Mark Rivers

Inputs of Nutrients and Fecal Bacteria to Freshwaters from Irrigated Agriculture: Case Studies in Australia and New Zealand

Increasing demand for global food production is leading to greater use of irrigation to supplement rainfall and enable more intensive use of land. Minimizing adverse impacts of this intensification on surface water and groundwater resources is of critical importance for the achievement of sustainable land use. In this paper we examine the linkages between irrigation runoff and resulting changes in quality of receiving surface waters and groundwaters in Australia and New Zealand. Case studies are used to illustrate impacts under different irrigation techniques (notably flood and sprinkler systems) and land uses, particularly where irrigation has led to intensification of land use. For flood irrigation, changes in surface water contaminant concentrations are directly influenced by the amount of runoff, and the intensity and kind of land use. Mitigation for flood irrigation is best achieved by optimizing irrigation efficiency. For sprinkler irrigation, leaching to groundwater is the main transport path for contaminants, notably nitrate. Mitigation measures for sprinkler irrigation should take into account irrigation efficiency and the proximity of intensive land uses to sensitive waters. Relating contaminant concentrations in receiving groundwaters to their dominant causes is often complicated by uncertainty about the subsurface flow paths and the possible pollutant sources, viz. drainage from irrigated land. This highlights the need for identification of the patterns and dynamics of surface and subsurface waters to identify such sources of contaminants and minimize their impacts on the receiving environments.

Application of Distributed Wireless Chloride Sensors to Environmental Monitoring: Initial Results

Over the next 30 years, it is anticipated that the world will need to source 70% more food to provide for the growing population, and it is likely that a significant amount of this will have to come from irrigated land. However, the quality of irrigation water is also important, and measuring the quality of this water will allow management decisions to be made. Soil salinity is an important parameter in crop yield, and in this paper, we describe a chloride sensor system based on a low-cost robust screen-printed chloride ion sensor, suitable for use in distributed sensor networks. Previously, this sensor has been used in controlled laboratory-based experiments, but here we provide evidence that the sensor will find application outside of the laboratory in field deployments. We report on three experiments using this sensor; one with a soil column, one using a fluvarium, and finally on an experiment in a greenhouse. All these give an insight into the movement of chloride over small distances with high temporal resolution. These initial experiments illustrate that the new sensors are viable and usable with relatively simple electronics, and although subject to ongoing development, they are currently capable of providing new scientific data at high spatial and temporal resolutions. Therefore, we conclude that such chloride sensors, coupled with a distributed wireless network, offer a new paradigm in hydrological monitoring and will enable new applications, such as irrigation using mixtures of potable and brackish water, with significant cost and resource saving.

Predicting discharge using a low complexity machine learning model

Enabling real time water quality management using collaborative networked farms.Discharge predictive model uses 3 simple field parameters and 12-month training data.M5 tree based proposed model, trained on real data, give R2 as 0.82 and RRMSE a 35.9%.80% of the residuals for the predicted values fall within ?2mm discharge depth/day error range.The proposed model gives comparable results when compared to contemporary research. This paper reports on the validation of a simplified discharge prediction model that is suitable for implementation on a resourced constrained system such as a wireless sensor network, which will allow their operation to become more proactive rather than reactive. The data-driven model, utilising an M5 decision tree modelling technique, is validated using a 12-month training data set derived from published measured data. Daily runoff and drainage is predicted, and the results are compared with existing data-driven models developed in this domain. Results for the model give an R2 of 0.82 and Root Relative Mean Square Error (RRMSE) of 35.9%. 80% of the residuals for the predicted test values fall within a ?2mm discharge depth/day error range. The main significance is that the proposed model gives comparable results with fewer samples and simpler parameters when compared to previous published research, which offers the potential for implementation in resource constrained monitoring and control systems.

How could sensor networks help with agricultural water management issues? Optimizing irrigation scheduling through networked soil-moisture sensors

Irrigated agriculture provides 40% of the Worlds food from 20% of the agricultural land but uses 70% of all global freshwater withdrawals. However, even supposedly efficient and well-managed irrigation systems waste up to 50% of the water applied to the crops under them. Meeting the food needs of an increasing world population from a static or even decreasing land base will, therefore require improved efficiencies in irrigated agriculture and better use of these finite water resources. The first part of this paper reports on a field-based research project which examined a suite of conventional and alternative irrigation systems which were installed at a farm in south west Australia and assessed and compared in terms of their Water Use Efficiency. All “alternative” systems outperformed the conventional surface (flood) irrigation systems with comparative water savings of around 50%. The second part of the paper assesses the potential Water Use Efficiency improvements at farm and system-scales which could be achieved through linking these irrigation systems to wireless soil-moisture sensor networks which are being developed by the authors and which are reported in detail in associate papers. Improving irrigation scheduling and management by better (and, where appropriate, automatic) links to near real-time soil moisture data is shown to produce water savings of up to 30 GL per year at the irrigation system scale.

Applications of a wireless chloride sensor in environmental monitoring

There is an established need to measure soil salinity, and wireless sensor networks offer the potential to achieve this, coupled with a suitable sensor. However, suitable sensors, up until very recently, have not been available. In this paper we report on the fabrication and calibration of a new low-cost, robust, screen-printed sensor for detecting chloride ions. We also report on two experiments using this sensor. The first is a laboratory-based experiment that shows how sensors can be used to validate modeling results by installing several sensors in a soil column and tracking the vertical migration of a chloride pulse in real time. The second is a trial of multiple sensors installed in a fluvarium (stream simulator) showing that distributed sensors are able to monitor real time changes in horizontal chloride flux in an emulated natural environment. We report on results from both surface flows as well as from sensors at a depth of a few mm in the fluvarium sediment, and differences and trends between the two are discussed. As an example of how such sensors are useful, we note that for the flow regime and sediment type tested, penetration of surface chloride into the river bed is unexpectedly slow and raises questions regarding the dynamics of pollutants in such systems. We conclude that such sensors, coupled with a distributed network, offer a new paradigm in hydrological monitoring and will enable new applications, such as irrigation using mixtures of potable and brackish water with significant cost and resource saving.

Data-driven low-complexity nitrate loss model utilizing sensor information — Towards collaborative farm management with wireless sensor networks

Excessive or poorly timed application of irrigation and fertilizers, coupled with the inherent inefficiency of nutrient uptake by crops result in nutrient fluxes into the water system. The ability to predict nutrient-rich discharges, in real time, can be very valuable to enable reuse mechanisms within farm systems. Wireless Sensor Networks (WSNs) offer an opportunity to monitor environmental systems with unprecedented temporal and spatial resolution. As part of our previous work, we proposed a novel framework (WQMCM) to combine increasingly common local farm-scale sensor networks across a catchment to learn and predict (using predictive models) the impact of catchment events on their downstream environments, allowing dynamic decision. Existing models use complex parameters which are difficult to extract and this, coupled with constraints on network nodes (battery life, computing power etc., availability of sensors) makes it necessary to develop simplified models for deployment within the networks. The paper investigates data-driven model for predicting daily total oxidized nitrate (TON) fluxes by seeking simplification in model parameters and using only a yearlong training data set. Data from a catchment in Ireland is used for training the model. Model simplification is investigated by abstracting details from an existing nitrate loss model. By using M5 decision tree model on the training samples of the proposed parameters, results give R2 as 0.92 and RRMSE as 0.26. The proposed novel model gives better results with fewer samples and simple parameters when compared to the traditional model. This shows promise for enabling real time nutrient control and management within the collaborative networked farm system.