A review of the numerical modelling of salt mobilization from groundwater-surface water interactions

Salinization of land and water is a significant challenge in most continents and particularly in arid and semi-arid regions. The need to accurately forecast surface and groundwater interactions has promoted the use of physically-based numerical modelling approaches in many studies. In this regard, two issues can be considered as the main research challenges. First, in contrast with surface water, there is generally less observed level and salinity data available for groundwater systems. These data are critical in the validation and verification of numerical models. The second challenge is to develop an integrated surface-groundwater numerical model that is capable of salt mobilization modelling but which can be validated and verified against accurate observed data. This paper reviews the current state of understanding of groundwater and surface water interactions with particular respect to the numerical modelling of salt mobilization. 3D physically-based fully coupled surface-subsurface numerical model with the capability of modelling density-dependent, saturated-unsaturated solute transport is an ideal tool for groundwater-surface water interaction studies. It is concluded that there is a clear need to develop modelling capabilities for the movement of salt to, from, and within wetlands to provide temporal predictions of wetland salinity which can be used to assess ecosystem outcomes.     

Simulation of streamflow with statistically downscaled daily rainfall using a hybrid of wavelet and GAMLSS models

ABSTRACT

A rainfall–streamflow model is proposed, in which a downscaled rainfall series and its wavelet-based decomposed sub-series at optimum lags were used as covariates in GAMLSS (Generalized Additive Model in Location, Scale and Shape). GAMLSS is applied in climate change impact assessment using CMIP5 general climate model to simulate daily streamflow in three sub-catchments of the Onkaparinga catchment, South Australia. The Spearman correlation and Nash-Sutcliffe efficiency between the observed and median simulated streamflow values were high and comparable for both the calibration and validation periods for each sub-catchment. We show that the GAMLSS has the capability to capture non-stationarity in the rainfall–streamflow process. It was also observed that the use of wavelet-based decomposed rainfall sub-series with optimum lags as covariates in the GAMLSS model captures the underlying physics of the rainfall–streamflow process. The development and application of an empirical rainfall–streamflow model that can be used to assess the impact of catchment-scale climate change on streamflow is demonstrated.     

Changing patterns in rainfall extremes in South Australia

Theoretical and Applied Climatology - Daily rainfall records from seven stations in South Australia, with record lengths from 50 to 137 years and a common period of 36 years, are...     

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.     

Non‐stationarity in rainfall and temperature in the Murray Darling Basin

Two analyses, one based on multiple regression and the other using the Holt–Winters algorithm, for investigating non‐stationarity in environmental time series are presented. They are applied to monthly rainfall and average maximum temperature time series of lengths between 38 and 108 years, from six stations in the Murray Darling Basin and four cities in eastern Australia. The first analysis focuses on the residuals after fitting regression models which allow for seasonal variation, the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI). The models provided evidence that rainfall is reduced during periods of negative SOI, and that the interaction between PDO and SOI pronounces this effect during periods of negative PDO. Following this, there was no evidence of any trend in either the PDO or SOI time series. The residuals from this regression were analysed with a cumulative sum (CUSUM) technique, and the statistical significance was assessed using a Monte Carlo method. The residuals were also analysed for volatility, autocorrelation, long‐range dependence and spatial correlation. For all ten rainfall and temperature time series, CUSUM plots of the residuals provided evidence of non‐stationarity for both temperature and rainfall, after removing seasonal effects and the effects of PDO and SOI. Rainfall was generally lower in the first half of the twentieth century and higher during the second half. However, it decreased again over the last 10 years. This pattern was highlighted with 5‐year moving average plots. The residuals for temperature showed a complementary pattern with increases in temperature corresponding to decreased rainfall. The second analysis decomposed the rainfall and temperature time series into random variation about an underlying level, trend and additive seasonal effects and changes in the level; trend and seasonal effects were tracked using a Holt–Winters algorithm. The results of this analysis were qualitatively similar to those of the regression analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Removal of organic matter from reservoir water: mechanisms underpinning surface chemistry of natural adsorbents

One of the key challenges in water treatment industry is the removal of organic compounds by cost-effective methods. This study evaluated the adsorptive removal of dissolved organic carbon (DOC) from reservoir water using fuller’s earth (FE) in comparison with natural (SQ) and modified quartz (MSQ) sands. The removal capacities of FE at different contact times, pH levels, adsorbent dosages and initial DOC concentrations were compared with both the quartz sands. The optimum DOC removals by FE and SQs were achieved at contact time of 60 and 30 min, pH level of 6 and 4, and at adsorbent dose of 1.5 g/150 mL and 10 g/100 mL, respectively. The adsorption capacity of FE (1.05 mg/g) was much higher compared to the MSQ (0.04 mg/g) and SQ (0.01 mg/g). Adsorption equilibrium data better fitted to the Freundlich model than to the Langmuir model, suggesting that adsorption occurred primarily through multilayer formation onto the surfaces of FE and SQ. The pseudo-second-order model described the uptake kinetics more effectively than the pseudo-first-order and intra-particle diffusion models, indicating that the mechanism was primarily governed by chemisorption. These observations were well supported by the physiochemical characteristics and charge behaviour of the adsorbents. In mass-transfer study, the results of liquid film diffusion model showed that the adsorption of DOC on FE was not controlled by film diffusion, but other mechanisms also played an essential role. This study demonstrates that FE is an effective adsorbent for the removal of DOC in surface water treatment.     

Influence of SOI, DMI and Niño3.4 on South Australian rainfall

The influences of climate drivers (SOI, DMI and Niño3.4) on South Australian (SA) rainfall are investigated in this study. Recent records of monthly rainfall and climate driver index values from 1981 to 2010 were analysed for 53 rainfall stations, located across eight SA natural resources management (NRM) regions. The Pearson, Kendall and Spearman correlation tests were applied between rainfall and climate drivers and between the climate drivers themselves. Both SA summer (December to February) and autumn (March to May) rainfalls were found not significantly influenced by climate indices. Winter rainfall in the south and east parts of SA was found strongly influenced by both SOI and DMI, particularly in July and August. Both SOI and DMI are inter-correlated in winter. Spring rainfall was found significantly influenced by DMI in the south and east parts of SA, particularly in September and October. In terms of ENSO phenomena, whilst both SOI and Niño3.4 are correlated, SOI was found more to be influential than Niño3.4 for SA winter and spring rainfall. Outcomes of the study are useful for stochastic rainfall generation and for developing downscaling techniques to generate rainfall projections in the region.     

Australian rainfall trends and their relation to the southern oscillation index

Rainfall is the key climate variable that governs the spatial and temporal availability of water. In this study we identified monthly rainfall trends and their relation to the southern oscillation index (SOI) at ten rainfall stations across Australia covering all state capital cities. The nonparametric Mann–Kendall (MK) test was used for identifying significant trends. The trend free pre‐whitening approach (TFPW) was used to remove the effects of serial correlation in the dataset. The trend beginning year was approximated using the cumulative summation (CUSUM) technique and the influence of the SOI was identified using graphical representations of the wavelet power spectrum (WPS). Decreasing trends of rainfall depth were observed at two stations, namely Perth airport for June and July rainfall starting in the 1970s and Sydney Observatory Hill for July rainfall starting in the 1930s. No significant trends were found in the Melbourne, Alice Springs and Townsville rainfall data. The remaining five stations showed increasing trends of monthly rainfall depth. The SOI was found to explain the increasing trends for the Adelaide (June) and Cairns (April) rainfall data and the decreasing trends for Sydney (July) rainfall. Other possible climatic factors affecting Australian rainfall are also discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model

Theoretical and Applied Climatology - In this study, the performance of the Generalized LInear Modelling of daily CLImate sequence (GLIMCLIM) statistical downscaling model was assessed to simulate...