Integration of 1401 Graduate Studies (Groundwater Management for Sustainable Farming Systems)

This report presents the integration of research studies carried out by the graduate students at UTS and UNSW as part of the CRC for Sustainable Rice Production Graduate Studies Program. It evaluates the methodologies and modelling scenarios in rice-based irrigation areas. Moreover, the report collates the research findings and conclusions to establish the benefits to rice industry. The main objective of the graduate studies was to develop strategies for managing groundwater for salinity mitigation at farm and regional scale. Through field experimentation and modelling approaches, the studies examined the impacts of land use on the environment and the effect of irrigation water with different quality levels on the rising watertable and the subsequent salinisation. These studies developed hydrogeological information base for rice growing areas mainly MIA (Murrumbidgee Irrigation Area) and WID (Wakool Irrigation District) has been developed that includes monitoring groundwater levels, groundwater quality, soil analysis and geophysical surveys. The modelling exercises show strong interaction between shallow and deep aquifer. The simulations show significant rise in groundwater levels during the rice crop season and fall during the fallow season. Subsurface lateral groundwater flows are dominant from east to west; from Narrandera to Hay. Groundwater monitoring indicated a rapid response to rainfall as well as irrigation events with a recharge estimation of about 80% for the shallow aquifer and 50% for the deep aquifer. The shallow aquifer (2 m) responds slightly faster than the deep aquifer (7 m) to irrigation events. Groundwater quality at Whitton (M.I.A) is classified as brine and therefore not suitable for irrigation. However, the irrigation water was classified as fresh. Sodium, Sulfate and Chloride were the most abundant elements found in the four water samples. The piezometers in irrigated paddocks showed substantially lower salinity indicating that irrigation water was recharging the aquifer. The deep aquifer piezometers monitoring displayed conductivity values of about 5 to 6 ms/cm. The geophysical resistivity imaging has shown a great promise for developing understanding about surface-ground water interactions and salinization. Large spatial variations in apparent resistivity were observed in irrigated and non-irrigated areas. Resistivity decreases with depth in a linear fashion. Variations in resistivity have been noticed in the upper 10 metre layer of soil indicating recharge zone. Increase of resistivity closer to rice paddocks during irrigation is due to the fresh water infiltrating to the aquifer. Irrigation events resulted in decreased resistivity at most depths, particularly at 15 m that reflecting rising water table or input of fresh water from the irrigated paddocks. These studies have shown a strong correlation between resistivity and electromagnetic responses from EM31 and EM34. The MODFLOW model developed by the UTS graduates with a 10 m minimum discretisation and a refined time scale (2 days stress period) simulated the groundwater dynamics with 80% accuracy. Six key parameters are identified influencing the system. They include rice ponding, precipitation, drainage, evapotranspiration deep leakage and lateral groundwater flow. The solute transport model revealed that the groundwater salinity is controlled by rising groundwater levels due to rice ponding. Salinity concentration is higher in top 2 metres below -2- ground surface. The solute transport model has successfully simulated salinity trends. The irrigated areas are affected by irrigation water salinity. The salinity of top 3 m profile is higher and decreases with depth. Groundwater salinity ranges from 1500 mg/l directly below and is approximately 2500 – 3000 mg/l in the fallow paddocks adjacent to the rice pond. According to the optimization results, an extensive bore network of several hundred pumping bores at shallow depths would be necessary to lower water levels around the irrigated area. However, it impossible to pump out the necessary groundwater volumes in order to lower water table to the targeted levels in low permeability areas as vertical hydraulic conductivity is one order of magnitude lower than horizontal hydraulic conductivity. The UNSW PhD (Xu, 2003) study in Wakool region predicted that about 2 kg/m2 salt will be added to root zone per one rice crop per season. This prediction quantifies to 20 t/ha per crop season each year. Moreover, if repeated irrigation with saline water is practiced, the salt concentration in root zone will continue to increase with time, which is alarming for future of rice industry. Therefore, careful decisions need to be done while working out the soil suitability for rice growers regarding existing soil salinity and the EC levels in irrigation water. The ponded rice irrigation is a major contributing factor to groundwater accessions resulting in rising watertables and subsequent salinity problem. The alternative use of fresh and low salinity water could be practiced on short-term basis for ponded irrigation as long as it does not affect rice growth or rice yield. This will help remove accumulated salts in the root zone by fresh water irrigation after the irrigation with water containing salts. The six graduate modelling studies described in this report are site specific. Efforts to apply these methods to other farms or regions will need to incorporate site specific information on cropping, topography and groundwater systems to describe and calibrate the salinisation processes

Hydro-climatic and Economic Evaluation of Seasonal Climate Forecasts for Risk Based Irrigation Management

This work is focused in the Murrumbidgee catchment to help understand the value of the seasonal forecasts to rice based cropping systems. The key activities of this project include: • An overview of water allocation in the Murrumbidgee Valley • Evaluation of commonly used seasonal forecasting methods used to predict rainfall • Development of a novel water allocation model on the basis of seasonal forecasts and historic allocation data • Economic analysis of the benefits from better irrigation forecasts in irrigated catchments The key findings include: • The current system of announcing allocations does not take into account seasonal climate forecasts of rainfall and flows in the catchment. End of the season allocations are made too late and pose a serious financial risk to farmers due to inadequate information being available at the start of the summer cropping period • The SST correlations with inflows to dams has provided promising results, which can be used to forecast flows to dams with lead times of around 1 year • Artificial Neural network (ANN) approaches which can learn from historic model simulations and SST predictions can be a way forward to link climate forecasts with risk management. Results of the ANN model show good correlations with the historic water allocation trends over any given season. This tool can be used to make informed cropping risk decisions • Irrigators utilising allocation forecast information can minimise the opportunity cost of forgone agricultural production. Undertaking decision analysis, it was estimated that the net benefit of allocation forecasts to the irrigators of the CIA is between $50,000 and$660,000 per year (equivalent to $0.68/ha and$8.56/ha). This was assuming that the CIA irrigators are collectively risk averse as their risk preference is unknown As part of this project a stakeholder workshop on climate variability, climate change and adaptation in the Murrumbidgee Basin was organised, to examine research ideas on climate research for efficient irrigation management. Participants included a number of interested participants from irrigation companies, NSW Agriculture, Department of Infrastructure Planning and Natural Resources (DIPNR), Murray Darling Basin Commission (MDBC) and the local community. There is a tremendous interest in climate and water issues due to the recent drought. The farming community needs tools which can link climate forecasts with smarter agricultural water management using a risk based approach. The key barrier to the adoption of existing climate forecast tools is their lack of proven utility and the risk adverse attitude of water allocation agencies