acceptable performance was obtained in estimating summer monsoon rainfall over Bangladesh after calibration and validation. This study predicts that in near future, summer monsoon rainfall on an average may decrease about ?0.5 % during the period 2015–2034 and future summer monsoon rainfall may increase about 0.4 % during the period 2075–2099.     

Stable isotope dynamics of groundwater interactions with Ganges river

Groundwater depletion has been an emerging crisis in recent years, especially in highly urbanized areas as a result of unregulated exploitation, thus leaving behind an insufficient volume of usable freshwater. Presently Ganges river basin, the sixth largest prolific fluvial system and sustaining a huge population in South Asia, is witnessed to face (i) aquifer vulnerability through surface waterborne pollutant and (ii) groundwater stress due to summer drying of river as a result of indiscriminate groundwater abstraction. The present study focuses on a detailed sub-hourly to seasonally varying interaction study and flux quantification between river Ganges and groundwater in the Indian subcontinent which is one of the first documentations done on a drying perennial river system that feeds an enormous population. Contributing parameters to the total discharge of a river at its middle course on both temporal and spatial scale is estimated through three-component hydrograph separation and end-member mixing analysis using high-resolution water isotope (δ¹⁸O and δ²H) and electrical conductivity data. Results from this model report groundwater discharge in river to be the highest in pre-monsoon, that is, 30%, whereas, during post-monsoon the contribution lowers to 25%; on the contrary, during peak monsoon, the flow direction reverses thus recharging the groundwater which is also justified using annual piezometric hydrographs of both river water and groundwater. River water-groundwater interaction also shows quantitative variability depending on river morphometry. The current study also provides insight on aquifer vulnerability as a result of pollutant mixing through interaction and plausible attempts towards groundwater management. The present study is one of the first in South Asian countries that provides temporally and spatially variable detailed quantification of baseflow and estimates contributing parameters to the river for a drying mega fluvial system.     

The relationship between the shear strength and water retention curve of unsaturated sand at different hydraulic phases

Acta Geotechnica - This technical paper focuses on evaluating the shear strength of unsaturated sand at drying, wetting and alternate drying–wetting cycles, as well as the correlation between...     

Stability analysis of multi-directional anchored rock slope subjected to surcharge and seismic loads

More than one set of anchors with different orientations can be an economical solution to completely stabilize the rock slope. In this note, a general analytical expression for the factor of safety of a multi-directional anchored rock slope (MDARS) against plane failure is derived, incorporating most of the practically occurring destabilizing forces under surcharge and seismic loading conditions. Several special cases of this expression are found to be similar to those reported in the literature. A graphical presentation shows that for any specific inclination of one set of anchors to the normal at failure plane, the variation in the inclination of the second set of anchors to the normal at failure plane, when greater than approximately 60°, does not cause a significant change in the factor of safety of the MDARS.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

Turbulence Study Around Bar in a Braided River Model

Water Resources - Turbulent flow is a flow regime which is described by the anarchic property changes. This includes the rapid variation of pressure, high momentum convection and flow velocity in...     

Multiple step ahead river flow modelling for south east tasmanian aquaculture

We develop multiple step ahead prediction models of river flow for locations in Tasmania (Australia) for decision support in aquaculture. In predicting river flows for multiple days ahead, we first statistically determine the maximum input lags of rainfall and river flow. We then use machine learning techniques in building models. In multiple step ahead prediction, we consider both static and dynamic approaches. In dynamic approach, one day prediction is served as input to two days ahead prediction. The experimental results demonstrate that, in general, a dynamic approach provides better accuracy in multiple day’s ahead prediction. For Duck Bay location using dynamic approach, support vector regression performs best over linear regression, M5P and multilayer perceptron. However, at Montagu Bay location, we find that M5P performs best over methods. We find that multiple step ahead prediction of river flow for each location requires modelling of lags with associated machine learning techniques.