Mat Gilfedder

Biophysical modelling of catchment-scale surface water and groundwater response to land-use change

The effect of land-use change on salt and water-balances of catchments in Australia has been significant. Impacts of these changes are often masked by large time lags between the changes and their subsequent expression. Successful management relies on information that allows these changes to be understood and predicted. In the absence of detailed hydrogeological and hydrographic data, a simple approach is required. A logistic function model is introduced, which weights changes in recharge to changes in discharge according to a characteristic time-scale and a rate of change. This response function approach is used to estimate the time lags for individual groundwater flow systems (GFS), which can be aggregated to estimate whole catchment behaviour. Using this model, the predicted future effect of a range of afforestation strategies on catchment salt load has been simulated for sub-catchments of the mid-Macquarie (New South Wales, Australia).

Prioritisation approach for estimating the biophysical impacts of land-use change on stream flow and salt export at a catchment scale

Predicting the impacts of land-use change on stream flow and stream salt export at a catchment scale is hampered by limited detailed measured data, particularly with regard to hydrogeological information. A recently developed modelling approach is presented that can be used to predict the variation in likely catchment response to changes in woody cover using only broadly available data. The Biophysical Capacity to Change (BC2C) model combines a downward approach for water balance, with groundwater response using groundwater flow systems (GFS) mapping to provide hydrogeological and salinity parameters, into a spatial model for estimating the impacts of changes in woody vegetation cover across large areas. The results from the model are compared to gauged flow and salinity data for 14 stream gauging stations across the Murrumbidgee catchment, in south-eastern Australia. Considering the limited calibration of the model, the results compare favourably in broad terms, and provide a useful starting point for consideration of the impacts of land-use change on stream flow and salt load, and to guide catchment managers towards areas where more detailed study can be undertaken.

Predicting Aquifer Response Time for Application in Catchment Modeling

It is well established that changes in catchment land use can lead to significant impacts on water resources. Where land-use changes increase evapotranspiration there is a resultant decrease in groundwater recharge, which in turn decreases groundwater discharge to streams. The response time of changes in groundwater discharge to a change in recharge is a key aspect of predicting impacts of land-use change on catchment water yield. Predicting these impacts across the large catchments relevant to water resource planning can require the estimation of groundwater response times from hundreds of aquifers. At this scale, detailed site-specific measured data are often absent, and available spatial data are limited. While numerical models can be applied, there is little advantage if there are no detailed data to parameterize them. Simple analytical methods are useful in this situation, as they allow the variability in groundwater response to be incorporated into catchment hydrological models, with minimal modeling overhead. This paper describes an analytical model which has been developed to capture some of the features of real, sloping aquifer systems. The derived groundwater response timescale can be used to parameterize a groundwater discharge function, allowing groundwater response to be predicted in relation to different broad catchment characteristics at a level of complexity which matches the available data. The results from the analytical model are compared to published field data and numerical model results, and provide an approach with broad application to inform water resource planning in other large, data-scarce catchments.

Probabilistic modelling and uncertainty analysis of flux and water balance changes in a regional aquifer system due to coal seam gas development

Large scale development of coal seam gas (CSG) is occurring in many sedimentary basins around the world including Australia, where commercial production of CSG has started in the Surat and Bowen basins. CSG development often involves extraction of large volumes of water that results in depressurising aquifers that overlie and/or underlie the coal seams thus perturbing their flow regimes. This can potentially impact regional aquifer systems that are used for many purposes such as irrigation, and stock and domestic water. In this study, we adopt a probabilistic approach to quantify the depressurisation of the Gunnedah coal seams and how this impacts fluxes to, and from the overlying Great Artesian Basin (GAB) Pilliga Sandstone aquifer. The proposed method is suitable when effects of a new resource development activity on the regional groundwater balance needs to be assessed and account for large scale uncertainties in the groundwater flow system and proposed activity. The results indicated that the extraction of water and gas from the coal seam could potentially induce additional fluxes from the Pilliga Sandstone to the deeper formations due to lowering pressure heads in the coal seams. The median value of the rise in the maximum flux from the Pilliga Sandstone to the deeper formations is estimated to be 85ML/year, which is considered insignificant as it forms only about 0.29% of the Long Term Annual Average Extraction Limit of 30GL/year from the groundwater management area. The probabilistic simulation of the water balance components indicates only small changes being induced by CSG development that influence interactions of the Pilliga Sandstone with the overlying and underlying formations and with the surface water courses. The current analyses that quantified the potential maximum impacts of resource developments and how they influences the regional water balance, would greatly underpin future management decisions.

Non-stationarity of low flows and their relevance to river modelling during drought periods

Changes in groundwater storage lead to a reduction in groundwater contribution to river flow and present as non-stationarity, especially during low-flow conditions. Conventional river models typically ignore this non-stationarity, and, hence, their predictions of declines in low flows during drought periods are likely to be compromised. The present study assesses non-stationarity and highlights its implications for river modelling. A quantile regression analysis showed non-stationarity of low flows in the Namoi catchment (Australia), with statistically significant downward trends in the 10th percentile of log-transformed baseflow (10-LTB). This highlighted the usefulness of the 10-LTB metric to identify non-stationarity and, hence, alert modellers to the importance of adopting models that explicitly account for groundwater processes when modelling such river systems.

Trajectory river modelling – a decision-support tool to help manage multiple risks associated with planning around variable water resources

The application of river-system models to inform water-resource planning and management is a growing global phenomenon. This requires models to be applied so that they are useful to water decision makers charged with setting targets that provide adequate water flows to sustain landholders and communities. This article examines why and how the innovative application of river-system models can facilitate interactions between water science and water management in Australia’s Murray–Darling Basin (the Basin). A trajectory river-modelling method was applied to run multiple short historical climate sequences through a river-system model to provide historical probabilities. These can allow better assessment of the risks and impacts associated with stream flow and water availability. This method allows known historical variability to be presented, and produces relevant results for a 10–15-year water-sharing plan lifetime. The benefits were demonstrated in the Basin’s Lachlan Catchment where modelled river-flow results demonstrated the increased variability between shorter 15-year sequences than for a single 114-year run. This approach highlighted the benefits of expressing modelling results as historical probabilities to inform short-term and strategic water-planning efforts.

Spatio-temporal evolution of water-related ecosystem services: Taihu Basin, China

Water-related ecosystem services (WESs) arise from the interaction between water ecosystems and their surrounding terrestrial ecosystems. They are critical for human well-being as well as for the whole ecological circle. An urgent service-oriented reform for the utilization and supervision of WESs can assist in avoiding ecological risks and achieving a more sustainable development in the Taihu Basin, China (THB). Spatially distributed models allow the multiple impacts of land use/land cover conversion and climate variation on WESs to be estimated and visualized efficiently, and such models can form a useful component in the toolbox for integrated water ecosystem management. The Integrated Valuation of Ecosystem Services and Tradeoffs model is used here to evaluate and visualize the spatio-temporal evolution of WESs in the THB from 2000 to 2010. Results indicate that water retention service experienced a decline from 2000 to 2005 with a recovery after 2005, while there was ongoing water scarcity in urban areas. Both the water purification service and the soil retention service underwent a slight decrease over the study period. Nutrients export mainly came from developed land and cultivated land, with the hilly areas in the south of the THB forming the primary area for soil loss. The quantity and distribution of WESs were impacted significantly by the shrinkage of cultivated land and the expansion of developed land. These findings will lay a foundation for a service-oriented management of WESs in the THB and support evidence-based decision making.