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Summary: The rapid economic development has caused heavy pollution in the Pearl River Delta Region (PRDR) and neighboring regions. In order to investigate the polluted characteristics, a coupled modelling approach has been deployed and the study area in the Pear River Delta is divided into two modelling systems: a river network that is simulated by a 1D hydrodynamic and water quality model, and an estuary that is modelled by a 3D model. The 1D river network model is coupled with the 3D estuary model based on the mass conservation criteria. The hydrodynamic (HD) model is based on 1D hydrostatic shallow water equations, and is solved by the Alternating Direction Implicit Approach. The water quality (WQ) model component is based on advection–diffusion equations. Some physical and bio-chemical processes are included in the WQ model to address the water pollution problems by different pollution substances, in particular, to the chemical oxygen demand (COD). The water quality data of the main rivers from 2004 to 2007 were collected, processed and evaluated. Because no detailed water quality data in the river is available, the COD source pollution intensity in the water quality model is taking the pollutant load from the confluence of the main five outlets of the Pearl River Estuary (PRE) for 1996–2007 as surrogate. The results show that the pollutants from the PRDR have a large impact on the quality of the Hong Kong seawater and much more serious in the wet season with high river discharge. An insight of the flow and transportation mechanism in the river and estuary system has been obtained through the paper, and a reliable basis to control water pollution of the Pearl River Delta is provided in this paper. [Copyright &y& Elsevier]

• A bed-steadying discharge is proposed. • A flow-sediment model and an LSTM model are integrated. • The riverbed deformation is predicted. River morphology plays an important role in water environment and resources. The dominant discharge (Q D) is a crucial indicator for understanding river morphology and bed evolution under the impact of various interacting processes. At present, the identification of Q D mainly depends on the analysis of a large number of hydrological data derived from measuring stations, leading to difficulty in obtaining detailed Q D distributions along the study reach. In this paper, Q D is approximately expressed as the bed-steadying discharge (Q S) which is based on major factors of water level and sediment-carrying capacity. Subsequently, an integrated model combining a numerical fluid-flow and sediment model with a deep-learning algorithm is applied to analyze the changing process of the Q S. The flow and sediment transport processes are simulated by the calibrated mathematical model, which are then adopted as the input sequences for the long short-term memory (LSTM) module. The verification results of the established LSTM model show robustness and accuracy in predicting the flow and sediment transport processes under multi-stage incoming flow and sediment conditions. Furthermore, the proposed integrated model is applied to identify the detailed process of Q S in the Middle Huaihe River. Results show that the changing process of Q S along the study reach is characterized by a particular trend of "increase-decrease-rapid increase" due to natural changes and human activities. Additionally, the Q S agrees well with Q D at the hydrological station, showing that Q S can be applied as an approximation for Q D along the study reach. By analyzing longitudinal and transverse profiles, the rationality of using Q S as obtained by the newly presented model is demonstrated. Its temporal variation is also consistent with the cross-sectional changes for the specified stations. [ABSTRACT FROM AUTHOR]

• A bed-steadying discharge is proposed. • A flow-sediment model and an LSTM model are integrated. • The riverbed deformation is predicted. River morphology plays an important role in water environment and resources. The dominant discharge (Q D) is a crucial indicator for understanding river morphology and bed evolution under the impact of various interacting processes. At present, the identification of Q D mainly depends on the analysis of a large number of hydrological data derived from measuring stations, leading to difficulty in obtaining detailed Q D distributions along the study reach. In this paper, Q D is approximately expressed as the bed-steadying discharge (Q S) which is based on major factors of water level and sediment-carrying capacity. Subsequently, an integrated model combining a numerical fluid-flow and sediment model with a deep-learning algorithm is applied to analyze the changing process of the Q S. The flow and sediment transport processes are simulated by the calibrated mathematical model, which are then adopted as the input sequences for the long short-term memory (LSTM) module. The verification results of the established LSTM model show robustness and accuracy in predicting the flow and sediment transport processes under multi-stage incoming flow and sediment conditions. Furthermore, the proposed integrated model is applied to identify the detailed process of Q S in the Middle Huaihe River. Results show that the changing process of Q S along the study reach is characterized by a particular trend of "increase-decrease-rapid increase" due to natural changes and human activities. Additionally, the Q S agrees well with Q D at the hydrological station, showing that Q S can be applied as an approximation for Q D along the study reach. By analyzing longitudinal and transverse profiles, the rationality of using Q S as obtained by the newly presented model is demonstrated. Its temporal variation is also consistent with the cross-sectional changes for the specified stations. [ABSTRACT FROM AUTHOR]

Summary: Most of the coal mines of China are mining under the ground, so the artesian test which is one of the aquifer tests is conducted normally several hundred of meters below the earth surface. And the target aquifer is with very high hydraulic pressure, sometimes more than 3MPa. Because of the high hydraulic pressure, it is most difficult to control the rate of flow out of the artesian well. Moreover, the velocity of flow out of the well cannot descend rapidly to zero, thus the analytical solution of Jacob and Lohman type curve for the artesian test will not be applicable. It is more reasonable if analyzing this test as a pumping test but with variable discharge. It is considered to rebuild that hydrogeological conceptual model in this paper. This conceptual model is similar with Theis model but with the variable discharge merely. And a general equation for any discharge variability is given. Its application for the linearly decreasing discharge is presented subsequently, and a type curve of this equation with linearly decreasing discharge will be given as well. Then a simple numerical model using FEFLOW will be built to simulate the linearly decreasing discharge from giving several different groups of the values of Transmissivity (T) and Storage coefficient (S). Both of them are much important hydrogeological parameters, and will be evaluated by using the type curve developed for this linear decreasing discharge well. The error between the given values of T and S in FEFLOW and the values of those calculated by matching point are much small. The solution gives really satisfactory values of these hydrogeological parameters. [ABSTRACT FROM AUTHOR]

Summary: Turbidity currents play a critical role in effective sediment and reservoir management in alluvial rivers. Yet previous estimations of reservoir turbidity currents are mostly based on simple empirical relations and it remains unclear how and to what extent physically based numerical models can resolve current evolution as compared against field observations. This paper presents a physically-based,process-resolved computational study of turbidity currents in the Xiaolangdi reservoir in the lower Yellow River, China. A coupled layer-averaged 2Dnumerical model is applied, which explicitly incorporates the interactions between the current, sediment transport and morphological evolution, and features a new well-balanced numerical scheme dealing with irregular topography. Two turbidity current events in July 2004 are numerically simulated to calibrate and validate the model. The current advance and the sediment transport rate computed by the model compare favourably with field measurements. These suggest the present model is a viable tool for determining the timing for operating the bottom outlets, which is critical for effective reservoir sediment management. The sediment entrainment flux and bed resistance are key factors dictating the evolution of turbidity current and warrant further investigations. [ABSTRACT FROM AUTHOR]

Summary: After the Yangtze River was closed by the Three Gorges Project (TGP) in 2003, erosion occurred from the dam site to the river mouth, especially in the middle and lower reaches of the Yangtze River. However, in some local areas of Chenglingji reach which holds the key position for flood management, there is actually deposition in contrast to the expected erosion. In this paper, a one dimensional mathematical model of the river network with sediment transport is used as the tool to simulate flow and fluvial processes. The calculation domain is from Yichang, which is downstream of the dam, to Hankou, the controlling node of flood management, 694km long in total. The model is calibrated based on the field data of hydrology and sediment transport during the period from October 2003 to October 2008. Then the model is utilized to simulate the erosion and deposition of the middle and lower reaches of the Yangtze River in the next two decades, and produce the results of a new river channel after river bed deformation occurs. The typical flood processes of 1954 and 1998 in the Yangtze River basin are used to check the flood management scheme for the research area, and results show that water storage of Three Gorges Reservoir (TGR) and a flood diversion program downstream of the Yangtze River should be taken into consideration. [Copyright &y& Elsevier]

Summary: In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT. [Copyright &y& Elsevier]