A decision support system for updating and incorporating climate change impacts into rainfall intensity-duration-frequency curves: Review of the stakeholder involvement process

Stakeholder involvement can serve to increase the quality of decision support systems (DSSs) and increase the perceived legitimacy of DSS outputs. Involving those who are ultimately affected by the outputs of DSSs in system design and development also reflects democratic principles. Importantly, stakeholder involvement can help ensure that the outputs of DSSs are used in decision-making processes. However, DSSs often fail due to poor engagement of stakeholder and end-user communities in the development and design of systems. The stakeholder engagement process applied in the development of the Computerized Tool for the Development of Intensity Duration Frequency Curves under Climate Change described here followed many of the tenants of best practices identified in the literature. While the engagement strategy was generally considered successful, over- and under-representation of some stakeholder groups and long term funding issues were weaknesses in the engagement process.     

A web-based screening model for climate risk to water supply systems in the northeastern United States

The aim of this study is to describe the development and application of a web-based decision support tool (ViRTUE) for performing climate risk evaluations of water supply systems. The tool is designed for small-scale water utilities in the northeastern United States that may lack the resources for detailed climate change risk investigations. Development of this tool demonstrates a relatively new approach to web application development using the Shiny framework for the R programming language to create an interactive environment for stakeholders and water managers to explore climate vulnerabilities. Using a decision-scaling framework, the tool allows the user to perform a climate stress test to evaluate the performance and vulnerability to water supply shortfalls of local reservoir systems over a wide range of potential climate change scenarios using a generic systems model. Probabilities of future climate conditions derived from climate projections then help inform utility operators of impending risk.     

A cloud-enabled remote visualization tool for time-varying climate data analytics

The ever-growing time-varying climate datasets pose challenges for efficient analytics using the current desktop-based or generic remote visualization tools. We present a tightly-coupled scalable cloud-enabled remote visualization tool that exploits the computational capabilities of Graphical Processing Units (GPUs). We implement three typical volumetric/3D visualization techniques to illustrate the enhanced performance offered by remote GPU clusters. Our development also enables fast deployment to facilitate the access of remote analytics tools by a wide range of end users.     

A daily weather generator for use in climate change studies

This paper describes the development of a weather generator for use in climate impact assessments of agricultural and water system management. The generator produces internally consistent series of meteorological variables including: rainfall, temperature, humidity, wind, sunshine, as well as derivation of potential evapotranspiration. The system produces series at a daily time resolution, using two stochastic models in series: first, for rainfall which produces an output series which is then used for a second model generating the other variables dependent on rainfall. The series are intended for single sites defined nationally across the UK at a 5 km resolution, but can be generated to be representative across small catchments (<1000 km²). Scenarios can be generated for the control period (1961–1990) based on observed data, as well as for the UK Climate Impacts Programme (UKCIP02) scenarios for three time slices (2020s, 2050s and 2080s). Future scenarios are generated by fitting the models to observations which have been perturbed by application of change factors derived from the UKCIP02 mean projected changes in that variable. These change factors are readily updated, as new scenarios become available, and with suitable calibration data the approach could be extended to any geographical region.     

A decision support system for the planning of the workload on a grain terminal

Ships, loaded with agricultural products, are handled by GEM at three different terminals in the port of Rotterdam. Each terminal consists of several berths and has both floating equipment and shore equipment. This paper describes the terminal system, the planning process and a menu driven computer planning model of the system. The planning model is split up into a first phase, in which berths are allocated, and a second phase, in which unloading equipment is assigned. The user of the model has the opportunity to manipulate several penalties and assign preferences to berths.     

A framework for integrated assessment of food production economics in South Asia under climate change

Agriculture is a sector vulnerable to climate change. The potential decline of crop yields from this vulnerability has important policy implications for food security in South Asia. In this study an integrated assessment modelling framework is proposed to link a global economic model with global climate models via an econometric model of crop productivity. It is then used to examine the impact of climate change on food security in individual South Asian countries by exploring the interaction between climate-induced productivity change and changes in food production and food prices. The results of our simulations suggest that unfavourable climate change can reduce food production significantly from the historical trend and create upward pressure on food prices. This, in turn, will have serious adverse impacts on food security in the South Asian region.     

A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology

We propose an inventory classification system based on the fuzzy analytic hierarchy process (AHP), a commonly used tool for multi-criteria decision making problems. We integrate fuzzy concepts with real inventory data and design a decision support system assisting a sensible multi-criteria inventory classification. We report on a study conducted in a small electrical appliances company and validate the design of the proposed multi-criteria inventory classification system and its underlying fuzzy AHP model.     

A Bayesian support tool for morphological design

Dynamic computer based support tools for the conceptual design phase have provided a long-standing challenge to develop. This is largely due to the ‘fluid’ nature of the conceptual design phase. Design evaluation methods, which form the basis of most computer design support tools, provide poor support for multiple outcomes. This research proposes a stochastic-based support tool that addresses this problem. A Bayesian Belief Network (BBN) is used to represent the causal links between design variables. Included in this research is an efficient method for learning a design domain network from previous design data in the structure of a morphological design chart. This induction algorithm is based on information content. A user interface is proposed to support dynamically searching the conceptual design space, based on a partial design specification. This support tool is empirically compared against a more traditional search process. While no compelling evidence is produced to support the stochastic-based approach, an interesting broader design search behaviour emerges from observations of the use of the stochastic design support tool.     

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.     

ODE: a tool for distributing object-oriented applications

Object-oriented applications are increasingly being deployed in distributed computing environments. Technologies, such as Java RMI, and architectures, such as CORBA, DCOM, and Enterprise Java Beans, are facilitating and enhancing this trend. The performance and eventual success of these applications is dependent on distribution decisions made by the application designer. This decision is a complex one, involving a large number of alternatives and multiple conflicting criteria. Rigorous approaches for effective distribution of object-oriented applications are still lacking. This paper describes the implementation of a practical and effective approach for distributing object-oriented applications. A prototype decision support system—object distribution environment (ODE)—that implements the approach in the form of a user-friendly tool for the design of distributed object-oriented applications is described. ODE has been successfully used in the distribution of a real world distributed object-oriented system.     

An assessment tool for land reuse with artificial intelligence method

This paper focuses on land resource consumption due to urban sprawl. Special attention is given to shrinking regions, characterized by economic decline, demographic change, and high unemployment rates. In these regions, vast terrain is abandoned and falls derelict. A geographic information system (GIS) based multi-criteria decision tool is introduced to determine the reuse potential of derelict terrain, to investigate the possible reuse options (housing, business and trade, industry, services, tourism and leisure, and re-greening), and to visualize the best reuse options for groups of sites on a regional scale. Achievement functions for attribute data are presented to assess the best reuse options based on a multi-attribute technique. The assessment tool developed is applied to a model region in Germany. The application of the assessment tool enables communities to become aware of their resources of derelict land and their reuse potential.     

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.     

Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas

Climatologically homogeneous regions in the Carolinas were delineated using a multi-step approach integrating in-situ and remotely-sensed data. We adopted a consensus clustering technique that obtains climate regions for precipitation and temperature separately. Both average linkage hierarchical and k-means non-hierarchical clustering methods were used to create weather station clusters. Using the resulting precipitation and temperature clusters as training data, we performed a machine-learning decision tree classification of remotely-sensed data (i.e., MODIS and TRMM) to map five precipitation classes and seven temperature classes for the Carolinas. These data were intersected to produce 17 consensus clusters for the Carolinas, and 16 climate regions when summarized by counties.The resultant climate regions showed rational climate regionalization reflecting controls on Carolina climate including topography, latitude, storm tracks, and proximity to the Atlantic Ocean. The use of remotely-sensed data effectively helped the delineation between weather station clusters and even detected consensus clusters that were not identified by intersecting weather station clusters grouped using only in-situ data. We compared the regions with the 15 existing National Climatic Data Center climate divisions using within- and between-cluster standard deviations for both in-situ and remotely-sensed data. Climate regions could improve the existing climate divisions in delineating climatologically homogeneous regions and in separating heterogeneous regions.     

A stepwise cluster analysis approach for downscaled climate projection – A Canadian case study

Downscaling techniques are used to obtain high-resolution climate projections for assessing the impacts of climate change at a regional scale. This study presents a statistical downscaling tool, SCADS, based on stepwise cluster analysis method. The SCADS uses a cluster tree to represent the complex relationship between large-scale atmospheric variables (namely predictors) and local surface variables (namely predictands). It can effectively deal with continuous and discrete variables, as well as nonlinear relations between predictors and predictands. By integrating ancillary functional modules of missing data detecting, correlation analysis, model calibration and graphing of cluster trees, the SCADS is capable of performing rapid development of downscaling scenarios for local weather variables under current and future climate forcing. An application of SCADS is demonstrated to obtain 10 km daily mean temperature and monthly precipitation projections for Toronto, Canada in 2070–2099. The contemporary reanalysis data derived from NARR is used for model calibration (1981–1990) and validation (1991–2000). The validated cluster trees are then applied for generating future climate projections.     

Evaluating methods to simulate crop rotations for climate impact assessments – A case study on the Canterbury plains of New Zealand

Most climate impact assessments for food production simulate single crops with re-initialised soil conditions. However, crop rotations with multiple crops are used in many agricultural regions worldwide. This case-study compares methods to aggregate outputs from simulations of multi-crop systems for climate impact assessments. The APSIM model was used to simulate four crops as monocultures (re-initialised or continuous) or as (single or multiple) instances of continuous rotations. We considered two contrasting climates and two soil types, with four production intensification scenarios (high/low water and nitrogen input). Results suggest that differences among the methods depend on the impact variable of interest and the degree of intensification. Detailed simulations (i.e. multiple runs of continuous rotations) were especially valuable for soil-related variables and limiting growth conditions. These results can indicate sources of uncertainty for large scale impact and adaptation assessments where simplifications of crop rotations are often necessary.     

Selection of target LEED credits based on project information and climatic factors using data mining techniques

Developed by the United States Green Building Council, Leadership in Energy and Environmental Design (LEED) is a credit-based rating system that provides third-party verification for green buildings. Selection of target credits is important yet challenging for LEED managers because various factors such as target certification grade level and building features need to be considered on a case-by-case basis. Local climatic factors could affect the selection of green building technologies and hence the target credits, but currently there is no research suggesting target LEED credits based on climatic factors. This paper presents a methodology for the selection of target LEED credits based on project information and climatic factors. This study focuses on projects certified with LEED for Existing Buildings (LEED-EB). Information of 912 projects and their surrounding climatic circumstances was collected and studied. 55 classification models for 47 LEED-EB credits were then constructed and optimized using three classification algorithms - Random Forests, AdaBoost Decision Tree, and Support Vector Machine (SVM). The results showed that Random Forests performed the best in most of the 55 classification models. With a combination of the three algorithms, the trained classification models were used to develop a web-based decision support system for LEED credit selection. The system was tested using 20 recently certified LEED projects, and the results showed that our system had an accuracy of 82.56%.     

A visual tool for user-interface development

VITUID is a visual tool for user interface development. It aims to help user-interface designers, who may be non-programmers, to specify and create interactive, graphical user interfaces through graphical interaction. VITUID lets designers specify the user command set and the human-computer dialogs by building a tree and specifying the behavior of each dialog via a dialog-control word, adopting the object-oriented approach. The main strategy of VITUID is to separate a user interface into an application-dependent part and an application-independent part and then let the designer specify only the application-dependent part.     

Searching for simplified farmers' crop choice models for integrated watershed management in Thailand: A data mining approach

This study used the C4.5 data mining algorithm to model farmers' crop choice in two watersheds in Thailand. Previous attempts in the Integrated Water Resource Assessment and Management Project to model farmers' crop choice produced large sets of decision rules. In order to produce simplified models of farmers' crop choice, data mining operations were applied for each soil series in the study areas. The resulting decision trees were much smaller in size. Land type, water availability, tenure, capital, labor availability as well as non-farm and livestock income were found to be important considerations in farmers' decision models. Profitability was also found important although it was represented in approximate ranges. Unlike the general wisdom on farmers' crop choice, these decision trees came with threshold values and sequential order of the important variables. The decision trees were validated using the remaining unused set of data, and their accuracy in predicting farmers' decisions was around 84%. Because of their simple structure, the decision trees produced in this study could be useful to analysts of water resource management as they can be integrated with biophysical models for sustainable watershed management.     

A simulation tool for managing environmental flows in regulated rivers

Environmental flows provide river flow regimes to restore and conserve aquatic ecosystems, creating considerably different demands compared to conventional water extraction. With increasing incorporation of environmental flows in water planning worldwide, governments require decision support tools to manage these flows in regulated rivers. We developed the Environmental Water Allocation Simulator with Hydrology (eWASH), a fast, flexible and user-friendly scenario-based hydrological modelling tool, supporting environmental flow management decisions for single- or multi-reservoir systems. Environmental flow demands and management rules are easily specified via the graphical user interface, and batch processing functions aid in uncertainty assessment. eWASH modelled main processes of complex regulated rivers and the tool is widely applicable. We calibrated eWASH for the Gwydir and Macquarie Rivers of Australia's Murray–Darling Basin. Modelled monthly environmental flow allocations exhibited Nash–Sutcliffe efficiencies of 0.55 for the Gwydir and 0.72 for the Macquarie catchments respectively when validated.