Integration of MONERIS and GREAT-ER in the decision support system for the German Elbe river basin

The Elbe-DSS is a computer based system for integrated river basin management of the German part of River Elbe basin. Simulation models are used to assess the efficiency of measures such as reforestation, changes of agricultural practices or the efficiency of wastewater treatment plants for achieving management targets. MONERIS and GREAT-ER are integrated into the Elbe-DSS to assess nutrient and pollutant loads. MONERIS calculates nutrient inputs from diffuse and point sources on a sub-catchment scale of about 1000 km². GREAT-ER is a tool for exposure assessment of point source emissions and considers fate in sewage treatment plants as well as degradation and transport in rivers. Both models make long-term predictions, but their spatial scales of operations differ. GREAT-ER divides the whole river network into small segments that are linked through a routing algorithm. The segments are coupled to MONERIS using accumulated flow length distribution. Linking the two models allows to distribute diffuse nutrient emissions calculated from MONERIS and point source emissions from GREAT-ER to the river network, where further elimination and transport processes are calculated. We exemplify the DSS in a study assessing the effects of different reforestation and erosion control measures on phosphate loads and concentrations in the river network.     

Elements of a decision support system for real-time management of dissolved oxygen in the San Joaquin River Deep Water Ship Channel

A decision support system (DSS) has been designed and will be implemented over the next three years to assist in the control and management of episodes of low dissolved oxygen (DO) in a Deep Water Ship Channel (DWSC), located near Stockton, California. The DSS integrates three information technology functions. The first part is the collection and management of data on flow, pollution loads and water quality. The second part is the simulation model which can forecast critical dissolved oxygen depletion in the DWSC and determine management actions necessary to improve dissolved oxygen concentrations. The third part is the graphical user interface, which facilitates the computer simulations and posting of the forecasted dissolved oxygen and remedial measures to a stakeholder group for implementations.     

Assessing water quality management options in the Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system

The widespread and relentless discharge of untreated wastewater into the Upper Litani Basin (ULB) river system in Lebanon has reached staggering levels rendering its water unfit for most uses especially during the drier times of the year. Despite the call by governmental and non-governmental agencies to develop several wastewater treatment plants and sewage networks in an effort to control this problem, these efforts do not seem to be coordinated or based on comprehensive and integrated assessments of current and projected conditions in the basin.This paper provides an overview of the development and implementation of an integrated decision support system (DSS) designed to help policy makers and other stakeholders have a clearer understanding of the key factors and processes involved in the sewage induced degradation of surface water quality in the ULB, and formulate, assess and evaluate alternative management plans. The DSS is developed based on the WEAP model, which provides a GIS based and visual simulation environment and scenario management and analysis capabilities. The DSS was used to assess two main water quality management plans taking into consideration hydrological, spatial and seasonal variabilities. An incremental cost-effectiveness analysis was conducted to identify best buy plans. The results have confirmed the gravity of this problem and demonstrated the importance of taking immediate action on curbing this onslaught on this valuable and scarce fresh water resource.     

Benchmarking enhancements to a decision support system for global crop production assessments

The Office of Global Analysis/International Production Assessment Division (OGA/IPAD) of the United States Department of Agriculture – Foreign Agricultural Service (USDA-FAS) has been assimilating new data and information products from agencies such as the National Aeronautics and Space Administration (NASA) into its operational decision support system (DSS). The FAS mission is to improve monthly estimates of global production of major agricultural commodities and provide US Government senior decision makers and the public the most accurate, timely, and objective assessment of the global food supply situation possible. These estimates are ultimately captured as the US governments’ official assessments of world food supply for the commodity markets and policy makers. The goal of this research was to measure changes in the quality and accuracy of decision support information resulting from the assimilation of new NASA products in the DSS. We gathered both qualitative and quantitative information through questionnaires and interviews to benchmark these changes. We used an interactive project lifecycle risk management tool developed for NASA mission spaceflight design and quality assurance (DDP – Defect Detection and Prevention) to do this. In this case, we used it to (1) quantify the change in DSS Objectives attained after assimilation of new products, and (2) evaluate the effectiveness of various Mitigation options against potential Risks. The change in Objectives attainment was considered the most important benchmarking indicator for examining the effectiveness of the assimilation of NASA products into OGA/IPAD’s DSS. From this research emerged a novel model for benchmarking DSSs that (1) promotes continuity and synergy within and between government agencies, (2) accommodates scientific, operational and architectural dynamics, and (3) facilitates transfer of knowledge among research, management, and decision-making agencies.     

The suite of lumped GR hydrological models in an R package

Lumped hydrological models are catchment-scale representations of the transformation of precipitation into discharge. They are widely-used tools for real-time flow forecasting, flood design and climate change impact assessment, and they are often used for training and educational purposes. This article presents an R-package, airGR, to facilitate the implementation of the GR lumped hydrological models (including GR4J) and a snow-accumulation and melt model. The package allows users to calibrate and run hourly to annual models on catchment sets and to analyse their outputs. While the core of the models is implemented in Fortran, the user can manage the input/output data within R. A number of options and plotting functions are proposed to ease automate tests and analyses of the results. The codes are flexible enough to include external models, other calibration routines or efficiency criteria. To illustrate the features of airGR, we present one application example for a French mountainous catchment.     

Development of a web-based decision support system for supporting integrated water resources management in Daegu city,South Korea

Demands on fresh water by human beings have been continuously increasing due to population growth, living standard improvement, and economic development. At the same time, many regions are suffering greatly from floods and droughts. Those are the results of ineffective management of water resources due to the associated complexities. In this study, a decision support system (DSS) was developed for supporting integrated water resources management in Daegu city, Republic of Korea. The developed DSS contained four subsystems including database, modelbase, and knowledgebase, as well as general user interface (GUI). It was then connected with the National Water Management Information System (WAMIS). A flow prediction could be conducted through the incorporated HEC-HMS Version 3.0.1. Also, an urban water demand forecasting model was developed using an artificial neural network (ANN) based model. At the same time, a water resources management model based on genetic algorithm (GA) was developed in the DSS, facilitating efficient allocation of water resources among different regions within a city. The result indicated that the developed DSS is very useful to deal with complex water resources management problems and could be further applied to similar cities in South Korea.     

Water resources of Cyprus under changing climatic conditions: Modelling approach,validation and limitations

We examine trends in the water resources of Cyprus by focussing on water flux changes in the important Kouris catchment. Our modelling approach is general and is a synthesis of an adapted conceptual daily rainfall-runoff model, radiation transfer models that use high resolution MODIS satellite climatological data and GCM scenarios for future climatic change. We used climatic data as input to our models, downscaled to the catchment resolution from two climate scenarios: the mild RCP2.6 and the extreme RCP8.5, to estimate water resources by the end of the 21st century. The models show that the present mean annual rainfall resource of 174 Mm³ will be reduced to 162 Mm³ and 132 Mm³, for the mild and extreme scenario, respectively. The present mean discharge of 21.5 Mm³ into the Kouris dam from the catchment will decrease to 16.6 Mm³ and 6.9 Mm³ under the mild and extreme scenario, respectively.     

A simulation-based risk network model for decision support in project risk management

This paper presents a decision support system (DSS) for the modeling and management of project risks and risk interactions. This is a crucial activity in project management, as projects are facing a growing complexity with higher uncertainties and tighter constraints. Existing classical methods have limitations for modeling the complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This will influence the effectiveness of decisions for risk response planning and will lead to unexpected and undesired behavior in the project. Based on the concepts of DSS and the classical steps of project risk management, we develop an integrated DSS framework including the identification, assessment and analysis of the risk network. In the network, the nodes are the risks and the edges represent the cause and effect potential interactions between risks. The proposed simulation-based model makes it possible to re-evaluate risks and their priorities, to suggest and test mitigation actions, and then to support project manager in making decisions regarding risk response actions. An example of application is provided to illustrate the utility of the model.     

Empirically assessing factors related to DSS benefits

This paper reports the results of a field study surveying 201 US business managers to test the relationship between decision support systems (DSS) benefits and a list of factors expected to affect them. DSS benefits considered are: decision quality, competitive edge, improved communication, cost reduction, increased productivity, time savings, overall satisfaction, overall cost effectiveness and total benefits. The determinants of DSS benefits fall into four broad categories encompassing attributes of the industry (strategic position and degree of competition), the organization (size of organization, task structuredness, frequency of use, quality of training, organization support and vendor support), the DSS (timeliness of output, completeness of output, accuracy of output, relevance of output, flexibility, range of alternatives and user-friendliness) and of the DSS user (age of user, experience with DSS, experience on job, education level, attitude of user and expectations of user). Correlation coefficients and incremental R square measures (stepwise regression) show that much of the variance (52-84%) in each of the benefits can be explained by the factors included in the study. Based on the results, managerial recommendations on how to proceed to increase DSS benefits are presented.     

User characteristics—DSS effectiveness linkage: an empirical assessment

Despite extensive research on various factors affecting the acceptance and effectiveness of decision support systems (DSS), considerable ambiguity still exists regarding the role and influence of user characteristics. Although researchers have advocated DSS effectiveness as a multi-dimensional construct, specific guidelines regarding its dimensions or the approach to derive it is lacking. The study reported here attempts to contribute to the existing body of knowledge by proposing a multi-dimensional construct for DSS effectiveness and identifying a comprehensive set of user characteristics that influences DSS effectiveness. It critically examines the relationship between these two sets through canonical correlation analysis technique. Thirty seven students, taking a graduate level course in financial management, in a large university located in the north eastern part of the United States participated in the study acting as surrogates for real-world managers. The results of the study highlight that user's domain-related expertise, system experience, gender, intelligence, and cognitive style have important influence on one or more dimensions of DSS effectiveness. However, their relative importance vary with the outcome measure of choice.     

Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture

Seasonal climate forecasts (SCFs) have received a lot of attention for climate risk management in agriculture. The question is, how can we use SCFs for informing decisions in agriculture? SCFs are provided in formats not so conducive for decision-making. The commonly issued tercile probabilities of most likely rainfall categories i.e., below normal (BN), near normal (NN) and above normal (AN), are not easy to translate into metrics useful for decision support. Linking SCF with crop models is one way that can produce useful information for supporting strategic and tactical decisions in crop production e.g., crop choices, management practices, insurance, etc. Here, we developed a decision support system (DSS) tool, Climate-Agriculture-Modeling and Decision Tool (CAMDT), that aims to facilitate translations of probabilistic SCFs to crop responses that can help decision makers adjust crop and water management practices that may improve outcomes given the expected climatic condition of the growing season.     

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.     

Contribution of rainfall vs. water management to Mediterranean wetland hydrology: Development of an interactive simulation tool to foster adaptation to climate variability

Mediterranean wetlands are increasingly managed to maintain their functions and services following modification in water allocation, embankment and climate change, calling for proactive and adaptive water management. In a first step, we used long-term monitoring of water levels in 37 adjacent embanked marshes in the Camargue as a repeated non-controlled experiment to build a hydrological model. Without information on water input/output by marsh users, we could nevertheless estimate evapotranspiration under flooding and dry conditions, and soil water coefficient. The model provided a high predictive accuracy (adjusted R² = 0.73–0.83) of monthly water levels when applied to an independent sample of 12 marshes. In a second step we developed an interactive decision-aid tool that allows users to visualize the impact of their management strategy (desired water level at a specific month) on subsequent water levels, and their consequence on different components of the ecosystem over a 10-yr period.     

A web-based tool for the development of Intensity Duration Frequency curves under changing climate

Intensity Duration Frequency (IDF) curves are among the most common tools used in water resources management. They are derived from historical rainfall records under the assumption of stationarity. Change of climatic conditions makes the use of historical data for development of IDFs for the future unjustifiable. The IDF_CC, a web based tool, is designed, developed and implemented to allow local water professionals to quickly develop estimates related to the impact of climate change on IDF curves for almost any local rain monitoring station in Canada. The primary objective of the presented work was to standardize the IDF update process and make the results of current research on climate change impacts on IDF curves accessible to everyone. The tool is developed in the form of a decision support system (DSS) and represents an important step in increasing the capacity of Canadian water professionals to respond to the impacts of climate change.     

Integration of models,data management,interfaces and training support in a drinking water treatment plant simulator

Water supply companies are gradually changing to a centralised, fully automated operation. The drivers for this change are the increase in efficiency and a better and more stable water quality. Fully automated treatment plants will require more sophisticated operator care than manually operated plants, so operation supervisors should periodically train in a drinking water treatment plant simulator. The successful first time setup of such a simulator is addressed in this paper. Environmental decision-support systems (EDSSs) were used as a blueprint for this simulator because the integration of different models is common in EDSSs. By applying a SCADA-like graphic user interface and several report options, even a group of end-users without specific modelling skills or knowledge can take advantage of the use of integrated hydraulic, water quality and process control models. The ‘Waterspot’ drinking water treatment plant simulator has been developed and applied to Dutch drinking water treatment plants. To demonstrate successful application, a case study is described for the drinking water treatment plant at Weesperkarspel.     

Decision making under uncertainty in a decision support system for the Red River

Decision support systems (DSSs) are increasingly being used in water management for the evaluation of impacts of policy measures under different scenarios. The exact impacts generally are unknown and surrounded with considerable uncertainties. It may therefore be difficult to make a selection of measures relevant for a particular water management problem. In order to support policy makers to make a strategic selection between different measures in a DSS while taking uncertainty into account, a methodology for the ranking of measures has been developed. The methodology has been applied to a pilot DSS for flood control in the Red River basin in Vietnam and China. The decision variable is the total flood damage and possible flood reducing measures are dike heightening, reforestation and the construction of a retention basin. The methodology consists of a Monte Carlo uncertainty analysis employing Latin Hypercube Sampling and a ranking procedure based on the significance of the difference between output distributions for different measures. The mean flood damage in the base situation is about 2.2 billion US$ for the year 1996 with a standard deviation due to parameter uncertainty of about 1 billion US$. Selected applications of the measures reforestation, dike heightening and the construction of a retention basin reduce the flood damage by about 5, 55 and 300 million US$, respectively. The construction of a retention basin significantly reduces flood damage in the Red River basin, while dike heightening and reforestation reduce flood damage, but not significantly.     

A model library for simulation and benchmarking of integrated urban wastewater systems

This paper presents a freely distributed, open-source toolbox to predict the behaviour of urban wastewater systems (UWS). The proposed library is used to develop a system-wide Benchmark Simulation Model (BSM-UWS) for evaluating (local/global) control strategies in urban wastewater systems (UWS). The set of models describe the dynamics of flow rates and major pollutants (COD, TSS, N and P) within the catchment (CT), sewer network (SN), wastewater treatment plant (WWTP) and river water system (RW) for a hypothetical, though realistic, UWS. Evaluation criteria are developed to allow for direct assessment of the river water quality instead of the traditional emission based metrics (for sewer overflows and WWTP discharge). Three case studies are included to illustrate the applicability of the proposed toolbox and also demonstrate the potential benefits of implementing integrated control in the BSM-UWS platform. Simulation results show that the integrated control strategy developed to maximize the utilization of the WWTP's capacity represents a balanced choice in comparison to other options. It also improves the river water quality criteria for unionized ammonia and dissolved oxygen by 62% and 6%, respectively.     

Modelling the long term water yield impact of wildfire and other forest disturbance in Eucalypt forests

Disturbance of forested catchments by fire, logging, or other natural or human induced events that alter the evapotranspiration regime may be a substantial threat to domestic, environmental and industrial water supplies. This paper describes the physically-based modelling of the long term changes in water yield from two wildfire affected catchments in north-eastern Victoria, Australia, and of fire and climate change scenarios in Melbourne's principal water supply catchment. The effect of scale, data availability and quality, and of forest species parameterisation are explored. The modelling demonstrates the importance of precipitation inputs, with Nash and Sutcliffe Coefficients of Efficiency of predicted versus observed monthly flows increasing from 0.5 to 0.8 with a higher density of rainfall stations, and where forest types are well parameterised. Total predicted flow volumes for the calibrations were within 1% of the observed for the Mitta Mitta River catchment and <4% for the Thomson River, but almost ?10% for the less well parameterised Tambo River. Despite the issues of data availability simulations demonstrated the potential for significant impacts to water supply in SE Australia from wildfire and climate change. For example, for the catchments modelled the moderate climate change impact on water yield was more pronounced than the worst fire scenario. Both modelled cases resulted in long term water yield declines exceeding 20%, with the climate change impact nearing 30%. A simulation using observed data for the first four post-fire years at the Mitta Mitta River catchment showed Macaque was able to accurately predict total flow.     

Implications of complexity and uncertainty for integrated modelling and impact assessment in river basins

The paper focuses on implications of complexity and uncertainty in climate change impact assessment at the river basin and regional scales. The study was performed using the process-based ecohydrological spatially semi distributed model SWIM (Soil and Water Integrated Model). The model integrates hydrological processes, vegetation/crop growth, erosion and nutrient dynamics in river basins. It was developed from the SWAT and MATSALU models for climate and land use change impact assessment. The study area is the German part of the Elbe River basin (about 100,000 km²). It is representative for semi-humid landscapes in Europe, where water availability during the summer season is the limiting factor for plant growth and crop yield. The validation method followed the multi-scale, multi-site and multi-criteria approach and enabled to reproduce (a) water discharge and nutrient load at the river outlet along with (b) local ecohydrological processes like water table dynamics in subbasins, nutrient fluxes and vegetation growth dynamics at multiple scales and sites. The uncertainty of climate impacts was evaluated using comprehensive Monte Carlo simulation experiments.