Real-time optimal control of river basin networks

River basins are key components of water supply grids. River basin operators must handle a complex set of objectives including runoff storage, flood control, supply for consumptive use, hydroelectric power generation, silting management, and maintenance of river basin ecology. At present, operators rely on a combination of simulation and optimization tools to help them make operational decisions. The complexity associated with this approach makes it suitable for long term planning but not daily or hourly operation. The consequence is that between longerterm optimized operation points, river basins are largely operated in open loop. This leads to operational inefficiencies most notably wasted water and poor ecological outcomes. This paper proposes a systematic approach using optimal control based on simple low order models for the real-time operation of entire river basin networks. © 2011 IFAC

Model predictive control of Murray-darling basin networks

River basins are the most significant component in water supply grids and are under increasing pressure from competing demands for fresh water. However, unlike energy grids which are managed very efficiently using closed-loop operation, water grids, and river basins in particular, are largely open-loop systems. One reason is the difficulty associated with developing suitable models and feedback controllers. This paper proposes a systematic approach using model predictive control based on simple low order models for the real-time operation of entire river basin networks. © 2011 IEEE

Streamflow and soil moisture forecasting with hybrid data intelligent machine learning approaches: case studies in the Australian Murray-Darling basin

For a drought-prone agricultural nation such as Australia, hydro-meteorological imbalances and increasing demand for water resources are immensely constraining terrestrial water reservoirs and regional-scale agricultural productivity. Two important components of the terrestrial water reservoir i.e., streamflow water level (SWL) and soil moisture (SM), are imperative both for agricultural and hydrological applications. Forecasted SWL and SM can enable prudent and sustainable decisionmaking for agriculture and water resources management. To feasibly emulate SWL and SM, machine learning data-intelligent models are a promising tool in today’s rapidly advancing data science era. Yet, the naturally chaotic characteristics of hydro-meteorological variables that can exhibit non-linearity and non-stationarity behaviors within the model dataset, is a key challenge for non-tuned machine learning models. Another important issue that could confound model accuracy or applicability is the selection of relevant features to emulate SWL and SM since the use of too fewer inputs can lead to insufficient information to construct an accurate model while the use of an excessive number and redundant model inputs could obscure the performance of the simulation algorithm. This research thesis focusses on the development of hybridized dataintelligent models in forecasting SWL and SM in the upper layer (surface to 0.2 m) and the lower layer (0.2–1.5 m depth) within the agricultural region of the Murray-Darling Basin, Australia. The SWL quantifies the availability of surface water resources, while, the upper layer SM (or the surface SM) is important for surface runoff, evaporation, and energy exchange at the Earth-Atmospheric interface. The lower layer (or the root zone) SM is essential for groundwater recharge purposes, plant uptake and transpiration. This research study is constructed upon four primary objectives designed for the forecasting of SWL and SM with subsequent robust evaluations by means of statistical metrics, in tandem with the diagnostic plots of observed and modeled datasets. The first objective establishes the importance of feature selection (or optimization) in the forecasting of monthly SWL at three study sites within the Murray-Darling Basin. Artificial neural network (ANN) model optimized with iterative input selection (IIS) algorithm named IIS-ANN is developed whereby the IIS algorithm achieves feature optimization. The IIS-ANN model outperforms the standalone models and a further hybridization is performed by integrating a nondecimated and advanced maximum overlap discrete wavelet transformation (MODWT) technique. The IIS selected inputs are transformed into wavelet subseries via MODWT to unveil the embedded features leading to IIS-W-ANN model. The IIS-W-ANN outperforms the comparative IIS-W-M5 Model Tree, IIS-based and standalone models. In the second objective, improved self-adaptive multi-resolution analysis (MRA) techniques, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are utilized to address the non-stationarity issues in forecasting monthly upper and lower layer soil moisture at seven sites. The SM time-series are decomposed using EEMD/CEEMDAN into respective intrinsic mode functions (IMFs) and residual components. Then the partial-auto correlation function based significant lags are utilized as inputs to the extreme learning machine (ELM) and random forest (RF) models. The hybrid EEMD-ELM yielded better results in comparison to the CEEMDAN-ELM, EEMD-RF, CEEMDAN-RF and the classical ELM and RF models. Since SM is contingent upon many influential meteorological, hydrological and atmospheric parameters, for the third objective sixty predictor inputs are collated in forecasting upper and lower layer soil moisture at four sites. An ANN-based ensemble committee of models (ANN-CoM) is developed integrating a two-phase feature optimization via Neighborhood Component Analysis based feature selection algorithm for regression (fsrnca) and a basic ELM. The ANN-CoM shows better predictive performance in comparison to the standalone second order Volterra, M5 Model Tree, RF, and ELM models. In the fourth objective, a new multivariate sequential EEMD based modelling is developed. The establishment of multivariate sequential EEMD is an advancement of the classical single input EEMD approach, achieving a further methodological improvement. This multivariate approach is developed to allow for the utilization of multiple inputs in forecasting SM. The multivariate sequential EEMD optimized with cross-correlation function and Boruta feature selection algorithm is integrated with the ELM model in emulating weekly SM at four sites. The resulting hybrid multivariate sequential EEMD-Boruta-ELM attained a better performance in comparison with the multivariate adaptive regression splines (MARS) counterpart (EEMD-Boruta-MARS) and standalone ELM and MARS models. The research study ascertains the applicability of feature selection algorithms integrated with appropriate MRA for improved hydrological forecasting. Forecasting at shorter and near-real-time horizons (i.e., weekly) would help reinforce scientific tenets in designing knowledge-based systems for precision agriculture and climate change adaptation policy formulations

Molecular assays for the detection of invasive tunicates and phylogeography of a tunicate invasion in Prince Edward Island

There are currently four invasive tunicate species which are causing significant challenges to the aquaculture industry in Atlantic Canada: Ciona intestinalis, Styela clava, Botryllus schlosseri and Botrylloides violaceus. These tunicates foul mussel socks, aquaculture gear, boat hulls and wharves. As with many aquatic invasive species, detection of these tunicates mostly occurs once adult populations have reached significant population sizes. However, once populations have reached these levels, there is often little that can be done to stop or slow the invasion. Having efficient and sensitive assays that could detect invasive tunicates at microscopic stages would be advantageous as management strategies could be implemented before populations spike to invasive levels. Such an assay would also be useful in monitoring mussel processing plant effluent so that invasive tunicate eggs and larvae are not spread through effluent water discharge to adjacent bays. Molecular assays have been developed in this study that can detect the four invasive tunicate species in both mussel processing effluent water and in bay water surrounding mussel leases. These assays are highly specific and have a sensitivity of detection of 1-5 eggs and/or larvae per water sample. In addition qRT-PCR assays have been developed that can detect and distinguish between different life stages of Ciona intestinalis (egg and larvae) in water samples. This qRT-PCR assay also has the capacity to evaluate viability of free swimming larvae so that nonviable larvae do not cause false positives during mussel processing plant effluent monitoring. The high throughput capacity, high specificity and sensitivity of these assays shows excellent potential for use as a monitoring tool for aquatic invasive species in screening ballast water, effluent waste from shellfish processing plants, as well in local bays and rivers. This study also used phylogenetic analyses of the cytochrome oxidase 1 gene to determine that populations of Botryllus schlosseri in Prince Edward Island have low genetic diversity. Only two haplotypes of B. schlosseri were found in this study in PEI, one which was found in all aquaculture bays tested and the other found only on native substrate in one bay. It is likely that local activity spread this species to other areas of Prince Edward Island after the initial invasion. Phylogeographic analysis suggests that this species was likely transported from Massachusetts to Nova Scotia and then was transported to Prince Edward Island via local boating activity or through the movement of aquaculture species. Through the development of these efficient detection methods and by determining source populations and possible vectors responsible for transporting invasive species to this region, it is hoped that new invasions of aquatic invasive species can be prevented and/or managed before they pose a risk to the aquaculture industry in Prince Edward Island

Strategies to address structural issues in hydrologic models

This thesis explores approaches for improving predictions of conceptual hydrologic models by addressing their structural issues, which typically arise from simplifications that occur when modelling complex hydrologic processes. The thesis documents the research undertaken to develop a generic and defensible modelling strategy. Hydrologic modelling is particularly challenging due to non-stationarity and limitations of data and in the methods used to analyse uncertainties. The thesis proposes two broad categories of methods to address these issues: (1) state uncertainty estimation and (2) model structure modification. A new Bayesian framework for estimating state uncertainty in hydrologic models was developed in Chapter 2, which was demonstrated in a synthetic study and a real-world case in the Bates catchment, Western Australia. This provided the theoretical underpinnings for Chapter 3, which presented a more practical approach named State and Parameter Uncertainty Estimation (SPUE). SPUE was applied to 46 catchments in Australia using the rainfall runoff model, GR4J. SPUE outperformed the classical approach of solely estimating uncertainty in hydrologic parameters based on validation fit to observed streamflow, reliability, and precision metrics. Chapter 4 explored a different approach, where direct modifications to the model structure were made to a river reach model, creating the River Bed/Bank Storage (RBS) model. This had the added advantage of more accurately representing the actual hydrological systems and their dynamic response to changing environmental conditions. In Chapter 5, the RBS model was further improved by combining it with SPUE, which resulted in better reliability and improved probability distributions. Overall, the thesis demonstrated that a comprehensive uncertainty formulation is essential for more accurate predictions of hydrologic models, and the use of both state uncertainty estimation and model structure modification methods can significantly enhance model performance

Integrating Knowledge for River Basin Management: Progress in Thailand

Resource /Energy Economics and Policy,

Geological factors influencing dryland salinity risk

Dryland salinity has become a major environmental problem in Australia through changes in land management incurred since European settlement. Given appropriate conditions of land management and clin1ate, salinisation follows the advent of discharge areas which arise through rises in groundwater level and/or impediments to groundwater flow. Impediments may be structures such as dams, roads and railway embankments but can also occur naturally through geological features. Geological structures such as dykes, faults and contact zones may be impediments to groundwater flow as can changes to hydraulic conductivity through changes in the permeability of rocks and soils. Geological factors have been incorporated into a predictive model to identify areas at risk to salinisation. The model is a Decision Tree Analysis run via the Knowledge SEEKER computer program. Results from this modelling exercise are comparable with other models based on surface and near-surface flow and topographic indices but suffer from being site specific. The study highlights the need for development of improved methods of mapping subsurface features and for widespread changes to the current system of land management

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.The two-layer model of Shuttlerworth and Wallace (SW) was evaluated to estimate actual evapotranspiration (ETa) above a drip-irrigated Merlot vineyard, located in the Talca Valley, Region del Maule, Chile (35° 25' LS; 71° 32' LW ; 136m above the sea level). An automatic weather system was installed in the center of the vineyard to measure climatic variables (air temperature, relative humidity, and wind speed) and energy balance components (solar radiation, net radiation, latent heat flux, sensible heat flux, and soil heat flux) during November and December 2006. Values of ETa estimated by the SW model were tested with latent heat flux measurements obtained from an eddy-covariance system on a 30 minute time interval. Results indicated that SW model was able to predict ETa with a root mean square error (RMSE) of 0.44 mm d-1 and mean absolute error (MAE) of 0.36 mm d-1. Furthermore, SW model predicted latent heat flux with RMSE and MAE of 32 W m-2 and 19W m-1, respectively

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.A In order to promote irrigation sustainability through reporting by irrigation water managers around Australia, we have developed an adaptive framework and methodology for improved triple-bottom-line reporting. The Irrigation Sustainability Assessment Framework (ISAF) was developed to provide a comprehensive framework for irrigation sustainability assessment and integrated triple-bottom-line reporting, and is structured to promote voluntary application of this framework across the irrigation industry, with monitoring, assessment and feedback into future planning, in a continual learning process. Used in this manner the framework serves not only as a "reporting tool", but also as a "planning tool" for introducing innovative technology and as a "processes implementation tool" for enhanced adoption of new scientific research findings across the irrigation industry. The ISAF was applied in case studies to selected rural irrigation sector organisations, with modifications to meet their specific interests and future planning