The influence of well hydraulics on the spatial distribution of well incrustations

In many cases, the operation of wells is hampered by the formation of mineral incrustations. From field studies, it is known that the distribution of incrustations in wells is quite inhomogeneous. Flow models were calculated to assess the hydraulic background of this phenomenon. For horizontal flow, the screen section facing the natural flow direction receives the majority of the total inflow. This phenomenon increases with increasing natural gradients of flow. The vertical distribution of water intake is also quite inhomogeneous. In partially penetrating wells, the uppermost part of the screen receives much more inflow than the deeper screen sections. These flow inhomogenities involve elevated flow velocities and may cause increased influx of shallow, oxygenated water, all conditions favorable for incrustation growth. Field investigations on incrusting wells clearly show that the identified screen areas of elevated flow are indeed much more prone to incrustation deposition. Such sections require more attention during rehabilitations. A suction flow control device can help to homogenize the inflow but can cause elevated entrance loss when affected by incrustation buildup itself.     

Hydrodynamic-phytoplankton model for short-term forecasts of phytoplankton in Lake Taihu, China

Phytoplankton biomass is an important factor for short-term forecasts of algal blooms. Our new hydrodynamic-phytoplankton model is primarily intended for simulating the spatial and temporal distribution of phytoplankton in Lake Taihu within a time frame of 1-5 days. The model combines two modules: a simple phytoplankton kinetics module for growth and loss; and a mass-transport module, which defines phytoplankton transport horizontally with a two dimensional hydrodynamic model. To adapt field data for model input and calibration, we introduce two simplifications: (a) exclusion of some processes related to phytoplankton dynamics like nutrient dynamics, sediment resuspension, mineralization and nitrification, and (b) use of monthly measured data of the nutrient state. Chlorophyll-α concentration, representing phytoplankton biomass, is the only state variable in the model. A sensitivity analysis was carried out to identify the most sensitive parameter set in the phytoplankton kinetics module. The model was calibrated with field data collected in 2008 and validated with additional data obtained in 2009. A comparison of simulated and observed chlorophyll-α concentration for 33 grid cells achieved an accuracy of 78.7%. However, mean percent error and mean absolute percent error were 13.4% and 58.2%, respectively, which implies that further improvement is necessary, e.g. by reducing uncertainty of the model input and by an improved parameter calibration.