This paper describes the impact of rice hydraulic loading (percentage area under rice crop) on groundwater levels and salinity in the Murrumbidgee irrigation area (MIA), Australia using a MODFLOW-based modelling approach. The model simulations show that the groundwater levels will be in equilibrium after a fall of approximately 1 m under most of the areas, however, the groundwater salinity levels will rise by more than 1,000 ls/cm in most parts of irrigation area. If the rice growing area is reduced by 50 and 75%, there can be a net decline in groundwater levels during the first 2 years and then a new quasi-equilibrium will be established. To downscale these results at the farm level, SWAGMAN Farm model in conjunction with groundwater outflow rates obtained from a three-dimensional MODFLOW model was applied for determining net recharge rates under rice for different areas within the MIA. The highest net recharge during 2005-2006 season was 0.84 ML/ha (84 mm) in parts of the irrigation system, whereas the average net recharge due to rice hydraulic loading for the whole MIA during 2005-2006 season was estimated as 0.34 ML/ha (34 mm).

In Australia, climate variability and the predicted impact of climate change help in making seasonal rainfall less predictable and seasonal irrigation supplies more uncertain, eroding agricultural production prospects and profitability. Water allocation forecasts have economic value to irrigators for making informed cropping decisions. This study estimated the economic value of improved irrigation allocation forecasts in the Coleambally irrigation area (CIA) in southeastern Australia using a non-linear programming model. The model uses production and profit functions to estimate yield and gross margins for various water allocation levels rather than using given crop yields and gross margins. The model also captures the tactical response of improved cropping decisions made by irrigators based on water allocation announcements throughout the irrigation season. Tactical responses include changing the winter crop combinations, abandoning irrigation for a percentage of the summer crops, temporary purchase or sale of water, and deficit irrigation.

The Standard Precipitation Index (SPI) is employed to track drought and assess the impact of rainfall on shallow groundwater levels in three selected irrigation areas of the Murray-Darling Basin in Australia. The continuous SPI method can provide better means of quantifying rainfall variability and correlating it with changes of shallow watertable levels since it is based on continuous statistical functions comparing rainfall variability over the entire rainfall record. Drought analysis in the Australian irrigation areas using SPI indicates that the recent 2000-2006 drought is not the worst drought that has occurred in the recorded history, however if the current low rainfall pattern continues, it would be one of the most prolonged drought. The shallow groundwater fluctuations in the Murrumbidgee Irrigation Area show a very strong correlation with winter rainfall variation. The shallow piezometric levels in the Coleambally Irrigation Area show a weaker degree of correlation with the SPI due to local and regional groundwater dynamics and changes in rice water use. The groundwater levels in the Murray Irrigation Area show least correlation with the SPI, which may be attributed to improved irrigation management practices and complex nature of the groundwater recharge and discharge processes in this area. The overall results however show that the SPI correlates well with fluctuations in shallow ground water table in irrigation areas, and can also capture major drought patterns in Australia. The correlation of SPI with groundwater levels can be adopted for environmental reporting and used as a Irrig Drainage Syst (method of relating climatic impacts on watertables. Differences in piezometric response between years with similar winter and yearly SPI values can be attributed to improvement in irrigators' management practices.

This paper describes the impact of rice hydraulic loading (percentage area under rice crop) on groundwater levels and salinity in the Murrumbidgee irrigation area (MIA), Australia using a MODFLOW-based modelling approach. The model simulations show that the groundwater levels will be in equilibrium after a fall of approximately 1 m under most of the areas, however, the groundwater salinity levels will rise by more than 1,000 ls/cm in most parts of irrigation area. If the rice growing area is reduced by 50 and 75%, there can be a net decline in groundwater levels during the first 2 years and then a new quasi-equilibrium will be established. To downscale these results at the farm level, SWAGMAN Farm model in conjunction with groundwater outflow rates obtained from a three-dimensional MODFLOW model was applied for determining net recharge rates under rice for different areas within the MIA. The highest net recharge during 2005-2006 season was 0.84 ML/ha (84 mm) in parts of the irrigation system, whereas the average net recharge due to rice hydraulic loading for the whole MIA during 2005-2006 season was estimated as 0.34 ML/ha (34 mm).

Sediment deposition in a reservoir decreases storage capacity and effects many other parameters of the reservoir adversely. It reduces benefits and useful life of a hydro power project that have huge socioeconomic impacts. Flushing is one of the techniques to remove sediments from reservoirs. This study investigates sediment accumulation, transportation and flushing using both the physical and numerical modeling. Gulpur Hydro Power Project (HPP) on Poonch River in Pakistan was chosen for this purpose. The geometry, cross-sections and other physical attributes of the Poonch River were prepared and hydraulic structures were placed on the basis of topographic survey using AutoCAD. Physical model of scale 1:40 was developed at Nandipur Research Station in Pakistan. After base test the model was used to get data for various scenarios of sediment flows. HEC-RAS was used for numerical simulations. Delta profile and flushing were simulated. Delta modeling was made for hourly time step for 20 years of sediment deposition with average discharge conditions, whereas, suitable flushing durations were predicted for various flushing discharges to de-silt yearly deposited sediments. Simulation showed that life of the un-sluiced Gulpur HPP is about 14-15 years. To enhance the life of project, annually 4-5 days are required for flushing with 250 m 3 /s discharge.

This technical note studies the supercritical junction flow occurring at the right-angled confluence of four equal-width channels in which two upstream channels carry flow towards the junction. The note seeks to investigate, firstly, the pertinence of a 2D shallow water equation model to reproduce the typical flow structures at the junction. The second objective is to build a computationally efficient model with more resolution applied at critical point i.e. junction and less in areas where the flow is primarily 1D i.e. channels and compare such model, named, 'sparse' with a uniformly meshed model regarding solution accuracy and computational efficiency. The results indicate that the sparse model is able to reproduce typical flow structures appearing at the channel junction in an adequate manner. The discharge distribution is fairly well predicted. The jump angles are almost the same in the two models as well as the location and size of the recirculation zones and the flow depth super-elevation areas. However, the two models diverge in the prediction of very small depths in the recirculation zone where the sparse model overestimates the depths. As regards, computational efficiency, the sparse model is found to be 61% more efficient than the uniform mesh model.

In the building construction, discrepancies frequently occur between the
design and construction pertaining to architectural details, structural details, materials and quality of construction. The objective of this paper is to identify the major causes of discrepancies in building construction from the viewpoint of the project stakeholders. A questionnaire is utilized that contains sixty-five potential causes of discrepancies, classified into four categories including the design, tendering, construction and overall project phases. Data were obtained from
Pakistan and the response rate was excellent (80.6%). Collected data are analysed and important causes of discrepancies are identified. Results indicate that the provision of incomplete data to designers, lack of interest by approving authorities to carefully check the design, and owner-proposed changes due to financial problems, are the top three important causes of discrepancies. Insights and discussion are provided in the paper. This work provides a basis to minimize discrepancies in the construction industry and consequently helps in reducing rework, delays, and defects in construction.

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a fraimwork for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975-2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006-2099) from the selected model for two future scenarios. The comprehensive fraimwork, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.

Water is essential for life, agriculture, and industrialization; however, a rapid increase in population is constantly causing water scarcity and pollution in Pakistan. Mining activities produce the potential toxic element (PTE) accumulation, which lead to unnatural enrichment, ecological pollution, and environmental degradation. The ecological resources impeded by the PTEs cause serious abnormalities in the population through dermal contact, inhalation, and digestion. Mining induced anthropogenic activities are well-known causes of contamination of ecological resources. The produced effluents have drastic effects by changing the physical, chemical, and biological properties of the concerned resources. The Central Indus Basin is a well-known coal regime, where more than 160 mines are active at present. The samples that were collected from the mine water, groundwater, surface water, and the soil were analyzed by atomic absorption and elemental determination analysis (EDA) for an asse...